Here's what actually issued. On December 13, 2022, Microsoft Technology Licensing, LLC was granted US11526812B2, "Generalized reinforcement learning agent," with inventors including Katja Hofmann and Sam Devlin from Microsoft's game-AI research. The single CPC code is G06N 20/20 (ensemble machine learning), a tight classification that itself hints at the approach.
Reinforcement learning trains an agent to act by trial and error, maximizing a reward signal. The hard, valuable problem is generalization: an agent that masters one task or environment usually fails to transfer to a new one. The title's word "generalized" points squarely at that — a method aimed at producing an agent whose learned behavior carries across tasks rather than overfitting to a single one.
Why does Microsoft hold this? Its research has long used games as testbeds for RL, and generalizable agents are the bridge from game-playing demos to useful, transferable decision-making systems. Owning a method for cross-task generalization is owning a piece of the path from narrow RL to broadly capable agents.
On scope, the tight CPC and the discipline together: granted B2, enforceable, but the claims describe a specific approach to a generalizing RL agent. "Generalized reinforcement learning" in the title is not a claim to all of transfer learning or all RL. Read the allowed claim language for what's actually fenced.
The takeaway: US11526812B2 is a method-specific RL grant from a lab with deep game-AI roots, addressing the field's central generalization problem — and a reminder that a narrow CPC like G06N 20/20 is itself a clue to how the claims are framed.