Preventing Patent Risks in Artificial Intelligence Industry for Sustainable Development

sustainability

Article Preventing Patent Risks in Artiﬁcial Intelligence Industry for Sustainable Development: A Multi-Level Network Analysis

Xi Yang 1,* and Xiang Yu 2

1 Center for Studies of Intellectual Property Rights, Zhongnan University of Economics and Law, Wuhan 430073, China 2 School of Management, Huazhong University of Science and Technology, Wuhan 430074, China; [email protected] * Correspondence: [email protected]; Tel.: +86-181-6273-0019

Received: 26 September 2020; Accepted: 16 October 2020; Published: 19 October 2020

Abstract: In recent years, assessing patent risks has attracted fast-growing attention from both researchers and practitioners in studies of technological innovation. Following the existing literature on risks and intellectual property (IP) risks, we define patent risks as the lack of understanding of the distribution of patents that lead to losing a key patent, increased research and development costs, and, potentially, infringement litigation. This paper aims to propose an explorative approach to investigating patent risks in the target technology field by integrating social network analysis and patent analysis. Compared to previous research, this study makes an important contribution toward identifying patent risks in the overall technological field by employing a patent-based multi-level network model that has not appeared in existing methodologies of patent risks. In order to verify the effectiveness of this approach, we take artificial intelligence (AI) as an example. Data collected from the Derwent Innovation Index (DII) database were used to build the patent-based multi-level network on patent risks from market, technology, and assignee perspectives. The results indicate that the lack of international collaborations among assignees and industry–university–research collaboration may lead to patent collaboration risks. Regarding patent market risks, the lack of overseas patent applications, especially the lack of distribution in the main competitive markets, is a key factor. As for patent technology risks, most of the leading assignees lack awareness of the distribution in the following technological fields: industrial electric equipment, engineering instrumentation, and automotive electrics. In summary, assignees from the U.S. with first mover advantages are still powerful leaders in the AI technology field. Although China is catching up very rapidly in the total number of AI patents, the apparent patent risks under the perspectives of collaboration, market, and technology will obviously hamper the catch-up efforts of China’s AI industry. We conclude that, in practice, the proposed patent-based multi-level network model not only plays an important role in helping stakeholders in the AI technological field to prevent patent risks, find new technology opportunities, and obtain sustainable development, but also has significance for guiding the industrial development of various emerging technology fields.

Keywords: patent risk; social network analysis; patent analysis; multi-level network; artiﬁcial intelligence; sustainable development

1. Introduction In the knowledge-based economy era, risks in the technological innovation process have been attracting increasing attention worldwide. Advancements in technological innovation (TI) are no

Sustainability 2020, 12, 8667; doi:10.3390/su12208667 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 8667 2 of 21 longer confined to the TI advancement of individual markets, technological fields, and assignees in a given technology field [1,2]. Many stakeholders have actively investigated whole TI networks and attempted to identify the central actors and prevent potential TI risks [3,4]. Based on probability theory, the concept of risk was first proposed by European scientist Pasco, and had been said to imply a calculus of probabilities and consequences of an unwanted event [5]. In recent years, most scholars defined risk from the theory of damage or the theory of uncertainty. Based on the theory of damage, Haynes regarded risk as the chance of damage or loss [6]. In 1901, Willett defined risk as the objectified uncertainty regarding the occurrence of an undesirable event [7]. Knight further distinguished between risk in the sense of a measurable probability and an uncertainty that cannot be measured, and defined risk as “measurable uncertainty” [8]. Considering the damage and uncertainty of risk, it is necessary to identify and prevent the risk before it results in possible damage. TI is an experimental procedure that covers uncertainty and unavoidable risks, especially intellectual property risks [9,10]. Patent information, which contains enormous and rich technical items, has been touted as one of the most important outputs of TI, and intellectual property risks have been identified [11,12]. Accordingly, the success of TI is closely related to the effective identification and prevention of patent risks. In the era of knowledge-based economies, artificial intelligence (AI) plays a vital role in maintaining learning experiences as well as improving learning efficiency [13]. AI technology is commonly considered to be promising in a wide range of human welfare sectors, including transportation, home/service robotics, healthcare, education, low-resource communities, public safety and security, employment and the workplace, and entertainment [14]. Many countries have made substantial efforts and research and development (R&D) investments in AI technology. For instance, the U.S. government has attached great importance to top-level strategic design in the AI technology field. In 2016, the White House released two important reports, namely “Preparing for the Future of Artificial Intelligence” and “The National Artificial Intelligence Research and Development Strategic Plan” [15]. In February 2019, the “American AI Initiative” was launched, which is the nation’s strategy for promoting American leadership in AI. One year after the American AI Initiative, the White House Office of Science and Technology Policy released “American AI Initiative: Year One Annual Report”, which summarizes the progress of AI in the U.S. [16]. Japan has also focused on the formulation of an AI strategy and planning. In March 2017, the “Artificial Intelligence Technology Strategy” was released by the Strategic Council for AI Technology. This strategy provided a three-phase development plan for AI: (1) the utilization and application of data-driven AI developed in various domains, (2) the public use of AI and data developed across various domains by approximately 2020, and (3) the creation of ecosystems built by connecting complex application services by approximately 2025 to 2030 [17]. China’s government has also released sets of innovation policies related to AI technology. In July 2017, the State Council announced “A Next Generation Artificial Intelligence Development Plan”, the most comprehensive of all national AI strategies, which divided China’s AI goals into three “Strategic Objectives” and proposed the goal of having China become the world leader in AI by 2030 [18]. In the “Report on the Work of the Government (2019)”, it expanded Intelligent Plus initiatives to facilitate transformation and upgrading in manufacturing [19]. In June 2019, the National Governance Committee for the New Generation Artificial Intelligence issued principles of next-generation artificial intelligence (AI) governance, pledging to develop responsible AI in China. While there has already been much attention to promoting the development of AI technology, it would seem that making breakthroughs and gaining competitive advantages in this field will necessarily require a comprehensive understanding of the risks involved in the technological innovation process. However, although patent information is widely regarded as the key to identify patent risks, the majority of previous studies focus on evaluating patent infringement risks, and they have failed to define the concept of patent risks. In this paper, we attempt to define patent risks as the lack of understanding of the distribution of patents that lead to losing a key patent, increased research and development costs, and, potentially, infringement litigation. Preventing patent risks is fundamental for assignees who own patents as well as stakeholders who make, use, or sell products containing Sustainability 2020, 12, 8667 3 of 21 patented technology. Moreover, few studies have explored patent risks from a more comprehensive perspective which integrates market-level, technology-level, and assignee-level patent data; even fewer studies have examined patent risks in the AI field from a comprehensive perspective. As a rapidly developing and emerging technological area, AI technology requires extensive attention to patent risks to foster more sustainable development outcomes and competitive advantages. Therefore, in order to better identify patent risks from patent information, it is of great importance to answer these two questions: (1) What are the existing main patent competitive markets, technological hotspots and fronts, and collaborative research patterns in AI technology? (2) What are the patent risks from the perspectives of collaboration, market, and technology? These concerns are critical for stakeholders to give valuable suggestions for future policy-making and obtain sustainable development in the field of AI technology globally. To solve these concerns, we propose a comprehensive framework for identifying patent risks. A patent-based social network approach has been used to identify the “elite” of French cancer researchers in 1999, and explore the relationships in TI [20]. Patent information in the social network, which contains enormous and rich technical items, has more credibility and practicability. Therefore, based on social network analysis and patent analysis, this framework explores patent risks from comprehensive perspectives as well as multi-levels, and has more advantages than the previous social network. This paper specifically selects the AI technology field as a case study, and collects and analyzes ten thousand AI patents from 1998 to 2018. The examination of the network of AI patents has the following aims: to outline the possibilities of using a patent-based multi-level network approach for research into patent risks; to describe the market network, technology network, and collaboration network of AI technology and how they are structured; and to identify patent risks under the perspectives of collaboration, market, and technology. Our final motivation is the overarching interest in helping stakeholders in the AI technological field to prevent patent risks, find new technology opportunities, and obtain sustainable development. The remainder of this paper is organized as follows. Section2 presents the literature related to multi-level network analysis and patent risks. In Section3, we introduce the research methodology and retrieval of patent information. The case study of patent risks in AI technology by employing a patent-based multi-level network is presented in Section4, where the feasibility of our method is demonstrated. We conclude and discuss the main findings, policy recommendations, contributions, future research directions, and limitations in Section5.

2. Literature Review

2.1. Multi-Level Network Analysis A multi-level network, an essential analytical perspective of social network analysis (SNA), can be defined as a network with nodes of several types, where the nature of ties differ according to the kind of nodes they connect [21,22]. It also means analyzing separately, then jointly, and integrating several levels of agency [23]. In general, organizations, individuals, countries or regions, and other different kinds of events can commonly be represented as nodes and their interactive relationships as ties. Compared to existing methodologies, the multi-level network analysis has the following advantages. First, the existing research on SNA was mainly from a single perspective (e.g., macro-level or micro-level), and paid little attention to the comprehensive framework [24]. In this study, we employed multi-level network analysis that provided an integrated analytical framework for exploring central actors and their organizations in the process of technological innovation from comprehensive levels, namely macro-level, meso-level, and micro-level. The multi-level network analysis extended the research perspectives of SNA. Considering much of the work in identifying scientific communities is only from a micro perspective, Bellotti developed a multi-level network that meaningfully described network structures of collaborations [25]: micro-level illustrated scientists’ collaborations, macro-level illustrated institutions’ collaborations, and meso-level combined network Sustainability 2020, 12, 8667 4 of 21 measures at a micro and macro levels [25]. Second, less attention has been paid to the meso-level network research. In this study, the multi-level network is regarded as an essential analytical tool measuring the meso-level of interaction in the field of technological innovation networks. By employing a two-mode network, the multi-level network analysis attaches great importance to the meso-level network that links the macro-level and micro-level together, which also makes up the lack of separation of the macro-level and micro-level. Based on the method of structural linked design, Lazega et al. contributed to the research of “duality”, and first used the multi-level network to measure the meso-level of integration [26]. Two-mode networks are commonly applied in multi-level network research, which focused on two sets of actors, or one set of actors and one set of events, and measured ties between nodes belonging to different sets [27,28]. Drawing on this deep research on multi-level networks, recent studies have employed multi-level networks in TI fields and in various industries and organizations. Bellotti et al. introduced multi-level network into scientific work; they analyzed collaboration networks between scientists, and illustrated how these networks are embedded in institutional levels [29]. Brennecke and Rank extended the multi-level network perspective by exploring the knowledge-sharing process between individuals and organizations [30]. Barnett et al. proposed a multi-level network analysis to study the URL citation network of global universities at both the institutional and national levels on the World Wide Web. This research also ascertains the antecedent factors that determine the structure of the network among world universities [31]. By employing a multi-level network approach, Meredith et al. aimed to investigate information-seeking ties and network structure among organizations in secondary schools [32]. In the related work, a multi-level network is considered a valuable tool for presenting different kinds of technological innovation networks in diverse domains, such as high-tech, steel structures, TV programs, and so on [23,33]. Furthermore, network data in the aforementioned studies are mainly derived from journals, the Google search engine, projects, surveys, questionnaires, etc. However, limited studies have focused on emerging technologies, which have been paid increasing attention by academicians and practitioners in the global era. When it comes to emerging technologies, it should be recognized that patent information contains rich, valuable, and essential technical items, and 80% of these technical items are able to be identified in patent information [34]. We argue, therefore, that the analytical tools of traditional multi-level network analysis can be expanded by the use of this rich patent information in a multi-level network framework.

2.2. Development of Patent Risks In the knowledge-based economy era, assessing patent risks has become more important in studies of technological innovation [35]. In practice, patent risk is regarded as a major threat for actors that aim to sustain their competitive advantage and obtain sustainable development in the fierce technological competition marketplace, and can cause loss or damage of assets potential [36]. According to the existing literature, the theoretical research on patent risk mainly focuses on the upper concept of patent risk, namely intellectual property risk. From the traditional risk management perspective, intellectual property risks refer to the analysis of what an individual or enterprise needs to be prepared for when deciding to protect their intellectual property (IP). Intellectual property risk arises from the complexity of various factors within the intellectual property system, and it falls within the realm of phenomena that are foreseeable; therefore, we can measure it, and the value loss resulting for a victim of IP theft is also measurable [37]. Although patent risks play an important role in IP risks, there are fewer studies that take into account the patent risks when stakeholders attempt to obtain sustainable development in technological competition. Following the existing literature on risks and IP risks, this paper attempts to define patent risks as the lack of understanding of the distribution of patents that lead to losing a key patent, increased research and development costs, and, potentially, infringement litigation. Patent risks affect more than just assignees who own patents; any stakeholder is vulnerable if it makes, uses, or sells products containing patented technology. Patent risks in the overall technological field can be Sustainability 2020, 12, 8667 5 of 21 effectively identified by exploring the distribution of patents, such as overseas patent applications, technological hotspots and fronts, and collaborative patenting activities. Most of the recent literature focuses on assessing patent risks in a scientific and systematic way. The existing research is more dependent on manual perusal of patent documents by experts to judge and predict risks [38]. However, the qualitative method is subjective, because its results mainly depend on individual knowledge [39]. Regarding the increasing number of patent data and the complexity of patent information, the above approaches are both time-consuming and labor-intensive and the results will be inaccurate [38]. Based on statistics and machine learning algorithms, other scholars argue that quantitative methods are more objective than the qualitative approach, and, thus, help to improve the validity of analysis. As the most useful indication of technologies, patent information contains rich, valuable, and essential technical items for promoting technological development for both organizations and industries [34]. Therefore, patent information is regarded as an objective data source in this study. In order to measure patent risks, several patent-based approaches have been introduced. According to the research on patent risks, the most widely used approach is to assess patent infringement risks by analyzing patent information [40]. Lee et al. attempted to propose a semantic patent claim analysis, and assessed patents with high risks of possible infringements [38]. By employing the subject–action–object (SAO) semantic technological similarity-based approach, Park and Yoon attempted to investigate patent infringement between a patent and product by generating a product–patent infringement map [4]. Other researchers applied patent claim maps [41], patent topic maps [42], and keyword-based patent maps [43] for this purpose. A more sophisticated approach to focus on the relationships between patent infringements is social network analysis (SNA). Recently, SNA has been regarded as an effective tool to explore many important phenomena depending on networks. Most of the recent studies have employed SNA for establishing patent collaboration networks at the country level or organizational level in various technological fields, such as care robots, information and communication technologies (ICTs), nanotechnology, etc., and identifying central researchers, institutions, publications, journals, and research clusters in this network [24,44]. However, a few works, although still limited, have applied SNA for assessing patent infringement risks. Based on SNA, Kim and Song built a patent-infringement lawsuits graph, and identified key actors in this graph by computing the network centrality. The proposed approach can be used to identify the key companies for patent-infringement lawsuits, assess patent infringement risks, and evaluate patent portfolios [3]. Tsai and An proposed to employ the SNA to analyze patent infringement relationships, explore the roles and influence of companies by computing their network centrality measures, and prevent patent infringement risks [45]. Unlike the existing studies attaching importance to specific patents infringed by other patents, the approach we propose focuses on all patents in the target technological field and attempts to develop a new and comprehensive patent-based multi-level network model for identifying patent risks from the perspectives of collaboration, market, and technology by integrating patent analysis and SNA. This approach helps both researchers and practitioners to prevent patent risks, find new technology opportunities, and obtain sustainable development.

3. Research Methodology and Framework This study represents an effort to identify patent risks from patent information. The scope of the specific technology field is based on related literature and the advice of technical experts. At first, we set up a suitable patent retrieval query and sorted patent data. Then, we handled patent data by employing Python software, and all these data were converted into the specific format to meet the requirements of the UCINET software (v6.0, Analytic Technologies, Lexington, KY, USA), which is a professional package for social network analysis [46]. In the methodology, building up the patent-based multi-level network model on patent risks was the key to the whole research process. This process was divided into two steps. First, we intended to build the market network, the technology network, and the patentee network, respectively. Second, by integrating the networks from different perspectives, a patentee–market network and a patentee–technology network were constructed by using Sustainability 2020, 12, 8667 6 of 21

two-modeSustainability network2020, 12, x analysis. After the data analysis process, it was of great importance for this study6 of to22 summarize and understand the results, and propose implications for stakeholders to formulate strategic planningstakeholders and to decision-making. formulate strategic Figure planning1 indicates and the decision-making. overall process ofFigure the proposed 1 indicates approach the overall for identifyingprocess of the patent proposed risks. approach for identifying patent risks.

Derwent Innovation Index

Patent Retrieval Artificial Intelligence Data Downloading Query Patent Database

Python Software

Data Handling Patent Database for UCINET

Patent-based Multi-level Network Model

Macro Level Micro Level

Data Analysis Market Network Patentee Network

Technology Network

Two-mode Network Meso Level Analysis

Patentee-market Network

Patentee-technology Network

FigureFigure 1.1. The outline of research process.process. 3.1. Retrieval of Patent Information 3.1. Retrieval of Patent Information Obtaining accurate patent data is an essential basis to build the patent-based multi-level network Obtaining accurate patent data is an essential basis to build the patent-based multi-level model proposed in this study. After setting a suitable patent retrieval query, we applied the powerful network model proposed in this study. After setting a suitable patent retrieval query, we applied the and professional patent research tool Derwent Innovation Index (DII) [47]. Taking the accuracy powerful and professional patent research tool Derwent Innovation Index (DII) [47]. Taking the of our patent retrieval into account, we read related literature, including books and articles in the AI accuracy of our patent retrieval into account, we read related literature, including books and articles technological field, and consulted technical experts’ advice. In order to collect the core keywords in the AI technological field, and consulted technical experts' advice. In order to collect the core for the patent search string, we followed advice from technical experts, and considered the research keywords for the patent search string, we followed advice from technical experts, and considered from Huang et al. [48]. However, we found that there are still some unrelated patents by pre-retrieval. the research from Huang et al. [48]. However, we found that there are still some unrelated patents by To address this problem, we adopted the international patent classification (IPC) to refine the retrieval pre-retrieval. To address this problem, we adopted the international patent classification (IPC) to query and retrieve a more accurate sample. In addition, we used broader criteria for selecting the AI refine the retrieval query and retrieve a more accurate sample. In addition, we used broader criteria patents. A patent was regarded as valid if the term appeared simultaneously in abstracts, claims, for selecting the AI patents. A patent was regarded as valid if the term appeared simultaneously in and titles. The patent retrieval query was shown by combining the keywords with IPC, using the “AND” abstracts, claims, and titles. The patent retrieval query was shown by combining the keywords with operator. TS = (“artif* intelli*” OR “comput* intelli*” OR “deep learn*” OR “machine learn*” OR IPC, using the “AND” operator. TS = (“artif* intelli*” OR “comput* intelli*” OR “deep learn*” OR “big data” OR “cloud comput*” OR “pattern recogn*” OR “neural network*” OR “data mining*” OR “machine learn*” OR “big data” OR “cloud comput*” OR “pattern recogn*” OR “neural network*” “natural language process*” OR “speech recogn*” OR “computer vision*” OR “gesture control” OR OR “data mining*” OR “natural language process*” OR “speech recogn*” OR “computer vision*” smart robot* OR “video recogn*” OR “image* recogn*” OR “voice translat*”) AND IP = (“G06N-003/00” OR “gesture control” OR smart robot* OR “video recogn*” OR “image* recogn*” OR “voice OR “G06N-003/02” OR “G06N-003/04” OR “G06N-003/06” OR “G06N-003/063” OR “G06N-003/067” translat*”) AND IP = (“G06N-003/00” OR “G06N-003/02” OR “G06N-003/04” OR “G06N-003/06” OR “G06N-003/063” OR “G06N-003/067” OR “G06N-003/08” OR “G06N-003/10” OR “G06N-003/12” OR “G06N-005/00” OR “G06N-005/02” OR “G06N-005/04” OR “G06N-007/00” OR “G06N-007/02” OR Sustainability 2020, 12, 8667 7 of 21

OR “G06N-003/08” OR “G06N-003/10” OR “G06N-003/12” OR “G06N-005/00” OR “G06N-005/02” OR “G06N-005Sustainability/04” 2020 OR, 12, x “G06N-007 /00” OR “G06N-007/02” OR “G06N-007/04” OR “G06N-007/06”7 of 22 OR “G06N-007/08” OR “G06N-099/00”). The timespan was from 1998 to 2018. We retrieve 13,647 patents from“G06N-007/04” DII. OR “G06N-007/06” OR “G06N-007/08” OR “G06N-099/00”). The timespan was from 1998Regarding to 2018. We the retrieve number 13,647 of patents, patents Figure from2 DII.illustrated a general overview and development trends of the topRegarding eight countries the number or regions of patents, leading Figure the AI2 il race.lustrated Japan a andgeneral the U.S.overview started and earlier development than other countriestrends of in the fieldtop eight of AI countries technology. or regions After the leadin yearg 2016,the AI the race. number Japan of and AI patentsthe U.S. in started these topearlier eight than other countries in the field of AI technology. After the year 2016, the number of AI patents in countries or regions entered a rapid growth phase. The U.S. is a powerful leader in the AI technology these top eight countries or regions entered a rapid growth phase. The U.S. is a powerful leader in field. One reason for this could be the first-mover advantages gained from entering the market the AI technology field. One reason for this could be the first-mover advantages gained from earlier. China, with a steadily increasing number of patents, serves as a technology catcher and first entering the market earlier. China, with a steadily increasing number of patents, serves as a outstripped the AI patents held by the U.S. in 2012. One practical reason for this is that China has technology catcher and first outstripped the AI patents held by the U.S. in 2012. One practical reason a largefor this market is that and China abundant has a fundinglarge market support. andThe abundant otherreason funding is thatsupport. China’s The science other reason and technology is that evaluationChina’s science system and has technology had an increasing evaluation emphasis system ha ons patentshad an increasing [49]. The emphasis number of on Korean patents patents [49]. experiencedThe number a rapidof Korean increase patents in 2018,experienced and Korea a rapid outstripped increase in Japan 2018, to and rank Ko thirdrea outstripped in the number Japan of to AI patents.rank third This in trend the number shows thatof AI Korea patents. attaches This tren greatd shows importance that Korea to the attaches AI technology, great importance and will to also the be a formidableAI technology, competitor and will in also this be field. a formidable competitor in this field.

FigureFigure 2. 2.Artiﬁcial Artificial intelligenceintelligence pa patentstents by by country/region. country/region.

3.2.3.2. Patent-Based Patent-Based Multi-Level Multi-Level Network Network ModelModel ByBy introducing introducing patent patent indicatorsindicators to the the previous previous multi-level multi-level network network model, model, this thisstudy study aims aimsto tobuild build a anew new and and comprehensive comprehensive framework framework for the for patent-based the patent-based multi-level multi-level network, network, which helps which helpsstakeholders stakeholders to identify to identify patent risks patent in risksthe specif in theic technological specific technological field. The proposed field. Thepatent-based proposed patent-basedmulti-level multi-levelnetwork attempts network to attemptsinvestigate to pa investigatetent risks patentthrough risks meso-level through and meso-level micro-level and micro-levelinvestigation. investigation. Patent indicators Patent indicatorsare easy to are choose easy to and choose assess and under assess the under macro the macromarket marketand andtechnology technology dimensions, dimensions, meso meso patentee–market patentee–market and and patentee–technology patentee–technology dimensions, dimensions, and and micro micro patenteepatentee dimension. dimension. In In addi addition,tion, patent patent indicators indicators include include all essential all essential factors of factors patent of risks. patent Levels, risks. Levels,dimensions, dimensions, and patent and indicators patent indicators can be matched can be one matched by one, and one can by be one, supported and can by bethe supportedrelevant byliterature. the relevant literature. FigureFigure3 represents 3 represents the the basic basic schema schema of of the the patent-based patent-based multi-levelmulti-level network network on on patent patent risks risks proposedproposed in in this this paper. paper. The market market network network and and the the technology technology network network from fromthe macro-level, the macro-level, the thepatentee–market patentee–market network network and the and patentee–tec the patentee–technologyhnology network networkfrom the frommeso-level, the meso-level,and the patentee network from the micro-level jointly constitute the patent-based multi-level model. Three types of nodes are shown in the diagram, namely: (1) circle nodes, representing patent filing offices Sustainability 2020, 12, 8667 8 of 21 and the patentee network from the micro-level jointly constitute the patent-based multi-level model. Three types of nodes are shown in the diagram, namely: (1) circle nodes, representing patent filingSustainability offices 2020 around, 12, x the world; (2) triangle nodes, representing a specific technological field,8 of based 22 on the Derwent Class Code (DC), a patent classification code by DII; and (3) square nodes representing around the world; (2) triangle nodes, representing a specific technological field, based on the patentees.Derwent Class Nodes Code can (DC), be also a patent distinguished classification using code di ffbyerent DII; colors.and (3) square The upper nodes market representing network representspatentees. the Nodes ties among can be country also distinguished/region filing patentsusing different in the given colors. technology The upper field. market Based network on patent analysis,represents a network the ties of among patent country/region geographic distribution filing patents with in regard the given to the technology number of field. patents Based between on eachpatent filing analysis, country a /networkregion appearedof patent geographic to be very di promisingstribution forwith market regard activitiesto the number [50]. of The patents upper technologybetween each network filing illustratescountry/region the relationsappeared amongto be very diff erentpromising technological for market fields, activities which [50]. helps The to digupper out researchtechnology hotspots network by illustrates employing the social relations network among analysis, different and technological obtain competitive fields, which advantage helps forto stakeholders dig out research [51]. The hotspots lower patentee by employing network so representscial network the patent analysis, collaboration and obtain situation competitive between assignees.advantage Considering for stakeholders the patent [51]. The collaboration lower patent betweenee network assignees represents is of fundamental the patent collaboration importance to identifysituation the between key patentee assignees. with the Considering highest technological the patent innovation collaboration capability between in theassignees whole network,is of wefundamental needed to focusimportance on the to patentidentify collaboration the key patentee of these with patentthe highest assignees technological [49]. By innovation employing two-modecapability networkin the whole analysis, network, the verticalwe needed lines to fo linkingcus on nodesthe patent in the collaboration lower network of these with patent nodes inassignees the upper [49]. network By employing form two networks,two-mode thenetwork patentee–market analysis, the network vertical andlines the linking patentee–technology nodes in the network.lower network When with investigating nodes in the the upper patentee–market network form network, two networks, we explored the patentee–market the patent distribution network ofand the topthe assigneespatentee–technology in the popular network. patent filingWhen offiinvestigatingce, and identified the patentee–market the patent market network, risks. Aswe for explored the patent distribution of the top assignees in the popular patent filing office, and the patentee–technology network, we found specific technological fields that the top assignees focus on, identified the patent market risks. As for the patentee–technology network, we found specific and identified the patent technology risks due to the lack of patent application in essential technological technological fields that the top assignees focus on, and identified the patent technology risks due to fields. Thus, the proposed patent-based multi-level network above could be examined separately at the lack of patent application in essential technological fields. Thus, the proposed patent-based first, and then jointly. multi-level network above could be examined separately at first, and then jointly.

Market network Technology network

Macro-level

Meso-level

Patentee-market Patentee-technology two-mode network two-mode network

Micro-level

Patentee network

Figure 3. Schema of the proposed patent-based multi-level network on patent risks. Figure 3. Schema of the proposed patent-based multi-level network on patent risks. Based on the constructed patent-based multi-level network, one can summarize and understand the technologicalBased on competitionthe constructed reﬂected patent-based by patent mu data,lti-level and thennetwork, identify one patentcan summarize risks. Employing and theunderstand proposed patent-basedthe technological multi-level competition network, reflected the followingby patent relevantdata, and aspects then identify of information patent risks. can be revealed.Employing (1) Thethe listproposed of the mainpatent-based distribution multi-level of patent network, competitive the following markets isrelevant suggested aspects by circleof nodesinformation in the market can be network. revealed. (2)(1) TechnologicalThe list of the hotspotsmain distribution and fronts of are patent indicated competitive by the betweennessmarkets is centrality,suggested degree by circle centrality, nodes in and the eigenvectormarket network. centrality (2) Technological of triangle hotspots nodes in and the fronts technology are indicated network. by the betweenness centrality, degree centrality, and eigenvector centrality of triangle nodes in the (3) The degree centrality of square nodes in the patentee network suggests the leading assignees with technology network. (3) The degree centrality of square nodes in the patentee network suggests the leading assignees with high collaboration capability. Those assignees lacking cooperation with other assignees, especially industry–university–research collaboration, suffer from patent collaboration risks. (4) The patent market risks are indicated by the uncomprehensive patent application of those Sustainability 2020, 12, x 9 of 22 leading assignees in the popular patent filing markets, which can be found in the patentee–market network. (5) Based on the patentee–technology network, the lack of patent distribution of those leading assignees in essential technological fields implies patent technology risks. The above aspects can support applications as represented in Figure 3.

4. Case Study Sustainability 2020, 12, 8667 9 of 21 4.1. Market Network highThe collaboration distribution capability. of the patent Those assigneesmarket is lacking of great cooperation significance with for other improving assignees, the especially level of technologicalindustry–university–research innovation capability. collaboration, In order su toff erexplore from patentthe distribution collaboration of the risks. patent (4) Themarket patent in differentmarket risks countries, are indicated and identify by the the uncomprehensive main competition patent market, application it is necessary of those to analyze leading the assignees market network.in the popular Using patent the Netdraw filing markets, visualization which cantool be in found UCINET, in the the patentee–market market network network. diagram (5) Basedof AI technologyon the patentee–technology in leading countries network, or regions the was lack drawn. of patent The distribution blue nodes represent of those leadingthe top assignees8 leading countriesin essential or regions technological in this fieldsfield, while implies the patent red nodes technology represent risks. the top The 10 above most popular aspects canpatent support filing officesapplications around as the represented world. A inlink Figure between3. a blue node and red node indicates the patent application of a leading country or region, and the thickness of the link represents the number of patents that a 4. Case Study leading country or region filed in the target patent filing office. 4.1. MarketAs shown Network in Figure 4, the U.S., the WIPO (World Intellectual Property Organization), and China are the top three most popular patent distribution countries or regions in the AI field, which indicatesThe distributionthe main competition of the patent marketmarkets is ofaround great significancethe world. forNearly improving all the the assignees level of technological pay close attentioninnovation to capability.the U.S. market, In order especially to explore those the distributionfrom Canada, of theGermany, patent marketthe U.K, in Taiwan, different and countries, Japan. Assigneesand identify from the the main U.K, competition Canada, Germany, market, it and is necessary the U.S. toattach analyze great the importance market network. to the patent Using applicationthe Netdraw at visualization the WIPO, while tool in assignees UCINET, from the market Taiwan, network Japan, diagramthe U.K, ofand AI technologythe U.S. focus in leadingon the patentcountries application or regions at was the drawn.CN (National The blue Intellectual nodes represent Property the topAdministration, 8 leading countries PRC). orIt can regions be clearly in this seenfield, that while many the red leading nodes countries represent or the regions top 10 most(e.g., popularChina, patentKorea, filingand Taiwan) offices around had the the largest world. numbersA link between of AI apatents blue node filed and by red applicants node indicates from thetheir patent own application country or of region, a leading which country indicate or region, the obviousand the thickness“home advantage” of the link representseffect in the the market number network of patents [52]. that Most a leading of the country leading or countries region filed or regions,in the target especially patent Asian filing countrie office. s or regions, lack patent applications in European markets.

EPO DE JP WIPO

the U.K Japan Germany Korea China

the U.S. Taiwan

Canada

TW KR CN US Figure 4. The market network diagram of artificial intelligence technology. US—United States FigurePatent 4. and The Trademark market network Office, diagram WIPO—World of artificial Intellectual intelligence Property technology. Organization, US—United CN—National States Patent andIntellectual Trademark Property Office, Administration, WIPO—World PRC, JP—Japanese Intellectual Patent Property Office, KR—KoreanOrganization, Intellectual CN—National Property IntellectualOffice, EPO—European Property Administration, Patent Office, DE—GermanPRC, JP—Japanese Patent Patent and Trademark Office, KR—Korean Office, GB—Intellectual Intellectual Property OOffice,ffice, United EPO—European Kingdom, TW—Taiwan Patent Offi Intellectualce, DE—German Property OPatentffice, CA—Canadianand Trademark Intellectual Office, GB—IntellectualProperty Office. Property Office, United Kingdom, TW—Taiwan Intellectual Property Office, CA—Canadian Intellectual Property Office. As shown in Figure4, the U.S., the WIPO (World Intellectual Property Organization), and China are the top three most popular patent distribution countries or regions in the AI field, which indicates the main competition markets around the world. Nearly all the assignees pay close attention to the U.S. market, especially those from Canada, Germany, the U.K, Taiwan, and Japan. Assignees from the U.K, Canada, Germany, and the U.S. attach great importance to the patent application at the WIPO, while assignees from Taiwan, Japan, the U.K, and the U.S. focus on the patent application at the CN Sustainability 2020, 12, 8667 10 of 21

(National Intellectual Property Administration, PRC). It can be clearly seen that many leading countries or regions (e.g., China, Korea, and Taiwan) had the largest numbers of AI patents ﬁled by applicants from their own country or region, which indicate the obvious “home advantage” eﬀect in the market network [52]. Most of the leading countries or regions, especially Asian countries or regions, lack patent applications in European markets.

4.2.Sustainability Technology 2020 Network, 12, x 10 of 22 4.2.The Technology technology Network network aims to explore valuable patent information for exploring research hotspotsThe and technology find technology network aims opportunities to explore byvaluable making patent a co-occurrence information for analysis exploring of theresearch given technologicalhotspots and field. find Thetechnology co-occurrence opportunities analysis by can making be complemented a co-occurrence by integratinganalysis of the the Derwent given Classtechnological Code (DC), field. which The categorized co-occurrence patent analysis documents can be throughcomplemented a simple by classification integrating the system Derwent for all technologies.Class Code (DC), Using which the DCcategorized can significantly patent document assist stakeholderss through a simple to enhance classification effective system and for precise all searchingtechnologies. capability Using in the a particular DC can significantly area of technology, assist stakeholders and identify to research enhance hotspots effective and and fronts. precise searchingAs can capability be seen in in Figure a particular5, each area node of technology, represents and a single identify DC, research and the hotspots link between and fronts. di fferent nodesAs is usedcan be to seen illustrate in Figure that 5, a each patent node has repres moreents than a single one DC. DC, We and measured the link between the co-occurrence different ofnodes the technological is used to illustrate field three that ways,a patent using has UCINETmore than [46 one]. TheDC. firstWe measured is betweenness the co-occurrence centrality. The of the size oftechnological the node represents field three the ways, betweenness using UCINET centrality. [46]. The A first DC is with betweenness a high score centrality. on this The measure size of the sits onnode many represents of the “shortest the betweenness paths” connecting centrality. A others DC with and a ishigh likely score to on have this a measure higher abilitysits on tomany control of thethe flow “shortest of information paths” connecting or the exchange others and of resourcesis likely to [ 53have]. Thea higher DC withability the to highest control betweennessthe flow of centralityinformation represents or the theexchange importance of resources and influence [53]. The power DC with of the the technology highest betweenness and indicates centrality the new researchrepresents front the and importance technology and opportunity influence power in this field.of the Thetechnology second measureand indicates we report the new here research is degree centralityfront and [54 technology], which captures opportunity information in this onfield. how The many second links measure to others we each report DC has,here andis degree reflects thecentrality hotspot of[54], the which technology. captures Degree information centrality on how depends many simply links to on others the number each DC of has, connections and reflects each nodethe has.hotspot An extensionof the technology. is eigenvector Degree centrality centrality [55 de],pends which simply ranks DCson the by numb hower well of theyconnections are connected each tonode highly has. connected An extension others. is Eigenvector eigenvector centrality centrality represents [55], which the needranks for DCs comprehensive by how well distribution they are inconnected the technological to highly field. connected Considering others. Eigenvector betweenness centrality centrality, represents degree the centrality, need for andcomprehensive eigenvector centrality,distribution the topin the 10 hotspotstechnological and frontsfield. Consid of AI technologyering betweenness are illustrated centrality, in Table degree1. centrality, and eigenvector centrality, the top 10 hotspots and fronts of AI technology are illustrated in Table 1.

P35 P36 Q66 B07 V08 C07 T03 S06 P81 E14 Q54 U23 P14 V04 P34 Q75 P84 Q14 Q17 U13 U12 X24 Q71 W03 P86 C06 V05 X26 X11 Q74 U22 U11 L03 D13 P85 A96 S04 W02 P31 Q69 X23 V07 P13 Q13 T07 X16 T02 S05 B04 P52 W04 M23 P82 V03 U14 V06 P62 Q51 U21 W01 D16 Q73 X22 W06 T04 X21 A89 Q47 X27 T05 S02 Q21 Q72 S03 D15 E36 Q35 X15 S01 J04 P55 A35 Q31 W05 P27 P33 U25 T06 X13 P22 V02 D14 X12 Q56 X25 Q52 T01 P41 A23 M29 Q34 Q12 A85 P56 P12 D21 M13 P21 U24 Q49 L02 W07 F09 P26 Q63 H01 H05 Q41 D22 M26 A95 P32 A17 L01 Q23 M24 K08 Q42 P43 K05 H02 H09 P71 P42 P72 P24 A32 E19 Q38 F01 A88 F05 J01 Q22 E17 P54 Q79 F07 P64 X14 Q25 P53 H06 A97 M21 K06 B02 P51 P15 M11 K03 H04 B01 Q46 Q55 M22 P25 F02 A41 Q77 A14 M28 D18

FigureFigure 5. 5.The The technology technology networknetwork diagram of artificial artiﬁcial intelligence intelligence technology. technology.

Table 1. Technological hotspots and fronts of artificial intelligence technology.

Derwent Betweenness Degree Eigenvector Technological Fields Class Code Centrality Centrality Centrality T01 Digital Computers 8557.116 170 0.293 T06 Process and Machine Control 1529.039 103 0.243 X25 Industrial Electric Equipment 606.145 71 0.187 S03 Scientific Instrumentation 597.972 76 0.213 T04 Computer Peripheral Equipment 373.217 68 0.207 W04 Audio/Video Recording and Systems 255.737 63 0.204 S02 Engineering Instrumentation 220.156 56 0.184 Sustainability 2020, 12, 8667 11 of 21

Table 1. Technological hotspots and fronts of artiﬁcial intelligence technology.

Derwent Betweenness Degree Eigenvector Technological Fields Class Code Centrality Centrality Centrality T01 Digital Computers 8557.116 170 0.293 T06 Process and Machine Control 1529.039 103 0.243 X25 Industrial Electric Equipment 606.145 71 0.187 S03 Scientiﬁc Instrumentation 597.972 76 0.213 T04 Computer Peripheral Equipment 373.217 68 0.207 W04 Audio/Video Recording and Systems 255.737 63 0.204 S02 Engineering Instrumentation 220.156 56 0.184 W01 Telephone and Data Transmission Systems 160.088 55 0.193 X22 Automotive Electrics 155.736 45 0.154 S05 Electrical Medical Equipment 130.458 48 0.171

T01 (Digital Computers) has the highest betweenness centrality, degree centrality, and eigenvector centrality in the AI field, suggesting that it plays the most important role as both a technological hotspot and front. As a crucial branch of computer science and digital computers, AI has been widely used in computer vision, natural language processing, automatic programming, and data mining in recent years. Regarding high betweenness centrality and degree centrality, as well as eigenvector centrality, T06 (Process and Machine Control), X25 (Industrial Electric Equipment), and S03 (Scientific Instrumentation) are ahead of other technological fields except for T01. The research on these three fields has been very active and plays a crucial role in the AI field. Intelligent manufacturing is an emerging area of interdisciplinary research integrating machine control theory and artificial intelligence, which can be widely applied in many fields such as engineering, agriculture, military aviation, etc. [56]. With the increasing complexity of electronic devices, computers have become imperative in electrical design. The application of AI in industrial electrical equipment is mainly embodied in electrical design, namely, the optimization involved in electrical equipment and computer aided design (CAD) technology [57]. In the coming era of artificial intelligence, scientific instruments have evolved from automation to intelligence. Scientific instruments in the fields of life sciences and medical treatments, such as the spectrometer, chromatograph, gene pulser transfection apparatus, cell fusion apparatus, and so on, have good prospects for development [58].

4.3. Patentee Network The patentee network illustrates the collaborative AI patenting activity between stakeholders, and aims to identify patent collaboration risks from the perspectives of collaborative patent applications, international collaborations, and industry–university–research collaboration. We would expect that links exist between patentees for the transmission of knowledge, for the generation of a collaborative community of patentees within the network, and to further a common technologically innovative collaboration opportunity. As shown in Figure6, each node represents an assignee, and the size of the node represents the degree centrality, i.e., assignees with higher degree centrality appear larger. The links indicate that two assignees collaborate on a patent and ﬁle a patent together. It is possible to capture the collaboration level of assignees and communities: the linkages of assignees in whichever community they operate and their collaboration with other assignees within the AI technology innovation system. Table2 illustrates the degree centrality of the top ten lead assignees in the AI ﬁeld, which helps stakeholders to identify patent collaboration risks from the perspective of a collaborative patent application. Regarding the geographical composition, the U.S. (6 patentees) leads China (1 patentee), Japan (1 patentee), Germany (1 patentee), and Korea (1 patentee), and takes the lion’s share, suggesting its leading position of jointly applied patents in the AI industry. It is clear that compared to assignees from the U.S., the collaborative patent application levels of assignees from China, Japan, Germany, and Korea are relatively low, which leads to patent collaboration risks. In addition, all the top ten lead Sustainability 2020, 12, 8667 12 of 21 assignees are from enterprises. It shows that enterprise, particularly large multinational corporations, is the main technology strength of collaborative patenting activity in the AI industry. However, the collaborative patent application levels of universities are relatively low, which indicates patent collaborationSustainability 2020 risks., 12, x 12 of 22

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BARNHILLGE O RKEZHU AD TECHNO H LO GIE S LLC MERTINS K O RAPTOR INT HOLDINGS PTY LTD MILOS C T SCHLESIGERBLOOM M J A NAVTE Q SHANGHAI TRADING CO LTD MCCLANAHAN C J NE URO CO MS INC BEEKMANN F BISCA R I CROSSLEY A LENO VO HEWLETT-PACKARD DEV CO LP BRANZANMALLE J C A NO KIA CO RP DO W NS O B JOHNSON & JOHNSON CONSUMER CO INC VINYALSNE URO CO O MS LNC ESSER S K MA X BRITS RBEIJING DEEP HI INTELLIGE NCE TECHNO LO GY CO LTD MORDECHAI E ARUZEBECK CORPF BAUMANNBAEKBARNHILL H S S D HIDO S GUTZWILLERSIGNAL L R SENSE INC MAASTRO CLINIC UBIQ INC ATIGEOASSULIN A CORP NEURALBOLT TECHNOLOGIES G LTD DE EP MIND TE CHNO LO GIES LT D FRONTEOJAEGER GKOULALAS-DIVANISINCFORMAN H G H A DEERE & CO GRUND R SHAKED D ENSUITE INFORMATIQUE FUJI XEROX CO LTD WURST M GAI Y M8 CORP LE Q UO C V GOOGLE INC IOVANOV V M JAPANYANGTANGO SCI&TE K CDMA CT CHNO CO RP LO GY AGENCY DAKAR R SAHITASIEMENS RMEDICAL L SOLUTIONS USA INC F RAUNHO F ER GES F O ERDERUNG ANGEW ANDTE N EV Figure 6. The patentee network diagram of artiﬁcial intelligence technology. Figure 6. The patentee network diagram of artificial intelligence technology.

TableTable 2. 2.Degree Degree centrality centrality ofof artiﬁcialartificial intelligenceintelligence lead assignees assignees from from jointly jointly applied applied patents. patents.

AssigneeAssignee Degree Degree Centrality Centrality Country Country Origin Origin SGCC-C 35 China SGCC-C 35 China ITLC-C 15 the U.S. ITLC-C 15 the U.S. SONY-CSONY-C 14 14 Japan Japan CODE-Non-standardCODE-Non-standard 14 14 the U.S. the U.S. MICT-CMICT-C 12 12 the U.S. the U.S. SIEI-CSIEI-C 11 11 Germany Germany IBMC-CIBMC-C 11 11 the U.S. the U.S. QCOM-CQCOM-C 11 11 the U.S. the U.S. SMSU-C 10 Korea SMSU-C 10 Korea BRAI-Non-standard 9 the U.S. BRAI-Non-standard 9 the U.S. SGCC-C—STATE GRID CORP CHINA, ITLC-C—INTEL CORP, SONY-C—SONY CORP, CODE-Non-standard—CODEXISSGCC-C—STATE GRID CORP MAYFLOWERHOLDING CHINA, ITLC-C—INTEL LLC, MICT-C—MICROSOFT CORP, SONY-C—SONY CORP,SIEI-C—SIEMENS CORP, AG,CODE-Non-standard—CODEXIS IBMC-C—International Business MAYFLOWER Machines Corp, HOLDING QCOM-C—QUALCOMM LLC, MICT-C—MICROSOFT INC, SMSU-C—Samsung CORP, ElectronicsSIEI-C—SIEMENS Co., Ltd., BRAI-Non-standard—BRAIN AG, IBMC-C—International CORP. Business Machines Corp, QCOM-C—QUALCOMM INC, SMSU-C—Samsung Electronics Co., Ltd., BRAI-Non-standard—BRAIN CORP. Examining the network as a whole via Figure6, we can identify patent collaboration risks from the perspectiveExamining of the international network as collaboration. a whole via Figure As shown 6, we incan Figure identify6, there patent are collaboration nine major componentsrisks from thatthe involve perspective more thanof international 10 partners, whichcollaboration. appear mostAs shown likely associatedin Figure with6, there jointly are applied nine major patents. Thecomponents largest network that involve with 50 more patentees than is10 largelypartners, dominated which appear by Chinese most likely actors. associated In this network, with jointly STATE applied patents. The largest network with 50 patentees is largely dominated by Chinese actors. In GRID CORP CHINA (SGCC-C), which until now has been very active on the AI fronts, possesses this network, STATE GRID CORP CHINA (SGCC-C), which until now has been very active on the a central role and appears to the driving force of commercial AI production in the power industry, AI fronts, possesses a central role and appears to the driving force of commercial AI production in especially those used in the industrial chain from power generation and transmission. There are the power industry, especially those used in the industrial chain from power generation and 38 patentees from enterprises, 12 from universities or research institutes, and no partners from other transmission. There are 38 patentees from enterprises, 12 from universities or research institutes, and countries.no partners The from second-largest other countries. network, The withsecond-large 23 partners,st network, is dominated with 23 by partners, U.S. patentees, is dominated particularly by INTELU.S. patentees, CORP (ITLC-C). particularly Another INTEL network CORP is(ITLC-C). dominated Another by 21 network actors from is dominated the U.S.; the by leading21 actors patentee from the U.S.; the leading patentee is QUALCOMM INC (QCOM-C). It can be observed that the patentee network has yet to demonstrate any assignee that appears to have a central role connecting a large number of international assignees, and, thus, serves as an international hub or cluster. Therefore, we Sustainability 2020, 12, 8667 13 of 21 is QUALCOMM INC (QCOM-C). It can be observed that the patentee network has yet to demonstrate any assignee that appears to have a central role connecting a large number of international assignees, and, thus, serves as an international hub or cluster. Therefore, we conclude that in the current period national collaboration still dominates. The lack of international collaborations among assignees may lead to patent collaboration risks and hinder the development speed of technological innovation process. Table3 presents the top twenty stakeholders leading the AI race, ranked by the number of patents. The table shows that there are thirteen patentees from enterprises and seven patentees from universities and research institutes. Among these top 20 patentees, we can see that China (10 patentees) and the U.S. (5 patentees) are major competitors in the AI industry. The patentees from the U.S., Japan, Korea, and Germany are entirely enterprises. Compared to lead assignees with high degree centrality (see Table2 and Figure6), most of the top 20 patentees from the U.S., Germany, and Korea, such as QCOM-C, ITLC-C, International Business Machines Corp. (IBMC-C), MICROSOFT CORP (MICT-C), SIEMENS AG (SIEI-C), and Samsung Electronics Co., Ltd. (SMSU-C), have good capability in technological innovation cooperation. It can be seen that most of the Chinese patentees are from universities, and there are only three enterprises, namely SGCC-C, BAIDU ON-LINE NETWORK TECHNOLOGY CO LTD (BIDU-C), CAS CAMBRICON TECHNOLOGY CO LTD (BEIJ-N). Except for SGCC-C, the collaborative patent application levels of Chinese patentees are low. Regarding the industry–university–research (IUR) collaboration, it will enhance the pace of industrialization of AI technology. Combining Table3 with Figure6, we can investigate the IUR collaboration of the top 20 assignees, and identify patent collaboration risks from the perspective of IUR collaboration. It can be seen that IBMC-C has patent collaboration with UNIV CORNELL (CORR-C), SMSU-C with UNIV ZUERICH (UYZU-C), and UNIV YONSEI (UYIA-C), NEC CORP (NIDE-C) with UNIV TOHOKU (TOHO-C), SIEI-C with UNIV JOHNS HOPKINS (UYJO-C) and UNIV BRITISH COLUMBIA (UYBR-C), and SGCC-C with UNIV HUAZHONG SCI & TECHNOLOGY (UYHZ-C) and UNIV WUHAN (UYWU-C). However, most of these top 20 assignees, especially assignees from Chinese universities lack IUR collaboration, which may lead to patent risks in technological innovation cooperation.

Table 3. Top 20 assignees holding AI patents.

Assignee Number of Patents Country Origin IBMC-C 941 the U.S. MICT-C 308 the U.S. GOOG-C 290 the U.S. SGCC-C 198 China SMSU-C 160 Korea NIDE-C 156 Japan UYQI-C 147 China QCOM-C 136 the U.S. ITLC-C 131 the U.S. SIEI-C 129 Germany UYSC-C 125 China UEST-C 120 China FUIT-C 103 Japan UYZH-C 103 China FABK-C 101 the U.S. UTIJ-C 95 China UYXN-C 93 China BIDU-C 90 China UYBT-C 82 China BEIJ-N 76 China GOOG-C—GOOGLE INC, NIDE-C—NEC CORP, UYQI-C—UNIV TSINGHUA, UYSC-C—UNIV SOUTH CHINA TECHNOLOGY, UEST-C—UNIV CHINA ELECTRONIC SCI & TECHNOLOGY, FUIT-C—FUJITSU LTD, UYZH-C—UNIV ZHEJIANG, FABK-C—FACEBOOK INC, UTIJ-C—UNIV TIANJIN, UYXN-C—UNIV XIDIAN, BIDU-C—BAIDU ON-LINE NETWORK TECHNOLOGY CO LTD, UYBT-C—UNIV BEIJING TECHNOLOGY, BEIJ-N—CAS CAMBRICON TECHNOLOGY CO LTD. Sustainability 2020, 12, x 14 of 22

SustainabilityLTD, UYBT-C—UNIV2020, 12, 8667 BEIJING TECHNOLOGY, BEIJ-N—CAS CAMBRICON TECHNOLOGY CO14 of 21 LTD.

4.4. Patentee–Market Patentee–Market Network How dodo thethe di differentfferent patentees patentees perform perform in diinff erentdifferent overseas overseas patent patent markets? markets? What What are the are patent the patentmarket market risks in risks the AI in technologythe AI technology field? To field? answer To theseanswer questions, these questions, a two-mode a two-mode network ofnetwork patentees of patenteesand patent and filing patent offices filing was offices constructed. was constructed. This diagram This displays diagram the displays possibility the of possibility patent market of patent risks marketbased on risks overseas based patenton overseas markets patent of patentees markets of from patentees different from countries. different As countries. an indicator As an of indicator seeking ofprotection seeking inprotection a geographical in a geographical scope, the more scope, comprehensive the more comprehensive a patentee files a patentee its patents files in its global patents patent in globalfiling o patentffices, thefiling broader offices, protection the broader of theirprotection patent of rights, their andpatent the rights, fewer and patent the market fewer patent risks incurred market risksby potential incurred patent by potential infringement. patent Basedinfringement. on the two-mode Based on network,the two-mode we measure network, the we connectedness measure the connectednessbetween patentee between and patent patentee filing and offi cepatent by employing filing office degree by employing centrality. degree The degree centrality. centrality The captures degree centralityinformation captures on how information many ties on each how patent many filing ties ea ochffi cepatent has tofiling patentees, office has based to patentees, on the two-mode based on thepatentee–market two-mode patentee–market network shown network in Figure shown7. The in squareFigure nodes7. The representsquare nodes eight represent patent filing eight o patentffices, whilefiling theoffices, circle while nodes the represent circle thenodes top represent 20 leading the stakeholders top 20 leading in the AIstakeholders field (see Table in 3the). The AI redfield nodes (see Tablerepresent 3). The patentees red nodes from China,represent green patentees from the from U.S., China, yellow green from Japan,from the pink U.S., from yellow Korea, from and orangeJapan, pinkfrom Germany.from Korea, The and sizes orange of the circlefrom nodesGermany. indicate The thesizes degree of the centrality circle nodes of the node.indicate The the larger degree the centralitycircle node, of thethe morenode. comprehensiveThe larger the circle the market node, the distribution more comprehensive of the patentee the inmarket the network, distribution which of theindicates patentee higher in the competitive network, advantageswhich indicates for the high keyer patentee. competitive The advantages link indicates for how the manykey patentee. patents Theeach link of the indicates 20 patentees how many has to patents every patenteach of filing the 20 offi patenteesce, and the has thickness to every of pa thetent link filing represents office, and the thenumber thickness of those of the patents. link represents the number of those patents.

UYXN-C

UTIJ-C QCOM-C CA ITLC-C SIEI-C TW UEST-C MICT-C SMSU-C UYZH-C GOOG-C IBMC-C UYBT-C CN

US DE BEIJ-N WIPO EPO JP KR GB

SGCC-C

UYSC-C FABK-C UYQI-C BIDU-C FUIT-C NIDE-C Figure 7. The patentee–market 2-mode network diagram of artificial intelligence technology. Figure 7. The patentee–market 2-mode network diagram of artificial intelligence technology. As shown in Figure7, GOOGLE INC (GOOG-C) and IBMC-C from the U.S. and SMSU-C from KoreaAs seem shown to bein theFigure most 7, centralGOOG organizations,LE INC (GOOG-C) and have and IBMC-C comprehensive from the layout U.S. inand the SMSU-C whole majorfrom Koreacompetitive seem market.to be the MICT-C most central and ITLC-C organizations, from the and U.S. have and SIEI-Ccomprehensive from Germany layout filed in the patents whole in major major competitivemarkets, except market. for the MICT-C U.K. It and is interesting ITLC-C from to find the that U.S. the and above SIEI-C six organizationsfrom Germany with filed better patents patent in majoroverseas’ markets, layout except are all for from the enterprises,U.K. It is interesting especially to largefind that multinational the above six enterprises organizations with firstwith moverbetter patentadvantages overseas’ in resource, layout are technology, all from R&Denterprises, investment, especially etc. Therefore,large multinational assignees enterprises from the U.S., with Korea, first moverand Germany advantages have in comprehensive resource, technology, distribution R&D in inve thestment, major competitiveetc. Therefore, market, assignees especially from the the U.S., U.S., WIPO,Korea, and China.Germany However, have comprehensive it can be clearly distributi seen fromon Figurein the 7major that Chinesecompetitive assignees market, are especially regarded theas latecomers U.S., WIPO, in and this China. field, extremely However, lacking it can be the clea overseasrly seen patent from layout.Figure 7 For that example, Chinese someassignees Chinese are regardeduniversities, as latecomers such as UNIV in this ZHEJIANG field, extremely (UYZH-C), lacking UNIV the overseas TIANJIN patent (UTIJ-C), layout. and For UNIVexample, XIDIAN some (UYXN-C)Chinese universities, just filed patentssuch as in UNIV their domesticZHEJIANG market. (UYZH-C), This showsUNIV thatTIANJIN so far (UTIJ-C), most of the and Chinese UNIV XIDIANassignees (UYXN-C) have focused just onfiled the patents domestic in market,their domest and haveic market. paid little This attention shows that to enteringso far most the globalof the Sustainability 2020, 12, x 15 of 22

ChineseSustainability assignees2020, 12, 8667 have focused on the domestic market, and have paid little attention to entering15 of 21 the global market. This lack of comprehensive overseas distribution of patent markets, especially in themarket. most Thispopular lack countries of comprehensive or regions overseas like the distributionU.S., WIPO, ofetc., patent will cause markets, patent especially market inrisks the when most Chinesepopular assignees countries face or regions fierce global like the competition U.S., WIPO, in etc., the future. will cause patent market risks when Chinese assignees face fierce global competition in the future. 4.5. Patentee–Technology Network 4.5. Patentee–TechnologyTo gain a general Networkunderstanding of specific technological fields for patentees from different countries,To gain the a patentee–technology general understanding network of specific is used technological by integrating fields the micro-level for patentees of frompatentees different and macro-levelcountries, the technological patentee–technology fields. The network patentee– is usedtechnology by integrating network thewas micro-level proposed ofto patenteeshelp key patenteesand macro-level to explore technological the important fields. technological The patentee–technology fields, identify networkthe potential was proposedof patent totechnology help key risks,patentees and toenhance explore their the important capability technological in patent distribution fields, identify of technological the potential domains. of patent technologyAmong the risks, key patentees,and enhance the their more capability comprehensive in patent patent distribution distributi of technologicalon of technological domains. domains, Among thethe key higher patentees, their competitivethe more comprehensive advantages, patentand the distribution fewer patent of technological technology risks domains, incurred the higherby fierce their technological competitive competition.advantages, andFigure the 8 fewerillustrates patent the technologypatentee–technology risks incurred two-mode by fierce network technological of AI technology. competition. The purpleFigure8 squares illustrates represent the patentee–technology the top 10 DCs in two-mode the core position network (see of AI Table technology. 4), while The the purple blue squaressquares indicaterepresent other the top DCs 10 DCsat the in theperiphery core position of the (see netw Tableork.4), The while circles the blue represent squares the indicate top 20 other leading DCs stakeholdersat the periphery (see of Table the network. 3), which The iscircles the same represent as the the patentee–market top 20 leading stakeholders network. The (see red Table color3), whichindicates is the assignees same as thefrom patentee–market China, green from network. the TheU.S., red yellow color indicatesfrom Japan, assignees pink fromfrom China,Korea, green and orangefrom the from U.S., Germany. yellow from The Japan,size of pinkthe circle from is Korea, determined and orange by the from betweenness Germany. centrality. The size of The the larger circle theis determined circles, the by more the betweenness important the centrality. assignee The is largerin the the network, circles, thewhich more indicates important the the assignee’s assignee competitiveis in the network, advantages which in indicates a given technological the assignee’s fiel competitived. The lines advantages represent the in anumber given technologicalof patents in thefield. technological The lines represent field that the an numberassigneeof has patents filed; th ine thethicker technological the line, the field higher that the an assigneefrequency has of filed;filed patentsthe thicker between the line, assignee the higher and technological the frequency field. of filed The patentscore/peripheral between positions assignee are and determined technological by thefield. size The of core nodes/peripheral and the positions thickness are of determined the links. byStakeholders the size of nodeswith andmore the relations thickness in ofthe the target links. technologicalStakeholders withfield moreand shorter relations paths in theare targetpositioned technological at the center field and and defined shorter as paths core areplayers positioned in the industry.at the center and defined as core players in the industry.

P24 Q34 P14 S06 Q73 A17 V03 A89 U23 P21 U12 X23 P36 UYZH-C P84 V02 W07 FABK-C P81 U13 UYQI-C V07 U22 NIDE-C A97 Q72 T07 QCOM-C UEST-C ITLC-C U24 BIDU-C W04 J01 P34 W05 A85 Q49 BEIJ-N S02 W06 W01 T05 SGCC-C UYXN-C S03 T01 L03 Q42 T04 UTIJ-C T06 U14 V04 J04 B04 W03 S05 Q54 P32 X22 W02 IBMC-C V08 S01 MICT-C P31 X27 P13 UYBT-C D16 U25 FUIT-C GOOG-C P62 H01 UYSC-C X15 X12 T02 E36 SIEI-C Q38 V06 SMSU-C U21 X16 D13 X25 P85 D15 P86 X13 Q74 U11 S04 X11 T03 M24 X21 Q52 P82 Q35 FigureFigure 8. 8. TheThe patentee–technology patentee–technology 2-mode 2-mode network network diagram diagram of of artificial artiﬁcial intelligence technology.

As shown in Table4, we measured the connectedness of the technological field three ways, as with the technology network, namely, betweenness centrality, degree centrality, and eigenvector centrality, and displayed the important technological fields in the patentee–technology network. It can be clearly seen that T01 (Digital Computers) and T04 (Computer Peripheral Equipment) are the most important technological fields for the top 20 leading stakeholders, followed by W01 (Telephone and Data Transmission Systems) and W04 (Audio/Video Recording and Systems). In addition, when comparing the important technological fields in the technology network (see Table1) and patentee–technology Sustainability 2020, 12, 8667 16 of 21 network (see Table4), those DCs in the technology network but not in the patentee–technology network indicate that the top 20 leading stakeholders lack patent application in those important technological fields. We find that X25 (Industrial Electric Equipment), S02 (Engineering Instrumentation), and X22 (Automotive Electrics) are three technological hotspots and fronts (see Table1) rather than important technological fields in the patentee–technology network, which indicates that the patent applications of the leading stakeholders in these three fields are weak. Due to the lack of patent distribution in the technological fields of X25, S02, and X22, most of the leading assignees will encounter the patent technology risks incurred by fierce technological competition. Taking X25 as an example, currently, there are eight stakeholders owning patents in this field, and the amount of patents is very few.

Table 4. Important technological ﬁelds in the patentee–technology network of artiﬁcial intelligence technology.

Derwent Betweenness Degree Eigenvector Technological Fields Class Code Centrality Centrality Centrality T01 Digital Computers 271.019 20 0.243 T04 Computer Peripheral Equipment 271.019 20 0.243 W01 Telephone and Data Transmission Systems 237.488 19 0.231 W04 Audio/Video Recording and Systems 233.145 19 0.232 S03 Scientiﬁc Instrumentation 172.673 14 0.195 T06 Process and Machine Control 171.000 15 0.201 S05 Electrical Medical Equipment 144.910 15 0.192 W02 Broadcasting, Radio and Line Transmission Systems 130.688 13 0.180 B04 Natural products and polymers 110.358 12 0.164 Sustainability 2020, 12, x 17 of 22 W06 Aviation, Marine and Radar Systems 107.909 13 0.170

X22

QCOM-C S02 UYZH-C

B04 S03 NIDE-C UYXN-C UYQI-C BEIJ-N T01 MICT-C SMSU-C IBMC-C UYSC-C W06 SIEI-C UYBT-C

UEST-C

UTIJ-C W01 SGCC-C W04 T04 W02

S05 ITLC-C T06 X25

FABK-C BIDU-C FUIT-C GOOG-C Figure 9. The patentee–technology sub-network diagram of artificial intelligence technology. Figure 9. The patentee–technology sub-network diagram of artificial intelligence technology. As can be expected from the patentee–technology sub-network results in Figure9, the top three 5.most Discussion important and technological Implications fields at the center of the network include T01 (Digital Computers), T04 (ComputerThis paper Peripheralpresents systematic Equipment), research and W01 on (Telephonepatent risks and in the Data AI Transmission technology field Systems), based which on a newis also patent-based in the technological multi-level hotspots network and model fronts that of AIintegrates patents patent (see Table analysis1). In with particular, social IBMC-C,network analysis.MICT-C, This and GOOG-Cstudy retrieved are centrally 13,647 locatedpatents inwithin T01 and the W01,AI technology while in the field case using of T04, the UNIVDII database, CHINA andELECTRONIC used social SCI network & TECHNOLOGY analysis to illustrate (UEST-C), an UYXN-C,d explore and the UNIV patent SOUTH risks behind CHINA these TECHNOLOGY networks. Furthermore,(UYSC-C) play integrating central roles. the Regarding networks thefrom comprehensive market, technology, patent distribution and patentee in the perspectives technological allowed fields, usthe to top construct five patentees a patentee–market from high to low network rank are SIEI-C,and patentee–technology SGCC-C, UEST-C, MICT-C, network and using SMSU-C, two-mode which networkindicates analysis, that they wherein have greater the details comprehensive of the patent competitive market risks advantages and patent and technology are the key risks competitors can be investigatedin the AI industry. intuitively Compared and comprehensively. to the enterprises fromTaking the the U.S., AI Germany, technology and field Korea, as Chinesea case study, assignees the proposed patent-based multi-level network approach has been shown to be valid and robust. The initial effort has contributed to the AI technology field by identifying patent risks from comprehensive perspectives, not only displaying current technological development in the AI industry, but also enlightening future patent sustainable development in collaboration, market, and technology. There are several interesting results that are worth noting. Regarding the market network, our study demonstrates that the U.S., WIPO, and China are the top three most popular patent filing countries or regions in the AI technology field, suggesting the main competition markets globally. As for the technology network, T01 (Digital Computers) dominates in the AI field, followed by T06 (Process and Machine Control), X25 (Industrial Electric Equipment), and S03 (Scientific Instrumentation). The research on the above technology fields has been very active and plays a crucial role in the AI field. When it comes to the patentee network, it is clear that compared to assignees from the U.S., the collaborative patent application levels of assignees from China, Japan, Germany, and Korea are relatively low, which leads to patent collaboration risks. In addition, we conclude that in the current period, national collaboration still dominates. The lack of international collaborations among assignees may lead to patent collaboration risks and hinder the development speed of the technological innovation process. Furthermore, most of the leading assignees, especially assignees from Chinese universities, lack industry–university–research (IUR) collaboration, which may lead to patent collaboration risks in the process of technological innovation cooperation. Regarding the patentee-market network, assignees from the U.S., Korea, and Germany have comprehensive distribution in the major competitive market, especially the U.S., WIPO, and China, and most of them belong to large multinational enterprises with first mover advantages (e.g., GOOG-C, IBMC-C, and SMSU-C). In addition, Chinese assignees are regarded as latecomers in this Sustainability 2020, 12, 8667 17 of 21 from non-state-owned enterprises or universities lack comprehensive patent applications in key technological fields, indicating their higher possibility of patent technology risks. For example, BIDU-C is a search engine giant, aiming to focus on future-looking fundamental research in AI and lead the national lab on deep learning. However, it does not have patent applications in key technological fields, such as S03, X25, and so on, suggesting patent risks in the distribution of technological domains.

5. Discussion and Implications This paper presents systematic research on patent risks in the AI technology field based on a new patent-based multi-level network model that integrates patent analysis with social network analysis. This study retrieved 13,647 patents within the AI technology field using the DII database, and used social network analysis to illustrate and explore the patent risks behind these networks. Furthermore, integrating the networks from market, technology, and patentee perspectives allowed us to construct a patentee–market network and patentee–technology network using two-mode network analysis, wherein the details of the patent market risks and patent technology risks can be investigated intuitively and comprehensively. Taking the AI technology field as a case study, the proposed patent-based multi-level network approach has been shown to be valid and robust. The initial effort has contributed to the AI technology field by identifying patent risks from comprehensive perspectives, not only displaying current technological development in the AI industry, but also enlightening future patent sustainable development in collaboration, market, and technology. There are several interesting results that are worth noting. Regarding the market network, our study demonstrates that the U.S., WIPO, and China are the top three most popular patent filing countries or regions in the AI technology field, suggesting the main competition markets globally. As for the technology network, T01 (Digital Computers) dominates in the AI field, followed by T06 (Process and Machine Control), X25 (Industrial Electric Equipment), and S03 (Scientific Instrumentation). The research on the above technology fields has been very active and plays a crucial role in the AI field. When it comes to the patentee network, it is clear that compared to assignees from the U.S., the collaborative patent application levels of assignees from China, Japan, Germany, and Korea are relatively low, which leads to patent collaboration risks. In addition, we conclude that in the current period, national collaboration still dominates. The lack of international collaborations among assignees may lead to patent collaboration risks and hinder the development speed of the technological innovation process. Furthermore, most of the leading assignees, especially assignees from Chinese universities, lack industry–university–research (IUR) collaboration, which may lead to patent collaboration risks in the process of technological innovation cooperation. Regarding the patentee-market network, assignees from the U.S., Korea, and Germany have comprehensive distribution in the major competitive market, especially the U.S., WIPO, and China, and most of them belong to large multinational enterprises with first mover advantages (e.g., GOOG-C, IBMC-C, and SMSU-C). In addition, Chinese assignees are regarded as latecomers in this field, severely lacking overseas patent applications. The lack of overseas patent applications, especially the lack of market distribution in the main competitive markets (e.g., the U.S., WIPO, etc.), will cause patent market risks when Chinese assignees face global competition in the future. As for the patentee–technology network, a great weakness is that most of the key patentees lack awareness of patent distribution in some important technological hotspots and fronts, including X25, S02 (Engineering Instrumentation), and X22 (Automotive Electrics). The lack of patent distribution in the key technological fields may cause patent technology risks incurred by fierce technological competition. Compared to the enterprises from the U.S., Germany, and Korea, most of the Chinese assignees, especially those from non-state-owned enterprises or universities, are lacking comprehensive patent distribution in key technological fields. The weakness in comprehensive patent distribution of technological domains may lead to patent technology risks, and become one reason why the AI field is not in a highly commercialized stage. Sustainability 2020, 12, 8667 18 of 21

The above findings are useful for policy recommendations on preventing patent risks in the AI industry. First, we recommend strengthening international collaborations as well as IUR collaboration to confront patent collaboration risks in the process of technological innovation cooperation. Encourage patentees to actively carry out international collaborations with foreign patentees with competitive advantages. Specifically, patentees from different countries or regions can establish an international technology cooperation alliance in the AI technology field, which aims to establish international technology collaborations and working relationships, to provide potential opportunity for international patent collaboration, and to facilitate the development and industrialization of AI technology. In order to promote the transformation of AI technology from basic research to commercial application and strengthen IUR collaboration, it is necessary to build a systematic IUR collaboration platform, including three types of stakeholders, (1) universities, laboratories, and research centers; (2) technology innovation centers and spin-off enterprises of universities; and (3) technology transfer offices (TTOs). Universities should build TTOs because they are regarded as bridges to link basic research in universities and commercial applications in the market. Second, speed up the pace of overseas patent distribution in the major competitive market, and prevent patent market risks in the competition of the global AI market. Most of the stakeholders need to pay close attention to patent distribution in European countries or regions. Considering that most of the Chinese assignees are severely lacking overseas patent applications, it is of great importance for Chinese stakeholders, especially universities, to file overseas patents in main competitive markets (e.g., the U.S., WIPO, etc.). Third, pay close attention to important technological fields and blank spaces in the AI industry, and prevent patent technology risks. On the one hand, stakeholders should focus on the fierce technological competition in the following important technological fields: Digital Computers, Process and Machine Control, Industrial Electric Equipment, and Scientific Instrumentation. In order to avoid the duplication of R&D investment and prevent patent technology risks, stakeholders need to attach great importance to the leading patentees and their core patents in the above technological fields. On the other hand, stakeholders should seize technological opportunity and make up for the lack of patent distribution in the following technological fields: Industrial Electric Equipment, Engineering Instrumentation, and Automotive Electrics. Once an assignee has a comprehensive patent distribution in important technological fields, especially those blank spaces, this assignee will have a better capability to prevent patent technology risks and become a leading assignee in this field. This study makes an important contribution toward identifying patent risks in the overall technological field by introducing a patent-based multi-level network model that has not appeared in the existing methodology of patent risk research. We make three contributions, inspired by recent advances in patent analysis as well as social network analysis. First, the proposed patent-based multi-level network model provides a new and comprehensive analytical framework for patent risk analysis in the overall technological field, which focuses on all the patents in the given technological field rather than those specific patents infringed by other patents. Our study also extends the research perspectives of patent risks, namely the market-level, technology-level, and patentee-level, and the patentee–market network and patentee–technology network integrating these three levels, and then identifies patent risks from comprehensive perspectives of collaboration, market, and technology. Second, it builds a patent-based multi-level networks model, which puts forward a new theoretical perspective for the existing research on patent risks. Owing to the simple process of retrieving patent data, this model offers a useful method for identifying patent risks in a target technology field. Third, this study uses a combination of methods to build the patent-based multi-level network, including data mining, patent analysis, two-mode network analysis, and social network analysis. By employing two-mode network analysis, the patentee–market network and patentee–technology network are proposed to analyze the patent market risk and patent technology risk. Furthermore, the proposed patent-based multi-level network model can be applied not only to AI, but to diverse other fields of emerging technology. Sustainability 2020, 12, 8667 19 of 21

Despite the above contributions, there are still several limitations that future studies could explore. First, the research methods used in this paper still need further development. In addition to patent analysis and social network analysis, other methodologies, for instance, patent roadmap analysis, main path analysis, and patent citation analysis, would also be considered useful tools to reach the expected research goal. Specifically, patent roadmap analysis and main path analysis have been proven to be efficient approaches to illustrating and visualizing dynamic competitive advantages, and contribute to identifying patent risks in the technological innovation process. Patent citation analysis attempts to measure the qualities of patents, which can help confront patent risks more comprehensively. Second, with the rapid development of an emerging technology, the number of patents will increase rapidly, which leads to the dynamical change in the research results. Future studies could put more effort into the dynamic evolution of patent risks based on the technology life cycle (TLC).

Author Contributions: Conceptualization, X.Y. (Xi Yang); Data curation, X.Y. (Xi Yang); Formal analysis, X.Y. (Xi Yang); Methodology, X.Y. (Xi Yang); Software, X.Y. (Xi Yang); Supervision, X.Y. (Xiang Yu); Visualization, X.Y. (Xi Yang); Writing—original draft, X.Y. (Xi Yang); Writing—review & editing, X.Y. (Xi Yang) and X.Y. (Xiang Yu). All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by the 111 Project (No. B18058) and the National Natural Science Foundation of China (No. 71072033). Acknowledgments: The first author would like to thank Martin G. Everett, the director of Mitchell Centre for Social Network Analysis, The University of Manchester. When I study at The University of Manchester, he not only provides me with very nice research conditions, but also offers me lots of valuable and constructive suggestions for my research. Conflicts of Interest: The authors declare no conflict of interest.

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