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.

De Facto Standard Patent Strategies and the Pitfalls of ‘Royalty-Free’: Lessons from Tesla, Qualcomm, and Google

 

That ‘Royalty-Free’ Gift… Can You Really Trust It? From Bluetooth to EV charging standards, we're diving into the complex world of patents and the calculated corporate strategies hidden behind the sweet promise of “free.” This article will give you a sharper eye for seeing what’s really going on in tech.

Hey there! In the world of tech, the term ‘Royalty-Free’ sounds pretty appealing, right? It feels like a free gift, and since it’s used in everyday things like Bluetooth, USB, and WebRTC, you might think you can use it without a second thought.

But is that really the case? Today, we’re going to dig into the complex issues lurking behind that attractive ‘royalty-free’ sign—namely, intellectual property (IP) problems and, sometimes, intentional strategic traps. The goal of this article is to help you see beyond the “Oh, it’s free!” mindset and understand the true nature of these technologies. So, where does the misunderstanding about ‘royalty-free’ begin?

 

🤔 “A Prefab House with a Free Frame?” The Real Face of Royalty-Free

The belief that ‘free means safe’ is actually the starting point for the biggest misconception. Royalty-free never means ‘zero risk.’ In reality, it’s just ‘a promise of a license with a very limited scope,’ not a complete hall pass from all patent issues.

To put it simply, it’s like a ‘prefab house where only the frame is free.’ The frame might not cost you anything, but you still have to pay for or figure out the crucial parts like walls, the roof, and plumbing on your own.

Bluetooth: ‘Enabling Technologies’ Are Not Covered

This becomes clear if you take a close look at Bluetooth’s Patent/Copyright License Agreement (PCLA). The royalty-free benefit is strictly limited to the ‘Compliant Portion’ of a certified product and only for ‘Necessary Claims’—patents that are technically essential to implement the standard and cannot be avoided.

More importantly, so-called ‘Enabling Technologies’ like semiconductor processes or operating systems are explicitly excluded from the license scope. The Bluetooth communication module itself might be covered, but the peripheral technologies needed to run it, like power management chips and audio codecs, can still be subject to separate patent disputes. In fact, more than 20 lawsuits were recently filed over Bluetooth’s frequency-hopping technology patents.

WebRTC: Google’s Umbrella Only Covers Google’s Code

The situation is similar with Google-led WebRTC. The royalty-free license Google provides generally applies only to ‘patents owned by Google’ and only when using the ‘original source code distributed by Google’ as-is. If a company modifies this code or adds new features to suit its service, the added parts are no longer under Google’s protective umbrella. This means they could be exposed to unexpected patent infringement lawsuits from third parties.

 

📊 Stories from the Players in the Game

So, what have actual companies experienced in this complex game? Let’s look at a few key examples to see the risks firsthand.

Case 1: The AV1 Codec – “Attacked by Wolves from Outside the Fence”

In response to the expensive royalties of the HEVC codec, tech giants like Google and Netflix formed the Alliance for Open Media (AOMedia) and created a royalty-free codec called ‘AV1.’ They even included a strong defensive clause preventing member companies from suing each other over patents, creating a solid “patent-safe zone.”

However, this fence only protected them from the patents of member companies. A patent pool operator named Sisvel appeared from outside the fence, claiming that AV1 was a “Copycat Codec” that infringed on their patents. They began demanding license fees from users (€0.24 per device). This case showed the limits of a consortium’s “permeable shield”—it couldn’t block attacks from the outside.

Case 2: Tesla’s NACS – “‘Our Friends’ Get In Free”

In 2014, Tesla pledged to let others use its patents, as long as they were “acting in Good Faith.” However, the term ‘Good Faith’ was essentially a promise “not to attack us in any way.” When a capacitor manufacturer sued a company that Tesla had acquired, Tesla countersued, claiming the lawsuit itself was a violation of good faith.

This strategy proved brilliant when the U.S. government’s 2021 Infrastructure Act offered subsidies only for the competing CCS1 standard. Facing a crisis, Tesla declared NACS an open standard, not only qualifying for government subsidies but also pulling competitors into its ecosystem under the condition that they wouldn’t attack Tesla. It was a smart move to solidify market dominance through ‘free and open’ access.

Case 3: Qualcomm – The Two Sides of Geopolitical Risk

Qualcomm’s “No License, No Chips” policy illustrates another dimension of the problem. Qualcomm tied its patent licensing agreements to the total price of a smartphone to maximize profits, a practice that led to a fine of over 1 trillion won from the Korea Fair Trade Commission, a decision upheld by the Supreme Court. Interestingly, however, a U.S. court ruled that the same business model did not violate antitrust laws. This case starkly shows the ‘geopolitical risk’—how the same action can lead to completely different legal outcomes depending on a country’s industrial policy and national interests.

 

💡 “The Razor and the Blade”: The Real Goal Behind Opening Up Tech

When companies open up their technology for free, there’s almost always a calculated reason behind it. Their goals can be summarized into three main categories.

Strategic Goal	Explanation (Analogy)	Key Example
Ecosystem Dominance & Customer Lock-in	“The razor is free, the blades are not.” Attract users with a free tool to lock them into your platform or service.	Microsoft (.NET → Azure)
Cost Avoidance & Reshaping Competition	“Group buying to avoid a pricey toll road.” Form a consortium to evade expensive royalties from a competitor’s tech and weaken its influence.	AOMedia (AV1 → HEVC)
Profit Maximization & Business Model Design	“Charging the buffet based on the customer’s weight, not the food’s.” Designing royalty calculations to maximize revenue.	Qualcomm (Chipset → Total Phone Price)

 

🛡️ Avoiding the “Patent Minefield”: The Importance of FTO Analysis

So, how can companies protect themselves amidst these potential risks? The most fundamental and crucial tool is Freedom to Operate (FTO) analysis.

Many people mistakenly believe, ‘I patented this technology, so I can use it freely.’ But that’s not how it works. For example, let’s say a competitor holds a patent for technology ‘A.’ Even if you patent an improvement, ‘A+B,’ by adding feature ‘B,’ you could still be infringing on their ‘A’ patent the moment you manufacture your product. Your patent gives you rights to ‘B,’ but it doesn’t grant you the right to use ‘A.’

FTO analysis is the process of drawing a map to see if your product might step on someone else’s ‘patent mine.’ It’s an essential step to identify loopholes in royalty-free licenses and uncover unexpected risks in advance. When you consider that a lawsuit can cost millions, the expense of an FTO analysis is a very affordable ‘insurance policy.’

 

📜 5 Key Strategic Principles for Your Company

Based on the cases we’ve examined, here are five principles to remember when dealing with royalty-free technology.

1. Principle 1: Always Get the Legal Basis in Writing. You need a formal agreement that specifies the license’s scope, terms, limitations, and termination clauses, not vague promises like “good faith.” The freer the tech, the more carefully you need to read the contract.
2. Principle 2: Understand the Provider’s Real Revenue Model. You need to map out how they ultimately monetize their value. Evaluate it from a long-term Total Cost of Ownership (TCO) perspective, considering platform lock-in, data usage, and more.
3. Principle 3: Analyze Beyond the Consortium’s “Defensive Shield.” It is essential to conduct an FTO analysis for patents held by non-members, especially Non-Practicing Entities (NPEs), and budget for potential royalty payments.
4. Principle 4: Assess the “Geopolitical Risk” of IP Enforcement. Review IP regulations and legal precedents in key markets and be flexible enough to adapt your strategy to local conditions.
5. Principle 5: If You Open Your Tech, Define Your Company’s “Azure.” When you open up a technology, you must set a clear ‘backend revenue model’ and Key Performance Indicators (KPIs) for the high-profit business you ultimately want to drive users toward.

💡

Must-Read! 5 Strategic Principles for Using “Free” Tech

1. Get It in Writing: Secure a formal contract, not vague promises like ‘good faith.’

2. Find the Real Revenue Model: Analyze the provider’s hidden motives, such as platform lock-in.

3. Look Beyond the Fence:

Always check for patent risks (FTO) from non-consortium members, especially NPEs.

4. Assess Geopolitical Risk: The same business model can face different legal judgments by country.

5. Define Your ‘Azure’: If you open your tech, have a clear backend revenue model to link it to.

Royalty-free isn’t risk-free. Thorough due diligence is not optional, it’s essential.

Frequently Asked Questions ❓

Q: Are ‘royalty-free’ and ‘open source’ the same thing?

A: They’re different. ‘Open source’ mainly refers to a ‘copyright’ license for using, modifying, and distributing source code. In contrast, ‘royalty-free’ is closer in meaning to being free from ‘patent’ usage fees. Even open-source software can require separate patents to implement its technology, so it isn’t free from the risk of patent infringement.

Q: Isn’t FTO analysis too expensive and difficult?

A: The cost varies depending on the technology’s scope, but compared to patent litigation costs that can run into the millions, an FTO analysis is a very economical ‘insurance policy.’ It’s much smarter to find ‘patent mines’ in advance to alter a design or secure necessary licenses.

Q: What exactly is the FRAND principle?

A: FRAND stands for ‘Fair, Reasonable, and Non-Discriminatory.’ For patents essential to implementing a ‘standard technology’ used by multiple companies (like in telecommunications), the patent holder is obligated to offer licenses to anyone under these FRAND terms. This was a key issue in the Qualcomm case.

Q: We’re a small startup. Where should we start?

A: The very first step is to list which royalty-free or open-source technologies are core to your business. Then, carefully read their license agreements. If anything is unclear, seeking advice from an external IP expert is the best way to protect your company in the long run.

After today’s discussion, I hope you have a better sense of the weight behind the term ‘royalty-free.’ It reminds me of the old saying, “There’s no such thing as a free lunch.” When you encounter a new technology, the right question isn’t, “What can I save?” but rather, “What are the hidden costs? Who is the player gaining the most from this ecosystem?” Now is the time for that kind of wisdom. If you have any more questions, feel free to ask in the comments! 😉