Patent Analytics:
Transform IP Strategy Into Intelligence
By Jieun Kim, Buyong Jeong, Daejung Kim
This book provides a practical guide to introduce and apply patent network analytics and visualization tools in your business. We incorporate case studies from renowned companies such as Apple, Dyson, Adobe, Bose, Samsung and more, to scrutinise how their underlying values of patent network drive innovation in their business. Finally, this book advances readers’ perspective of patent gazettes as big data and as a tool for innovation analytics when coupled with Artificial Intelligence.
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About Authors
Jieun Kim
Jieun Kim is an associate professor at the Graduate School of Technology and Innovation Management, Hanyang University. She received her M.S. and Ph.D. degrees in Industrial Engineering from the Arts et Métiers ParisTech in 2008 and 2011, respectively. In 2012, she was awarded the Leverhulme Research Fellowship (UK). She was a vising associate professor at Human Communication Technologies Lab, University of British Columbia in Vancouver from 2020 to 2021.
She is an experienced speaker at international conferences and Master’s programs in design innovation (Royal College of London, Ecole de design Nantes, Hong Kong Polytechnic University, National University of Singapore) and in business schools (ASB/MIT Sloan program, University of Nantes).
Buyong Jeong
Buyong Jeong is a deputy director in the Korean Intellectual Property Office(KIPO). Before joining KIPO in 2015, he worked as a patent attorney at PLUS International IP Law firm (2009-2014).
He has been involved in various projects and policies for the Trademark & Design Examination bureau of KIPO. His legal and practical expertise is supported by his academic background, having obtaining bachelor's and master's degrees in Industrial design from KAIST (Korea Advanced Institute of Science and Technology).
Daejung Kim
Daejung Kim is a data scientist specializing in intellectual property and a senior lecturer at Hankyong National University. He holds a Ph.D. in technology and innovation management from Hanyang University.
He is a frequent speaker and consultant, and workshop leader in strategic technology and management solutions for many law firms and legal departments.
About the Book
Innovation unfolds in facets of our lives: in technology, design, business, etc. These areas are seemingly disjointed. However, through the innovative ways of analyzing, measuring and visualizing innovative indicators, we can gain pertinent business insight into the interconnection of these diverse fields.
Through the prisms of a data scientist, a patent attorney, and a designer, this book demystifies the complexity of patent data and its structure and reveals hidden connections in patent data by employing elaborate data analytics and visualizations using a network map. The milestone of 10 million US patents today, enables innovation practitioners to leverage the benefits of patents as big data.
Cross references between patents and collaborating inventor networks enable us to assess the reliance on and impact of patent data and to identify sources of innovation. This book provides a practical guide to introduce and apply patent network analytics and visualization tools in your business.
We incorporate case studies from renowned companies such as Apple, Dyson, Adobe, Bose, Samsung and more, to scrutinise how their underlying values of patent network drive innovation in their business. Finally, this book advances readers’ perspective of patent gazettes as big data and as a tool for innovation analytics when coupled with Artificial Intelligence.
Table of Content
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Part I: Patent as Data2 A Brief History of Patents 2.1 The Prelude of the Patent System 2.2 The First Patent with Claims 2.3 The Great Fire and Patent Numbering 2.4 Genesis of Citations 2.5 Summary References 3 Understanding Patent Data 3.1 Patents, Designs, and Trademarks 3.2 A Walk Through of Patent Data Fields 3.3 Same Same, But Different Design Patents 3.4 Comprehending Trademark Data 3.5 Summary References 4 Claims, “Legally, Less is More!” 4.1 Disentangling Patent Claims 4.2 Broad or Narrow: All-Elements Rule 4.3 Anatomy of Patent Claims 4.4 The Butterfly Effect of Design Patents: Partial Claims of Drill Bits 4.4 Summary References
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Part II: Network Analytics5 Basic Network Concepts 5.1 Why Does Patent Network Analysis Matter? 5.2 Basic Concept of Network and Graph Theory 5.3 Network Metrics 5.4 Summary References 6 Patent Citations Analysis 6.1 The Meaning of Patent Citations 6.2 How to Scale Up Patent Citation Networks 6.3 Pitfalls and Best Practices in Using Patent Citation Data 6.4 Summary References 7 Patent Data through a Visual Lens 7.1 Unexpected Encounters 7.2 Six Basic Charts 7.3 Network Visualisation 7.4 Summary References 8 How to Study Patent Network Analysis 8.1 Research Design 8.2 Choosing Network Analysis Tools 8.3 Four Practical Steps for Patent Network Analysis 8.4 Summary References
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Part III: Uncover Corporate Innovation with Patent Analytics9 Is Innovation Design- or Technology-Driven? Dyson 9.1 Dyson: From Bagless Vacuum Cleaner to Bladeless Hair Dryer 9.2 Dyson’s Patent Citation Analysis: A Complete Network 9.3 Technology or Design First? Ego Networks of the Bladeless Fan 9.4 Forecasting Dyson’s Next Innovation References 10 Predict Strategic Pivot Points: Bose 10.1 Bose's New Neat! Innovation Pivots 10.2 Core Innovation: Better Sound 10.3 Four Innovation Pivots: Beyond Sound 10.4 Summary References 11 Who Drives Innovation? Apple 11.1 The Shapes of Internal Collaborations: Apple and Google 11.2 Apple's Inventor Network: One-mode Network 11.3 Apple's Inventor-Technology Network: Two-mode Network 11.4 Summary References 12 Knowledge Acquisition and Assimilation after M&As: Adobe 12.1 Adobe M&A Activities 12.2 Inventor Network Analysis as a Proxy of Innovation Assimilation 12.3 Evolution of Adobe’s Inventor Network 12.4 Knowledge Diffusion in Design and Technology 12.5 Summary References 13 Learn to Build Design Innovation Team: Samsung vs LG 13.1 A Look at Samsung and LG’s Patenting Activities 13.2 Diversification of Product Innovation 13.3 Different Design Team Structure 13.4 Summary References
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Part IV: Future Developments with AI14 Is Trademark the First Sparring Partner of AI? 14.1 The Great Wall: A Trademark Powerhouse 14.2 How AI Changes Trademarks Searches 14.3 Use case: AI-based Trademark Search for Brand Protection 14.4 Summary Reference 15 Legal Technologies in Action 15.1 Background: AI and IP 15.2 Five AI Applications in IP 15.3 The Rise of Legal Technology 15.4 Summary References
What will you learn?
Different stakeholders approach patent data and analytics with different purposes. We define patent analytics as the data science of analyzing large amount of patent in-formation, to discover relationships, trends and patterns for decision making rooted in the business context. The importance of accommodating interdisciplinary view-points in patent analytics includes:
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Business managers and professionals looking to improve their innovation portfolio and tooling with patent analytic techniques aiming to exploit highly detailed, accurate, and actionable insights on patent data to bolster informed decision-making.
Business Managers
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Data analysts who seek to gain a deeper understanding of the special structures, knowledge, and economic values that underlie patent data and close the gap between big data analytics capacities and the particular needs of legal professionals.
Data Analysts
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Legal professionals need to harness the power of patent analytics to practically improve IP research and legal-services delivery and envisage the emerging legal technology landscape.
Legal Professionals
Contact
Jieun Kim Ph.D.
Professor, Graduate School of Technology & Innovation Management, Hanyang University
222 Wangsimli-ro, Hanyang University, Seoul, South Korea
Email : jkim2@hanyang.ac.kr
Webpage : imagine.hanyang.ac.kr