Patenting AI Medical Devices After Alice
How to identify the technical improvement that survives eligibility scrutiny when machine learning is embedded in a regulated device.
Insights
Practical writing on patent strategy at the intersection of medicine, engineering, and artificial intelligence — for the executives and counsel who make IP decisions.
How to identify the technical improvement that survives eligibility scrutiny when machine learning is embedded in a regulated device.
Where the defensible invention usually lives — data pipelines, model architecture, hardware interactions — and how to claim it.
Building a portfolio when your product spans clinical workflow, software, and learned models.
The third-party risks founders overlook — and how they resurface, expensively, during a financing or acquisition.
How investors and acquirers separate defensible coverage from a stack of filings that don't read on the product.
Framing language-model innovations so the claims rest on technical contribution, not abstract function.
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