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36 Santa Clara High Tech. L. J. 433 (2019-2020)
The Promise of Machine Learning for Patent Landscaping

handle is hein.journals/sccj36 and id is 433 raw text is: THE PROMISE OF MACHINE LEARNING FOR PATENT LANDSCAPING Andrew A. Toole Chief Economist, U.S. Patent and Trademark Office Nicholas A. Pairolero Economist, U.S. Patent and Trademark Office James Q. Forman Data Scientist, Google LLC' Alexander V Giczy Data Scientist, U.S. Patent and Trademark Office (Addx Corporation) ABSTRACT Patent landscaping involves the identification of patents in a specific technology area to understand the business, economic, and policy implications of technological change. Traditionally, patent landscapes were constructed using keyword and classification queries, a labor-intensive process that produced results limited to the scope of the query. In this paper, we discuss the advantages and disadvantages of using machine learning to produce patent landscapes. Machine learning leverages traditional queries to construct the data necessary to train machine learning models, and the models allow the resultant landscapes to extend more broadly into areas of technology not expected a priori. The models, however, are black boxes that limit transparency regarding their underlying reasoning. To illustrate these points, we summarize two landscapes we recently conducted, one in mineral mining and another in artificial intelligence. DISCLAIMER: The views expressed are those of the individual authors and do not necessarily reflect the official positions of the Office of the Chief Economist or the U.S. Patent and Trademark Office. 'Formerly at the U.S. Patent and Trademark Office (Addx Corporation).

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