###### Abstract
Feature encoding (or transformation) methods also require a human reader who will subjectively set the value of features, such as whether a brief raises an argument of patent obviousness or not. NLP models, by contrast, learn semantic relationships and word-meaning in the course of training, alleviating time-consuming review to subjectively encode documents. Moreover, if a predictive NLP model can also be _interpretable_ or _explainable_, then the model can derive additional useful insight [19]. For example, an interpretable model may reveal what linguistic patterns are particularly persuasive, or changes over time in how judges evaluate particular fact patterns and legal argument. In this paper, we demonstrate a proof-of-concept of analyzing litigation briefs and opinions using two diverse interpretable machine learning methodologies: (1) Extreme gradient boosting for ensemble decision tree modeling (XGBoost) based on the term frequency-inverse document frequency (TF-IDF) of individual words, and (2) a whole-document contextual convolutional neural network (CNN)-based model that includes attention layers for highlighting potentially significant features. We analyze administrative litigation before the United States Patent Trial & Appeal Board (PTAB) to resolve post-grant patent challenges called post-grant review (PGR), inter partes review (IPR), and covered business method patent reviews (CBM). IPR, PGR, and CBM cases are similar proceedings in which a party petitions the PTAB to challenge the validity of a patent held by a patent owner [20; 21]. This paper focuses on the first stage of these proceedings, when a panel of PTAB judges decides whether to institute a trial with evidence on the petitioner's brief supporting their challenge, usually taking into account a preliminary response brief submitted by the patent owner. In these proceedings, the petitioner (i.e., challenger) is almost always the defendant in a patent lawsuit brought in U.S. federal court by the patent owner. As such, these are generally high stakes cases litigated by sophisticated lawyers. The legal and factual issues are specific to particular patent validity challenges--but the text of the parties' briefs is generally unstructured and has a lot of stylistic variation as lawyers attempt to craft the briefs to be as persuasive to the decision-makers as possible. Therefore, while PTAB represents a closed universe of a particular kind of litigation, it nonetheless poses a highly challenging textual analysis problem. Adding to the complexity of the challenge is that while all the relevant documents are publicly available, they are available only as PDF files which must be converted to text, a frequently noisy process (while clean text is available through commercial databases, those databases generally do not offer bulk download options and have restrictive terms of use.) Soon, all documents in U.S. litigation proceedings will be freely available as well; however, they will likely be only available in a similar format. In sum, our PTAB analysis represents a realistic, practically relevant problem. To validate and demonstrate as a proof-of-concept interpretable machine learning to provide insight in the analysis of litigation documents, this paper focuses on two analytical tasks: (1) Deriving the outcome of the proceeding from the written opinion prepared by PTAB judges, and (2) predicting the outcome of the case based on the patent owner's preliminary response brief. The goal of the studies shown here is to demonstrate that interpretable machine learning has the potential to obtain practically useful insight from litigation brief text as a component of litigation data analytics. The paper proceeds as follows. Section 2 describes the legal problem, reviews relevant literature on machine learning and NLP in the legal field, and specifically reviews related work applying deep learning methods and interpretation methods to litigation outcome prediction.