is a registered patent attorney at Schwegman Lundberg & Woessner. His practice includes patent prosecution (US and foreign), patent validity, and strategic IP counseling, in a broad range of technical arts, including computer software and hardware, networking, semiconductor manufacturing, network storage, gaming (including virtual and augmented reality, controllers, head-mounted displays), data analysis, machine learning, virtual machines, and security.
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In Part I of this series we examined the mathematics exception to patentability and the historical underpinnings of its justification. In Part II, we will continue to examine the case history around patenting of mathematic principles.
The mathematics exception for subject matter eligibility is overbroad because it was improperly justified under the premise that mathematics is like a law of nature. This is absurd because mathematics is everywhere, and excepting mathematics means excepting virtually everything. Recent court decisions declare that “[m]athematical calculations and formulas are not patent eligible,” SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161 (2018)(“SAP AM.”), based on older decisions, such as Parker v. Flook, 437 U.S. 584 (1978) (“Flook”) and Gottschalk v. Benson, 409 U.S. 63 (1972) (“Benson”).
The USPTO’s Update on Subject Matter Eligibility of October 2019 (“OCT2019 PEG”) states, “The 2019 PEG defines ‘mathematical concepts’ as mathematical relationships, mathematical formulas or equations, and mathematical calculations,” and “where a formula or equation is written in text format that should also be considered as falling within this grouping.” This means that one can have a mathematical concept without even writing any mathematics. The USPTO can assert this illogical and absurd statement because the justification for the underlying mathematical exception itself is also illogical and absurd.
When discussing patentable inventions with data scientists, I often hear them dismiss their inventions under arguments such as these: “We’re using the same tools as everyone else,” “Augmenting data for the training set is well known,” “A similar thing has been done for car-bumper design” (said by the designer of a churro-making machine), “Configuring the neural-network hyperparameters is trivial,” and worst of all, “It’s obvious.” Data scientists often believe that their accomplishments are not patentable, but in-depth exploration of their work often uncovers patentable ideas. I am referring to data scientists that use machine-learning (ML) tools to uncover intrinsic relationships within a large corpus of data. Other data scientists design and improve these ML tools, and their work may also result in patentable ideas, which is a topic for discussing another day.