Evaluating and ranking patents using weighted citations

Sooyoung Oh, Zhen Lei, Prasenjit Mitra, John Yen

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

5 Citations (Scopus)

Abstract

Citation counts have been widely used in a digital library for purposes such as ranking scientific publications and evaluating patents. This paper demonstrates that distinguishing different types of citations could rank better for these purposes. We differentiate patent citations along two dimensions (assignees and technologies) into four types, and propose a weighted citation approach for assessing and ranking patents. We investigate five weight learning methods and compare their performance. Our weighted citation method performs consistently better than simple citation counts, in terms of rank correlations with patent renewal status. The estimated weights on different citations are consistent with economic insights on patent citations. Our study points to an interesting and promising research line on patent citation and network analysis that has not been explored.

Original languageEnglish
Title of host publicationProceedings of the ACM/IEEE Joint Conference on Digital Libraries
Pages281-284
Number of pages4
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event12th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '12 - Washington, DC
Duration: 10 Jun 201214 Jun 2012

Other

Other12th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '12
CityWashington, DC
Period10/6/1214/6/12

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Keywords

  • patent citation
  • patent ranking
  • patent renewal
  • weighted citation

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Oh, S., Lei, Z., Mitra, P., & Yen, J. (2012). Evaluating and ranking patents using weighted citations. In Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (pp. 281-284) https://doi.org/10.1145/2232817.2232870