Tabrez Y. Ebrahim*
Abstract
Artificial intelligence (“AI”) has attracted significant attention and has imposed challenges for society. Yet surprisingly, scholars have paid little attention to the impediments AI imposes on patent law’s disclosure function from the lenses of theory and policy. Patents are conditioned on inventors describing their inventions, but the inner workings and the use of AI in the inventive process are not properly understood or are largely unknown. The lack of transparency of the parameters of the AI inventive process or the use of AI makes it difficult to enable a future use of AI to achieve the same end state. While patent law’s enablement doctrine focuses on the particular result of the invention process, in contrast, this Article suggests that AI presents a lack of transparency and difficulty in replication that profoundly and fundamentally challenge disclosure theory in patent law. A reasonable onlooker or a patent examiner may find it difficult to explain the inner workings of AI. But even more pressing is a non-detection problem—an overall lack of disclosure of unidentified AI inventions, or knowing whether the particular end state was produced by the use of AI.
The complexities of AI require enhancing the disclosure requirement since the peculiar characteristics of the end state cannot be described by the inventive process that produced it. This Article introduces a taxonomy of AI and argues that an enhanced AI patent disclosure requirement mitigates concerns surrounding the explainability of AI-based tools and the inherent inscrutability of AI-generated output. Such emphasis of patent disclosure for AI may steer some inventors toward trade secrecy and push others to seek patent protection against would-be patent infringers despite added ex ante costs and efforts. Utilitarian and Lockean theories suggest justifications for enhanced AI patent disclosure while recognizing some objections. Turning to the prescriptive, this Article proposes and assesses, as means for achieving enhanced disclosure, a variety of disclosure-specific incentives and data deposits for AI. It concludes by offering insights for innovation and for a future empirical study to verify its theoretical underpinnings.
*Associate Professor of Law, California Western School of Law; Visiting Associate Professor, University of California, San Diego; Ostrom Visiting Scholar, Indiana University (Bloomington); Visiting Fellow, University of Nebraska (Lincoln): Nebraska Governance & Technology Center; Thomas Edison Innovation Fellow & Leonardo da Vinci Fellow, George Mason University Antonin Scalia Law School; Visiting Scholar, University of California, Los Angeles School of Law; Registered U.S. patent attorney; J.D., Northwestern University Pritzker School of Law; M.B.A., Northwestern University Kellogg School of Management; LL.M., University of Houston Law Center; Graduate Entrepreneurship Certificate, Stanford Graduate School of Business; M.S. Mechanical Engineering, Stanford University School of Engineering; B.S. Mechanical Engineering, University of Texas at Austin Cockrell School of Engineering.
I am grateful for helpful comments, feedback, and suggestions from Michael Risch, Ted Sichelman, Brenda Simon, Thomas D. Barton, Robert A. Bohrer, Shawn Miller, Lisa Ramsey, Anjanette Raymond, Daniel R. Cahoy, Sonia Katyal, Tejas Narechania, Jonathan Barnett, Eric Claeys, John Duffy, Sean O’Connor, Ashish Bharadwaj, Loletta Dardin, Charles Delmotte, H. Tomás Gómez-Arostegui, Taorui Guan, Devlin Hartline, Christa Laser, Daryl Lim, Kevin Madigan, Talha Syed, James Stern, Seth C. Oranburg, Agnieszka McPeak, Gregory Day, Nicole Iannarone, Emily Loza de Siles, Eric C. Chaffee, Robert F. Kravetz, Ashley London, Aman Gebru, Elizabeth I. Winston, A. Michael Froomkin, Mason Marks, Larry DiMatteo, Robert W. Emerson, Robert E. Thomas, Colleen M. Baker, Lawrence Trautman, George Cameron, David Orozco, Thomas Freeman, Christopher Guzelian, Daniel Herron, Michelle Romero, Tyler Smith, Brian Haney, Jihwang Yeo, Sikander Khan, Erica Pascal, Ryan Hsu, Kevin R. Tamm, and Daniel R. Peterson.
Thanks to the following forums for presenting this Article and their participants for insightful comments: 2020 Huber Hurst Research Seminar at University of Florida Warrington College of Business, The Junior #FutureLaw Workshop 4.0 at Duquesne University School of Law, Ostrom Workshop’s Colloquium Series at Indiana University (Bloomington), Junior Intellectual Property Scholars Association (JIPSA) at George Washington University School of Law, We Robot 2019 at University of Miami School of Law, PatCon 9 (The Annual Patent Conference) at University of Kansas School of Law, and the 9th Annual Patent Law Conference at University of San Diego School of Law. Thanks to the Academy of Legal Studies in Business (ALSB) Interdisciplinary Section for selecting this Article for its inaugural “Best Paper Award” at the 2020 ALSB Annual Conference and to ALSB members for thoughtful comments.
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