Our capstone project sees us utilizing machine learning to address an issue of “bad patents”, whereby new patent filings with a high level of similarity to older filings cause disputes which in turn cause significant wastage of time and money on litigation in court. If every new patent filing could be compared against an entire database of older patents before a decision is made, that would solve the issue, but the sheer number of patents in existence make this unfeasible if using humans. Machine learning, on the other hand, allows such actions to be fully automated, and thus presents a viable solution to the problem. Over the past nine months, we developed a multi-part software solution involving large-scale data retrieval and analysis, the implementation and training of a support vector machine learning model, and the creation of an interactive graphical user interface.

In this paper, I discuss in detail my specific contributions, which include creating a connected infrastructure and writing the graphical user interface using web tools. My work in those areas enhanced team collaboration, improved operating efficiency, and provided our target audience with an interactive portal for actually utilizing our work. Other critical technical work, such as the model training and data parsing, is analyzed inside the papers written by my teammates David Winer, Joong Hwa Lee, Dany Srage, and William Ho. I then follow up with an analysis of how our project incorporates key facets of engineering leadership, such as marketing strategy, competitive analysis, and ethical forethought. Our combined efforts resulted in a novel and robust software solution which we believe satisfies the needs of our target audience. In the long run, we hope our work will help steer the United States patent system back towards its original purpose of fostering innovation, while also contributing to increased public interest in machine learning as a tool to solve broad problems.




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