Patent classification using ontology-based patent network analysis

Meng Jung Shih*, Duen-Ren Liu

*Corresponding author for this work

Research output: Contribution to conferencePaper

8 Scopus citations

Abstract

Patent management is increasingly important for organizations to sustain their competitive advantage. The classification of patents is essential for patent management and industrial analysis. In this study, we propose a novel patent network-based classification method to analyze query patents and predict their classes. The proposed patent network, which contains various types of nodes that represent different features extracted from patent documents, is constructed based on the relationship metrics derived from patent metadata. The novel approach analyzes reachable nodes in the patent ontology network to calculate their relevance to query patents, after which it uses the modified k-nearest neighbor classifier to classify query patents. We evaluate the performance of the proposed approach on a test dataset of patent documents obtained from the United States Patent and Trademark Office (USPTO), and compare it with the performance of the three conventional methods. The results demonstrate that the proposed patent network-based approach outperforms the conventional approaches.

Original languageEnglish
Pages962-972
Number of pages11
StatePublished - 1 Dec 2010
Event14th Pacific Asia Conference on Information Systems, PACIS 2010 - Taipei, Taiwan
Duration: 9 Jul 201012 Jul 2010

Conference

Conference14th Pacific Asia Conference on Information Systems, PACIS 2010
CountryTaiwan
CityTaipei
Period9/07/1012/07/10

Keywords

  • K-nearest neighbor
  • Patent classification
  • Patent network analysis
  • Patent ontology network

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