THE EVOLVING PATENT LANDSCAPE AT THE INTERSECTION OF BIOTECHNOLOGY, PHARMACEUTICALS, AND ARTIFICIAL INTELLIGENCE
The rapid evolution of artificial intelligence (AI) is reshaping numerous industries, with biotechnology and pharmaceuticals at the forefront of this transformation. As AI becomes increasingly integrated into these fields, it presents both opportunities and challenges from a patent law perspective, particularly within the frameworks of UK and European patent law. This article explores the evolving patent landscape at the intersection of biotechnology, pharmaceuticals, and AI, focusing on the potential implications of AI on patentability, and on enforcement of granted patent rights.
THE CONFLUENCE OF AI, BIOTECHNOLOGY, AND PHARMACEUTICALS
Biotechnology and pharmaceuticals have long been fields driven by innovation, with patents serving as critical tools for protecting new inventions and fostering further research and development. In recent years, AI has emerged as a powerful tool within these industries, aiding in drug discovery, patient diagnosis, treatment planning, and the prediction of molecular behaviours.
THE INTEGRATION OF AI INTO BIOTECH AND PHARMA IS NOT JUST A TREND; IT REPRESENTS A PARADIGM SHIFT IN HOW INNOVATION IS APPROACHED AND MANAGED.Patents are vital in this landscape, granting inventors a temporary monopoly over their inventions, thus providing the incentive necessary to invest in costly and time-consuming research. As AI becomes more entwined with biotechnology and pharmaceuticals, however, questions arise about how these innovations can be effectively protected under existing patent law.
PATENTABILITY CHALLENGES IN AI-ENHANCED BIOTECH/PHARMA INNOVATIONS
Patent applications must meet specific criteria to be granted, including novelty, inventiveness, and industrial applicability (1, 2). The subject matter of the patent, however, is equally crucial, particularly for innovations at the intersection of AI and biotech/pharma. There are longstanding exclusions in patent law that can affect the patentability of certain inventions (1, 2). For example, UK and European patent laws exclude mathematical methods and computer programs “as such” from patentability, which directly impacts AI-related inventions, as these often involve algorithmic processes and software implementations.
In biotechnology and pharmaceuticals, there are additional exclusions specific to the field, such as methods of medical treatment and diagnosis and essentially biological processes (1, 2). These exclusions are intended to prevent patents from hindering life-saving medical treatments, or from granting monopolies over natural biological processes. When AI is integrated into biotech/pharma innovations, however, these exclusions can compound, creating significant hurdles for inventors seeking patent protection.
TRENDS IN PATENT APPLICATIONS AND GRANT RATES
Over the past decade, there has been a dramatic increase in the number of patent applications mentioning AI and machine learning (3), reflecting the growing importance of these technologies across all sectors. This trend is mirrored in biotech/pharma-specific applications, indicating a rising interest in AI-enhanced innovations within these fields.
A closer examination of patent grant rates, however, reveals a potential disparity. When analysing the number of granted patents as a percentage of published applications, this suggests that the grant rate for AI-related biotech/pharma applications is approximately 23% lower than the rate for biotech/pharma applications which do not mention AI or machine learning (4). This potential discrepancy suggests that incorporating AI into these innovations introduces additional challenges that may affect their patentability.
NAVIGATING PATENTABILITY AND EXAMINATION CHALLENGES
The patenting process involves several stages, from drafting the patent application, to its examination and possible grant, and any subsequent enforcement. Each of these stages presents unique challenges when dealing with AI-enhanced biotech/pharma innovations.
When drafting patent applications, inventors and their patent attorneys must navigate a complex landscape of subject matter exclusions and case law. For instance, the patentability of antibodies has been an increasingly contentious issue over recent times, particularly concerning the inventiveness of complex, but now readily understood and catalogued, interactions between antibody components and their therapeutic effects. Similarly, in AI-related inventions, the focus often lies on whether the AI implementation contributes a technical “real-world” effect beyond mere algorithmic processing, a criterion that is crucial for overcoming the inventiveness requirement for computer-implemented inventions.
During examination, patent applications undergo rigorous scrutiny, particularly in the UK and Europe, known for their stringent patent examination processes. For biotech/pharma patents, inventiveness is often supported by data demonstrating the technical effect of the invention. In AI-related patents, the reliance on training data and the need for clear disclosure of how the AI model functions can complicate the examination process. The patent application must enable a skilled person to recreate the AI invention based on the information provided, which can be challenging when dealing with complex
AI models.
IMPLICATIONS FOR PATENT ENFORCEMENT
Once a patent is granted, its enforceability becomes a critical concern.
AI-enhanced biotech/pharma patents often involve processes that are distributed across multiple systems or platforms, such as client-server architectures. This distribution can complicate patent enforcement, as the steps of a patented process might be performed by different parties. A patent that requires multiple infringing parties to perform different steps of a patented process may provide weaker protection, making it more difficult to enforce against any single infringer.
THE FUTURE OF PATENTABILITY IN AI-DRIVEN INNOVATIONS
Looking ahead, the role of AI in biotech and pharmaceuticals raises broader questions about the future of patentability. In the case of antibodies, for example, developing antibodies against a target antigen has become routine, making it increasingly challenging to argue for the inventiveness of such inventions. Similarly, as AI models become more sophisticated and widely used, the inventiveness threshold for AI-related inventions may rise, making it more difficult to obtain patent protection for solutions that rely heavily on AI.
This trend is particularly relevant in the context of large language models (LLMs) and other AI systems that operate in a “black-box” manner, where the internal workings of the model are often not fully understood. This lack of transparency can create challenges for meeting the “plausibility” requirement, which mandates that the technical effect of an invention must be apparent from the patent application as originally filed. With specific reference to biotech/pharma inventions, while AI can aid in generating in-silico data to support plausibility, there is a risk that the ease of generating such data could diminish the perceived inventiveness of the invention. A question may also arise where, if an AI system simulates multiple possible therapeutic targets, and selects the best of these, who is the inventor in this instance?
AI AS AN INVENTOR: A PARADIGM SHIFT?
This provides a convenient segue into a recent, heavily-debated issue within the intellectual property field on whether AI can be listed as an inventor on a patent application.
The UK and European patent systems currently require that inventors be natural persons with legal capacity, which excludes AI systems from being named as inventors. This could, however, change as AI continues to advance and play a more significant role in the invention process. The DABUS case (5, 6), which involved an AI system being named as an inventor, sparked considerable debate but acted to highlight that a shift in current patent law, at least within the UK and Europe, would be required in order to accommodate an AI being listed as an inventor for
AI-generated inventions.
POTENTIAL RISKS FOR ONGOING INVESTMENT
One of the most significant risks posed by the evolving patent landscape in AI crossover technologies with biotech and pharmaceuticals is the potential cooling effect this may have on ongoing investment. Investment in these sectors is often predicated on the assumption that patents will provide a temporary monopoly, ensuring a return on the substantial capital required for research and development. As AI complicates the patentability of innovations, however, with the potential consequence of lowering patent grant rates, investors may grow hesitant to fund projects where patent protection is uncertain. This uncertainty can undermine the financial viability of pioneering research at the intersection of AI, biotech, and pharma, potentially stifling innovation and slowing the development of new therapies and technologies. The inability to secure robust patent protection might lead to a shift in investment strategies, with funds being diverted away from high-risk, high-reward AI-enhanced biotech/pharma projects, toward more traditional, less complex areas where patent security is more assured. The complex effects of AI-involvement on patentability could of course have long-term implications for the pace of innovation in these critical sectors, ultimately impacting the development of new treatments and technologies that rely on AI-driven advancements.
CONCLUSION
The intersection of AI, biotechnology, and pharmaceuticals presents a complex and evolving patent landscape. As AI becomes more integrated into these fields, it introduces new challenges for patentability and enforcement, particularly within the frameworks of UK and European patent law. The trend towards AI-enhanced innovations in biotech/pharma is likely to continue, necessitating a deep understanding of the divergent patentability restrictions and case law governing these distinct fields. While AI offers tremendous potential for advancing biotechnology and pharmaceuticals, it also raises the bar for inventiveness and challenges existing patent frameworks, calling for ongoing adaptation and innovation in patent law. Addressing these patent challenges is crucial not only for fostering continued innovation but also for ensuring that the investment needed to drive AI-enhanced biotech and pharmaceutical advancements remains strong and confident in the promise of secure intellectual property protection.
REFERENCES AND NOTES
- Patents Act 1977, c. 37. Available from: https://www.legislation.gov.uk/ukpga/1977/37
- European Patent Office. European Patent Convention (EPC) (Internet). Available from: https://www.epo.org/law-practice/legal-texts/epc.html
- Silvestre F, Lam A. Artificial Intelligence Related Inventions: Artificial Intelligence Patent Landscape. The Royal Society; 2023. Available from: https://royalsociety.org/-/media/policy/projects/science-in-the-age-of-ai/science-ai-related-inventions-report.pdf
- World Intellectual Property Organization. PATENTSCOPE (Internet). Available from: https://patentscope.wipo.int/
- Thaler v Comptroller-General of Patents (2021) EWCA Civ 1374.
- European Patent Office. Decision in J 08/20 (Internet). 2021. Available from: https://www.epo.org/law-practice/case-law-appeals/recent/j200008eu1.html
