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Patent analytics : transforming IP strategy into intelligence /

Bibliographic Details
Main Author: Kim, Jieun
Corporate Author: ProQuest (Firm)
Other Authors: Jeong, Buyong, Kim, Daejung
Format: eBook
Language:English
Published: Singapore : Springer, 2021.
Subjects:
Patents > Data processing.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • 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