In October 2025, the U.S. Patent and Trademark Office (USPTO) launched the “Artificial Intelligence Search Automated Pilot Program,” or ASAP!, an initiative to identify potential prior art using artificial intelligence (AI) tools before a patent application is examined. Original, noncontinuing, nonprovisional utility applications filed under 35 U.S.C. 111(a) on or after October 20, 2025, and on or before April 20, 2026 are eligible to participate in ASAP!. The goal of the Pilot Program is to improve efficiency by giving applicants an early glimpse of potential prior art. Applicants who participate in ASAP! can petition to receive an Automated Search Results Notice (ASRN), which lists up to ten relevant references. While Applicants are not required to respond to ASRN, the search provides the Applicants an early opportunity to identify and address prior art-based rejections and/or make strategic decisions on their filings.
However, the search results of this program may be less nuanced for inventions in certain technologies including life sciences and biotechnology applications. For example, prior art for biotechnology inventions is often embedded in databases, deposited biological material records or datasets including sequence listings or experimental data that may not be fully captured by AI search tools. Furthermore, claims may include functional language, for example how a claimed protein binds to a particular target, the meaning of which hinges on details found in the body of the specification, which an AI model may miss entirely. As a result, the ASRN may overlook important references or over-flag superficially similar art, creating both blind spots and false positives in the biotech space.
Therefore, at this time, for biotech applicants, an ASRN may be useful as a supplementary data point, but is not a substitute for expert human judgment and analysis. As the USPTO gathers feedback from this program and evaluates the alignment between AI-generated results and examiner findings, future programs may incorporate context-aware search algorithms that can seamlessly integrate complex biological databases and datasets for improving search precision.
Editor: Brenden S. Gingrich,Ph.D.
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