Can AI file your patent? At SF Tech Week, Landfall IP founders Nathan Mutter and Michael Drapkin led a roundtable on where AI helps founders in real ways, and where the decisions that are hard to undo still call for experienced judgment.
Why do the same AI models produce strong patents for some firms and weak ones for others?
In a recent conversation with Dwarkesh Patel, OpenAI researcher Noam Brown discussed the agents used in OpenAI’s work on the Navier–Stokes problem. He cautioned against concluding that AI is now better than mathematicians at everything. Its capabilities are jagged: exceptional at some tasks, weaker at others, including deciding which questions are worth pursuing.
Patent practice has its own jagged edge.
AI can read a disclosure and draft with remarkable thoroughness. It can flag antecedent basis errors, trace support for claim limitations, compare embodiments, and identify technical details that may have been lost along the way. Those capabilities should change how we work.
But the most consequential question is harder to check: What should this patent protect? That takes an understanding of the invention, the client’s business, likely competitors, and what infringement might actually look like. The quality of that decision may become clear only years after filing.
The sequence of work matters. An attorney develops the claim strategy around those goals. AI helps explore alternatives and pressure-test the choices. The specification and drawings are built to support the strategy. AI then helps verify the finished work with a level of consistency and thoroughness that was previously difficult to achieve.
Reverse that sequence—ask a model to generate an entire application and have a lawyer check it at the end—and a polished draft can still protect the wrong thing.
As access to powerful models spreads, the model alone will tell you less about the quality of a patent. The difference will be whether a firm knows where to apply human judgment and how to build AI into the work around it.
Source: LinkedIn Post by Michael Drapkin
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