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001438250 001__ 1438250
001438250 003__ OCoLC
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001438250 019__ $$a1259669206$$a1266811080$$a1284934233
001438250 020__ $$a9789811629303$$q(electronic bk.)
001438250 020__ $$a9811629307$$q(electronic bk.)
001438250 020__ $$z9789811629297
001438250 0247_ $$a10.1007/978-981-16-2930-3$$2doi
001438250 035__ $$aSP(OCoLC)1260347780
001438250 040__ $$aEBLCP$$beng$$erda$$epn$$cEBLCP$$dYDX$$dGW5XE$$dOCLCO$$dOCLCF$$dDCT$$dUKAHL$$dN$T$$dOCLCQ$$dWAU$$dOCLCQ
001438250 049__ $$aISEA
001438250 050_4 $$aT211
001438250 08204 $$a608$$223
001438250 1001_ $$aKim, Jieun,$$eauthor.
001438250 24510 $$aPatent analytics :$$btransforming IP strategy into intelligence /$$cJieun Kim, Buyong Jeong, Daejung Kim.
001438250 264_1 $$aSingapore :$$bSpringer,$$c[2021]
001438250 300__ $$a1 online resource (xxii, 206 pages) :$$bcolor illustrations.
001438250 336__ $$atext$$btxt$$2rdacontent
001438250 337__ $$acomputer$$bc$$2rdamedia
001438250 338__ $$aonline resource$$bcr$$2rdacarrier
001438250 347__ $$atext file$$2rdaft
001438250 347__ $$bPDF
001438250 504__ $$aIncludes bibliographical references.
001438250 5050_ $$aAbout 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
001438250 5058_ $$a3.2.1 INID Codes and Bibliographic Data -- 3.2.2 Patent Numbering System and Kind-Of-Documents -- 3.2.3 Patent Classification System -- 3.2.4 International Patent Classification (INID Code: 51) -- 3.2.5 Cooperative Patent Classification (INID Code: 52) -- 3.3 Same Same, but Different Design Patents -- 3.4 Comprehending Trademark Data -- 3.5 Summary -- References -- 4 Claims, "Legally, Less is More!" -- 4.1 Disentangling Patent Claims -- 4.2 Broad or Narrow: All-Elements Rule -- 4.3 Anatomy of Patent Claims -- 4.4 The Butterfly Effect of Design Patents -- 4.5 Summary -- References -- Part II Network Analytics -- 5 Basic Network Concepts -- 5.1 Why Does Patent Network Analysis Matter? -- 5.2 Basic Concept of Network and Graph Theory -- 5.2.1 Node, Edges, and Attributes -- 5.2.2 Undirected and Directed Network
001438250 5058_ $$a5.2.3 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
001438250 5058_ $$a8.3 Four Practical Steps for Patent Network Analysis -- 8.4 Summary -- References -- Part III Uncover Corporate Innovation with Patent Analytics -- 9 Is Innovation Design-or Technology-Driven? Dyson -- 9.1 Dyson: From Bagless Vacuum Cleaner to Bladeless Hairdryer -- 9.2 Dyson's Patent Citation Analysis: A Complete Network -- 9.3 Technology or Design First? Ego Networks of the Bladeless Fan -- 9.4 Forecasting Dyson's Next Innovation -- 10 Predict Strategic Pivot Points: Bose -- 10.1 Bose's New Neat! Innovation Pivots -- 10.2 Core Innovation: Better Sound -- 10.3 Four Innovation Pivots: Beyond Sound -- 10.3.1 Technology Pivot: Suspension Seats for Vehicles -- 10.3.2 Customer Segment Pivot: High-Tech Cooktops -- 10.3.3 Platform Pivot: Audio AR Sunglasses -- 10.3.4 Zoom-In Pivot: Noise-Masking Sleepbuds -- 10.4 Summary
001438250 506__ $$aAccess limited to authorized users.
001438250 520__ $$aThrough the prisms of a data scientist, a patent attorney, and a designer, this book demystifies the complexity of patent data and its structure and reveals their hidden connections by employing elaborate data analytics and visualizations using a network map. This book provides a practical guide to introduce and apply patent network analytics and visualization tools in your business. We incorporate case studies from renowned companies such as Apple, Dyson, Adobe, Bose, Samsung and more, to scrutinise how their underlying values of patent network drive innovation in their business. Finally, this book advances readers' perspective of patent gazettes as big data and as a tool for innovation analytics when coupled with Artificial Intelligence.
001438250 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed July 28, 2021).
001438250 650_0 $$aPatents$$xData processing.
001438250 655_0 $$aElectronic books.
001438250 7001_ $$aJeong, Buyong.
001438250 7001_ $$aKim, Daejung.
001438250 77608 $$iPrint version:$$aKim, Jieun.$$tPatent Analytics.$$dSingapore : Springer Singapore Pte. Limited, ©2021$$z9789811629297
001438250 852__ $$bebk
001438250 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-2930-3$$zOnline Access$$91397441.1
001438250 909CO $$ooai:library.usi.edu:1438250$$pGLOBAL_SET
001438250 980__ $$aBIB
001438250 980__ $$aEBOOK
001438250 982__ $$aEbook
001438250 983__ $$aOnline
001438250 994__ $$a92$$bISE