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Patent Analytics: Transforming IP Strategy into Intelligence 2021 ed. [Kõva köide]

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  • Formaat: Hardback, 206 pages, kõrgus x laius: 235x155 mm, kaal: 512 g, 117 Illustrations, color; 28 Illustrations, black and white; XXII, 206 p. 145 illus., 117 illus. in color., 1 Hardback
  • Ilmumisaeg: 06-Jul-2021
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811629293
  • ISBN-13: 9789811629297
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Patent Analytics: Transforming IP Strategy into Intelligence 2021 ed.Suurem pilt
  • Formaat: Hardback, 206 pages, kõrgus x laius: 235x155 mm, kaal: 512 g, 117 Illustrations, color; 28 Illustrations, black and white; XXII, 206 p. 145 illus., 117 illus. in color., 1 Hardback
  • Ilmumisaeg: 06-Jul-2021
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811629293
  • ISBN-13: 9789811629297
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789811629297.html
Märksõnad:
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
1(10)
1.1 The Prism of Patent Big Data
1(4)
1.1.1 The Vs to the Patent Big Data Paradigm
1(1)
1.1.2 Coping with Patent Big Data Complexity
2(2)
1.1.3 Harnessing Patent Big Data Analytics to Make a Difference
4(1)
1.2 Overview of the Book
5(3)
1.2.1 Part I: Patent as Data
5(1)
1.2.2 Part II: Network Analytics
6(1)
1.2.3 Part III: Uncover Corporate Innovation with Patent Analytics
7(1)
1.2.4 Part IV: Future Developments with AI
7(1)
References
8(3)
Part I Patent as Data
2 A Brief History of Patents
11(10)
2.1 The Prelude of the Patent System
11(1)
2.2 The First Patent with Claims
12(1)
2.3 The Great Fire and Patent Numbering
12(4)
2.4 Genesis of Citations
16(3)
2.5 Summary
19(1)
References
19(2)
3 Understanding Patent Data
21(20)
3.1 Patents, Designs, and Trademarks
21(3)
3.2 A Walk Through of Patent Data Fields
24(8)
3.2.1 INID Codes and Bibliographic Data
24(3)
3.2.2 Patent Numbering System and Kind-Of-Documents
27(3)
3.2.3 Patent Classification System
30(1)
3.2.4 International Patent Classification (INID Code: 51)
31(1)
3.2.5 Cooperative Patent Classification (INID Code: 52)
31(1)
3.3 Same Same, but Different Design Patents
32(3)
3.4 Comprehending Trademark Data
35(3)
3.5 Summary
38(1)
References
39(2)
4 Claims, "Legally, Less is More!"
41(16)
4.1 Disentangling Patent Claims
41(2)
4.2 Broad or Narrow: All-Elements Rule
43(2)
4.3 Anatomy of Patent Claims
45(4)
4.4 The Butterfly Effect of Design Patents
49(4)
4.5 Summary
53(1)
References
54(3)
Part II Network Analytics
5 Basic Network Concepts
57(16)
5.1 Why Does Patent Network Analysis Matter?
57(1)
5.2 Basic Concept of Network and Graph Theory
58(4)
5.2.1 Node, Edges, and Attributes
58(1)
5.2.2 Undirected and Directed Network
59(1)
5.2.3 One-Mode and Two-Mode Networks
59(2)
5.2.4 Ego Networks and Complete Networks
61(1)
5.3 Network Metrics
62(7)
5.3.1 Centrality
62(5)
5.3.2 Network Diameter and Density
67(1)
5.3.3 Clustering and Modularity
68(1)
5.4 Summary
69(2)
References
71(2)
6 Patent Citations Analysis
73(10)
6.1 The Meaning of Patent Citations
73(3)
6.2 How to Scale up Patent Citation Networks
76(3)
6.3 Pitfalls and Best Practices in Using Patent Citation Data
79(2)
6.4 Summary
81(1)
References
81(2)
7 Patent Data Through a Visual Lens
83(14)
7.1 Unexpected Encounters
83(4)
7.2 Six Basic Charts
87(5)
7.2.1 Bar, Line, and Pie Charts
87(2)
7.2.2 Geospatial Visualizations
89(1)
7.2.3 Bubble Charts
89(1)
7.2.4 Treemaps
90(2)
7.3 Network Visualizations
92(4)
7.4 Summary
96(1)
References
96(1)
8 How to Study Patent Network Analysis
97(20)
8.1 Research Design
97(2)
8.2 Choosing Network Analysis Tools
99(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.