Evaluating and ranking patents using weighted citations

Sooyoung Oh, Zhen Lei, Prasenjit Mitra, John Yen

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

8 Scopus citations

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 (US)
Title of host publicationJCDL '12 - Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries
Pages281-284
Number of pages4
DOIs
StatePublished - 2012
Event12th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '12 - Washington, DC, United States
Duration: Jun 10 2012Jun 14 2012

Publication series

NameProceedings of the ACM/IEEE Joint Conference on Digital Libraries
ISSN (Print)1552-5996

Other

Other12th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '12
Country/TerritoryUnited States
CityWashington, DC
Period6/10/126/14/12

All Science Journal Classification (ASJC) codes

  • General Engineering

Fingerprint

Dive into the research topics of 'Evaluating and ranking patents using weighted citations'. Together they form a unique fingerprint.

Cite this