Here's the question under the hood: when a speech recognizer hears audio and guesses words, those guesses are imperfect. How do you fix them automatically? One answer is to treat correction as a translation problem, translate the noisy guess into the clean intended text. That's sequence-to-sequence.
The grant US10573296B1 (issued February 25, 2020) describes reconciliation between a simulator and recognizer output using exactly this seq-to-seq mapping, with CPC tags in G10L (speech processing) and G06N 20/00 (machine learning generally). The same family includes US10559299B1 from two weeks earlier. Together they document a learned correction layer sitting on top of recognition.
The mechanism matters because it's the ancestor of how modern systems work. Today's speech and language models are seq-to-seq at their core, audio-to-text, text-to-text, one sequence in, another out. This 2020 grant is a narrow, applied instance of the architecture that would soon swallow the whole field.
Connect it to the sector story: by 2020, the transformer-driven seq-to-seq revolution was underway in research, and patents like this show it diffusing into shipped speech products. The reconciliation framing, use a learned model to clean up another system's output, also prefigures today's ensembles where one model checks another.
The careful note: this is a granted patent, so it's an enforceable right, but its scope is whatever the claims say, not the broad idea of seq-to-seq. The value here is historical, a dated, concrete marker of when learned sequence mapping became production speech infrastructure.