Predicting construction litigation outcome using particle swarm optimization
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Legal docket-entry classification: where machine learning stumbles
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Risk analysis for intellectual property litigation
Proceedings of the 13th International Conference on Artificial Intelligence and Law
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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.