AI technology and patenting in the life sciences part 2: When should AI-based life science innovation be patented?

AI technology and patenting in the life sciences part 2: When should AI-based life science innovatio

First published with Lawtext Publishing's Bio-Science Law Review - Volume 18 - Issue 6

In the first instalment of this mini-series, Sarah and I discussed what can be patented at the intersection of AI/ML and the Life Sciences. But just that you are able to patent something does not necessarily mean that this is always a good idea. Here we are looking at how you can develop a strategy for patenting in the interdisciplinary field.

When should AI-based life science innovation be patented?

Patenting AI innovation in the context of life science is in many ways no different from patenting other software-based inventions and the same concerns apply. Broad protection may be difficult to obtain and the pace of technological development can mean that a patented technology becomes obsolete early on in the life of a patent. Infringement of software patent claims can be difficult to detect, in particular if the technology can be used in the cloud or behind closed doors to analyse data. For example, it would be, on the face of it, impossible to know if a competitor’s new candidate compound was found using an already patented drug discovery AI platform. At the same time, in such scenarios where the AI technology is used for discovery rather than as the product itself, the discovery, that is, the new target compound or new diagnostic test, may itself be patentable, providing a route to patent protection that can avoid some of the above problems.

With all this in mind, where a commercial area involves AI technology for discovery, rather than as the product itself, one strategy is to focus on patent protection, for example, for the target compound discovered using the AI technology and to take steps to protect the AI technology as a trade secret. This may still be possible in situations where the AI technology is part of a product, for example a diagnostic system, if the innovative aspects of the AI can be kept secret. A common scenario where this might be the case is if the AI technology runs in the cloud. In such scenarios where the AI technology can be protected as a trade secret, this can have serious benefits over patenting, most significantly avoiding disclosure of proprietary know-how that would be necessary in a patent application and protection that lasts as long as secrecy can be maintained. However, in case that keeping the AI technology secret is not an option, for example because its disclosure is required for regulatory or marketing purposes, because engineers and scientists insist on being able to publish or because of high workforce mobility, the case for patenting becomes stronger. While patenting AI technology faces the same challenges as patenting software in general, if the technology will be disclosed in any event, the alternative is that the technology will become available for free to the public (including competitors) much earlier than the lifetime of patent protection would be.


In summary, the right patent strategy for any one AI technology will depend on factors such as the likely lifetime of the technology (in terms of its relevance), the ability to detect whether the technology is used, the ability to keep the technology secret, and the availability of other avenues for patenting that could be used to protect the particular commercial area. In this respect AI technology in the life sciences is not much different from any other software innovation.

Please contact Alexander Korenberg or Sarah Lau if you have any questions or would like to discuss this topic further.

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◄ Read part one  |  Read part three ►

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