Artificial Intelligence And The Law

Artificial Intelligence And The Law

Although the notion of “explainable artificial intelligence” (AI) has been suggested as a necessary component of governing AI technology, at least for the reason that transparency leads to trust and better management of AI systems in the wild, one area of US law already places a burden on AI developers and producers to explain how their AI technology works: patent law.  Patent law’s focus on how systems work was not borne from a Congressional mandate. Rather, the Supreme Court gets all the credit–or blame, as some might contend–for this legal development, which began with the Court’s 2014 decision in Alice Corp. Pty Ltd. v. CLS Bank International. Alice established the legal framework for assessing whether an invention fits in one of the patent law’s patent-eligible categories (i.e., any “new and useful process, machine, manufacture, or composition of matter” or improvements thereof) or is a patent-ineligible concept (i.e., law of nature, natural phenomenon, or abstract idea).  Alice Corp. Pty Ltd. v. CLS Bank International, 134 S. Ct. 2347, 2354–55 (2014); 35 USC § 101.

Understanding how the idea of “explaining AI” came to be following Alice, one must look at the very nature of AI technology.  At their core, AI systems based on machine learning models generally transform input data into actionable output data, a process US courts and the Patent Office have historically found to be patent-ineligible.  Consider a decision by the US Court of Appeals for the Federal Circuit, whose judges are selected for their technical acumen as much as for their understanding of the nuances of patent and other areas of law, that issued around the same time as Alice: “a process that employs mathematical algorithms to manipulate existing information to generate additional information is not patent-eligible.”  Digitech Image Techs, LLC v. Elecs. v. Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014).  While Alice did not specifically address AI or mandate anything resembling explainable AI, it nevertheless spawned a progeny of Federal Circuit, the district court, and Patent Office decisions that did just that.

Artificial Intelligence, Meet Alice

In a bit of ironic foreshadowing, the Supreme Court issued Alice in the same year that major advances in AI technologies were being announced, such as Google’s deep neural network architecture that prevailed in the 2014 ImageNet challenge (ILSVCR) and Ian Goodfellow’s generative adversarial network (GAN) model, both of which were major contributions to the field of computer vision. Even as more breakthroughs were being announced, US courts and the Patent Office began issuing Alicedecisions regarding AI technologies and explaining why it’s crucial for inventors to explain how their AI inventions work to satisfy the second half of Alice’s 2-part test.

In Purepredictive, Inc. v. H2O.AI, Inc., for example, the US District Court for the Northern District of California considered the claims of US Patent 8,880,446, which, according to the patent’s owner, involves “AI driving machine learning ensembling.”  The district court characterized the patent as being directed to a software method that performs “predictive analytics” in three steps.  Purepredictive, Inc. v. H2O.AI, Inc., slip op., No. 17-cv-03049-WHO (N.D. Cal. Aug. 29, 2017).  In the method’s first step, it receives data and generates “learned functions,” or, for example, regressions from that data. Second, it evaluates the effectiveness of those learned functions at making accurate predictions based on the test data. Finally, it selects the most effective learned functions and creates a rule set for additional data input. The court found the claims invalid on the grounds that they “are directed to the abstract concept of the manipulation of mathematical functions and make use of computers only as tools, rather than provide a specific improvement on a computer-related technology.” The claimed method, the district court said, is merely “directed to a mental process” performed by a computer, and “the abstract concept of using mathematical algorithms to perform predictive analytics” by collecting and analyzing information.  The court explained that the claims “are mathematical processes that not only could be performed by humans but also go to the general abstract concept of predictive analytics rather than any specific application.”

In Ex Parte Lyren, the Patent Office’s Appeals Board, made up of three administrative law judges, rejected a claim directed to customizing video on a computer as being abstract and thus not patent-eligible.  In doing so, the board disagreed with the inventor, who argued the claimed computer system, which generated and displayed a customized video by evaluating a user’s intention to purchase a product and information in the user’s profile, was an improvement in the technical field of generating videos. The claimed customized video, the Board found, could be any video modified in any way.  That is, the rejected claims were not directed to the details of how the video was modified, but rather to the result of modifying the video.  Citing precedent, the board reiterated that “[i]n applying the principles emerging from the developing body of law on abstract ideas under section 101, … claims that are ‘so result-focused, so functional, as to effectively cover any solution to an identified problem’ are frequently held ineligible under section 101.”  Ex ParteLyren, No. 2016-008571 (PTAB, June 25, 2018) (citing Affinity Labs of Texas,LLC v. DirecTV, LLC, 838 F.3d 1253, 1265 (Fed. Cir. 2016) (quoting Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1356 (Fed. Cir, 2016)); see also Ex parte Colcernian et al., No. 2018-002705 (PTAB, Oct. 1, 2018) (rejecting claims that use result-oriented language as not reciting the specificity necessary to show how the claimed computer processor’s operations differ from prior human methods, and thus are not directed to a technological improvement but rather are directed to an abstract idea).

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