Patent maintenance recommendation with patent information network model

Xin Jin, Scott Spangler, Ying Chen, Keke Cai, Rui Ma, Li Zhang, Xian Wu, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Patents are of crucial importance for businesses, because they provide legal protection for the invented techniques, processes or products. A patent can be held for up to 20 years. However, large maintenance fees need to be paid to keep it enforceable. If the patent is deemed not valuable, the owner may decide to abandon it by stopping paying the maintenance fees to reduce the cost. For large companies or organizations, making such decisions is difficult because too many patents need to be investigated. In this paper, we introduce the new patent mining problem of automatic patent maintenance prediction, and propose a systematic solution to analyze patents for recommending patent maintenance decision. We model the patents as a heterogeneous time-evolving information network and propose new patent features to build model for a ranked prediction on whether to maintain or abandon a patent. In addition, a network-based refinement approach is proposed to further improve the performance. We have conducted experiments on the large scale United States Patent and Trademark Office (USPTO) database which contains over four million granted patents. The results show that our technique can achieve high performance.

Original languageEnglish (US)
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining, ICDM 2011
Pages280-289
Number of pages10
DOIs
StatePublished - Dec 1 2011
Event11th IEEE International Conference on Data Mining, ICDM 2011 - Vancouver, BC, Canada
Duration: Dec 11 2011Dec 14 2011

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other11th IEEE International Conference on Data Mining, ICDM 2011
CountryCanada
CityVancouver, BC
Period12/11/1112/14/11

Keywords

  • Patent information network
  • Patent maintenance
  • Patent mining
  • Prediction
  • Ranking

ASJC Scopus subject areas

  • Engineering(all)

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