LLM-Powered Patent Search from A to Z: From Basic Prompts to Advanced Strategy

 

Still Stumped by Patent Searches with LLMs? This post breaks down how to use the latest AI Large Language Models (LLMs) to maximize the accuracy and efficiency of your patent searches, including specific model selection methods and advanced ‘deep research’ prompting techniques.

Hi there! Have you ever spent days, or even weeks, lost in a sea of patent documents, trying to find that one piece of information you need? I’ve definitely been there. The anxiety of wondering, ‘Is my idea truly novel?’ can keep you up at night. But thanks to the latest Large Language Models (LLMs), the whole paradigm of patent searching is changing. It’s even possible for an AI to conduct its own ‘deep research’ by diving into multiple sources. Today, I’m going to share some practical examples of ‘prompt engineering’ that I’ve learned firsthand to help you unlock 200% of your LLM’s potential!

Prompt Engineering Tricks to Boost Accuracy by 200%

Choosing the right AI model is important, but the success of your patent search ultimately depends on how you ask your questions. That’s where ‘prompt engineering’ comes in. It’s the key to making the AI accurately grasp your intent and deliver the best possible results. Let’s dive into some real-world examples.

Heads Up!
LLMs are not perfect. They can sometimes confidently present false information, a phenomenon known as ‘hallucination.’ It’s crucial to get into the habit of cross-referencing any patent numbers or critical details the AI provides with an official database.

 

1. Using Chain-of-Thought for Step-by-Step Reasoning

When you have a complex analysis task, asking the AI to ‘show its work’ by thinking step-by-step can reduce logical errors and improve accuracy.

Prompt Example:
Analyze the validity of a patent for an ‘autonomous driving technology that fuses camera and LiDAR sensor data’ by following these steps.

Step 1: Define the core technical components (camera, LiDAR, data fusion).
Step 2: Based on the defined components, generate 5 sets of search keywords for the USPTO database.
Step 3: From the search results, select the 3 most similar prior art patents.
Step 4: Compare the key claims of the selected patents with our technology, and provide your final opinion on the patentability of our tech.

 

2. Using Real-Time External Information (RAG & ReAct)

LLMs only know information up to their last training date. To get the latest patent data, you need to instruct them to search external databases in real-time.

Prompt Example:
You are a patent analyst. Using your search tool, find all patent publications on KIPRIS related to ‘Quantum Dot Displays’ published since January 1, 2024.

1. Organize the list of patents by application number, title of invention, and applicant.
2. Summarize the overall technology trends and analyze the core technical focus of the top 3 applicants.
3. Based on your analysis, predict which technologies in this field are likely to be promising over the next two years.

 

3. Activating the “Deep Research” Function

The latest LLMs can do more than just a single search. They have ‘deep research’ capabilities that can synthesize information from multiple websites, academic papers, and technical documents to create a comprehensive report, much like a human researcher.

Prompt Example:
Activate your deep research function. Write an in-depth report on the global R&D trends for ‘next-generation semiconductor materials using Graphene.’ The report must include the following:

1. The main challenges of the current technology and the latest research trends aimed at solving them (reference and summarize at least 3 reputable academic papers or tech articles).
2. An analysis of the top 5 companies and research institutions leading this field and their key patent portfolios.
3. The expected technology development roadmap and market outlook for the next 5 years.
4. Clearly cite the source (URL) for all information referenced in the report.

 

4. Exploring Multiple Paths (Tree of Thoughts)

This is useful for solving strategic problems with no single right answer, like designing around a patent or charting a new R&D direction. You have the AI explore and evaluate multiple possible scenarios.

Prompt Example:
Propose three new design concepts for a ‘secondary battery electrode structure’ that do not infringe on claim 1 of U.S. Patent ‘US 1234567 B2’.

1. For each design, clearly explain which elements of the original patent were changed and how.
2. Evaluate the technical advantages, expected performance, and potential drawbacks of each design.
3. Select the design you believe has the highest likelihood of avoiding infringement and achieving commercial success, and provide a detailed argument for your choice.

💡 Pro Tip!
The common thread in all great prompts is that they give the AI a clear ‘role,’ explain the ‘context,’ and demand a ‘specific output format.’ Just remembering these three things will dramatically improve your results.

💡

LLM Patent Search: Key Takeaways

Assign a Role: Give the AI a specific expert role, like “You are a patent attorney.”

Step-by-Step Thinking: For complex analyses, instruct the AI to use step-by-step reasoning (CoT) to improve logical accuracy.

Advanced Strategies:

Use Deep Research and Tree of Thoughts to generate expert-level reports.

Cross-Verification is a Must: Always be aware of AI hallucinations and verify important information against original sources.

One smart question can save you hours of work.

Frequently Asked Questions

Q: Is the ‘deep research’ function available on all LLMs?

A: No, not yet. It’s more of an advanced feature typically found in the latest premium versions of LLMs like Perplexity, Gemini, and ChatGPT. However, you can mimic a similar effect by using the standard search function and asking questions in multiple, sequential steps.

Q: Can I trust the search results from an LLM 100%?

A: No, you absolutely cannot. An LLM is a powerful assistant, not a substitute for a qualified expert’s final judgment. Due to hallucinations, it can invent patent numbers or misrepresent content. It is essential to always verify its findings against the original documents and have them reviewed by a professional.

Q: Prompt engineering seems complicated. Where should I start?

A: An easy way to start is by modifying the examples shown today. Just applying three techniques—’assigning a role,’ ‘specifying the format,’ and ‘requesting step-by-step thinking’—will dramatically improve the quality of your results.

Patent searching is no longer the tedious, uphill battle it once was. How you wield the powerful tool of LLMs can change the speed of your R&D and business. I hope you’ll use the tips I’ve shared today to create smarter innovations with AI. If you have any more questions, feel free to ask in the comments!