Why your choice in AI tools matter for patent searching

What’s at stake in choosing an AI-enabled patent search tool.

With the proliferation of AI-based patent searching tools, it is getting harder to determine what kind of tool will work best for your business. If you or your clients are conducting patent research in areas with cutting edge technology, tool selection is even more important if you want to be sure you are protecting your innovations.

Tradeoffs of natural language queries

Today, many patent search tools offer natural language searching in addition to Boolean queries to facilitate faster, easier information gathering. Often, this takes the form of a Large Language Model (LLM) hosted on a web page or an application integrated into the patent intelligence software. It’s important to note that this LLM interface can serve as an orchestration layer, allowing the provider to monitor queries and enhance their model using insights derived from user interactions. Does that mean that using an LLM within the context of your patent search creates a direct security risk? No, though it does not rule out the possibility of your search input incidentally guiding future searches of other users.

While the impact of this learning process on innovation research is difficult to measure, it’s conceivable that if two companies are researching the same field, the second researcher could benefit from the LLM’s enhanced capabilities gained through interactions with the first researcher.

A real-world AI example

For instance, if you ask ChatGPT for a recipe that happens to include gluten, it may respond by asking if you have a gluten allergy. Why? Likely because someone else with a gluten allergy previously asked a similar question, prompting the LLM to “learn” to check for dietary restrictions. So, while the LLM doesn’t share the recipe itself, nor would it reveal your proprietary ideas, if you’re developing a unique technology, wouldn’t you want to be certain that a competitor isn’t “guided” in their research because the LLM learned from your interactions?

Understand the implications of the tools you choose

The key takeaway is not to avoid LLMs in your patent search workflow, but rather to ask your solution partner(s) about the security of your data in this context. You should know if your interactions are providing contextual learning for their LLM (or their LLM provider). Are they developing their own learning model for an LLM that is learning from all the queries of all their clients? Be aware of what is most important to your business and how your LLM interactions are being used.

The FluidityIQ difference – proprietary information stays protected

At FluidityIQ, we are LLM-agnostic. We are committed to using the best of breed LLM and the architecture of our platform reflects this, with the capacity to swap models based on their relative performance. More importantly, we’ve designed our patent search tool with your security at the forefront: unlike other providers, our orchestration layer does not “learn” from your searches. With us, your ideas remain your own, as do your questions.

Ready to learn more?

For more information about FluidityIQ and how to best leverage AI in your patent research, reach out to us directly at info@fluidityiq.com.