Recently, Sameer Vadera was a panelist on the "Subject Matter Eligibility, Inventorship, and Artificial Intelligence" panel co-hosted by the Pauline …

A software-related invention is likely to be patent eligible if the invention, as claimed, improves the functioning of a computer.

During the lunch service, attendees will work in groups at their tables to identify issues within proposed hypotheticals. Some issues may be practical in nature while others address policy considerations that are currently being evaluated by the USPTO. This panel will address techniques for and considerations in planning an IP strategy including training data, ML models, and output data. This panel will discuss best practices to address issues of inventorship, adequacy of disclosure, subject matter eligibility, and more. During the lunch service, attendees will work in groups at their tables to identify issues within proposed hypotheticals.

The patentability of artificial intelligence (AI) has been increasingly scrutinized in light of the surge in AI technology development and the ambiguity regarding the interpretation of software-related patents. The Federal Circuit has gradually refined the criteria for determining subject matter eligibility for software-related patents, and based in part on such jurisprudence, earlier this year the U.S. Patent and Trademark Office (USPTO) released revised guidance on examining patent subject matter eligibility under 35 U.S.C. §101. See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Considering the advances in AI technology and intellectual property law, how do these recent developments shape the outlook of AI patentability?

The use of artificial intelligence (AI) in life sciences, or "Life Tech", has increased at a rapid pace. According to World Intellectual Property Organization (WIPO), there has been "a shift from theoretical research to the use of AI technologies in commercial products and services," as reflected in the change in ratio of scientific papers to patent applications over the past decade.1 Indeed, while research into AI began in earnest in the 1950s, more than 1.6 million scientific papers have been published on AI, with more than half of identified AI inventions in the last six years alone.2,3 A review article in Nature Medicine reported last year that despite few peer-reviewed publications on use of machine learning technologies in medical devices, FDA approvals of AI as medical devices have been accelerating.4 Many of these FDA approvals relate to image analysis for diagnostic purposes, such as QuantX, the first AI platform to evaluate breast abnormalities; Aidoc, which detects acute intracranial hemorrhages in head CT scans, assisting radiologists to prioritize patient injuries; and IDx-DR, which analyzes retinal images to detect diabetic retinopathy.