Disclosing AI Inventions - Part II: Describing and Enabling AI Inventions

In Part I of this series on Disclosing AI Inventions, we discussed the basics of machine learning and the unique disclosure challenges presented by the “black box” nature of trained machine learning models. Nevertheless, current U.S. patent laws are generally viewed as sufficient to ensure adequate disclosure of machine learning inventions to the public, and it will be left to the courts to shape the details of disclosure requirements through interpretation of existing patent laws. In this Part II, we discuss techniques for disclosing machine learning inventions in compliance with the written description and enablement requirements of 35 U.S.C. 112(a).

The test for the sufficiency of the written description is whether the patent disclosure reasonably conveys to those skilled in the art that the inventor had possession of the claimed subject matter as of the filing date.  Enablement requires that the invention be described in such a way that allows one skilled in the art to make and use the invention without undue experimentation.  Thus, compliance with the written description and enablement requirements is a fact-specific determination that will depend on the type of machine learning improvement that is being claimed in the patent or application.

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