Here's what actually issued. On February 25, 2020, salesforce.com, inc. was granted US10573295B2, "End-to-end speech recognition with policy learning," inventors Yingbo Zhou and Caiming Xiong. The CPC list combines speech classes (G10L 15/063, G10L 15/14, G10L 15/16) with neural-network learning G06N 3/084 and probabilistic G06N 7/005.
Two ideas are stacked here. "End-to-end" means the system maps audio directly to text in a single trained model, rather than chaining separate acoustic, pronunciation, and language components — the older, more brittle pipeline. "Policy learning" borrows from reinforcement learning: instead of only minimizing a frame-level error, the model is trained against an objective tied to the quality of the final transcription, treating recognition decisions as actions to optimize.
The combination is the point. Optimizing end-to-end against a reward-like signal can align training with what users actually care about — accurate transcripts — rather than a proxy loss. For Salesforce, whose research arm produced a steady stream of NLP and speech work in this period, the grant secures a method that ties a modern training objective to a modern architecture.
On scope, the discipline applies. Granted B2, enforceable, but the claims describe a specific end-to-end-plus-policy training method. They do not cover end-to-end speech recognition generally, nor reinforcement learning generally. The independent claim is the boundary.
The takeaway: US10573295B2 is a good example of how 2020-era speech IP blended sequence modeling with reinforcement-style objectives, and how a software company's research lab converts published techniques into enforceable, method-specific grants.