AI adoption across IP workflows has jumped to 85% in the last two years.

If your prosecution team adopted AI for prior art search recently, you’ve probably noticed that searches come back faster and the outputs look complete. What’s harder to verify is whether the AI surfaced the strongest references or just the most visible ones.

And you usually find out something was missed when the examiner cites art your team should have caught before filing. The AI search doesn’t fail in any obvious way. It returns a ranked list, the references look on-topic, your team moves forward. The issue is what the tool processed but didn’t weigh right.

A method claim where the steps in the reference run in a different order. A negative limitation like “without” or “in the absence of” that the AI scanned past. A Japanese counterpart of a returned reference that ranked low and never got opened. Nothing on the surface tells you what the AI caught and what it didn’t. The output looks thorough, so it gets treated as thorough. You have to actively map out the tool’s blindspots to catch these gaps.

To give teams a framework for this, our senior researchers built a working checklist for AI-assisted searches. They recently ran a 60-minute session walking through it, covering 13 patent search pitfalls no one talks about and the review checks that catch each one. None of it is theoretical. It comes from failures they’ve worked through in their own searches. They also cover where AI performs reliably, so you know where to lean on it and where expert review still matters.

Get the free recording and slide deck here: https://greyb.com/resources/webinars/ai-in-patent-search/