IPC Code Analysis of Patent Documents Using Association Rules and Maps - Patent Analysis of Database Technology

Patent documents are the results of researched and developed technologies. Patent is a protecting system of inventors’ right for their technologies by a government. Also, patent is an important intellectual property of a company. R&D strategy has been depended on patent management. For efficient management of patent, we need to analyze patent data. In this paper, we propose a method for analyzing international patent classification (IPC) code as a patent analysis. We introduce association rules and maps for IPC code analysis. To verify our improved the performance, we will make experiments using searched patent documents of database technology.

[1]  Darrell L. Mann,et al.  Better technology forecasting using systematic innovation methods , 2003 .

[2]  Sung-Hae Jun,et al.  Patent and Statistics, What's the Connection? , 2010 .

[3]  Alan L. Porter,et al.  On the Future of Technological Forecasting , 2001 .

[4]  Scott Shane,et al.  Technological Opportunities and New Firm Creation , 2001, Manag. Sci..

[5]  Khaled Khelif,et al.  Supporting Patent Mining by using Ontology-based Semantic Annotations , 2007, IEEE/WIC/ACM International Conference on Web Intelligence (WI'07).

[6]  Roberto J. Bayardo,et al.  Mining the most interesting rules , 1999, KDD '99.

[7]  M.W. Brinn,et al.  Investigation of forward citation count as a patent analysis method , 2003, IEEE Systems and Information Engineering Design Symposium, 2003.

[8]  Christian N. Madu,et al.  Setting priorities for the IT industry in Taiwan-A Delphi study , 1991 .

[9]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[10]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.

[11]  H. Ernst.  Patent applications and subsequent changes of performance: evidence from time-series cross-section analyses on the firm level , 2001 .

[12]  Kurt Hornik,et al.  The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets , 2011, J. Mach. Learn. Res..

[13]  Sunghae Jun,et al.  Forecasting Vacant Technology of Patent Analysis System using Self Organizing Map and Matrix Analysis , 2010 .

[14]  Yuen-Hsien Tseng,et al.  Text mining techniques for patent analysis , 2007, Inf. Process. Manag..

[15]  J. Lerner.  The Importance of Patent Scope: An Empirical Analysis , 1994 .

[16]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[17]  Mirko Markič,et al.  Management Standards Integration in Service Providing Organizations , 2012 .

[18]  Tao Huang,et al.  The Support Vector Machine Classification System for Patent Document Information Importance Analysis , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[19]  F. Woudenberg.  An Evaluation of Delphi , 1991 .

[20]  Heikki Mannila,et al.  Verkamo: Fast Discovery of Association Rules , 1996, KDD 1996.

[21]  Kurt Hornik,et al.  Introduction to arules – A computational environment for mining association rules and frequent item sets , 2009 .

[22]  Soung Hie Kim,et al.  A Delphi technology forecasting approach using a semi-Markov concept , 1991 .

[23]  V. Mitchell.  Using Delphi to Forecast in New Technology Industries , 1992 .

[24]  Alan L. Porter,et al.  Automated extraction and visualization of information for technological intelligence and forecasting , 2002 .

[25]  Yongtae Park,et al.  Development of New Technology Forecasting Algorithm: Hybrid Approach for Morphology Analysis and Conjoint Analysis of Patent Information , 2007, IEEE Transactions on Engineering Management.

[26]  Byungun Yoon,et al.  Patent analysis for technology forecasting: Sector-specific applications , 2008, 2008 IEEE International Engineering Management Conference.