  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.
