Data Tech

Dockets for federal court cases contain a wealth of information for lawyers to develop a litigation strategy, but the information is locked up in semi-structured text. Manually deriving the outcomes for each party (e.g., settlement, verdict) would be very labor intensive. We used Natural Language Processing (NLP) techniques and deep learning methods in order to scale the automatic analysis of millions of US federal court dockets. The automatically extracted information is fed into a Litigation Analytics tool that is used by lawyers to plan how they approach concrete litigations.