Ephemeral learning of machine learning model(s)
US12126845B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 23, 2021 |
| Grant date | Oct 22, 2024 |
| Priority date | — |
| Expiry date | Nov 18, 2042 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04N21/232
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
Implementations disclosed herein are directed to ephemeral learning of machine learning (“ML”) model(s) based on gradient(s) generated at a remote system (e.g., remote server(s)). Processor(s) of the remote system can receive stream(s) of audio data capturing spoken utterance(s) from a client device of a user. A fulfillment pipeline can process the stream(s) of audio data to cause certain fulfillment(s) of the spoken utterance(s) to be performed. Meanwhile, a training pipeline can process the stream(s) of audio data to generate gradient(s) using unsupervised learning techniques. Subsequent to the processing by the fulfillment pipeline and/or the training pipeline, the stream(s) of audio data are discarded by the remote system. Accordingly, the ML model(s) can be trained at the remote system without storing or logging of the stream(s) of audio data by non-transient memory thereof, thereby providing more efficient training mechanisms for training the ML model(s) and also increasing security of user data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.