Identifying patent monetization entities

Mihai Surdeanu, Sara Jeruss

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

2 Scopus citations

Abstract

The United States has seen an explosion in patent litigation lawsuits in recent years. Recent studies indicate that a large proportion of these lawsuits, increasing from 22% in 2007 to 40% in 2011, were filed by patent monetization entities (PMEs), i.e., companies that hold patents, license patents, and file patent lawsuits, but do not sell products or provide services practicing the technologies described in their patents. We introduce a classifier that identifies which patent litigation lawsuits are initiated by PMEs. Using features extracted from the entities' litigation behavior, the patents they asserted, and their presence on the web, the proposed classifier correctly separates PMEs from operating companies with a F1 score of 85%. We believe that such a classifier will be a useful tool to policy makers and patent litigators, allowing them to gain a clearer picture of the 37, 000+ patent lawsuits filed to date and assessing newly filed cases in real time.

Original languageEnglish (US)
Title of host publicationProceedings of the 14th International Conference on Artificial Intelligence and Law, ICAIL 2013
Pages131-139
Number of pages9
DOIs
StatePublished - Sep 11 2013
Event14th International Conference on Artificial Intelligence and Law, ICAIL 2013 - Rome, Italy
Duration: Jun 10 2013Jun 14 2013

Publication series

NameProceedings of the International Conference on Artificial Intelligence and Law

Other

Other14th International Conference on Artificial Intelligence and Law, ICAIL 2013
CountryItaly
CityRome
Period6/10/136/14/13

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Law

Cite this

Surdeanu, M., & Jeruss, S. (2013). Identifying patent monetization entities. In Proceedings of the 14th International Conference on Artificial Intelligence and Law, ICAIL 2013 (pp. 131-139). (Proceedings of the International Conference on Artificial Intelligence and Law). https://doi.org/10.1145/2514601.2514616