Conference Abstracts and Biographies
Bringing Patent Information to the Engineering Community
The American Society of Mechanical Engineers (ASME) recently launched an initiative to support engineers in the bioengineering and medical devices industries. As part of this initiative, known as the Alliance of Advanced Biomedical Engineering, we identified the top challenges expressed by our stakeholders, which include:
We are looking to connect the ASME/AABME community with PIUG, as we believe such a connection can enable exchange of information and instruction that can ultimately solve these challenges.
ASME, a not-for-profit professional society and standards developing organization, was founded in 1880 to advance a mission of improving the quality human life. The mission continues to this day, serving 130,000 members worldwide.
Christine Reilley is business development director of Healthcare for the American Society of Mechanical Engineers (ASME), based in New York City. As director, she leads the ASME Alliance of Advanced Biomedical Engineering (AABME), which focuses on creating and growing the Society’s portfolio of programs, products, and services in this area.
Christine had previously served as program manager in the ASME Emerging Technologies unit, developing content and conferences in areas focusing on bioengineering, nanotechnology, thermofluids, and materials. Previously, she spent more than 10 years in ASME Codes and Standards Publishing as an editor, overseeing the production of codes.
She earned an MS in Biomedical Engineering with a concentration in Tissue Engineering and Biomaterials from New Jersey Institute of Technology (NJIT). She received a BA in Journalism and Mass Media with a minor in Biological Sciences from Rutgers University, Douglass College.
Information-Analysis-Insight-Action: An IP Manager’s Perspective
Though specific factors vary by industry, businesses in general (and R&D departments in particular) face ever-increasing pressures to remain viable and growing: compressed innovation cycles having accelerated product development schedules, all while seeking a sustainable competitive advantage and reasonable risk-tolerance for potential litigation. Against the backdrop of that environment, the community of IP professionals (patent attorneys/agents, IP managers, information scientists/analysts, etc.) must contend with the continuing massive increase in patent filing activity and exponential growth in available digital information. This presentation will focus on sharing some of the key principles, strategies, and frameworks an IP Manager has utilized to address some of these challenges, thereby driving more effective decision-making and actions with key stakeholders.
Mike is currently Patent Liaison for Prosthetics R&D (Lab SBU) at Dentsply Sirona. He also provides independent IP consultancy as the principal at SF IP Strategy and Patent Advisory Services. Prior to his current positions, Mike retired from a 24-year career in Product Development at Procter & Gamble, the last 9 years of which he was IP Manager for their global Skin Care and Color Cosmetics brands, interfacing with R&D, Legal, and Marketing. In this role, he applied his prior technical expertise in product formulation, process engineering, and patent analysis to develop and execute IP strategies protecting more than 300 new patentable inventions, manage and defend the existing patent portfolio, and provide technical competitive intelligence for strategic business planning. Mike is a registered US patent agent, is a co-inventor on 5 granted US patents and 7 pending US patent applications, and has a BS in chemical engineering from the University of Delaware.
Application of Patent Classification Systems to “Unconventional” Searches
Many discussions have been presented, at PIUG conferences and on other occasions, demonstrating the fundamental importance of the application of various patent classification systems to conduct effective searches. These discussions have focused on the methodical and skillful application of various patent classification systems to solving challenges with well-defined search scopes. For example, “conventional” search requests often arrive in the form of novelty, state-of-the-art, freedom-to-operate, or invalidity that allow definitive search concepts to be formulated with clarity. Searchers define these concepts with appropriate tools including patent classification schemes to devise search strategies to achieve appropriate scope coverage.
Yet, as existing technologies progress and mature, not to mention the accelerated pace of advancements in new science and technologies, the need to explore “unconventional” ideas has been elevated at almost all business levels. This requires a searcher to discover “unknowns” outside conventional search boundaries. We have seen increased amount of “unconventional” search requests in recent years. These requests often share a common characteristic – the search boundary is open or cannot be effectively defined. This emerging challenge requires innovative approaches and solutions.
This presentation hopes to jump-start necessary discussions and sharing of knowledge, experiences and tips in overcoming the challenge. The application of patent classification schemes will be discussed as an effective approach to “unconventional” searches.
Hongbo Liu joined the Information Research and Analysis (IR&A) group, ExxonMobil Research & Engineering Company, in April 2012. In addition to patent and literature searching, large amounts of his time have been dedicated to competitive intelligence covering a wide range of emerging technologies. As such, he has developed practical knowledge and skills in competitive analysis. Prior to Exxon Mobil, he had a 10-year R&D career at Ethicon – a Johnson & Johnson company. He was a recipient of Johnson & Johnson’s prestigious Philip B. Hofmann Research Scientist Award. Hongbo holds a B.S. degree in chemistry from Peking University and Ph.D. in organic/polymer chemistry from Rutgers University. He is also the inventor of six US grant patents and author of over 20 publications.
LOE (Loss of Exclusivity) for Pharmaceutical Products
LOE, or Loss of Exclusivity, is an important date in a pharmaceutical product’s lifecycle which indicates the date of first possible generic competition in the market. This presentation will cover the basics of Loss of Exclusivity projections for small molecule pharmaceutical products in major markets including the United States and Europe. An overview of LOE, including the different types of exclusivity, and useful resources for practitioners will be discussed.
Jason has been a Chemistry Patent Searcher in the Intellectual Property Group at Pfizer for the past 10 years, working with attorneys in both the Innovative Health business as well as the Essential Health business. In 2014, Jason also began assisting with Pfizer’s Exclusivity Data Management programs and is now responsible for providing LOE information to all of Pfizer’s Essential Health businesses around the globe. Prior to joining the searching world, Jason was a Medicinal Chemist with Wyeth for six years. Jason received his PhD in Organic Chemistry from the Massachusetts Institute of Technology and BS in Chemistry from Villanova University. Jason also holds an MBA degree from DeSales University. Jason is a member of the Patent Information Users Group and the American Chemical Society.
Money Talks: Reasonable Royalties, Licensing Pitfalls, and M&A Portfolio Valuation Trends and Analytics.
When evaluating a licensing or acquisition opportunity you face numerous challenges, ranging from portfolio validation, technology alignment, strength and valuation assessments, to contract drafting. This session will address key search strategies, metrics, and indicators for these tasks, and how to create instructive analytics. This involves a balancing act: the most comprehensive search yielding the most relevant and actionable intelligence.
Laura is a registered US patent attorney and is a member of the Minnesota Bar. Laura earned her B.S. from the College of Biological Sciences at the University of Minnesota, and graduated magna cum laude from William Mitchell College of Law where she served as Editor-in-Chief of the Cybaris® Intellectual Property Law Review. Prior to joining Clarivate Analytics, Laura gained corporate industry experience in pharmaceutical regulatory compliance, patent licensing, and M&A due diligence.
Artificial Intelligence and Patents: How the U.S. Patent System should prepare itself for the latest technological revolution
Artificial Intelligence (“AI”) has the ability to create its own inventions. Although our patent system does not explicitly require a “human” inventor, it has not yet been faced with the possibility of a “machine” inventor. The patent world thrives on the notion of human creativity, which faces a big threat from AI. In simple terms, AI is a machine’s ability to independently improve itself without the need for human intervention. These are the systems that learn to perform tasks on their own. AI is already in use in companies worldwide, and it is imperative that our patent system be vary of AI’s legal implications and prepare for it. AI poses two big hurdles to the U.S. patent system. First, the pace of AI’s revolution may completely eliminate the ambivalent Alice standard, which fails to provide clarity on the patentability of abstract ideas. Litigations on AI patents would most likely focus on this heightened subject-matter eligibility standard and it is foreseeable that the standard would need improvisations. Second, in case of AI, the liability for patent infringement would fall on machines and not humans. The current patent law does not provide any guidance on such a scenario. It is no secret that the world is preparing for the tremendous impact that AI is about to make and it remains to be seen how the U.S. Patent Law adapts to this magnanimous technological advancement. This article explores the hurdles posed by AI in the field of patent law and discusses various changes that our patent system can imbibe to prepare for the AI revolution.
Mansi Parikh is an attorney at the law firm of Schumann Hanlon Margulies LLC, where she chairs the Intellectual Property group. She is admitted to practice law in New York and New Jersey. Her intellectual property practice puts emphasis on trademark clearances and enforcement, patent clearances, due diligence analysis, copyright filing and enforcement, freedom-to-operate investigations, and the assignment or licensing of intellectual property rights. In addition, she is also involved in commercial litigation, residential and commercial real estate transactions, and contract drafting and negotiations. In her previous work as an Associate at a well-known law firm in New Jersey, Mansi was involved in patent litigation, as well as non-infringement and invalidity opinion work.
Mansi obtained her J.D. from Rutgers School of Law, Newark, NJ. As a Clinical Research Associate at the Intellectual Property Law Clinic at Rutgers Law School, she focused on trademark prosecution and routinely advised small business entrepreneurs on IP risks for their companies. Mansi has a highly technical life sciences background, through her 5.5 years of work at a renowned biotechnology company, where she was involved in developing antibodies for neurological disorders. She has a Master of Science degree in Biological and Pharmaceutical Biotechnology, and a Bachelor of Engineering degree in Biotechnology. Mansi is the author of her firm’s blog and a member of NJSBA, NYSBA, NJIPLA and NYIPLA.
Unique Opportunities for Artificial Intelligence: People, Process and Technology
The USPTO makes and distributes a vast amount of data each day both internally and externally. USPTO has an enterprise data inventory that includes patent, trademark, policy, and other related data that is distributed to the public for consumption. Consumers, both domestic and international, of our data include: independent inventors, startups to large companies, law firms, IP intensive industries, strategic patent analytics companies, firms, academia, other government agencies, IP5 Offices (EPO, SIPO, KIPO and JPO), and the public at large.
Enterprise measurement reaches across all business units. What is the impact of a change in the work product of one business unit on the data flow of another business unit? Slight modifications in processes may ripple across multiple work products – sometimes positive, sometimes negative. It is critical to improve pendency to better ensure the timely delivery of innovative goods and services to market and the related economic growth and creation of new or higher-paying jobs. This is accomplished with precise quality work product measurement(s) across the enterprise in an exponentially growing information age for the Agency mission of timely delivery of high quality patents and trademarks.
How do other federal agencies execute on their similar missions? Sister science based agencies (NASA, NSF, NOAA, NIH and the FDA) have met this challenge for more accurate measurement, actionable business intelligence and quality work products by implementing advanced analytic solutions. These next generation solutions include building a data science practice, machine learning (ML) and artificial intelligence (AI). Should the USPTO should adopt these next generation enterprise analytic solutions to successfully meet our agency mission?
Given an increasing workload and newer work products, how does the USPTO successfully execute its mission ensuring the reliability of issued patents and high quality examination of trademark applications so that rights holders and the public have confidence in patent grants and trademark registrations when they make plans to invent and invest?
Thomas A. Beach is Chief Data Strategist and Portfolio Manager of the USPTO’s Patents End to End (PE2E) and Patent Trial & Appeal Board End-to-End (PTAB) initiatives. In these roles, he serves as part adviser, part steward for improving data quality, part evangelist for data sharing, part technologist, and part developer of new data products. He also works to ensure that agency systems keep pace with private sector technology to provide effective and modern IT for patent application examination Beach’s USPTO career spans a variety of key roles. He was the founder and portfolio manager of the Digital Service & Big Data (DSBD) initiative, which worked to unleash and unlock the value of patent and trademark data through data science and machine learning to ensure that patents and trademarks are of the highest quality.
He also served as a senior advisor in the Office of the Under Secretary of Commerce for Intellectual Property, working to advance the USPTO’s mission of delivering timely and quality patents and trademarks. As a patent examiner and supervisor, he covered Offshore Oil & Gas Technologies and Business Methods (Fixed Income & Stock Trading and Portfolio Management). A graduate of the Georgia Institute of Technology in Engineering, Beach received his Master’s Degree from Georgetown University’s McDonough School of Business. He has been a guest speaker at the White House Open Data Summit, Scheller College of Business at Georgia Tech, and the Harvard Business School. Most recently, Thomas was nominated for FedScoop Top 50 Federal Leaders of 2018.
AI meets IP: Leveraging Artificial Intelligence and Machine learning to improve IP Search, Discovery and Monitoring
Patent exploration technology has not evolved to deal with the exponential growth of patent data and its changing terminology. This limitation is principally because patent exploration is still rooted in iterative terminology based search and filter methodology. We observe three principal areas that limit the success of a search based methodology: Search is limited by terminology; the user must have latest subject matter knowledge of the field of invention and know the right combination of terms for the search to produce good results. Search is disjointed; the user must first create many search queries, perform iterative searches and then manually correlate across these independent search results to form a complete picture. Search is Reactive; any changes in the clients input or underlying patent data requires the user to update their queries and rerun the search process again. Machine Learning and Artificial Intelligence can be brought to bear on this challenge and is ideally suited to achieve a simple, learning system that allows the legal professional to effectively explore the patent landscape. Machine Learning provides a vehicle to learn the legal professionals interests through simple interaction, while AI uses that learned interest model to evaluate and recommend patents that are highly correlated to it. Both the learning and recommendations are iterative and update dynamically with human feedback. This talk will explore the application of ML and AI to the patent landscape and its potential for Search, Discovery and Monitoring.
Tom brings over 30 years of experience in the industrial and healthcare technology space. Tom has BS degree in Electrical Engineering form Georgia Tech. He is a graduate of GE’s management development course and is a Six Sigma master black belt. Prior to joining ConvergentAI, Tom held leadership positions in technology companies including COO at a $30M healthcare software and services company, VP of strategy & business development for the $500M healthcare data connectivity and analytics division of McKesson, GM of commercial software for $30M solution at GE Healthcare, and CIO of a $300M sensing solutions business at GE Industrial Systems.
Leveraging IBM’s Watson to aid in the monetization and management of IBM's Patent portfolio.
IP Advisor with Watson is a tool which dramatically shows the benefits of using IBM's Watson’s Cognitive Technologies. Attempting to monetize the IBM Patent Portfolio can be an extremely time consuming and difficult task. There are many sources of data, all in various formats, which requires many different skills to perform a detailed and proper analysis.
An example use case is finding Evidence-of-Use to show a company is infringing on a Patent. Hunting for the evidence and making the connections from documentation to Patent Claims requires many different skills such as enhanced search techniques, subject expertise and patent analysis. Another critical need is TIME. IBM Watson can ingest, digest and understand the documentation available enables the user to effectively read and understand this large amount of data and to make connections between the documentation and the Patent portfolio.
This session will describe how IP Advisor with Watson helps reduce the complexities faced by the user. We will also discuss how we leveraged Watson, the issues in developing a Watson application, and benefits of AI (Augmented Intelligence).
Tom Fleischman, with 36 years at IBM, is an IBM Master Inventor/Senior Engineer and has over 17 years experience working in IBM’s Technology & Intellectual Property Organization. His roles in T&IP have been as a Patent Engineer, Tools Development and Support and Technical Consulting. Tom is a recognized expert in Patent, Company and Industry Landscaping, Infringement Identification, Patent Analytics and Tools.
Recently, Tom has been a team lead and been developing a Cognitive Watson based IP Solution to gather and analyze the huge amounts of unstructured data relevant to various Intellectual Property use cases. Prior to working in T&IP, Tom had worked to test and qualify various components used in IBM equipment, as an Advanced Semiconductor Test Engineer and as an Application Engineer in the Advanced MLC Packaging Organization in IBM. Tom Fleischman is a graduate of Polytechnic Institute of NY (Brooklyn Polytechnic in 1982) has a BS Degree in Electrical Engineering and Computer Science.
Top 10 Reasons Why Professional Searchers Crush It!
While most PIUG members probably don’t need convincing, your clients - members of the C-Suite, research directors, the legal staff - may need to be reminded from time to time of the value you provide as a professional searcher. This presentation is intended to remind you of why you are the best thing that ever happened to the people you serve. We’ll discuss some of the skills, talents and experiences you and your colleagues bring to the table as you help your organization move forward in today’s competitive business environment.
Matt manages the Science IP team, as well as conducting research on behalf of Science IP clients. He became a member of the team in 2010, after spending six years as a Senior Application Specialist providing STN and SciFinder technical training to CAS customers. He holds a Master of Science degree in plant pathology from the University of Minnesota and a Bachelor of Science degree in molecular biology from Purdue University. In addition to his research experience at CAS, Matt was an information consultant with Thomson Reuters and a biologist at Rohm and Haas. Matt is a member of the Patent Information Users Group (PIUG) and the American Chemical Society (ACS).