Predicting litigation likelihood and time to litigation for patents
This work addresses the costly and time-consuming issue of patent lawsuits for companies, allowing better budget and time allocation in patent portfolio management, but it appears incremental as it builds on existing methods with different features and algorithms.
The paper tackled the problem of forecasting patent litigation likelihood and time to litigation by developing predictive models using textual and non-textual features, resulting in improved state-of-the-art predictions with more realistic data.
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.