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 their hidden connections by employing elaborate data analytics and visualizations using a network map. 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.
Author(s): Jieun Kim, Buyong Jeong, Daejung Kim
Publisher: Springer
Year: 2021
Language: English
Pages: 228
City: Singapore
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
