Document Type


Publication Date



In this Article, we describe how to use artificial intelligence (AI) techniques to partially automate a type of legal analysis, determining whether a patent claim satisfies the definiteness requirement. Although fully automating such a high-level cognitive task is well beyond state-of-the-art AI, we show that AI can nevertheless assist the decision maker in making this determination. Specifically, the use of custom AI technology can aid the decision maker by (1) mining patent text to rapidly bring relevant information to the decision maker attention, and (2) suggesting simple inferences that can be drawn from that information.

We begin by summarizing the law related to patent claim indefiniteness. A summary of existing case law allows us to identify the types of information that can be relevant to the legal determination of indefiniteness. This in turn guides us in designing AI software that processes a patent text to extract information that can be relevant to the legal analysis of indefiniteness. Some types of relevant information include whether terms in a claim are defined in the patent, whether terms in a claim are not mentioned in the patent specification, whether the claim includes nonstandard terms coined by the drafter of the patent, whether the claim relies on vaguely-specified measurements, and whether the patent specification discloses structure corresponding to a means-plus-function limitation.

The AI software rapidly processes a patent text and identifies information that is relevant to the legal analysis. The software then provides the human decision maker with this information as well as simple metrics and inferences, such as the percentage of claim terms that are defined explicitly or by example, and whether terms that are coined by the drafter should be defined or renamed. This can provide the user with insights about a patent much faster than if the user read the entirety of the patent to locate the same information unaided.

Moreover, the software can aggregate the various types of information to "score" a claim (e.g., from 0 to 100) based on its risk of being deemed indefinite. For example, a claim containing only defined terms and lacking any vague measurements would score much lower in terms of risk than a claim with terms that are not only undefined but do not even appear in the patent specification. Once each claim in a patent is assigned such an indefiniteness score, the patent itself can be given an overall indefiniteness score.

Scoring groups of patents in this manner has further advantages even if the scores are blunt measurements. AI software ranks a group of patents (e.g., all patents owned by a company) by indefiniteness scores. This allows a very large set of patents to be quickly searched for patents that have the highest, or lowest, indefiniteness score. The results of such a search could be, e.g., the patents to target for detailed review in litigation, post-grant proceedings, or licensing negotiations.

Finally, we present some considerations for refining and augmenting the proposed methods for partially automating the indefiniteness analysis, and more broadly other types of legal analysis.