Here's what actually issued. On November 15, 2022, Amazon Technologies, Inc. was granted US11501173B1, "Reinforcement learning for training compression policies for machine learning models," inventors including Gurumurthy Swaminathan and Ragav Venkatesan. The CPC codes are G06N 5/003 and G06N 20/00 — machine learning broadly.
Two AI techniques are stacked. Model compression shrinks a trained network so it runs cheaper; the open question is always how to compress — which layers to prune, how aggressively, at what bit-width. This grant's answer is to learn the compression policy itself with reinforcement learning: an RL agent treats compression choices as actions, gets rewarded for small-and-accurate results, and discovers a compression strategy rather than hand-tuning one.
The strategic logic is cloud economics. Amazon runs models at enormous scale on AWS; any method that automatically finds better compression policies translates into lower serving cost across a huge fleet. Owning the RL-for-compression method protects an efficiency lever that compounds with volume.
On scope, the standard line: granted B1, enforceable, but the claims describe using reinforcement learning to train compression policies specifically. They do not cover model compression generally, nor reinforcement learning generally — only the particular marriage of the two as claimed. Read claim 1 for the boundary.
The takeaway: US11501173B1 is a nice example of meta-learning IP — using one AI technique (RL) to automate another (compression) — from a hyperscaler whose incentive is to drive down the per-inference cost at fleet scale.