###### Abstract  
A key challenge for artificial intelligence in the legal field is to determine from the text of a party's litigation brief whether, and why, it will succeed or fail. This paper shows a proof-of-concept test case from the United States: predicting outcomes of post-grant inter parties review (IPR) proceedings for invalidating patents. The objectives are to compare decision-tree and deep learning methods, validate interpretability methods, and demonstrate outcome prediction based on party briefs. Specifically, this study compares and validates two distinct approaches: (1) representing documents with term frequency inverse document frequency (TF-IDF), training XGBoost gradient-boosted decision-tree models, and using SHAP for interpretation. (2) Deep learning of document text in context, using convolutional neural networks (CNN) with attention, and comparing LIME and attention visualization for interpretability. The methods are validated on the task of automatically determining case outcomes from unstructured written decision opinions, and then used to predict trial institution or denial based on the patent owner's preliminary response brief. The results show how interpretable deep learning architecture classifies successful/unsuccessful response briefs on temporally separated training and test sets. More accurate prediction remains challenging, likely due to the fact-specific, technical nature of patent cases and changes in applicable law and jurisprudence over time. L 202220203 April 202202  
and electronic documents, as well as in the area of contracts, patents, and form documents in litigation such as Complaint and Answer litigation pleadings [2; 6; 15]. These applications either involve short lengths of text (e.g., search query and target phases), structured text that follows standard formats (contracts and pleadings), or technical and specialized subject matter characterized by jargon words and phrases that can readily characterize documents (patents). NLP is still highly experimental in the area of litigation briefs and opinions. Briefs are documents written by attorneys to argue their case on a preliminary issue (generally called a \"motion\") or for judgment. Opinions are written by judges to explain their reasoning to justify their decision, guide future litigators, and persuade judges on a higher court who may hear an appeal. Because of the persuasive element, briefs and opinions are written in a more loose style and with less formal language than precise technical documents like contracts and patents. Analyzing briefs and opinions is therefore a very difficult task for computers [16]. Machine learning in litigation has thus primarily focused on non-textual features, such as the identity of the judge, and encoding the legal and factual issues and the type of argument. For example, to predict the outcome of a dispositive summary judgment motion in a patent lawsuit, a machine learning model can be trained to predict the chance of success based on such features as (i) the subject matter coding of the patent, (ii) the age of the patent, (iii) licensing history of the patent, (iv) the kind of arguments that the defendant has raised (patent ineligibility, prior art, infringement), and (v) the court where the dispute will be heard (e.g., in the United States, Texas courts tend to be more favorable for plaintiffs and California courts to defendants) [17; 18]. A feature subset-based model of the text of litigation briefs will miss additional information that would be found in the full text. By contrast, a model based on the natural language in full context can be more flexible, efficient, and less biased than using feature subsets. Specifically, a predictive NLP model based on the text of briefs will naturally derive patterns of factual and legal issues that are in dispute, and how the parties have argued their cases. There is no model designer who must make subjective choices of what keywords or issue codes to use to characterize the text, which are choices that can reflect a bias or miss certain issues.