WIPO Technology Trends 2019 Artificial Intelligence

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This publication is not intended to reflect the views of the © WIPO, 2019 Member States or the WIPO Secretariat. First published 2019 The mention of specific companies or products of manu- facturers does not imply that they are endorsed or recom- World Intellectual Property Organization mended by WIPO in preference to others of a similar nature 34, chemin des Colombettes, P.O. Box 18 that are not mentioned. CH-1211 Geneva 20, Switzerland

Photo credit: cover montage created with images by ISBN: 978-92-805-3007-0 © Margarita Lyr / iStock / Getty Images Plus and © Daria Dombrovskaya / iStock / Getty Images Plus Attribution 3.0 IGO Printed in Switzerland (CC BY 3.0 IGO) Artificial intelligence is a new digital frontier that will have a profound impact on the world, transforming the way we live and work.

WIPO Director General, Francis Gurry Preface 1 Introduction 3 Evolution 7 of AI patent The past, present and future ���� of AI: what research and applications innovation trends can reveal; the data used in this report and and how it is analyzed; and a scheme for categorizing scientific Foreword AI technologies. publications 8 18 ���� The historical development of AI innovation: analysis of trends in patents and scientific literature since 2 the emergence of AI, and About the Trends breakdown by techniques, functional applications and contributors in artificial application fields. 10 intelligence 38 ���� Overall trends emerging from the data and analysis of changes over time, by region and industry and the most 4 Acknowl- prominent entities. Key edgments 30 players in 12 AI patenting ���� The top applicants for AI patents: how companies and universities/public research organizations compare, which Executive entities are most active in each area and where they summary are filing. 13 58 ���� 104 trends trends filings Analysis of the use of AI of AI use of the Analysis 82 Comprehensive analysis of filed, including both first and and first both including filed, 5 geographical trends,geographical on based 6 of patent of the offices where patents are are patents where offices the technologies: data on and oppositions. acquisitions, funding, open related to AI AI to related source and patent and source litigation filings. subsequent Geography Market

7 138 120 The opportunities presented presented opportunities The AI and policyAI IP system IP Views from AI experts experts AI from Views 8 on the key policy and key the on and policy of AI and the from arising and culture, and how AI and IP IP and how AI and culture, and applications, plus examples of responses in the promotion of innovation. jurisdictions. various in by AI for business, society society business, by for AI or enacted proposed policies rights interact with other each by AI raised issues regulatory Key issues Key issues The future The

150 146 Selected AI Selected AI ���� ���� Further and terms categories reading

5 WIPO Technology Trends 2019 This new report aims to shed light on the trends in innovation in AI since the field first developed in the 1950s.

WIPO Director General, Francis Gurry The analysis offers unique insights into trends into trends insights unique offers analysis The to we contribute hope report, this Through AI is fast becoming part of our everyday lives, lives, everyday of our part becoming fast is AI Preface Given these widely held reservations and and reservations held widely these Given find out how AI research has so far developed – developed far so has howresearch AI out find developedfirst in the has Research 1950s. as (AI) intelligence artificial features edition first series, flagship other sectors in which AI innovation is being being innovation is AI which in sectors other contributionsexpert are openly available on and patent methodology datasets, search and by commentary accompanied and detail field the since innovation in AI in trends the on to important this clarity bring and evidence to basis have afactual essential is it concerns, misconceptions numerous are There culture. how shop, we travel work, and changing well as for those general readers who want to want who readers general those for well as complete AI; more in world’s experts leading of businesses range awide on impacts with I am pleased to present the first report in a new in a report first the to present pleased I am the WIPO website. We hope that this report will will report website. We this WIPO the that hope scientific on data well as as to inventions, AI other and of patents study adetailed on Based ways many the of discovering beginning the technology acutting-edge is AI as series the of launch the for topic afitting It is theme. the used in AI, such as machine learning and and learning machine as such AI, in used application fields (i.e., those industries and and (i.e., fields industries those application speech processing, natural language as going. is it where and in discussed are findings data activity. These of debate. area in and of AI, nature the about misgivings and activities. and in AI techniques (i.e., the different approaches approaches (i.e., different techniques the AI in 20 of the than more from perspectives industry related data of patent involved analysis the light to shed aims new report this information, and society – business, challenge indeed –and on have will AI impact an which in other. each with Yetinteract at only we are prove an invaluable resource for businesses, businesses, for resource prove invaluable an publications, litigation filingsand acquisition to the it poses challenge humankind. particular processing and computerprocessing vision) and AI fuzzy logic),fuzzy AI functional applications (such innovation AI. in about discussions policy for researchers and policymakers in the field, as as field, the in policymakers and researchers WIPO Technology TrendsWIPO . This This report identifies the key players in in the keyplayers identifies report This Together, the of analysis trends technological AI from both the corporate and public public and corporate the both from AI findings of the report is that 50 percent of all AI AI all of percent 50 is that report of the findings years – a remarkable illustration of how rapidly of how rapidly illustration –aremarkable years development of human-centered and ethical AI address experts AI from inputs the and data debate away from speculative interpretation interpretation awaydebate speculative from new avaluable are here collected experts I hope that this contribution will help to shift to help will shift contribution this that I hope to benefit all. to benefit the future of AI, its governance and the IP IP the and governance its of AI, future the thereby informing on global policymaking as the regulation and control of data, the the of data, control and regulation the as and toward evidence-based projections, projections, towardand evidence-based AI. on base to knowledge agrowing addition sectors across different research areas and and areas research different across sectors intellectual property (IP) protection and the the and (IP) protection property intellectual of role the research, of further incentivization of the analysis the Furthermore, industries. innovation is advancing in this field. patents have been published in just the last five last the just in published havepatents been striking most of the One into practice). put presented in this report and the voices of AI of AI voices the and report this in presented framework that supports it.framework that supports many of the policy issues raised by AI, such by such AI, raised issues policy of the many Director General Francis GURRY

7 WIPO Technology Trends 2019 8 Foreword “automated” the process of playing chess. I chess. of playing process the “automated” This report illustrates some of those of those some illustrates report This me, among for out stands moment This Adam Coates, came into my office with a with into my office came Coates, Adam transform to develop and continues AI As we had and steroids, on automation is AI Andrew Ng Foreword CEO, Landing AI When Kasparov resigned, I jumped out Kasparov of I resigned, When jumped chart showing that the more data you fed to you fed a data more the that showing chart way. this chess champion! chess was at Stanford in the 2000s, my PhD student, my PhD student, 2000s, the in Stanford at was chess school of my high captain once was I was a graduate student at MIT in the 1990s. 1990s. the in MIT at student agraduate I was match chess watching final the I remember that AI brings. to unemployment inequality, and we but should event for asad not was This to computers. the leave it to and happy Iwas retire moment that to competitively, play at used but and team and fastest growing technique in AI. When I I When AI. in technique growing fastest and possibility and value of the sight never lose also scalability and the and DARPAscalability Challenge. Grand in lessons learning, of deep rise the as such and deeplearning.ai and its findings is that deep learning is the biggest biggest is the learning deep is that findings its create will it industry, Ihope after industry between Deep Blue and Gary Kasparov while while Kasparov Gary and Blue Deep between possibilities and where they are arising. One of of One arising. they are where and possibilities have a duty to address serious issues relating relating issues serious to address have aduty We experiences. joyful similarly more many to give up thrilled Iwas me; contrary, the on of AI, development the in milestones many had finally triumphed over the human community AI excitement in –the my chair

“unsupervised learning” – learning without – learning” learning “unsupervised 10. Manufacturing lines, for example, use use example, for lines, 10. Manufacturing Google Brain in 2011. in Brain Google learning deep then, Since We need to find ways to train computers on on to computers We find ways to train need With this potential to transform every industry industry every to transform potential this With computer vision to check for defects in parts parts in defects for to check vision computer discovery,That and find. we could computers decisions to become true Internet companies. to Internet true companies. decisions become all across continue companies incumbent as example, what trucks should dispatched be deep through today value driven is economic of alot the But your speech. recognize or cat on and images, natural speech language world the in value economic massive creating Effective on. to train of defects examples when, what to products to recommend what a to identify acomputer for means it what training datasets as small as 100, as even small or as datasets training recognize usually can atoddler For example, we have still But way along to field. the in go to work. make it to computing distributed of using idea the trend early, and their leaders made strategic strategic made early, leaders their trend and companies era, Internet did. the In Internet the will today value and massive driving is it But –for data This world). the in spreadsheet Excel today. to tends focus media the AI, covering In grail,” “holy this Even already is AI without user – is more specific to individual companies companies individual to specific more –is user a cat after just one encounter, but a computer encounter, one acomputer just but after a cat and create so much economic value, AI value, AI economic much so create and intuitive. or it’s visual as not because attention less gets data Structured industries. and understand can Everyone human. very are still needs more than one example to learn. to example learn. one than more needs still of to creation the led networks, neural up scale spreadsheets (only thanspreadsheets the bigger biggest including Microsoft and Apple saw the Internet Internet the saw Apple and Microsoft including AI. with themselves transform industries performed. We started looking for the biggest biggest the for looking We started performed. presents just as great a technological shift as as shift atechnological great as just presents of data types those because processing have never will they million a hopefully but more data and created the powerful computers computers powerful the created and data more we’ve as collected progress great made has network neural the better the network, neural machine learning applied to massive Excel Excel to massive applied learning machine learning on “structured data.” Think of this as data.” as of this Think “structured on learning of AI. grail aholy –remains data labelled 1. This meant investing meant This in the right technology, They will then be able to build internal AI AI to internal build able be then will They We are seeing a small number of incumbent of incumbent number asmall We seeing are 2. own AI transformations. navigate their industries all in organizations released Irecently solutions. AI unique create projects. AI to pilot build expertise domain today lieopportunities of outside the software a similar undergo transformation companies In order to help more people and businesses businesses and people to more help order In 3. the following three actions: the and forward industries their push that teams and data its leverage can organization Every to Internet solely limited not is transformation AI But companies. AI-driven now fully they are products. their and adaptingand how they communicated about search companies. The biggest untapped untapped biggest The companies. search industry in industries such as agriculture, agriculture, as such in industries industry projects; AI pilot building and talent AI hiring in leaders were Baidu and Google era. AI the in building teams, developing a digital strategy strategy adigital developing teams, building harness the potential of AI, we should consider consider we of AI, should potential the harness healthcare and manufacturing.

Today, it’s but AI, aPhD to get learn you can Companies haveCompanies as universities joined When companies and universities or or universities and companies When Continue promoting the free and open open and free the promoting Continue can learn this lesson and move and away from lesson this learn can repository arXiv The research. AI open relevant problems, understand data and government organizations work together, that allow you to learn, and at-home online at-home online and allow youthat to learn, university researchers can access more more access can researchers university and corporations can understand the latest latest the understand can corporations and sharing of AI knowledge and resources Promote increased understanding of AI of understanding increased Promote Build more partnerships public–private paywalled journals. paywalled because it dramatically accelerates the in technology.breakthroughs For example, benefit. can sides both free dissemination of ideas. Other fields fields Other of ideas. dissemination free not the only option. Traditional jobs degrees, to AI the difference ahuge made has revolution, GitHub the has as hosting service leading forces for publishing free and and free publishing for forces leading AI Transformation Playbook to help to help : : : Today, research AI of amount asignificant To towards work gap, this and close AI will transform every facet of society. It facet every transform will AI Governments should invest heavily in educating educating in invest heavily should Governments States and China. These countries are home are home countries States China. These and of regions and organizations. In building a a building In organizations. and of regions of AI concentration the decentralizing with great education, to compete with the the with to compete education, great with widely shared. governments businesses, empower we must It’s very difficult for other countries, even those even those countries, other for difficult It’s very those two countries. world, the in universities to best of the many the creation of an AI ecosystem to ensure to ensure ecosystem AI of an creation the partner- public–private enter and citizens their technology, invest education. in we must until now been focused on a small number number asmall on focused nowuntil been and created thoughtful regulations that enable regulations that createdand thoughtful enable funding have provided governments their and United the in place taking is education and automation to ensure that the benefits of AI are are AI of benefits the that to ensure automation by impacted may be who citizens and ships to adopt AI-powered systems safely. systems AI-powered to adopt ships innovation. But the United States and China China and States United the innovation. But business, engineering and investing and engineering of business, talent brings tremendous promise to improve our promise tremendous brings fairer and more equitable AI-driven society, AI-driven equitable more and fairer have also built incredible business ecosystems. ecosystems. business incredible have built also highlights, innovation and technology AI has long-term, sustainable growth. As this report report this As growth. sustainable long-term, we require will world live it the in, but and lives flexibility people will havewill people learn. to flexibility on important problems. The more widely widely more The problems. important on accessible information we have, information more the accessible work and to learn ways people for good all learning programs (such as MOOCs) are are MOOCs) as (such programs learning

9 WIPO Technology Trends 2019 10 About the contributors About the contributors the About protection and ethical concerns. legal data questions, and regulatory technology, AI of impact and uses potential addressing issues such as existing and to the information revealed in patent data, and add complement context comments and viewpoints Their innovation. and policy, experts in AI, data, intellectual property, includesThis from contributions report Andreessen Horowitz Andreessen Yoon Chae Yoon Brynjolfsson Erik Nick Bostrom GBenzell Seth Dario Floreano Flaim G. John Kay Firth-Butterfield Boi Faltings Myriam Côté Chen Frank Competence inCompetence Robotics of the Swiss National Center of of Center National Swiss of the global of head the IP group at Initiative on the Digital Economy Digital the Initiative on Institute of Technology (MIT) MIT Initiative on the Digital Economy Digital the Initiative on MIT Baker McKenzie and Institute Future of Humanity Laboratory of IntelligentLaboratory Systems Baker McKenzie (WEF) Forum Economic (EPFL) Lausanne de fédérale École polytechnique (Mila) Algorithms Learning for Institute Montreal at Humanity author of author associate at the Massachusetts at EPFL and founding director director EPFLat founding and World the at Learning Machine and at Lab AI of the Director and Dangers, Strategies Superintelligence: Paths, is a senior associate associate asenior is is a partner at at apartner is is Professor of AI of AI Professor is is Director of AI for for of AI Director is is a partner and and apartner is is Director of the of the Director is is postdoctoral is Director of the of the Director is

is Director at the the at Director is is Head of AI of AI Head is

Martin Ford Martin Dominique Foray Paul Nemitz Motohashi Kazuyuki Miguel Luengo-Oroz Ben Lorica Kai-Fu Lee Konstantinos Karachalios Malcolm Johnson Jay Iorio Ventures and author of author Ventures and Scientist at UN Global Pulse Global UN at Scientist General ofSecretary the of of University of Tokyo University Union (ITU) Superpowers: Silicon China, Valley, IEEE Standards Association IEEE Association Standards International Telecommunications Management for InnovationManagement at the Council Management Economics at EPFL at Economics the European Commission at O’Reilly Media at of IEEE’s amember and in the Department of Technologyin the Department of Director Managing is and the Threat of a Jobless Future ofaJobless Threat the and and the New World Order World New the and Rise of the Robots: Technology Technology Robots: ofthe Rise is afuturist is is founder of Sinovation is Chief Data Scientist is a futurist and author author and afuturist is is Principal Adviser to Adviser Principal is is Professor of of Professor is is Deputy is Deputy is Professor Professor is is Chief Data Data Chief is AI AI

Training, and Deputy Dean of the of the Dean Training, Deputy and Technology Laboratory Media (MIT) Hefa Song Rosalind Picard Eleonore Pauwels Herbert Zech Herbert Haifeng Wang Aristotelis Tsirigos Petr Šrámek Computing Research Group at at Group Research Computing at the Cybertechnologies Czech Republic of the of Industry Confederation (CAS) of Sciences Academy Chinese Sciences Law and Intellectual Intellectual and Law Sciences Incubator co-founder and Startup of Basel of Bioinformatics Applied of the the at AI on Platform of the UN University’s Center for for Center University’s UN Intellectual Property School at the School Intellectual Property Management, Deputy Director of of Director Deputy Management, Medicine the Center for IPR Research and and IPR for Research Center the and of Science Institute the Institutethe Massachusetts of Research Policy Fellow Emerging on Research Property Law at the University University the at Law Property Baidu at President Laboratories at NYU School of and Director of the Affective Affective of the Director and

is Professor of of Professor is is the founder of AI of AI founder the is is Professor of Life Life of Professor is is Senior Vice is Senior Vice is founder is is Director

11 WIPO Technology Trends 2019 Acknowledgments

The report was prepared under the direction prepared the visualizations and contributed of Francis Gurry (Director General) and Yo together with Alex Riechel to reviewing and Takagi (Assistant Director General, Global finalizing the report. Vipin Saroha prepared Infrastructure Sector), supervised by Alejandro the maps. Julie Summers provided valuable Roca Campaña (Senior Director, Access administrative support. Additional data to Information and Knowledge Division), and comments for the comparison of the under the responsibility of Irene Kitsara (IP report findings with overall patent statistics Information Officer, Access to Information and were provided by Kyle Bergquist, Mosahid Knowledge Division). Khan, Julio Raffo and Hao Zhou, all from the Economics and Statistics Division. The report draws on commissioned background research, based on search Gratitude is due to the 27 leading experts in AI strategy and methodology developed by the and contributors to the report who shared their core analytics team led by Irene Kitsara (WIPO), views, provided their comments on different consisting of Sophie Gojon, Adrien Migeon and aspects which enriched and contextualized the Philippe Petit (CNRS Innovation) and Patrice report findings – their time and contribution are Lopez (science-miner). The AI dimensions used much appreciated. Thanks also to Andrew Ng for the report and the related glossary were for providing the Foreword of the report, and to developed by Patrice Lopez, who also provided Bridget Hickey for her facilitation. expert advice on AI in patent literature, with inputs by the core team, the WIPO Advanced The report also draws on helpful input Technologies Application Center (ATAC) and received in the conceptualization phase team members of Mila (Simon Blackburn, from Phillippa Biggs (Senior Policy Analyst Pierre Luc Carrier, Mathieu Germain, Margaux at ITU), Virginia Dignum (Assistant Professor Luck, Gaétan Marceau Caron and Joao at the Faculty of Technology, Policy and Felipe Santos). Management, Delft University of Technology), Kay Firth-Butterfield (Head, AI and Machine Interviews were conducted and AI expert Learning at WEF), Jay Iorio (futurist), contributions were compiled by James Nurton, Konstantinos Karachalios (Managing Director who was the editor of the report, under the at IEEE Standards Association), and the responsibility of Charlotte Beauchamp (Head, law committee members of the IEEE Global Editorial and Design Section). Case studies Initiative on Ethics of Autonomous and were kindly provided by contributors, Angela Intelligent Systems, as well as from WIPO Harp (IBM Research), Mohamad Ali Mahfouz colleagues Carsten Fink, Allison Mages, (Microsoft Switzerland) and Sven Zirnite Christophe Mazenc, Bruno Pouliquen, Ning (Siemens Healthcare). Xu; and on experience drawn from previous WIPO publications from the Economics and The report team benefited greatly from Statistics Division, shared by Mosahid Khan, external reviews of the draft chapters by Julio Raffo and Sacha Vincent-Wunsch. Boi Faltings (EPFL), Patrice Lopez (science- miner) and Alexandros Tsirigos (NYU Medical Gratitude is also due to the WIPO School), and by WIPO colleagues Carsten Fink Communications Division, in particular (Chief Economist), Akshat Dewan and Bruno Charlotte Beauchamp for all the valuable Pouliquen (from ATAC), and Marco Aleman, contributions and support throughout the Allison Mages and Tomoko Miyamoto (from preparation of the publication, Edwin Hassink WIPO’s Patent Law Division). and Sheyda Navab for the design of the report and Ed Harris for helpful inputs; and to staff The core team was skillfully assisted by in the Printing Plant for their high-quality colleagues from WIPO’s Access to Information services. Gratitude is expressed to everyone and Knowledge Division, under the direction who worked hard and constructively towards of Andrew Czajkowski. Thanks are due in creating a new publication type and meeting

Acknowledgments particular to Alica Daly, who validated the data, challenging deadlines.

12 Artificial intelligenceArtificial (AI) is increasingly Executive summary Andrew Ng, Landing AI and deeplearning.ai transformed by AI. to going be not is which industry an imagine hardly electricity. can I AI is the new cancer, predict an epidemic and improve improve and epidemic an predict cancer, of detection enhance yields, crop can improve weather forecasting, boost of seemingly unrelated data points, AI effect: detecting patterns among billions patterns detecting effect: advancing computational processing processing advancing computational autonomous vehicles to medical diagnosis developmentsdriving important to advanced manufacturing. As AI moves moves AI As manufacturing. advanced to industrial productivity. industrial in technology and business, from marketplace, its growth is fueled by a bya fueled is growth its marketplace, from the theoretical realm to the global global the to realm theoretical the from power, with potentially revolutionary rapidly and data digitized of profusion 1950s, innovators and researchers have filed have filed 1950s, researchers and innovators This publication is among the first to first the among is publication This AI development, and the location of future of future location the and development, AI AI-related technologies grouped to grouped technologies three reflect AI-related through patent analytics Technology trends can be discerned from theory tofrom commercial theory application AI-related inventions are booming, shifting vision; and application fields, including WIPO Technology Trends investigates the WIPO has devised a new framework for the the for anew framework devised has WIPO Since artificial intelligence emerged in the in the emerged intelligence artificial Since companies and what and institutionscompanies are leading insights intooffering how innovation in this field over half of the identified inventions have been haveinventions been identified ofover the half questions, data protection and ethical concerns. the globe, from across addressing experts key trends, identify that analysis and data such AI, in used of techniques AI: dimensions growth markets. In addition, it includes contributions from AI AI from contributions includes it addition, In to review past and current trends in AI, while while AI, in trends current and to review past analyzes it era: AI emerging the in trends data patent in WIPO’s on Drawing expertise For each of these areas, this report provides provides report this areas, of these For each telecommunications and transportation. understanding of developmentsunderstanding in the field, with Notably, AI-related patenting is growing rapidly: rapidly: growing is patenting Notably, AI-related analytics, this first publication in the series series in the publication first this analytics, applications for nearly 340,000 AI-related AI-related 340,000 nearly for applications activity, including acquisitions and litigation. machineas learning; applications, functional systematically research trends in AI technology technology AI in trends research systematically scientific publications. and computer processing such as speech in order to discover which fields show the show the fields to which discover order in years. likelyis to coming develop the in inventions and published over 1.6 million ofimpact technology, AI regulatory and legal and uses potential and existing as such issues patent,publishing scientific and other data published since 2013. market and spread geographical players, largest amount of innovative activity, amount AI which largest

13 WIPO Technology Trends 2019 14 Executive summary 175 2013 from percent to 2016, 2,399 reaching patent in upsurge 12 of an advance in years The growth rates observed in the identified identified the in observed rates growth The Those AI functional applications with the revolutionizing techniques learning machine The AI-related patentAI-related higher data are noticeably AIAmong functional applications, computer and networks, neural and learning deep are AI

vision, which includes image recognition, is the Whilepublications scientific AIon date back control methods, which both grew on average average on grew both which methods, control 20,195 with by average 28 percent, annual on more in included is and patents in disclosed products of in technologies AI commercial 2001, around approximately started only AI on the inpublications boom scientific decades, (134,777 patent documents). Filings of machine with 6,506 patent filings in 2016. filings patent 6,506 with 9,567with 2013). in 8:1 2010 in to 3:1 2016 in of a –indicative Machine learning is theMachine learning dominant technique AI than the average annual growth rate for patents patents rate for growth annual average the than showed learning deep filings: of patent terms in techniques AI growing fastest the are these than one-third of all inventions identified across all areas of technology, 10 was which areas all across applications filed in 2016). (21,011 of 24 average an percent patent period, over same the percent arate of 46 at rate of growth annual average impressive an and services. Moreover, of scientific ratio the applications. shift from theoretical research to the use to use the research theoretical from shift in 49 percent of all AI-related patents (167,038 patents AI-related of all 49percent in patent documents), growing annually by in grew 2016; filings patent networks neural and in 2016 filed applications patent (compared from decreased to has inventions papers percent between 2013 between 2016. and percent ayear.by percent 55 2013period to 2016 and robotics for AI were highest growth rates in patent filings in the in the filings patent in rates growth highest popular.most Computer vision is mentioned learning-related patentlearning-related have grown annually Some areas of AI are growing more more growing are AI of areas Some quickly than others… than quickly 15 percent of all identified patent documents), documents), patent 15 identified of all percent Twenty application fields were identified in identified Twentywere fields application AI patent data. These include, in order of of order in include, These data. patent AI AI disclose only not patents AI-related AI-related telecommunications still grew grew still telecommunications AI-related

Within telecommunications, the most growth growth most the telecommunications, Within rate of high same the showing not While 2013 2016, and in 2016. filings 6,684 with computing and human–computer interaction human–computer computing and exploring exploitation the of commercial AI. growth as transportation, patent filings in filings patent transportation, as growth in 2016). filings 5,569 with growth, boom The (67 percent aerospace/avionics are category of things). (HCI) (11 percent). Other sectors featuring in (11 in (HCI) featuring sectors Other percent). Many AI-related technologies can find use find Many can technologies AI-related transportation (15 medical life and percent), transportation was one least at and analysis present the techniques and applications, they often also than 8,700than filings). 20 just representing 2006–2016: period the transportation the within emerging Rapidly patent AI-related in rates growth highest the is Transportation industries. to multiple refer that AI in of patents number large the the results include banking; entertainment; annually by an average of 23 percent between between of by 23 average percent an annually accounted for one-third of applications (more annual (42 vehicles percent autonomous 1,813 with growth, annual in 2016) filings and growth annual percent a33 with applications, showing fields those among features also by shown as industries, different across agriculture; and networks (including social sciences (12 percent), and personal devices, devices, (12sciences personal and percent), are industries and sectors many that shows security; industry and manufacturing; industry security; in transportation technologies becomes becomes technologies in transportation percent of applications in 2006, by 2016 2006, in of applications it percent 2013between 2016 and (8,764in 2016). filings it results, overall the in only not prominent magnitude: (mentioned telecommunications in identified total of the percent 62 in mentioned more evident when we look at trends over trends at we look when evident more Internet the and cities smart networks, refer to an application field or industry. Analysis Analysis industry. or field to application an refer …and many AI patents include different industries… different inventions that can be applied in in applied be can that inventions This pattern applies across most AI AI most across applies pattern This AI patent applicants, while only four are are four only while applicants, patent AI or application technique, AI an mention AI China, dominate patenting activity and (U.S.) America of States United the those from Japan, inCompanies, particular Companies represent 26 out of the top of 26 the 30 out represent Companies vision telecommunication with transportation, field field combinationin withanother. Themost finance (28 percent). notable in 2016,filings category this within and in 2016,informatics filings medical including Other sectors and sub-categories within within sub-categories and sectors Other electronics companies are particularly are particularly companies electronics future. near the in AI in developments combinations to areas suggest watch for rapid with computer vision; learning deep computer growth); agriculture (32 percent); computing (37 recognizes which percent), computing affective of sub-field the in occurred growth (18 percent growth) and public health (17 health (18 public and growth) percent grew (17 sciences medical and Life percent). was seen by computer networks/Internet (17 networks/Internet by computer seen was top 20 filing patents,AI-related companies 12 techniques, applications and fields.Of the universities or public organizations. research Nearly 70 percent of inventions related to of related inventions 70 percent Nearly and two are from China. Japanese consumer consumer Japanese China. from are two and U.S. the from are three Japan, in based are with ontology engineering security; and 2013 between 2016,annually and 3,977 with of 11 average an grew HCI and percent sectors with notable growth in patent filings filings patent in growth notable with sectors in government (30 percent); and banking and and banking and percent); (30 government in (47 cities annual percent smart include: percent growth). Personal devices, computing computing devices, growth). Personal percent 4,112 with period, by 12 same the in percent broadcasting television and radio and percent) frequent combinations in patent filings are: filings in patent combinations frequent heavily represented. heavily machine and processing; natural language emotion.human learning with life and medical sciences. These These sciences. medical life and with learning …while certain AI techniques, techniques, AI …while certain applications and industries appear toapplications appear and industries be linked. closely Toyota and Bosch, which are prominent in in Toyota prominent are which Bosch, and patenting across AI-related areas different AI in leaders are Microsoft and IBM Grid Corporation of China has leaped into leaped has of China Corporation Grid Samsung in life and medical sciences. Some Some sciences. medical life in and Samsung (5,102)Samsung NEC (4,406). and State The companies lead in specific industries. in lead specific companies with of acompanies high specialization degree are not limitingcompanies to their activity well-known that do companies not feature of nature, observations from draw which In certain techniques and fields, the highest highest the fields, and techniques certain In patent of AI portfolio largest the has IBM to specialized data may explain why certain why certain may explain data to specialized and Philips Siemens, and transportation, learning, deep for highly ranks which Baidu, techniques of bio-inspired approaches, learning machine to the in 2016, particularly anby filings patent its topthe 20, increasing are topthe fiveapplicants (5,223),Toshiba among the top overall players in AI patents are are patents AI in top the players overall among include Examples field. that in expertise and of aform machines, vector support and 2013 from annually of 70average percent out Rounding field. or industry a specific these that indicating fields, and applications applications with inventions, 8,290 followed social networks. Industry expertise and access access and expertise Industry networks. social learning. supervised include Facebook and Tencent in networks and Tencent and and Facebook networks in include portfolios span a range of AI techniques, techniques, of AI arange span portfolios companies’ Both 5,930. with by Microsoft nonetheless prominent in certain areas; these areas; these in prominent certain nonetheless of patentnumbers originate applications from identified inventions. identified all of one-third than included in more in patents and is technique disclosed AI dominant the is learning Machine

15 WIPO Technology Trends 2019 16 Executive summary The U.S. and China are the two most popular popular most two the are U.S. China The and There are 167 universities and public research research 167 public are and There universities Telecommunications Institute Research (ETRI) of Korea’s and Republic The Electronics

universities dominating research in specific fields, with Chinese Universities contribute significantly to AI CAS has the largest deep learning portfolio portfolio CAS learning deep the has largest in strong particularly are organizations Chinese make 17 up organizations top of the Chinese 20 fields such as distributed AI, some machine machine some AI, as distributed such fields 20 are from the U.S., the from 20 are of 19 Republic the from offices for filing AI patents, in line with with line in patents, AI filing for offices organizations ranked among the top 500 top the 500 among ranked organizations of organizations rates growth the beating or publications. scientific topof the 20 AI-related in (CEA) is in 185 in is (CEA) (235 patent families). organizations Chinese with over 2,500 patent families and over 20,000 over 20,000 and over families with patent 2,500 Energies and Atomic Energy Commission public Four European Japan. 4from Korea and the technique emerging The learning. of deep AI, in of companies dominance the Despite universities and public organizations research universities and public organizations research applicants overall. applicants patent top the 30 among ranks and are consolidating their lead, with patent filings 10 well as as patenting AI in players academic stands out as second in patent filing among among filing patent in second as out stands Moreover,AI. on published papers scientific is ranked 159 ranked is which Institute, Fraunhofer German the is (CAS), of Sciences Academy Chinese the is patent applicants. Of these, 110 these, Of applicants. patent Chinese, are 2013 from year per topercent 2016, matching AI selected in inventions in role aleading play from most otherfrom most countries. having grown on average by more than 20 20 than by average on more grown having neurorobotics. research organizations feature in the top 500 top the in 500 feature organizations research list; the highest-placed European institution leading public organization research applicant neuroscience/ techniques and learning The U.S. and China are the main targets targets main the are China and U.S. The for AI patent filing… patent AI for th th , while the French Alternative Alternative French the , while position. 1st in terms of acquisitions of AI companies. companies. of AI 1st of acquisitions terms in This suggests that targets are being acquired acquired being are targets that suggests This Japan. These three offices account for for 78 account offices three These Japan. Just 4 percent of patent applications first first applications of patent 4percent Just Apple and Microsoft have also been active active have been also Microsoft and Apple

X Development) ranks 10 ranks X Development) Certain companies, such as IBM and Intel, and IBM as such companies, Certain currently universities and companies Chinese five or more jurisdictions. jurisdictions. more fiveor filed in China are subsequently filed in filed subsequently are China in filed 2012, 103 reaching 2017. in Alphabet Although 2016. of number The in acquisitions identified Out of the top three filing offices, 40 percent percent 40 offices, filing top of the three Out of acquisitions having taken place since other jurisdictions. and Japan in filed first applications of patent (including Google, DeepMind, Waymo and Waymo and DeepMind, Google, (including which allows patent applicants to file in multiple tomultiple in file applicants allows patent which with small or non-existent patent portfolios. In total, 434 companies in the AI sector have sector AI the in companies total,In 434 32 percent of patent applications first filed in filed first applications of patent 32 percent Many patent applications are extended to more to more extended are applications patent Many than one jurisdiction. One-third of all AI patent patent AI of all One-third jurisdiction. one than top the targets among fourth ranks PCT route target mature companies. The majority of of majority The companies. mature target since year every increased has sector AI the the U.S. only, with toChina tend in file compared elsewhere. filed also U.S.the subsequently are after their first filing and 8 percent are filed in filed are percent and 8 filing first their after applications are filed in additional jurisdictions of WIPO’s use PCT System, increasing an acquired companies are, however, companies acquired startups particularly from other countries, applicants in acquisitions. 3,814with filed, inventions ranks in it total, jurisdictions by filing a single application. The The application. bysingle a filing jurisdictions percent of total patent filings. There has been been has There filings. patent of total percent followed by fields, other in trends patenting been acquired since 1998, with 53 percent 1998, percent 53 since with acquired been for AI patent filings. patent AI for …but are filings Acquisitions complement internal increasingly international increasingly research and IP strategies IP and research th in the number of of number the in 1,264 patent AI familiesin identified litigation, The potential The of impact already has AI societal offers report this in presented analysis The Technologies International. over litigation in plaintiffs top three The 4,231 in patent opposition cases worldwide. worldwide. cases 4,231 opposition patent in American Vehicular Sciences and Automotive and Sciences Vehicular American AI patents are Nuance Communications,

Overall, the of amount litigation identified of digital data and its effect on IP systems; systems; IP on effect its and data of digital considerations; to access and ownership widest the to mix maximize policy correct on the willhave to reflect of stakeholders awhole. as society and economy applicants. other on patent applications. However, none of with its expected effect on the workforce, the the workforce, the on effect expected its with 74 U.S.,with the in and of cases percent In many cases, organizations that cooperate cooperate that organizations cases, many In Policymakers will have will to toPolicymakers move quickly conjunction in viewed be must AI regard, this of ownership shares topthe 20 applicants availability of an appropriately skilled skilled appropriately of an availability ethical and legal regulations addressing and shape the direction of AI’s direction the evolution. Avariety shape intelligence artificial to which extent the shows is playing an increasingly important role in a a in role important increasingly an playing is 1 than low (less relatively is report the in co-assignees as credited are research in keep up with AI-related developments and and developments AI-related with up keep been identified – and much more is to come. In In is come. to more much and – identified been which litigated), being of patents percent possible benefits from AI, with particular particular with AI, from benefits possible for other assets, including talent, data, know- data, talent, including assets, other for focus on AI-related strategies, policies, laws policies, strategies, AI-related on focus how and other IP. other how and new insights into trends in AI innovation. AI It in into trends new insights have prove.to been There difficult may be infringement and to market the yetnot come have products that to due fact the may be with portfolio AI of its 1 percent than more range of otherrange and technological activities. Cooperation in AI research is limited, limited, is research AI in Cooperation Technology inform can trends but so is conflict is so but policymaking on the future of AI of future the on policymaking This report documents how AI-powered This report on the future of AI and the policy and and policy the and of AI future the on to decision- provide aims that contribution workforce; and investment strategy and and strategy investment and workforce; technologies are rapidly entering are rapidly global technologies an improved knowledge base for discussions discussions for base knowledge improved an from experts at the cutting edge of AI. It is a a It is of AI. edge cutting the at experts from makers in the public and private sectors with with sectors private and public the in makers markets and together brings viewpoints related funding. regulatory framework for this fast-moving area. publications. scientific AI-related in as 10 of the top 20 AI patenting as well in players academic up 17 of the top 20 organizations make Chinese

17 WIPO Technology Trends 2019 1 Introduction

A few decades ago, it was only humans who could play chess or read handwriting. Having been the focus of research in artificial intelligence (AI) for several years, both are now routinely done by machines. Today, researchers are working on many more applications of AI which will revolutionize the ways in which we work, communicate, study and enjoy ourselves. Products and services incorporating such innovation will become part of people’s day-to-day lives within the next few years as we embark on what some AI experts describe as the age of implementation.

Yet AI remains a challenging subject for many people. Definitions vary, have changed over time and are in some cases contentious. The technology is complex and wide-ranging, potentially affecting many different areas of human activity. And AI raises complex questions about privacy, trust and autonomy that are difficult to grapple with, and this has led to fears about humans themselves being under threat.

According to many observers, the current AI boom began about seven years ago. It followed a series of ups and downs, often referred to as “AI summers and winters.” The growth in computing power and power and computing in growth The Annex (see 150–154). pages any perform to successfully able systems AI predictions you make.”predictions you have, of data set the better the bigger The relevant. are ones which out figure I will and give examples me basically: is technique of x-rays. of “The anumber analysis on based cancer has apatient whether or court, in testimony on vote would based how ajury predict you says, can matters.” he For example, predictions on business, health and legal making have of algorithms you can sets ... observation, from technique old where a very of of aresurgence “an offshoot as AI describes Frank Chen Horowitz partner Andreessen report, this for Interviewed investment. research on by data the demonstrated –as diminished and grown alternately has AI in interest as winters,” to “AI and as referred summers often downs, and of ups followed aseries ago. It seven years about began boom AI current the to observers, many According wave AI The Such concepts are not something that current current that something not are concepts Such of data to be compiled and shared, has opened opened has shared, and to compiled be of data volumes large which enables connectedness, the in reading of further list the in found be can brain. human the surpass to far of amachine general intelligence or superintelligence; namely, artificial as such concepts from distinguished down into broken be can and chapters, and of techniques range awide encompasses or no intervention. human definition This For the purposes of this report, AI systems systems AI report, of this For purposes the these concepts and other approaches to AI to AI approaches other and concepts these only therefore they are and permits, technology ability hypothetical the or brain human the techniques and applications included in this limited with by humans performed typically addressed in passing in this report. More on on More report. this in passing in addressed subsequent in we as see will applications, systems; that learning as primarily viewed are systems, known as “narrow AI.” “narrow to as is known be systems, This intellectual task that could be undertaken by undertaken be could that task intellectual atask at better become can that machines is, many different categories of technology. The of technology. The categories different many report refer to individual tasks performed by AI by AI performed tasks to individual refer report 1987–1993 of toward AI. form this funding and of research focus the in change a and new successes brings systems expert of knowledge-based rise The 1980–1987 research. AI in interest and funding to first the leads winter”, “AI reduced with programs of AI capacities limited the with Overly coupled high expectations 1974–1980 approaches. problem-solving funding in promising, logic-based government enjoy of AI years golden The 1956–1974 discipline. academic an as founded is AI and conference aDartmouth at intelligence” termThe “artificial coined is 1956 history ofA short AI Go. game board complicated the in champion aworld beats AlphaGo 2016 in and autonomously Google 2012, navigate cars driverless Google In potential. AI the about optimism and funding of increased anew era heralding learning, deep and networks neural in mainly learning, in machine breakthroughs power allow for computational and ness connected- of data, availability Increased 2012–today quiz Jeopardy. at thechampions TV human two Watson beats IBM and Siri recommendations. In 2011, releases Apple provide to systems automated uses 2002, Amazon In chess. at Kasparov champion world beats IBM’s DeepBlue 1997, In data-driven. becomes AI and help of increased computational power the with marked are successes New increases. and returns AI about Optimism 1993–2011 expensive to update and maintain. prove and limitations show their systems expert as investors, and governments by perceptions negative it with brings 1987. in industry hardware hype AI The of collapse the sudden specialized the with “AI starts second winter” The

19 WIPO Technology Trends 2019 20 1 Introduction The Rise of the Robots ofthe Rise The The impact of impact The is on technologies humans AI Assessing the state of AI of state the Assessing Chapter 2 highlights the main trends across all all across trends main the 2highlights Chapter you do is encapsulated in data, at some point point some at data, in encapsulated you is do employment and growing inequality. “In the the “In inequality. growing and employment categorizations and classificationsare used methodology, of the adescription data: of patent of developed being technologies those and into types the insight provides of applications documents are publicly available once of Patents technologies. AI provide a valuable which fields they cover. they fields which be will to automation vulnerable most be will have will governments and employers while areas, different in skilled havewill to become by greater the reinforced turn in are which Much of this report therefore focuses on analysis analysis on focuses therefore report of this Much into substance the Moreover, deeper digging that are emerging, what they are applied to and to and applied they are what emerging, are that which identify over and time changes track to possible is it data, patent analyzing By of work.” kind that do can that If what predictable. and routine are that those of loss the with to deal how best to address this report, this in we as see will But, to data. up many new opportunities for up many technologies, AI new opportunities Now is therefore a good time to take a close to time take aclose agood Now therefore is accumulation and analysis of data raises ofaccumulation analysis and data raises access of and availability, collection systematic areas, while Chapters 3 to 6 explore the data data the 3to 6explore Chapters while areas, invention. of the details technical and application date of patent applicant, of the name the as Moreover, on. focused application patent are in patents related to AI over time, Chapter 4 to over 4 AI time, related Chapter patents in trends at 3looks Chapter detail. greater in AI an be will there that bet good it’s apretty jurisdictions are seeing most patenting activity. patenting most seeing are jurisdictions presented in the next section of this chapter. of this section next the in presented published, and include useful information, such further questions forfurther AI researchers. means of assessing trends in research as they they as research in trends of assessing means that 10 jobs next “the to 20 years,” predicts, he reveal the areas of innovation that inventors of inventors innovation that areas reveal the look at the state of research and exploitation exploitation and of research state the at look of Ford, author Martin likely to profound. be , thinks many workers workers many , thinks 7 and 8, look at public policies and the future future the and policies public at 8,look 7 and The analysis of the data in this report is is report this in data of the analysis The analysis includes also therefore report This This section explains the methodology used explains the section This used methodology with the combined insights offered, The Chapters report, in this twochapters final The How the research was done filing patents, Chapter 5 focuses on geographical geographical on 5 focuses Chapter patents, filing comments givencomments by invited some AI experts, bycomplemented the viewpoints and oppositions and open source investment. acquisitions on information relevant other by well as as byover geography and time of scientific publications, identifying trends and companies prominent most the discusses data were collected, classified and categorized.and data were collected, classified how Innovation, the by and CNRS conducted this throughout presented trends on data issues with along concern, acentral clearly fast, so is evolving that afield In contributions. of these some on detail more in draw and of AI, 10–11. pages on found be can contributors Some ofconcerns. these contributors Patents, however,Patents, of the apart provide only trends. market at 6looks Chapter and trends technological developments, issues of concern developments,technological of concern issues governments, businesses, that role to the is this universities and public organizations research and funding as well as patent litigation and and litigation patent well as as funding and and regulationand of including technologies, AI the privacy Related bias. and data access, around subject area. In addition, Chapter 6 provides 6provides Chapter addition, In area. subject sent written submissions and others were were others and submissions written sent general more addressing others and studies in AI and the policy responses in place. place. in responses policy the and AI in institutions should play in the development intergovernmental organizations and educational areas some but to make predictions, hard is it of Alist research. the during interviewed picture, as much research is never patented. never is patented. research much as picture, for the analysis in this report, which was was which report, this in analysis the for case or AI in issues specific on focusing merit particular attention. Data ownership is is ownership Data attention. particular merit report, should provide a valuable guide to key guide a valuable provide should report, related intellectual (IP) property framework. The bulk of this report comprises analysis of of analysis comprises report of this bulk The AI technologies may be described in patent patent in described may be technologies AI ���� discoveries, scientific theories, mathematical mathematical discoveries, scientific theories, it whether to see application each examines databases non-patent and patent in format data developed protected and by in-house trade data on patents and scientific publications,and (EPO). In many jurisdictions, the patent office office patent the (EPO). jurisdictions, many In Patents are thus generally granted by national by national granted generally thus Patents are Patents rights. property intellectual Patents are astructured in collected systematically they are technical inventions, patents scientific and administered by the European Patent Office Patent Office by European the administered granted. is patent a and filed was application they provide that meaning territorial, are applications. patent in described or articles scientific in published be may not advances established an as economists and academia draw and to analyze of data source appropriate an they provide accessed, publicly be can and as addition, In technologies. AI in trends analyze disclosed by patents, protected applications, and research about tell us can each what and such as the European Patent (EPC) Convention European the as such developments research protecting and sharing of means used commonly most the As secrets. or platforms, collaboration or projects source invention Where disclosed. be sufficiently inventive (i.e., step have a “non-obvious”), be is software whereas patentable, not general in subject apatentable under fall invention must trends, technology analyze and to track indicator inthroughpublications, open scientific shared innovation trends. how they differ to distinguish important is it patent offices – or through regional systems, systems, regional or through – offices patent an where jurisdiction the in only protection technologies’ in mind thatbearing certain ways to useful particularly provide publications patents for the same invention are filed in in filed invention are same the for patents claimed the and application, industrial potential novel be (i.e., jurisdictions), some in patentable not be part of the state of the art), involve art), of the state of the an part be not programs computer and methods (e.g., question in jurisdictions the in matter requirements, including that the certain meets meaningful conclusions. Patents and scientific literature are habitually used by industry by industry used habitually are literature Methodology and data per se per are are Once a patent is granted, a patent holder has, in in has, holder a patent granted, is a patent Once general, the right to exclude others from making, making, from to exclude right the others general, by withdrawn are or granted not are and criteria patentability of the more or one meet not do circumstances.certain may lapse it though jurisdictions, date most in In many fields of technology, and across all all of across technology,and fields many In those purposes, the invention, claimed those purposes, without Apatent reasons. different for applicants the under term protection patent the to extend of term The years. may take this several though 18 published months normally Patents are on preparation and collection boxthe Data on using, selling, offering for sale, or importing for for importing or sale, for offering selling, using, patentunder examination. and other requirements, the patent is granted, granted, is patent the requirements, other and application all meets the patentability criteria the that decides office patent filing. If the after jurisdictions, many patent applications filed paid, and some jurisdictions have provisions not are fees renewal applicable the if before, filing the from 20 years ordinarily is protection invention. to 22) asingle equate page and (see family patent same of the members being family may for which include patents members have been granted, others not granted or still still or granted not others granted, have been as described they are jurisdictions, numerous performed routinely by human experts. by human routinely performed tasks specialized more to master forward moving quickly is AI capabilities, human ordinary these Beyond emulating (AI). intelligence artificial are called algorithms navigation. Collectively, computer these and speech vision, as such capabilities, slowly human-like acquiring fundamental are that algorithms by powerful possible made is this All new world. complex this navigate effectively to us more allowing developed, being are services automated response, mobile applications and In environment. demanding and experiencing an increasingly complex we are individuals, society. As our transform to fundamentally begun has information and services products, globalization of the and abundance urbanization, accelerated years, recent In

NYU School of Medicine Aristotelis Tsirigos, definitionA AI of

21 WIPO Technology Trends 2019 22 1 Introduction equivalent processes such as as such processes equivalent invalidation proceedings, orobservation other invention. of the ownership of the determination where an invention is created by an employee, by employee, an invention created is an where Many patent offices provide mechanisms for for mechanisms provide offices patent Many third parties to patent challenge third rightsthrough parties cases In person. legal or natural to another inventor an speaking, Generally consent. their and litigation regarding AI patents are analyzed analyzed are patents AI regarding litigation and administrative opposition/ namely mechanisms, in Chapter 6. Chapter in proceedings (litigation).proceedings Trends in oppositions many jurisdictions provide a special rule for the the for rule aspecial provide jurisdictions many assigned be can to which apatent, right the has review. Patents can also be challenged in court court in review. challenged be also Patents can patent publications with scientific publications. compare helps also day and to present the closer is that insight provides year publication the on based data date. Studying priority earliest the 18 made are after months publications patent first general, In art. prior of the part becomes thus toavailable and public) the (i.e., made published first is application apatent which date the date on is publication The patents. for data date to present publication earliest the use report this in graphs Some made. invention the actually was when time the to reference of point closest application).the is It (priority office patent at a filed was family patent in a application patent first the date the when is adate. This include that analysis and graphs of the most in used date is priority earliest the report, this in data For patent once. counted only is it family patent a of members are there several where that even ensures This filing. first of offices other WIPO, USPTO, INPI (France), then DMPA and Kingdom) United (the IPO (Germany), UK EPO, preference: of order this in offices filing different the among from chosen document patent by single one represented is family patent each analysis, and graphs the In 150–154). pages (see Annex the in reading of further section the in found be can analysis may have the on type family patent of the choice the impact the and families patent about information More jurisdictions. different in protection patent seeking data priority exact the invention sharing same the together grouping used, were FamPat the families report current the for families; patent of definitions different inventive are and step.novelty There of purposes the for data of priority pieces more or one share family the in applications number, other and priority the as known is what has family the in application earliest The content. technical similar or same relate that to the offices different in patents those all includes family Apatent into families. patent grouped are patents These authorities. 100 than patenting more from patents granted and indexes applications patent collection FamPat The by Questel. provided FamPat the collection from extracted are Patent data exhaustive. be may not like source any data clustering scheme (see to report this paper background the in available is databases of these coverage The databases. Orbit and Darts-IP on based is data 15, June between 2018 extracted were and 29, June 2018. and Scopus Elsevier Litigation 31, using March on prepared extracted have 2018.data been publication Scientific were and Orbit Questel database patent the using prepared have data been patent The Data collection preparation and , available at at , available inter partes inter www.wipo.int/tech_trends/en/artificial_intelligence

Among the different data sources available, available, sources data different the Among Once published, the information in a patent is is apatent in information the published, Once or individual) and inventor(s), and athorough individual) and or (organization applicant of the date, name the the art.” the invention, of the including description technical and once a patent has expired or lapsed, it is is it lapsed, or expired has apatent once and purposes academic or research for analyze and to study read, researchers for available much include also They databases. accessible in collected are They AI. in trends analyzing part of the public domain. public of the part of “state as known solutions technical previous patents provide several advantages for relevant information, such as the application application the as such information, relevant

Data collection method and ) and and ) This process resulted in a total list of 339,828 list atotal in resulted process This AI. The data in this report are based on the the on based are report this in data The AI. definition agreed of an lack of the because AI ���� classification codes used by patent offices and and offices patent by used codes classification below, discussed scheme classification the to however can difficult it be documents, report, of this For purposes the databases. topatent a file choose companies where cases those in particular in information, unique contain jurisdictions except patented, those in be cannot the with compared and detail in discussed web resources. Samples of the results were were results of the Samples web resources. areview on of existing based selected were limits time no with searched, haveworld been can as they publications scientific on working However, much research is not patented but However, but patented not is research much that follow. The patent families related to AI to AI follow. related that families patent The patent in of information availability the Despite patent related the and Patent applications thetherefore information complement available an extended list of specific keywords, which which keywords, of specific list extended an constitutes of what concepts changing the and application and not publish a scientific paper. allowing publications Scientific period. a grace identify exactly which patent families relate to relate families patent which exactly identify report this in used numbers total The imposed. in information the while databases, patent in novel therefore and considered longer no is it publication, ascientific in invention disclosed is are AI to related publications scientific in patent families from different offices around the the around offices different from families patent to researchers of use may be publications patent an Once field. same the in activity patenting the in analyzed have been also publications insteadThese published in scientific journals. patent families related to AI, and these patents patents these to and AI, related families patent appendix. the in provided families, based on the total collection of 59.3 collection total the on based families, chapters the in analysis of the basis the form patent using by identified be can AI in families million patent families at the time of the search. of the time the at families patent million are analysis tool. data of the Fullmining details atext- using validated and checked manually represent about 0.6 percent of all patent patent of all 0.6 percent about represent results. overall to these refer chapter, each In trends report. this for research literature, well-established hierarchies and Counting categorizing and patents • ���� on the three main ones: main three the on drew report this for search patent The offices. to their technical features. This facilitates facilitates This features. technical to their patent to classify codes use Patent examiners applications and other documents according according other and applications documents searching and examination. There are several several are There examination. and searching patent classification systems used by patent patent by used systems classification patent International Patent (IPC): Classification maintained by the World Intellectual Patent classification experimental conditions in laboratory were They of implementation. age an there’s and likely toof discovery be we’reInternet, age of the end the at of the notion the or systems, operating computer or recognition of speech history the at humanity. If you look to contribution social largest the be will – that applications toactual fit the known really honed technologies all making be will it think I personally everything. penetrate will AI Eventually warehousing/transportation/delivery. including logistics, automotive, and manufacturing, and transportation healthcare, retail, as such areas see will of money. we that After transactions immediate are there where – those affected be will that industries biggest the are e-commerce and financial and Internet probably five next the years, In thing.most important the 100 be will –that percent applicable so that technologies they become can tweaking usage? and Fixing mainstream are Which drivers? momentum the are ones Which adopters? early the are Which industry. in yet not applied and Kai-Fu Lee, Sinovation Ventures adopt AI? How will industry

23 WIPO Technology Trends 2019

24 1 Introduction

Expert systems Expert 1.1.Figure techniques AI

Description logistics Description Logic programming (general) programming Logic programming Logic

engineering Ontology

Multi-task learning Multi-task

Fuzzy logic Reinforced learning Reinforced

Unsupervised learning Unsupervised Supervised learning Supervised

Machine learning Machine learning (general) learning Machine Bio-inspired approaches Bio-inspired

Latent representation Latent Instance-based learning Instance-based Probabilistic

reasoning

Rule learning Rule

Probabilistic graphical models graphical Probabilistic Logical and relational learning relational and Logical Classification and regression trees

Deep learning Deep Support vector machines Neural networks

• • • • • 1.1 the on to 1.3. based chosen was This The scheme comprises three main categories: main three comprises scheme The are technologies AI this report, Throughout AI technologies over technologies AI time. (ACM) for Computing Machinery Association ���� Computing Classification Scheme, which has Scheme,which has Computing Classification of providing a clear analytical framework for the the for framework analytical aclear of providing of deep emergence the as such developments several identified report CPC. of the This case the in 250,000 to symbols),or around rising technologies, this scheme has the thistechnologies, has advantage scheme Each of these classificationcontains schemes adapted to take technological of account recent figures in illustrated scheme the using analyzed scheme was last updated in 2012, in updated last was scheme been has it perspectives and use different definitions of AI AI of definitions different use and perspectives this As years. 50 over past the developed been hundred classification codes relevantcodes classification hundred AI.to classes (so-called codes 100,000 than more report and the presentation of the evolution of evolution of the presentation the and report learning. While AI experts may have different may have different experts AI While learning. AI application fields: different fields, areas or areas fields, different fields: application AI AI functional applications: functions such to ameans as used may be techniques AI of statistical forms advanced techniques: AI China’s (CNIPA). Patent Office Patent Cooperative (CPC): Classification disciplines where AI techniques or functional functional or techniques AI where disciplines typically performed by humans; different different by humans; performed typically codes classification F-termFI list: these and by used and (WIPO) Organization Property as speech or computer vision which can be be can which vision computer or speech as as such models, mathematical and (JPO). Japan in used and developed are by used IPC, the also on is it based and EPO the and (USPTO) Trademarkand Office as transportation, agriculture or life and life and or agriculture transportation, as applications may findapplication, such systems, allowing the of computation tasks implement AI different functions. jointly developed by the United States Patent States by United the developed jointly medical sciences. medical expert and logic fuzzy learning, machine 100 than offices. more realized using one or more AI techniques. AI more or one using realized Categorization of AI technologies AI of Categorization This scheme is used for categorizing both both categorizing for used is scheme This (using keywords). and keywords) and scientific publications patent publications (using classificationcode at close to human performance levels in levels in performance to human close at outstanding, with functioning algorithms AI so have been translation machine and to recognition, speech computer vision applied learning in deep breakthroughs topic, recent research amarginal as years earlier in Seen Institute. of the director scientific the and field of the Yoshua one being of Bengio the founders with 1990s, the Mila’s since research of heart the at been has learning Deep AI. on based revolution industrial anew fueling is learning deep a nutshell, In disruptive. likely to profoundly are be economy and industry, society on impacts its clear became soon it that cases, some Myriam Côté, Mila revolutionlearning deep The

25 WIPO Technology Trends 2019

26 1 Introduction Computer vision (general) vision Computer

Image and video segmentation Figure 1.2. AI functional applications Augmented reality

Character recognition Biometrics

Object tracking methods Scene understanding Control Computer vision scheduling Planning and Robotics representation Knowledge reasoning

and Sentiment analysis Sentiment processing

language

Morphology Natural Semantics

processing Natural language generation language Natural

Speech Dialogue

Distributed AI Distributed

Machine translation Machine

Information extraction Information Natural language processing (general) processing language Natural Phonology Predictive analytics Speech processing (general)

Speech recognition

Speech synthesis

Speech-to-speech Speaker recognition Speaker • • • • • • • • • • • • • • • • Figure 1.3. AI application fields application AI 1.3. Figure VoIP Videoconferencing Telephony broadcasting Radio and television Computer networks/internet Public health Physiological parameter monitoring Nutrition/food science Neuroscience/neurorobotics Medical informatics imaging Medical Genetics/genomics discovery Drug Biomechanics Biological engineering Bioinformatics government Computing in

Entertainment

Military

munications Telecom- sciences Life and Life

medical

and publishing management Cartography Document Transportation Networks • • • • • • • traffic engineering traffic Transportation and Driver/vehicle recognition Autonomous vehicles Aerospace/avionics Internet of things (IoT) things of Internet Social networks cities Smart Education Banking finance

and

manufacturing Security Business Industry and Industry engineering sciences Physical • • • • • • • • and Privacy/anonymity Cybersecurity Cryptography Authentication surveillance Anomaly detection/ Enterprise computing e-commerce Customer service management humanities Arts and Arts Energy

Law, social behavioral computing sciences Personal devices, and HCI and • • • • behavioral sciences and social Law, Industrial property and PC applications Personal computers computing Affective Agriculture

27 WIPO Technology Trends 2019 28 Chapter title

Photo © Siemens Healthineers. Note: The system is currently under development and not for sale. Its future availability cannot be guaranteed. Photo © Siemens Healthineers. Note: The system is currently under development and not for sale. Its future availability cannot be guaranteed. improve patient experience and transform the delivery of care. and every minute saved by optimizing significantly can processes major time savings. Time is acritical factor in many areas of healthcare, scenarios not only improves treatment the but also potential offers for simulation of different The prognoses. physicians develop precise more This is excellent an example of using digitalization and AIto help resynchronization therapy could also besuccessful in the real patient. pumping was of corrected, indication an the as heart it virtual served that the electrodes, and virtually generated electrical pulses. If the asynchronous cardiologists created adigital twin of the patient’s virtually implanted heart, implanted on the right ventricle, the other one on ventricle. the left Heidelberg The pacemaker that resynchronizes the using beating two electrodes, heart one for patients suffering from chronic congestive failure. heart It involves advanced an University of Heidelberg. Cardiac resynchronization therapy is atreatment option of these algorithms in cardiac resynchronization in aresearch project at the models of organs on based vast amounts of data. Cardiologists tested the use Siemens Healthineers is developing intelligent algorithms that generate digital to treatment on acomputer before the actual intervention. planning toVirtual then can visualize beperformed its responses simulates the physiological of processes apatient’s most vital organ. was on Based MRI and ECG the heart. measurements, the model organ first The to beprecisely simulated using the digitaltwin method tailored treatments, paving the way for the expansion of precision medicine. physiological model. This holds the potential for evaluating the effectiveness of are to used approximate of acombined individualized parts multi-scale to train learning deep neural networks. In step, asecond the neural networks actionable insights. First, millions of examples of curated data are leveraged a technology that links the real and digital worlds, using AIto turn data into right“the treatment for the right patient at the right time.” of They are part remain imperceptible, representing the next step towards the goal of providing Digital twins mirror reality and detect can problems that would otherwise twin digital The Case study by Siemens by study Case be cannot availability future Its sale. for not and development under currently is system The Note: Healthineers. ©Siemens Photo

guaranteed.

29 WIPO Technology Trends 2019 2 Trends in artificial intelligence

Looking first at trends in AI techniques, machine learning predominates, representing a One of the most striking characteristics of research in artificial intelligence (AI) is the massive 89 percent rapid growth that has been seen over the past five years. The impressive numbers of filings mentioning of patent filings in this period and the decrease in the ratio of number of scientific this AI technique papers to inventions are indicative of a shift from theoretical research to the use of AI and 40 percent of technologies in commercial products and services. This trend is also reflected in the all AI-related types of patents being filed, with significant growth in specific AI applications and patents. sector-specific fields.

In this chapter, we present the overall trends in AI, including the behavior of its key players, geographical trends, and acquisition and enforcement trends, using the three categories of 1) AI techniques, 2) AI functional applications, and 3) AI application fields, illustrated in figures 1.1 to 1.3 in Chapter 1. The growth rates reported below are based on the average annual growth rate of patent filings from 2013 to 2016. The findings are analyzed in more detail in Chapters 3, 4, 5 and 6 that follow. Turning to trends in AI functional applications, AI filings concerning both robotics and control control and robotics both concerning filings AI by grew 24 and patents percent AI-related AI-related of all percent 40 and technique AI Trends in AI applications functional techniques AI in Trends vision was mentioned in 49 percent of all of all 49percent in mentioned was vision Within machine learning, every AI technique technique AI every learning, machine Within Within speech processing, speech-to-speech Within computer vision – the top functional While these three functional applications are 28 (though it still only accounts for 1 percent 1percent for accounts only still 28 it (though grew by 49 percent. Other techniques with with techniques Other bygrew 49percent. of natural language applications). processing by analysis sentiment and bygrown percent 33 example, while those for planning/scheduling fast. growing and emerging are others of filings, 2013during to 2016. top areas two other The computer vision, which includes image Looking first at trends in AI techniques, AI in techniques, at trends first Looking the most important in terms of the total number number total of the terms in important most the understanding one of 28 percent. Within Within of 28 one percent. understanding a massive 89 percent of filings mentioning this this mentioning of filings percent 89 a massive annual growth rate of 31 percent and scene scene and rate of 31 growth percent annual average an seen has –biometrics application (13 processing percent). speech and showed an increase in annual filing numbers filing numbers in annual increase showed an in functional applications are natural language 175 an with over AI, in the increase percent processing (14 percent of all AI-related patents) patents) (14 AI-related of all processing percent Multi-taskperiod. the learning, fastest, next by 19programming percent. by grew 28 percent learning Machine patents. for the same period, but some stand out. Deep Deep out. stand some but period, same the for 2013from to 2016; fuzzy period, same the in notable increases were neural networks, latent latent networks, neural were increases notable representing predominates, learning machine has grown by 15 percent, and speech by grown speech 15has and percent, has semantics processing, natural language have by grown 37 percent. for by percent, 55 have increased methods recognition, is the popular. most Computer learning. unsupervised and representation learning is the fastest growing technique technique growing fastest the is learning by logic grown 16 and has logic percent Telecommunications, the most second AI-related patents),AI-related (15 telecommunications Trends in AI application fields application AI in Trends of applications in 2006, by 2016 2006, in of applications accounted it bygrown 12 percent. with annual growth rates of at least 30 percent percent 30 least of at rates growth annual with 8,700 filings). the proportion of filingsmentioning thebusiness, proportion boom the over ten years, trends at Looking the top fields, Lastly, application AI in at around 24 percent during this period, but but period, this during 24 around at percent agriculture, and computing in government, important application field, has remained application field, has remained important becomes technologies in transportation (15 of all percent transportation are industries between 2013between 2016. and transportation, are industries Growing percent). (12 sciences medical life and and percent), for one-third of applications (with more than than (with more of applications one-third for more evident: representing just 20 percent 20 percent just representing evident: more recognition and speaker recognition have both recognition speaker and recognition need for the combination of different AI AI of different combination the for need industry. For application systems, the AI future to the dominate aiming players for opportunity another be could AI for chips with framework learning Deep applicable. more practically technologies to make AI software with hardware to combine atrend is For industry, there and in scientific publications. filings patent in to trends the correspond developments applications. Such practical in utilized been have and already potential industrial vast have shown also processing natural language and processing speech for functional applications, computer vision, And made. been has progress significant and well studied been has ten years, past the for nearly learning deep particularly techniques, AI machine learning, Regarding scenarios. business with to integrated be need also systems application AI serious. more getting is techniques with functional applications Haifeng Wang, Baidu reflect reality? theDoes data

31 WIPO Technology Trends 2019 owning quite large portfolios of patents related Deep learning is to the deep learning sub-category of machine learning, followed by Alphabet, Siemens, the fastest growing Xiaomi, Microsoft, Samsung, IBM and NEC. technique in AI, Organizations that cooperate in research may be credited as co-assignees on patent with an 175 percent applications. However, the data indicate that co-ownership of patents is rare for most increase between technologies. None of the top 20 applicants 2013 and 2016. co-owns more than 1 percent of its AI portfolio. Around one-fifth of the top 500 applicants, ranked by number of patents, are from universities and public research organizations document management and publishing or life from China. The highest placed such and medical sciences has decreased. organization is the Chinese Academy of Sciences, which has 2,652 patent families, placing it 17th in the overall list of applicants. Key players Patenting activity from Chinese universities and public research organizations has seen Companies represent 26 of the top 30 significant growth (between 20 and 80 percent applicants, most of them active in consumer annually on average between 2013 and electronics, telecommunications and/or 2016), while patenting activity from top U.S. software, as well as in sectors such as electric universities and public research organizations utility and automobile manufacture. Just has diminished (by between 20 and 26 percent four of the top 30 are a university or public annually) from 2013 to 2016. research organization. Among universities and public research IBM is the company with the largest patent organizations, computer vision is the main portfolio (8,290 applications), followed by functional application mentioned in patent Microsoft (5,930 applications). Of the top 20 portfolios (as with companies), while machine companies, 12 are based in Japan, three are learning and neural networks are the most from the United States of America (U.S.) and frequently mentioned techniques. two are from China. The top universities/public organizations make Computer vision is the main functional the vast majority of their priority patent filings application mentioned in patents by the in their country of origin. Fraunhofer is the main top companies (19 out of 20), though IBM exception, with some priority filings also made has a greater focus on natural language in the U.S. or via the European patent route. processing. Machine learning is by far the most represented AI technique in the top applicants’ portfolios. Geographical trends

One notable trend concerns the leaders in deep Looking at those offices where patents are filed, learning, the fastest-growing area of machine it is possible to identify trends in developments learning. The Chinese Academy of Sciences in AI research. The first patent filings in AI were possesses the largest patent portfolio explicitly made in Japan in the early 1980s, but this dealing with deep learning techniques (235 office was subsequently overtaken by both patent families), and most of the main portfolios the U.S. and China. Since 2014, China has led in this field have been filed by Chinese the world in the number of first patent filings in

2 Trends in artificialintelligence in Trends 2 universities. Baidu leads among companies AI, followed by the U.S. Together, these three

32 The Chinese and U.S. offices lead in all all in lead U.S. and offices Chinese The of patent percent 40 with compares This at Two-thirds filed are families patent of AI 4 percent protected in another jurisdiction. jurisdiction. another in protected 4 percent 434 companies in the AI sector have been have been sector AI the in companies 434 Available data on acquisitions indicates that that indicates acquisitions on Available data Acquisition of AI patents Canada Australia. and vast majority of acquired companies in the field field the in companies of acquired majority vast filing subsequent applications after the first first the after applications subsequent filing single a by filing jurisdictions multiple in file The international. more becoming are filings 0.6percent just and jurisdictions more fiveor in the lead the and also U.S. China filings. While it is too early to assess the impact of AI of AI impact the to assess too early is it While of those first filed in the U.S. that are then thethen inare that filed U.S. first of those in filed are only, percent 9 office while one patent of total for percent 78 account offices of AI are U.S. ones (283 acquired companies), companies), (283 U.S. acquired are of AI ones (IP) property intellectual its complement and of acompany’s strategy part be can activity.economic For example, acquisitions and into business insight provide can data ontology engineering. computer vision processing, and speech made include the U.S.,one been has China, with 25 companies. acquired (U.K.) second ranks Kingdom United the while in out of Korea stands Republic the while though Japan is prominent in fuzzy logic, logic, fuzzy in prominent is Japan though techniques and functional applications, for offices Popular Patent Office. European to allows applicants which PCT System, However, patent that indications are there technologies on individuals and society, certain society, certain and individuals on technologies application, is extensively used, as is the the is as used, extensively is application, percent and 32 Japan in filed first applications Thevast than more at 10 filed offices. are acquisitions have taken place since 2016. The of percent 1998, 53 that since and acquired subsequentlyelsewhere. filed protection development and efforts. focused onfocused the domestic market, with only majority of Chinese applications seem to be to be seem applications of Chinese majority ofnumber publications. scientific (scientific findings)and patents (new articles ofresearch evolution the by tracing captured be can technology and ofscience co-development This research and private enterprise. theblurring distinction academic between research and technological progress, scientific interaction between demands field this in change oftechnological speed sheer The works. brain human how the of mechanism the from comes discovery) drug new and engines ofjet maintenance as autonomous driving, condition-based such applications AI for (used network neural ofadeep idea the example, an As computer and science cognitive science. between by interaction the driven been has innovations AI in progress Recent rights. IP and of data policy the concerning private ownership be developed in line with competition technology.proprietary They should also and science open between tension for potential the recognizing time same the at while sectors, private and public both cover should AI promoting Policies field. and technology proprietary in the AI science open between to interplay the alive to be need therefore Policymakers over inventions. rights IP obtains it as over time increases sector private the by played role the but technology, of AI contributesScience to the development moving job). by or appointment ajoint through (either patenting activities at a private company involved laterorganizations became in in publications publicAI-related research – those whoprocess had published this to contributes firms private and academia between ofpeople crossover earlier. The rising ofpublications volume publications and patents, but with the scientific both in trend upward an technologies). My own shows research University of Tokyo of University Kazuyuki Motohashi, AI-innovations in logy techno- and Science

33 WIPO Technology Trends 2019 34 2 Trends in artificial intelligence Advances in information (ICTs) technologies communication and in are driving global changes security,and development. rights, human and peace UN: of the pillars AI of three towards the benefits the advance to and promote hopes the on cars driving self- and of autonomous implications the Germany, considering is UNECE and including driving, autonomous for frameworks issuing are countries first The findings. its published 2019,January In ILO’s the benefits. to their access equitable and technologies of AI development inclusive and safe trusted, ensure XPRIZEUN agencies, Foundation ACM and to foster global multi-stakeholder to dialogue sister with year, partnership in “AI the Every of AI. Summit hosts ITU Good” implications for the to consider hard working is UN the SDGs, of the to achievement the contributing in efforts their in agency UN of every work the impacts potentially AI that Recognizing divide, one with profound globally. implications for inequality of digital form sophisticated more another up of opening arisk is there If not, collaboration. unprecedented willbothrequire and — good social for solutions the delivering as difficult as may be employment on impact disruptive its as such challenges socio-economic and complex multifaceted. and that are very challenges Navigating ethical, AI-related technical, creates AI opportunities, the with along that However, recognition public growing is there sectors. industrial different across consumption and production to responsible monitor, used manage be and can plan AI data. piecemeal, smaller, in more or unnoticed may pass that correlations and patterns identify and datasets huge to analyze ability its in to Akey lies AI advantage students. individual for packages education to personalize used be can analysis data and AI education, In of diagnosis. speed and accuracy the improving techniques, and tools medical traditional complement help and of doctors methods improve working the can AI health, In causes. root their and hunger and poverty to analyze used be can 17 of the techniques every and tools AI SDGs. and to achieve each help used be can AI and of data how big examples many are There Development (SDGs), Goals Sustainable to and drive in development. transformations toward the progress to measure decision-making, evidence-based more to enable data big to unlock the value opportunities Without of doubt, societies. and offers AI our economies other, each behave with shaping and way the to forces the –from we communicate society our Malcolm Johnson, ITU development action humanitarian and sustainable How support help data big can AI and Vienna ConventionVienna on Road Traffic Global Commission on the Future the on of Work Commission Global

. In these and other ways, UN the other and these . In

Examples of AI applications

Boi Faltings, EPFL

Distributed AI can be used to optimize resource sharing without appearing to U.S. companies also lead the way as acquirers. place restrictions on people’s behavior. Six out of the top 20 companies have acquired To take one example, recent work has AI companies. Ten companies have made shown how to best place electric vehicle at least five acquisitions in this field and charging stations so that users will between them have made 79 acquisitions find them naturally and conveniently in total. Alphabet, Apple and Microsoft have available. Another use of distributed AI is been the most active entities, with 18, 11 and to enable intelligent infrastructure such nine AI-related acquisitions, respectively. The as smart grids. These connect intelligent number of acquisitions identified in the AI devices such as heating and washing sector has increased every year since 2012, machines to renewable energy supplies reaching 103 in 2017. so that demand on the devices can be matched continuously to the available Funding provides further insight into AI activity. electricity supply, without noticeably As of May 2018, 2,868 companies related to AI affecting the comfort of their users. Such have been identified as receiving a disclosed a technology is indispensable for the amount of funding (44 percent of 6,538 large-scale take up renewable energy, companies). This represents about US$46 which is difficult to store. billion in funding in total. Another area where AI can have a huge impact is digital medicine. For instance, Enforcement of AI patents utilizing recent advances in deep learning, a smartphone app can detect skin cancer Turning to legal disputes over AI patents, at an early stage using an image taken available data on litigation and opposition from a cellphone camera. In the future it cases from different regions can be analyzed will be possible to detect diseases from to identify trends over time, as well as the most data collected by wearable sensors, and active parties as plaintiffs and defendants: to suggest optimal treatments to prevent 1,264 AI patent families are mentioned in these diseases from developing. This will litigation cases and 4,231 are mentioned in however require a major data collection opposition cases for the period 1975 to 2017 effort and possibly new advances in (years correspond to earliest priority years ensuring data privacy. of the patents implicated in the litigation/ opposition cases). There are 492 patent AI also has the potential to have a large, families mentioned in both types of dispute. beneficial influence on the tertiary sector. Machine translation, for example, The top three plaintiffs in litigation cases are allows people to communicate and do Nuance Communications, American Vehicular business across language barriers, and Services and Automotive Technologies thus creates many new opportunities, International, while Microsoft, Apple and not only for profit, but also for enriching Alphabet are the top defendants. The biggest people’s lives. filers of oppositions to AI patents are Siemens, Daimler and Giesecke+Devrient, while the main defendants in oppositions are Samsung, LG Corporation and Hyundai. Technology Trends Report 2018 Trends Technology

35 36 Chapter title

Photo: © alengo / Getty Images Photo: © alengo / Getty Images happiness cent and per lower 30 stress if you go to hour an bed earlier tonight personalizedbased recommendations, such “40 as percent higher chance of increasinglybecome accurate. app The also has the ability to make evidence- (20 percent higher than yesterday).” she the As uses AIapp, its forecasts percent chance of higher stress” or “40 percent chance of becoming sick it might indicate for tomorrow percent “30 chance of feeling happy,” “80 forecast, but personalized to predict her mood, stress and health. For example, AIappThe provides her privately with information that is like aweather continuously collects data related to her sleep, stress and physical activity. receiving texts and calls, and to communicate watch, with her smart which consents to have it securely track her sleep, activity, mood, times of sending/ prevent this from happening, she downloads anew AI app on her phone, and where she has anearly 50 percent chance of experiencing depression. To the other and your daughter is entering ahighly demanding university program Imagine your family has ahistory of depression on one side and panic attacks on computing Affective Case study by Rosalind Picard, MIT Media Laboratory Media MIT Picard, Rosalind by study Case goals (e.g., increasing focus and calm, or improving their sleep regularity). the user’s affective state in ways that help the successfully user achieve their body of work in affective computing, where systems sense and respond to lacks emotional intelligence. This latter of challenge amuch larger is part or annoying, which is what happens with most of today’s technology that that are inspiring and successful, opposed as to being irritating, frustrating are supporting improved health), but also engineering ways to make suggestions evidence-based behaviors to suggest for (if the each person recommendations solve this requires not only lots of data mining and modelling to which learn Another area of active research is making personalized recommendations. To thisuses passive data to predict the scores given by atrained psychiatrist. wearables, so that the not does user have to enter anything manually, and then Another approach requires only passive data and from sensed smartphones toto whether learn try the is likely person to “severely become depressed.” providesa person every day, these and uses in arecurrent neural network 345,000 days of data, the approach takes input as the answers to questions for your forecast. personal In one application where there were more than in your data to guide the selection of the machine learning method used learningdeep –or using ahybrid approach that allows personalized variations machine learning, and either using the data to train neural deep networks – mood forecasting rely upon getting large sets of labelled data for supervised regulate their emotions. most The successful methods today for solving with technology that helps individual an better understand, monitor and This describes area an of active research in the field of affectivecomputing, and talk with afriend today –here are some you might consider to call.”

37 WIPO Technology Trends 2019 3 Evolution of AI patent applications and scientific publications

A significant growth of patents in a field is usually observed Key findings long after scientific • Nearly 340,000 patent families and more than 1.6 million scientific papers related to publications. There artificial intelligence were published from 1960 until early 2018. is a 10-year delay • The number of patent applications filed annually in the AI field grew by a factor of 6.5 for most techniques, between 2011 and 2017. • The boom in patent applications, oriented with the exception towards the industrial application of technical solutions, lags that in scientific of deep learning. publications by about 10 years. In addition, the ratio of scientific articles to patents published is reducing, suggesting a greater interest in the practical use of AI technologies. • The AI techniques on which the patent literature focuses most extensively are machine learning, followed by logic programming (expert systems) and fuzzy logic. The most predominant AI functional applications are computer vision, natural language processing and speech processing. • The AI application fields most commonly mentioned in patent literature include telecommunications, transportation, and life and medical sciences, but almost all fields show a growth in patenting activity in recent years. • • ���� Historical development grew by about 8 percent a year on average, average, on ayear 8percent bygrew about invention. corresponding by patent the represented is and once Between 2006 and 2011, 2006 Between patent publications 2012. in accelerated 1980s, and early the backLooking over time, the data show that family,” “patent terms the report this “patent to intelligence artificial were published than 1.6 related million papers scientific Nearly 340,000 patent families and more more and families patent 340,000 Nearly an average of 28 percent ayear. of 28 average an actual percent The application,”or “invention” filing” “patent may of purposes the For earliest. filed application 55,660 in 2017. in 55,660 a6.5-fold represents This since 2013 – a remarkable recent increase in in increase 2013since recent –aremarkable increase in annual filings filings over a annual in 12-yearincrease since constantly grown has field the in interest 1, counted is Chapter in family patent each be used interchangeably, referring to the referring interchangeably, used be 2018. 1960 early and explained between As patent publications. published have been field AI the in patents of all percent 53 that means It also period. 2012 between but 2017, and they by grew number of published applications year per rose from 8,515 from rose to 12,473 2006 in 2011 in and the and member family patent representative There are also strong linkages between between linkages strong also are There patenting in increases marked most The co-occurs with computer visionco-occurs applications. often learning deep For example, clusters. were the fastest growing AI functional functional AI growing fastest the were the fastest growing application fields. application growing fastest the growth annual average an had learning Deep applications, and aerospace/avionics (67 percent) 55 (both methods control and 2013 between 2016 and activity a features percent) and smart cities (47 cities were percent) smart and percent) machine learning technique, machine learning. learning deep rate of 175 percent in this period. Robotics rate of 175 Robotics period. this in percent The AI patent boom The majority of patent families (68 percent percent (68 families of patent majority The AI is a major topic in scientific literature, with with literature, scientific in topic amajor is AI Scientific literature Categorization of AI technologies Categorization 2007. of ratio annual the shows 3.2 Figure date. Figure 3.1date. Figure scientific in trends shows also are family.of apatent Patent applications or 232,423or two inventions) into least at fall categories, of these more to or one refer can mention 75 while percent technique, AI one 1, Chapter in we 44 that know described and 2002 to 18 between doubling percent 1996 2001, and between almost of 8percent typically published 18 months after the priority priority the 18 after published months typically 3.1Figure patent AI in trends the shows the three areas as they are mentioned in in mentioned they are as areas three the between overlaps the shows 3.3 Figure content.technological Using the scheme to their according categorized be Patents can that period. 2015, in patent per papers suggesting three Figure 3.1 patent compares filings also a total of 1,636,649 papers published up to up published of 1,636,649a total papers applications published from 1960 to 2017, application field.Since patent documents a functional application and 62 an percent and uses practical the in interest increased an and scientific publications since 1960. It scientific papers to patent papers scientific ratioThe families. scientific AI-related in boom the that shows industrial applications of applications industrial during technologies AI publications from 1960 to 2017. of amember publication earliest the on based patent documents. least at mention patents AI of all percent rate growth annual average an with patents, in 10 that before about years started papers fell from eight papers per patent in 2010 in patent per to papers just eight from fell mid-2018.

39 WIPO Technology Trends 2019 40 3 Evolution of AI patent applications and scientific publications 0 2 4 6 8 dropped from 8 to 1 in 2010 8to 1in from 2016 to 3to 1in dropped families patent to publications scientific of ratio The year publication earliest by families patent to publications scientific of Ratio 3.2. Figure 2012 2017between and annually percent 5.6by publications scientific and 28 of percent average by an grew families patent AI year publication earliest by publications scientific and families 3.1. patent AI Figure 100,000 50,000 0 1967 1962 1977 1967 1987 1972 1997 1977 2007 1982 2017 1987 one category than more into fall patents AI-related of percent 68 over combined: often are technologies AI fieldsand their overlaps techniques, functional application applications, AI to related families Patent 3.3. Figure 15,959 fields Application 1992 41,886 1997 Techniques 22,699 2002 104,485 47,580 2007 38,472 Functional applications 2012 Scientfic publications Scientfic Patent families 2017 66,044 This number includes documents that documents refer includes number This patent that indicate statistics The A total of 150,637A total dealing families patent ���� Go in 2016. in Go Ng Brain’s by Andrew Google headed team various is techniques. AI the Machine learning or 40 percent of all AI patent families. This This families. patent AI of all percent 40 or to one show only the and technique, dominant that those well as as to techniques AI only detail below. more in discussed are areas three of the each AI of scientific percent 64 techniques: AI on 14 and (47,580 percent categories, inventions) with the development or use of a specific AI AI of aspecific use or development the with Machine learning represents 89 percent of percent 89 represents learning Machine techniques by total number of applications. of applications. by number total techniques of AI breakdown the shows 3.5 Figure for trends patent the 3.4 illustrates Figure 2018. to up early published were technique for publications scientific and patents in trends of patent percent 44 with compared technique, and Alphabet’s Google DeepMind AlphaGo AlphaGo DeepMind Google Alphabet’s and years. recent in in filings increase a significant families. patent AI all of percent 44 represents and fields, application also mention functional applications and/or implementation”. of AI now age the in are The on filings their to tend focus applicants application field. patent of all 31 only percent representing alone, industrial applications, unlike scientific inventions) mention only a technique and 15 publications mention at one least specific likely to more focus are which publications (15,959percent an only inventions) mention a only inventions) mention 66,044 percent, category into asingle fall that families patent beating a human in the complex board game game board complex the in ahuman beating technique, to AI an related families patent fall into all three categories. Among those those Among categories. into three all fall families. As pointed out by Kai-Fu Lee, “we by Lee, Kai-Fu out pointed As families. functional application, while 22 (22,699 percent (104,473families (63 inventions), majority the reliable cat image recognition in 2012 in recognition image cat by reliable made thanks progress the reflects tomachine learning in landmark applications such as as such applications landmark in learning AI techniques 1980s. A recent, though moderate, increase increase moderate, though 1980s. Arecent, The growth in publications relating to AI to AI relating publications in growth The Turning a total literature, to scientific the neural and learning deep in interest recent This AI techniques, namely ontology engineering ontology techniques,AI namely engineering GitHub, a collaborative platform for open open for platform acollaborative GitHub, filings in the different sub-categories, we can can we sub-categories, different in the filings machine than common less much While filings. Looking at the history of AI development, development, AI of history at the Looking filings. 2017, respectively. described (representing 54 percent of scientific of scientific percent 54 (representing described of 1,050,631publications scientific dealing 2014, in learning deep to 3,871 3,276 and in 2013 between 2016.growth and machine Other with AI techniques have been published published have been techniques AI with It is worth noting that, if we look beyond the the beyond we if that, look noting worth It is Machine learning is the most common field field common most the is learning Machine total numbers of patent families and examine examine and families of patent numbers total 1,500 2,000 and between with techniques, techniques is similar to that seen with patent patent with to seen that similar is techniques the totalpublicationscollection. scientific with field, the in growth recent biggest the up to mid-2018, representing 64 percent of of toup mid-2018, percent 64 representing and probabilistic reasoning, represent a very avery represent reasoning, probabilistic and and fuzzy logic (see figure 3.6). figure (see logic fuzzy and 2017,and repositories 238 GitHub from of repositories number increasing a constantly 175 impressive an annual average percent source software development, which evidence software source by far demonstrates learning deep that see instead the average annual growth rate of rate of growth annual average the instead two these for is evident filings patent in it is interesting to note when certain techniques techniques to certain note when interesting is it years, raterecent in growth filing in increase percent of all patent families). patent of all percent in 2015 filings and 2016.priority However, other of patent percent (with 99.5 programming publications), followed by logic programming families related to expert systems) and fuzzy fuzzy systems) and to expert related families mentioning neural networks and 43 mentioning 43 and networks mentioning neural 2014 between techniques these mentioning from extracted data by confirmed is networks (46 percent). networks neural and (49 percent) learning multi-task namely low number of filings in the field (less than 1 than (less field in the of filings low number late the ratesince filing show asteady logic logic as such techniques AI other learning, learning techniques show a similar very steep steep very show asimilar techniques learning

41 WIPO Technology Trends 2019 42 3 Evolution of AI patent applications and scientific publications Note: A patent refer may to more than or one sub-category category to an AI technique AI to an related families patent of percent 89 representing technique, AI dominant the is learning Machine and sub-categories categories technique AI for families Patent 3.5. Figure 2011 between 2016 and annually 26 percent of average by an grew learning Machine year priority earliest by techniques AI top for families Patent 3.4. Figure 0 Multi-task learning Multi-task Latent representation Logical and Logical relational learning learning Instance-based Probabilistic reasoning Probabilistic engineering Ontology programming Logic (general) logics Description Reinforcement learning Reinforcement Note: A patent refer may to more than one category 10,000 15,000 20,000 Unsupervised learning Unsupervised Rule learning Deep learning 5,000 Classification and regression trees and regression Classification Bio-inspired approaches Bio-inspired Fuzzy logic Fuzzy Support vector machines Support vector models graphical Probabilistic Expert systems Expert programming Logic 0 Supervised learning Supervised 1981 Machine learning Machine (general) 50,000 Neural networks 1986 100,000 1991 Machine learning Machine 150,000 Sub-category Category 1996 of the total for AI for total the of ashare as techniques AI to related publications scientific and families Patent 3.6. Figure higher than patent families for AI techniques AI for families patent than higher generally is publications scientific of share The category refer Note: may publication to A more patent than or one scientific programming 2001 Probabilistic engineering Fuzzy logic Fuzzy reasoning Ontology Machine learning Logic 0% 2006 20% 2011 Scientific publications Scientific Patent families 40% Machine learning Machine 2016 Logic programming Logic Fuzzy logic Fuzzy • • • • • A breakdown of scientific publishing in various in various publishing of scientific A breakdown When scientific publications scientific on When various inappeared scientific first publications, using Ontology engineering and probabilistic and Ontology engineering with patenting activity (see figure 3.7), figure (see twomain activity patenting with trends are stand out: stand are trends activity. As with patent activity, expert systems systems activity, patent with expert activity. As are still under the chosen threshold of 200 of 200 threshold chosen the under still are at 200 least publications: were there when year first the abenchmark as is the most common approach. Description Description approach. common most the is patenting with compared and 3.8 figure in patent filings. patent publication rate has declined since then). since declined rate has publication the 2007 in but publications 500 at peaked publications a year (probabilistic reasoning machine learning approaches are compared are compared approaches machine learning represented more in scientific literature than in in than literature scientific in more represented reasoning are emerging techniques, and and techniques, emerging are reasoning logics and logic programming (general) are is presented approaches logic programming 1991: fuzzy logic appears in the scientific scientific the in 1991: appears logic fuzzy are 1985: techniques learning machine 1982: logic programming is the first AI AI first the is 1982: programming logic They then show moderate growth until 2002 2002 until growth show moderate then They 25 percent between 2002 and 2005, before before 2005, and 2002 between 25 percent characterized by a very high annual growth growth annual high by avery characterized trend and is significantly more common in in common more significantly is and trend exception to an overall the forms learning Rule learning. task trend Asimilar corpus). corresponding the are significantly approaches Bio-inspired the scientific literature.This technique is in addressed to substantially be technique and then an average annual growth rate of rate of growth annual average an then and 1982 in to 2,986 papers 208 average, from slowing down between 2008 and 2013. and 2008 down between slowing substantially described from 1985 onwards. is observed in neural networks, machine machine networks, neural in observed is of percentage of (in terms filings patent in patents than inpublications. scientific 1985). in papers more common in scientific publications than then. since increase moderate rate from the outset (146 outset the on rate from ayear percent learning (general approaches) multi- and (general learning 1991 around a shown literature has and • Each of the different types of machine of machine types different of the Each and differences. Some examples of these of these examples Some differences. and however,are, similarities interesting some illustrated in figure 3.9 are: 3.9 in figure illustrated patent filings over the period studied. There There studied. period filings over the patent number of both scientific publicationsand learning shows a significant increase in the increase shows a significant learning Supervised learning, vectorSupervised support all show continuous growth in the number of of number the in growth show continuous all scientific publications. machines, deep learning, classification and and classification learning, deep machines, regression tree and instance based-learning based-learning instance and tree regression relapse as early as possible? as early as relapse to predict and to monitor wedo need of data type What side-effects? minimize dosage? How we do optimal the is What B? drug Aversus to drug better respond apatient will For example, patient. each answer clinically questions critical for and potential full to its allow AI realize will data of this availability the turn, In subtype. and type disease each in patients from data of clinical amount of avast collection systematic the willrequire field this in Progress patient. individual each for tailored of therapies medicine, thatprecision is, the design towardof field focus the their shifting established companies are and startups A growing of labs, number academic patients’ lives. protectand our rightswhile improving balance to right strike the possible is it setbacks, our view occasional and is that to Despite challenges these data. access fair and issues ownership concerns, privacy research, in subjects human related tochallenges the protection of many forward bringing of AI, success the for essential is to data Clearly, access cultural and ethical norms. existing with aclash is there forward, leap new big a that technology big promises under-served populations? with As every in care effective we deliver How can NYU School of Medicine Aristotelis Tsirigos, of precision medicine research: the example public Data and

43 WIPO Technology Trends 2019 44 3 Evolution of AI patent applications and scientific publications Note: A patent or scientific publication may refer Note: may publication to A more patent than or one scientific category refer Note: may publication to A more patent than or one scientific sub-category Logical and Logical relational publications than in patent filings scientific in higher significantly represented is logics description sub-categories, programming logic Among AI for total the of ashare as Figure Patent 3.8. families and scientific publicationsrelated to logicprogramming sub-categories scientificpublications than families patent in highly more represented are learning supervised and learning Rule AI for total the of ashare as Figure 3.7. Patent families and scientific publicationsrelated to machinelearning sub-categories Supervised learning Supervised Logic programming Logic Multi-task learning Multi-task Description logics Description Classification and Classification Machine learning Machine graphical models graphical Neural networks regression trees regression Expert systems Expert Support vector Reinforcement Deep learning Unsupervised Rule learning Probabilistic Bio-inspired approaches machines (general) (general) learning learning learning 0% 0% 2% 1% 4% 2% 6% 8% 3% 10% 4% 12% 5% 14% Scientific publications Scientific Patent families Scientific publications Scientific Patent families 16% 6% 18% 7% 20% 8% patent filing in 1999 of burst a saw which learning rule for except publications, scientific follows usually families patent in Growth and year priority year,publication earliest respectively by publications, scientific and families patent learning Machine 3.9. Figure Computer vision represents 49 percent of patent families related to a functional application to afunctional related families patent of percent 49 represents vision Computer Figure 3.10. Patent families for functional application categories and sub-categories Note: A patent refer may to more than or one sub-category category Note: A patent refer may to more than or one sub-category category 0 0 Sentiment analysis Morphology Sentiment analysis Morphology Natural languagegeneration Natural Knowledge and representation reasoning languagegeneration Natural Phonology Speech-to-speech SP (general) Knowledge representation Knowledge and representation reasoning Phonology Speech-to-speech SP (general) Distributed AI Distributed AI Distributed Dialogue Dialogue Control methods Control methods Control 10,000 20,000 Augmented reality Augmented reality Semantics Semantics Speech synthesis Speech Speech synthesis Speech Object tracking Object tracking Object Predictive analytics Predictive analytics Predictive Machine translation Machine translation Machine NLP (general) NLP (general) Robotics Planning and scheduling Robotics Planning and scheduling 300 600 Information extraction Information extraction Image and segmentation video Image and segmentation video 0 Scene understanding Scene understanding Scene 0 Speaker recognition Speaker Speaker recognition Speaker 50,000 50,000 Speech recognition Speech Speech recognition Speech Biometrics Biometrics Speech processing Speech Speech processing Speech Patent families Natural language processing Natural language processing Neural networks Rule learning Character recognition Character recognition Character 1996 1996 100,000 100,000 2016 2016 10,000 2,000 4,000 5,000 0 150,000 150,000 0 Support vector machines Support vector Scientific publications Scientific Deep learning Computer vision Computer vision Sub-category Category Sub-category Category 1996 1996 200,000 200,000 2016 2016 10,000 20,000 300 600 0 0 Note: A patent refer may to more than or one sub-category category 0 Phonology Speech-to-speech SP (general) Knowledge representation Knowledge and representation reasoning Sentiment analysis languagegeneration Natural Morphology Distributed AI Distributed Control methods Control Dialogue Augmented reality Speech synthesis Speech Semantics Object tracking Object Predictive analytics Predictive Machine translation Machine Robotics Planning and scheduling NLP (general) Information extraction Image and segmentation video Neural networks Scene understanding Scene Speaker recognition Speaker Rule learning 1996 1996 50,000 Speech recognition Speech Biometrics Speech processing Speech Natural language processing Character recognition Character 2016 2016 100,000 10,000 2,000 4,000 5,000 0 0 Support vector machines Support vector 150,000 Deep learning 1996 1996 Computer vision Sub-category Category 2016 2016 200,000

45 WIPO Technology Trends 2019 46 3 Evolution of AI patent applications and scientific publications Computer vision grew by an average of 23 percent annually between 2011 between 2016 and annually 23 of percent average by an grew vision Computer year priority earliest by applications functional top for 3.11. families Figure Patent Information extraction grew by 24 percent and semantics by 33 percent between 2013 2016 and between by percent 33 semantics and by 24 percent grew extraction Information year priority earliest by sub-categories processing language natural top for families 3.13. Patent Figure vision sub-categories computer other all 2013, since surpassing percent 30 of average by an grown has Biometrics year priority earliest by sub-categories vision computer top for families 3.12. Patent Figure Note: A patent refer may to more than one sub-category Note: A patent refer may to more than one category 10,000 20,000 Note: A patent refer may to more than one sub-category 1,000 2,000 2,000 4,000 6,000 0 0 0 1981 1981 1981 1986 1986 1986 1991 1991 1991 1996 1996 1996 2001 2001 2001 Image and segmentation video 2006 2006 2006 Information extraction 2011 2011 2011 Natural language processing Planning and scheduling Scene understanding Scene recognition Character 2016 2016 2016 Speech processing Speech Machine translation Machine Semantics Biometrics Computer vision NLP (general) Dialogue • • • The three AI functional applications with the A total of 256,456 patent families related to AI to AI related of 256,456A total families patent ���� vision, natural language and speech processing growth rate of robotics and control methods methods control and rate of robotics growth to functional AI only may refer documents to These AI. related families patent of all Moreover, it is the areas with a relatively small small Moreover, arelatively with areas the is it them occurring in about 10,000 patent families. 10,000 families. patent about in occurring them 3.11Figure filing shows the overfor top time underlines the importance of three these the importance underlines 2018, 75 toup early percent represents which application fields. applications or include AI techniques or AI inventions related to planning scheduling, and patent families related to AI, respectively. This to respectively. This AI, related families patent 14 13 of all and percent, percent percent 3.10). (figure 49 processing represent These between 2013between 2016 and percent. 55 was four functional applications. Beyond these fields, functional applications to the field ofAI. functional applications have published been most growth recently; the average annual annual average the recently; growth most ofnumber that applications have shown the of each literature, patent the in frequent most of number highest patent families are computer robotics and predictive analytics are the next next the are analytics predictive and robotics 2016, with 96 percent and 1752016, and percent a 96 with percent year on average, respectively; patents grew grew patents average, respectively; on year deep learning. deep delay publications between and patents of slower periods with growth overall (a significant growth of patents in a field field in a patents of growth (a significant (general), probabilistic graphical model, Most types of machine learning show a show a learning of machine types Most Deep learning has an untypical profile with with profile untypical an has learning Deep Neural networks, machine learning networks, machine learning Neural scientific publications grewfrom 654. 2013 rate from growth ahigh showing to scientific publications. is usually observed long after scientific scientific after long observed usually is increase, or even temporary decrease, in both scientific publicationsand patents a10-year is for delay There publications). bio-inspired approaches, unsupervised from a starting point of 118 point astarting from 2013, in whereas most techniques, with the exception the of with techniques, most learning and reinforcement learning show show learning reinforcement and learning AI functional applicationsAI functional • • • • • • Among noteworthy findings are: findings noteworthy Among of patent families: computer vision, natural natural vision, computer families: of patent turn a detailed breakdown of each of the three three of the of each breakdown adetailed turn 3.12,Figure 3.13 figure figure and 3.14show in functional applications with applications functional the number highest language processing and speech processing. processing. speech and processing language The number of patents related to related of patents number The Character recognition is the recognition Character leading Within the speech processing category, processing speech the Within 2010;since filings translation; machine Within the processing natural language Scene understanding, although appearing although appearing understanding, Scene of patent families, as patent documents documents patent as families, of patent are: sub-categories largest the category, years, recent in recognition character which accounts for 39 percent of the annual annual of the percent 39 for accounts which Biometrics has, however, overtaken of patent number total the of date and terms address more than one speech processing processing speech one than more address percent 50 for recognition speaker and amarked seen has which semantics, and 2016. and 2014 between stable was reality augmented ofaccounting for the numbers highest speech recognition accounts for 86 percent percent 86 for accounts recognition speech in both vision, computer in sub-category increase in filings since 2010.since in filings increase data), big (including extraction information 1980s. the in patent filings since 2012.since filings patent families, with the first patent filings occurring occurring filings patent first the with families, later, is likewise showinggrowth. significant Frank Chen, Andreessen Horowitz now.right that’s red-hot discovery –and drug more efficient circuits, biological and biologyAI – of intersection the at investment We see alot of

47 WIPO Technology Trends 2019 48 3 Evolution of AI patent applications and scientific publications scientificpublications in are they than families patent in represented highly more generally are applications functional AI AI for total the of a share Figure 3.15. Patent families and scientificpublications related to AIapplications functional as processing to speech related families patent of percent 86 represents recognition Speech year priority earliest by sub-categories processing speech top for families 3.14. Patent Figure Note: A patent or scientific publication may refer Note: may publication to A more patent than or one scientific category representation and reasoning Distributed AI Distributed Planning and Note: A patent refer may to more than one sub-category 1,000 2,000 3,000 Knowledge processing processing scheduling Computer Predictive language Robotics analytics methods Speech 0 Control Natural vision 1981 0% 1986 10% 1991 20% 1996 2001 30% 2006 Scientific publications Scientific Patent families 40% 2011 Speech recognition Speech 50% 2016 Speaker recognition Speaker Speech synthesis Speech SP (general) • • • • • • • • The order of appearance of AI functional functional of AI of appearance order The As with patent filings, computer vision and and vision computer filings, patent with As of mid-2018,As 777,251 papers scientific Within the computer vision category, 200 publications, is as follows: as is publications, 200 In terms of trends in scientific publications, publications, scientific in of trends terms In For the natural language processing category,For the processing natural language there are three distinct patterns (see figure 3.16): figure (see patterns distinct three are there the totalpublicationscollection. scientific are the most represented in scientific scientific in represented most the are as from the first year when there were at least least at were there when year first the from as applications in scientific publications, defined publications, 11 and scientific AI of all percent information extraction and semantics are the the are semantics and extraction information is better represented. better is percent with 20 publications, scientific in biometrics and image and segmentation video papers than in patents whereas distributed AI AI distributed whereas patents in than papers scientific in represented less is processing 47 of percent representing published, been most represented, while for speech processing processing speech for while represented, most natural language processing are prominent are prominent processing natural language respectively (see figure 3.15). figure (see respectively However, speech relating to had applications functional AI literature, followed by recognition. character 1996: distributed AI 1996: distributed 1992: processing natural language 1982 to 1986: planning robotics, 1970: computer vision General growth with a period of slower of slower aperiod with growth General Continuous growth: natural language vision, control methods, distributed AI, AI, distributed methods, control vision, 2003: predictive analytics and and analytics predictive 2003: control methods. control filing trends. annual in their correlation Less regular growth: regular knowledge Less 19,524 Furthermore, type. patent and scheduling, processing, and speech sub-categories, explaining the marked sub-categories, increase (or temporary decrease): computer knowledge representation and reasoning and representation knowledge processing, and robotics. planning scheduling and processing and predictive analytics families are classified in both of these both of these in classified are families representation and reasoning, speech speech reasoning, and representation

AI, knowledge representation and reasoning, reasoning, and representation knowledge AI, ���� Comparing the dateComparing ofpublications scientific 20 different application fields identified in figure figure in identified fields application 20 different of all AI patent documents. patent AI of all computer interaction (HCI). Together, four these sciences, followed medical byeach, life and (10delay to 15 language natural in exists years) In all, 209,910 AI-related patent families (62 all, (62 In 209,910 families patent AI-related 3.17. mentioned fields applications top two The the number of patent filings specifying an an specifying filings of patent number the to identifying AI related Patent documents filings 50,000 than more with transportation, research, to move group basic from latter this two the between exists delay no or little that usually described in scientific literature, to application field has boomed since 2011,since boomed has field application the in percent 42 mentioned are fields application human– and computing devices, personal and into fall families patent These fields. application case the activity, generally patenting is it and similar to those obtained from patent filings. patent from obtained to those similar specific application emerging began specific fields for may have it that longer taken suggests a significant whereas processing, speech it is speech recognition. These findings are are findings These recognition. speech is it in the mid-1990s the in 3.18). figure (see While and telecommunications are patents AI in and analytics predictive vision, computer in proportion of these patent families in any given any in given families patent of these proportion several to or one refer total) of the percent exploitation potential. withpractical commercial planning scheduling, and robotics. and This distributed methods, control processing, AI application fields the future. innovation in sustained willensure and demand high in are AI and robotics in adegree with researchers and Students world. to real lab the the from several results being rapidly deployed with systems, intelligent in research for funding industrial and of governmental up a is ramp- major witnessing decade This robotics. and AI in applications patent of increase an to see I’m surprised not

Dario Floreano, EPFL real world From the to lab the

49 WIPO Technology Trends 2019 50 3 Evolution of AI patent applications and scientific publications Note: A patent refer may to more than or one sub-category category and predictive analytics vision computer for families patent in growth and publications scientific in growth between exists lag Little year, respectively publication and year Figure 3.16. Functional application patent families and scientific publications by earliest priority are the top four application fields mentioned in patent documents and represent 24, 24, and 19 represent percent 17 and documents patent in mentioned fields application four top the are Telecommunications, transportation, life and medical sciences, and personal devices, computing and HCI sub-categories and categories field application for 3.17. families Figure Patent of all patent families related to AI application fields, respectively fields, application to AI related families patent all of 0 Videoconferencing VoIP Drug discovery Nutrition/food science Biomechanics 10,000 20,000 Note: A patent refer may to more than or one sub-category category 0 Biological engineering Biological Public health Public Neuroscience/neurorobotics Bioinformatics Videoconferencing Privacy/anonymity VoIP Drug discovery Cryptography Nutrition/food science Biomechanics Genetics/genomics Biological engineering Biological Public health Public Neuroscience/neurorobotics Bioinformatics Medical informatics Medical Privacy/anonymity Aerospace/avionics 0 Cryptography Genetics/genomics Anomaly detection/surveillance Anomaly Medical imaging Medical Driver/vehicle recognition Driver/vehicle Medical informatics Medical Aerospace/avionics Anomaly detection/surveillance Anomaly Transportation Transportation and engineering traffic Medical imaging Medical Driver/vehicle recognition Driver/vehicle Cybersecurity Transportation Transportation and engineering traffic Patent families Computer networks/internet Cybersecurity 20,000 Physiological parameter monitoring Physiological Computer vision Authentication Computer networks/internet 20,000 Physiological parameter monitoring Physiological Authentication 1996 Radio and television Radio broadcasting and television Telephony Radio and television Radio broadcasting and television Telephony Autonomous vehicles Autonomous Security Autonomous vehicles Autonomous Security 2016 40,000 40,000 Life and sciences medical 1,000 1,500 500 Life and sciences medical 0 Scientific publications Scientific Transportation Telecommunications Sub-category Category Predictive analytics Predictive Transportation Telecommunications 60,000 Sub-category Category 1996 60,000 2016 0 Industrial property Industrial Law, social and sciences Law, behavioral social Customer service Customer Affective computing Affective Law, social and sciences Law, behavioral social 10,000 15,000 Note: A patent refer may to more than or one sub-category category 0 0 Smart cities 5,000 Military Social networks Social Industrial property Industrial Videoconferencing Agriculture VoIP Customer service Customer computing Affective Law, social and sciences Law, behavioral social Drug discovery Nutrition/food science Biomechanics Law, social and sciences Law, behavioral social Computing in government Biological engineering Biological Banking and Banking finance Internet of (IoT)things Public health Public Smart cities Entertainment Neuroscience/neurorobotics Bioinformatics Social networks Social Military Enterprise computing Enterprise Privacy/anonymity Agriculture 0 Cryptography Genetics/genomics Computing in government Cartography Banking and Banking finance Internet of (IoT)things Education Entertainment Arts and Arts humanities Energy management Energy Medical informatics Medical Enterprise computing Enterprise Aerospace/avionics Natural language processing Anomaly detection/surveillance Anomaly Cartography Education Networks Medical imaging Medical Arts and Arts humanities Driver/vehicle recognition Driver/vehicle Energy management Energy Transportation Transportation and engineering traffic Networks Cybersecurity 20,000 e-commerce Computer networks/internet 20,000 20,000 Physical sciences and engineering sciences Physical Physiological parameter monitoring Physiological Industry and manufacturing Industry Authentication 1996 e-commerce Physical sciences and engineering sciences Physical Industry and manufacturing Industry Document management Document and publishing Business Radio and television Radio broadcasting and television Telephony Document management Document and publishing Business Autonomous vehicles Autonomous Security 2016 Personal computers and PC computers applications Personal 40,000 Personal devices, computing and computing HCI devices, Personal Personal computers and PC computers applications Personal 40,000 40,000 Personal devices, computing and computing HCI devices, Personal 2,000 4,000 6,000 Life and sciences medical 0 Transportation Telecommunications 60,000 Sub-category Category 60,000 60,000 1996 Robotics 2016 0 Industrial property Industrial Law, social and sciences Law, behavioral social Customer service Customer Affective computing Affective Law, social and sciences Law, behavioral social Smart cities Military networks Social Agriculture Computing in government Banking and Banking finance Internet of (IoT)things Entertainment Enterprise computing Enterprise Cartography Education Arts and Arts humanities Energy management Energy Networks 20,000 e-commerce Physical sciences and engineering sciences Physical Industry and manufacturing Industry Document management Document and publishing Business Personal computers and PC computers applications Personal 40,000 Personal devices, computing and computing HCI devices, Personal 60,000 The most remarkable growth rates observed and trends Taking emerging at look acloser As we have seen, there are significant significant are we there haveAs seen, Cross-analysis of categories filings over time in the sub-categories within within sub-categories in the filings over time 27.7 percent. fields application 2016 different the across total of the to percent 65 corresponding 2000s, year has been almost constant since the early early the since constant almost been has year examine these crossovers in more detail by detail more in crossovers these examine used categories three the between crossovers services customer percent; rate of 46.9 growth for period same the in observed rates growth with agriculture, rate of 32.9 percent, growth distinct two least at mention total) of the to percent 44 (corresponding families of these not often are inventions AI-related average. on (a sub-category of business), with 37.7 with of business), (a sub-category percent, of networks), average with (a sub-category within AI in the recognition and analysis of of analysis and recognition the in AI within rate. growth annual a42.2with percent of sub-categories the at closer looking when 30.3 percent, and banking and finance, with with government, in computing 32.3 percent, 37.1 interest increasing an indicating percent, 66.7 percent, followed by autonomous vehicles, vehicles, followed by autonomous 66.7 percent, tagged in different application fields. application different in tagged to patents AI analyze (techniques, functional rate of growth annual average highest the aerospace/avionics sub-categories: these has the top application field transportation, found be can results interesting Further 2013 period the rate in growth average the to applications and application fields). We can of (a sub-category computing affective and cities smart are fields application other across application fields. Figure 3.19shows patent annual average an with transportation, are and their sub-categories, the most remarkable application fields. identified in the patent search results. It is is It results. search patent in the identified personal devices, computing and HCI), and with computing devices, personal the at looks one when instructive particularly focusing on the number of patent families families of patent number the on focusing human emotions. human limited to a single application field: 71 percent field: percent 71 application tolimited asingle

• • • • • • • • Taking a closer look at these three functional functional three Taking these at look acloser AI technique and an AI functional application an mention families patent of 86,052 A total ���� Computer vision is the top functional application dominant interest in: interest dominant other functional applications, primarily natural (63 percent, which is the highest percentage percentage highest the is which percent, (63 together. techniques machine Some learning the boom in deep learning had begun in 2013, in begun had learning deep in boom the vision computer for used mainly is learning Deep are particularly associated with associated specific are particularly applications – deep learning, natural language natural learning, language –applications deep techniques). allamong machine learning interest started to increase in its usefulness for for usefulness its in to increase started interest processing, and speech processing – reveals a a – reveals processing speech and processing, functional applications. are: These for this AI technique. No more than a year after after ayear than more No technique. AI this for language processing and speech processing. speech and processing language 47.9 percent, respectively). vision (63.2 percent, 53.2 percent and and percent 53.2 percent, (63.2 vision Supervised learning for natural language learning Supervised character recognition in recognition computer vision, character (67.5 percent). (67.5 Deep learning, support vector machines machines vector support learning, Deep Rule learning for knowledge representation forRule knowledge representation learning Probabilistic for models graphical speech for planning and approaches Bio-inspired and unsupervised learning for computer computer for learning unsupervised and speech recognition and speakerspeech recognition in natural language semantics scheduling applications (13.6 percent). in speech processing. speech in processing, and processing (22.2 percent). (10.3 knowledge and processing percent) (19.1processing percent), natural language representation (9.6 percent). representation Functional applications and techniques levels. reward to different react participants of how market by observations determined may be information for paid rewards For example, of mechanisms. operation actual the in also but design, the in only of use not increasing behavior observation, toward shifted has this of AI, all for As Boi Faltings, EPFL behavior observation towardsShift

51 WIPO Technology Trends 2019 52 3 Evolution of AI patent applications and scientific publications telecommunications overtaking all other fields and transportation with the in 1990s, emerged fields application to AI related families Patent year priority earliest by categories field application top for families 3.18. Patent Figure Note: A patent refer may to more than one category 2,000 4,000 6,000 8,000 0 1981 1986 1991 1996 Personal devices, computing and computing HCI devices, Personal 2001 Life and sciences medical 2006 2011 Telecommunications 2016 and publishing management Document manufacturing Business Security Transportation Industry and Industry fields; computer vision with telecommunications and transportation telecommunications and sciences medical and life the with frequently most co-occurs learning Machine and functional applications learning machine with fields application of co-occurrence by families Patent 3.20. Figure 2013 to 2016. Over the same three years, aerospace/avionics grew even faster, at 67 at faster, percent even grew aerospace/avionics years, 2013 three to 2016. same the Over from 2011, from annually to 42 percent annually rising percent 35 of average an grew vehicles Autonomous Figure 3.19. Patent families sub-categories by for earliest year transportation priority Document management Document and publishing Personal devices, computing and computing HCI devices, Personal Note: A patent refer may to more than one sub-category Law, social and sciences Law, behavioral social 2,000 4,000 Physical sciences and engineering sciences Physical 0 Industry and manufacturing Industry 1981 Life and sciences medical Computing in government Energy management Energy Telecommunications Banking and Banking finance Arts and Arts humanities Transportation Entertainment Cartography Agriculture Education Networks Business 1986 Security Military 18,772 11,585 13,741 16,201 1,300 1,430 2,368 2,583 1,822 3,766 3,276 3,914 2,489 5,296 8,330 9,569 9,709 6,841 8,813

780 Machine learning 1991 11,530 17,235 17,098 17,164 21,744 22,871 1,343 1,196 2,047 2,587 2,890 1,056 3,334 3,767 4,852 3,659 5,397 5,573 7,968

404 Computer vision 1996 1,055 1,610 1,642 2,669 2,350 1,284 3,031 5,850 9,526 3,033 3,818 7,920 2,330 7,553 Natural language 550 370 291 938 737 397 processing 12,549 1,087 1,951 2,615 1,498 1,183 2,422 3,291 3,075 2,504 6,678 3,997

121 269 126 493 444 309 759 798 Speech processing 2001 Transportation Transportation and engineering traffic 14,030 1,540 1,262 1,162 1,494 1,625 3,496 Driver/vehicle recognition Driver/vehicle

443 778 149 309 734 697 284 237 343 271 163 Control methods 25 87 2,404 1,381 1,401 1,617 1,663 3,614 2,601

Planning and 2006 153 241 282 435 380 199 944 697 365 273 789 721 517 scheduling 1,073 1,988 1,416 5,080 2,476

255 415 135 528 336 257 372 371 380 679 350 221 793 Robotics 37 99 Autonomous vehicles Autonomous 2011 Knowledge 1,213 1,820 1,698 1,838 1,292 394 243 189 187 365 532 203 630 444 880 795 761 123 110 representation and 82 reasoning 1,086 2,585 1,694 1,069 1,533

111 138 449 213 133 299 425 247 277 570 720 431 594 866 Predictive analytics 2016 65 Aerospace/avionics

335 183 171 382 189 243 428 223 533 516 Distributed AI 23 73 48 81 71 41 98 56 44 83

53 WIPO Technology Trends 2019 54 3 Evolution of AI patent applications and scientific publications Patent families containing at least one PCT application have grown by an average 13 percent since 2009 since 13 percent average by an grown have application PCT one least at containing families Patent year priority earliest by application, EP or aPCT with families patent AI 3.21. Figure 6,000 7,000 1,000 2,000 3,000 4,000 5,000 0 1975 1985 1995 2005 2015 member anEP with Patent families member aPCT with Patent families The growth in scientific publicationsand the A total of 89,466 patent families simultaneously simultaneously families patent of 89,466 A total ���� ���� first column). first Themainmachine learning in every used is learning machine While field. computing and HCI fields. 3.20, figure (see life sciences with dealing within a short period of only about three years. years. three about of only period ashort within telecommunications, and personal devices, devices, personal and telecommunications, are life sciences with associated techniques proven has that atechnique is learning Deep approaches are mainly used in the life sciences, life sciences, the in used mainly are approaches programming Logic trees. regression and few years. over coming the applications supervised learning, support vector machines, machines, vector support learning, supervised sector, it is in cited patents in particular subsequent boom in patent applications that its potential in applications various functional page 45) suggest an expectation that deep deep that expectation an suggest 45) page bio-inspired approaches, and classification followed just a year later (see figure 3.9 on on 3.9 figure (see later ayear followed just mention an AI technique and an AI application application AI an and technique AI an mention learning will deliver added value to many such to value such many added deliver will learning The latest ratio is about 1.5 PCT applications for every European one. European 1.5 every for about is ratio PCT applications latest The since PCT 2009 applications have more become numerous than European applications. and ones, of European number the than faster increasing is of PCT applications number The period. same in the of technology fields all across families patent 10 of with percent compared Database, Statistics WIPO the in to data member, according 2015, and EP an 2000 had (EP). families 18 patent Between of AI application percent 51,397 a European include (15.1report in this identified families patent AI the of all percent) 2011 from of 28 percent to 2017. 2011, Since to average an slightly 2000s. the in fallen has percent 40 steady proportion this 1995 10 before than to a less percent from increased of PCT applications proportion The period. same in the of technology fields all across 12 families with of patent compared percent and 2015, 2000 between member aPCT had families patent AI of identified 25 percent Database, Statistics WIPO 3.21). the from figure drawn (see data on application Based PCT one least at include report in this identified families patent AI of all (20 percent) 69,383 applicants. of patent plans international of the indicators are therefore and jurisdiction, one than more in apatent for to apply used be can that tools Patent (EPC) Convention are European and Treaty (PCT) Patent Cooperation The Percentage of PCT and European applications Applicationand fields techniques • • • • • A total of 152,065A total families patent ���� Computer vision is used in all application fields, fields, application all in used is vision Computer Some functional applications are particularly transportation (seetransportation figure 3.20, row). second associated with application fields. These are: These fields. application with associated application and an AI application field. simultaneously mention an AI functional mention functional AI an simultaneously but especially forbut especially telecommunications and Control methods and robotics for for robotics and methods Control Speech processing and natural language natural language and processing Speech document management and publishing and management document transportation applications.transportation Planning for scheduling and natural language and Predictive analytics applicationstransportation Natural language processing in the field of processing language Natural and HCI industry and manufacturingindustry and computing devices, personal of field the in processing forprocessing applications business forprocessing telecommunications and also Applicationand fields functional applications functional

55 WIPO Technology Trends 2019 56 Chapter title

Photo: © Vizerskaya / Getty Images Photo: © Vizerskaya / Getty Images and advanced machine learning algorithms to sift through hundreds of of hundreds through to sift algorithms learning machine advanced and materials, historical data trends. success and industry Philyra new uses AIproduct– an composition system that about learn can formulas, raw Mixing and scientific artistic thought into one big pot resulted in Philyra of flavorsand fragrances, got together to explore the possibilities. of at Symrise, IBM and skilled researchers aglobal perfumers producer pathways? were These some of the questions that popped up when agroup crafting new scents? What if it could them assist in identifying novel creative What if AIcould from learn and fragrance augment experts their of process detergent, deodorant, air freshener and, of course, cologne and perfume. forming apositive or negative opinion about everyday products like laundry is one of the components most important aconsumer when considers have aknack for and science bringing together, art knowing that scent talent –one that takes years of experience to develop. perfumers Seasoned abilityThe to afragrance that craft leaves apositive impression is quite a and scienceBringing together art Case study by IBM Research and Symrise and Research IBM by study Case businesses accelerate and scale their creative design process. potential to bemade available to help aservice as any number of materials. While this is still research today, the technology has the wellas industrial as products like adhesives, lubricants or construction detergent, like products laundry or consumer shampoo and cosmetics AI systems like Philyra also can beapplied to designing flavors, and predict novel combinations that have never on the seen been market before. analyzecan thousands of formulas well as historical as data to identify patterns for hundreds of have can years. Now perfumers AIapprentice an by their side that have something humans is explored winning a perfume of designing science and Philyra demonstrates how in assist can AI domains where creativity is key. art The between the scents, the more novel is predicted the perfume to be. similar scents to existing commercial fragrances. larger The the difference So, Philyra is learning adistance model to identify fragrances that have course,Of novelty is amajor driver when is come to crafting afragrance. objectives, such creating as aunique fragrance for Brazilian millennials. design specific that fit combinations to new generate scent learning machine previously designed fragrance formulas. With this wealth of data, Philyra uses fragrance materials and historical information that captures the of success of thousands of fragrance-formulas, fragrance families (e.g. or floral), fruity raw was developed with adata-driven approach, relying on adatabase of hundreds amount of ingredient an make can or break anew This is why perfume. Philyra Creating afine fragrance requires precision,as even the slightest change in the fragrance market to design entirely new fragrance formulas. of fragrance combinations, it detect can the whitespaces in the global patterns and novel combinations. Philyra As explores the entire landscape thousands of formulas and thousands of raw materials, helping identify

57 WIPO Technology Trends 2019 4 Key players in AI patenting

Key findings

• Companies represent 26 of the top 30 patent applicants. Most of these are conglomerates active in consumer electronics, telecommunications and/or software, as well as sectors such as electric power and automobile manufacturing. Just four of the top 30 are universities or public research organizations. • IBM has the largest portfolio of AI patents with 8,290 patent applications, followed by Microsoft with 5,930 patent applications. • Of the top 20 companies, 12 are Japanese. • The main functional application mentioned by the top companies in their patent applications is computer vision (19 out of 20), though IBM has a greater focus on natural language processing. • Machine learning is by far the most represented AI technique in the top applicants’ portfolios. • Patent co-ownership is rare: no entity among the top 20 applicants co-owns more than one percent of its AI portfolio, similar to other areas of technology. IBM has the largest • Seven out of the top 20 companies have acquired AI companies. Among them, portfolio of AI Alphabet has acquired the largest number (18), while at the same time reducing patents with 8,290 its patent filing activity over the last patent applications, several years. followed by Microsoft with 5,930 patent applications. • • • • • The topThe universities/public research leading institutionThe universities/ among As with companies, computer vision is the the is vision computer companies, with As (CAS) 17 (ranked of Sciences Academy Chinese Academy of Sciences (CAS), is of which Sciences Academy Chinese 20 players academic are in China. Samsung, IBM and NEC. and IBM Samsung, of machine Baidu, and learning, followed of their majority make vast the organizations other from most organizations countries. annually), percent 60 20 and (between growth (Fraunhofer being the main exception, with exception, with main the being (Fraunhofer the leader in the deep learning sub-category sub-category inthe learning the leader deep significant seen has that area Key an in players Patent route). European the of the topthe patent universities/ portfolios Patenting by activity universities/ Chinese around 100around institutions Chinese in the top 500 patent holders, while 17 while holders, top of patent the out 500 some priority filings also in the in the via or also U.S. filings priority some in the overall results). Altogether, there are results). overall the Altogether,in are there by Alphabet, Siemens, Xiaomi, Microsoft, Microsoft, Xiaomi, Siemens, by Alphabet, origin of country in their filings patent priority public organizations. research Machine public organizations research is the Chinese frequently mentioned AI techniques. techniques. AI mentioned frequently main functional application mentioned in of rates growth the beating or matching recent growth, namely deep learning, are the the are learning, deep namely growth, recent organizationsresearch significant has seen learning and neural networks are the most most the are networks neural and learning th

This chapter looks in more detail at the the at detail more in looks chapter This The top applicants applicants top The field, based on the number of AI-related patent patent AI-related of number on the based field, dominate the list, accounting for 26 of the top universities and both companies comprise as patents, most the filing are organizations or applicant patent of the details contain well as track trends over time and by geography by over geography and time trends track well as In Chapter 3, we examined some of the broad broad of the some 3, we examined Chapter In 30 applicants, whereas universities30 applicants, whereas and public Figure 4.1Figure AI the in top the applicants lists trends in artificial intelligence (AI) research as applications and AI application fields. available public information. Further analysis and public organizations. research For the AI the in named top of the applicants activity and category. and companies which to identify possible also subsidiaries have their under subsidiaries grouped been in Chapter 1: AI techniques, AI functional 1: functional Chapter AI in techniques, AI out set areas three the on based provided is parent company or institution, based on on based institution, or company parent relevant, where research, of this purposes 3. These Chapter in discussed families patent is it assignee), the as known (also patentee publications. patent Because documents families identified in their portfolio. Companies Companies familiesin identified portfolio. their research organizations account for just four. just for account organizations research revealed by patenting scientific and activity data. pool and share to mechanisms have to develop better they to will compete, nations For Western example. for data, medical and modeling recognition, behavior human for speech training thehas data collections best China than in China and other countries, in of data amounts vast to collecting fewer obstacles far are –there of China the strong surprisingly positionparticular in observations, of the many explains This data. own most that the ones the often are patents AI-related most the generate that organizations the by data, innovations AI-related Since are enabled Boi Faltings, EPFL China’s strong position

59 WIPO Technology Trends 2019 60 4 Key players in AI patenting Companies represent 26 of the top 30 AI patent applicants worldwide applicants patent AI 30 top the 26 of represent Companies families patent of number by applicants 4.1. Top patent Figure 30 Denso; and Nokia includes Alcatel and Denso; includes Nokia Google, and Alphabet Deepmind Waymo Sanyo; X includes Technologies, Toyota Development; includes PFU;includes Note: Fujitsu includes Panasonic 0 1,000 Zhejiang University Xidian University Hewlett Packard Intel Baidu Nokia Philips Nuance Communications Nuance Sharp 2,000 Bosch Electronics and Telecommunications Research Institute (ETRI)Institute Research and Telecommunications Electronics LG Corporation LG Ricoh Chinese Academy of Sciences (CAS) of Sciences Academy Chinese Mitsubishi State GridCorporationofChina(SGCC) Nippon TelegraphandTelephone(NTT) 3,000 Toyota Sony Siemens 4,000 Alphabet Canon Hitachi Panasonic Fujitsu NEC 5,000 Samsung Toshiba University/public reserach organization reserach University/public Company 6,000 Microsoft 7,000 8,000 IBM 17 The highest ranked highest U.S.The public organization Academy of Sciences (CAS), ranks of which Sciences Academy ���� ���� China’s dominance is further evident if we if evident China’s further is dominance portfolios Bosch). The and (Siemens Germany Outside of China and the Republic of Korea, Republic the and of China Outside public and top of the 20 universities Out 12 top the 20 Japanese companies, are Of States of America (U.S.), Europe. and of America Japan States consider the list of the top 500 applicants: applicants: top of the 500 list the consider patent 500 than more with organizations below. detail more in examined are companies top applicant of the patent however, AI conglomerates; biggest two the auto manufacturer one (SGCC) and company (244) Fraunhofer and (244) the ranked highest from two and LG and Corporation) (Samsung (Toyota) included. while the National Institute of Advanced of Advanced Institute National the while with Microsoft, and families, 8,920with patent Industrial Science and Technology and Science Industrial (AIST) 30 applicants in figure 4.1 figure in applicants 30 conglomerates are Most of the companies listed among the top the among listed companies of the Most there are no universities or public research research public or universities no are there InstituteResearch (ETRI). Korean Electronics and Telecommunications two The of Korea. Republic the in three of Korea Republic the from companies two utility electric one also is there though telecommunications and/or sectors, software universities and public organizations research active in the consumer electronics, electronics, consumer the in active 5,930 patent families). The top 20 also includes includes top 20 families). The also patent 5,930 in Europe. in Japan in organization ranked highest the is (400 patent of California University the is United the of Korea, Republic the China, in portfolios belong to belong U.S. (IBM, portfolios companies families) followed by (389),families) U.S. the Navy families. Figure 4.2 shows the ranked highest majority (17)majority the remaining and in China are research organizations in the AI field, the the vast field, AI the in organizations research largest portfolios belong to belong the Chinese portfolios largest th in the overall top 30 applicants, and and applicants, top overall the in 30 Top public and 20 universities Top 20 companies research organizations research China is represented by more than 100 than by more represented is China Europe have four each (see figure 4.3). figure (see haveEurope each four and 20, have Japan while Korea each around institutions; the U.S. and the Republic of U.S. the Republic the and institutions; 2. 1. 5. 4. 3.

Strengthening the transformation the transformation Strengthening reached 14,881.reached applications of patent number the 2016 in and Bureau, Development Technologyand Promotion and Science in the established was Office Management Intellectual Property regulations. and policies of series in a Rights,” first the Property Work the of Intellectual Strengthening “Severalissued Opinions on Further (CAS) of Sciences Academy Chinese strategy. 2007, In property the intellectual of an implementation and application through the formulation and (IP) creation property intellectual RMB12than billion. of more value acontract with services) developmenttechnology technical and self-implementation, price-for-share, 7,000 (transfer, license, assets IP transformed and CAS transferred to 2016, From 2008 achievements. of and scientific technological 203. processing speech 246, processing natural language and 715, 417,learning vision computer applications from to 2008 2018: machine resulting in the following patent institutions, research of AI a number established CAS has rights. IP through and service. transfer management, IP in engaged of 2016,end CAS 1,891 had people the at and trained have been people 16,000 2008, Since information. and Building an IP work system. The system. The work IP an Building of objectives of the Clarification Vigorously carrying out IP training training IP out carrying Vigorously Strengthening AI technology innovationStrengthening technology AI Hefa Song, CAS (CAS)Sciences Academy ofChinese atsuccess the Five steps to

61 WIPO Technology Trends 2019 62 4 Key players in AI patenting public organizations research and universities among filings patent in second and first rank Korea) of (Republic ETRI and CAS (China) families patent of number by selected in locations, organizations research public and universities among applicants Top patent 4.2. Figure patent applicants patent 500 top the of one-fifth than more for account organizations research public and universities Chinese 500 top the in patent applicants, by number of organizations organizations research public and universities of origin Geographical 4.3. Figure Taiwan Province of Taiwan China Province

Russian Federation Russian United States of

Republic of Republic Korea EuropeEurope JapanJapan U.S. RepublicRepublic ofof KoreaKorea ChinaChina America 0 0 Saudi Arabia Singapore National Institute National of (NICT)Institute Information Technology and Communications National Institute National of (NICT)Institute Information Technology and Communications Europe Japan Commissariat à l'énergie Commissariat (CEA) atomique et alternatives énergies aux Columbia University Commissariat à l'énergie Commissariat (CEA) atomique et alternatives énergies aux Columbia University China Pohang University of Science and Technology (POSTECH)and of Technology Science Pohang University Pohang University of Science and Technology (POSTECH)and of Technology Science Pohang University Fraunhofer and (AIST) Technology Science Industrial National of Institute Advanced Fraunhofer and (AIST) Technology Science Industrial National of Institute Advanced U.S. Massachusetts Institute of (MIT)Institute Technology Massachusetts Massachusetts Institute of (MIT)Institute Technology Massachusetts U.S. Navy U.S. Navy University of California University University of California University 0 500 500 Korea Advanced Institute of Science and (KAIST) of Technology Institute Science Korea Advanced Korea Advanced Institute of Science and (KAIST) of Technology Institute Science Korea Advanced Southeast University (SEU) University Southeast Southeast University (SEU) University Southeast South China University of (SCUT)Technology South China University of (SCUT)Technology South China University 20 1,000 1,000 Tianjin University Tianjin University Chongqing University Chongqing University Beihang University (BUAA) Beihang University Beihang University (BUAA) Beihang University Tsinghua Tsinghua University Tsinghua Tsinghua University Beijing University of (BJUT) Technology Beijing University of (BJUT) Technology Beijing University Industry Academic Cooperation FoundationAcademic Korea Industry (IACF) Industry Academic Cooperation FoundationAcademic Korea Industry (IACF) Zhejiang University Zhejiang University 1,500 1,500 Xidian University Xidian University 40 2,000 2,000 Electronics and Telecommunications Research Institute (ETRI)Institute Research and Telecommunications Electronics Electronics and Telecommunications Research Institute (ETRI)Institute Research and Telecommunications Electronics 60 2,500 2,500 80 Chinese Academy of Sciences (CAS) of Sciences Academy Chinese Chinese Academy of Sciences (CAS) of Sciences Academy Chinese 3,000 3,000 100 3,500 3,500 The topThe players in the patent AI collection of number publications large scientific The Top applicants bycategory Top applicants Scientific publications – top20 can be further analyzed using the scheme scheme the using analyzed further be can organizations have greater scientific publication 10 with list, the top of the dominates 20 out to top the in 20 contrast in is organizations the small number of universities and public public and of universities number small the activity than patentingactivity activity. top unlike respective the Singaporean; two and (CAS) of Science Academy Chinese the and 4.4). figure (see again China applicants research public and to universities attributed shows that U.S. and French public research research U.S. public that shows French and patent applicants list, there are six U.S. public U.S. six public are there list, applicants patent public organizations, research one is Japanese public institutions research being Chinese featuring among the top 20 organizations. This This top the 20 organizations. among featuring research organizationsresearch and one French one top 20 remaining the Among first. ranking top the patent among organizations research industry. the entire of to cultivate ecosystem the capabilities and it is continuously widening its AI byor formed its leading technologies AI on based is Baidu’s business existing of that. proportion alarge for accounts R&D AI and billion, US$2 about is investment R&D annual Its engineers. 10,000 than R&D more with world, the in players top the AI now is among Baidu technologies and promote applications. Al together, develop AI to better aiming Group, to departments Al-related bring Intelligence group, business the Artificial 2017, March In anew up set Baidu learning. of deep study the on focusing institute in-house 2013, the world's Baidu announced first In areas. other and graph, knowledge learning, computer vision,learning, deep machine processing, speech processing, into natural language efforts R&D pouring 2010, in layout of Al its started Baidu Haifeng Wang, Baidu AI at Baidu

1,039 patent families), Alphabet and IBM (both (both IBM 1,039 and families), patent Alphabet The topic of neural networks is central to the central is networks of neural topic The mentioning any kind portfolios largest The AI techniques includes 25 categories and and 25 categories includes techniques AI ���� CAS and Xidian University). Xidian CAS and field field ofmachineand learning, the largest 20 entities are companies. Nonetheless, 20 Nonetheless, entities are companies. Support vectorSupport machines, unsupervised Samsung) by and (e.g., universities Chinese dominate certain AI techniques, in particular particular in techniques, AI certain dominate top of the all learning, rule In categories. categories and sub-categories, see clear leader emerges and most of the top AI top of the AI most and emerges leader clear (e.g.,electronics NEC, NTT, and Hitachi of to techniques belong machine learning each AI technique and sub-category. category with 731) own the largest patent portfolios 731)with portfolios patent own largest the IBM (3,566) and Microsoft (3,079): of Microsoft each and (3,566) IBM 300 patent families in this field. Microsoft (with (with Microsoft field. this in families patent 300 Microsoft and IBM (with 492 and 431 and (with 492 IBM patent and Microsoft 650 patent families, respectively). However, respectively). families, patent 650 no Based on the backgroundBased other major data, for twice account almost companies these in applicants 4.5 top the shows two Figure universities and public organizations research approaches, unsupervised learning and and learning unsupervised approaches, top most in rank companies general, in and, all across top the 20 on to players data access applicants in probabilistic models. graphical applicant next the as families patent many as support-vector machines, bio-inspired 10 in leads IBM of these, sub-categories. 1, Chapter in into out the set insight providing int/tech_trends/en/artificial_intelligence in information or technology consumer (CAS with field this in families). patent 1860 learning. instance-based players listed in Figure 4.1 Figure in listed than more players possess (with 677 SGCC and to Siemens belong explicitly naming this technology portfolios of machine any kind with dealing portfolios families, respectively) are the top two top the two are respectively) families, mentioning supervised learning techniques. techniques. learning supervised mentioning research organizationsresearch in each category. For leading companies and universities and public and universities and leading companies learning and bio-inspired approaches are owned bylearning active conglomerates AI techniques www.wipo. .

63 WIPO Technology Trends 2019 64 4 Key players in AI patenting one each in Japan and France and Japan in each one and Singapore in two U.S.,the in six China, in are publications scientific AI in 10 20 organizations top the of publications, by number of articles scientific AI producing organizations research public and Top universities 20 4.4. Figure Singapore Europe Japan China U.S. 0 5,000 Wuhan University Georgia Institute Georgia of Institute Technology Stanford University University of Tokyo University (SEU) University Southeast Massachusetts Institute of (MIT)Institute Technology Massachusetts Huazhong University of Science and of Technology Science University Huazhong National University of Singapore National University IEEE Beihang University (BUAA) Beihang University 10,000 Nanyang Technological University Technological Nanyang Zhejiang University Shanghai Jiao Tong University (SJTU) Shanghai TongJiao University Harbin Institute Harbin of (HIT)Institute Technology Centre National (CRNS) de Scientifique la Recherche Carnegie Mellon University Ministry of China Education Ministry University of California University 15,000 Tsinghua Tsinghua University 20,000 Chinese Academy of Sciences (CAS) of Sciences Academy Chinese 25,000 30,000 IBM and Microsoft rank first and second in most AI techniques AI most in second and first rank Microsoft and IBM of number patent by families sub-category and category technique AI each for applicants Top patent two 4.5. Figure Note: A patent refer may to more than or one sub-category category relational learning Classification and Classification Machine learning Machine learning Machine graphical models graphical Neural networks regression trees regression Instance-based Note: A patent refer may to more than or one sub-category category relational learning Classification and Classification Support vector Reinforcement Machine learning Machine learning Machine graphical models graphical representation Deep learning Unsupervised Neural networks regression trees regression Rule learning Instance-based Support vector Probabilistic Reinforcement Bio-inspired representation approaches Logical and Logical Deep learning Supervised Unsupervised Rule learning machines Multi-task Probabilistic Bio-inspired approaches Logical and Logical (general) Supervised learning learning learning learning learning machines Multi-task (general) Latent learning learning learning learning learning Latent 0 Microsoft IBM Microsoft CAS IBM Microsoft Samsung IBM CAS Xidian University 0 CAS CAS SGCC Baidu Microsoft Microsoft IBM Microsoft CAS IBM Microsoft Samsung IBM CAS Xidian University CAS IBM Microsoft Zhejiang University CAS CAS SGCC Baidu Microsoft CAS CAS IBM Microsoft SGCC IBM Zhejiang University Microsoft CAS IBM SGCC IBM SGCC Microsoft Siemens Alphabet 1,000 IBM SGCC Siemens Alphabet 1,000 Microsoft Microsoft IBM Microsoft Microsoft IBM 2,000 2,000 3,000 3,000 Microsoft Microsoft IBM IBM Description logics Description Expert systems Expert Description logics Description programming programming Expert systems Expert Probabilistic engineering Fuzzy logic Fuzzy programming programming reasoning Category Probabilistic engineering Ontology (general) Fuzzy logic Fuzzy reasoning Ontology (general) Logic Logic Logic Logic 0 IBM Microsoft IACF Microsoft IBM IACF IBM IBM 0 Microsoft IBM IACF IBM IBM Microsoft IACF Siemens Siemens Siemens IBM Omron Siemens Siemens Siemens IBM IBM Omron IBM IBM 1,000 Sub-category 1,000 2,000 2,000 3,000 Sub-category Category 3,000 Sub-category Category

65 WIPO Technology Trends 2019 66 4 Key players in AI patenting and sub-categories Different companies feature as top patent applicants across AI functional application categories families patent of number by sub-category and category application functional AI each for applicants Top patent two 4.6. Figure Scene understanding Scene Note: A patent refer may to more than or one sub-category category Machine translation Machine Sentiment analysis Note: A patent refer may to more than or one sub-category category Scene understanding Scene Augmented reality Natural language Natural Natural language Natural Machine translation Machine Image and video Sentiment analysis Computer vision Computer vision Augmented reality Object tracking Object Natural language Natural Natural language Natural Image and video NLP (general) Computer vision Computer vision segmentation Object tracking Object Morphology NLP (general) Information recognition segmentation processing generation Semantics Biometrics Character extraction Morphology (general) Dialogue Information recognition processing generation Semantics Biometrics Character extraction (general) Dialogue 0 Microsoft IBM IBM ETRI Microsoft NTT 0 Microsoft Microsoft Microsoft Samsung Microsoft IBM Samsung IBM IBM ETRI Microsoft Microsoft Microsoft NTT Canon Samsung Microsoft Alphabet Microsoft Microsoft Samsung Samsung Microsoft IBM Microsoft Microsoft Microsoft Canon Samsung Alphabet IBM Toyota IBM 1,000 Microsoft IBM Microsoft Sony IBM Toshiba Toyota IBM Samsung 1,000 IBM IBM Sony Toshiba Samsung IBM Samsung Canon Samsung 2,000 Canon Microsoft 2,000 Toshiba Microsoft Toshiba 3,000 IBM 3,000 IBM Samsung Toshiba Samsung Toshiba 4,000 4,000 Speaker recognition Speaker Speech recognition Speech Predictive analytics Predictive Speech processing Speech representation and representation Speech-to-speech Speech synthesis Speech Speaker recognition Speaker Speech recognition Speech Predictive analytics Predictive Speech processing Speech Control methods Control representation and representation Speech-to-speech Speech synthesis Speech Control methods Control Distributed AI Distributed Planning and Category SP (general) Knowledge Distributed AI Distributed scheduling Phonology Planning and SP (general) reasoning Robotics Knowledge scheduling Phonology reasoning Robotics 0 Panasonic NTT Toshiba NTT Samsung Zhejiang University Alphabet 0 SGCC SGCC IBM IBM Sony Panasonic NTT Toshiba NTT Samsung Zhejiang University Alphabet Panasonic Nuance Communications Nuance Samsung SGCC SGCC IBM IBM Hitachi Sony Microsoft SGCC Toyota Panasonic Nuance Communications Nuance Samsung Hitachi Microsoft 1,000 SGCC Toyota NEC Panasonic Sub-category 1,000 NEC Panasonic Panasonic Panasonic IBM Panasonic Nuance Communications Nuance IBM Panasonic Nuance Communications Nuance Nuance Communications Nuance Nuance Communications Nuance 2,000 2,000 3,000 Sub-category Category 3,000 Sub-category Category 4,000 4,000 The CAS possesses the largest patent portfolio portfolio patent largest the CAS possesses The ���� Xidian University and South China University University China South and University Xidian Chinese universities (e.g., universities Chinese Zhejiang University, vision. Two research institutes, CAS and ETRI, Two ETRI, CASvision. and institutes, research Samsung, Canon, Fujitsu and NEC) dominate one Chinese, the other Korean, also possess possess also Korean, other the Chinese, one activecompanies electronics, in consumer categories and sub-categories. In general, involving logic owns portfolio fuzzy the largest of Technology). own companies Some quite explicitly dealing with techniques learning deep (with 640 patent families), Toshiba patent while (with 640 is (namely biometrics, image and video of the families), most (with 235 patent and Universities and public organizations research IBM is once again dominant, leading in nine of of nine in leading dominant, again once is IBM Microsoft, Samsung, IBM and NEC. Omron NEC. and Omron IBM Samsung, Microsoft, top for character recognition (with 1,988 recognition top character for tracking). Toyota understanding in leads scene the largest functional application, computer on the backgroundBased conducted analysis and methods control in topthe 20 players Companies sub-categories. and the categories leading the among diversity agreater is there 4.6Figure applicants top the shows two families), patent while (with 330 techniques universities’ portfolios. and 581 patent families, respectively). respectively). 581and families, patent in Chinese mentioned mainly topics are segmentation, augmented reality and object object and reality augmented segmentation, heldby are field in this portfolios significant imaging, telephony and software (i.e., Toshiba, software and telephony imaging, in each of the 31 AI functional applications knowledge representation and reasoning. reasoning. and representation knowledge players compared withplayers techniques, AI though compared patent families). patent 1,000 than families patent of more portfolios four out of six computer vision sub-categories Korean and Japanese report, this for followed Xiaomi, by Siemens, Alphabet, mentioning computer vision. leads Samsung ranking second in the deep learning category, category, learning deep the in second ranking lead in distributed AI. distributed in lead of all for account and areas, most in lead by are led technologies learning IBM (with 440 rule systems) and (expert programming logic in the field, Baidu, namely portfolios large AI functional applicationsAI functional The landscape looks very different in patent patent in different very looks landscape The Toyota, Bosch, Honda, Ford and General A similarly diverse range of organizations of organizations range diverse A similarly ���� filings related to distributed AI, predictive predictive AI, to distributed related filings Sony, Samsung, IBM and Alphabet. Sony, Alphabet. and IBM Samsung, characterizes the top 20 players for AI AI for top the 20 players characterizes company for knowledge representation In control methods, the largest patent patent largest the methods, control In the by far possess Microsoft and IBM Motors). Communications Nuance owns the Panasonic, Microsoft, NEC, Toshiba, Microsoft, NTT,Panasonic, are mainly owned by Chinese universities and and universities by owned Chinese mainly are planning scheduling, and and which analytics 1,200 than (with more reasoning and public organizations. research applicationsprocessing (with 1,776 patent to belong including a companies, portfolios families). patent by Toshiba leading the Fujitsu. also is IBM and followed third, comes Alphabet processing. families). other The players major possessing more than 800 patent families in the field, are are field, the in families patent 800 than more field (e.g., transportation the in active number largest patent portfolio dealing with speech dealing with speech patentlargest portfolio involving portfolios natural language largest Excerpt from his book, book, his from Excerpt minds. world’sof the AI brightest share outsized an Alphabet bought has spree spending That budget. own R&D of Google’s half than to less amounts research science computer and math for funding U.S.government: federal even own its dwarfs Google funding, company. any other of than it terms In to harnessing devoted resources more has and learning deep in potential the to see companies earliest of the one It was above rest. the shoulders and head Waymo, stands subsidiary driving self- its and owns DeepMind which company,precisely, parent its Alphabet, More it. at shot best the has Google world, corporate the in discovered be to destined is learning deep next If the

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67 WIPO Technology Trends 2019 68 4 Key players in AI patenting business sector dominateSpecialized companies in their families patent of number by field application AI each for applicants 4.7. Top patent Figure two Note: A patent refer may to more than one category Energy management Energy Telecommunications Banking and Banking finance Arts and Arts humanities behavioral sciences behavioral computing and computing HCI Physical sciences Physical Personal devices, Personal management and Life and medical and engineering Law, social and Law, social Transportation manufacturing Entertainment Computing in Computing Industry and Industry Cartography government Agriculture Document publishing Education Networks Business sciences Security Military 0 Thomson Thomson Reuters SGCC Raytheon Samsung China University Agricultural Hitachi Deere IBM IBM Microsoft Microsoft Siemens IBM Microsoft Microsoft Microsoft Hitachi Sony Siemens IBM Sony Microsoft Alphabet Samsung IBM SGCC IBM SGCC Samsung Microsoft Philips Microsoft 1,000 IBM Microsoft IBM Siemens IBM Microsoft Bosch 2,000 Toyota The largest portfolios dealing with life and life and with dealing portfolios largest The of Toyota sub-categories the in leads field The ofis dominated telecommunications Zhejiang University) are also represented represented also are University) Zhejiang Alphabet and IBM also possess large portfolios portfolios large possess also IBM and Alphabet vehicle recognition (with 677 families). patent recognition vehicle companies also active also technology companies in medical (eight top of the 20).cities 4.7 Figure the shows public and universities no are there categories (such as CAS, University of California and and of California CAS, University as (such Samsung), while and Philips (Siemens, (with 1,013 or driver and families) patent (with 457 315 and respectively), families, patent University is ranked first in this field with 45 with field in this first ranked is University In transportation, the largest portfolios belong belong portfolios the largest In transportation, transportation sub-category with the highest sub-category transportation 236 with player patenting main the is Boeing Toyota, (including Hyundai). Europe and Bosch or Asia from suppliers or to auto manufacturers LG Sony. and Nokia, are telecoms field. application AI each in applicants top two which in categories The top 20 patentees. universities and public organizations research universities and public organizations research universities and public organizations research accounting for 10 for accounting Tianjin top of the 20 players. neurorobotics, and active in neurosciences and University top the 20 players. among 3. Chapter in identified as 2013–2016, period the rate in growth average autonomous vehicles (with 1,387 patent (with 32 VoIP well as as videoconferencing and and computer networks/internet sub-categories can examine we which fields, application in radio and television broadcasting (with 428 broadcasting television and radio in many In identified. field application every in patent families in aerospace/avionics, the in players prominent families). Other patent patent families each). however, Samsung, leads in leads the telephonyby Microsoft companies. sector.by industry outnumber Companies patent families. public organizations research are particularly families), transportation or traffic engineering engineering families), or traffic transportation neuroscience/ are prominently most feature medical sciences belong to belong multinational sciences medical mentioning applications. transportation-related (10 neurorobotics top of smart the 20) and research organizations identified among the identified organizations research Alibaba (all of the patents being mainly for mainly being (all patents of the Alibaba Other players are mainly active in the field field the in active mainly are players Other emerged. Youngemerged. in specialized companies devices, human–computer computing and cybersecurity, 120 in anomaly detection and and 120 detection cybersecurity, anomaly in Microsoft. and Samsung as such companies cities applications) is led by Microsoft (with 328 by Microsoft led is applications) cities to Patents related electronics. of consumer Microsoft and IBM categories. other of the purposes).e-commerce corpus). patent overall the on (with 385 patent families) and Microsoft leads leads Microsoft and families) patent (with 385 with 1,145with respectively. families, patent 933 and IBM and Microsoft. Alphabet and Verizon and Alphabet Microsoft. and IBM Microsoft, IBM and Samsung have filed the have the filed Samsung and IBM Microsoft, this field, such as Affectiva, possess patent patent possess as Affectiva, such field, this However, Samsung is top for authentication However, top authentication is for Samsung applications, IBM leads atLooking security Familiar industry names are at the top the at of many are names industry Familiar applicationsBusiness are dominated by Networks applications (including social and SGCC (with 518 SGCC and families), to patent a and activity patenting show astrong also surveillance and 107 in privacy/anonymity). interaction (HCI). Affective computing is a a is computing (HCI). Affective interaction in cryptography (with 91 families), patent cryptography in in families patent (with 266 sectors three in industry and manufacturingindustry applications are with along field, this in and Toshiba portfolios comparable in size to those of larger of size in larger to those comparable portfolios patent families), followed by IBM and Alphabet. families),patent Alphabet. followed by and IBM followed by BBK Electronics (which ranks 46 ranks (which followed by Electronics BBK networks, Internet of things (IoT) and smart (IoT) of smart things and Internet networks, by (with 528 owned IBM families) mainly patent recent trend, and no clear leader has yet has leader clear no and trend, recent largest number of patent families for personal personal for families of patent number largest lesser extent Siemens. publishing, and management document lead promising technologies. regulate the public adoption of these future as governmental agencies near the in products in translate likely most will that a new field of birth the indicates universities applications in neurorobotics from of patent dominance current The Dario Floreano, EPFL neurorobotics Universities in lead

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69 WIPO Technology Trends 2019 70 4 Key players in AI patenting Companies established in specific industries hold top positions in many AI application fields application AI many in positions top hold industries specific in established Companies field application AI by applicants Top patent 4.9. Figure decreasing patent filing rate filing patent decreasing a had companies big other while annually, by 70 percent average on grew SGCC by filed families patent of number The 2013–2016 period the for companies top by filed families patent in rate growth annual Average 4.8. Figure Note: A patent refer may to more than Highlighted to refers one text the for category. applicant. top each category LG Corporation LG Panasonic Mitsubishi Samsung Microsoft Alphabet Siemens Toshiba Toyota Hitachi Canon Fujitsu SGCC Bosch Sharp Ricoh Sony NEC NTT IBM 1,469 1,987 333 286 209 415 184 538 163 487 190 501 278 451 424 735 299 88 42 56 Transportation 1,438 1,050

169 336 495 268 153 160 922 176 323 203 130 409 306 200 293 137 709 Personal devices, 72 computing and HCI 198 274 538 458 142 374 755 134 494 273 438 179 754 524 759 338 253 195 185 593 Telecommunications

1,223 Document 270 326 496 521 439 196 170 203 265 367 251 177 351 119 944 17 14 43 71 management and publishing 1,127 188 390 372 158 595 322 129 368 171 319 113 553 447 401 380 129 119 Life and medical 92 55 sciences 161 299 293 322 446 261 107 317 121 377 212 486 297 351 118 184 206 92 54 72 Security 232 194 194 183 115 197 780 935 168 173 463

26 60 21 95 61 50 94 56 14 Business

132 266 518 131 105 192 546 199 110 Industry and 36 46 28 81 96 27 88 49 50 58 61 manufacturing

267 108 323 165 101 148 155 112 256 230 Physical sciences and 85 33 36 22 23 69 57 55 33 53

engineering -20% Alphabet 173 142 164 646 140 141 155 34 97 21 51 94 22 93 43 25 15 18

Energy management Hitachi 7 6 NEC Sharp Samsung Toshiba Sony 163 176 145 209 150 158 267 Bosch 0% 62 55 97 42 84 98 73 89 40 51 74 Arts and humanities 9 6 Mitsubishi Ricoh Canon NTT Panasonic 241 308 332 148 135 61 54 28 13 43 44 53 57 58 45 30 73 88 58 14 LG Corporation LG

Networks Siemens Toyota Microsoft Fujitsu 20% 106 151 215 80 37 58 35 14 73 24 80 36 63 49 15 90 66 31 25 67 Education 361 114 218 184 40% 11 21 42 10 21 14 21 17 23 31 12 67 39 Cartography 8 7 9 IBM 236 314 13 82 13 22 11 10 55 11 62 45 11 17 17 15 12 Entertainment 8 1 7 60% SGCC Computing in 19 50 32 31 16 55 44 24 31 22 47 14 96 10 81 37 34 18 38

3 government This section focuses on the patent portfolios portfolios patent the on focuses section This clear the is of China Corporation Grid State The Although IBM and Microsoft are the two two the are Microsoft and IBM Although ���� analyzed portfolios Top applicants’ Chinese state-owned electric utility company company utility electric state-owned Chinese for CNH and University Agricultural China in active most are universities Chinese activeCompanies entertainment in electronic Social network patents are also owned by owned also are patents network Social SGCC enjoyed the greatest growth from 2013 from growth greatest the enjoyed SGCC example, filings by Alphabet itself declined in in declined itself Alphabet by filings example, however, might, This indicate decreasing. simply in show filing companies increases significant trends, filing analyzing topof the applicants, computing applications for governmental sector. electronics (such as Sony, Microsoft, Konami, Nintendo Konami, Sony, Nintendo as (such Microsoft, more cities), filed has player no (IoT) smart and two technological categories (Internet of categories two technological things Verizon Tencent. and Facebook, other the In the impact of different business strategies: for for strategies: business of different impact the to growth 2016 70 percent aremarkable with and co-ownership of portfolios, content the more than has 100 companies these patent (containing top 20 energy. for Cartography 100 far. so than patents activity since 2012, since even activity or stable afew are the portfolios, biggest the with applicants acquisition coverage. and geographical of None military. for Systems BAE and Martin Lockheed agriculture, Raytheon, and agriculture Husqvarna, Deere, and military: consumer the in active players other with along AI-related large fairly possess Disney) and some Chinese universities also appear in the the in appear also universities Chinese some patent portfolios in the entertainment category, category, entertainment the in portfolios patent IBM. and Microsoft by Alphabet, families, however. families, matters, while specialized companies dominate while companies specialized matters, rate (see figure 4.8). While in general most most 4.8).general figure in rateWhile (see localization and positioning and localization technologies) is led energy, in and 647 with leader families, patent Top company patent applicants: filingtrends • • • • • • • • • • AI companies since 2009 (see below). (see 2009 since companies AI We can also look at where particular particular where at look We also can Within telecommunications, all the major companies’ intellectual property (IP) strategy companies’ intellectual property activecompanies sectors, in industrial numerous Theapplication fields symbols. classification or keywords on active, based are companies wide area of applications (see figure 4.9). figure (see of applications area wide which may or may not lead to disclosure of a of a to disclosure lead may not may or which Patent filing in different sectors is linked to the is linked to the sectors Patentin different filing spanning a patentthey portfolios possess major are top of the applicants most Because HCI, telecommunications, life transportation, 18 acquired has other Alphabet but period, that Less common applicationLess fieldsare mentioned and/ortechnology the nature of the technology and medical sciences and security. and sciences medical and artificial_intelligence which for data report, this for a background specialized patent portfolios (as can be seen seen be (as can portfolios patent specialized is available at at available is more have applicants some However, involved. publishing, devices, personal computing and and management are document portfolios players (Microsoft, Samsung, IBM, Samsung, LG,players (Microsoft, include: These top of the by applicants. some as conducted analysis portfolio patent players from figure 4.9 and also based on the top on the top based also and 4.9 figure from most often mentioned in the top applicants’ top the in applicants’ mentioned often most Toyota (transportation, 69 percent). Arts and humanities (which includes Arts Cartography (which includes geolocation- Siemens (life and medical sciences, sciences, medical and (life Siemens 23 percent) management, (energy SGCC Industry and manufacturing by SGCC. manufacturing and Industry 32 percent) 32 Mitsubishi (transportation, 19Mitsubishi (transportation, percent) Microsoft devices, (personal computing Bosch (transportation, 78 percent) LG 24 (telecommunications, percent) applications related to music) by Sony applications) by Alphabet interaction (HCI), human-computer and 24 includes smart-city and social network network and social includes smart-city percent of the company’s AI portfolio) company’s of the portfolio) AI percent related technologies) (which networks and www.wipo.int/tech_trends/en/ ), as: such

71 WIPO Technology Trends 2019 72 4 Key players in AI patenting contrary to other top patent applicants who focus principally principally focus who applicants patent top to other contrary processing, language natural on focus astrong has IBM functional application AI by 4.10. applicants TopFigure patent on computer vision Highlighted text refers Highlighted to refers text the for applicant top each category Note: A patent refer may to more than one category LG Corporation LG Panasonic Mitsubishi Samsung Microsoft Alphabet Siemens Toshiba Toyota Hitachi Canon Fujitsu SGCC Bosch Sharp Ricoh Sony NEC NTT IBM 1,394 3,417 2,477 1,792 1,086 3,365 1,993 2,726 1,336 2,778 1,441 2,428 1,362 2,683 2,310 2,727 3,282 1,568 567 687 Computer vision 1,013 1,316 1,094 1,076 1,133 507 977 374 332 935 341 962 589 549 526 608 587 839

39 92 Speech processing 1,809 2,962

641 436 207 396 358 509 318 406 472 569 207 177 373 640 420 924 Natural language 84 51 processing 493 300 292 351 218 225 224 151 449 105 285 176 96 61 42 38 79 33 17 Control methods 5

1,213 Knowledge 177 112 125 131 255 461 202 176 200

47 15 84 32 75 96 35 20 representation and 2 8 reasoning 105 236 148 370 195

62 29 51 24 76 25 45 64 46 48 88 56 36 46 77 Robotics

139 480 130 209 137 Planning and 44 82 37 11 65 16 32 23 48 49 13 46 15 30 40 scheduling 186 171 214 108 23 75 48 52 55 27 40 78 36 10 53 35 11 Predictive analytics 8 9 8 160

10 14 10 Distributed AI 5 5 4 5 0 3 8 9 3 9 4 8 4 2 0 2 Highlighted text refers Highlighted to refers text the for applicant top each category Note: A patent refer may to more than one category patent families patent applicants’ top all of percent 92 Machine learning represents over AI technique by 4.11.Figure applicants Top patent LG Corporation LG Panasonic Mitsubishi Samsung Microsoft Alphabet Siemens Toshiba Toyota Hitachi Canon Fujitsu SGCC Bosch Sharp Ricoh Sony NEC NTT IBM 1,229 1,689 1,770 1,257 1,057 1,294 1,314 3,079 3,566 1,302 1,070 1,801 582 923 329 502 917 271 584 298 Machine learning 207 277 246 101 157 100 214 444 213

27 25 37 82 55 17 85 46 28 70 Logic programming 8 141 305 126 151 161 106 172 246

48 21 19 88 41 15 72 30 78 48 81 53 Fuzzy logic 114

19 15 31 17 19 Ontology engineering 0 6 4 1 3 0 1 1 7 1 1 7 2 1

Probabilistic 30 27 1 0 1 9 1 0 2 4 3 1 1 2 0 2 3 2 0 7 reasoning • • • • • • • • • • • • • The main functional application present in 4.11). for framework main the networks, Neural Alphabet, Panasonic, Sony, Panasonic, etc.)Alphabet, have filed vision (see figure 4.10). figure (see vision functional main is the It traffic and transportation well as as vehicles Siemens is the major player in life and medical medical life in player and major the is Siemens Some portfolios have a strong focus on other other on have focus astrong portfolios Some cited in a large proportion of several of the top of the of several proportion alarge in cited driver and or vehicleengineering recognition. (36 percent of its AI portfolio, compared with with compared portfolio, AI of its percent (36 with the notable exception of IBM, which exception which of IBM, notable the with 32 percent for computer vision). computer for 32 percent Machine learning is the main AI technique, and and technique, AI main the is learning Machine transportation, Toyota large a filed has transportation, applicant’s portfolios: 19 in portfolios, AI application 20 of the largest computer is portfolios top the applicants’ all sciences among the top 20 and clearly top clearly the 20 and among sciences in the top applicants’ portfolios. Some specific specific Some portfolios. top the in applicants’ figure (see portfolios top the in applicants’ technique AI represented most the by is it far physiological parameters. functional applications: more on processing natural language focuses numerous patentsnumerous related to telephony. In machine learning techniques are explicitly explicitly are techniques learning machine mentioned machine frequently is learning, also ofnumber patents related to autonomous leads in medical imaging and monitoring of of monitoring and imaging medical in leads Control methods: Bosch, Siemens, Support vector machines: SGCC machines: vector Support Supervised learning: IBM, IBM, learning: Supervised Toshiba, LG Panasonic, processing: Speech of the portfolios of: IBM, SGCC and Siemens. and of: SGCC IBM, portfolios of the Information extraction: IBM, Fujitsu, IBM, SGCC. extraction: Information Microsoft, Alphabet Mitsubishi, LG, Toyota Expert systems represent a large proportion proportion alarge represent systems Expert Bio-inspired approaches: SGCC Rule learning: IBM Panasonic, Siemens Probabilistic models: graphical Microsoft, Knowledge representation and Robotics: Sony Planning scheduling: and SGCC Natural language processing: IBM, processing: Sharp language Natural reasoning: NEC reasoning: 10 Chinese public organizations, plus the top the plus organizations, 10 public Chinese The co-ownership of patents can be obtained obtained be can of patents co-ownership The This section focuses on the patent portfolios portfolios patent the on focuses section This Acquisitions are explored further in Chapter 6. Chapter in further explored are Acquisitions Canon, companies:AI Alphabet, IBM, ���� ���� vision to task management. Other companies companies vision Other to management. task Of these, Alphabet has acquired the most, most, the acquired has Alphabet these, Of Seven of the top 20 companies have acquired have acquired Seven top of the 20 companies that companies only the are NTT and Sony of patent families is rare in the top applicants’ top the in applicants’ rare is families of patent geographical scope,geographical the universities and public To coverage. give abroad geographical filing trends, analyzing applicants, organization research public and topof the university or universities with co-ownership country, while with 18 acquisitions since 2009, including six six including 2009, 18with since acquisitions Microsoft (nine), (four). (five) IBM Samsung Microsoft and Panasonic,Microsoft, and Siemens. Samsung From this, the data suggests that co-ownership co-ownership that suggests From data the this, the content of portfolios, co-ownership and and co-ownership of portfolios, content the to have made of a number acquisitions include and the top two from Japan and Europe. and Japan from top the two and of percent one than more co-owns applicants since 2016, spanning sectors from computer computer 2016, from since sectors spanning extent. significant to any organizations such with ownership share its most Co-ownership occurs AI portfolios. public institutions is marginal; Samsung, SGCC, Samsung, marginal; is institutions public top the 20 among entity no portfolios: from patent data, if patent applications have applications patent if data, patent from four from the Republic of Korea and the U.S., the of Korea and Republic the from four same the from companies between frequently more than one assignee. more than one assignee. research organizationsresearch are considered the top Co-ownership and acquisitions Co-ownership University and public research public and research University terms. fair on to data this access to share forced were corporations these if accelerated dramatically be could that result. Innovationthe new services and research for accessed be cannot and corporations by owned large is Data organization filing trends organization Boi Faltings, EPFL the market? fromAl research to thingcritical to bring What is the most

73 WIPO Technology Trends 2019 74 4 Key players in AI patenting patent offices patent top the across patents file applicants top the of Most filing of office by applicants patent Figure 4.12. Patent applications for top corporate applications. Note: EPO the is European PCT Patent WIPO Ofiice. represents LG Corporation LG located in Japan and China, respectively. China, and Japan in located being activity patent of their over percent with 98 countries, home exclusively their in almost SGCC,and file NTT namely twoapplicants, patent top the contrast, In office. one than more at filed have been applications patent of their majority the that member, one than indicating and Sony Siemens, ToyotahaveSamsung, more Canon, Bosch, by filed families of patent percent while 50 India, over including of filing offices seven major as many as uses Microsoft applicants. filingforeign patent by for target activity,popular filing the with most U.S. the in which they exhibit most are domiciled. international significant Nonetheless, patent country the in principally file applicants these that suggests data The offices. patent selected for 4.12Figure applicants by filings patent of patent top distribution geographical the shows Geographical of distribution filing by top patentapplicants Panasonic Mitsubishi Samsung Microsoft Alphabet Siemens Toshiba Toyota Hitachi Canon Fujitsu SGCC Bosch Sharp Ricoh Sony NEC NTT IBM 2,603 2,195 5,811 7,990 1,668 1,905 2,456 3,695 1,869 2,172 3,566 1,857 1,959 1,042 741 818 865 942 U.S. 2,602 1,550 1,281 3,947 3,952 3,404 2,642 4,936 2,376 3,910 2,726 3,909 2,446 578 851 371 653 Japan 1,507 1,584 1,394 2,680 692 239 519 884 596 686 584 626 898 836 626 311 852 498 437 China 1,091 1,346 2,070 1,473 1,280 255 358 777 848 751 763 903 566 WIPO 1,132 1,448 1,226 1,466 204 471 627 655 524 790 844 498 316 750 553 334 EPO 4,146 1,986 424 497 773 Republic of Korea 1,859 1,650 724 364 Germany 624 India 20 percent than more of growth have all organizations universities Chinese and public research 2013–2016organizations, universities and public research top by filed families patent in rate Figure 4.13. Average annual growth POSTECH Columbia University U.S. Navy 0% Fraunhofer University of California University CEA ETRI MIT IACF KAIST Chongqing University NICT CAS Zhejiang University Xidian University BJUT BUAA Tsinghua Tsinghua University 50% SCUT SEU Tianjin University 100% AIST Turning to AI functional applications, computer of the Chinese oftop filings number total The Chongqing University, Tianjin University University, TsinghuaCAS, Xidian University, Chinese organizations continues to grow, with vision is dominant for all the organizations organizations the all for dominant is vision filed five patents infive patents of filed 2016. activity The filing 2013 to 2016 4.13). figure (see Other organizations with a positive growth growth apositive with organizations Other Some organizations included in figure 4.15 graphical models are also strongly cited in the the in cited strongly also are models graphical as such entities Chinese for only of portfolios 5percent than more in mentioned explicitly is learning Deep organizations’ portfolios. declining. or from 20 percent than rate of more growth organization havingeach average an annual with isorganizations high the compared (see figure 4.15). figure (see almost is vision Computer 4.14). figure (see and learning Supervised of rates filing (Japan, 21 up The percent). (NICT) (see figure 4.2).AIST (Japan) had the highest U.S., European institutions and Japanese Information Technology Communications and Machine learning and neural networks are are networks neural and Machine learning the only functional application cited in the top portfolios. Bio-inspired approaches and probabilistic techniques, AI mentioned frequently most the stable either are listed organizations other the Natural language processing is the second is the second processing language Natural and South China University of South Chinaand Technology. University theyas are in the top companies’ portfolios rate, however growth only it annual average and Korean universities and public research 9 percent) and the National Institute of of Institute National the and 9 percent) support-vector techniques are also frequently portfolio of Xidian University and predominant predominant and University of Xidian portfolio functional applications: of Technology (BJUT). University Beijing for have a particular specialization in certain certain in specialization have aparticular public and top of the universities majority predominates processing natural language research public and university the in mentioned research organizations.research ranked functional application in the vast up of Korea, (Republic KAIST rate include listed, except for NICT in Japan, where where Japan, except in NICT listed, for • • • • • • The two main application fields mentioned mentioned fields application main two The 4.16). Zhejiang include to rule this Exceptions document management and publishing). and management document are transportation organization portfolios University (with 176 patent families in industry (with 176University industry in families patent and manufacturing), Xidian University (with 107 University Xidian manufacturing), and figure (see sciences medical life and and in telecommunications), and NICT (with 41 NICT and telecommunications), in in research public and top the in university Technology (SCUT)) Chongqing University, Southeast Control University methods (Beihang of Korea (IACF), KAIST, POSTECH, NICT) (U.S. Navy, Columbia University, AIST) U.S. Navy,(BUAA), CEA) University (SEU) University (SEU)) University University, Tsinghua University, (SEU), University University, Southeast Industry Academic Cooperation Foundation Cooperation Academic Industry (CAS, Tsinghua extraction Information Robotics (South of China University Predictive (Zhejiang University, analytics (BUAA), Southeast University Beihang Planning (Zhejiang scheduling and reasoning and representation Knowledge make the resulting transfer technology and research AI-related to conduct are working together closely companies universities, institutions research and innovation cultivation. talent and Chinese touniversities technological accelerate Chinese with working Baidu as such enterprises of AI-related to efforts the thanks China in pool talent AI of the growth acontinuous is results these key Behind of other players. ahead steps afew may be and progress great made some functional applications, we have for and smaller; and smaller becoming is giants AI other and institutions research and universities Chinese between gap the techniques, AI some For worldwide. peers top-class their with up now is catching universities in in technologies AI Chinese Research cooperative laboratories. talent training programs and setting up AI implementing by example for smooth, Haifeng Wang, Baidu China in industry Universities and

75 WIPO Technology Trends 2019 76 4 Key players in AI patenting reasoning (AIST and Zhejiang University) Zhejiang and (AIST reasoning (IACF)engineering and probabilistic ontology for except techniques, AI most across patenting in first CAS ranks byorganizations AI technique among universities and public research 4.14. applicants TopFigure patent Highlighted text refers Highlighted to refers text the for applicant top each category Note: A patent refer may to more than one category University of California University Chongqing University Columbia University Tsinghua Tsinghua University Zhejiang University Tianjin University Xidian University Fraunhofer POSTECH U.S. Navy KAIST BUAA SCUT BJUT NICT ETRI IACF AIST CEA SEU CAS MIT 1,129 1,066 1,860 104 117 135 135 165 171 234 245 516 599 641 661 740 761 750 866 897

75 99 Machine learning 128

14 12 17 11 28 32 82 28 32 22 41 53 43 47 60 Logic programming 6 5 4 6 5 8

12 21 33 12 31 13 57 39 41 56 54 35 53 48 54 62 Fuzzy logic 8 0 1 2 6 0

26 35 28 10 14 Ontology engineering 0 1 0 1 0 1 1 0 9 2 1 1 6 6 7 8 0

Probabilistic

0 0 0 4 1 0 0 0 0 1 1 2 0 1 1 0 2 2 1 4 2 3 reasoning Highlighted text refers Highlighted to refers text the for applicant top each category Note: A patent refer may to more than one category University of California University Chongqing University Columbia University Tsinghua Tsinghua University Zhejiang University Tianjin University Xidian University Fraunhofer POSTECH on computer vision focus instead which organizations, research public and universities top to other contrary processing, language natural on focus astrong has NICT by AI functional application universities and public research organizations among 4.15. applicants TopFigure patent U.S. Navy KAIST BUAA SCUT BJUT NICT ETRI IACF AIST CEA CAS SEU MIT 1,108 1,036 1,364 129 148 134 215 175 120 293 619 254 443 430 402 418 466 529 533

55 88 98 Computer vision

114 204 487 108 160 152 166 399 Natural language 98 20 33 11 25 62 64 71 57 92 59 6 8 9 processing 468 206

39 61 57 16 26 20 25 61 61 95 34 67 17 32 18 79 35 36 11 Speech processing 1

Planning and 11 10 10 29 35 96 63 47 53 62 70 87 84 29 94 6 2 1 1 6 6 5 scheduling 111 117

12 11 40 27 53 40 38 58 42 57 50 36 Predictive analytics 4 5 0 2 5 7 2 5

18 14 21 40 15 67 78 28 25 31 21 77 34 71 69 13 62 Control methods 2 5 4 7 8 105

12 16 13 12 12 10 35 56 33 74 30 31 23 68 46 16 67 Robotics 5 6 3 6

12 38 30 24 41 36 23 37 72 53 33 Distributed AI 1 2 0 0 0 0 6 2 1 2 2 universities and public research organizations top many for fields application AI predominant two the are transportation and sciences medical and Life field application AI by organizations research public and universities among 4.16. applicants TopFigure patent Note: A patent refer may to more than Highlighted to refers one text the for category. applicant. top each category University of California University Chongqing University Columbia University Tsinghua Tsinghua University Zhejiang University Tianjin University Xidian University Fraunhofer POSTECH U.S. Navy KAIST BUAA SCUT BJUT NICT ETRI IACF AIST CEA SEU CAS MIT

102 197 204 120 113 111 123 164 308 Life and medical 65 47 11 55 72 28 74 67 94 96 79 94

9 sciences 183 188 174 154 256 122 208 124 208

29 34 14 25 30 65 31 22 45 99 54 89 Transportation 2 122 175 139 102 103 107 222

19 30 17 25 16 35 19 68 59 98 88 61 81 99 Telecommunications 8 119 170

12 14 22 16 13 33 51 60 36 48 46 67 58 63 72 34 81 Security 3 4 5

100 176 Industry and 17 14 14 14 12 10 45 30 59 75 50 57 51 70 36 88

8 1 9 1 manufacturing

135 Personal devices, 15 11 23 25 28 48 14 36 53 26 70 35 34 35 52 59 65 22 93

6 2 computing and HCI Document 109 124

41 17 28 12 10 50 60 17 20 26 18 39 49 63 59 22 management and 8 5 8 6 publishing

Physical sciences and 18 25 21 22 46 22 85 22 60 22 15 20 24 40 30 34 29 44 60 2 6 3 engineering

30 16 88 43 42 61 10 55 22 33 36 Energy management 9 0 0 8 7 3 5 2 7 8 5

24 42 29 27 24 22 44 26 25 23 36 32 82 Networks 1 3 7 3 6 3 6 9 0

Computing in 23 19 26 34 18 43 24 21 36 26 36 66 1 2 1 1 2 3 4 9 3 8 government

14 21 49 50 22 25 14 32 14 16 24 10 35 Business 0 4 0 8 9 7 1 5 6

12 28 23 13 11 20 20 23 25 19 13 93 Cartography 4 2 1 1 2 6 9 5 1 7

26 14 11 19 47 16 10 11 13 15 18 11 38 Arts and humanities 3 6 3 8 7 5 4 5 7

18 19 28 36 19 13 13 13 38 Education 2 5 4 8 1 7 2 8 5 7 5 9 7

77 WIPO Technology Trends 2019 78 4 Key players in AI patenting Note: EPO the is European PCTPatent WIPO Ofiice. represents applications. Japan fileJapan applications many across jurisdictions and U.S. the Europe, from organizations research public and Universities filing of office by organizations research public and universities Figure 4.17. Patent applications for top patent applicants among University of California University Chongqing University Columbia University Tsinghua Tsinghua University Zhejiang University its patent applications have been filed at more than one office. one than more at filed have been applications patent its of majority vast the that member, one than indicating more having families patent of its percent over with 90 (CEA), Commission and Atomic Energy Energies Alternative the and French offices, Institute, eight filing which significant at major has activity country. exceptions Notable to this include rule the Fraunhofer home U.S.in the their exclusively and Navy,China nearly file which international patent filing activity, locatedthose in in particular universities and public organizations research have generally lower though, contrast In origin. of country in their filings patent their of to tend make majority the organizations research public and universities applicants, patent top to other Similar offices. patent selected for organizations research public and by top universities 4.17Figure filings of patent distribution geographical the shows public research organizations Geographical of distribution filing by topuniversities and Tianjin University Xidian University Fraunhofer POSTECH U.S. Navy KAIST BUAA SCUT BJUT NICT IACF ETRI AIST CEA SEU CAS MIT 1,392 1,423 1,130 1,021 1,190 2,609 850 887 920 995 59 33 31 China 1,243 1,806 221 550 Republic of Korea 387 388 268 188 114 170 863 156 55 54 65 97 U.S. 243 168 132 126 144 26 52 57 WIPO 171 205 69 39 54 30 77 Japan 102 139 172 29 39 38 EPO 205 France 177 23 Germany 69 36 33 Australia

61 Canada

39 Austria Transportation is the main application field field application main the is Transportation As in the portfolios of those companies most most companies of those portfolios the in As ���� California. Co-ownership of patents among U.S. among of patents Co-ownership California. focus University) Columbia and MIT California, Samsung co-owns patent families with with families patent co-owns Samsung organization. SGCC co-owns patent families families patent co-owns SGCC organization. research public or university the as country originating from thecompanies same industrial mostly they are co-owners, (such as Massachusetts General Hospital and and Hospital General (such Massachusetts as with most of the top Chinese universities universities top of the Chinese most with Korea, as well as with the University of of University the with well as as Korea, life in and strong are organizations two and (AIST organizations Japanese top two The engineering. and sciences physical the in applications patent numerous file they also top U.S. The of Korea. universities Republic universities and public organizations’ research universities and public organizations research universities and public organizations research National Institutes of Health). of Institutes National life in sciences have portfolios strong NICT) and public organizations, research while (with 30 CEA for management energy and public and top the universities among unique is that apattern humanities, and arts in also but telecommunications, and and public organizations research (University of several organizations in the Republic of of Republic the in organizations several manufacturing and industry in specialization is typically shared with shared institutions healthcare is typically patent families is relatively rare among the the among rare relatively is families patent of co-ownership patents, AI filing in prolific families). patent the top of in the four two in and portfolios for Fraunhofer (with 17 Fraunhofer for families), patent mentioned in six of the top of the 10 six in mentioned Chinese most active in this field. Where there are are there Where field. this in active most a with transportation, and sciences medical though sciences, medical life and on mainly research organizations. In Europe, the top the Europe, In organizations. research Co-ownership country of origin. of country their in filings patent priority their of majority make the vast organizations public research and top universities The

79 WIPO Technology Trends 2019 80 Chapter title

Photo: © baranozdemir / Getty Images Photo: © baranozdemir / Getty Images patterns, so is afarmer able to better plan what they plant where. FarmBeats’ aerial imaging capabilities precisely document flooding Because afarm is often located next to ariver, flooding is aproblem. time the planting of so the seeds, gets farmer amore productive harvest. pH. Information on soil temperature and moisture levels help can better waterpercent less for irrigation lime percent and 44 less to control soil generated by FarmBeats is agame changer. use up can Afarmer to 30 helpcan increase farm productivity, and also help reduce costs. data The Data, coupled with the farmer’s knowledge and intuition about their farm, to enable data-driven farming and help make farmers better decisions. yield. Microsoft’s FarmBeats project aims to provide end-to-end an approach of their soil make can of informed all sorts decisions that save money and boost soil But sensors. who afarmer knows the temperature, pH, and moisture level When most people think of groundbreaking digital technology, they don’t picture Improving agriculture with AI and IoT Case study by Microsoft food production in acontext of limited arable land and water. global the world to the tools around increase they significantly need team believes that the technology AI-enabled will give farmers Microsoft’s goal is thus to enable data-driven farming. FarmBeats The predications on soil temperature and moisture levels for the entire farm. map. machine The learning pipeline then can use the resulting maps to make learning algorithms to stitch images and sensor values together into adata whitevia space TV to your computer, where device edge an machine uses map of the farm. Data from both the and the sensors phone are transmitted you don’t). You walk around the fields with creatingthecamera, an aerial down to either adrone (if you have money to spare) or ahelium balloon (if – in the ground. You then phone your attach with smart the facing camera number of –one every sensors couple hundred meters, instead of 10 meters wholeThe FarmBeats system is powered by solar panels. You place asmall over them the way same that data gets transmitted via broadband. in the remote areas where most farms are located, so data besent can whilesee flipping through channels.These gaps arein spectrum plentiful TV, you’ve white seen spaces before. They’re the “snow” you’ll sometimes broadcast spectrum.spaces are If unused TV you’ve ever watched old an but FarmBeats relies on aclever workaround: white space. it TV uses White data. Most farm data systems require expensive transmitters to connect, broadband Akey speeds. innovation of FarmBeats is in how transmit sensors percent of people living in areas don’t rural have to access even the slowest often no power in the field, or Internet in the farms; in the United States, 20 However, getting data from beextremely afarm can difficult since there is

81 WIPO Technology Trends 2019 5 Geography of patent filings

For Western Key findings nations to • The first patents related to AI were filed with the Japanese patent office at the beginning compete, they will of the 1980s. In following years, the number of filings in Japan stagnated, while filings in have to develop the U.S. and, later, in China increased. • China and the U.S. are now leading better mechanisms research in the field of AI in applied as well as more fundamental research, based to share and on analysis of both patent filing data and scientific publications. pool data. • In 2014, the number of first filings in China surpassed that of the U.S. However, only Boi Faltings, EPFL four percent of patents first filed in China are subsequently filed in another jurisdiction. • Other major patent offices receiving patent filings in the AI field are France, Germany, the Republic of Korea and the U.K., while India is emerging as a new target for patent filing. • The European Patent route is mainly used by European applicants to seek protection in several countries directly from first patent filing, but also by U.S. patent applicants, whereas the PCT route is used mainly by applicants in the U.S., Japan and China. • One-third of applications are filed with two or more offices. The main office of second filing is the International Bureau of the World Intellectual Property Organization (WIPO), followed by the United States Patent and Trademark Office (USPTO) and the European Patent Office (EPO). • China and the U.S. lead in patent filings in all AI techniques and functional applications, though their predominance is challenged by Japan, in the categories of fuzzy logic, computer vision and speech processing, and the Republic of Korea, in ontology engineering. • China and the U.S. also lead in patent filings in all AI application fields, challenged only by Japan (arts and humanities, document management and publishing) and the Republic of Korea (military applications). This chapter looks at data on where AI patent patent AI where on data at looks chapter This where office the to refers filing first of office The by WIPO, Treaty a is administered (PCT), Geographical of distribution patent filings valid in that jurisdiction. Often, but not always, not but Often, jurisdiction. that in valid filing is taking place, including both office of both office including place, taking is filing filings).The route the under PatentCooperation non-resident or resident from come filings Office (EPO), which grant patents covering covering patents (EPO), grant which Office way to is One look AI to in evaluate trends established by the European Patent Convention Patent Convention by European the established (EPC), administered by the European Patent Patent by(EPC), European the administered where the invention is expected to have invention expected a the is where the application is first filed, and the office(s) of the and filed, first is application the only are by office apatent Patents granted these whether see over and time changes track responsible office apatent has jurisdiction Each are referred to as members of a patent family. of apatent to members as referred are invention other in same the for applications home their in first file will applicant a patent from. came they where affiliations, scientific Similarly, at look we can applicants. route the as such systems, regional some also governed Patents are place. taking is activity patenting most where jurisdictions those at second filing refers to any patent office where where any office patent to refers filing second jurisdictions relate to the same invention, they to relate same the jurisdictions patent subsequent as known (also jurisdictions jurisdiction. In many particularly cases, patent applicant may choose to file further tofurther file may choose applicant patent broad application or considerable value, the publications and, on based their authors’ territory. given in by protection laws providing protection may subsequently be sought. be may subsequently protection functional applications and application fields, are There patents. granting and examining for multiple jurisdictions. Where filings in different in different filings Where jurisdictions. multiple thatmechanism facilitates extension to techniques, AI in trends the at closely more look also and filed, being are patents AI most where identify can we national/regional offices, in trends comparing By country. one than more first filing and the offices of subsequent filings filings subsequent of offices the and filing first filing, with first filings generally ahead due to ahead generally filings first with filing, office of second filing for inventors overseas, for filing inventors overseas, second of office an being from 2007 about in shifted office and haveoffices shown considerable growth may applicant the that office patent certain attorney patent the where area the or company where located office patent the with coincide This filings. office in into patterns the closer (offices of second filing), before then looking looking filing), then before second of (offices wish to explore first. In addition, information information addition, In first. to explore wish perceived are regions and countries which In summary, the International Bureau of WIPO of WIPO Bureau International the summary, In to an office of first filing for Chinese inventors, Chinese inventors, for filing first of to office an offices. patent these of a practice and process grant patent the even linked to be could it or based; is team may filing first of sector, public the office the from foremost of applicants, For anumber about 2003. The U.S. office has followed a followed has U.S. The office 2003. about of Korea Republic The fast. growing is and extension as used mainly EPO are the and at applicants non-resident or resident among market. potential or important already an as subsequent ones; moreover, this jurisdiction moreover, ones; jurisdiction this subsequent single one is there when mainly amarket, see in its use as an office of first and second second and first of office an as use its in patent China The years. several last the in filings of patent distribution the on included is into insight valuable provide can information patent application filed, not be followed to filed, by application patent more established path, with a parallel increase increase aparallel with path, established more to since China trend followed asimilar has of a headquarters the with may coincide research takes place or where the applicants applicants the where or place takes research States. Member with coordination in pursues it which Europe, in AI for aStrategy presented has this purpose, the European Commission For use. and development of AI forefront EU the for to the at be action coordinated international competition requires However, competitive. to remain fierce applications AI adopt to develop and need that sectors manufacturing and healthcare transport, world-leading has and robotics in strong EU also is The AI. of field the in startups and laboratories researchers, world-class has Europe European Commission Paul Nemitz, in EuropeAI

83 WIPO Technology Trends 2019 84 5 Geography of patent filings Note: The color is based on the number of scientific publications by location of entities authors are affiliated with affiliated are authors entities of location by publications scientific of number the on based is color The Note: offices patent at filed applications patent of number the on based is color The Note: AI research and patent protection for AI-related inventions occurs around the world the around occurs inventions AI-related for protection patent and research AI publications by(bottom) geographical affiliation scientific of number and (top) office patent by applications patent of 5.1. Number Figure 100,000 -360,000 1,000 -99,999 1-999 No data 100,000-160,000 10,000-99,999 1,000-9,999 100-999 1-99 0 No data 100,000 - 360,000 - 100,000 99,999 - 1,000 1-999 data No 100,000-160,000 10,000-99,999 1,000-9,999 100-999 1-99 0 data No The patent offices of China and the the and U.S.China of offices patent The AI and potential as a market for AI-related AI-related for amarket as potential and AI ���� ���� filing (i.e., considered as an improtant market market improtant an as (i.e., filing considered second a or filing first a either are filings filing. first exponentially in recent years), U.S. while recent in exponentially of AI-related scientific publications.Over 5.1 figure comparison, By results. total of the acombine high of number innovations in (U.S.) offices patent China and of America opposite almost followed an has it as example country. Japan is the perhaps most interesting (whose applications have increasing been distribution geographical the shows (bottom) 5.1 figure (see 5.2). (top) figure Bothand U.K., which with 96,359 scientific publications 300,000 publications300,000 have published been Most filings are made at the United States States United at the made are filings Most the of number publications scientific than the times three have than more countries these that in inventors of AI community large the as an office of second filing than an office of office an than filing second of office an as show the most dynamic trends in filings but but filings in trends dynamic most show the in China are made by patentees Chinese third. ranked is percent 20 represent PCT filings inventions. publications) and the U.S. (327,880). Both (341,833 China in by organizations scientific more to used be appears and topath China, by organizations in other juridsdictions). for different reasons: the majority of filings of filings majority the reasons: different for Office compared trends Office Topfiling of offices brokers. power important power, are is today’s In knowledge policymakers IP and economy centralization. power, to up on may law be IP processing it to new limits and create information of digital economics the given But brains. and eyes, ears human of the limits the in power inherent of concentration the on limits to biological Technology the us allowed overcome has

Seth G. Benzell and Erik Brynjolfsson, MIT Initiative on the Digital Economy Digital the on Initiative MIT Brynjolfsson, Erik and Benzell G. Seth roleThe of IP policy in the knowledge economy The results from AI-related filings in China in filings AI-related from results The The low numbers of filings for Germany may be maybe Germany for of filings low numbers The ���� geographical distribution filing: first of Offices WIPO statistics and patent analysis reports, reports, analysis patent and statistics WIPO Some other jurisdictions show a less dynamic dynamic show aless jurisdictions other Some observed in China, as shown in different different in shown as China, in observed trends to filing patent overall the correspond of first filings for all patent families (see figure (see 5.4). families patent all for filings of first the and of Korea Republic Japan, (China, offices the to use prefer applicants that todue fact the 2000 between showdecrease to a office only increased notably has filings patent in growth (with 328,935 patent families) and the top four top the four and (with families) 328,935 patent U.S.) together account for 86 percent of the total total of the percent 86 for U.S.) account together the data relating to granted, cited or extended extended or cited to granted, relating data the at by looking made be can analysis Further the topoffices filings, main first at 10 Looking Germany. in protection EPO to secure account for 97 percent of all AI patent filings filings patent AI of all 97 for percent account 2010, and again have filings increased but by Fink (2013). paper arelated in analyzed and since 2010. in recent years (see figure 5.3). Germany is the is the 5.3).Germany figure (see years recent in EPO, the the and where Japan particular in be seen as a validation by independent patent patent by independent avalidation as seen be could it granted, been has afamily in patent one 5.5).least figure at If (see families patent still nonetheless but growing of number filings, Top offices of first filing Topfirst of offices

85 WIPO Technology Trends 2019 86 5 Geography of patent filings Note: EPO the is European Patent WIPO Office. to refers PCT applications. Japan, while WIPO and the EPO are also often used often also are EPO the and WIPO while Japan, by followed China, and of U.S. offices patent the in filed are applications patent of number greatest The office patent by applications patent of number Overall 5.2. Figure Taiwan Province of Taiwan China Province Hong Kong SAR, China Hong KongSAR, Russian Federation Russian Republic of Republic Korea New Zealand South Africa Netherlands Singapore Argentina Germany Denmark Australia Sweden Canada Norway Finland Mexico Austria France Japan WIPO China Spain Brazil Israel India EPO U.S. U.K. Italy 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 The number of patent applications filed in China grew by an average of 25 percent since 2009 since percent of 25 average an by grew China in filed applications patent of number The date priority earliest by offices different for applications patent of Number 5.3. Figure Note: EPO the is European Patent WIPO Office. to refers PCT applications. 10,000 12,000 14,000 2,000 4,000 6,000 8,000 0 1974 1984 1994 2004 2014 U.S. Republic of Republic Korea Germany EPO WIPO Japan China

87 WIPO Technology Trends 2019 88 5 Geography of patent filings patent offices of the U.S. and Japan and U.S. the of offices patent the at filed are families patent cited highly and grant one least at with families patent of number highest The filing first of offices patent top for share extension and patent by office families cited highly and member granted one least at with families of Number 5.5. Figure by the patent offices of Japan and the Republic of Korea Republic the and Japan of offices patent by the followed filing, first of offices as chosen frequently most are which offices patent the are U.S. the and China office patent by filings first of Number 5.4. Figure Republic of Republic Korea Republic of Republic Korea Germany Germany Japan Japan China China U.S. U.S. 0 0 5 Families Families with at one least grant 20,000 10,000 4 3 40,000 2 20,000 60,000 30,000 1 0 40,000 5 3 4 2 10,000 Highly cited families cited Highly 50,000 20,000 60,000 30,000 70,000 1 0% 5 80,000 Extension share Extension 20% 90,000 4 3 100,000 40% 2 1 Considering these three criteria, the ranking three criteria, these Considering filed in China are subsequently filed in other in other filed subsequently are China in filed patent of number in first ranks China Whereas that mind in bear has to one although field, offices with regards to families with at least least at with families to regards with offices five these among last ranks China of filings, cited/extendedgranted/highly patent families dramatically. changes filing first of of offices applicant patent of the desire the of both of self-citations the including of citations, than to tend have patents citations more older protection patent inventionof which the for inventiveness and novelty of the examiners (in this case, where the patent is mentioned mentioned is patent the where case, this (in when estimatingwhen the of impact a patent. to commercialize the invention in multiple invention the multiple in to commercialize to (i.e., share) of patents extension Extension are considered: just 4 percent of patents first first of patents 4percent just considered: are certain in a of finding keypatents a means patents) published later in 20 times least at several patent offices provides an indication indication an provides offices patent several in figure 5.5 are considered as a percentage percentage as a considered are 5.5 figure in considered be not should and importance invention of the on impact the indicates families of patent Citation sought. being is jurisdictions, compared with from 25 from to with 63 compared jurisdictions, patent applicants which do not have the same have not do same the which applicants patent percent in all other offices; and when the data data the when and offices; other all in percent families, its ranking falls markedly when only only when markedly falls ranking its families, markets and the market size. market the and markets recent ones, and that there are different types types different are there that and ones, recent later inventions. Citations are often used as as used often are Citations inventions. later technologies. AI core other and HCI vision, speech, in strong for decades been has Japan interactions. their social–emotional all for AI require which robotics, dominant in humanoid AI, especially in aleader been long has –it AI in by Japan'sI'm leadership surprised not MIT Media Laboratory Rosalind Picard, AI in Japan

• • • The strengths of other geographical areas can can areas geographical of other strengths The AI have been filed more recently than those in in those than recently more filed haveAI been go could that factors many are there Although other jurisdictions. by explained be also could grants and citations However, market. overseas low the rate of the on based last and patent, granted one the fact that many Chinese patents related to related patents Chinese many that fact the only filed of applications percentage high the for reason one figures, these toward explaining also be identified: be also in China could be that Chinese applicants are are applicants Chinese that be could China in more interested in the domestic rather than than rather domestic the in interested more ofnumber citations. The U.S. patent office has the largest largest the has U.S. office The patent country this in made inventions that indicates This families. patent cited highly in U.S. first The ranks Around 60 percent of patent applications applications of patent percent 60 Around China combined. Japan and filed in other jurisdictions (i.e., have more jurisdictions in other filed United the of offices patent the in filed first one granted member, with double that of of that member, double with granted one (including self-citations). (including U.S. applicants being cited in other U.S. patents U.S. other in cited patents being U.S. applicants of chances the increases U.S. which patents, than one patent family member). family patent one than subsequently are (U.K.) France and Kingdom in of citations toward number ahigh tendency number of patent families including at least least at including families of patent number general a reflect Itmay also impact. have agreat ahead. pulls one which see to interesting It willbe China. one in and Valley Silicon in one AI: in ecosystems 10 next wethe have will years parallel your $10 for put Over investment. million You cost. the through- more much get atenth of or take aquarter might effort in China, that whereas on labeling data, money of that million $2 spend would company.AI In the U.S., the company yousay invested $10 into million asmall let's U.S. the in than For instance, China in now,Right further goes your money Frank Chen, Andreessen Horowitz China’s advantage data

89 WIPO Technology Trends 2019 90 5 Geography of patent filings since 2006 percent 29 of rate growth annual average an U.S., thewith overtaking filing, first of office top the is China year priority earliest by offices patent selected for filings first of Number 5.6. Figure contrast to most other geographical territories in publications, scientific than families patent of share alarger for account Japan and U.S., China The location that in filed families patent to compared with, Figure 5.7. Scientificpublications for topgeographical locations ofareentities authors affiliated Note: EPO the is European Patent WIPO Office. to refers PCT applications. 10,000 15,000 Germany 5,000 Canada France Japan China Spain India U.K. U.S. Italy 0 0% 1975 5% 1985 10% 15% 1995 20% Patent families publications Scientific 2005 25% 30% Republic of Republic Korea 2015 U.S. WIPO EPO Japan China • • 14 percent, respectively). Non-European Japanese applicants (both 3 percent). The PCT PCT The 3percent). (both applicants Japanese Among regional and international and regional filingAmong routes, ���� ���� China (20China percent). German-based are by office patent German of first filing by earliest priority year. priority earliest filing by of first are,however, There office. to exceptions some 12,415 of the percent (95 companies patent the at made filings first all almost example, For of protection. area primary the as country (12 and followed by percent), Canadian (2.7 (2.6 the percent), Japanese the percent), Israeli (1.9 percent) and the Netherlands (1.2 (1.9Israeli Netherlands the and percent) U.S. the that office noteworthy patent It is Most applicants choose the office of their own of their office the choose applicants Most Figure 5.6 shows the trend in the top six offices offices top the in six 5.6Figure trend the shows U.S. and the (26 (27 percent) Japan percent), by led applicants U.S. filings, patent European most used is route patent European the rule: this EPO (with 1,529 or any filings) other first applicants account for a smaller proportion of of proportion asmaller for account applicants (with 16 and applicants percent French and Canadian the above example for applicants, over the own office their chose applicants is chosen above any other office by foreign by foreign office above any other chosen is percent of applications), followed by Dutch followed of by applications), Dutch percent offices. percent) frequently by German applicants (with 28 applicants by German frequently German and Germany) in filed first families route is most widely used by applicants from from by applicants used widely most is route 746 compared with respectively 330 and 150 and 330 746 respectively with compared Canadian applicants). first filings for Swiss applicants). Swiss for filings first and applicants, Israeli for filings first 2,779 compared with 535 first filings for for filings 2,779 first 535 with compared office (1,237 compared with respectively 900 900 (1,237office respectively with compared (1,982 408 with own country compared Israeli and Canadian applicants choose choose Canadian and applicants Israeli Dutch and Swiss applicants choose the U.S. the choose applicants Swiss and Dutch more office the in patent U.S. tofirst file and 182 first filings for Dutch applicants, and and applicants, Dutch 182for and filings first patent office over the EPO and their own their and over EPO the office patent frequently than they do the office of their of their office the they do than frequently Resident and non-resident filings non-resident and Resident Offices of first filing: change over time over change filing: first of Offices • • • • • • One of the notable differences between each each between differences notable of the One growth rate among all the offices. Looking at at Looking offices. the all rate among growth first office each which in is the year office the top offices in more detail: more in topthe offices However, today, China the has annual highest as an office of first filing. Japan and the and U.S. Japan filing. first of office an as respectively, while China reached it in 2002. 2002. in it reached respectively, China while 1979 the in threshold this 1986, and reached the of threshold reached patent 200 filings 1986, then increased again between 1986 between again 1986, increased then Japan: was among although the country China: filings with the China patent office first in 2014.first China have in since filings First 2,000 patent applications per year since the the since year per applications patent 2,000 overtaken first filings in the in an filings thanks to U.S. first overtaken 10 (with past the in years exponentially 10over past the years. economic downturn. continuously ever since (with around 10,000 mid-1980s) the (in early have and grown 1982 from to decreased families of patent (mid-2000s) and have seen stable growth growth stable have and seen (mid-2000s) U.S.: filings at the U.S. patent office began began U.S.: at the office patent filings U.S. 33,000 patent families in 2016). in families patent 33,000 was China the first to innovate in the AI field (as early early innovate(as to fieldAI first in the the PCT: international patent applications and stable seen have filings EPO: first Germany,France, the and U.K. the the with of Korea:Korean filings Republic and to 2002 2000 from periods two the and 1991 and has been stable at around around at 1991stable and been has and 1980s), of the beginning the as number the impressive average annual growth rate of 43 rate of 43 growth annual average impressive percent since 2013. since percent have late (2002) but relatively grown began recently more began office patent 2015), in applications patent except for 1990s. of the beginning from 2008 to 2012, 2008 from an was there when have grown strongly, 2010. after especially moderate increases. ranked second for first filings in 2009 and and 2009 in filings first for second ranked advantage.economic into expertise their to turn ecosystem an yet has places of these none However, of implementation, terms in are several other strong countries. there and talent extraordinary has Canada Ithink of research, terms In Kai-Fu Lee, Sinovation Ventures emerging? are countries Which

91 WIPO Technology Trends 2019 92 5 Geography of patent filings 6 to 10 6 to while only 5 percent have more than six members six than more have 5percent only while member, one only have families Two patent of thirds number of family members by families patent of Percentage 5.8. Figure for other offices other for true is opposite the whereas filings, subsequent than filings first more receive Japan and U.S. the China, subsequent filing of offices top for filings subsequent of number and filings first of Number 5.9. Figure Note: EPO the is European Patent WIPO Office. to refers PCT applications. 5 4 3 2 1 0% Japan WIPO China EPO U.S. 0 20% 10,000 40% 20,000 30,000 60% 40,000 50,000 60,000 70,000 80,000 First First filings filings Subsequent 90,000 100,000 The lack of a grace period in Europe – as –as Europe in period of agrace lack The with organizations of those location The Two mentioning. are worth other countries Although not appearing in the notAlthough top appearing historical ���� China, Japan and the U.S. the and However, Japan China, are there filing, whereas some other countries record filing, some record countries other whereas opposed to U.S., the allows –that example opposed for public and universities todue strategy IP the may be This patenting. in than research of their of Japan), Spain, Italy, ninth, and ranks which first is for there than publications of scientific highly also are filing first of of top offices list the in prominently feature that countries such where publications, such in described positioninggeographical of the research growth, although the of number patent families of average (with an years recent during growth which is 10 which authors of scientific publicationsare 33 percent in the 3 years up to up 2015). 3years the in percent 33 filings First the patentability of AI-related subject matter. subject of AI-related patentability the of awareness alack toward protection, IP cultural to adhere, required they are to which researchers 5.7Figure European that indicates terms in activity less is of Korea there Republic filings first for eighth ranked was total, India about the patent system, or laws governing laws system, governing or patent the about organization or university the within attitudes results the publishing in active more much are as to the indication an provide can affiliated 5.1 include 5.7). figure These and (bottom) some differences: notably, in the case of the notably, of the case the in differences: some information has been obtained from Scopus Scopus from obtained been has information havein Russia shown also annual significant 2015in rate of annual ahigh enjoyed has and patent filings. These latter include India, which which India, include latter These filings. patent following, the national or institutional policies of the Most report. of this purpose the for higher activity in publications compared with in publications compared activity higher research organizations in Europe may be may be Europe in organizations research inpublications scientific ranks fourth (ahead ranked for scientific publications figure(see 100 year). just per (around small remains Growth levelled off in the most recent year year recent most the in off levelled Growth one-off event. one-off seen whether this represents a trend or is a a is or atrend represents this whether seen for which data is available. It remains to be to be It available. remains is data which for Comparison with publications scientific Comparison th . The U.K. is ranked third. ranked is U.K. . The A substantial majority of patent applications applications of patent majority A substantial ���� ofsecond filing Offices China, where 43 percent of patent applications applications of patent percent 43 where China, corresponding proportions for the U.S., the for Japan proportions corresponding explain partly also could article of ascientific (see figure 5.8). This pattern is particularly particularly is pattern 5.8). This figure (see within a certain period after the publication publication the after period acertain within these results. are not extended to other jurisdictions. The The jurisdictions. to other extended not are (227,627) filing:percent 67 first the after AI of jurisdictions to additional extended not are pronounced among patent among filed in applications pronounced office one only with filed are families patent application to patent a file applicants patent It’s also hard for them to transition to a to transition them for It’s hard also advantagean for developing countries. of less willbecome lowerhaving wages and to markets closer move production will it’s to That – and your home. close fit to exact an ashoe manufacture can factory alocal and ashoe for measured be you that can Imagine work. same the do can of technology aid the with fewer people where Germany in to ones out losing to are make textiles used that robots in the world. factories In Indonesia, industrial for market now largest is the init developed does China countries. than workforce of the proportion a larger employs manufacturing where countries, disruptive in lower-wage developing more be fact in could of automation impact The everywhere. It manifest will levels. income higher hits country the before disappearing are jobs factory namely de-industrialization, what call premature economists experiencing are Brazil like and Mexico Countries vanishing. is topath prosperity that because this to experience country manufacturing. China might the be last through rich become countries Historically, that. to afford enough rich they’re not because economy service Top offices Martin Ford,Martin futurist markets? have developing on What impact will AI

93 WIPO Technology Trends 2019 94 5 Geography of patent filings fuzzy logic in the U.S. than in China in than U.S. the in logic fuzzy in filings more slightly are there while offices, two these in to parity close is programming logic in filings of number The U.S. by the followed China, in filed are patents learning machine most The patent applications for AI different techniques of number by 5.10. offices TopFigure patent Note: A patent refer may to more than one category

Probabilistic reasoning Ontology engineering Fuzzy logic Logic programming Machine learning 0 Australia of Republic Korea Japan China U.S. Canada Japan of Republic Korea China U.S. Germany of Republic Korea Republic of Republic Korea Germany Japan Japan China U.S. China U.S. Germany Republic of Republic Korea 20,000 Japan 40,000 U.S. 60,000 China fourth in scientificfourth learning machine publications and logic fuzzy in third ranks India filing, patent for offices top the among appear not does it While AI techniques number of scientific publications for different Figure 5.11. Top geographicalby affiliation Note: A scientific publication may refer may publication to more Note: than A one scientific category

Ontology engineering Probabilistic reasoning Fuzzy logic Logic programming Machine learning 0 Italy U.S. China U.K. Germany Canada Germany U.K. China U.S. U.K. of) Republic Iran (Islamic France India U.S. Germany U.K. China China 50,000 U.S. Japan India U.K. 100,000 150,000 U.S. 200,000 China • • • • • • • The main office of second filing is the the is filing second of office main The chosen offices of subsequent filing can be can filing subsequent of offices chosen in the top are Canada appear that 10 list offices 5.9.EPO,and figure shownChina in as office International Bureau of WIPO (18 of WIPO Bureau of International percent Looking at the top offices of first filing, the filing, first of top the at offices Looking only (2,100 office, percent one 1.9 than patent and Australia. families), patent all followed by U.S. the patent 26 of percent,15 Korea are Republic the and summarized as follows: as summarized patent families being extended there. Two there. extended being other families patent (112,201) families of patent percent more at filed 33 the respectively. Of 9percent, and percent families) are filed with more than than more with 10 offices. filed are families) ranks fourth with around 10 percent of all 10 of all around with percent fourth ranks The main chosen office of second filing is filing second of office chosen main The Japan: unusually, WIPO is not used very very unusually,Japan: used not is WIPO Germany: these patents have a high ratio of of have ratio patents ahigh these Germany: China: patent these filings areseldom WIPO (31.5 percent of patent families with an (31.5WIPO an with families of patent percent closely followed by the U.S. patent office followed by U.S. the office patent closely percent), EPO the are (34.9 filing of second extensions in general. The preferred offices to U.S. the (25.0extended percent). percent), (39.3 office of second choice (12.8often U.S. preferred the The is percent). U.S. the at office). patent priority earliest 5 than (less jurisdictions to other extended (60.7 percent), ahead of the Chinese, Chinese, of the (60.7 ahead percent), (31.4 (30.6 WIPO percent). and percent) percent). (13.0 (44.5 percent). (44.5 U.S.: extensions are observed, numerous the U.S.the followed by (62.0 WIPO percent) is filing second of office EPO: main the to percent). 36.8 30.8 (from offices Japanese and European the during PCT:chosen office main the there to Japan, of Korea: similarly Republic U.S.the WIPO. and are offices patent favored two the they are, Europe but also Asia, Canada and Australia. Australia. and Canada Asia, also but Europe ahead of China (14.1 of China ahead EPO the and percent) is not much use of PCT applications (9.4 of PCT applications use much not is in a wide variety of market areas: mostly mostly areas: of market variety awide in percent) and patents are most commonly commonly most are patents and percent) percent of first filings with this office). When When office). withthis filings of first percent national phase isnational the phase U.S. patent office

• The remainder of this chapter looks at the the at looks chapter of this remainder The Japan (fuzzy logic), the Republic of Korea of Korea logic), Republic the (fuzzy Japan AI techniques are filed. are techniques AI ���� andapplications fields application Breakdown by techniques, functional Canada (ontology to engineering) seem be U.S. the offices top the two and are China geographical trendsgeographical in applications for the (ontology engineering) and Germany (fuzzy (ontology (fuzzy Germany engineering) and with notable strengths in AI techniques are are techniques AI in strengths notable with AI remaining the in U.S. the while leads they are not countries where many patents for for patents many where countries not they are of number the on well, as based markets target althoughtechnique the volumes categories, of identified technologies AI three categories applications and AI application fields. subsequent filingsalthough countries, insubsequent these in these categories are lower.in categories these countries Other 1: functional Chapter AI in techniques, AI for all AI techniques (see figure 5.10). figure (see techniques AI all for China logic). Australia (probabilistic reasoning) and and reasoning) (probabilistic logic). Australia patent on machineleads learning filings, The U.S. patent office is the main office of office main is the U.S. office The patent first filing. first office main the is theEPO while filing, first of second filing when France is the office of office is the when France filing of second 40.0 WIPO (from 55.6and to percent). office France and the U.K.: the main offices of of U.K.: offices the main and the France second filing are the EPO,the are filing the patent U.S. second second filing when the U.K. is the office of office is the the when U.K. filing second alot. term, long- but alot, worth not short-term, –so, GDP of its atiny percentage only world’s of the but two-thirds population, for account perhaps Chinese The notprobably in developed countries. make inroads some internationally but mobile and AI willChinese technologies Ithink America. South potentially and India probably and Africa, East, Middle the Asia, –south-east demographics those developing with countries similar to into get opportunity agood has China AI techniques Kai-Fu Lee, Andreessen Horowitz developing markets and China

95 WIPO Technology Trends 2019 96 5 Geography of patent filings practice. European the and prong, to second its respect with particularly framework, of the aconvergence indicate developments recent the believe that others although framework, Alice the than stringent U.S. the less may be outside standard eligibility matter subject- patent believe the that some Thus, hardware. to tied sufficiently or with implemented are if they offices non-U.S. these in patented be can inventions software speaking, Generally • • • • Public,” 2017: July in issued the from Recommendations and Views on Report USPTO’s Matter: Subject Eligible “Patent the in eligibility, discussed as subject-matter on standards have different jurisdictions Other eligibility. subject-matter on clarity further bring Guidance,” can Eligibility Matter “2019 the Subject (USPTO)’sPatent Revised particularly Trademarkand Office guidelines, Patent States United the and of test, Alice’s application two-step the on insights meaningful v. Inc. AT&T LLCServices, Mobility LLC v. LP Holdings, Hotels.com, computer computer generic to functions.” perform Lower decisions, such as court ageneric “require than activity, to more do failing conventional” routine, understood, a“well- is elements of their each because any inventive without concept idea abstract Alice application.” into a“patent-eligible The idea abstract the transform would that concept” any “inventive provide elements claimed the so, if whether (2) and idea; determining abstract an as such to concept, (1) apatent-ineligible invention directed the is whether determining Alice Corporation v. CLS International Bank 2014 Court’s U.S. the with inventions Supreme in “computer-implemented” and decision software for stringent more became requirement eligibility subject-matter patent this on standard The phenomena. natural and laws of nature ideas, excluding abstract as courts of matter,” by the composition or interpreted is and manufacture, machine, “process, (U.S.C.) Code States United 35 §101, to a matter subject patent-eligible limits which invention arguably is AI an on apatent to obtaining hurdle legal U.S., the In biggest the specified emerging fields. technology specified other to and AI pertaining applications patent for examination accelerated introduced of Korea recently eligible.” patent is Republic The combination the implicates that method of the combination, a and computer-readable the containing medium software in is with conjunction claimed hardware,software then the combination, the operating computer if that “indicate they also but patent-eligible, not are se per programs (KIPO)’s computer state that Office guidelines Property Intellectual Korean The matter. subject eligible of patent- scope of the patentability.” abroadening by viewed as is many revision This from excluded be not will characteristics invention” “technical has that program-related 2017, April in revised guidelines to examination the according China, In a“computer to hardware. tied expressly inventive are steps claimed their as long so patent-eligible, being inventions software view Many resources.” hardware using by implemented to “specifically be required are aspects processing information its if invention patentable is asoftware Japan, In to character” the patentable. has be machine requisiteand learning “technical AI 3.3.1), invention on an (G-II of whether learning” assessment the on guidance providing machine and intelligence of “Artificial Examination for Guidelines published recently also run.” is EPO the addition, any In program when inevitably occur which effects those beyond effect technical afurther invention causes “claimed the if avoided be can exclusion matter”, this subject eligible ‘as patent such’ from computer for exclude programs explicitly 52(2) “Article although 52(3) Europe, In Patent (EPC) and Convention European of the Court held that the patent claims on “intermediated settlement” are directed to an to an directed are settlement” “intermediated on claims patent the that held Court

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148 scientific publicationsand the overall The United States and China lead in in lead China and States United The for offices main Two the among not offices The trend is similar when scientific publications AI, planning and scheduling, predictive predictive scheduling, and planning AI, U.S. the and China areas, other the in As ���� Canada (fifth in knowledge representation and and knowledge in Canadarepresentation (fifth volume of publications is very low). U.K. The very is ofvolume publications each functional application category, except Japan where processing, except speech for dominate patents for AI functional applications, of terms in few years next the within evident with first ranks Germany where engineering, (see figure 5.12). figure (see the detail, more in Looking U.S. Patent Office has the most control method method control most the has U.S. Patent Office with patent filings (see figure figure filings (see patent with 5.13). However, lower patenting in or eighth is it whereas 10 with Iran and Turkey; and Iran they do, however, fourth rank to AI functional applications, compared with AI in top the offices also top are overall are that categories all in top spots two the taking exception (with the of ontology technique unlike in patents, India is highly ranked for for ranked highly is India unlike patents, in applications. The Republic of Korea seems to of Korea seems Republic The applications. and robotics. Typically,analytics those offices fuzzy in ninth, respectively, publications for and strengths has India that suggests activity. This AI each for second or first rank China and 5.11). figure (see considered are The U.S. scientific publications inalmost all functional different all the for publications scientific is the second ranking country behind the U.S. the behind country ranking second the is techniques. AI for patents for is it even more become might that research AI in learning, machine in fourth and logic fuzzy in processing, India (fifth in distributed AI) and and AI) in distributed (fifth India processing, in distributed leads China while filings, patent of Republic Islamic the are AI in filing patent patenting activity. be more visible in scientific publications related related publications scientific in visible more be functional applications, similar to the case language natural in fifth is which Australia, for much visible less in scientific publications than reasoning and predictive analytics). predictive and reasoning compared learning, in machine third ranks logic. Conversely, the Republic of Korea is of Korea is Conversely,logic. Republic the AI functional applicationsAI functional th for patent filings. India ranks third third ranks India filings. patent for ���� Germany, which features in transportation and and transportation in Germany, features which top the two U.S. the among and feature China fields (see figure figure (see fields 5.14). feature to offices Other energy management. Australia and Canada Canada Australia and management. energy publishing; and management document and application all almost for filing first of offices computer vision processing. and speech techniques: it ranks sixth in robotics and 10 and robotics in sixth ranks it techniques: seem to be target market areas as well. as areas market to target be seem include the Republic of Korea, ranked second second ranked of Korea, Republic the include for military; Japan, which is in third place for for place third in is which Japan, military; for predictive analytics predictive analytics scheduling, and planning AI, distributed in patent applications in leads China while filings, patent method control has Office the most U.S.The Patent and robotics. AI application fields th in in

97 WIPO Technology Trends 2019 98 5 Geography of patent filings the top filing specific in applicationsoffices among are India and Canada Germany, Australia, while processing, speech in position second holds Japan most, in second and applications functional all for filing patent in first rank U.S. the and China Although functional applications AI different for applications patent of number by offices 5.12. Top patent Figure Note: A patent refer may to more than one category Note: A patent refer may to more than one category

Knowledge representation Knowledge representation Natural language Natural language Distributed AI Control methods Predictive analytics Planning and scheduling DistributedRobotics AI SpeechControl processing methods Predictive analytics PlanningComputer and scheduling vision Robotics Speech processing Computer vision and reasoning and reasoning processing processing 0 0 Germany Canada of Republic Korea Japan U.S. China India of Republic Korea Japan U.S. of Republic Korea Germany Japan Canada of Republic Korea Canada of Republic Korea Japan U.S. China India of Republic Korea Japan U.S. of Republic Korea Germany Japan Canada of Republic Korea Germany of Republic Korea Germany of Republic Korea Germany China Japan China Japan Japan of Republic Korea Japan of Republic Korea China China Australia Australia Japan U.S. U.S. Japan Germany Germany U.S. U.S. U.S. U.S. U.S. U.S. China China China China Republic of Republic Korea Republic of Republic Korea Republic of Republic Korea Republic of Republic Korea China China Japan Japan Germany Germany 20,000 20,000 China China Japan Japan China China U.S. U.S. Republic of Republic Korea Republic of Republic Korea U.S. U.S. 40,000 40,000 Japan Japan 60,000 60,000 China China 80,000 80,000 U.S. U.S. Note: A patent refer may to more than one category

Knowledge representation Natural language Distributed AI Control methods Predictive analytics Planning and scheduling Robotics Speech processing Computer vision and reasoning processing 0 Canada of Republic Korea Japan U.S. China India of Republic Korea Japan U.S. of Republic Korea Germany Japan Canada of Republic Korea Germany of Republic Korea Germany China Japan Japan of Republic Korea China Australia U.S. Japan Germany U.S. U.S. U.S. China China Republic of Republic Korea Republic of Republic Korea China Japan Germany 20,000 China Japan China U.S. Republic of Republic Korea U.S. 40,000 Japan 60,000 China 80,000 U.S. specific categories in publications in positions prominent hold U.K. the and India Germany, Canada, Australia, functional applications Figure 5.13. Top geographical affiliations by number of scientificpublications fordifferent AI Note: A scientific publication may refer may publication to more Note: than A one scientific category refer may publication to more Note: than A one scientific category

Knowledge representation Knowledge representation Natural language Natural language Control methods Predictive analytics Speech processing PlanningControl and methods scheduling PredictiveDistributed analytics AI Robotics Speech processing PlanningComputer and scheduling vision Distributed AI Robotics Computer vision and reasoning and reasoning processing processing 0 0 Iran South Korea Spain U.K. Iran (Islamic Republic of) Republic Iran (Islamic of Republic Korea Spain U.K. India France Japan U.S. India France Japan U.S. U.K. Germany U.K. Germany China China China China U.S. U.S. India U.K. U.K. India Germany U.S. India U.S. India Germany U.K. France U.K. France China China U.K. U.K. India India Germany Japan Japan Germany U.K. U.K. China China Japan Germany Japan Germany U.K. U.K. U.S. China India U.S. China India U.S. U.S. 20,000 20,000 China China India India U.S. U.S. U.K. U.K. U.S. U.S. Japan Japan China China 40,000 40,000 China China U.S. U.S. 60,000 60,000 China China U.S. U.S. Note: A scientific publication may refer may publication to more Note: than A one scientific category

Knowledge representation Natural language Control methods Predictive analytics Speech processing Planning and scheduling Distributed AI Robotics Computer vision and reasoning processing 0 Spain U.K. Iran (Islamic Republic of) Republic Iran (Islamic of Republic Korea India Japan U.S. France U.K. Germany China China U.S. U.K. India U.S. India Germany U.K. France China U.K. India Japan Germany U.K. China Japan Germany U.K. U.S. U.S. China India 20,000 China India U.S. U.K. U.S. Japan China 40,000 China U.S. 60,000 China U.S.

99 WIPO Technology Trends 2019 100 5 Geography of patent filings Note: A patent refer may to more than one category Note: A patent refer may to more than one category The patent offices of China and the U.S. rank first and second in all AI application fields application AI all in second and first rank U.S. the and China of offices patent The fields application AI identified 20 for office patent by families patent of 5.14. Number Figure

Industry and Physical sciences and Document management PersonalIndustry devices, and Physical sciences and Document management Personal devices, Networks Business NetworksSecurity Life and medical sciences TelecommunicationsBusiness Transportation Security Life and medical sciences Telecommunications Transportation manufacturing engineering and publishing computingmanufacturing and HCI engineering and publishing computing and HCI 0 0 Canada Canada Republic of Republic Korea of Republic Korea Germany Germany Australia Canada Australia Canada Japan Japan Republic of Republic Korea of Republic Korea Germany Germany Republic of Republic Korea of Republic Korea Australia Australia Germany Canada Germany Canada Republic of Republic Korea of Republic Korea Japan Japan Japan Japan Germany Germany Republic of Republic Korea Canada Republic of Republic Korea Canada Republic of Republic Korea Republic of Republic Korea Republic of Republic Korea Republic of Republic Korea U.S. U.S. China China Japan Japan Japan Japan China China Japan Japan of Republic Korea Republic of Republic Korea China China China China Republic of Republic Korea Republic of Republic Korea Japan Japan 10,000 10,000 U.S. U.S. Japan Japan China China Germany Germany U.S. U.S. Japan Japan Japan Japan China China China China U.S. U.S. U.S. U.S. U.S. U.S. China China 20,000 20,000 China China China China U.S. U.S. U.S. U.S. U.S. U.S. U.S. U.S.

Law, social and behavioral Law, social and behavioral Agriculture Military Banking and finance Computing in government Entertainment CartographyAgriculture EducationMilitary EnergyBanking management and finance ComputingArts and inhumanities government Entertainment Cartography Education Energy management Arts and humanities sciences sciences Note: A patent refer may to more than one category 0 0 Australia Canada of Republic Korea Australia Canada of Republic Korea Japan Japan Industry and Physical sciences and Document management Personal devices, Canada Canada Canada Canada Canada Canada Australia Australia of Republic Korea

Republic of Republic Korea Canada NetworksU.S. Canada Business Security Life and medical sciences Telecommunications Transportation Canada U.S. Canada Canada China Republic of Republic Korea China Japan of Republic Korea Canada manufacturingJapan engineering and publishing computing and HCI Republic of Republic Korea Japan Japan of Republic Korea Republic of Republic Korea of Republic Korea Republic of Republic Korea of Republic Korea Japan Taiwan, Province Taiwan,of China Province Japan Taiwan Province of Taiwan China Province Japan Japan Japan Japan Republic of Republic Korea of Republic Korea 0 China Germany of Republic Korea Germany China Republic of Republic Korea Republic of Republic Korea Republic of Republic Korea 2,000 2,000 Canada Japan U.S. U.S. Japan Republic of Republic Korea China of Republic Korea Republic of Republic Korea China China Germany China Australia Canada U.S. Japan U.S. China Republic of Republic Korea China Japan Japan Germany Japan Japan U.S. Australia Republic of Republic Korea U.S. Germany Canada Republic of Republic Korea Japan Japan China Germany China Republic of Republic Korea Canada China China Japan Republic of Republic Korea Japan Republic of Republic Korea U.S. U.S. U.S. China Japan Japan 4,000 China China 4,000 China China Japan Republic of Republic Korea China China U.S. China Republic of Republic Korea U.S. U.S. Japan U.S. 10,000 U.S. U.S. Japan U.S. China U.S. Germany U.S. U.S. Japan U.S. U.S. China Japan China 6,000 6,000 China China U.S. U.S. U.S. China 8,000 20,000 8,000 China China U.S. U.S. U.S. U.S.

Law, social and behavioral Agriculture Military Banking and finance Computing in government Entertainment Cartography Education Energy management Arts and humanities sciences 0 Australia Canada of Republic Korea Japan Canada Canada Canada Australia Republic of Republic Korea Canada U.S. Canada China Japan of Republic Korea Canada Japan of Republic Korea Republic of Republic Korea of Republic Korea Japan Taiwan Province of Taiwan China Province Japan Japan Republic of Republic Korea China Germany Republic of Republic Korea Republic of Republic Korea 2,000 U.S. Japan Republic of Republic Korea China China U.S. China Japan Japan U.S. China China Japan U.S. 4,000 China China

U.S.

U.S. U.S.

U.S. U.S. China 6,000 8,000 Note: A patent refer may to more than one category Note: A patent refer may to more than one category

Industry and Physical sciences and Document management PersonalIndustry devices, and Physical sciences and Document management Personal devices, Networks Business SecurityNetworks Life and medical sciences TelecommunicationsBusiness Transportation Security Life and medical sciences Telecommunications Transportation manufacturing engineering and publishing computingmanufacturing and HCI engineering and publishing computing and HCI 0 0 Canada Canada Republic of Republic Korea of Republic Korea Germany Germany Australia Canada Australia Canada Japan Japan Republic of Republic Korea of Republic Korea Germany Germany Republic of Republic Korea Australia Republic of Republic Korea Australia Germany Canada Germany Canada Republic of Republic Korea of Republic Korea Japan Japan Japan Japan Germany Germany Republic of Republic Korea Canada of Republic Korea Canada Republic of Republic Korea of Republic Korea Republic of Republic Korea of Republic Korea U.S. U.S. China China Japan Japan Japan Japan China China Japan of Republic Korea Japan of Republic Korea China China China China Republic of Republic Korea of Republic Korea Japan Japan 10,000 10,000 U.S. U.S. Japan Japan China China Germany Germany U.S. U.S. Japan Japan Japan Japan China China China China U.S. U.S. U.S. U.S. U.S. U.S. China China 20,000 20,000 China China China China U.S. U.S. U.S. U.S. U.S. U.S. U.S. U.S. fields (continued) application AI identified 20 for office patent by families patent of 5.14. Number Figure

Law, social and behavioral Law, social and behavioral Agriculture Military Banking and finance Computing in government Entertainment CartographyAgriculture EducationMilitary EnergyBanking management and finance ArtsComputing and humanities in government Entertainment Cartography Education Energy management Arts and humanities sciences sciences Note: A patent refer may to more than one category 0 0 Australia Canada of Republic Korea Australia Canada of Republic Korea Japan Japan Industry and IndustryPhysical and sciences Physicaland sciences and Document managementDocument management Personal devices, Personal devices, Canada Canada Canada Canada Canada Canada Australia Australia

Republic of Republic Korea Canada Networks Networks of Republic Korea Canada Business Business Security Security Life and medical sciencesLife and medicalTelecommunications sciences TelecommunicationsTransportation Transportation U.S. U.S. Canada Canada Republic of Republic Korea China Japan of Republic Korea Canada China Japan manufacturingCanada manufacturingengineering engineering and publishing and publishing computing and HCIcomputing and HCI Japan Japan of Republic Korea Republic of Republic Korea Republic of Republic Korea of Republic Korea Republic of Republic Korea Republic of Republic Korea Japan Japan Japan of Taiwan China Province Japan Taiwan Province of Taiwan China Province Japan Japan Republic of Republic Korea Republic of Republic Korea 0 0 Germany China China Germany Republic of Republic Korea Republic of Republic Korea Canada Republic of Republic Korea of Republic Korea 2,000 2,000 Canada Japan U.S. U.S. Japan Republic of Republic Korea Canada Germany Canada Japan Australia Republic of Republic Korea Republic of Republic Korea Republic of Republic Korea of Republic Korea China China Germany Germany China China Australia Canada Japan of Republic Korea Australia U.S. U.S. Germany Canada Japan of Republic Korea of Republic Korea China China Japan Germany Japan Japan Germany Japan Japan Australia Republic of Republic Korea of Republic Korea U.S. U.S. Canada Germany Canada Republic of Republic Korea of Republic Korea of Republic Korea Japan United States United of States America China Japan Japan Japan Germany China China China Japan of Republic Korea Canada 10,000 China of Republic Korea China China China Republic of Republic Korea Japan of Republic Korea Japan Japan Republic of Republic Korea U.S. U.S. U.S. China Japan Japan Japan China China 4,000 4,000 Germany United States United of States America China China Republic of Republic Korea Japan China China China Japan China Republic of Republic Korea U.S. U.S. Japan Japan U.S. U.S. 10,000 China U.S. China United States United of States America Japan U.S. U.S. China United of States America United of States America China Germany U.S. U.S. U.S. 20,000 Japan U.S. U.S. Japan China China 6,000 6,000 China China U.S. China U.S. China U.S. China United States United of States America United of States America United States United of States America United States United of States America 30,000 20,000 8,000 8,000 China China U.S. U.S. 40,000 U.S. U.S.

Law, social and behavioralLaw, social and behavioral Agriculture AgricultureMilitary MilitaryBanking and financeBankingComputing and finance in governmentComputing in governmentEntertainment EntertainmentCartography CartographyEducation EducationEnergy managementEnergy managementArts and humanitiesArts and humanities sciences sciences 0 0 Australia Canada of Republic Korea Australia Canada of Republic Korea Japan Japan Canada Canada Canada Canada Canada Canada Australia Republic of Republic Korea Australia United States United of States America Canada Republic of Republic Korea Canada Canada Canada U.S. Canada China Republic of Republic Korea Canada Japan Republic of Republic Korea China Japan Republic of Republic Korea Japan of Republic Korea Japan Republic of Republic Korea of Republic Korea of Republic Korea Japan Republic of Republic Korea Taiwan, Province Taiwan,of China Province Japan Japan of Taiwan China Province Japan Japan Japan Republic of Republic Korea of Republic Korea Republic of Republic Korea Germany Germany China China Republic of Republic Korea Republic of Republic Korea Republic of Republic Korea 2,000 2,000 Japan United States United of States America Japan U.S. Republic of Republic Korea China of Republic Korea China China China United States United of States America U.S. China China Japan Japan Japan Japan United States United of States America U.S. China China China China Japan Japan United States United of States America U.S. 4,000 China 4,000 China China China United States United of States America U.S. United States United of States America U.S. United States United of States America U.S. United States United of States America U.S. United States United of States America U.S. China China 6,000 6,000 8,000 8,000

101 WIPO Technology Trends 2019 102 Chapter title

Photo: Artist's impression of lung cancer diagnosis made with imagery © NYU School of Medicine Photo: Artist's impression of lung cancer diagnosis made with imagery © NYU School of Medicine were independently asked to diagnose the test same set of patients’ tumors. was 97 percent, slightly better than of the three performance pathologists who weeks –we tested of the our AIsystem performance and found that its accuracy healthy and diseased lungs. After training was complete –it took about two we images obtained about used 800,000 from about 1,200 samples from both are shown each image and told what the diagnosis is. To train these networks, specifically Inceptionv3 (a tool made available Google).by Neural networks In our study, we AItechnique an used called convolutional neural networks, diagnosis. However, this manual is time-consuming process and prone to error. who use microscopes to examine the details of each tumor and deliver a CancerThe Genome Atlas. images These are typically prepared by pathologists identified a large set of imaging data, made availableas a public resource by which we tested whether we automate can lung diagnosis cancer using AI. We laboratoryOur at the NYU School of Medicine recently launched anew study in diagnosis cancer lung for AI Using Case study by Aristotelis Tsirigos, NYU School of Medicine of School NYU Tsirigos, Aristotelis by study Case genetic mutations beimproved can if more examples are for used training. withcancer more than percent 80 accuracy. accuracy The of the AImodels on AI was able to predict the mutational status of akey cancer-driving gene in lung and comparison with the normal genetic material of the patient. same Intriguingly, called sequencing,a process DNA which allows the reading of the tumor’s DNA alone, such genetic as mutations. Typically, genetic mutations are determined by images: tumor characteristics that human cannot experts discern from images We then explored whether extract AIcan additional information from these proposed model (3) Aggregated prediction using tiles of the slide for Inception V3. Inception for slide the of tiles using prediction Aggregated (3) model proposed the for slide the of tiles using prediction (2) Aggregated (TCGA-LUSC) carcinoma cell squamous Visualization of images and classification heatmaps: (1) Original whole slide image with lung 1 2 3

103 WIPO Technology Trends 2019 6 Market trends related to AI

Key findings

• 434 companies in the AI sector have been acquired since 1998. • 53 percent of acquisitions have taken place since 2016. • The vast majority of acquired companies in the AI field are U.S. (283 acquired companies), while the U.K. ranks second with 25 acquired companies. • Ten companies have made at least five acquisitions in this field and between them have made 79 acquisitions in total. • Alphabet, Apple and Microsoft have been the most active entities, with 18, 11 and nine AI-related acquisitions, respectively. • As of May 2018, based on public information, 2,868 companies active in AI have been identified as receiving funding (44 percent of 6,538 AI companies). This represents about US$46 billion in funding. • 1,264 patent families are mentioned in litigation cases, corresponding to 0.37 percent of all AI-related patent families; 4,231 are mentioned in opposition cases (equivalent to 1.25 percent of the identified AI-related patent families) and 492 in both I predict that in the types of dispute. • The top three plaintiffs in litigation cases next five years, AI are Nuance Communications, American Vehicular Services and Automotive adoption across Technologies International while Microsoft, Apple and Alphabet are the top defendants. multiple industries • The biggest filers of oppositions to AI patents are Siemens, Daimler and – especially outside Giesecke+Devrient, while the main defendants in oppositions are Samsung, the software LG Corporation and Hyundai. industry – will drive massive global GDP growth.

Andrew Ng, Landing AI and deeplearning.ai Artificial intelligence (AI) is a dynamic field, field, dynamic is a (AI) intelligence Artificial in the AI sector AI the in activity (M&A) acquisition and Merger field field that will have significant impact, a not only considered to be commercially important. toconsidered be commercially important. as weaknesses, and strengths comparative provide can oppositions additional insight on data While available. is data such that extent and agriculture,entertainment among other efficiency, but also on health, transportation, and productivity increased with business, on research in investment by substantial driven well as which technologies and markets are are markets and technologies which well as the CrunchBase database, which includes which database, includes the CrunchBase from compiled was AI in activity M&A on Data an AI category. This is a large database, but but database, alarge is This category. AI an To to measure areas. means some provide a also It is knowledge. in advances rapid and statistics on patent litigation/oppositions to the into which patents are challenged, where and and where challenged, are patents into which and activity, funding, acquisition and merger in trends at looks chapter this in data the impact, between which parties. This data may illustrate may illustrate data This parties. which between and litigation on data trends, and/or potential market and into research insight provides chapters earlier in analyzed applications patent may from lack information companies about it For example, comprehensive. be may not ahead. is AI not a panacea. a and lotdollar of companies opportunity we’ve of billion- dozens got industry over-rotatednot computer the In AI. on (3) are and data; transaction business their to want may not give Google – i.e. who with companies partnering don’t have giants the that to adataset box; the (2) outside havethink access to ready teams ambitious have smart, (1) that startups for we look space, AI we’re the in when acompany at looking For instance, avalley in of giants. them differentiate will what about thoughtful be to need startups so AI, in investing alot Facebook). are companies large These and Microsoft, Google, (i.e. Amazon, giants of the shadow the in make money can that are pockets the where about we think investing startups, in When Frank Chen, Andreessen Horowitz investor’sThe view

The vast majority of acquired companies of companies acquired vastThe majority CrunchBase lists 434 companies in the AI sector sector AI the in companies 434 lists CrunchBase countries for acquired companies are among the the are among for companies acquired countries 6.2). All six of the top figure (see companies have may not may or apatent companies (see Chapter 5). Chapter However,(see exception the of with (283 companies), acquired while the United are AI of field the in CrunchBase) in (indexed with an acceleration evident in the number of of number the in evident acceleration an with Kingdom (U.K.) ranks second, with 25 acquired 25 with acquired (U.K.) second, ranks Kingdom 19986.1), since (figure acquired havethat been acquisitions have taken place since 2016. 2012. since acquisitions of Moreover, percent 53 portfolio. The data was extracted in May in 2018. extracted was data The portfolio. non-English speaking countries, while countries, these speaking non-English leaders in patents AI publications scientific and leaders (U.S.) of America States United the in located development of an AI-on-demand AI-on-demand of an development the support also will Commission The encourage testing and experimentation. and Europe across centers research AI strengthen and connect will it health; to transport from key in AI sectors, of development the It willsupport robotics. and data big on example for partnerships, public–private existing from of funding billion €2.5 additional an to trigger expected is investment This innovation program. and 2020 research 2018–2020 Horizon the under period to investment €1.5its the for billion the European Commission is increasing of 2020. efforts, end To these support by billion the €20 innovation by least at and research AI in investments to increase willneed sectors) private and (public Union European The of key arange sectors. 2020 across by investments total in million €500 than to is more mobilize aim the Investments, Fund Strategic for European the With to invest AI. in support additional with startups and to companies provide mobilized be will Investments Strategic FundAdditionally, for European the EU users. the in all for resources to AI relevant access providing platform European Commission Paul Nemitz, Europe in Investment

105 WIPO Technology Trends 2019 106 6 Market trends related to AI Alphabet accounts for 4 percent of acquisitions overall acquisitions of 4percent for accounts Alphabet companies acquiring top by acquired companies of Number 6.3. Figure Figure 6.2. Acquisitions in the AI sector by country of acquired company from 1998 to 2018 to 1998 from company acquired of country by sector AI the in Acquisitions 6.2. Figure 2017 and 2012 between 2012 and by percent 33 2000 and between average on by 5percent grew Acquisitions year acquisition by sector AI the in acquisitions of 6.1. Number Figure More than two-thirds of companies acquired since 1998 have been from the U.S. the from 1998 been have since acquired companies of two-thirds than More Netherlands Switzerland Salesforce Facebook Germany Microsoft Alphabet Sweden Belgium Canada Amazon Verizon France 100 China Spain Brazil 20 40 60 80 Israel Apple Cisco India U.K. U.S. 0 Intel IBM 0 0 2 50 4 2002 100 6 8 2007 150 10 12 200 2012 Before 2016 2016 Since 14 250 16 18 2017 • • • • 11 and nine AI-related acquisitions, respectively. respectively. 11 acquisitions, AI-related nine and Ten have companies five made at least 85 in disclosed only was price acquisition The Apple has acquired seven AI companies since since companies seven AI acquired has Apple 10 these All are big tech multinationals companies ���� Google, has acquired six. However, six. acquired has VerizonGoogle, has 2016, while Alphabet, the parent company of of company 2016, parent the Alphabet, while decreased their patent filing activity in the last last in the activity filing patent their decreased Alphabet that noting worth It is companies. topof the 10 top the 10 among listed patenting prominently feature also companies of these impossible it making acquisitions, of 435 out with the compared U.S.countries coverage may of database not the CrunchBase (see figure 6.3). figure (see India, Asian countries such as China and the the and China as such countries Asian India, Moreover, there has been an acceleration in the the in acceleration an Moreover, been has there Republic of Korea are far less well represented in in well represented less far of Korea are Republic the total number of acquisitions in the AI field). field). AI the in of acquisitions number total the However, prices. about here to generalize among patent assignees in the AI field, with three three with field, AI the in assignees patent among have them between and field this in acquisitions with of acquisitions examples some are acquisitions than they are in the patent/scientific several years (see Chapter 4). Chapter (see years several is among the top patent applicants to have top applicants the patent among is past two years, with 34 acquisitions since 2016 since acquisitions 34 with years, two past information: pricing in comprehensive as be non-English-speaking above, the mentioned As rankings. publications for Verizon is an aggregation of acquisitions Microsoft and Apple U.S. the from Alphabet, made by recently acquired Yahoo and AOL. Most Yahoo Most acquired AOL. and by recently made 2016. since companies any AI Data acquired not 18, with entities, active most three the have been (18 of 79 atotal of acquisitions made percent Vivid Smart Home was acquired by acquired was Home Smart Vivid Orbital ATKOrbital (global in aerospace leader US$400 million. US$400 US$7.8 billion. Movidius was acquired by Intel 2016 in acquired was Movidius for DeepMind was acquired by Google in 2014 in by Google acquired was DeepMind 2012 in Blackstone billion. US$2 for and defense technologies)and was acquired by Northrop Grumman in 2017 in Grumman by for Northrop for US$500 million. US$500 for Top companies for acquisitions • • • • There are certain trends that can be observed observed be can that trends certain are There in companies: acquired 17 specialize in computer vision, 1417 vision, computer in natural in specialize The vast majority of acquired companies of companies acquired vastThe majority The majority of the top majority The 10 companies Technological are trends similar to those virtual assistants, big data analytics for for analytics data big assistants, virtual While the acquired companies are diverse, diverse, are companies acquired the While years old). others seem more interested in integrating in integrating interested more seem others havecompanies patent larger portfolios no with 79)of the startups young are image example) and for entertainment, analytics. predictive in eight and extraction ofdominance computer vision technologies machine in specialize information) disclosed scientific and patents the in observed (37 patent families for DeepMind, 38 for for 38 DeepMind, for (37 families patent Most ofMost (46 the companies acquired out the acquisition process (such as Microsoft), Microsoft), as (such process acquisition the to tend DemandTec). companies some While acquire significant patent portfolios through acquire patent significant portfolios 79 of the that out (53 companies acquired more to tend acquire afew players although old), years age: three (median startups are some applications occur frequently. the and companies acquired the in seen is less marked than in the patent collection: collection: patent the in than marked less is patent portfolios. But two of the acquired acquired of the two But portfolios. patent of the majority alarge analysis: publications have acquired startups specializing in in specializing startups have acquired 10 is companies of acquired age median Intel the and IBM (for companies mature recognition (photos,recognition etc.). systemsrecommendation (advertising and language processing, 14 processing, language in information Variouslearning. are applications functional wrong. going they are where is that and targeting their technologies own industries on focused are countries Most of that. aware are Idon’t countries many but think one, has extent to some Israel ecosystem. an such with countries other no are there U.S. the China from and Aside the to scientists improve the technologies. to that push use and needs user on focus relentlessly the and right application areas ecosystem to drive the to technologies (VC) You capital venture astrong need Kai-Fu Lee, Sinovation Ventures ecosystem A strong VC

107 WIPO Technology Trends 2019 108 6 Market trends related to AI • The CrunchBase database also includes data data includes also database CrunchBase The Funding in the AI sector AI the in Funding category. It shows that, as of May 2018, as It that, shows category. 2,868 AI the in tagged companies for funding on As mentioned above, mentioned As the CrunchBase or Vivid Smart Home), most of the public Home), public of the most Smart Vivid or of have not details full does database (for example, Apple, Verizon Salesforce). and Apple, example, (for technologies intotechnologies than rather their products acquisition by Alphabet. acquisition DeepMind bythe or IBM acquisition well above are US$100 prices acquisition even Nonetheless, if acquisition prices. such as acquiring existing patent portfolios some acquisitions were at the range of of range the at were acquisitions some in some cases, for example the DemandTec the example for cases, some in billions (such as the cases of Orbital ATK of Orbital cases the as (such billions million, and reach up to half a million dollars dollars amillion to up half reach and million, emerge. will what see are to waiting and success, significant without mostly pilots, AI have small done funding corporations stages. Larger round A/B or late seed on primarily Traditional VCs focused are startups. of AI majority the constitute which companies, stage early for true especially is This sources. funding AI-specialized of alack still is investor community, there over-hyped is AI Even though the in and public services. education healthcare, as such affected populationsare large thosewhere fields then bemade in can impact societal radar. biggest The startups’ the on be should retail, and services professional value added, such as manufacturing, gross for potential highest the with industries term, short the In startups. AI for opportunity into ahuge translates in theopportunities world, which it globally.scale are countless There and solution, automated to an create cognition, a use set of technologies AI existingan human requiring problem you take –where applications AI vertical –so-called problem to aniche applied technologies AI in is trend current The Petr Šrámek, AI Startup Incubator Startup AI Šrámek, Petr for startups Challenges

• • • • • • • The amounts of funding range from US$1,000 from range of funding amounts The Technology (both in founded 2015). They also originating from China, companies, These ACORN OakNorth Holdings and CloudWalk CloudWalk and Holdings OakNorth ACORN one just in US$1 billion received which AI, venture capital (series A to F), grant, debt, debt, Ato grant, F), (series capital venture company specializing in recommendation company specializing in recommendation related tocompanies have AI identified been either doeither not have patent big AI-related 2016) in (founded AI exception of Argo and above has listed seven of the companies one Ford as such Motorcompanies, Company or by capital venture funded are companies US$46 billion in funding. in billion US$46 It is surprising that these funded companies companies thatIt funded these is surprising Most of these AI companies are mature (i.e., mature are companies AI of these Most to US$3.1 company. largest per billion The the most),the have all, except at or for none seed, angel, include of funding types Different U.S.the U.K., the and have received all amount wasamount by received Toutiao, a Chinese (44 of funding amount adisclosed receiving as seven funding rounds. Seven have companies which mining, data on based products system software and networks. and software except Argo for of funding, rounds several percent of the 6,538 companies related to AI to AI related companies 6,538 of the percent portfolios (up to four patent families at at (up families to patent four portfolios private from funding receive Some banks. Most market. secondary and equity private from transportation to banking, e-commerce, e-commerce, to banking, transportation from Toutiao. Weibo or funding AI Argo Only funding funding round. more than six years old in general), with the the with general), in old years six than more had (Cloudera: initial an NYSE). public offering received more than US$300 million in funding: in million US$300 than more received in seven investors from funding received represent various sectors of applications, of applications, sectors various represent listed in CrunchBase). This represents about about represents This CrunchBase). in listed Toutiao ACORN OakNorth Holdings Ltd Holdings OakNorth ACORN AI Argo Vivint Smart Home Smart Vivint Cloudera (Hadoop)Cloudera CloudWalk Technology. Wish • • • The funding of startups is a particular priority priority aparticular is of startups funding The To many of AI, development the promote Open source Open CloudWalk Technology, has which already 22 published patents families despite being being despite families patents 22 published 2013 to 2017. to 2013 Out ofOut the top 10 patenting companies effective means of sharing and promoting AI AI promoting and of sharing means effective innovation)open provide approaches an from 4.5-fold increased systems AI developing developing 2.1-fold systems AI increased 2018 report Index by AI the indicates compiled 7. Chapter in detail in discussed are policies similar and of these established public–private Some partnerships. and incentives have place in put governments include: These observed. be can companies and funding in trends Certain grants. or funding venture to via AI related companies technologies, duetechnologies, to either the of cost filing (or source open developers, For many of U.S. active startups number the that annual venture capital investment into startups into startups investment capital venture annual investment among the top the 10 among investment patenting five4, Chapter in havein invested identified patent families, but created in 1999). in created but families, patent founded only three years ago, and Vivint (32 (32 Vivint ago, and years three only founded from January 2015 2018, January from to January while for governments, given their role in developing new technologies and generating jobs. Data new jobs. Data generating and technologies Algorithms (MILA) in 2016 in million). (MILA) (US$3.4 Algorithms AI products (through private companies) (through investments in laboratories), such Investments are often directed toward directed often are Investments toward directed only not are Investments Republic of Korea investing in both Chinese Chinese of Korea investing both in Republic two of the top patenting (IBM companies Pivaclouds funding received has from 2017 in Lab (US$240 Alphabet million) and and U.S.and companies. AI and Microsoft). investing MIT-IBM in IBM as Watson AI investing in Montreal Institute for Learning Learning for Institute investing in Montreal but also toward AI fundamental research research toward fundamental AI also but foreign companies, such as Samsung in the Samsung as such foreign companies, One ofOne the main collaborative developer of two areas of AI attracting a lot of interest (see (see of alot interest attracting of AI areas of two analysis of trends. It shows a constant increase increase It aconstant shows of trends. analysis patent of the of awareness alack or a patent source activity more appropriate for very fast- very for appropriate more activity source open consider also developers system. Many in the number of software projects related to to related projects of software number the in platforms. different across spread is information platforms, GitHub, allows for some indicative indicative some allows for GitHub, platforms, is developments. activity source paced Open neural networks and deep learning as examples examples as learning deep and networks neural toharder than patenting measure activity, the as short-term gain that exclusivity may may exclusivity that gain short-term the to despite have gain, players less environments is, inevitably, slower. All in such ofpace progress technological tocompany.belongs a specific The to innovations that IP our tying in result are often application-specificand atcommunity large. Such constraints to available the made be can that of publications number the limiting and open discussions among researchers preventing flow information, of free the jeopardize often which constraints, IP with projects industry in to engage reluctant We very are partnerships: regarding institute of our policy general datasets. This culture is reflected in the open and libraries open code, source open research, collaboration, open of open aculture has model of our core the at community research The traditional of innovation. models industrial behind premises the question and to need domain adopt agile philosophies disruptive this in players that recognize too They appreciative. increasingly are partners our of which a characteristic and fast-paced research environment, to ahealthy leads necessarily policy open This patents. writing from refraining and manner atimely in by publishing domain public the in strategies algorithmic new of our all and code of our all share through philanthropic donations. We is partners research from support through which we welcome financial mechanism principal the reason, For this to offer. appear Myriam Côté, Mila sourceOpen at Mila

109 WIPO Technology Trends 2019 110 6 Market trends related to AI originate from the U.S. the from originate families patent litigated of 70 percent than More litigation, by litigation jurisdiction in involved families patent of Number 6.4. Figure Republic of Republic Korea Germany France Japan China U.K. U.S. 0 200 400 600 800 account for 4 percent of litigated patent families patent litigated of 4percent for account Sciences and Automotive Technologies International Nuance Communications, American Vehicular of litigated patent families number by plaintiffs Top litigation 6.5. Figure American Vehicular Sciences Vehicular American Abbyy USA Software Abbyy House Cross Match Technologies Match Cross Automotive Technologies Automotive Nuance Communications Nuance Public Patent Public Foundation Pictometry International Pictometry Pavilion Technologies Pavilion Intellectual Ventures Intellectual Magna Electronics Phoenix Solutions Phoenix Excel Innovations Excel LG Corporation LG Object Video Object Blast Motion Blast International RWS Group RWS IPEngine Microsoft Masimo Cognex Ultratec iRobot Captel UPEK Apple Cytyc IBM 0 10 20

100) published in December 2018, December in measured 100) published The patentThe families involved in the litigation Accessing, gathering and analyzing this data is is data this analyzing and gathering Accessing, ���� Litigation and oppositions Chapter 3). In addition, the AI Index, a project aproject Index, 3). AI the Chapter addition, In cases were filed between between (775 1997and 2007 filed were cases to belong the named do not necessarily oppositions or the arising appeals from WIPO’s in further discussed (see databases of various coverage limited the mind in bearing difficult, area. technology agiven within efforts case. Litigation and opposition information can of types both in mentioned 492 and cases, in 1,264 mentioned reveals data families patent not Though comprehensive, this databases. within the Stanford 100 Year Stanford the within (AI AI on Study Most patent families involved in litigation involved litigation in families patent Most 6.4 shows the most popular jurisdictions for for jurisdictions popular 6.4 most the shows them, which are discussed separately below. separately discussed are which them, patent include not does litigation on Data yet extensive may be which methodology), the trends/en/artificial_intelligence Data on litigation and oppositions involving AI years the in dramatically increased both Learn, Each GitHub. on they “starred” were times Numbers relating to plaintiffs refer only to only refer to plaintiffs relating Numbers assist in completing the picture about the IP IP the about picture the completing in assist TensorFlow scikit- learning, and machine and Property Indicators 2018 Indicators Property situation in a given field and the enforcement enforcement the and field agiven in situation most two the for of stars number The software. of usage and interest developer indicates star of by number the projects software some infringement cases. patents is drawn from the Darts-IP and Orbit Orbit and Darts-IP the from drawn is patents learning for packages deep software popular patent in families mentioned litigation). Figure 1,264 total of the percent 62 or families patent collection. to patent AI the only but player not exhaustive, and the differences between between differences the and exhaustive, not litigation (a patent family may be involved may be (a family patent litigation are that issues procedures, and systems legal litigation 4,231 cases, in mentioned opposition to up 2018.leading Litigation trends Litigation . www.wipo.int/tech_ World Intellectual for more on The conclusion is the same when looking at at looking when same the is conclusion The field, perhaps reflecting the factmost thatfield, perhaps One should also bear in mind that over half over that half mind in bear also should One of identified patent families are very recent recent are very families patent of identified (with 352), HCI and medical lifecomputing and of litigated patent families broadly follows of this technology. competitor’s product. to difficult may be it as patent of their which is probably due to the recent emergence emergence to due recent the probably is which and 96 for accounts programming logic while technologies may make it very difficult for a for difficult may very make it technologies of AI-related nature the that is mind in to bear transportation AI (autonomous driving) is yet is to driving) (autonomous AI transportation in fewer for cases accounts transportation (with 425) the has telecommunications though it is notable that learning no deep 421 for account cases, of the techniques atLooking the involved technology in the and business (with 218). business that and means This machine Within respectively. fivecases, and 926 cases) were filed in the U.S. Something in the Something U.S. filed were 926 cases) sciences (with 308), transportation (with 234) (with 308), transportation sciences in cases in more than one jurisdiction): 73 jurisdiction): one than more in cases in identify how it has been embedded in a a in embedded been how has it identify patent assignee to identify the infringement the to infringement patent identify assignee (or cases litigation identified of the percent be commercialized. this in of patents to number the proportion however, fields, application In trends. patenting far, so involved litigation in been has patent forprobabilistic account just seven reasoning functional applications where the proportion the where applications functional proportion and engineering 59. for Ontology logic fuzzy most cases, followed by personal devices, devices, followed by personal cases, most learning, no particular technique stands out, out, technique stands nolearning, particular litigated patent families, machine learning investments. AI about all thinking are firms capital venture and companies, venture 500 Fortune arms, inamounts technology. AI Corporate Toyota, and Motors they're investing large General example for manufacturers, top the at 10 If you look auto startups. to small companies, public large, – from investmentstechnology the board across AI in We're increase arapid seeing Frank Chen, Andreessen Horowitz investment? AI in What are the trends

111 WIPO Technology Trends 2019 112 6 Market trends related to AI The most active companies as plaintiffs in the the in plaintiffs as companies active most The ���� case may involve several patent families. may involve families. patent case several one course, Of patents. owns the company commercialization of many recently patented of which damages, usually only once arise It is notable that entities owned by Acacia by owned Acacia entities that notable It is 6.5, along with the number of patent families families of patent number the with 6.5, along Research, a large patent assertion entity, the when they evolve years, coming the in a product has come to market. It will thus It thus will to market. come has a product linked to is emergence the litigation that and in litigation cases. Patent assertion entities entities assertion Patent cases. litigation in involved in litigation, of whether the regardless families patent of AI number of the order in listed are Companies cases. involved these in figure in listed are cases litigation identified have will inventions place. taken be interesting to follow the statistics as as to follow statistics the interesting be feature prominently among the top plaintiffs top the plaintiffs among prominently feature Top plaintiffs analytics.” predictive to perform algorithms mathematical of using concept abstract the and process to“directed amental being for analytics predictive AI-driven on invalid claims as patent held also of California 2017, in asignal.” case recognize another In and to identify humans District Northern the of ability effective highly “the to model mind,” seeking human the with merely undertaken long idea of “an abstract implementation computer to ageneral-purpose directed being for claims invalidated patent of California District Northern the for Court District the 2015, in For inventions. example, computer-implemented and software AI, on claims patent invalidating decisions court more well as as examination, patent during rejections (USPTO) Trademark Patent and Office States United more have there been aresult, As activities. and tasks human perform to better or automate often is of AI goal the because inventions AI Alice Corporation v. CLS International Bank the under paper” and apen by using or “a mind” human human the “in process,” mental “ordinary an through performed be could that matter subject covering for claims of patent invalidations courts’ the are to report this relevance particular Of ideas. abstract as patents method business and software of numerous invalidated have claims since courts different to evolve, U.S. in idea” law continues patent and “abstract an constitutes precisely What John G. Flaim and Yoon Chae, Baker McKenzie Baker Yoon Chae, and Flaim G. John “Abstract idea” in U.S. jurisprudence

test. This creates a tension with patenting patenting with atension creates This test. • • enforced in the coming few years. coming the in enforced are portfolios patent AI how these observe prominent against rights patent their enforce develop and acquire and patent portfolios Looking in more depth at the main plaintiffs: main the at depth more in Looking businesses. Again, it will be interesting to interesting be will it Again, businesses. manufacturers or other downstream downstream other or manufacturers The cases comprise four infringement infringement four comprise cases The American VehicularAmerican (U.S.) Sciences is a 2010. Note that Nuance acquired Vlingo 2010. Vlingo acquired Note Nuance that organizations worldwide. and consumers the cases were initiated between 2008 and and 2008 between initiated were cases the Tellme two) an and and Networks Nuance Communications (U.S.) is a plaintiff actions against Abbyy, Lexmark (times keypadand solutions for businesses, imaging speech, providing company a listed subsidiary of Acacia Research and owns and Research of Acacia subsidiary in 2011. in It is involving families. 26 patent AI cases in reexamination petition against Vlingo. All All Vlingo. against petition reexamination ex-parte

• • • 14 patents in the 2006 case and six in the the in six and 14 case 2006 the in patents These five AI patent families are also also are families patent fiveAI These AVS, with As these Holdings. TK and TRW Apple is involved as a plaintiff in seven seven in involved is aplaintiff as Apple Automotive Technologies International (U.S.) 2011 2018. and Apple’s case, one for Except 13 ATI these case. defending 2008 also is Siemens, Nissan, Kia, Hyundai, against 2008) Orbit. It has filed almost 60 suits since 2012.since suits 60 almost filed It has Orbit. declaratory action for non-infringement, action for non-infringement, declaratory defending party. involvingcases five patents belonging on involve attacks multiple cases of these by filed AVSagainst cases other Toyota, owned by Automotive Technologies 13of 24 including patent families AI patent (Samsung, Motorola, HTC) between filed 21, Hyundai, against (dated December 2006) (Phoenix’s was reassigned patent portfolio International. This was filed in 2013 filed was This International. against involving atotal case one launched It has Microsoft’s relative inactivity in litigation litigation in inactivity relative Microsoft’s one in aplaintiff as named is Microsoft 1, (dated February second the and Motors, to Nuance Communications in 2013.) in 2010. filed and Solutions Phoenix being of them six to collection, AI the General and BMW Honda, Kia, Elesys, patent. same the AVS is manufacturers. car other and BMW to Toyota. in cited not are patents AI The at a time, making the AI-related patents patents AI-related the atime, making at to patents four involved three only actions involveactions quite patent large portfolios: automotive of some It assigned companies. actions launched from 2004 to 2011 against five the for infringement known is and 26 in adefendant also several entities, most of them belonging belonging of them most entities, several involving 13 AI patent families, the first involving 13 first the families, patent AI 2012.in cases ATItwoinfringement filed has its patents Vehicular to American Sciences Research, of Acacia subsidiary another is involving several patents to belonging a is Apple which in cases involved eight in infringement actions against competitors involving these 13 involving some these families; patent AI patents in numerous cases. numerous in patents families, some of which were previously previously were of which some families, major technological items in these cases. items technological major in cases. these more than 180 patent families according to 180 according than more families patent reflects its company-wideits reflects strategy: the inter partes inter reviews • A ranking of entities involved as defendants in in involved defendants as of entities A ranking ���� defendants are: involved in families of patent number the on to the company named). The main players as as players main The named). to company the of (regardless they whether the cases belong litigation cases is shown in figure 6.6, based 6.6, based figure in shown is cases litigation cases have been filed by diverse parties, parties, by diverse filed have been cases Microsoft: a defending party in 31 cases 31 in cases party adefending Microsoft: Darts-IP database includes only 25 cases for for 25 only cases includes database Darts-IP these 31 cases are mentioned in 161 in mentioned are 31 cases these cases to 2018. involved in families patent 55 The attacked it. attacked in total. Microsoft has also filed filed also has total.in Microsoft and manufacturers inventors, large including involving 55 patent AI families. These portfolio. patent entire its non-practicing entities (NPEs), from 1998 review that have companies against actions Top defendants vastly improved their natural language –they've checkers example: grammar day. every AI with One they interact by don't Many that AI. realize touched that's being not industry of an think Ican't services. legal and education, healthcare, Also trucks. delivery ships, planes, buses, –cars, systems into went of autonomous a lot money by 2017 In AI. upended being 2018, and that’s not industry There's amajor not diagnoses. to with help side bytool their AI an use doesn't who won't adoctor visit even advances technology further. You the now when from afew years imagine It's to Now email. AI. thanks all an writing you'rewhen auto-fill or responses email technology. with Same the suggested to AI thanks systems processing Frank Chen, Andreessen Horowitz in? interested investors are What inter partes inter

113 WIPO Technology Trends 2019 114 6 Market trends related to AI percent of litigated patent families since 1960 since families patent litigated of percent 12 for account Alphabet and Apple Microsoft, of litigated patent families number by defendants Top litigation 6.6. Figure Motorola Mobility Motorola Holdings Mobility Nuance Communications Nuance KIA Motors KIA America Motors Adobe Systems General Motors General Facebook Samsung Microsoft Alphabet Amazon Hyundai Verizon Toyota Oracle Apple Cisco BMW Sony HTC IBM Dell 0 20 40 the Korean patent office (KIPO) office patent Korean the at filed been have families patent opposed of Half office patent by families patent opposed of 6.7. Number Figure number of patent families of other top opponents top other of families patent of number filedSiemens oppositions for more than double the opposed patents for selected patent offices of number by Top opponents 6.8. Figure ZF TRW Holdings Automotive Republic of Republic Korea Octrooibureau Van Octrooibureau der Lely Interessengemeinschaft für Interessengemeinschaft Rundfunkschutzrechte Germany Australia Giesecke+Devrient Japan China Brazil EPO U.K. U.S. Continental 0 Samsung Alphabet Siemens Amazon Daimler Oticon Valeo Apple 0 1,000 20 2,000 40 • • The number of oppositions filed over time has has over time filed of oppositions number The ���� Oppositions Certain rules generally apply toCertain oppositions. Office (EPO) and at the United States Patent States United at the and (EPO) Office only or on more than one ground. The Chinese Chinese The ground. one than more on or only KIPO, at the filed have been oppositions of the procedure opposition the in patents other Many patent systems allow third parties parties allow third systems patent Many the German patent office, the European Patent the European office, patent German the year per families of opposed proportion the to cite possible may be it For example, to file oppositions against granted patents and Trademark Office (USPTO). Decisions to (USPTO). Decisions Trademarkand Office to holder patent the for possible be also and to file oppositionsanonymously. It may increased consistentlyincreased since the late 1980s, but the in conducted normally procedures in been assumed that when a patent is not fully fully not is apatent when that assumed been patent filings on average). In the AI field, most most on average).field, AI filings patent In the for the purposes of this research, it has has it research, of this purposes the for modify the claims to avoid opposition; claims the the modify jurisdictions. some in appealed be may also revoke patents can be made on one ground ground one on made revoke be can patents AI (1.2 yearly of the constant percent remains holder. patent revoked the for awin is it cases in these Decisions office. IP relevant Alphabet: cited as a defendant in 24 cases 24 in adefendant cases as cited Alphabet: 23 in cases adefendant as mentioned Apple: filed between and 2017. between 1999 filed Of the 20 22 infringement actions, 16 actions, brought were 22 infringement two and actions 22 infringement where Apple is a plaintiff (including (including aplaintiff is Apple where are operating companies. The same patent same The companies. are operating actions, one identified plaintiffs, eight and NPEs are 12 plaintiffs, identified involving 44 AI patent families, including including families, patent AI involving 44 21 patents: involving AI 46 infringement petitions against NPEs. is Alphabet party, aplaintiff by As NPEs. petition one and administrative hearing, families are also mentioned in 47 in mentioned cases also are families mainly involved in actions such as as such involved actions in mainly partes partes partes Global dynamics, geographical dynamics, Global and technological aspects reviews or reviews or of the Out petitions. reexamination reviews). ex partes inter partes inter reexamination reexamination reexamination reexamination inter inter inter inter inter inter

• • • The main companies for filing main companies The oppositions to to have filed organizations main The ���� oppositions against the largest number of number AI the against oppositions largest the number of patent families for which these these which for families of patent number the and Japanese offices are also frequently used used frequently also are offices Japanese and patents are: as parties. players opposing are identified indicating 6.8, figure in shown are patents for oppositions in the AI field (see figure figure 6.7). (see field AI the in oppositions for 1991. filed were 46 47 the Of oppositions, Giesecke+Devrient: a firm technology that 22 AI patent families (21 before the EPO and (21 EPO and the families before patent 22 AI Siemens (Germany):Siemens filed 47 oppositions Siemens has been filing oppositions been has Siemens covering 48 AI patent families from 1991 from families patent AI 48 covering of the oppositions were filed from from 2001 to filed were oppositions of the Fewone undecided. are available, decisions filed brought all It has clients. cases, 17 assist, parking or detection object available, the is information where cases, won It was four, Germany). in one 16 lost (Audi, Nissan, Toyota, Nissan, (Audi, Volkswagen, Volvo) were inventive step – non-obviousness in in inventivewere –non-obviousness step to 2017. cases, won eight has it these, Of technologies (roadtechnologies recognition, signals – differences ascertaining and cases two involving cases 22 filed (Germany): Daimler ascertaining differences – no novelty. –no Most differences ascertaining solutions to management identity and and concerning directly applicable patentsattacked owned by competitors successful In undecided. remain two and – differences ascertaining on two and favor its in were decisions available, three inventive one and step – non-obviousness is information where cases, successful its but there were two reported cases of of cases reported two were there but EPO, the before involving patent 22 AI provides payment, communication secure on inventivebased step – non-obviousness Germany. in one In EPO and the before families, winning nine, losing seven, nine, with losing winning families, for instance). no novelty (in one case). Daimler mostly mostly case). Daimler one (in novelty no novelty. no since field AI the in non-anonymously reasons given for the decisions in its favor its in decisions the for given reasons lost 37 and three remain undecided. undecided. remain 37 three lost and Top opposing parties

115 WIPO Technology Trends 2019 116 6 Market trends related to AI other top defendant any of patents opposed of number the double than more in defendant was Samsung company Korean The offices patent selected for patents opposed of number by defendants Top opposition 6.9. Figure Corporation and Hyundai. and Corporation LG Samsung, are oppositions in defendants main the while Giesecke+Devrient, and Daimler to AI patents are Siemens, oppositions of filers biggest The Korea Advanced Institute of Institute Korea Advanced Nuance Communications Nuance Science and Technology Science LG Corporation LG Bizmodeline Qualcomm Panasonic Samsung Microsoft Alphabet Siemens Hyundai Bosch Apple Nokia Sony Intel 0 20 40 60 80 100 120 140 160 • • • The top defending parties in oppositions in oppositions topThe defending parties ���� in patent opposition cases. The main players are: players main The cases. opposition patent in 6.9, figure in shown are involving patents AI families in their portfolio that have been involved involved have that been portfolio their in families ranked by counting the of number patent Technologies a patent. against Chinese When a Samsung patent is revoked is patent or aSamsung When devices security and (times 2) fingerprints 2004 and involved and 2004 that technologies included Samsung (Republic of Korea): a defendant in in of Korea): adefendant (Republic Samsung defendant in 11 in defendant two (eight Korea, in cases times). (two of claims –clarity of disclosure inventive for assessment step issue general procedures. opposition in atarget often oppositions/considered proportion portfolio seven Japan, in Korea, in were oppositions claims (once). None of the patents at stake (once). stake at patents of the claims None inventive for assessment step issue general on based available, they are are decisions (four times), and novelty – global assessment assessment –global times),(four novelty and (24 times), inventive –non-obviousness step (50/167) other with compared quite high is 2).(times (eight times) and completeness of disclosure of disclosure completeness (eight and times) was the subject of other legal cases, such as as such cases, legal of other subject the was It is worth noting that not all of these 167 of these all not that noting worth It is 85 AI patent families. It has won one, lost won lost one, It has families. patent AI 85 LG Corporation (Republic of Korea): a (Republic LG Corporation Hyundai (Republic of Korea): a defendant of Korea): adefendant (Republic Hyundai a cancellation proceeding filed Huawei by filed proceeding a cancellation actions; the action only other legal sufficiency well as as differences asserting 37 cases, 50 the Of undecided. are eight and and sufficiency of of disclosureand sufficiency – clarity 50 cases from 2005 to 2017, 2005 from cases 50 involving 167 AI serial number reading number device method, and serial involving Samsung’s concerned portfolio involving 73 AI patent families. It was won won It was involving families. 73 patent AI of Korea, Republic the in all cases, nine in actions. infringement EPO), the before one involving and Brazil in patent families were used in infringement infringement in used were families patent 30 won 12 It has lost families. patent cases, players, indicating that Samsung is quite quite is Samsung that indicating players, four before the EPO and two in China. The The China. in two EPO and the before four nine and one remains undecided. Where Where nine undecided. one and remains abill card, electronic an manufacturing refused, the main causes are classified as a as classified are causes main the refused, Top defending parties

zero and lost five cases, while four are are four while fivecases, lost and zero combination (twice). None of the patents at at patents of the None (twice). combination inventive for assessment step issue general (five times) and inventive (fiveand times) stepinvention – by with the first being filed in filed being 2014. first the with Where undecided. These cases are relatively recent, recent, relatively are cases These undecided. available, on were the decisions based stake was the subject of other cases. of other subject the was stake

117 WIPO Technology Trends 2019 118 Chapter title

Photo: © Empatica Photo: © Empatica of potential life lost out of all the neurological diseases, and kills someone every Sudden unexplained death in epilepsy (SUDEP) is the number two cause of years helped by the AIrunning on your wrist, probably just saved your life! And, …, another breath, …, and then you recover and are fine. Your friend, you gently and says your name. You can’t speak but you take abreath. face-down in bed, blue and not breathing. She flips you over.She shakes nearest friend to rushes your dorm room to check on you and finds you tiny map on their the smartphone location of where you are. Quickly, your AI, detects the and calls attack your caregiver list and shows them on a such aneurological watch, and your attack smart continuously running breathingto start again and to recover completely. One day, you have just no comfortably-wearable technology do can yet), then you are more likely is there to stimulate you (flip you over, give you a gentle shake; things that When this condition it occurs, cause can you to stop breathing. If somebody Imagine you have aneurological condition that belife-threatening. can Seizure-detection algorithm Case study by Rosalind Picard, MIT Media Laboratory Media MIT Picard, Rosalind by study Case development, aimed at being able to give people advance notice of aseizure. future wearables and machine learning are active areas of research and today the wearable only can to detect seizures and alert they as happen, event timing, so it bereviewed can later by amedical professional. Whereas calls and text In messages. addition, the additional logs software the data and of on apaired (perhaps software smartphone) and makes that alerts issues seizure. When it detects such event, an it communicates with another piece where it runs continuously, looking for events that might beadangerous resultingThe vector trained support machine is programmed into every watch, wearer’s wrist to labels likely to begiven to the data by human. expert an enabling it to amathematical learn function that maps data from sensed the of the data. labels The and data are to used train vector the support machine, neurologist expert an to provide amedically accurate label for each time chunk learning thatsupervised is trained by collecting lots of wearable data and asking the time of the FDA’s acceptance vector was asupport machine, aform of algorithm, built using machine learning. AIalgorithm The within the watch at save lives. Inside, the watch is continuously running aseizure-detection Administration) 2018, in January and has already credited been with helping convulsive seizures. It was approved by the FDA (U.S. Food and Drug watchthe smart first running AI to detect potentially life-threatening seven to nine minutes. wearable The described here is the Empatica Embrace,

119 WIPO Technology Trends 2019 7 Key issues arising from AI and policy responses

The impact of AI

The spread of low-cost graphic processors allowing the performance of huge computational loads is extending the AI revolution to beyond the big multinational companies and impacting businesses and academic organizations the world over.

In particular, AI is expected to have a fundamental impact on the Fourth Industrial Revolution (4IR), a term coined by Klaus Schwab in his book of that title. Schwab sees the 4IR being characterized by a number of emerging technologies – including AI, robotics, the Internet of things (IoT), 3D printing and It is important autonomous vehicles – which are fusing the physical, digital and biological worlds, and that the various affecting all disciplines, economies, industries regulatory and and governments. Ultimately, almost every activity and sector will other governance benefit from the use of AI. The impact of AI can already be seen in applications that people use mechanisms are every day, in transport, health, finance, law and other areas. AI will also transform productivity, thought about with some studies estimating it could reduce conversion costs in industrial operations by up now; the fast to 20 percent. pace of change As with every new technology, AI offers advantages to early adopters. However, it also in this technology poses many challenges. AI is affecting the workplace, replacing skills and so threatening is such that we jobs and incomes. Concerns around data are myriad, and include ethical questions, from cannot wait. the fear of security breaches and hacking, to issues around privacy and consent, to potential Kay Firth-Butterfield, WEF bias in algorithms and the evaluation of data. A number of writers and thinkers have recently have thinkers recently and of writers A number Policies and regulation: some perspectives area. In many areas of the world, job loss which which loss job world, of the areas many In area. of the stability to political the and workforce a to their find way disruption minimize to that wish to companies jobs: conscious “Socially on impact an sees already (WEF), Forum World Economic the at Learning Machine and AI of Head Like Ford, Kay Firth-Butterfield, low-wage compete.” can countries in people mean also technologies these effect: drivenbe down. There’s the also globalization will wages or replaced be either will People shrink. will workforce of the a percentage as jobs good in people of those number The inequality. willincrease that and areas more to dynamic that apply will technology This of years. like looked anumber for has food fast what is That to replace. easy are people so wage training, job.minimum It require doesn’t –a do anyone could into something turns it and education, and training required that job agood you had Once effect. a de-skilling often is story. That augmentation this about too sanguine are people many but happen, will things of those both that is belief My newwhere work technologies with people? augmentation, about more it is or people, thiswhether will technology substitute for inequality.about There’s a debate about It’s also unemployment. “It’s about just not explains: He effects. to its mitigate guarantee income to abasic provide governments for on impact jobsdramatic have and argued of author Ford,Some commentators, such Martin as ���� exhaustive, a selection of perspectives is is of perspectives aselection exhaustive, on business, the economy, education and the landscape. addressed the likelyaddressed of impact technologies AI presented below to illustrate the complexity of of complexity the to below illustrate presented regulation and private actors. By no means means no By actors. private and regulation leisure, and commented on the role of of role the on commented and leisure, Employment The Rise of the Robots ofthe Rise The , foresee a a , foresee ���� wait.” we that cannot such is now; the of pace fast in change this technology are thoughtgovernance about mechanisms other that and the various regulatory important is it Therefore, of AI. use the about made are decisions wrong the if lost be can value accountability and privacy. Substantial brand four broad main categories; transparency, bias, into fall problems “These resources: of human area the in AI using when problems potential to aware be of the need companies that warns also She to businesses.” useful generally not is change, in and instability marketsgeo-political to led has by retraining mitigated been not has visual recognition systems that can be used used be can that systems recognition visual Others have sought to identify some of the of the some to have identify sought Others cyberattacks and creating mischief.” Future of Humanity Institute sees both risks risks both sees Institute Future of Humanity are concerns aboutare autonomous concerns and cars in this “We see technology: of AI applications near-term and present in benefits well as as in technology. Nick of Bostrom Oxford’s key security risks posed by radical advances advances by radical posed risks key security for surveillance. On a smaller level, there level, there asmaller On surveillance. for military applications,military such as drones, and in Security Security funding. 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121 WIPO Technology Trends 2019 122 7 Key issues arising from AI and policy responses Four views superintelligence on Frank Chen, Horowitz Andreessen Ford,Martin futurist Nick Bostrom, Future of Institute Humanity Kay Firth-Butterfield, WEF Kay Firth-Butterfield, necessary. so is stage this at through thought to be governance for why need is the This multiplying. issues these we see will to market, come robots and cars as such objects AI-enabled more as and, loans and jobs to obtain individual’s an ability sentencing, and policing predictive in it be AI, of narrow use the in again and time seen are areas problem four These of privacy, bias, transparency. and accountability issues the and AI narrow about Additionally, to concerned be we area. this need rightly,are, and on working superintelligence and intelligence general artificial an immediate concern. immediate an it’s but at, to not laugh something not is machines intelligent of truly threat The 10 next the in to issue 20 an years. become will which example, for race and gender around data, the in of There’s bias risk also surveillance. and privacy well as inequality, as and labor, on unemployment for impact the potential the as such term, short the in concerns immediate more the from us distracts this about risks. with associated be should of ways it avariety are There careful. we Ithink have to reason, very For that be invention ever. important most the be would it moment: awatershed be would superintelligence Machine of interest. domains all virtually in minds human best of the performance cognitive the outperforms greatly that system intellectual implicit set of desires. no are there that is intelligence human from different what’sbut fundamentally there’s intelligence, human a“you.” algorithms, may have AI super-sophisticated of At core the techniques. own AI its requires each and intelligences, thousand a are there is reality The thing. asingle-dimensional as intelligence to label easy may have quotient]. It’s IQs low [emotional high EQs have who too very people : I take that issue seriously. The danger is that worrying worrying that is danger seriously. The issue : Itake that : There are many who worry about controlling controlling about worry who many are : There : Most intelligence is orthogonal: some some orthogonal: is intelligence : Most

: Superintelligence is any any is : Superintelligence

“glorified data optimizationprocess.” Shesees AI Superpowers: Silicon China, Valley, and the AI as tied not only to algorithmic design, but but design, to algorithmic only not tied as AI ���� Gmail replies are now auto-replied. "Here's now auto-replied. are replies Gmail Why wouldn't I do that? That's free cancer cancer Why wouldn't That's free that? Ido data about concerns express many While embodied in ubiquitous embodied facial recognition say: would of alot people Ithink of this, Horowitz,capitalAndreessen firm suggests exacerbate behavior prejudice. and undesirable new ways of security.” need will that questions would we want that? I think eventually we eventually wewould Ithink that? want Kai-Fu Lee, author of the recently published published recently of the author Lee, Kai-Fu this is the challenge of use facial recognition the of challenge developing fair accurate and Fellow of United Pauwels, Research Eleonore they're what capturing about to dishonest be skin for photos our they scan would that If they told us Instagram. and to Facebook to give prepared may be people some that that also in technologies AI He observes up some of their privacy in order to get the to the get order in privacy of their some up Nations University (UNU), describes AI as a a as AI (UNU), describes University Nations and predictive policing algorithms by law algorithms policing predictive and and quality data markets, financial and it: to “When train used to datasets the also personal our we selling don'tand them want aton of we photos upload a speculation: how numbers, would a thousand people thata of are undecipherable, bunch numbers New World Order selection, or inselection, the functioning of hospitals job predictions, justice criminal systems, screening!" Chen, solution, One suggests of they percent now 40 want, that services even or reinforce unwittingly can media social integrity risks are amplified. One example of of One example amplified. are risks integrity is to as why amachine explanations including is greater transparency in AI applications, out opt or in opt If you could information. cancer, of skin signs early and irregularities know? What would it cause? These are all all are cause? it would These What know? privacy, Frank Chen, partner at the venture venture the at privacy, partner Frank Chen, “AI hacking: and also is breaches by security making a particular recommendation. amaking particular might. we But don't want companies these hacked Ifhumans. someone in changed and to ways in inexplicable together multiplied Data privacy ethics Data and , stresses the threat posed posed threat the , stresses There is a concern that there will be a race to arace be will there that aconcern is There A lot of the concerns about AI are linked to are a AI about concerns ofA lot the ���� offences, while the European Union (EU) (EU) Union European the while offences, could develop its country owneach data disproportionate surveillance in low-income create Palantir like and PredPol platforms data, crime historical on trained –when enforcement crime, but there are side effects to that.” effects side are there but crime, withcountries the laws strictest have the least may not this that believes Lee But countries. this term, medium the In of AI. development website. These every for permutation of every government “The practice: in model effective with the possibility of imprisonment for such such for of imprisonment possibility the with whether more optimistic or more pessimistic in will which corporations damage then will those in to individuals athreat pose would Lee welcomes the effort made in passing passing in made effort the welcomes Lee Protection Regulation (GDPR). Kai-Fu similar Leeaddressed when concerns turn force countries to adopt stricter laws. It’s stricter to adopt countries force turn the of risk highest damaging individuals, which regimes privacy data liberal to implement the will whereby bottom countries compete an is it whether questions but GDPR, the and Blackand communities.” and ethical questions, uncertainty around and ethical questions, uncertainty the likely to more enable are these as sick and tired and just click ‘yes’. click just and tired tiny and sick A very its own culture. For example, China has strict strict has China For example, own culture. its that predicts He report. this for interviewed is playing manager. product It’s doing the protection and data privacy laws in line with laws with line in privacy data and protection be the case: “More data collection involves collection data “More case: the be windows.”pop-up ‘no’ clock will and alot of people percentage get up; coming people keep windows pop-up choice the –let’s user force givebrute every has recently implemented the General Data Data General the implemented recently has negative that loops feedback recommend how an AI-enhanced future will look and and willlook future AI-enhanced how an the with annoyed get just will of people number alarge while convenience the on out miss laws on selling and using data without consent, consent, without data using and laws selling on lack of specific regulations addressing legal legal lack addressing regulations of specific that argue you could punishment: like capital Superintelligence

123 WIPO Technology Trends 2019 124 7 Key issues arising from AI and policy responses described in the patent documents captured captured documents patent the in described tasks of individual care taking AI beyond goes happens of what question the words, other (so-called “narrow AI”), which is what current current what is AI”), which “narrow (so-called when machines exceed the ability of human of human ability the exceed machines when technological developmentstechnological allow what and is –in of superintelligence to development the scenarios are likely to materialize. One of the of the likely One to are materialize. scenarios by the current report. report. by current the brains in general intelligence. Superintelligence most widely discussed concerns is linked linked is concerns discussed widely most different countries – users, language, – users, countries different in different is ecosystem entire The standardized. are developed so those products countries by its to default products exports U.S. The it’s to succeed. hard issues, Even users. no regulatory assuming acquire and aproduct advertise expectations, how you build and economy. into real the Al of integration the on resources leverage also policies these studies, theoretical the on the emphasis Besides cooperation. development internationalresources and AI, covering capital, IP protection, human the development topolicies of support government numerous has issued 2016. since plan strategic The national the in included been has AI Kai-Fu Lee, Sinovation Ventures National solutions Haifeng Wang, Baidu policyAI in China

“Currently companies are not investing as much much investing as not are companies “Currently Taking all these concerns related to AI and its its to and AI related Taking concerns these all Andreessen Horowitz partner Frank Chen Horowitz partner Andreessen contributing we experts the haveAs seen, Although striking a more positive note than note than positive amore striking Although to allow took aleap They Florida. and Arizona ���� countries will make their own decisions – those –those own decisions make will their countries to be has it expect who “Regulators cars: shouldorganizations play in the development capable of, some of the technology is still in its its in still is of, technology of the some capable and to control corporations byof AI large development will betoward monopolization people’s influence to positively of potential alot is “There corporations: big from come on the road while cars other cities driverless who wantcompanies to develop using that we have an arms race dynamic. So I think Ithink So dynamic. race we have arms an process? the in acatalyst be or lead will who weapons. I am not optimistic and I think the the Ithink and optimistic not Iam weapons. who regulate will less technology likely attract In particular, in whatever companies field will this will be dominated by sector.” private the dominated be will this the about views have differing to report this they should doing. be probably causes The that agrees the Faltings, Firth-Butterfield the to data required develop the technology.” goes of research majority overwhelming the will change that of EPFL Faltings Boi argues technology. We’re of use the with this seeing understanding of the what and technology it is understanding agrees, pointingagrees, to the example of autonomous Bostrom’s In of AI. words: regulation “The and are waiting to see how it plays out. how out. plays it to waiting see are autonomous vehicles in the U.S., specifically sharing and access to information. However, to information. access and sharing in research and development (R&D) and AI as as AI and (R&D) development and research in profound. be will business for of AI implications robot and surveillance marketing, into digital perfect will hold back progress. Cities and and Cities progress. back willhold perfect becomes ubiquitous in robots, cars, home home cars, robots, in ubiquitous becomes future into impact is another account, question for that are principally in three areas: a lack of of alack areas: three in principally are that for more active governments become, the more more the become, governments active more healthcare, entertainment and other areas: technology AI as strategy to AI develop an need to they have only society, as access manipulate roles that governments and private sector sector private and governments that roles lives, for example in medicine, resource resource medicine, in example for lives, Leading the future of AI of future the Leading As is often the case with emerging emerging with case the often is As that businesses some see we can aresult, As National and regional responses to AI Given the questions about AI outlined above, outlined AI about questions the Given on governments to adopt public policies that that policies public to adopt governments on other, the on to address expected they be will to promote pressure be will there hand, one the on adilemma: face will governments governments it.” may support example in facial recognition air conditioning, hit. will it developments which and from come regulation where will about of understanding where it really is not necessary to use it, for for it, to use necessary not is really it where technologies, technological advances may advances technological technologies, and other businesses holding back until otherand businesses they places in AI to use trying looking forward are scientific development and economic progress; developmentscientific progress; economic and what thesee innovation is how space and infancy and not yet generally useful, and a lack alack and useful, yet not generally and infancy be faster than the frameworks that seek to seek that frameworks the than faster be rights. their and citizens protecting incumbent therefore It is people’s concerns. foster competitiveness while at the same time time same the at while competitiveness foster including expertise. AI-specific skills, digital advanced in training for support strengthened 2027) include will framework multiannual financial (2021– the under EU'sProposals next creativity. and entrepreneurship (STEM), mathematics and engineering in technology, science, competencies skills, digital Fund, support and Social the European from support financial with schemes training dedicated up set Europe, in talent AI more keep and to attract partnerships education Commission business– will support European Pillar of Rights. The Social market transitions, building on the labor support and systems training and education their to modernize States Member is encouraging Commission why European is the This transformed. be will most and disappear will others but created, be will jobs many intelligence, dawn of the artificial With European Commission Paul Nemitz, EU policy framework

AI or address the legal and ethical concerns concerns ethical and legal the address or AI Various initiatives regulations and policies, verifiability and accountability AI,of the and accountability verifiability contribute to discussions about appropriate appropriate about to discussions contribute can report this in presented analysis and data of playing catch-up, policy regulation and In the light of these challenges, it is crucial that that crucial is it challenges, of these light the In try to anticipate the possible yet unknown yet unknown possible the to anticipate try and avoidance of bias, and the mitigation of of mitigation the and of bias, avoidance and of adoption or investment the encourage and implications of technology, leading to too either policy decisions are based on evidence. The The evidence. on based are decisions policy research AI incentivize to try and benefits negative on impacts employment. regulation. too little or much right to privacy, the right to equal treatment to privacy,right treatment to equal right the potential its and AI around optimism the reflect instead that, be also It could them. regulate linked to AI. The latter include the transparency, transparency, the include latter linked to The AI. Canada, China, and now China, and Canada, France, all US, –the nationalized very is ofA lot AI follow suit. will regions other environments, in welcoming regulatory take longer. new As thrive technologies can which roads, and infrastructure public on relying than rather Rwanda, in facilities to medical blood drops and collects that Zipline called acompany in regulated. Andreessen Horowitz invested that in countries are less happen example. Today, drone deployments most Taketechnologies. an as drones in of the usher AI-backed generation next will envelope, the to push aren't afraid regulation, with who Those less progress. back to hold will deploy perfect be must that whoregulators expect regulation We where to likely will get apoint overnational battle technology. a we such had since awhile It's been out. how plays that to see interesting be level. It will down to city government the from investment direct making is China timelast got technology this national. the Ican't remember countries. their in encourage and stimulate the AI economy have national how strategies around to Frank Chen, Andreessen Horowitz the world Regulation around

125 WIPO Technology Trends 2019 126 7 Key issues arising from AI and policy responses declaratory nature, indicating an intention intention an indicating nature, declaratory Responses to AI can be of a general, of ageneral, be to can AI Responses the taken approach by several countries. address them. It also indicates differences in in differences indicates It also them. address policies and regulation in this area. This chapter chapter This area. this in regulation and policies from AI, along with examples of the policies, policies, of the examples with along AI, from now presents a number of the issues that arise arise that issues of the anumber now presents laws, strategies and other initiatives trying to trying initiatives other and laws, strategies (OCEANIS).Systems Intelligent and Autonomous in Ethics for Community Open the and Intelligence Systems, the Council on Global Extended Intelligent and of Autonomous Ethics on the as activities Initiative such Global through areas, these investing all in engaging and been has Association IEEE Standards The over others. all dominant more and powerful ever more groups to make only not ever smaller awhole, as humanity serve and co-exist to to able be machines and humans for to do we need what about message the home drive and convince may help well told astory or image An artists. yes and also philosophers, sociologists, political lawyers, actors, experts, science human include and to attract made be to has effort Aconscious technicians. among to amatter reduced be cannot this challenge themselves, because reinvent to almost significantly, evolve even to they but have this, close to still standardization ecosystems come Some of actors. variety agreat among consensus abroad of producing capable are transparency, and and rules robust on based are inclusive, and open universally are that processes we need challenge, tomorrow. To this singular address now, them we need and not codes, and material, guidelines, standards such educational as assets, practical of We techno-solutionism. concrete, need transformation against the prevailing spirit cultural achieveto this suffice not will Goodwill communities.alone scientific technical skills of the involved techno- of anticipative reflection to the traditional To we have risks, mitigate to alayer add Konstantinos Karachalios, IEEE reflection Adding anticipative

The The Artificial Intelligence Research andDevelopment Automation, and the Economy AI forAI Europe, other among announcing for of AI importance the highlighting or AI Cooperation on Artificial Intelligence in 2018Intelligence Artificial on Cooperation Government in achieving its goal of maintaining of maintaining goal its in achieving Government of a Select Committee on Artificial Intelligence Intelligence Artificial on Committee of aSelect to aims and opportunities, AI and challenges in approach thecentric global context.” The ahuman- promoting AI, secure and ethical issued It also of AI. development ethical the on guidelines set and investment, and policy on Europe’sensuring in competitiveness the U.S. leadership in AI. in U.S. leadership Strategic Plan was announced towas announced advise the White on House In the the In Member States also signed a Declaration of of aDeclaration signed also States Member Made in Europe, which states “the ambition is is ambition “the states which Europe, in Made aHigh-Level Moreover, established EC the has the period 2018–2020 under the Horizon 2020 2018–2020 Horizon the under period the AI in investment to aim increase its things arelated in inclusion its through country the three reports in 2016: in reports three released future, the for of AI role growing the the global context. AI EC report, Intelligence of Artificial Use and Development Group on AI to makeExpert recommendations by from AI, raised issues important most the at the national or regional level to invest in regional or national the at Future of Artificial Intelligence ofArtificial Future Perspective support this effort, the EC is increasing its its EC the increasing is effort, this support plan. or strategy support the development ofsupport European action in social, economic, ethical and questions. legal in which they agreed to work together on on to together work they agreed which in to to AI €1.5 dedicated investment for billion 2018 April on in issued aCommunication interagency AI R&D priorities and to support the the to support and priorities R&D AI interagency 2018 the on Plan December in aCoordinated billion from now until the end of 2020. now end the To until from billion provides an overview of European policies, AI AI policies, of European overview an provides for developing deploying and cutting-edge, region world-leading the to Europe become for research and innovation program. The EU The innovation program. and research €20 innovation by least and at research research and deployment of AI, to dealing with with to of AI, dealing deployment and research European Commission (EC) U.S. , the White House, acknowledging Artificial Intelligence:Artificial The European , published in the same month, month, same the in , published . In May 2018, the establishment May. In 2018, establishment the Artificial Intelligence, ; and ; and ; Preparing for the , for instance, instance, , for The National Technology convened a meeting to the mark This is supplemented by government local followed was by areport, This Artificial Intelligence: Towards a French and A number of policies aim at supporting supporting at aim of policies A number in soon to published be expected are plans AI well as as sectors, vital various in investment AI ���� Centennial 2071 objectives. This strategy aims aims 2071 strategy This Centennial objectives. value. The first Minister of State for Artificial Artificial State for of Minister first value. The 2017, the UAE announced Mohammed Sheikh Germany Strategy for AI as a major part of the country’s country’s of the part amajor as AI for Strategy enabling interoperability, privacy, security and and privacy, interoperability, security enabling values, and principles guidelines, standards, economic high with market anew vital create 2017 of December in and Ministry the entities, special up a setting of this Plan, launch official 1trillion. RNB passing industries of AI output defense. and ecology transport, health, on while developing transparent ethical and which included 50 about recommendations. Intelligence was also appointed. the In Information and TechnologyIndustry released In In France IA (France AI Plan) on March 21, Plan) March AI on (France IA France 2017, the way of this fast-developing field. to avoid awish stated signatories The trust. of field in the in the world to make UAE the first Industry. AI of New-Generation Development governmental 15 involving Office Promotion November 2017,November and of Science Ministry the Development AI Plan, Generation Next setting any unnecessary regulation that could get in any unnecessary a Three-Year to Plan Promote the Action to to companies AI incentives provide and world’s the to become country the for agoal as European StrategyEuropean in Mayin AI, 2018 for to data access to enhance states base themselves in respective provinces. In In provinces. respective in themselves base to designed regions promotepolicies different the by with 2030, innovation center primary afocus with to data access better promoting research and development in the field of AI. of field the in development and research China France AI and publicAI and research/funding United Arab Emirates (UAE) made a joint statement on AI collaboration collaboration AI on statement ajoint made , the State Council issued in 2017 in issued Council State , the the and and , the Government published its Finland , delivered in March 2018, March in , delivered , while the the , while For a Meaningful Nordic-Baltic Nordic-Baltic , in October October , in

One Road, it seeks to encourage the setting Road,One to the it seeks setting encourage cooperation centers and joint research centers centers research joint and centers cooperation Belt of One strategy the on Based organizations. In In In to promote and apply AI technology. AI apply to and promote research public world’sand the top universities on the strengthening research Plan proposes up of international science and technology up of technology and international science Intelligence Task Force framework of AI standards to cooperate with with to cooperate standards of AI framework China India disciplines and coalitions. and disciplines of diverse perspectives the require that procedures auditing and datasets training collectively establish standards for to together work must institutions national governments international and Citizens, privateunknowns. industry, future and risks potential anticipate to foresight by inclusive informed be should regularization and standardization horizon, the on (AGI) intelligence general institutional silos. With artificial in overcome can’t be challenge this –but AI responsible developing for of principles own sets their releasing are IBM and Intel, Microsoft, Google, as such Multinational corporations countries’ populations ecosystems. and of other data value, the high avery for commodify, AI know-how tothe harness have that states when rise might tensions biology on such a grand scale. Geopolitical physiologythrough behaviors, human and sift and to monitor equipped been species our has Never before untested. remains May in 2018,into effect efficacy its but data protection to legislation date, came holisticmost national comprehensive and ProtectionData Regulation (GDPR), the EU’s The disruption. General potential to the combat of global scale necessary may be treaty evenor amultilateral innovation, collaborative national policies AI in leaders the willbe who determine will ownership data where aworld In , the 2018, the , the Next Generation AI Development Development AI Generation Next , the Center for Policy Research Eleonore Pauwels, UN University responsible AI to developing Multilateral solutions Report of the Artificial Artificial ofthe Report focuses on focuses public

127 WIPO Technology Trends 2019 128 7 Key issues arising from AI and policy responses (ICT). technologies communication and new information other and AI in inherent benefits and risks the with deal and allocation, efficient and distribution its enable of data, production to the promote suitable framework law the to a provide challenged and established an information economy has This own right. its in good tradeable a become has information progress, Driven by economies. technological of modern keyhave aspects become use and distribution production, Their society. of modern keyas concepts regarded widely are information and Data rights. property other and protection personality property, of intellectual area the in found be can property as alike. data Data application and software comprising by machines processed be away in can that encoded information as understood be can Data usefulness. privacy and between trade-off the in call judgment make a all wethey’re so can for it using what data they’re what and collecting about open to more be need Companies is: didn’t that.” “I you know doing were of anger root The US. the in hacked got That’s Equifax when what happened by acompany. collected being is data they don’t their realize when is people upsets What data. that to use intentions their with transparent being at job better to a do need data personal aggregated have who companies all I think Frank Chen, Andreessen Horowitz good common Data privacy and the Zech,Herbert of University Basel Data information and

document in the UN on big data and and data big on UN the in document 2017). (UNDG official Note, first the This adopted by the UN Development Group Agenda 2030 of the achievement the for Data Note Big on Guidance the is UN included indocument an official obligations of data handling were moral the and ethics which in example Arecent good. public the for of data re-use and use responsible the and accountability ensure also and privacy beyond go that needed are frameworks or mobile telecommunication. New e-commerce financialas services, such industries priority in data big from anonymization of sharing and insights To the for exist date, standards no meters. smart and phones mobile support that services for analytics have business built privacy-preserving Google and Microsoft, Apple, real-time. in data generate that of devices to millions impressively, differential privacy can scale More organizations. many within intelligence business privacy-preserving paves tool way the for source open Their that provides robust privacy assurances). guarantee (a privacy formal differential results that adhere to state-of-the-art that lets and analysts get submit queries tool source open an in resulted has Lab Berkeley’s UC and RISE Uber between onrelies a A database. collaboration intelligence business settings, many In governance. data for procedures operating of standard part as included is ethics data that ensuring of importance the privacy, stresses UN Global Pulse Miguel Luengo-Oroz, addressed being Data concerns Ben Lorica, O’Reilly Media privacy tool source open An

their behalf. on bargaining collective directly or enabling by either citizens empower can Governments information. of their use the for aprice power negotiating in market have little individuals ordinary individuals are close substitutes, then different from data if or data, additional from returns decreasing faces If AI differences. also but commonalities, some willbe U.S. there and –and China laws –EU, of data havewill sets three we Maybe it. we Ithink tweak will but design, I don’t good it’s think avery efforts. first of the one is GDPR The project. of acrowdsourcing beginning the at we Ithink are expectations. user and cultures country, different the given every for valid is that answer one is there Idoubt not. is what and legal is management and privacy and what data individual with how toout deal figuring in waters We uncharted in are something figuring out. U.S. the and is GDPR the EU has The have by punished imprisonment. been which case would Cambridge Analytica Facebook- the in as such consent, user without data use and sell that to companies privacy, respect with but to individual respect laws, with not data strong has laws. China data different willhave country Eventually every

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in this area have not changed very much much very have changed not area this in methods and tools The records. medical sensitive on based of diseases models for data, exampleunderlying to develop of the privacy the preserving while work to techniques AI enable can techniques privacy). These of differential guarantees (with the data original this about give information no but data original the as models same the from allow learning data machine forthat learning artificial and more recently mechanisms for the privacy of agents, the participating preserve that optimization multi-agent for mechanisms finding is contributions we have major which in made area One data. health and the environment, as well as research and utilities public from data covers This easier. to sharing make data measures and re-use for data more up to open legislation is proposing Commission material for the technologies, AI most raw the is data Because investment. to create that environment an stimulates The European Commission will continue problems. to such shifted has of interest focus the learning, machine in interest large to current the however, 20 years; over past the due Boi Faltings, EPFL data Anonymized European Commission Paul Nemitz, Data in the EU

129 WIPO Technology Trends 2019 130 7 Key issues arising from AI and policy responses The The The 2017, startups for incubator world’s the largest opened in Paris. The Station F campus covers covers Fcampus Station The Paris. in opened June In projects. public–private host could over the key long term, identifying technologies to industry transfer and research discovery cooperation between the Government, industry the Technology AI engaged Strategy Council AI for capacity computing for demand centers of research excellence. world-class AI research designed to nurture to nurture designed research AI world-class Institute of Science and Technology (KAIST), Technology and (KAIST), of Science Institute the In In In data and AI for institute anational as Institute the In 34,000 square meters, can accommodate accommodate can meters, square 34,000 Mission toMission coordinate activities. AI-related Intelligence Ministerial National Artificial their commercialization conducted through conducted their commercialization objectives defining to aroadmap up draw “Ellis.” called to be Scientists to combined. be resources and tools that allows available knowledge, algorithms, up to 3,000 workstations available to available 1,000 workstations toup 3,000 and other services. which AI” for “Center aFrench creating and academia. and is institute This Europe. in top talent retain and ecosystem AI European the to host a platform Discussion Paper Discussion science research university Korea Advanced Korea Advanced university research science funds venture integrates directly and startups, public other with to together work science include promoting a support policy for for policy asupport promoting include provides European funding for the creation of of creation the for funding European provides business, Hanwha Systems, and the state-run state-run the and Systems, Hanwha business, for R&D related to AI technologies and and to technologies AI related R&D for multinational European institute devoted to avast, for plans have ambitious up drawn research community. research U.K. of the behalf on to negotiate and research to coordinate councils or entities research Turing Alan the establishing recommends Inter- of an funding the including research, France Japan EU Intelligence Artificial for Strategy National Republic of Korea of Republic U.K. Horizon 2020 (2014–2020) project project (2014–2020) 2020 Horizon , the Japan Revitalization Strategy has has Strategy Revitalization Japan , the , proposals in the France AI Plan Plan AI France the in , proposals , the , the Growing AI in the UK the in AI Growing also recommends funding for for funding recommends also , the leading defense report report

The two parties recently opened a joint ajoint opened recently parties two The Technology a new announced US$1 (MIT) ���� vehicles and the Internet of things. It is It is of things. Internet the and vehicles various studies into how of technologies the 2018, the Massachusetts Institute of of Institute 2018, Massachusetts the Several the national strategies address of College Schwarzman Computing. expected to be finished by 2020.Similarly, to finished be in expected to accommodate autonomousengineered question of AI and jobs. and of AI question A. Stephen new MIT the be will endeavor largest single the initiative marks This of AI. by presented challenges and opportunities will not only enhance and support human skills, skills, human support and enhance only not will as AI designate to history in first the was In In In In the the In the In transforming Dubai into a smart city. into asmart Dubai transforming the on utilized be can Revolution Industrial Fourth weapons. to military to applied be technologies Revolution Commission, which is directly being is issue This income. and to jobs threat systems AI that have aconcern voiced Experts rise the and of computing prevalence the transportation in Israel. as the “King Abdullah Economic City” is being being is City” Economic Abdullah “King the as addressed at the national level by a range of of level by arange national the at addressed of this At heart the institution. academic October In priority. R&D Administration an ILS240 about worth program a five-year in AI to mitigate job losses or preparing for the the for preparing or losses to job AI in mitigate by AI aU.S. and computing in investment policies, aimed either at creating new skill sets sets new skill creating at either aimed policies, a posing them, replace extent to will some but billion to commitment the global addressing future battlefields. out willcarry Hanwha and university the from have launched a project to co-develop AI AI to co-develop aproject have launched new jobs that might be created through AI. through created be might that new jobs smart million) to promote (US$66.2 million research center at KAIST where researchers researchers where KAIST at center research Israel Saudi Arabia UAE AI and jobs and AI U.S. Republic of Korea of Republic , the Government has authorized authorized has Government , the , the Smart Dubai strategic plan is is plan strategic Dubai Smart , the , the FY2019 Budget Request FY2019, the Request Budget , the megacity project known known project megacity , the , the 4 , the th Industrial Industrial “people-centered” AI development, AI proposing “people-centered” AI training. 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Data is fed into AI systems to train to systems into train AI fed is Data of AI. workforce. the on of AI effect aon national employment design and strategy offering are platforms private and of Israel, 2019 from schools graduate to 2022, and working with other ministries to include AI in AI to include ministries other with working In the the In In the In Memorandum was which prioritized signed Memorandum them, and the more relevant that data is to is the data that relevant more the and them, development the in role acentral plays Data theto of impact automated observe driving a for need the addressing is principles Driving AI. in women and men Emirati 2018, January In the curriculum. national the universities, including the Open University University Open the including universities, and support them in grouping information, information, grouping in them support and the negative to reduce retraining at aimed aparticular with education, (STEM) math and answerable to the President, considered 5,000 AI personnel over the next five over next the personnel AI 5,000 system by 2020, and the Ministry of AI is of AI bysystem 2020, Ministry the and university existing at research AI for support intended final use. use. final intended forecasts, accurate more providing in and results grouping patterns, identifying AI an with airports at officers immigration Mayin 2018 to train strategy R&D AI an personnel in high-tech industry.personnel Well-known of skilled number the increasing for program a to resolution a national implement passed perform better the more the data used to train to train used data the more the better perform focus on computer science education. new data is presented. 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131 WIPO Technology Trends 2019 132 7 Key issues arising from AI and policy responses The GDPR at the European level addresses level addresses European the at GDPR The ���� Consumers haveConsumers about concerned become Some regulation has already been developed been regulation already Some has discrimination, for example in assessing for for assessing in example for discrimination, ethical questions, including the grouping and given the large amounts of personal data data of personal amounts large the given solutions, from technological mostlycome believedevelopment. that experts Some the shared be should players byowned bigger ownership. debate Another is data whether of a development the about discussions mitigated. be could to as how risk this question in the developmentopportunities of the and AI adominant gain of data amounts own large (e.g., universities and startups) from equal equal from (e.g., startups) and universities what way, and the need for explicit consent. way,what consent. explicit for need the and and developers AI for both situation win-win to provide institutions sector public with Further concerns are linked to a series of of linked to are aseries concerns Further to its use for certain purposes. Examples Examples purposes. certain for to use its that will be generated by new applications by new applications generated be will that services useful gaining between tradeoff the in and to used be information personal the to privacy, right the transparency, for need the to privacy. address are ongoing There and e-commerce. especially likely to compounded, are be These information. personal up giving and answer to data and privacy concerns can which companies those that risk the is arises may involve that information geospatial and such as autonomous vehicles, smart meters such autonomous as meters vehicles, smart data. of the subjects IPan as such framework, regulatory specific consent and about know data of the subjects interpretation of data by machines which may may which by machines of data interpretation information and control by citizens about about by citizens control and information and nature legal the to address instrument, include patient information by collected position, thereby excluding smaller players players excluding smaller thereby position, that issue Another data. sensitive or personal providers, privatepublic or service health health insurance or profiling a subject in the area area in the subject a profiling or insurance health more equal opportunities in AI research and and research AI in opportunities equal more related to anonymizing data, to a create data, to anonymizing related database and regulation of datasets data, lead to undesired and biased results and and results biased and to undesired lead AI and ethics AI and debate on this issue within society at large and and large at society within issue this on debate into algorithms, values ethical embedding and decisions AI for liability and of accountability have Moreover, justice. concerns of criminal In the the In trust and concerns about the possible unethical unethical possible the about concerns and trust and ethics about questions raises Plan Reform training. through and transparency, information stresses Plan extent to some addressed are issues Ethical the evaluation of data and ensuring transparency and harm to to both benefit used be can and tool use of AI. It established an ethics charter with with charter ethics an It established of AI. use and a study of standards and procedures for for procedures and of standards astudy and also Plan Humanity for AI The awareness. actions. Strategies to concerns these address in many policies. In In policies. many in aconclusion. way reach the in systems AI include using a human-centric AI framework, been expressed about the use of AI which as a a as which of AI use the about expressed been for the ethical development of AI and promote promote and of AI development ethical the for notedformally itsto desire lay the foundations making sure unconscious biases are removed in in removed are biases unconscious sure making issue related the and well-being, life and human respect to intelligencerespect information technology predictive power of one algorithm to algorithm power of one predictive We the from go will ever recommend. interventions that no doctor would treatment unsafe in result can it data, curated poorly on trained are platforms diagnostic medical AI-enhanced When deployment. and bad design AI ward off to necessary trans-disciplinarity of the we to prey may alack fall then context, given not is IoB the within data If the (IoB). of Bodies Internet the networks of set this –we call behaviors and of our biometrics, vital signs, emotions record refined ever an more analyze and andbiosensors algorithms will capture of Networks bio-control. and social for stage the setting minds, and genomes to bodies, our access unprecedented to networks, giving algorithmic them surrender We unwittingly may soon utmost importance. of the procedures auditing AI and standards the data quality next, making Republic of Korea of Republic Center for Policy Research Eleonore Pauwels, UN University InternetThe of Bodies France

, the Regulatory , the Regulatory , the France AI AI France , the The co-development of AI technologies applied of applied technologies AI co-development The designed bydesigned the Robotics, company Hanson development. algorithm and data collection In In KAIST president to state that the university had had university the to that state president KAIST the above led mentioned weapons to military became a full citizen in October 2017. October in citizen afull became no intention of engaging in the development of lethal autonomous weapons systems (LAWS). systems weapons autonomous lethal Saudi Arabia interest. public the in framing democratic asubstantial U.S., the in without far so corporations of afew mega hands the in development AI and of data concentration in the China, and unsustainable equally systems scoring social the in emerging is it as of people, control mass and surveillance massive for AI and technology of digital use developmentfrom the unsustainable and Europe differentiates This investments. AI of profitability and use sustainable the ensuring in keylaw be will factors and ethics AI, of European positioning geostrategic the in that clear It is products. of defective case in producers and consumers for clarity legal of developments, technological to ensure light the in Directive Liability Product the of interpretation the on guidance issue mid-2019By also will Commission the Alliance. AI aEuropean in stakeholders together brought allCommission relevant To help develop guidelines, these the Ethics New and in Technologies. Science on Group European of the work the on protection and transparency, and building data on those as laws such existing and intoRights, taking principles account of Fundamental EU's the on Charter of 2018, by end the development based AI on guidelines ethical draft toAI present on level ahigh group asked Commission European The new values. mean not making. New should technologies decision- biased potentially or liability to related those as such questions, legal and new ethical may raise AI technology, any transformative with As , Sophia, a robot humanoid , Sophia, European Commission Paul Nemitz, and framework legal Providing ethical an

This forms part of the EC’s of the three- part proposed forms This The Society Japanese the now constitutes This adraft in summarized were discussions These Japanese Society for Artificial Intelligence for Artificial Society Japanese Artificial Intelligence (AI HLEG), including a set HLEG), set a (AI including Intelligence Artificial Commission’s on Group High-Level Expert by European the produced Guidelines of half 2016 first the during of Ethics Code of ethical guidelines considering principles principles of ethical guidelines considering effectively communicate it to at large. society (JSAI) was established in 2014 in (JSAI) established was exploring is and In In technology and society, and technology striving and to research/ AI between relationship the approved in 2017. in approved to feedback. online open became soon and such as data protection and transparency. transparency. and protection data as such in December 2018 Ethics AI of the December in adraft pronged strategy to increase public and private private and public to increase strategy pronged for Artificial Intelligence Guidelines, Ethical for Artificial Japan European Commission (EC) and understanding of these problems in in problems of these understanding and level of awareness general the increase we to intend projects, cooperative and dialogue Through recommendations. of possible legislative actions and feasibility and validate coherence the to background context and necessary technical the by providing practices governancerules, best guidelines and of to elaboration the to contribute ours as such institutes research for important increasingly willbe time, it At same the socially responsible application of AI. and ethical to the to promote take action willing is and issues important by these concerned is Mila values, core of its part As horizon. the on appearing changes the to time Now the lives. forecast is our of more impacts and this on technology more we willsee revolution. Soon, AI wavemajor an in first of the We part are on a global scale. life of human improvethat conditions the ways responsible ethically and humane that in theensuring is technology used intelligent development of guidelines for to the prerequisite essential and urgent an is This allies. international our with and of communities our local the members , the Ethics Committee of the of the Committee Ethics , the Myriam Coté, Mila forAI humanity

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133 WIPO Technology Trends 2019 134 7 Key issues arising from AI and policy responses • • • learning: machine in techniques privacy-preserving recent some Prize). are Here Netflix the on example is the attacks de-anonymization can place users’ privacy at risk (an early simple data anonymization techniques that long has acknowledged community learning machine The AI. for tools and methods building privacy-preserving Researchers and entrepreneurs are • computation ofcomputation complex models develop of a tools class that allow to is goal whose area emerging an Homomorphic encryption: this is privacy. differential that models to adhere learning to deep examine beginning are active research area and researchers to an be continues learning machine and privacy differential between privacy: theDifferential interplay mobile devices. on into services nicely fits thus and data, withoutmodel sharing learning machine a centralized training allows for it by Google, introduced Learning: Federated computations.”machine learning blockchain compatible networks with to want They “make models. and to data share companies securely allow will that infrastructure decentralized source, open building is Labs Computable For example, cryptocurrencies. use that structures incentive and ledgers distributed toare looking blockchains, use who by startups mainly driven area an is this Decentralization: technologies. speech vision and computer in done work preliminary been has There data. over encrypted Ben Lorica, O’Reilly Media to privacy Technology solutions

relationships with competitors, suppliers, their within by or contract voluntarily and policies that organizations apply agreements industry bodies, professional oversight certification, by schemes, incentive private endorse, or constrain which norms of social emergence the WEF Protocols), (IEEE and of standards include:These the development use and measures. governance agile to is use AI of governance in way of succeeding best however we the believe that necessary; is regulation cases some in that know At WEF we the countries. democratic in to especially regulate, long so takes it because AI as such technologies paced fast- behind lags it often So of them. one only is by government regulation and of governance forms many are There in AI.especially face of fast-paced change, technological too slow the in are measures governance traditional where arena an in needed alternative to regulation, particularly asofter provide of AI trajectory the of influencing methods indirect These discussions. these for abaseline set help can credibility and authority moral the technologies, these government’s on own standards its setting debates industry as And, government. beyond sold are that even products for standards of these adoption private-sector drive powercan buying significant time, governments’ At same the citizens. for technology AI design the of benefit governments to responsibly deploy and guidelines willThese empower initiatives. governance agile of these part as importance great of is (AS) Systems Autonomous and (AI) ConsiderationsIntelligence in Artificial Ethical Initiative on Global of the work the such As customers. and partners Kay WEF Firth-Butterfield, regulation standards and Governments,

The development of AI often takes place in in place takes often of AI development The Two worth are linked to initiatives ethics other A vision for prioritizing well-being human AI-related innovation.AI-related principles published has that Driving Automated ���� Germany Science and ICT and the National IT Promotion Promotion IT National the and ICT and Science the Second, engagement about AI and its influences on on influences its and AI about engagement in companies which the biggest consortium ethicalestablish implementations and social government. of AI. use public the in citizens’ rights and accountability AI with extensively dealing changes and ensure an appropriate ethical and well-being in a given cultural context. context. cultural agiven in well-being In the the In formulating and “studying are aims stated Its the public’s understanding of AI, and serving serving and of AI, public’sthe understanding aligningtechnologies, them to defined values hundred several from of input result the and of citizens protection of the importance the announced in March 2018 of March in Ministry the announced that certain and environment source open an and discussion for platform open an as in this as players top (identified AI in active and ethical principles that prioritize human to how best about discussion public advance industry,academia, civil society, policy and Systems allocation of liability, the acknowledged and some United Nations participate. agencies (UN) is applied to public decision-making, stressing socio-economic for prepare AI, in investment Ethics of Autonomous and Intelligent Intelligent and Autonomous of Ethics with autonomous and intelligent systems people and society.” and people on advancing technologies, AI practices best six across continents representing participants policies aim at encouraging this as a path to a path as this encouraging at aim policies for intelligent and autonomous systems and and systems autonomous and intelligent for noting. First, the the First, noting. AI when discrimination to and avoid bias need report), academic institutions, AI experts and and experts AI institutions, academic report), legal framework. AI and open innovation open and AI Republic of Korea of Republic published has an Ethics Commission on on Commission Ethics an has Partnership on AI on Partnership Ethically Aligned Design Aligned Ethically IEEE Global Initiative on Ethically Aligned Design: Design: Aligned Ethically , the Government Government , the is a technology aims to to aims , The policies outlined above are just afew just above are outlined policies The June 2018, recommending a data protection 2018, protection June adata recommending Also in March 2018, March in Also of AI development to the are promote Agency American industries, by removing regulatory by regulatory removing industries, American Industry, American the Government announced Wide engagement Vehicles Policy; various strategies in the 2016 the in strategies Policy;Vehicles various Big 2016 report companies with IPcompanies rights. by codes of source togiven opening the related products and services commercialize support Financial systems. operating open and industries to traditional software of open ownership, sharing rights and usage policies, ownership, policies, usage rightsand sharing concerns in this field. this in concerns and interests many the they balance ensure governments ways of the which in examples In the the In to AI and other core technologies and support support and technologies core other to and AI for software of application development the through the adoption of technologies AI to have for relevant enabling policies socially this field to engage in this process and provide provide and process in this engage to field this promotion the for to frameworks provide PlanData devoted to innovation; open while the U.S. recent Other technologies. initiatives include learning. for platforms of open creation the in AI) for Strategy (National Paper Discussion and big data, notably through the application application the through notably data, big and and applications. This was followed was by a This applications. and generated income for incentives tax well as as and regulation of AI applications. The next next The applications. of AI regulation and to need the responding are legislators and initiative. AI for Data Open an to update 2016 the an Automated Federal is to be given to software companies to companies to to given is be software informed input as policies are developed to developed are policies as input informed in stakeholders all on incumbent It is initiatives. of new creation the to enable objective its projects, especially a data policy to include to include policy adata especially projects, barriers to the deployment of AI-powered of AI-powered to deployment the barriers few years are likely to see many more similar similar more many likely to are see few years framework, sectoral regulatory guidelines and U.S. , at the May the 2018, at for AI on Summit Preparing the Future ofAI Future the Preparing India launched a Plan launched included included

135 WIPO Technology Trends 2019 136 Chapter title

Photo: © UN Global Pulse, 2016 Photo: © UN Global Pulse, 2016 Case studies by Miguel Luengo-Oroz, UN Global Pulse key is that AIaugment human intelligence rather than beasubstitute for it. when humanitarian assessing performance and conflictsatellite imagery. The iterative human-centered AIsystem is proposed in order to increase analysts’ analysis is key to supporting critical operations on the ground. In one project, an dealing with conflictand humanitarian scenarios, precision satellitein image the accurate mapping of structures in different conditions and locations. When automated exist, Although populations. methods they have proven inadequate for reliable method for mapping structures in settlements built to house displaced emergency. manual The analysis of satellite has thus the far imagery been most is key to allocating sufficient resourcesandassistance duringan humanitarian scenarios. Forcertain instance, estimate an of the size of adisplaced population However, the inherent errors in any automated method might beunacceptable in A key characteristic of AItools is the possibility to automatize tasks. certain intelligence human for substitute than rather augmentation as AI provision of warnings early of epidemics or low-scale natural disasters. to the education ranging from and subjects corruption on public concerns Rutooro. Monitoring public conversations allows for the understanding of of the languages spoken in Uganda, including Luganda, Acholi, Lugbara and public discussions in radio broadcasts into text that beread can in several University in South Africa built recognition speech technology that converts those not always heard. Together, UN Global Pulse and the Stellenbosch of the world’s languages. This provides the possibility of giving avoice to generation is the facilitation of the real-time translation of more and more One application where recent AItechniques have the outperformed preceding to information facilitating access Speech recognition and text-to-speech

137 WIPO Technology Trends 2019 8 The future of AI and the IP system

The focus of this report has been the analysis of current and recent trends in AI-related technologies, as measured by statistical data on patents and scientific publications. In this final chapter, the future of this technology is reviewed, including the opportunities that the next generation of AI might bring and the interaction between AI and the intellectual property (IP) system. As with previous chapters, it draws on comments and contributions from a range of international AI experts.

The big question may not be what the next breakthrough will be, but rather how existing emergent technologies will be applied across different areas. • • • Although the nature of AI does not allow for the the allow for not does of AI nature the Although it brings opportunities the and AI of future The could provide an indication of the direction in in direction of the indication an provide could which the field is heading. is field the which a number of trends have been identified which which identified have been of trends a number based on the analysis of patent data, scientific scientific data, of patent analysis the on based forecasting of what the next big thing will be, be, will thing big next the of what forecasting literature and business information related to AI, related information business and literature The fields with the most patent applications applications patent most the with fields The patent most that shows analysis The diagnostics and predictions. However, predictions. and growth diagnostics medical related and data of medical collection life and or airplanes, and drones cars, driving exploring the application of technologies. AI to banking, to education entertainment in general in observed those with compared with an AI application field. This report report This field. application AI an with It is clear that we are going through a a through going we that are clear It is to AI such as transportation, including self- including transportation, as to such AI to continue. expected be can trend this 2013–2016 period the higher much are which 2013. since place AI in taken boom This are the ones which have already attracted a a attracted have which already ones the are to AI they an refer as focus, application applications have a commercial that indicate of technology,clearly fields all in the increasing numbers of scientific in the numbers increasing indicating that sectors across the board are are board the across sectors that indicating that sectors identifies 20 fields/industry patent documents refer to, ranging from to, from refer ranging documents patent patenting activity, with growth combined publications and patent applications, with functional application or are combined medical sciences with applications such as as such applications with sciences medical having AI in activity patenting of the half rates across different AI technologies over technologies AI different across rates reflected also is this and of AI renaissance lot of media attention and have been linked linked have and been attention oflot media • • • This may be linked to an identified need to need linked to identified may be an This where however, are, areas There certain At the same time, the analysis suggests that that suggests analysis time, the At same the A number of experts raise the issue of issue the raise of experts A number Companies feature as top patent applicants top applicants patent as feature Companies view of their importance in the context of context the in importance ofview their emerging technologies, such as robotics, the robotics, as such the technologies, emerging develop skills in at AI a national or regional recent the through seen as governments, develop, or whether there will befurther distributed AI for functional applications; combination with them. Internet of things and cryptography used for for used cryptography and of things Internet the combination of with technologies AI other the dominant deep learning and neural neural and the learning dominant deep to and of accessibility importance the of alot for aconcern is talent AI Building talent by industry. by populated mainly are topthe players availability of large amounts of data, in of data, amounts of large availability of AI to democratization the allow for and adoption of of a policies. number AI-related acquisition of related or AI technologies and neuroscience/neurorobotics and and learning unsupervised approaches, in developments to further lead and areas some government policies. Some of the Some policies. government some use. its from benefit can everyone that so be will It fields. application for cities smart support-vector machines, bio-inspired interesting how to cooperation observe techniques; AI for learning instance-based techniques, of AI majority vast the in blockchain, revolutionize can AI means other between industry and universities may universities and industry between public institutions. research include These functional applications and industries. nearly all areas to benefit from the use of AI. AI. of use the from to benefit areas all nearly networks, and this is addressed through through addressed is this and networks, rates in other fields show the potential for for potential show the fields other in rates level to prepare society to match demand to demand match society level to prepare

139 WIPO Technology Trends 2019 140 8 The future of AI and the IP system What will be the big next thing in AI? Martin Ford,Martin futurist Frank Chen, Horowitz Andreessen Rosalind Picard, MIT Media Laboratory Haifeng Wang, Baidu Kai-Fu Lee, Sinovation Ventures Sinovation Lee, Kai-Fu understanding. Each of these has a chance of making a big difference. abig of making achance has of these Each understanding. language or semantics or computing, quantum thinking, statistical and something our thinking AI, about and connecting combination of neuroscience entirely new capabilities bring will and society across impact generally, have will Ibelieve AI enormous an More to this happen. enabling Technology online come will environments. warehouse in true is same The food. of fast automation the on working companies of three Iknow area. one are cars yet.haven’t this Self-driving seen we because just of it to dismissive mistake be ahuge be would it Ithink AI. beginning. the just only is this but ways. We’ve different many so in aways got and to go, services legal education, You’ll trucks. healthcare, delivery in AI with ships, interact planes, buses, – cars, systems you’ll soon, autonomous But fully see apps. in by little, little and phone, your on now, and Right globe. your email in the AI you around see industry, and every across development and toward research funds of those deployment the is seeing you’re what and to few start going years past the for into AI money IoT and processes) devices. easy-to-describe other and (financial transactions robots, wearables/smartphones, interactions around in transportation, especially – (HCI) interaction etc.) human–computer and forecasting, health, better diagnostics/monitoring/alerting/mining the combinations for and microbiome imaging/ for AI (especially healthcare in growth huge to see we continue will scenarios vertical and data real with customized applications AI combinations of functional different applications and integration with hardware, applicationssynergized functional with knowledge, mechanisms, learning in lie: AI opportunities and human-like the where may challenges consider : I remain convinced there will be an enormous impact from from impact enormous an be will there convinced : Iremain : While we may hardly name one next big thing in Al, we Al, in thing big next one name we mayWhile hardly . : The next breakthrough could come from the the from come could breakthrough next : The : Venture capital firms have been pouring pouring have been firms : Venture capital : Of the areas where I have expertise, Ihave expertise, where areas the : Of .

As has been pointed out by a number of expert of expert by anumber out pointed been has As existing will technologies applied be emergent what be may not question big the contributors, what Kai-Fu Lee in Chapter 3 calls the “AI the 3calls Chapter in Lee Kai-Fu what technologies, including artificial intelligence, includingtechnologies, artificial how rather but willbe, breakthrough next the as Jay Iorio predict that, thanks to increased to increased thanks that, predict Jay Iorio as we as experience areas different across interconnectivity, the different emergent emergent different the interconnectivity, implementation age.” Some futurists such AI approaches, or the monopolization approaches, AI talk to report the contributing experts AI of certain approaches by owners of by owners approaches of certain the developmentcould encourage of other public for particularly to data, of access with smaller datasets, which may have which datasets, smaller with techniques working and providing results results providing and working techniques about the challenges and opportunities for opportunities and theabout challenges implications lack for The data requirements. proprietary data. proprietary research organizationsresearch or smaller players, future. near the in patentability AI regarding developments judicial and legislative and discussions academic and political more we seeing willlikely be policies, governmental on well as as advantage, Givencompanies’on AI’s competitive effects and economic technological significant §101 stringent aless for aneed standard. implying as by some interpreted may be hearing the during senators the with manner. overlybroad discussions an His in applied related innovations,” that “negatively can innovation impact in other and these areas” if software- and diagnostics to medical relates it as eligibility, particularly matter of subject into area the of uncertainty adegree have introduced decisions Court “Supreme recent that and “At neutral” 101 said: Iancu, to technology Section be level, ahigh approach the should 18, April on by Senate 2018, the Director, held USPTO Andrei appointed hearing recently the oversight aUSPTO During approach). European restrictive more the with convergent U.S. practice making law brought, case Alice the changes the instance for (see to change subject is statute likeand any other flux, in is courts §101 by the States interpretation its and United the in inventions software on protection of patent status the But cases. infringement patent in defense invalidity an as raised being and challenges prosecution of providing contexts in §101 suits, patent especially based roles, to central play likely will continue For granted. U.S.-and filed being applications patent of AI proliferation the given increase, will likely jurisdictions other and States United in the filed suits patent of AI number The

John G. Flaim and Yoon Chae, Baker McKenzie Baker Yoon Chae, and Flaim G. John Future of patent protection in U.S. ongoing for hundreds of years, and as Keynes Keynes as and of years, hundreds for ongoing the making AI outweigh They harm. foresee by are immense. technologies these offered digital the complete will This areas. of these Martin Ford, believe the benefits of AI will will AI of Ford, benefits believe the Martin Even with whose commentators reservations in the previous discussed technologies chapter, that the about challenges Despite concerns transformation underway and will process (IoT), of things Internet the augmented us more productive, a trend that has been been has that atrend productive, more us and profound, such as Nick Bostrom and and Bostrom Nick as such profound, and societies. and environment, already interconnected an is indicated by the analysis results which most experts agree that the possibilities possibilities the that agree experts most to AI regard with to addressed be need economies lives, our on impact have agreat regard to “superintelligence” are substantial substantial are to “superintelligence” regard some for specifically of AI to use the refer reality, into converge will virtual and reality

141 WIPO Technology Trends 2019 142 8 The future of AI and the IP system compensating for his biases. Humans are are Humans biases. his for compensating to augmenting his own intelligence and and own his intelligence to augmenting forward looks He to bring. promises AI that theFrank new opportunities embraces Chen the community. dehumanizing no are there when to afuture irrational while AI can help make us become become make us help can AI while irrational jobs, there is more space for leisure, and more more and leisure, for space more is there jobs, predicted back in the 1930s, it may even lead 1930s, the in may even it back lead predicted better decision-makers. He foresees AI’s foresees He decision-makers. better to or to arts the themselves dedicate people of reality. perception actual our becomes over that, time, of illusion environment intelligent an be could disruptive fundamentally even more But areas. other and decision-making, crucial enforcement, law warfare, in systems of intelligent understandably, the about potential uses concern, widespread is of privacy). There (to nothing say itself reality and identity of self, conceptions our in shift profound to a lead could and connections animal into those splice will reality augmented IoT systems, of intelligent full-time and convergence The tell us. senses our what to than lives our central more is nothing applications, remarkable vertical perhaps the of all importance potential the despite But elsewhere. and clothing smart apatient’s from coming data of real-time on mountains including based diagnostics ways, many in positively transformed be could alone Healthcare not. perhaps some positive, –some generation next over the society in changes to major lead to going inevitably are systems Intelligent Jay Iorio, futurist environment interconnectedan augmented into reality IoT full-time and systems, intelligent of Convergence

Such a focus on empathy will in its turn have turn its in will empathy on afocus Such on both education and software design. software and education both on its in will empathy. empathy on afocus Such that systems will shift to people with such skills, skills, such with to people shift will systems that turn have on impact an society, Chen, argues an impact on impact an society, Chen, predicting argues such skills,such in turn having a long-term impact is not able to do and emphasize those parts of of parts those emphasize to and do able not is predicting that systems will shift to people with with to people shift will systems that predicting AI things the on to to allow us focus potential humanity that are hard to automate, such as as to automate, such hard are that humanity in the development of technology. core – which regions lead cannot transitional and advanced less many news for good is This sectors. of applications ever-expanding and range awide in a wave innovations of complementary about bringing thus future, foreseeable in technology the purpose a general to become potential the has AI that of innovation is economics the in made by argument A recent scholars activities. productive toward more of asector transition the or markets declining facing of industries sector,of diversification atraditional the the as such modernization economies, structural transformation in regional trigger help could applications of AI indomains which the development a few) natural are just (to mention tourism, agrifood and marine resources healthcare, as such Sectors advantage. competitive interregional and capability domestic to future create assets the region’scomplements productive the development of applications AI which in strategy specialization a smart design they can (a if “if”) big game the However, of out be not will regions these Dominique Foray, EPFL technology purpose AI as ageneral

• AI is expected to revolutionize processes to revolutionize processes expected is AI report, of this chapters previous the in seen As AI and the IP system in the future the in system IP the and AI Some of the questions likely to arise include: likely to arise questions of the Some education and software design. software and education will affect and be incorporated into IP rights into rights IP incorporated be and affect will that AI will also affect intellectual property property intellectual affect also will AI that and practices will interact with the strategy of strategy the with interact will practices and is foreseen It of fields. range awide across in turn having a long-term impact on both both on impact along-term having turn in process: on the one hand, AI developments developments AI hand, one the on process: managing innovation in AI. onmanagement; the other hand, IP policies likely is to atwo-way be This management. rights, in particular patent their rights, and rights, in particular datasets and databases form a form databases and datasets of assessment The of AI. development what is its legal nature and whether data, data, whether and nature legal its is what and ownership of data is central to the central is of data ownership and Data and the IP system IP the and Data 4. 3. 2. 1. be considered: conclusions were that four should areas Our subject. this on a white paper Revolution Fellow, Yoon Chae, I authored Industrial Fourth the for a WEF Centre quite be significant. could with Together system patent the on of AI impact The

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sui generis

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143 WIPO Technology Trends 2019 144 8 The future of AI and the IP system These trends reveal trends These how have technologies AI in patent, trends report, this Throughout Such questions may call for related regulation regulation related for may call questions Such depend on a number of other factors whose whose factors of other anumber on depend as the AI, contextualize to field the in experts from perspectives important but different, current the are evolved what over and time what involve. technologies AI about of issues of existing interpretation acertain or only discussed currently are that questions will add to the existing knowledge base and and base knowledge to existing add will the In addition, as AI develops, some of the some develops, AI as addition, In and future trends in technologies, target range abroad on discussion a meaningful sector in the next few years. next the in sector policy on discussions to vital contribute so offered also has report This of AI. segments have data been business and scientific itself and impact humanity as a whole. We awhole. as humanity impact and itself implications extend beyond far the technology and patent of AI, inventorship the include presented to provide fact-based evidence for evidence to fact-based provide presented further development adoption and offurther will AI hope the unique features of this publication publication of this features unique the hope different the key within and players markets by AI. infringement rights IP generally more hypothetically These may real issues. become relating to AI in both the private and public public and private the to both in AI relating related questions. answer and gaps to cover possible regulations Various discussions are taking place on on place taking are Various discussions confirmation of an intended move intended an toward of confirmation which can support the work of patent work the support can which tools and instruments, including including instruments, and tools using, to technologies AI facilitate services, among the top 20 applicants in this field is field this in top the 20 applicants among The side. attorneys/applicants’ patent and included in the various IP administration IP administration in the various included including patent image classification, patent classification. or even exploring, already are offices patent addition, In professionals. patent of profiles different to support solutions and products providers information of patent presence offices patent the on both professionals, how AI-specific provisions howshouldbe AI-specific machine translation and customer service. recognition, state-of-the-art searches, searches, recognition, state-of-the-art fundamental challenge is how challenge fundamental the using algorithmic systems, the most mainly invention produced owns an relatedproblems the specific to who thehence “my house” doctrine. Beyond “private” “closed”, and means believe it “open,” even now though people most means “patent” that never forget should We promise. to original its again rises it that demand circumstances The knowledge economy, evolve. must global of the system, apillar patent The technology. and science basic to access open on agreement an for Commons, similar to existing proposals Systems Intelligent and Autonomous of an idea the into weight supporting its throw could WIPO particular, In fast. very out running is time and to enacted be they now need but past, the in proposed have been scenarios and proposals related innovation ecosystems. Several software- for necessary incentives collaboration the kill) not (or least at of more promote could system patent Konstantinos Karachalios, IEEE timeA for action

Annex

145 WIPO Technology Trends 2019 146 Selected AI categories and terms Selected AI categories and terms These techniques are defined below. defined are techniques These AI techniques are different core algorithmic AI techniquesAI Classification and regressiontrees completely true and completely false). Fuzzy in experts by human manually expressed level ahigh of human requiring usually domain, values, continuous takes tree of a regression simple where intelligence, swarm and data, genetic evolution to mechanisms better which is not based on the usual “true or or “true usual the on based not is which truth” (where the “true” value ranges between between ranges value “true” the (where truth” simple are which of rules of aset form the of terms in world the to understand tries that outcome The belongs. data to which class the of facts representations tree-like use that a hierarchy of concepts. Most deep learning learning deep Most of concepts. a hierarchy as such value, is adiscrete tree a classification and their sometimes possible consequences, toadapt decisions new new and problems by inspired systems, biological approaches solves within complex problems a specialized of ahouse. price the as such Instance-based learning Instance-based is expertise This expertise. and intelligence interaction at group-level. include genetic algorithms, which mimic Fuzzy logic system Expert Deep learning Bio-inspired approaches predictive models to support decision-making predictive decision-making to models support false” assessment, but rather on “degrees of of “degrees on rather but assessment, false” non-numerical information. non-numerical make decisions on based imprecise and network. aneural in of layers number the by increasing implemented are models referred to as decision trees. The outcome of of outcome The trees. to decision as referred can agents by individual implemented rules These technique. aprecise than rather approaches used to implement AI functions. functions. AI to implement used approaches learning algorithms that new algorithms compare problem learning people that principle the on relies logic tests. logical tolead behavior sophisticated robust and via : a decision-making approach approach : a decision-making : a machine learning approach : approach a machine learning : a computer system that that system : acomputer : a family of machine of machine : afamily : a family of AI of AI : afamily : A typical example is a program that identifies identifies that aprogram is example A typical Ontology engineering gained while solving one problem and applying whilegained applying solving and one problem because learning “instance-based” called generally organized in successive layers of of layers successive in organized generally exploiting commonalities and differences tocomputers make without having decisions with cases seen in training and can adapt adapt can and training in seen cases with Machine learning algorithms build on algorithms a model Machine learning training instances themselves. instances training It is data. unseen to previously model the to the for methodologies building ontologies, therefore acquire their own knowledge. task. the to it perform program to explicitly and in deep learning, where it is often used for for used often is it where learning, deep in and approach where a single model is used to used is model asingle where approach email. spam filters and algorithms and statistical models to allow solve multiple learning tasks at the same time, time, same the at tasks learning multiple solve to order in data training as used data sample it to a different but related problem. related but toit adifferent of words, distribution statistical the from inferred the from directly hypotheses constructs it in a particular domain are formally represented. formally are domain a particular in and data, from patterns extract and identify to achieve a order in steps, intermediary Logic programming Latent representation Neural network Multi-task learning Machine learning performing transfer learning, i.e. learning, transfer performing knowledge usually is it where example, for processing, previous one as an input. an as one previous is network The inputs. data multiple process The brain. of the structures by neural the the various tasks. between goal. particular functions, each layer each functions, using the output of the functions (neurons) working together to namely the way concepts and their relationship relationship their and way the concepts namely of many framework aconnected is network additional make without specifying decisions, representation is applied in natural language in is natural applied language representation Latent observed. directly than rather inferred are that of variables representation : a learning process inspired inspired process : alearning : an AI process that uses uses that process AI : an : a machine learning learning : a machine : uses facts and rules to rules and facts : uses : a set of tasks related related of tasks : aset : the mathematical mathematical the :

groups ofgroups data. The points. data unseen classify and groups identifies thepoints data data, that grouped new, categorize can system AI the output, of input- examples these on Based dataset. training the form and correctly categorized manually have which been (input) of data rules These of data. newclassification unseen a to pursue and error and by trial decisions to seeks approach This objective. complex models the where of probabilities, distribution which identify and generalizewhich automatically identify and logic deductive combines which Unsupervised learning Unsupervised that has not been labeled or classified. Unlike or classified. that labeled not has been lines that separate the different the boundary different the how separate to identifies that, (output) categories certain in information the or independence the dependence statistical under uncertainty in data. in data. uncertainty under use a representation graph-based for defining unseen data intocategories. unseen the predefined are most challenging to group and, based on on based and, to group challenging most are vectorSupport machine adopted form of machine In learning. learning Supervised are usually simple conditional tests. or prediction for to used be of rules a set supervised learning, the system has not been been not has system the learning, supervised of grouping expected the learning supervised is provided to the computer through examples examples through to computer the provided is correct to learn agents software incentivize Rule learning Reinforcement learning Probabilistic reasoning Probabilistic graphical models punishment for learning how to attain a a how to attain learning for punishment to relations logical model theory probability for representing complex domains using using domains complex representing for hidden patterns or commonalities in data data in commonalities or patterns hidden from comes machine” vector “support name relationships between data. data. between relationships learning algorithm that finds and analyzes thatanalyzes algorithm and finds learning labeled/ that algorithm analyzes learning long-term reward. of asystem and reward uses that learning : machine learning methods methods : machine learning : the most widely widely most : the : a type of machine of machine : atype : an AI approach approach AI : an : an area of machine of machine area : an : a supervised : a supervised : a framework : aframework AI functional applications cover the functions Augmented reality AI systems can by design aim at solving solving at aim by design can systems AI AI functional applications functional AI Computer vision Character recognition computer-generated information and sensory of a real-world environment, where elements data. the classifies it which in groups computational power. tasks, decision-making and complex learning Distributed agents. individual the connecting learning autonomous multiple distributed, vision Computer videos. and images digital understand and see how with computers deals (OCR). or reader recognition optical character A text. into it machine-encoded converting or gait as such traits, behavioral and iris, or that can be used in other processes. other in used be can that application provides an interactive experience and provide partial solutions which are then then are which solutions partial provide and independently data agents which process into aform information complex extracting and of the field of application. These functional functional These application. of field of the stimulus, understanding what is being seen, seen, being is what understanding stimulus, avisual sensing or “seeing” including systems, vision by biological performed tasks all spans as known also is it recognition, of image subset Itspeech. combines computer vision with pattern vascular fingerprint, face, as such involving high requiring data and sets large integrated, through nodes communication Distributed AI knowledge of physiology human behavior. and Biometrics people based on physiological characteristics, on physiological characteristics, based people provided with a predefined set of classes, but but classes, of set apredefined with provided from the real-world are “augmented” by “augmented” are real-world the from rather identifies patterns and createsand labels/ patterns identifies rather reading typed, handwritten or printed text and and text printed or handwritten typed, reading applications categorized are as follows. layered over with the natural environment. environment. over natural the layered with performed byperformed AI techniques, independent : deals with the recognition of of recognition the with : deals : systems consisting of of consisting : systems : an interdisciplinary field that that field interdisciplinary : an : this computer vision : the process of of process : the

147 WIPO Technology Trends 2019 148 Selected AI categories and terms Object tracking operation of to able operation machines follow step-by- techniques of statistical avariety events using over avideo. in time objects moving more or what understand can computers that so data are representations complex tasks. These into multiple image down adigital of breaking these tasks. tasks. these to perform techniques of AI implementation the to and historical current analyze facts. information representing to dedicated field the usually based on the way humans represent represent way the on humans based usually Scene understanding autonomy. hardware Robotics with combines level of acertain with and automatically vehicles. unmanned and toalgorithms (natural) human analyze language and sub-sets) and of sets relations building and to solve by acomputer usable world the about the change or to simplify order in image, an pixel in to every alabel assigning a video, step complex instructions oractions perform by execution for sequences action or strategies solve problems. semi-structured textual sources. structured information from unstructured or the constituting images or analyzing segments in real-time, of perceiving, analyzing and and analyzing of perceiving, real-time, in robots autonomous as such agents, intelligent them. with interact Information extraction This to analyze. easier and meaningful more is Image and video segmentation Robotics Predictive analytics Planning/scheduling languageNatural processing rules through instance (for reason knowledge, Knowledge representation and reasoning predictions about future or otherwise unknown unknown otherwise or future about predictions etc.) curves, (lines, boundaries in images. and objects to locate used typically is process humans have written or said and further further and said or have written humans representation of an image into something that that into something image of an representation : the design, construction and and : the design, construction : the process of locating one one of locating process : the : the process of making of making process : the : the realization of of realization the : : the process, often often process, : the : the task of extracting of extracting task : the : use of of : use : the process process : the : AI technologies can be applied to multiple to multiple applied be can technologies AI AI applicationAI fields extraction, analysis and categorization of new identifying further for of reasoning in concepts and of topics disambiguation to 3D the respect with context in objects and of ascene interpretation an elaborating of financial systems, from the approval of approval of the from systems, of financial (de-noised). enhanced (anotherdifferent voice or another language) or output and input the where systems end the assessment of risks. Automated trading trading Automated of risks. assessment the into text. them translating fields, assummarized below. affective state or opinion from text, social social text, from opinion or state affective Sentiment analysis facts. and associations Semantics already deeply integrated into many aspects into aspects many integrated deeply already be can which signal, voice araw audio are Speech-to-speech application Speaker recognition synthesis Speech recognition Speech signals,analysis of including speech speech Speech processing sensors information.sensors spatial, relationships functional, semantic and the layout, and its scene, of the structure systems involve the use of complex AI AI involvesystems of complex use the synthesis. speech identifying words in spokenidentifying of and language Banking and finance between objects. between person from the characteristics of their voice. of their characteristics the from person media activity, audio, video or biometric biometric or activity, audio, video media human speech. human speech. raw text, image or video, and the application application the and video, or raw image text, recognition, natural language processing and and recognition, processing natural language loans, to the management of assets and and of assets to management the loans, : the automatic recognition and and recognition automatic : the : the artificial production of of production artificial : the : the identification, : systems involving involving systems : : the process of of process : the : the identification of a of identification : the : Machine learning is is learning : Machine : an end-to- : an AI-powered document management systems management document AI-powered financial on to have impact an predicted is AI Generative design systems are able to able are systems design Generative Over the past two decades, AI has been been has AI decades, two past the Over diagnostic systems are a very promising promising avery are systems diagnostic market of the monitoring Continuous goals. quickly generate, explore optimize and design downtime malfunction. algorithms AI and to unplanned related to costs limit expected customer data. could also enhance security and protect exploit to better expected are analytics data documents (including automatic translation). extraction, structuring and conversion of improvingcontinuously automatic data decisions to make and faster data customer and marketing improving for used commonly with future, the in services customer detection fraud systems Modern decisions. with the objective of following their on impact Improved clustering advanced and document the supply of raw materials. of raw materials. supply the tasks and make autonomous decisions. and industry on to have impact major the huge volume of documents that exist. real-time. in market the application of new machine learning alternatives from a set of high-level design into insights and trends to identify algorithms product and personalization advertising, advances in biometric systems. actively new learn threats. potential security trading fast to make extremely algorithms staffing, inventory, energy consumption and and consumption inventory, energy staffing, the with to cope companies help also should specialized chatbots and voice assistants, increasing complexity of products, engineering engineering of products, complexity increasing andIndustry manufacturing Life and medical sciences Document management and publishing Business processes and quality regulations. Improved Improved regulations. quality and processes by exploiting safety improving for and products by AI tools could help proactively to optimize proactively help could by tools AI manufacturing. Predictive maintenance is is maintenance Predictive manufacturing. robots are expected to handle more cognitive cognitive more to handle expected are robots recommendations. Many companies rely on AI systemsrecommendation for financial : AI techniques are already already are techniques : AI : Automatic : Automatic : AI is likely is : AI : AI will improve traffic management by reducing reducing by management improve will AI traffic a for anew enabler as considered also is AI Transportation Telecommunications vast range of military requirements, including including requirements, of military range vast emissions and enhance road safety, road that and enhance and emissions customer services. U.S. states and the U.K. and AI techniques are are techniques AI and U.K. the U.S. and states techniques such as face detection, as techniques such behavior is frequently also personalization cited Drug techniques. results have Recent shown that it traffic jams and make possible crewless cargo cargo crewless makepossible and jams traffic of prediction and detection to anomaly to improve network performance, thanks several in to used be started has technology Security by helping costs development reduce and data of clinical amounts of large availability by The AI. driven akey of progress as marker autonomous vehicles will save costs, lower willsave costs, vehicles autonomous transportation in used have been approaches also integrated in mass surveillance programs. to make enough mature are crowd analysis and surveillance issurveillance developing quickly, sometimes theselect most promising hypotheses and ships and fully automated package delivery. package automated fully and ships that predicted 1980s. the widely It is since by optimizing also and degradations, service systems and defense/offense decisions. “active” the more without cameras surveillance in conjunction with smart city technologies. AI AI technologies. city in with conjunction smart from benefited intrusion-detection) has accuracy expert human to surpass possible is intelligence, surveillance, reconnaissance, focus on more targeted research. targeted more on focus of detection as such tasks, narrow several for new opportunities in by telecoms new helping opportunities machine learning since the 1990s. Automated Automated 1990s. the since learning machine discovery to improve drug predicted is AI mean arteries. in of atherosclerosis risks or melanoma need for human supervision. Predictive policing policing Predictive supervision. human for need logistics, battlefield planning, battlefield logistics, weapons : (spam Cyber-security filtering, : Fuzzy logic and other AI AI other and logic : Fuzzy : AI is expected to drive expected is : AI

149 WIPO Technology Trends 2019 150 Further reading Further reading Further Truth About AI from the People Building It Building People the from Truth AI About Frontier? Digital Next The York: Packet Publishing. January 17,January 2019). Cockburn, I., R. Henderson and S. Stern Stern S. and I., Henderson R. Cockburn, Electronic Security; American aNew for Center dam/Deloitte/at/Documents/human-capital/ deepai.org/data-science-glossary/a (accessed on innovation. of Existential of Risk; University Cambridge; (accessed March 29, 2018). March (accessed (2017). intelligence of impact The artificial University of Oxford; Centre for the Study Study the for Centre of Oxford; University Press. University McKinsey Global Institute. Global McKinsey Bergstein, B. (ed.)Bergstein, (2017). artificial The Furman, J. and Seamans, R. (2018). R. Seamans, J.Furman, and Ford, (2018). M. Revolution. Munich: European Patent Office. EPO. (2017). Industrial Fourth the Patents and (2016).Deloitte DeepAI. J. al. (2017). et Bughin, (2017). McAfee A. and E. The Brynjolfsson, Frontier Foundation; OpenAI. al. (2018). et M. Brundage, (2016). N. Bostrom, abstract=3186591 (accessed January 22, 2019).abstract=3186591 January (accessed artificial-intelligence-innovation-report.pdf Paths, Dangers, Strategies Review Economy Report Economics Intelligence of Artificial Business Review of Artificial Intelligence: Forecasting, Prevention, story/2017/07/the-business-of-artificial- intelligence (accessed January 24, 2019). January (accessed intelligence intelligence issue. business of artificial ofintelligence.business artificial and Mitigation and . https://www2.deloitte.com/content/ , 120(6). , Data Science Glossary . Available at SSRN: https://ssrn.com/ . Available SSRN: at . Future of Humanity Institute; . Future of Humanity NBER Conference on the the on Conference NBER Artificial Intelligence Innovation Intelligence Artificial Architects of Intelligence: The The ofIntelligence: Architects . https://hbr.org/cover- . MIT Technology Superintelligence: Artificial Intelligence: Discussion Paper, Discussion . Oxford: Oxford . Oxford: Oxford The Malicious Use . https:// . Harvard , Toronto. , AI and the the and AI . New New . — (2018).— (2018). Loukides M. — and 17, 2019). 17, York: Mifflin. Houghton Telecommunications Union. AI Applications of Tomorrow Goodfellow, (2016). al I. et (2018). M. man Giles, The GANfather: The al. (2017). et A. Ghandeharioun, Objective Gartner. Cambridge, MA: MIT Press. http://www. Press. MIT MA: Cambridge, 2017. 325–332. IEEE, TX: Antonio, San Computing and Intelligent Interaction (ACII) deeplearningbook.org (accessed January January (accessed deeplearningbook.org oreilly.com/ideas/data-collection-and-data- Sea. Mediterranean the in operations rescue and search quantify and describe (accessed January 22, 2019). January (accessed Seventh International Conference on Affective Software Development Silicon Valley, and the New World Order Summit Report who’s given of imagination. the machines gift ITU (2017).ITU tools-for-the-ai-applications-of-tomorrow Radar. https://www.oreilly.com/ideas/building- B. (2018).Lorica, K.-F.Lee, (2018). Deep learning. Y.,LeCun, Y. Hinton (2015). G. and Bengio al. (2018). et Pham toHoffmann fusion Data assessment of depressive symptoms with with symptoms of depressive assessment MIT Technology Review MIT Learning Machine and ofPrivacy Age the in Markets 5 it-glossary/ (accessed March 29, 2018). March (accessed it-glossary/ machine learning and wearable sensors data. data. sensors wearable and machine learning https://www.oreilly.com/ideas/what-machine- markets-in-the-age-of-privacy-and-machine- and Advanced Analytics learning (accessed January 22, 2019). January (accessed learning th International Conference on Data Science . O’Reilly On Our Radar. Our . O’Reilly On https://www. IT Glossary What Machine Learning Means for for Means Learning Machine What AI for Good Global Nature . Geneva: International AI Superpowers: China, China, Superpowers: AI Data Collection and Data Data and Collection Data . https://www.gartner.com/ . , 521, 436–444. . O’Reilly On Our Radar. Our . O’Reilly On , 121(2), 49–53. . Turin:. IEEE. Deep Learning Building Tools for the . O’Reilly On Our Our . O’Reilly On 2018 IEEE . New New . . , — (2018).— Technology Review A: Society Royal ofthe Transactions January 22, 2019).January Yearning Artificial Intelligence: Foundations of of Foundations Intelligence: Artificial 29, 2018). 29, Quinn, J.A. et al. (2018). et J.A. Quinn, Humanitarian Agents Computational Quarterly F.Onorati, al. (2017). et clinical Multicenter Schwab, K. (2017).Schwab, K. convulsive seizure detectors. detectors. seizure convulsive difference-between-deep-learning-machine- (11), 1870–1879. (11), 24, 2019). January (accessed 22, 2019). January (accessed Sciences McAfee, A. and E. Brynjolfsson (2017). Brynjolfsson Machine, E. and A. McAfee, Marr, B. (2016). Rotman, D.Rotman, (2018). into jobs. AI Making (2017). Mackworth A.K. Poole, and D.L. S.W. and Pauwels, E. (2018). Denton Searching Future. Digital Our Crowd: Harnessing Platform, Ng, A. (forthcoming). (forthcoming). A. Ng, York,New NY: W. W. &Company. Norton applications of machine learning with remote- with learning of machine applications multimodal wearable of improved assessment Revolution Mathematical, Physical and Engineering Learning Machine Learning, Deep Between to lead your company into the AI era AI the into company your to lead sensing data: Review and case study in in study case Review and data: sensing sites/bernardmarr/2016/12/08/what-is-the- info/2 for privacy in the Internet of Bodies. of Bodies. Internet the in privacy for mlyearning.org/ (accessed January 24, 2019). January (accessed mlyearning.org/ refugee mapping. settlement and AI? and landing.ai/ai-transformation-playbook/ learning-and-ai/#707f26da26cf (accessed learning-means-for-software-development e /html/ArtInt2 Forbes. https://www.forbes.com/ Forbes. . Draft copy available at http://www. at available copy . Draft AI Transformation Playbook: How , 376 (2128). 376 , (May). . New York:. New Group. Publishing Crown What Is the Difference Difference the Is What e , 121(4), 10–17. The Fourth Industrial Industrial Fourth The .html (accessed March March .html (accessed Machine Learning Learning Machine , 2 nd Edition. http://artint. Edition. Philosophical Epilepsia . https:// . The Wilson , 58 , 58 MIT MIT 1-artificial-intelligence-defined.html (accessed 1-artificial-intelligence-defined.html (accessed Thurman, D.J., D.C. Hesdorffer and J.A. French French J.A. and D.J., D.C.Thurman, Hesdorffer Taylor, al. (2017). et S.A. Personalized January 18, 2019).January Annual Report Assessing the public health burden. burden. health public the Assessing Vasisht, D. al. (2017). et IoT An FarmBeats: Conferences Steering Committee, 715–724. Committee, Steering Conferences van Duin, S. and N. Bakhshi (2017). Bakhshi N. and S. van Duin, USENIX Symposium on Networked Systems Systems Networked on USENIX Symposium Uganda Suhara, Y.,Suhara, Y. (2017). Pentland A.S. Xu and Stone, P. al. (2016). et Y.Shoham, al. (2018). et (2017). Barocas S. and A. Selbst, Social Affairs (2018). Affairs Social com/nl/nl/pages/data-analytics/articles/part- (2014). epilepsy: in death unexpected Sudden University. United Nations Global Pulse (2017). Pulse Global Nations United United Nations. and of Economic Department Nations United Social Survey 2018: Frontier technologies 2018: Survey technologies Social Frontier MA: USENIX. MA: 29, 2018).March Perth, Australia: International World Wide Web World Wide International Australia: Perth, Deepmood: Forecasting mood depressed PredictingTomorrowsMoods.pdf (accessed affect.media.mit.edu/pdfs/17.TaylorJaques- International Conference on World Wide Web 2030 in Life Report Design and Implementation and Design Intelligence Defined in Content Radio to Analyse Learning Machine 55(10), 1479–1485. 55(10), on Affective Computing, PP(99) Computing, Affective on based on self-reported histories via recurrent recurrent via histories self-reported on based platform for data-driven agriculture. agriculture. data-driven for platform neural networks. networks. neural mood, stress, and health. health. and stress, mood, tomorrow's predicting for learning multitask for development sustainable . The AI Now Institute (1–2). Now Institute AI . The . Geneva: United Nations. United . Geneva: . Stanford, CA: Stanford University. University. CA: Stanford . Stanford, . Stanford, CA: Stanford CA: Stanford . Stanford, Proceedings of the 26 ofthe Proceedings . https://www2.deloitte. World Economic and Artificial Intelligence and The AI Index 2018 Index AI The IEEE Transactions Transactions IEEE . Boston, . New York:. New . https:// . AI Now 2017 Now AI Artificial Artificial Using Using 14 Epilepsia th th

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151 WIPO Technology Trends 2019 152 Further reading 17, 2019). 17, 2019). 17, Artificial Intelligence Artificial Turning intoFinland a A European Perspective Access Now (2018).Access Group on Artificial Intelligence (2018).Intelligence Artificial on Group (ed.) M. Craglia, (2018). Wikipedia contributors. contributors. Wikipedia (2018).WIPO Policy and regulatory frameworks of Economic Affairs and Employment Employment and Affairs of Economic technology. to and science basic access open SCRIPT-ed Intelligence for Europe. Europe. for Intelligence Ministry, 47/2017. Property Organization. Property Finnish Government (2017). Government Finnish High-LevelEuropean Commission Expert Committee Social and Economic European the Parliament, the European Council, the Council, European Commission (2018). Communication Union.European Luxembourg: Publications of the Office (2004). Economic Maskus K.E. J.H. and Barton, uploads/2018/11/mapping_regulatory_ and the Committee of the Regions on Artificial Artificial on Regions of the Committee the and January (accessed artificial_intelligence Indicators 2018 Indicators Intelligence Objective and Recommendations ofArtificial Application the in Country Leading Disciplines Scientific and Capabilities AI: of Main Definition Europe IntelligenceProposals for in Artificial Europe perspectives on a multilateral agreement on on agreement amultilateral on perspectives January (accessed proposals_for_AI_in_EU.pdf from the Commission to the European to European the Commission the from https://www.accessnow.org/cms/assets/ https://en.wikipedia.org/wiki/Glossary_of_ for Measures intelligence . Brussels. . Brussels. , 1(3). , . Wikipedia, The Free Encyclopedia, Free Encyclopedia, The . Wikipedia, . Publications of the Ministry Ministry of the . Publications World Intellectual Property . Geneva: World Intellectual World Intellectual . Geneva: Mapping Regulatory Mapping Regulatory Artificial Intelligence: Artificial Intelligence in Artificial Glossary ofGlossary artificial , EUR 29425 EN. Finland’s Age of of Age Finland’s A . — (2016). — (2016). Transactions A Technology Council (2016). Automation, and the Economy Autor, D. and A. Salomons (2017).Autor, Salomons D. A. and Robocalypse WhiteHouse-ArtificialIntelligencePreparations. files/PDF/2017/Rapport_synthese_France_IA_. Employment and productivity and Employment 2142 (June), 1–74. (June), 2142 2019). 23, Growth for and Challenges Regional Opportunities deklaration-slutlig-webb.pdf (accessed January deklaration-slutlig-webb.pdf January (accessed employment? employment? United States National Science and and Science National States United whitehouse.gov/sites/whitehouse.gov/files/ Foray, D., P.A. Smart (2009). B. Hall and David Foray, D. (2015). Report.pdf (accessed January 22, 2019). January (accessed Report.pdf République Française (2017). Nordic Council of Ministers (2018). of Ministers Council Nordic Nemitz, P. (2018). Constitutional democracy and technology in the age of artificial in technology and the age of artificial Nordic-Baltic Region Innovation Policies Intelligence Research and Development Strategic Plan (FIA) Intelligencede Artificielle Rapport se/49a602/globalassets/regeringen/dokument/ concept. The specialisation: strategic_plan.pdf (accessed January 22, 2019). January (accessed strategic_plan.pdf intelligence. intelligence. images/EMBARGOED%20AI%20Economy%20 pdf (accessed January 22, 2019). January (accessed pdf 13, 2018). December (accessed pdf naringsdepartementet/20180514_nmr_ now: Does productivity growth threaten threaten growth productivity now: Does https://www.nitrd.gov/PUBS/national_ai_rd_ synthèse . Brussels: European Commission. . https://www.economie.gouv.fr/files/ Preparing for the Future of Artificial ofArtificial Future the for Preparing National Artificial Intelligence . https://info.publicintelligence.net/ Royal Society Philosophical Philosophical Society Royal ECB Forum on Central Banking , 376: 20180089. Smart Specialisation: Smart . London: Routledge. . https://www.regeringen. . Artificial Intelligence, Knowledge for France France . https://www. AI in the the in AI . , Technology Employment. and January 22, 2019).January 18, 2019)January Artificial Intelligence Talent Pool? Gerbert P.Gerbert al. (2015). et Cambridge, NBER. MA: Worker-level evidence. evidence. Worker-level Data privacy and ethics Oneworld Publications. Sachs (2015). Robots are us: Some economics (2015).Sachs economics us: Some are Robots engineered_products_project_business_ computerisation? to jobs are How susceptible of employment: of Minneapolis. replacement. of human (2017). Artificial intelligence and the modern modern the and (2017). intelligence Artificial www.bcg.com/en-gb/publications/2015/ Minneapolis, MN: Federal Reserve Bank Bank Minneapolis, MN: Reserve Federal Benzell, S.G., G. Kotlikoff, G. LaGarda and J.D. S.G., and Benzell, Kotlikoff, G. LaGarda G. Boucher, P. al. (2014). et J. (2018).Kahn, (2013).Frey, Osborne future C.B. M.A. The and Robots: Ford, (2016). M. of the Rise The and W.,Dauth, J. Suedekum Findeisen, S. C. E., Syverson and D. Rock, Brynjolfsson, N. Woessner (2018). Woessner N. to robots: Adjusting artificial-intelligence-talent-pool (accessed (accessed artificial-intelligence-talent-pool articles/2018-02-07/just-how-shallow-is-the- statistics. and No. 20941 No. Experiencing Ethics Through “Things”: Open Industries Paper 13 No. Paper Working ofMinneapolis Bank of Productivity and Growth in Manufacturing Manufacturing in Growth and of Productivity Working Employment Technology and on of Mass Unemployment industry_4_future_productivity_growth_ productivity paradox: A clash of expectations of expectations Aclash paradox: productivity https://www. bloomberg.com/news/ (accessed manufacturing_industries.aspx . Oxford: Oxford Martin Programme on on Programme Martin Oxford . Oxford: Technology and the Threat Threat the Technology and . Boston Consulting Group. https:// Group. Consulting . Boston . Cambridge, MA: NBER. MA: . Cambridge, NBER Working Paper No. 24001 No. Paper Working NBER Just How Shallow Is the the Is Shallow How Just Oxford Martin Programme Programme Martin Oxford Federal Reserve Industry 4.0: The Future 4.0: Future The Industry . New York:. New NBER Working Paper Paper Working NBER Ethics Dialogues: Dialogues: Ethics Bloomberg. Bloomberg. . . Transactions of the Royal Society A: A: Society Royal ofthe Transactions Trade & Industry Discussion Paper 17-E-006 Paper Discussion Trade &Industry Tokyo: RIETI. Tokyo: January 17,January 2019). A global patent analysis. Council of Europe (2017). of Europe Council Cath, C. al. (2018). et artificial Governing Geneva: WIPO.Geneva: Patent information 2 of Europe. of opportunities. and challenges do different definitions really matter? matter? really definitions different do (2017). International patent families: from Sciences Scientometrics Scientometrics Intermediaries (MSI-NET). Strasbourg: Council (MSI-NET). Intermediaries IEEE (2018).IEEE Martínez, C. (2011).Martínez, When Patent families: European Union.European Luxembourg: Publications of the Office technology invention:technology A global patent F.Hidemichi, (2017). Shunsuke M. and Trends (2017).Fujii Managi S. and H. Trends priority and surge. patent worldwide the Exploring (2013). Zhou H. C., and Fink, Khan M. Y. A., Mohnen M. and Dechezleprêtre, Ménière analysis. analysis. intelligence artificial in shifts priority and indicators. to statistical strategies application Human Rights. Study on the human rights rights human the on Study Rights. Human Mathematical, Physical and Engineering Sensors IoT, Wearable and Drones Civil Economy, Trade Industry and Economic Working Research No. Paper 12 techniques and possible regulatory techniques and regulatory possible dimensions of automated data processing processing data ofautomated dimensions shifts in artificial intelligence invention: intechnology artificial shifts intelligence: Ethical, and legal technical implications . https://ethicsinaction.ieee.org/ (accessed , 376 (2133). 376 , Research Institute ofEconomy, Institute Research . Committee of Experts on Internet Internet on of Experts . Committee Ethically Aligned Design, version version Design, Aligned Ethically , 86(1), 39–63. 828 – 793 111(2), , Research Institute of of Institute Research Algorithms and and Algorithms , 17-E-066. , Philosophical WIPO WIPO . . .

153 WIPO Technology Trends 2019 154 Further reading — (2017). — (2015). 18-P-017 AI-Generated Inventions: Is a Reform of the ofthe aReform Is Inventions: AI-Generated WIPO (2018).WIPO Competition Law Competition content/pkg/FR-2019-01-07/pdf/2018-28282. 1–32. of Japan, (2019). (2019). United States Patent and Trademark Office Trademark Patent and Office States United (2015).UKIPO Intellectual property Science Motohashi, K. (2018). K. driven AI Motohashi, Understanding Property, Foundation for Intellectual Property Property, Foundation for Intellectual Property (2018). A. Ramalho, access. vs. property Exclusive Kerber, W. (2016). of data: Governance organization. and technology (1994). E. assets, Information Brynjolfsson, Organization. Property Organization. Property Office. Property Intellectual Kingdom United Edition. Newport: articles and patents. patents. and articles Indicators 2018 Indicators Paper Discussion Economy, Trade &Industry Public Eligibility Guidance Needed? System Patent and Property ofIntellectual Review Reports Landscape on Views and Recommendations from the the from Recommendations and Views on innovation by linked of dataset scientific pdf (accessed January 8,2019). January (accessed pdf for analysing and interpreting patent data patent interpreting and analysing for . Washington, D.C.: USPTO. 2019 Revised Patent Subject Matter Matter 2019 Subject Patent Revised , 40 (12),, 40 1645–1662. . Tokyo: RIETI. Patent Eligible Subject Matter: Report Guidelines forGuidelines Patent Preparing World Intellectual Property The Patent Guide: A handbook Ahandbook Guide: Patent The . Geneva: World Intellectual World Intellectual . Geneva: , 47(7), 759–762. 47(7), , . https://www.govinfo.gov/ . . Geneva: World Intellectual World Intellectual . Geneva: Patentability of Research Institute of of Institute Research Institute of Intellectual Management International , 2 nd

Zech, H. (2016). H. Zech, adata for framework Alegal Cologny/Geneva: WEF Centre for the Fourth WIPO (1991).WIPO World Economic Forum (2018). Forum World Economic economy in the European Digital Single Market: Market: Single Digital European the in economy Industrial Revolution.Industrial March 25– 27, 25– March 1991. Rights to use data. data. to use Rights Intelligence Intellectual Property Aspects of Artificial Property Law &Practice Law Property Intelligence Collides with Patent Law the WIPO Worldwide Symposium on the the on Symposium Worldwide WIPO the , Stanford University, Stanford CA, CA, University, Stanford , Stanford Conference Proceedings of of Proceedings Conference Journal of Intellectual , 11(6), 460–470. Artificial Artificial .

WIPO Technology Trends 2019: Artificial Intelligence reveals patterns in innovation in artificial intelligence (AI) and gives insights into where future developments may lie.

Based on analysis of data including AI-related patent filings, scientific publications, litigation filings and acquisition activity, the report reveals the fastest growing AI techniques, such as deep learning, and AI functional applications, such as robotics. It also presents trends in the fields in which AI innovation is being put into practice, revealing the top players in AI from industry and academia and the geographical distribution of AI-related patent protection and scientific publications.

AI raises many policy questions, such as the regulation and control of data, the incentivization of further research and the role of intellectual property (IP) protection. The analysis offers new evidence-based perspectives on these and other governance issues.

The report is the first in a new series from WIPO tracking the development of technologies through the analysis of data on innovation activities. Its findings are accompanied throughout by commentary and industry perspectives from more than 20 of the world’s leading experts in AI, making it of particular interest to business leaders, researchers and policymakers.

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