Predicting litigation likelihood and time to litigation for patents

TL;DR

This paper presents advanced predictive models for estimating patent litigation likelihood and timing, utilizing novel features and algorithms to improve accuracy in a challenging, low-incidence domain.

Contribution

It introduces new feature sets and sophisticated algorithms for more accurate patent litigation prediction and time estimation, addressing data scarcity issues.

Findings

Improved accuracy over previous models in predicting litigation likelihood.

Effective estimation of time to litigation using modified models.

Enhanced feature engineering contributes to better predictive performance.

Abstract

Patent lawsuits are costly and time-consuming. An ability to forecast a patent litigation and time to litigation allows companies to better allocate budget and time in managing their patent portfolios. We develop predictive models for estimating the likelihood of litigation for patents and the expected time to litigation based on both textual and non-textual features. Our work focuses on improving the state-of-the-art by relying on a different set of features and employing more sophisticated algorithms with more realistic data. The rate of patent litigations is very low, which consequently makes the problem difficult. The initial model for predicting the likelihood is further modified to capture a time-to-litigation perspective.