Patent analytics : transforming IP strategy into intelligence / / Jieun Kim, Buyong Jeong, Daejung Kim

• Autore: Kim Jieun
• Pubblicazione: Gateway East, Singapore : , : Springer, , [2021]; ©2021
• Descrizione fisica: 1 online resource (217 pages)
• Disciplina: 608.773
• Soggetto topico: Patents - Data processing
• Persona (resp. second.): JeongBuyong; KimDaejung
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Intro -- Foreword -- Contents -- About the Authors -- Abbreviations -- List of Figures -- List of Tables -- 1 Introduction -- 1.1 The Prism of Patent Big Data -- 1.1.1 The Vs to the Patent Big Data Paradigm -- 1.1.2 Coping with Patent Big Data Complexity -- 1.1.3 Harnessing Patent Big Data Analytics to Make a Difference -- 1.2 Overview of the Book -- 1.2.1 Part I: Patent as Data -- 1.2.2 Part II: Network Analytics -- 1.2.3 Part III: Uncover Corporate Innovation with Patent Analytics -- 1.2.4 Part IV: Future Developments with AI -- References -- Part I Patent as Data -- 2 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 -- 3.2.1 INID Codes and Bibliographic Data -- 3.2.2 Patent Numbering System and Kind-Of-Documents -- 3.2.3 Patent Classification System -- 3.2.4 International Patent Classification (INID Code: 51) -- 3.2.5 Cooperative Patent Classification (INID Code: 52) -- 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 -- 4.5 Summary -- References -- Part II Network Analytics -- 5 Basic Network Concepts -- 5.1 Why Does Patent Network Analysis Matter? -- 5.2 Basic Concept of Network and Graph Theory -- 5.2.1 Node, Edges, and Attributes -- 5.2.2 Undirected and Directed Network -- 5.2.3 One-Mode and Two-Mode Networks -- 5.2.4 Ego Networks and Complete Networks -- 5.3 Network Metrics -- 5.3.1 Centrality. ; 5.3.2 Network Diameter and Density -- 5.3.3 Clustering and Modularity -- 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 -- 7.2.1 Bar, Line, and Pie Charts -- 7.2.2 Geospatial Visualizations -- 7.2.3 Bubble Charts -- 7.2.4 Treemaps -- 7.3 Network Visualizations -- 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 -- Part III Uncover Corporate Innovation with Patent Analytics -- 9 Is Innovation Design-or Technology-Driven? Dyson -- 9.1 Dyson: From Bagless Vacuum Cleaner to Bladeless Hairdryer -- 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 -- 10.3.1 Technology Pivot: Suspension Seats for Vehicles -- 10.3.2 Customer Segment Pivot: High-Tech Cooktops -- 10.3.3 Platform Pivot: Audio AR Sunglasses -- 10.3.4 Zoom-In Pivot: Noise-Masking Sleepbuds -- 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 Versus LG -- 13.1 A Look at Samsung and LG's Patenting Activities -- 13.2 Diversification of Product Innovation -- 13.3 Different Structure of Design Team -- 13.4 Summary -- References -- Part IV Future Developments with AI -- 14 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 -- References -- 15 Legal Technologies in Action -- 15.1 Background: AI and IP -- 15.2 Five AI Applications in IP -- 15.2.1 Automatic Classification -- 15.2.2 Machine Translation -- 15.2.3 Examination and Formality Checks -- 15.2.4 Image Search and Recognition -- 15.2.5 Helpdesk Bots -- 15.3 The Rise of Legal Technology -- 15.4 Summary -- References -- Afterword.
• Titolo autorizzato: Patent analytics
• ISBN: 981-16-2930-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910490025603321
• Lo trovi qui: Univ. Federico II