A.I. for A.I. — US Patent Regulator Uses Machine Learning to Analyze Complex A.I. Patents

Every year, the U.S. Patent and Trademark Office (USPTO) reviews thousands of patent applications involving artificial intelligence (A.I.), the core technology behind so many consumer products and industrial inventions today. As interest in A.I. patents grew and the definition of A.I. broadened in recent years, it’s difficult to keep an accurate tab of every A.I. patent going through the system and track how the trend changes over time. To tackle that problem, the USPTO recently utilized its own A.I. capabilities to identify A.I.-related patents from as early as the 1970s in a sea of documents.

Earlier this month, the USPTO’s Office of the Chief Economist (OCE) released a pair of data files called the Artificial Intelligence Patent Dataset (AIPD), generated using a machine learning approach that analyzed patent text and citations in all U.S. patent documents recorded since 1976. One of the files identifies patents issued and pre-grant publications (PGPubs) published between 1976 and 2020 that contain one or more of eight A.I. technology components under the USPTO’s definition.

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