Patenting Artificial Intelligence: Innovation Spike Follows Broader Market Trend

If you received a US patent for a machine learning invention recently, count yourself among a record number of innovators named on artificial intelligence technology patents issued in 2017. There’s also good chance you worked for one of the top companies earning patents for machine learning, neural network, and other AI technologies, namely IBM, Amazon, Cisco, Google, and Microsoft, according to public patent records (available through mid-December). This year’s increase in the number of issued patents reflects similar record increases in the level of investment dollars flowing to AI start-ups and the number of AI tech sector M&A deals in 2017.

As the chart indicates, US patents directed to “machine learning” jumped over 20% in 2017 compared to 2016, and that follows an even larger estimated 38% annual increase between 2015 and 2016. Even discounting the patents that merely mention machine learning in passing, the numbers are still quite impressive, especially given the US Supreme Court’s 2014 Alice Corp. Pty Ltd. v. CLS Bank decision, which led to the invalidation of many software and business method patents and likely also put the brakes on software-related patent application filings (as explained here) beginning in 2014. So the recent jump in issued patents for “machine learning,” “artificial intelligence,” and “neural network” inventions suggests that specific applications of those technologies remain patentable despite Alice.

A jump in issued patents in a highly competitive, increasingly crowded market segment, could lead to an uptick in patent-related infringement. Already, estimates by some suggest that 35% more companies expect to face IP litigation in 2018 compared to 2017.

Federal Circuit: AI, IoT, and Robotics in “Danger” Due to Uncertainty Surrounding Patent Abstraction Test

In Purepredictive, Inc. v. H2O.ai, Inc., the U.S. District Court for the Northern District of California (J. Orrick) granted Mountain View-based H2O.ai’s motion to dismiss a patent infringement complaint. In doing so, the court found that the claims of asserted U.S. patent 8,880,446 were 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.”

Decisions like this hardly make news these days, what with the frequency by which software patents are being invalidated by district courts across the country following the Supreme Court’s 2014 Alice Corp. Pty Ltd. v. CLS Bank decision. Perhaps that is why the U.S. Court of Appeals for the Federal Circuit, the specialized appeals court for patent cases based in Washington, DC, chose a recent case to publicly acknowledge that “great uncertainty yet remains” concerning Alice’s patent-eligibility test, despite the large number of post-Alice cases that have “attempted to provide practical guidance.”  Calling the uncertainty “dangerous” for some of today’s “most important inventions in computing” (specifically identifying medical diagnostics, artificial intelligence (AI), the Internet of Things (IoT), and robotics), the Federal Circuit expressed concern that perhaps Alice has gone too far, a belief shared by others, especially smaller technology companies whose value is tied to their software intellectual property.

Utah-based Purepredictive says its ‘446 patent 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. In the method’s first step, the court said, 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. This method, the district court found, 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.

Alice critics have long pointed to the subjective nature of Alice’s patent-eligibility test. Under Alice, for subject matter of a patent claim to be patent eligible under 35 U.S.C. § 101, it may not be “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea. If it is, however, it may nevertheless be patentable subject matter if the particular elements of the claim, considered both individually and as an ordered combination, add enough to transform the nature of the claim into a patent-eligible application. This two-part test has led to the invalidation of many software patents as “abstract,” and presents an obstacle for inventors of new software tools seeking patent protection for their inventions.

In the Purepredictive case, the district court found that the claim’s method “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.” The “could be performed by humans” query would seem problematic for many software-based patent claims, including those directed to AI algorithms, despite the recognition that humans could never perform the same feat as many AI algorithms in a lifetime due to the enormous domain space these algorithms are tasked with evaluating.

In any event, while Alice’s abstract test will continue to pose challenges to those seeking patents, time will tell whether it will have “dangerous” impacts on the burgeoning AI, IoT, and robotics industries suggested by the Federal Circuit.

Sources:

Purepredictive, Inc. v. H2O.AI, Inc., slip op., No. 17-cv-03049-WHO (N.D. Cal. Aug. 29, 2017).

Smart Systems Innovations, LLC v. Chicago Transit Authority, slip. op. No. 2016-1233 (Fed. Cir. Oct. 18, 2017) (citing Alice Corp. Pty Ltd. v. CLS Bank, 134 S. Ct. 2347, 2354-55 (2014)).