Patent Analysis and Mining for Business Intelligence, 1st ed. 2029
SpringerBriefs in Computer Science Series

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Language: English

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· 15.5x23.5 cm · Paperback

This book provides a comprehensive compendium of recent research on business intelligence-oriented patent data analysis and mining. Through the book,  the readers will gain an essential understanding of the following topics: (1) text mining modeling for patent documents, including statistics modeling and key phrase extraction mining; (2) the patent retrieval method, including chuck based retrieval and retrieval fusion method; and (3) integrated business solutions for stock dynamics, technology prospecting, and minimizing legal exposure. This book provides an informative and insightful reference guide for researchers who are newcomers to patent data mining and business intelligence, as well as for professionals and practitioners from industry.

1. Introduction
1.1 Intellectual Property and Patent Mining
1.2 Challenges and Progresses
1.3 From Patent Mining to Business Intelligence
1.4 Overview of the Book
References
2. Survey of Patent Mining
2.1 Introduction
2.2 Patent Analysis
2.2.1 Patent Statistics and Visualization
2.2.2 Patent Retrieval
2.2.3 Patent Translate
2.3 Patent Mining
2.3.1 Patent Classification
2.3.2 Patent Recommendation
2.3.3 Patent Prediction
2.4 Tools for Patent Analysis and Mining
2.4.1 Preprocessing
2.4.2 Analysis and Visualization
2.4.3 Patent Map
2.5 Discussion
References
3. Text Mining Methods for Patent
3.1 Introduction
3.2 Concepts and Data Description
3.3 Sememe Statistics Modeling
3.4 Key-Phrase Extraction
3.5 Patent Classification
3.6 Performance Evaluation
3.7 Related Work
3.8 Discussion
References
4. Patent Retrieval Methods
4.1 Introduction
4.2 Chunk-based Modeling
4.3 Textual Chunk Retrieval
4.4 Retrieval Fusion Modeling
4.5 Performance Evaluation
4.6 Related Work
4.7 Discussion
References
5. Patent Mining Application for Business Intelligence
5.1 Introduction
5.2 Patent Activities on Stock Dynamics
5.3 Patent Mining for Technology Prospecting
5.4 Patent Recommendation for Minimizing Legal Exposure
5.5 Performance Evaluation
5.6 Related Work
5.7 Discussion
References
6. Conclusion
6.1 Promising Topics
6.2 The Prospects
Appendix

Dr. Bo Jin is an Associate Professor in Dalian University of Technology. He received his Ph.D. in Computer Science in 2009. His general area of research is data mining and knowledge discovery. He has published prolifically in refereed journals and conference proceedings (60+ papers), e.g., SIGKDD, ICDM, and PAKDD. He has served regularly in the program committees of a number of conferences and is a reviewer for the leading academic journals in his fields, e.g., SIGKDD, ICDM, DASFAA, SDM, TKDE, and SpringPlus. He is a senior member of ACM, IEEE, and CCF.


Dr. Hui Xiong received his Ph.D. in Computer Science from the University of Minnesota - Twin Cities, USA, in 2005, the B.E. degree in Automation from the University of Science and Technology of China (USTC), Hefei, China, and the M.S. degree in Computer Science from the National University of Singapore (NUS), Singapore. He is currently a Professor and Vice Chair in the Management Science and Information Systems Department, and the Director of Rutgers Center for Information Assurance, at Rutgers, the State University of New Jersey, where he received a two-year early promotion/tenure (2009), the Rutgers University Board of Trustees Research Fellowship for Scholarly Excellence (2009), the ICDM-2011 Best Research Paper Award (2011), an IBM ESA Innovation Award (2008), the Junior Faculty Teaching Excellence Award (2007), the Junior Faculty Research Award (2008), and Dean's Award for Meritorious Research (2010, 2011, 2013) at Rutgers Business School.

 

Dr. Xiong's general area of research is data and knowledge engineering, with a focus on developing effective and efficient data analysis techniques for emerging data intensive applications. His research has been supported in part by the National Science Foundation (NSF), IBM Research, SAP Corporation, Panasonic USA Inc., Awarepoint Corp., Citrix Systems Inc., and Rutgers University. He has published prolifically

The first systematic and comprehensive book on patent data analysis and mining

Equips readers to predict the technological trends and certain business strategies using data mining and analytics techniques

Elaborate the applications of patent analysis and mining in stock market