US 12,488,801 B2
Method and system for personalising machine learning models
Alberto Gil C. P. Ramos, Staines (GB); Abhinav Mehrotra, Staines (GB); and Sourav Bhattacharya, Staines (GB)
Assigned to SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR)
Filed by SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR)
Filed on Jul. 11, 2023, as Appl. No. 18/220,567.
Application 18/220,567 is a continuation of application No. PCT/KR2023/000693, filed on Jan. 13, 2023.
Claims priority of application No. 22179714 (EP), filed on Jun. 17, 2022.
Prior Publication US 2023/0410818 A1, Dec. 21, 2023
Int. Cl. G10L 17/18 (2013.01); G10L 13/08 (2013.01); G10L 17/04 (2013.01); G10L 21/0208 (2013.01)
CPC G10L 17/18 (2013.01) [G10L 13/08 (2013.01); G10L 17/04 (2013.01); G10L 21/0208 (2013.01)] 13 Claims
OG exemplary drawing
 
1. A computer-implemented method for training, on a server, a machine learning, ML, model having a first neural network and a second neural network, the method comprising:
obtaining a training dataset comprising a plurality of pairs of data items to be processed by the ML model, each pair of data items comprising a conditioning vector computed from data associated with an individual user and an input data item comprising a plurality of samples, wherein a sample from among the plurality of samples is a text sample or an audio sample; and
training the ML model by jointly:
training the first neural network of the ML model to alter weights of at least one layer of the second neural network to personalize the weights for individual users using the conditioning vector of the pairs of data items; and
training the second neural network of the ML model to process the input data item of the pairs of data items using the personalized weights for the at least one layer, and to generate output data using the personalized weights.