Patent registration prediction methodology using multivariate statistics

Won Gyo Jung, Sang Sung Park, Dong Sik Jang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Whether a patent is registered or not is usually based on the subjective judgment of the patent examiners. However, the patent examiners may determine whether the patent is registered or not according to their personal knowledge, backgrounds etc. In this paper, we propose a novel patent registration method based on patent data. The method estimates whether a patent is registered or not by utilizing the objective past history of patent data instead of existing methods of subjective judgments. The proposed method constructs an estimation model by applying multivariate statistics algorithm. In the prediction model, the application date, activity index, IPC code and similarity of registration refusal are set to the input values, and patent registration and rejection are set to the output values. We believe that our method will contribute to improved reliability of patent registration in that it achieves highly reliable estimation results through the past history of patent data, contrary to most previous methods of subjective judgments by patent agents.

Original languageEnglish
Pages (from-to)2219-2226
Number of pages8
JournalIEICE Transactions on Information and Systems
Issue number11
Publication statusPublished - 2011 Nov


  • Data mining
  • Neural network
  • Patent
  • Pattern recognition
  • Text mining

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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