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E-raamat: Patent Analytics: Transforming IP Strategy into Intelligence

Buyong Jeong, Daejung Kim, Jieun Kim

Formaat: EPUB+DRM
Ilmumisaeg: 06-Jul-2021
Kirjastus: Springer Verlag, Singapore
Keel: eng
ISBN-13: 9789811629303

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Formaat: EPUB+DRM
Ilmumisaeg: 06-Jul-2021
Kirjastus: Springer Verlag, Singapore
Keel: eng
ISBN-13: 9789811629303

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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.

1 Introduction

(10)

1.1 The Prism of Patent Big Data

(4)

1.1.1 The Vs to the Patent Big Data Paradigm

(1)

1.1.2 Coping with Patent Big Data Complexity

(2)

1.1.3 Harnessing Patent Big Data Analytics to Make a Difference

(1)

1.2 Overview of the Book

(3)

1.2.1 Part I: Patent as Data

(1)

1.2.2 Part II: Network Analytics

(1)

1.2.3 Part III: Uncover Corporate Innovation with Patent Analytics

(1)

1.2.4 Part IV: Future Developments with AI

(1)

References

(3)

Part I Patent as Data

2 A Brief History of Patents

(10)

2.1 The Prelude of the Patent System

(1)

2.2 The First Patent with Claims

(1)

2.3 The Great Fire and Patent Numbering

(4)

2.4 Genesis of Citations

(3)

2.5 Summary

(1)

References

(2)

3 Understanding Patent Data

(20)

3.1 Patents, Designs, and Trademarks

(3)

3.2 A Walk Through of Patent Data Fields

(8)

3.2.1 INID Codes and Bibliographic Data

(3)

3.2.2 Patent Numbering System and Kind-Of-Documents

(3)

3.2.3 Patent Classification System

(1)

3.2.4 International Patent Classification (INID Code: 51)

(1)

3.2.5 Cooperative Patent Classification (INID Code: 52)

(1)

3.3 Same Same, but Different Design Patents

(3)

3.4 Comprehending Trademark Data

(3)

3.5 Summary

(1)

References

(2)

4 Claims, "Legally, Less is More!"

(16)

4.1 Disentangling Patent Claims

(2)

4.2 Broad or Narrow: All-Elements Rule

(2)

4.3 Anatomy of Patent Claims

(4)

4.4 The Butterfly Effect of Design Patents

(4)

4.5 Summary

(1)

References

(3)

Part II Network Analytics

5 Basic Network Concepts

(16)

5.1 Why Does Patent Network Analysis Matter?

(1)

5.2 Basic Concept of Network and Graph Theory

(4)

5.2.1 Node, Edges, and Attributes

(1)

5.2.2 Undirected and Directed Network

(1)

5.2.3 One-Mode and Two-Mode Networks

(2)

5.2.4 Ego Networks and Complete Networks

(1)

5.3 Network Metrics

(7)

5.3.1 Centrality

(5)

5.3.2 Network Diameter and Density

(1)

5.3.3 Clustering and Modularity

(1)

5.4 Summary

(2)

References

(2)

6 Patent Citations Analysis

(10)

6.1 The Meaning of Patent Citations

(3)

6.2 How to Scale up Patent Citation Networks

(3)

6.3 Pitfalls and Best Practices in Using Patent Citation Data

(2)

6.4 Summary

(1)

References

(2)

7 Patent Data Through a Visual Lens

(14)

7.1 Unexpected Encounters

(4)

7.2 Six Basic Charts

(5)

7.2.1 Bar, Line, and Pie Charts

(2)

7.2.2 Geospatial Visualizations

(1)

7.2.3 Bubble Charts

(1)

7.2.4 Treemaps

(2)

7.3 Network Visualizations

(4)

7.4 Summary

(1)

References

(1)

8 How to Study Patent Network Analysis

(20)

8.1 Research Design

(2)

8.2 Choosing Network Analysis Tools

(5)

8.3 Four Practical Steps for Patent Network Analysis

104

(10)

8.4 Summary

114

(1)

References

114

(3)

Part III Uncover Corporate Innovation with Patent Analytics

9 Is Innovation Design-or Technology-Driven? Dyson

117

(10)

9.1 Dyson: From Bagless Vacuum Cleaner to Bladeless Hairdryer

117

(1)

9.2 Dyson's Patent Citation Analysis: A Complete Network

118

(3)

9.3 Technology or Design First? Ego Networks of the Bladeless Fan

121

(3)

9.4 Forecasting Dyson's Next Innovation

124

(2)

References

126

(1)

10 Predict Strategic Pivot Points: Bose

127

(12)

10.1 Bose's New Neat! Innovation Pivots

127

(2)

10.2 Core Innovation: Better Sound

129

(4)

10.3 Four Innovation Pivots: Beyond Sound

133

(5)

10.3.1 Technology Pivot: Suspension Seats for Vehicles

134

(1)

10.3.2 Customer Segment Pivot: High-Tech Cooktops

135

(1)

10.3.3 Platform Pivot: Audio AR Sunglasses

136

(1)

10.3.4 Zoom-In Pivot: Noise-Masking Sleepbuds

137

(1)

10.4 Summary

138

(1)

References

138

(1)

11 Who Drives Innovation? Apple

139

(10)

11.1 The Shapes of Internal Collaborations: Apple and Google

139

(3)

11.2 Apple's Inventor Network: One-Mode Network

142

(3)

11.3 Apple's Inventor-Technology Network: Two-Mode Network

145

(3)

11.4 Summary

148

(1)

References

148

(1)

12 Knowledge Acquisition and Assimilation After M&As: Adobe

149

(12)

12.1 Adobe M&A Activities

149

(2)

12.2 Inventor Network Analysis as a Proxy of Innovation Assimilation

151

(1)

12.3 Evolution of Adobe's Inventor Network

152

(4)

12.4 Knowledge Diffusion in Design and Technology

156

(2)

12.5 Summary

158

(1)

References

158

(3)

13 Learn to Build Design Innovation Team: Samsung Versus LG

161

(14)

13.1 A Look at Samsung and LG's Patenting Activities

161

(1)

13.2 Diversification of Product Innovation

162

(4)

13.3 Different Structure of Design Team

166

(5)

13.4 Summary

171

(1)

References

172

(3)

Part IV Future Developments with AI

14 Is Trademark the First Sparring Partner of AI?

175

(12)

14.1 The Great Wall: A Trademark Powerhouse

175

(1)

14.2 How AI Changes Trademarks Searches

176

(6)

14.3 Use Case: AI-Based Trademark Search for Brand Protection

182

(3)

14.4 Summary

185

(1)

References

186

(1)

15 Legal Technologies in Action

187

(18)

15.1 Background: AI and IP

187

(1)

15.2 Five AI Applications in IP

188

(9)

15.2.1 Automatic Classification

188

(2)

15.2.2 Machine Translation

190

(2)

15.2.3 Examination and Formality Checks

192

(1)

15.2.4 Image Search and Recognition

193

(1)

15.2.5 Helpdesk Bots

194

(3)

15.3 The Rise of Legal Technology

197

(5)

15.4 Summary

202

(1)

References

203

(2)

Afterword

205

Jieun Kim is an associate professor at the Graduate School of Technology and Innovation Management, Hanyang University and co-directs an interdisciplinary research lab - Imagine X lab since 2013. She has a BA in Industrial Design from KAIST (2007) and an MS and PHD in Industrial Engineering from Arts et Métiers ParisTech, Paris (2008/2011), followed by the Leverhulme Research Fellowship (2012) at Royal College of Art in London. She was a visiting associate professor at Human Communication Technologies Lab, University of British Columbia (2020). She served as a general co-chair of ACM TVX 2018 and continued to contribute to many international design and innovation management communities as reviewers and speakers.

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 (20092014). He has been involved in various projects and policies for the Trademark and Design Examination bureau of KIPO. His legal and practical expertise is supported by his academic background, having obtaining bachelors and masters degrees in Industrial design from Korea Advanced Institute of Science and Technology (KAIST). He is the co-author of the Korean chapter for AIPPI Law Series: Design Rights, Functionality and Scope of Protection (by Christopher V. Carani / Wolters Kluwer 2017).

Daejung Kim is a data scientist specializing in intellectual property and a senior lecturer at Hankyong National University. He holds his Ph.D. in technology and innovation management from Hanyang University. He is a frequent speaker and consultant in strategic technology and management solutions for many law firms and corporate legal departments.

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