How Far Can LLMs Go in Patent Claim Construction?
I have been continually testing the limits of large language models (LLMs) like Gemini and Claude to see exactly how much of the patent analysis workflow they can genuinely handle.
Through this process, it has become abundantly clear that the roles of SaaS platforms and expert services are rapidly realigning. This doesn’t mean experts will disappear. Rather, their role is shifting from executing every manual task to designing and verifying the systems that ensure AI operates correctly.
Today, I want to show you how to leverage LLMs to perform Patent Claim Construction at a near-expert level. I will explain this as concretely as possible so that even beginners can follow along—assuming “beginner” means someone with a basic grasp of using interfaces like Gemini or Claude.
The High Hurdle of Claim Construction
Claim construction is the starting point for any critical patent analysis, whether you are conducting a Freedom-To-Operate (FTO) clearance or a patentability search. A patent document is broadly divided into the patent claims (what the inventor legally seeks to protect) and the detailed description (which explains the invention so it can be easily reproduced).
However, interpreting these claims is surprisingly difficult. Claim construction is ultimately the act of defining the legal boundaries of a patent right. How you define these borders clearly separates the novices from the experts. Accurate claim construction requires a deep understanding of legal principles, case law, technical background, and years of practical experience. Mere reading comprehension is not enough; relying on it alone will likely lead to failure when setting boundaries in real-world disputes.
This is a challenging task even for seasoned professionals. In practice, we often rely on peer review to ensure objectivity. Ultimately, claim construction is not judged by you, but by a third party—who strives for an objective interpretation based solely on facts and the written record.
The Right Approach to LLMs: Treat Them as a Clueless ‘New Hire’
Now, let’s assign this highly complex task to an LLM. However, you cannot simply hand claim construction over to a model trained only on general knowledge. You must inject the text of the target patent, relevant background art, legal principles, and the prosecution history to create a highly tailored, case-specific working environment for the AI.
Many people are surprised when they see the results I achieve with LLMs. They often mention that they tried similar tasks but got vastly inferior output. I always give the same advice to those still getting used to leveraging AI:
“You need to treat the LLM like a new hire who has absolutely zero knowledge or experience in the specific task you are assigning.”
Of course, LLMs already possess a vast understanding of general vocabulary and syntax, making them easier to manage than an actual new hire who needs everything spelled out. But if a term has a highly specific meaning in a particular case or technical context, you must explicitly define it or train the model using reference materials.
A Step-by-Step Workflow for Claim Construction
Let’s use claim construction as an example. If you hand a new hire a patent publication and simply say, “Construe Claim 1,” they will panic. They will try their best using their baseline knowledge, but the result will likely be inadequate.
Instead, you would first have them research and summarize the relevant case law and legal principles regarding claim construction. You would ask them to present their findings to check their understanding. An experienced practitioner would then step in to share unwritten practical conventions and key judgment points that aren’t always explicitly stated in textbooks or rulings.
The fundamental principle of claim construction is that claims must be understood objectively, based on the patent’s detailed description, the Common General Knowledge (CGK) in the art, and the Applicant’s Intent as revealed during examination. Conversely, you cannot arbitrarily import limitations or meanings from other patents or external technical literature.
Building on these core principles, you would then instruct the new hire to design a framework and workflow for the actual analysis.
The essential inputs for patent interpretation are the patent publication and the File Wrapper (Prosecution History). The file wrapper primarily contains the examiner’s Office Actions, as well as the applicant’s Remarks and Amendments. These documents reveal how the applicant surrendered certain claim scopes to secure the patent. You would also have them research related prior cases, similar patents, and especially litigation outcomes regarding Patent Families or foreign counterparts.
Finally, you would ask them to compile all this data into a Claim Chart. Since a new hire might not be familiar with this format, you must provide templates and specific guidelines on how to accurately populate each section.
Combining NotebookLM with Prompt Engineering
This exact workflow can be applied almost verbatim to an LLM. Given the right data and procedures, an LLM can produce stable results much faster than training a human junior associate. The bottleneck isn’t the model’s intelligence; it’s what you input, the sequence of tasks you assign, and how you verify the output.
I frequently perform this work in Google’s NotebookLM. Because NotebookLM is inherently designed to ground its responses in the provided source documents, it is highly effective at reducing baseless extrapolation (hallucinations) and driving data-backed workflows.
I start by using NotebookLM’s deep research features to compile case law and legal principles. Once verified, these common methodologies are synthesized and injected as core instructions (prompts) to create a Gemini GEM or a Claude Skill. Providing concrete examples of input materials and a sample Claim Chart makes a massive difference in output quality.
In my next post, I will break down exactly how I instruct the LLM for claim construction, sharing the specific prompts, workflows, and frameworks I use step-by-step. I will also take a real-world litigated patent and compare the LLM’s claim construction against the actual court or tribunal ruling.
Naturally, this approach has its limits. Unlike in actual litigation, the opposing party’s counterarguments may not be fully represented initially. However, in a real case, you can continuously refine the precision of the analysis by feeding the model the opposing counsel’s arguments, rebuttal evidence, and specific prior art.
