公开(公告)号：US20240054392A1

公开(公告)日：2024-02-15

申请号：US18009178

申请日：2022-04-01

Applicant: Google LLC

Inventor： Wei Huang ,  Alexander E. Mayorov

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for using transfer machine learning to predict attributes are described. In one aspect, a method includes receiving, from a client device of a user, a digital component request that includes at least input contextual information for a display environment in which a selected digital component will be displayed. The contextual information is converted into input data that includes input feature values for a transfer machine learning model trained to output predictions of user attributes of users based on feature values for features representing display environments. The transfer machine learning model is trained using training data for subscriber users obtained from a data pipeline associated with electronic resources to which the subscriber users are subscribed and adapted to predict user attributes of non-subscribing users viewing electronic resources to which the non-subscribing users are not subscribed.

公开(公告)号：US20240202360A1

公开(公告)日：2024-06-20

申请号：US18388763

申请日：2023-11-10

Applicant: Google LLC

Inventor： Gang Wang ,  Alexander E. Mayorov

IPC: G06F21/62 ,  G06F21/53

CPC classification number: G06F21/6245 ,  G06F21/53

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting and distributing digital components to client devices in ways that protect user privacy and confidential data of content platforms and/or digital component providers are described. In one aspect, a method includes receiving, by a secure distribution system and from a client device of a user, a digital component request that includes, for each of multiple content platforms that distribute digital components to users, a corresponding user embedding comprising weights indicative of the relevance of multiple features to the user. The secure distribution system provides each user embedding as input to a respective isolated execution environment for the content platform corresponding to the user embedding, wherein the secure distribution system hosts each isolated execution environment. Digital component selection data generated based on the user embedding is received from each isolated execution environment.

公开(公告)号：US20220318644A1

公开(公告)日：2022-10-06

申请号：US17608221

申请日：2020-10-14

Applicant: Google LLC

Inventor： Wei Huang ,  Joshua Patrick Gardner ,  Michael William Daub ,  Alexander E. Mayorov

IPC: G06N5/02

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing digital components to a client device. Methods can include assigning a temporary group identifier to a client device that identifies a particular group, from among a plurality different groups, that includes the client device based on a current period of user activity on the client device. A training set is generated for training a machine learning model that generates user characteristics. A request for digital component is received from the client device that includes the temporary group identifier currently assigned to the client device, a subset of activity features and one or more additional features that are based on the client device. The machine learning model generates one or more user characteristics based on which one or more digital components are selected and transmitted to the client device.