## 6 Conclusions  
This paper shows a proof-of-concept for predicting litigation outcomes from party briefs with interpretable machine learning methods that can identify what terms and parts of briefs were relevant to outcome prediction. The test case in this paper is a real-world, high-stakes litigation problem involving United States patent law: predicting the outcome of post-grant inter parties review (IPR) trial institutions for invalidating patents. Using outcome prediction based on the unstructured written institution decisions as validation, both XGBoost/TF-IDF (decision tree classification using term frequency-based document representation) and deep learning of text in context (with a novel multiscale CNN-Attention model architecture) are able to classify decisions according to outcome. Both XGBoost/TF-IDF and the multiscale CNN-Attention architecture are also able to provide interpretable results that can accurately identify key terms relevant to the outcome prediction. At a proof-of-concept level, we show that end-to-end deep learning with the multiscale CNN-Attention deep learning architecture can also predict the outcome of PTAB institution decisions, with performance better than chance. Visualizing the attention layers of the trained deep learning models reveals key winning arguments, such as those based on the redundancy of PTAB proceedings with trial court cases on the same patents. Notably, these results come with important caveats that need to be addressed in future work: changes in jurisprudence and governing law over time limit the generalizability of predictions based on past cases to future cases, training data sets are limited in size, repeat patent litigants may skew results, and even after pre-processing, document noise continues to limit machine learning performance. Subject to the foregoing caveats, however, the results in this paper reinforce the promise of using machine learning to analyze litigant briefs and potentially identify likely successful strategies and textual patterns in briefs. Moreover, while the proceedings analyzed in this paper occur in an administrative body, the U.S. Patent Trial & Appeal Board (PTAB), they are otherwise identical to adversarial proceedings in courts. Conceptualization, B.A.S., G.L.R. ; methodology, B.A.S. ; software, B.A.S. ; validation, B.A.S. ; formal analysis, B.A.S., G.L.R. ; investigation, B.A.S. ; resources, G.L.R. ; data curation, B.A.S. ; writing--original draft preparation, B.A.S. ; writing--review and editing, G.L.R. ; visualization, B.A.S. ; supervision, G.L.R. ; project administration, G.L.R. ; funding acquisition, G.L.R. All authors have read and agreed to the published version of the manuscript. This work is supported by United States National Science Foundation (NSF) grants 1936791, 1919691 and 2107108. Not applicable. The data obtained from the United States Patent and Trademark Office for this study, in particular the briefs and written deciscions, as well as other metadata for patent review proceedings and their encoded outcomes, are provided at the paper's GitHub, located at [[https://github.com/EESI/PTAB](https://github.com/EESI/PTAB)]([https://github.com/EESI/PTAB](https://github.com/EESI/PTAB)), accessed on 3 April 2022. Data were downloaded from the PTAB Open Data site, located at [[https://developer.uspto.gov/ptab-web/](https://developer.uspto.gov/ptab-web/)]([https://developer.uspto.gov/ptab-web/](https://developer.uspto.gov/ptab-web/)), accessed on 3 April 2022. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The following abbreviations are used in this manuscript: