公开(公告)号：US20250111285A1

公开(公告)日：2025-04-03

申请号：US18902137

申请日：2024-09-30

Applicant: Google LLC

Inventor： Yan Liu ,  Chuizheng Meng ,  Yihe Dong ,  Sercan Omer Arik ,  Tomas Pfister

IPC: G06N20/00

Abstract: A machine-learned model includes an encoder having a feature block configured to embed input data into a plurality of features in an embedding space. The input data includes multiple components such as covariate, treatment, and output components. The encoder includes one or more encoding layers, each including a temporal attention block and a feature-wise attention block. The temporal attention block is configured to obtain the embedded input data and apply temporal causal attention along a time dimension in parallel for each feature of the plurality of features to generate temporal embeddings. The feature-wise attention block is configured to obtain the temporal embeddings and generate component representations such as a covariate representation, a treatment representation, and an output representation.

公开(公告)号：US20230244732A1

公开(公告)日：2023-08-03

申请号：US18194989

申请日：2023-04-03

Applicant: Google LLC

Inventor： Nitya Narasimhan ,  Yan Liu ,  Gregory L. Singer

IPC: G06F16/9535 ,  G06Q30/02

CPC classification number: G06F16/9535 ,  G06Q30/02

Abstract: Techniques and/or apparatuses receive an indication that a user has entered a rating of first media content, determine, responsive to the indication that the user has entered the rating of the first media content, whether the user consumed the first media content prior to entering the rating. Responsive to a determination that the user did not consume the first media content prior to entering the rating, the techniques and/or apparatuses provide an indication that the user did not consume the first media content prior to entering the rating or weight the rating based on the determination that the user did not consume the first media content prior to entering the rating of the first media content.