Augmented inventorship: rethinking patent law in the age of generative AI

Abstract Generative artificial intelligence (AI) now participates in tasks constitutive of invention—problem framing, hypothesis generation, and design—yet patent doctrine remains anchored to a natural-person rule that offers limited guidance for AI-intensive workflows. This Article advances augmented inventorship, a conservative but operationally modern attribution doctrine that preserves human inventorship while making AI’s generative role legible and auditable at the moment of conception. Drawing on an analogy to augmented immunology, the framework identifies two design criteria—directability (independent and substantive human intellectual judgment steering model behavior or selection) and traceability (a reviewable, claim-centered record linking human reasons to claim elements)—and translates them into a proportionate evidentiary practice: a Computational Traceability Report and a Human–Machine Contribution Statement. These instruments are content-rich but code-light. They support enablement and sufficiency, clarify claim drafting and construction, reduce prosecution and litigation error costs, and balance evidentiary transparency with trade-secret sensitivity through proportional disclosure. Situated within—and distinguished from—the growing literature on AI inventorship and disclosure, the doctrine aligns with existing law (US conception and significant-contribution standards; the UK’s ‘actual deviser’; EPC sufficiency) and is compatible with TRIPS disclosure norms. Rather than demanding ‘more disclosure’ in the abstract, augmented inventorship supplies an administrable grammar for human accountability in AI-assisted research.