View source for Google LLC (20250016387). EPHEMERAL LEARNING OF MACHINE LEARNING MODEL(S)

=EPHEMERAL LEARNING OF MACHINE LEARNING MODEL(S)=


==Organization Name==

[[:Category:Google LLC|Google LLC]]


[[Category:Google LLC]]

==Inventor(s)==

[[:Category:Françoise Beaufays of Mountain View CA (US)|Françoise Beaufays of Mountain View CA (US)]][[Category:Françoise Beaufays of Mountain View CA (US)]]

[[:Category:Khe Chai Sim of Dublin CA (US)|Khe Chai Sim of Dublin CA (US)]][[Category:Khe Chai Sim of Dublin CA (US)]]

[[:Category:Trevor Strohman of Sunnyvale CA (US)|Trevor Strohman of Sunnyvale CA (US)]][[Category:Trevor Strohman of Sunnyvale CA (US)]]

[[:Category:Oren Litvin of New York City NY (US)|Oren Litvin of New York City NY (US)]][[Category:Oren Litvin of New York City NY (US)]]

==EPHEMERAL LEARNING OF MACHINE LEARNING MODEL(S)==


This abstract first appeared for US patent application 20250016387 titled 'EPHEMERAL LEARNING OF MACHINE LEARNING MODEL(S)




==Original Abstract Submitted==

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.



[[Category:H04N21/233]]
[[Category:G06F18/214]]
[[Category:G06N20/00]]
[[Category:H04N21/232]]
[[Category:CPC_H04N21/233]]