US 12,079,307 B2
Personalized data model utilizing closed data
Jane Huang, Kirkland, WA (US); Li He, Redmond, WA (US); and Ian Porteous, Mercer Island, WA (US)
Assigned to Google LLC, Mountain View, CA (US)
Appl. No. 16/971,253
Filed by GOOGLE LLC, Mountain View, CA (US)
PCT Filed Nov. 27, 2019, PCT No. PCT/US2019/063604
§ 371(c)(1), (2) Date Aug. 19, 2020,
PCT Pub. No. WO2021/107948, PCT Pub. Date Jun. 3, 2021.
Prior Publication US 2021/0397892 A1, Dec. 23, 2021
Int. Cl. G06F 18/21 (2023.01); G06F 18/211 (2023.01); G06F 18/214 (2023.01); G06N 3/045 (2023.01)
CPC G06F 18/2148 (2023.01) [G06F 18/211 (2023.01); G06N 3/045 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A method for training of a machine-learning architecture, the method comprising:
receiving, by one or more processing circuits, a data set;
determining, by the one or more processing circuits, a first portion of the data set associated with a plurality of entities, wherein the first portion of the data set comprises an open data set including characteristics associated with the plurality of entities;
training, by the one or more processing circuits and utilizing the first portion of the data set, a machine learning entity model, wherein the machine learning entity model is trained to output predictions from subsequently received data;
determining, by the one or more processing circuits, a second portion of the data set associated with a first subset of entities of the plurality of entities, wherein the second portion of the data set comprises a closed data set specific to the first subset of entities;
determining, by the one or more processing circuits, a second subset of entities, wherein the second subset of entities does not include any entities in the first subset of entities, and wherein the second portion of the data set is not shared with the second subset of entities;
freezing, by the one or more processing circuits, one or more parameters of the machine learning entity model associated with the first portion of the data set associated with the plurality of entities such that the one or more parameters of the machine learning entity model remain fixed during a subsequent training of the machine learning entity model using the second portion of the data set and such that one or more non-frozen parameters of the trained machine learning entity model are trained to output predictions specific to the first subset of entities and are not associated with the second subset of entities; and
training, by the one or more processing circuits and utilizing the second portion of the data set, the machine learning entity model.