Concerns over patenting AI inventions is flat wrong – Robust and rising patenting of medical AI inventions now

Mateo Aboy et al, 4-17-2023 (Aboy, M. Centre for Law, Medicine, and Life Sciences (LML), Faculty of Law, University of Cambridge, Cambridge, UK, Price, W.N. University of Michigan Law School, Ann Arbor, MI, USA & Raker, S. University of Michigan Law School, Ann Arbor, MI, USA). Mapping the patent landscape of medical machine learning. Nat Biotechnol 41, 461–468 (2023). -- MoState JG

The landscape of patents on MML has several implications for existing scholarship and for policy going forward. First, fears that patents would be unavailable for MML inventions seem to be largely unwarranted. Our results show that after a decade of relatively low growth, since 2013 MML patent grants have been growing rapidly, with a CAGR of 38.51%. In 2021, more than 700 patents were granted containing claims directed to AI/ML for medical applications. The allowance rate for these MML patents has steadily increased, from 48% in 2007 to 64% in 2012, and looks to be staying relatively high. The majority of the MML patents granted over the past 20 years are in force (81.73%), but 18.27% are now expired and are already in the public domain available for society to use freely. Second, the doctrinal instability around patentability of AI in the United States also seems not to have dissuaded MML patenting at the USPTO. Despite the relatively greater stability regarding subject-matter eligibility of the EPO compared with the US jurisprudence, our results indicate that the USPTO has been the patent office of choice over the past 20 years (87% USPTO versus 13% EPO in 2021). And even though AI/ML applications are often affected by 35 USC 101 subject-matter eligibility rejections based on Alice, our results indicate that the post-Alice allowance rates for MML inventions are likely to range between 55% and 70%. Inventors’ preference for the USPTO notwithstanding, the fact is that European-headquartered corporations are among the top MML patent owners. Over the past 20 years, MML patenting activity has been led by large corporations active in the medical technologies field, such as Siemens, Philips, Medtronic, Samsung and GE. More recently, we also find technology companies such as Google and Microsoft among the top MML patent owners, as well as universities such as University of California, Case Western Reserve and Stanford. The two clear leaders, Siemens and Philips, are two of the three largest manufacturers of MRI scanners (GE, the third, is fifth among MML patentees). This area concentration is congruent with the high rate of image-input patents and with the dominance of radiology among US Food and Drug Administration (FDA)-cleared or FDA-classified1 MML-based products (representing three-quarters of all FDA-listed MML products13). Nevertheless, despite this moderate imaging focus, MML patenting more generally is not especially concentrated: the top 20 S5 patentees together represent only ~28% of all MML patents. Third, patents referencing MML are actually about MML inventions. Given the hype over AI and machine learning, one might expect that many patents would reference these techniques in the abstract, but that the references would be buzzwords without real importance to the claimed invention. Although this might once have been the case, it is not today. In 58% of MML patents, AI/ML limitations appear in the broadest (independent) claims. This represents a sharp increase: merely a decade ago, only 20% of MML patents had such AI/ML limitations as part of the independent claims. Claim practice is also evolving as the technology does, with “machine learning” emerging as the most prevalent limitation since 2017, followed by “neural network,” “AI” and “deep learning.” Fourth, despite the MML hype, our results show that claims are relatively conservative. There are relatively few patents directed at fully automatic AI-based diagnosis. Most of the MML claims analyzed instead focus on measurement and analysis (27%) and detection and classification (36%). In the case of diagnosis, images are the most prevalent input signal to the AI/ML system. We suspect that this pattern may change, and the current focus on earlier clinical tasks reflects capabilities and the current state of knowledge—but for now, most MML patents are farther from clinical decisions, even as policy debates revolve around MML systems that focus on the point of care itself14,15. More work remains to be done. Further research is required to investigate the scope of protection for these MML patent claims, their validity in light of current subject-matter eligibility requirements and AI/ML prior art, and the sufficiency of disclosure of the patent specifications3. But an early look suggests that patenting remains an important player in innovation incentives for AI/ML in medicine, with a range of inventors actively seeking patent protection in surprisingly conservative applications.