Patent applications are currently an expensive process, requiring many work hours and high economic costs for creating, editing, and submitting applications to the United States Patent and Trademark Office. This project utilized machine learning methods to analyze method claim text and ultimately determine whether a method claims application would be rejected. This project aims to help stakeholders leverage the results from these models to streamline their patent application process and to implement the models in the future for better determining which words may contribute to their application being rejected.