The US Patent and Trademark Office announced an opt-in automated search pilot program in an October 2025 Federal Register notice, another step in the growing use of artificial intelligence in patent examination. The tool identifies up to 10 potentially relevant prior art documents using an application's cooperative patent classification designation, specification, claims, and abstract. Applicants can consider these references before formal examination and, if appropriate, amend their claims to expedite prosecution.
The PTO introduced a "similarity search" option four years ago that enabled examiners to identify additional references similar to the application under review. To date, these AI-based tools have not fundamentally altered the examination process, but the rapid pace of AI advancement suggests their influence will grow.
Two Types of AI Search Tools
There is no clear, unambiguous definition of an "AI search tool," but any such system will rely on a custom-trained model, an existing AI model, or a combination of both to identify prior art references that may anticipate or render obvious an applicant's claims. These tools will likely fall into two categories. Examiner assistant tools improve the search results presented to an examiner for review. Agentic tools can autonomously search relevant databases and evaluate results in an iterative, reasoned manner to produce traditional search results or even draft initial office actions.
The key differentiator from traditional search methods is the use of learned associations developed during training. Language models encode the semantic meaning of an input passage into a high-dimensional vector space where different directions correspond to different semantic features, such as technology fields and system functionality. The tool then compares the application's vector with vectors from prior art references to identify relevant documents without relying solely on matching identical or synonymous text. Agentic systems would operate similarly but would also review identified references, conduct additional searching, and generate a summary report or office action draft that includes the references and the reasons the agent believes they are relevant.
Both types of tools could increase the number of on-point prior art references cited by the patent office and reduce the time it takes to examine individual filings. However, this per-filing speed-up may not translate into meaningfully shorter pendency at the PTO. The higher volume of better prior art may require applicants to engage in additional rounds of prosecution or appeals, leading to more narrowing claim amendments and even abandonment of weak cases.
How to Prepare
No single step guarantees success, but the most proactive measure applicants can take is to file thorough, detailed specifications focused on particularized technical improvements in systems, processes, devices, and methods. Applicants should have a clear engineer- or scientist-level understanding of the technical features and operation of the invention they are seeking to patent. This includes providing detailed examples and clearly articulating how the invention operates or is intended to operate.
While applicants are not required to constructively reduce their invention to practice before filing, those that are closer to that goal will be better positioned for success. More prophetic and less developed patent applications may lack the disclosure necessary to support strong claim amendments needed to overcome the increased volume of relevant prior art that AI-based search tools may uncover. This warning is especially relevant for computer- and software-related inventions, which often are filed earlier in the product development cycle compared with mechanical, chemical, or biological inventions. As with all technologies, the key disclosure elements are not the outcomes that a system, device, or method achieves, but what the invention is and how it achieves those outcomes.
Why this matters for legal professionals
AI-driven prior art searching will likely force patent prosecutors to craft applications with far more technical depth and specificity than in the past. For patent agents and attorneys, understanding how these tools work can inform filing strategies and claim drafting. AI Learning Path for Patent Agents offers focused training on the intersection of AI and patent practice, helping legal professionals adapt to a changing examination process. The firms and in-house teams that invest now in both strong technical disclosure and AI literacy will be better equipped to handle a future where AI-generated prior art citations are the norm.
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