NSF Proposal Writing Workshop June 2019 Prior Art Searching

Total Page:16

File Type:pdf, Size:1020Kb

NSF Proposal Writing Workshop June 2019 Prior Art Searching NSF Proposal Writing Workshop June 2019 Prior Art Searching Gregory Sechrist Licensing Associate Who we are: The Office of Technology Commercialization exists to Understand, Catalyze, Support and Protect the intellectual property of Ole Miss employees. We also work with students and innovators in the Oxford community. Much of the scholarly research conducted at the University of Mississippi has applications in the commercial sector. We help inventors realize and harness this potential. Commercialization rewards both the researcher and the University, and ultimately, benefits the larger public. UNIVERSITY OF MISSISSIPPI © Disclaimer This Is Not Legal Advice. * I am not an attorney in the University of Mississippi Office of the General Counsel, nor am I legal counsel for the Office of Technology Commercialization or the University. I am not licensed to practice law in Mississippi and the information that I provide does not constitute legal advice or counsel. UNIVERSITY OF MISSISSIPPI © NSF Clause on Intellectual Property Inventions made during NSF-assisted research are governed by the Foundation's standard Patent Rights clause which is invoked by article 21 of our Grant General Conditions published in the Grant Policy Guide. Normally, awardees are allowed to retain principal rights to these inventions, provided they disclose them to the Patent Assistant in the Office of the General Counsel. Rights to copyrightable material ("subject writings") created during NSF-assisted activities are governed by article 18 of our Grant General Conditions. • 730 Intellectual Property - Link UNIVERSITY OF MISSISSIPPI © What is Prior Art? • Patents are for new inventions • Public disclosure is the date your invention is shared with the “public” • USA has 1 year grace period to file application once public, the rest of the world is gone • Use NDA’s and pay attention to the terms of a contract / proposal • Prior Art - ANYTHING that can be used against you which is relevant to your claims of originality that exists ANYWHERE BEFORE you file • Literally can be anything in the public domain – Patents, Publications, Thesis, YouTube, Goods for Sale • Does not need to exist exactly as your invention - can be a combination if obvious to combine • Just because you have a patent on a product does not mean you can produce it if the “pieces” of it are protected by other patents that belong to someone else UNIVERSITY OF MISSISSIPPI © Prior Art Searching Using Lens.org and Google • Searching for patents is similar to searching for publications • Patent Search Tools Comparison Guide • Lens.org • Can create structured keyword and classification searches and Boolean searches • Can create folders, save and share searches, create notes, download results • Free IP trend analysis tools • Can search for scholarly articles separately • Video Tutorials • Google Patents • Keyword search using Boolean operators and group by classification • Good translation capabilities • Can include grants, scholarly articles, and patents in same search • Basic structured search filtering and IP trend analysis tools UNIVERSITY OF MISSISSIPPI © Prior Art Searching Using Publication tools • Basically, search for similar publications, articles, etc. • Lens.org and Google Patents are free and offer both publication and patent searches • The Library subscribes to several resources for publication searching • One Search on library page • Library has access to full-text versions of a lot of articles or they can get them • Use the librarians – https://libraries.olemiss.edu/request-a-library-research- consultations/ • You can be your own prior art UNIVERSITY OF MISSISSIPPI © Tips for Talking Patents • Talk about the value, NOT how it specifically works • Think of the invention as your secret sauce, don’t disclose more than you need to • Market and IP trends can show value • If someone outside of the University wants to know the technical information we can setup a NDA • Be wary of language in contracts regarding intellectual property. Some will tell you not to disclose intellectual property or what you are disclosing belongs to them. • Prior art is not limited by time, jurisdiction, or patents. Literally can be anything in the public domain which came before you. • Filing a non-provisional application is considered a public disclosure • Do not lie about having a patent or patent application • Provisional applications are not disclosed. UNIVERSITY OF MISSISSIPPI © 5 Practical Tips for IP 1. The terms in a contract are important, please ask questions • We have seen proposals that say the company owns whatever you submit 2. Protect confidential information • Establish security policies, use NDA’s, and label things as “Confidential” 3. Do your homework so you don’t waste your time 4. DO NOT publically disclose inventions until you are ready • This starts clocks you cannot stop 5. Use your university resources, both the tools and the people UNIVERSITY OF MISSISSIPPI © What Type of IP Do You Need • Copyrights – Creative Works • Examples: Art, literature, pictures, movies, music, code • Trademarks - Branding • Examples: Phrases, designs, logos • Trade Secrets – Confidential Business Secrets • Examples: Recipes, algorithms, manufacturing process, customer lists, business strategies • Patents - Inventions • Examples: Methods of manufacture, products, processes, compositions of matter, software UNIVERSITY OF MISSISSIPPI © .
Recommended publications
  • Arguments for USPTO Error in Granting U.S. Patent 9,904,924 to Square, Inc
    Arguments for USPTO Error in Granting U.S. Patent 9,904,924 to Square, Inc. Umar Ahmed Badami Annabel Guo [email protected] [email protected] Samantha Jackman Kathryn Ng Sharon Samuel [email protected] [email protected] [email protected] Brandon Theiss* [email protected] New Jersey’s Governor’s School of Engineering and Technology July 24, 2020 *Corresponding Author Abstract—The United States Patent and Trademark Office how their invention works. When the patent expires 2, the (USPTO) erred in granting U.S. Patent 9,904,924 to Square Inc. public is permitted to make, use, or sell the invention without U.S. Patent 9,904,924 claims a system for the transfer of funds the inventor’s permission. from a sender to a recipient via email. However, this system was disclosed prior to the U.S. Patent 9,904,924’s effective filing For an inventor to patent their invention, there are some date of March 15, 2013. Claims 1 and 9 were disclosed by U.S. requirements that must be fulfilled as per the U.S. Code. One 8,762,272 and U.S. Patent 8,725,635 while claim 20 was taught by requirement for patentability, as stated under 35 U.S.C. § 101 a combination of U.S. Pat. App. Publication 2009/0006233, U.S. Patent 10,395,223, and U.S. Patent 10,515,345, thereby rendering is that the invention must be useful, defined as having “a at least one of the claims of U.S. Patent 9,904,924 invalid.
    [Show full text]
  • Application Number
    Intellectual Property India· Application Number. 4302WHE/2014 0 Home (h ttp://i pin di a.gov. in/index. htm) Contact Us (hrtp :/I i pi ndia.gov. in/ contact-us. h tm) Feed back (http:/ I i pi nd ia .gov. in/feedback. h tm) FAQ s (http:// i pi nd i a .gov. i n/faq-patents .h tm) Si tem a p (http://i pi nd i a .gov. i n/sitemap. htm) ., v ~~i&l lnck; ,, P ..1renr Advat<r.~:d. Se.1rch System (http:/ I i pi ndia. gov. in/index. htm) (http:/ /ipin dia .gov.in/index.h t Application Number: 4302/CHE/2014 Bib/agraphic Data Complete Specification Application Status Invention Title PHARMACEUTICAL COMPOSITIONS COMPRISING GERANIAL AS ATPASE INHIBITORS "" Publication Number 27/2016 Publication Date 2016/07/01 Publication Type INA Application Number 4302/CHE/2014 Application Filing Date 2014/09/03 Priority Number Priority Country Priority Date Field Of Invention (F/11) PHARMACEUTICALS Classification (IPC) A61 K-31 /00 Inventor Name Address Country Nationality V SAISHA Department of Biotechnology, BMSCE, PB No. 1908, Bull Temple Road, Bangalore · 560019, Karnataka (India) IN IN http://i pi ndi aservi ces .g ov.1 nlpubli csearchlneltvpublicsearchlpatent_detail.php?UC ID= SGF6l.J Di1JN zJhSjJ KYkhZM jJGaF dzUG 1mSW1 NZ2F TaWCZ01\f\JRzkxRjF GST 0%30 L"'S~·il/5·31~2'0~13 ' lir~iiA~~~~=:"~ l~~&~f&;~g:~c,~;~(i) I #GS/4, N~G,A;Sf.\Em H,t.;Ciii) lNOfAN g, f>c, G. JYOf:IHI, #,3'6/:t7~B-.f0l Ofv1~RSAR®Vf4:AAI PAPW ·~OFr~G~; [email protected];Afi<APlaM•M#lN~R()AP, ~SklSM'ANAGWE>l, ·BA'NGJl;llQRE-560004'• IJIJDIAI'f .
    [Show full text]
  • Artificial Intelligence, Economics, and Industrial Organization Hal Varian1
    Artificial Intelligence, Economics, and Industrial Organization Hal Varian1 Nov 2017 Revised June 2018 Introduction Machine learning overview What can machine learning do? What factors are scarce? Important characteristics of data Data ownership and data access Decreasing marginal returns Structure of ML-using industries Machine learning and vertical integration Firm size and boundaries Pricing Price differentiation Returns to scale Supply side returns to scale Demand side returns to scale Learning by doing Algorithmic collusion Structure of ML-provision industries Pricing of ML services Policy questions Security Privacy 1 I am a full time employee of Google, LLC, a private company. I am also an emeritus professor at UC Berkeley. Carl Shapiro and I started drafting this chapter with the goal of producing a joint work. Unfortunately, Carl became very busy and had to drop out of the project. I am grateful to him for the time he was able to put in. I also would like to thank Judy Chevalier and the participants of the NBER Economics of AI conference in Toronto, Fall 2017. Explanations Summary References Introduction Machine learning (ML) and artificial intelligence (AI) have been around for many years. However, in the last 5 years, remarkable progress has been made using multilayered neural networks in diverse areas such as image recognition, speech recognition, and machine translation. AI is a general purpose technology that is likely to impact many industries. In this chapter I consider how machine learning availability might affect the industrial organization of both firms that provide AI services and industries that adopt AI technology. My intent is not to ​ ​ ​ ​ provide an extensive overview of this rapidly-evolving area, but instead to provide a short summary of some of the forces at work and to describe some possible areas for future research.
    [Show full text]
  • Data Analytics: the Future of Legal JEREMIAH CHAN1
    International In-house Counsel Journal Vol. 9, No. 34, Winter 2016, 1 Data Analytics: The Future of Legal JEREMIAH CHAN1 Legal Director, Google Inc., USA & JAY YONAMINE Senior Data Scientist, Google Inc., USA & NIGEL HSU Head of Patent Operations, Verily Life Sciences LLC, USA INTRODUCTION During the 2002 season of Major League Baseball (MLB), the Oakland Athletics and New York Yankees both won an astounding 103 games, a feat only topped three times by any team in the last 24 years. Despite identical records, the New York Yankees payroll on opening day was $125,928,583 compared to only $39,679,746 for the Oakland Athletics. On a per win basis, the Oakland Athletics paid $385,240 per win compared to the New York Yankee’s $1,222,608. The New York Yankees were not an outlier. All other MLB teams (excluding Oakland) paid on average $853,252 per win in 2002. So how were the Oakland Athletics able to generate wins with over two to three times the cost efficiency of other teams in the league? By now, the answer has been well documented by Michael Lewis in the book and subsequent movie Moneyball, in which Lewis recounts how Oakland Athletics’ General Manager Billy Beane replaced traditional approaches to scouting and roster management with a rigorous use of data and statistical models. This is often referred to as “data analytics.” The quest for increased efficiency is certainly not unique to MLB. Be it a widget factory, technology company, or legal department, the desire to maximize the quality of outcomes while maintaining or reducing costs is ubiquitous.
    [Show full text]
  • Improving Google Patents with European Patent Office Patents And
    Improving Google Patents with European Patent Office patents and the Prior Art Finder Tuesday, August 14, 2012 at 10:23 AM ET Posted by Jon Orwant, Engineering Manager Cross-posted from the Google Research blog At Google, we're constantly trying to make important collections of information more useful to the world. Since 2006, we’ve let people discover, search, and read United States patents online. Starting this week, you can do the same for the millions of ideas that have been submitted to the European Patent Office, such as this one. [An example Moisture-tight edible film dispenser and method thereof EP 1692064 B1Abstract (text from WO2005051822A1]. Typically, patents are granted only if an invention is new and not obvious. To explain why an invention is new, inventors will usually cite prior art such as earlier patent applications or journal articles. Determining the novelty of a patent can be difficult, requiring a laborious search through many sources, and so we’ve built a Prior Art Finder to make this process easier. With a single click, it searches multiple sources for related content that existed at the time the patent was filed. From googlepublicpolicy.blogspot.com/2012/08/improving-google-patents-with-european.html 21 April 2013 Patent pages now feature a “Find prior art” button that instantly pulls together information relevant to the patent application. The Prior Art Finder identifies key phrases from the text of the patent, combines them into a search query, and displays relevant results from Google Patents, Google Scholar, Google Books, and the rest of the web.
    [Show full text]
  • Google Patents
    USPTO University Outreach: Patent Searching and Search Resources Presenter Date Objectives • Where can you search? • What are the benefits of searching? • When should you search? and most importantly: • How do you search using USPTO search tools? • What are some important strategies? Overview of IP: Why Get a Patent? • A patent can be – Used to gain entry to a market – Used to exclude others from a market – Used as a marketing tool to promote unique aspects of a product – Sold or licensed, like other property What is patentable? compositi on Manufact NEW, USEFUL, Machine ure NONOBVIOUS, ENABLED & CLEARLY DESCRIBED Method/ Improvements or process thereof Types of Patents • Utility - New and useful process, machine, article of manufacture, or composition of matter, or any new and useful improvement thereof Term – 20 years from earliest effective filing date • Design - Any new, original and ornamental design Term - 15 years from issue date • Plant - Whoever invents or discovers and asexually produces any distinct and new variety of plant... Term – 20 years from earliest effective filing date Overview of IP: What is a Patent? • A Property Right – Right to exclude others from making, using, selling, offering for sale or importing the claimed invention – Limited term – Territorial: protection only in territory that granted patent; NO world-wide patent • Government grants the property right in exchange for the disclosure of the invention What Type of Application? • Provisional – abandoned after one year, no claims required, written disclosure must meet same requirements as non- provisional, not allowed for design. • Non-Provisional - claims required, written disclosure must meet requirements of 35 USC 112(a).
    [Show full text]
  • Machine Learning for Marketers
    Machine Learning for Marketers A COMPREHENSIVE GUIDE TO MACHINE LEARNING CONTENTS pg 3 Introduction pg 4 CH 1 The Basics of Machine Learning pg 9 CH. 2 Supervised vs Unsupervised Learning and Other Essential Jargon pg 13 CH. 3 What Marketers can Accomplish with Machine Learning pg 18 CH. 4 Successful Machine Learning Use Cases pg 26 CH. 5 How Machine Learning Guides SEO pg 30 CH. 6 Chatbots: The Machine Learning you are Already Interacting with pg 36 CH. 7 How to Set Up a Chatbot pg 45 CH. 8 How Marketers Can Get Started with Machine Learning pg 58 CH. 9 Most Effective Machine Learning Models pg 65 CH. 10 How to Deploy Models Online pg 72 CH. 11 How Data Scientists Take Modeling to the Next Level pg 79 CH. 12 Common Problems with Machine Learning pg 84 CH. 13 Machine Learning Quick Start INTRODUCTION Machine learning is a term thrown around in technol- ogy circles with an ever-increasing intensity. Major technology companies have attached themselves to this buzzword to receive capital investments, and every major technology company is pushing its even shinier parentartificial intelligence (AI). The reality is that Machine Learning as a concept is as days that only lives and breathes data science? We cre- old as computing itself. As early as 1950, Alan Turing was ated this guide for the marketers among us whom we asking the question, “Can computers think?” In 1969, know and love by giving them simpler tools that don’t Arthur Samuel helped define machine learning specifi- require coding for machine learning.
    [Show full text]
  • Implications of the Google's US 8,996,429 B1 Patent in Cloud
    DOI: 10.5772/intechopen.70279 Provisional chapter Chapter 8 Implications of the Google’s US 8,996,429 B1 Patent in ImplicationsCloud Robotics-Based of the Google’s Therapeutic US 8,996,429 Researches B1 Patent in Cloud Robotics-Based Therapeutic Researches Eduard Fosch Villaronga and Jordi Albo-Canals Eduard Fosch Villaronga and Jordi Albo-Canals Additional information is available at the end of the chapter Additional information is available at the end of the chapter http://dx.doi.org/10.5772/intechopen.70279 Abstract Intended for being informative to both legal and engineer communities, this chapter raises awareness on the implications of recent patents in the field of human-robot interaction (HRI) studies. Google patented the use of cloud robotics to create robot personality(-ies). The broad claims of the patent could hamper many HRI research projects in the field. One of the possible frustrated research lines is related to robotic therapies because the person- alization of the robot accelerates the process of engagement, which is extremely beneficial for robotic cognitive therapies. This chapter presents, therefore, the scientific examina- tion, description, and comparison of the Tufts University CEEO project “Data Analysis and Collection through Robotic Companions and LEGO® Engineering with Children on the Autism Spectrum project” and the US 8,996,429 B1 Patent on the Methods and Systems for Robot Personality Development of Google. Some remarks on ethical implica- tions of the patent will close the chapter and open the discussion to both communities. Keywords: cognitive therapeutic robots, cloud robotics, Google patent, personality, personalization, ASD research, ethical implications 1.
    [Show full text]
  • Do Patents Disclose Useful Information?
    Harvard Journal of Law & Technology Volume 25, Number 2 Spring 2012 DO PATENTS DISCLOSE USEFUL INFORMATION? Lisa Larrimore Ouellette* TABLE OF CONTENTS I. INTRODUCTION .............................................................................. 532 II. THE DISCLOSURE JUSTIFICATION FOR PATENTS .......................... 536 A. Legal Requirements and Judicial Interpretation ...................... 536 B. Is Disclosure a Compelling Justification for Patents? ............. 540 C. Previous Surveys of the Value of Patent Disclosures .............. 547 III. UTILITY OF PATENTS FOR NANOTECH RESEARCHERS ................ 552 A. Survey Method and Summary Statistics ................................... 553 B. Why Do Scientists Read Patents (or Not)? ............................... 557 1. Reasons Researchers Avoid Patents ...................................... 557 2. Reasons Researchers Read Patents ........................................ 559 C. Do Patents Contain Useful Technical Information? ................ 560 D. Are Patents Reproducible? ...................................................... 562 E. Do Researchers Worry About Willful Infringement? ............... 564 IV. NANOTECHNOLOGY PATENT CASE STUDIES .............................. 566 A. Carbon Nanotube Resonators .................................................. 566 B. Carbon Nanotube Sensors ........................................................ 569 V. IMPLICATIONS FOR PATENT POLICY ............................................. 571 A. Strengthen Enforcement of Disclosure Requirements
    [Show full text]
  • The Future According to Google
    Travis: THE FUTURE ACCORDING TO GOOGLE THE FUTURE ACCORDING TO GOOGLE: TECHNOLOGY POLICY FROM THE STANDPOINT OF AMERICA'S FASTEST-GROWING TECHNOLOGY COMPANY By Hannibal Travis* 11 YALE J.L. & TECH. 209 (2009) As the fastest-growing technology company in the United States,' Google has been at the center of some of the most contentious technology policy disputes of recent years. In the federal courts, these disputes focus on the fair or noncommercial use of copyrighted work and trademarks on the Internet. In Congress, Google is leading the charge in favor of laws protecting innovative Internet companies from discriminatory or exorbitant charges by broadband and wireless infrastructure providers. It has also been a vocal opponent of excessive governmental control over Internet content. Copyright lawsuits arising out of search engines and user- generated content sites such as Google Video and YouTube have the potential to change the rules governing communication over the Internet. Similarly, trademark litigation alleging that comparative and Internet keyword-based advertising are infringing may limit the ability of technology companies and their customers to compete online. Many technology companies also believe that injunctive relief obtained by the owners of patents in comparatively minor components of complex software-enabled products may chill innovation and divert capital away from applied research. But it seems that the power of infrastructure providers to favor allied content providers has truly spooked technology leaders like Google. Meanwhile, Google, other technology and Internet companies, and members of Congress have demanded action to limit foreign governments' ability to block U.S.-based Web content from being accessed by persons present in their territory.
    [Show full text]
  • Overview of the Patent System and Procedure in Sri Lanka
    Overview of the Patent System and Procedure in Sri Lanka Outline n What is intellectual property? n Types of intellectual property n Evolution of the domestic system n International Arena n Why do intellectual property rights matter? n Patent system n Patenting Procedure n Patent CooperationTreaty 2 What is Intellectual Property (IP)? n IP is the term used for types of property that result from creations of human mind, the intellect. n IP is an asset and has a monetary value. n IP can be owned, transferred, sold or licensed. n A kind of intangible property and has no physical form. n IP is divided into two general categories n Industrial Property n Copyrights 3 Types of Intellectual Property n Industrial Property – assets created for the advancement of technology, industry and trade. n Patents n Trademarks n Industrial Designs n Geographical Indications n Copyrights and related rights – original expressions and “work of authorship” 4 Evolution of the domestic system n The first patent was granted on November 22, 1860 in Sri Lanka under the British Inventor’s Ordinance of 1859. n The Patents Ordinance of 1906 (based on the English Patent Law). n The English Law of Trademarks in 1888 under the Ordinance No. 14 of 1888. n The Design Ordinance in 1904. n The Copyright Ordinance No 12 of 1908 is the first copyright law in Sri Lanka. 5 Evolution of the domestic system n The Code of Intellectual Property Act N0.52 of 1979 – turning point in the evolution of IP law and administration in Sri Lanka. n New regime – The Intellectual Property Act, N0.36 of 2003 which provides the law relating to IP and for an efficient procedure for registration, control and administration.
    [Show full text]
  • Google Patents
    Patent Research and Analysis - Google Patents Cynthia Dixon, Ford Khorsandian, Ellen Krabbe, Steve Sampson, Ian Wetherbee Overview and Coverage Non-patent literature Google Patents includes over 87 million patent To make prior art searching easier, Google Patents includes a publications from 17 patent offices around the world, as well as copy of the technical documents and books indexed in Google many more technical documents and books indexed in Google Scholar and Google Books. These documents have been Scholar and Google Books. machine-classified using the Cooperative Patent Classification scheme commonly used in patent searching to make finding It currently indexes full-text documents from the following documents easier. You can browse the citation metrics for the major patent offices of interest for attorneys or agents in the top publications in Google Scholar Metrics to get an overview of United States: US, EP, WO, CN, JP, AU. Bibliographic information the types and sources of papers included, and see Google is available for over 100 countries included in DocDB. Scholar help for details on what papers are included. You can see the real-time number of you can go to https://patents.google.com/coverage. These totals include all Searching patents and published applications in the index, but complete coverage cannot be guaranteed. From the homepage, you can begin your search in a few ways: • Enter a patent publication or application number, such as Many documents have full-text description and claims available. [US9014905B1], [9014905], or [US 14/166,502]. It is The "Since ..." dates listed on the statistics graph show when preferable to use the country code, no spaces and no slashes.
    [Show full text]