Applying Machine Learning To A New Field Does Not Impart Patent Eligibility

The U.S. Court of Appeals for the Federal Circuit recently affirmed a district court's dismissal of a complaint for infringement of machine learning patents based on subject matter ineligibility of the patent claims under the Patent Act, 35 U.S.C. § 101. The district court had concluded that the claims recited known machine learning algorithms and did not provide a specific method for improving the algorithm, nor did they add "significantly more" to the abstract idea of using a generic machine learning algorithm in a specific field. Thus, the claims were deemed to be invalid. On appeal, the Federal Circuit distilled the matter down to the question of whether applying established methods of machine learning to new fields imparts patent eligibility, concluding that it does not. This decision highlights the importance of drafting claims related to machine learning algorithms to avoid "generic" language and, where possible, tying inventive methods to physical improvements