Abstract
Examining industrial applicability and utility standards in patent law remains a cornerstone of the patent system frameworks. While scholarly discourse has extensively debated other patentability criteria, such as novelty and inventive step tests across jurisdictions, the specific impact of advancements in AI tools on industrial applicability remains largely unexplored. This gap is critical because AI tools, such as predictive analytics, can challenge traditional interpretations of utility tests by blurring the lines between credible potential applications as set in case law, and hypothetical conjectures.
This paper investigates how AI’s capabilities affect the thresholds for deeming patent claims “plausible” or “speculative” under industrial applicability tests. This study evaluates legal frameworks in the European Patent Convention, the United Kingdom, and the United States, analysing case law, statutory provisions, and examination guidelines. Key cases, such as Human Genome Sciences v. Eli Lilly, are used to assess current standards through comparative legal and socio-economic approaches critically. The findings highlight the need for a nuanced reinterpretation of industrial applicability and utility standards to account for AI’s predictive capabilities. The study advocates for harmonised, stricter legal frameworks that safeguard against speculative patents while strengthening related criteria such as patent eligibility and morality as interpreted in Articles 53(a) of the EPC. This research also situates the discussion within a postmodern framework, emphasising a shift from rigid, traditional patent standards to a more responsive, human-centric approach that reflects contemporary societal and technological complexities introduced by AI.
