公开(公告)号：US20250068913A1

公开(公告)日：2025-02-27

申请号：US18828690

申请日：2024-09-09

Applicant: GOOGLE LLC

Inventor： Brian Strope ,  Yun-Hsuan Sung ,  Matthew Henderson ,  Rami Al-Rfou' ,  Raymond Kurzweil

IPC: G06N3/084 ,  G06F16/00 ,  G06F16/335 ,  G06N3/044 ,  G06N3/045 ,  G06N5/04

Abstract: Systems, methods, and computer readable media related to information retrieval. Some implementations are related to training and/or using a relevance model for information retrieval. The relevance model includes an input neural network model and a subsequent content neural network model. The input neural network model and the subsequent content neural network model can be separate, but trained and/or used cooperatively. The input neural network model and the subsequent content neural network model can be “separate” in that separate inputs are applied to the neural network models, and each of the neural network models is used to generate its own feature vector based on its applied input. A comparison of the feature vectors generated based on the separate network models can then be performed, where the comparison indicates relevance of the input applied to the input neural network model to the separate input applied to the subsequent content neural network model.

公开(公告)号：US12086720B2

公开(公告)日：2024-09-10

申请号：US17502343

申请日：2021-10-15

Applicant: Google LLC

Inventor： Brian Strope ,  Yun-hsuan Sung ,  Matthew Henderson ,  Rami Al-Rfou' ,  Raymond Kurzweil

IPC: G06N3/08 ,  G06F16/00 ,  G06F16/335 ,  G06N3/045 ,  G06N3/084 ,  G06N5/04 ,  G06N3/044

CPC classification number: G06N3/084 ,  G06F16/00 ,  G06F16/335 ,  G06N3/045 ,  G06N5/04 ,  G06N3/044

Abstract: Systems, methods, and computer readable media related to information retrieval. Some implementations are related to training and/or using a relevance model for information retrieval. The relevance model includes an input neural network model and a subsequent content neural network model. The input neural network model and the subsequent content neural network model can be separate, but trained and/or used cooperatively. The input neural network model and the subsequent content neural network model can be “separate” in that separate inputs are applied to the neural network models, and each of the neural network models is used to generate its own feature vector based on its applied input. A comparison of the feature vectors generated based on the separate network models can then be performed, where the comparison indicates relevance of the input applied to the input neural network model to the separate input applied to the subsequent content neural network model.

公开(公告)号：US20220036197A1

公开(公告)日：2022-02-03

申请号：US17502343

申请日：2021-10-15

Applicant: Google LLC

Inventor： Brian Strope ,  Yun-hsuan Sung ,  Matthew Henderson ,  Rami Al-Rfou' ,  Raymond Kurzweil

IPC: G06N3/08 ,  G06N5/04 ,  G06F16/00 ,  G06N3/04 ,  G06F16/335

Abstract: Systems, methods, and computer readable media related to information retrieval. Some implementations are related to training and/or using a relevance model for information retrieval. The relevance model includes an input neural network model and a subsequent content neural network model. The input neural network model and the subsequent content neural network model can be separate, but trained and/or used cooperatively. The input neural network model and the subsequent content neural network model can be “separate” in that separate inputs are applied to the neural network models, and each of the neural network models is used to generate its own feature vector based on its applied input. A comparison of the feature vectors generated based on the separate network models can then be performed, where the comparison indicates relevance of the input applied to the input neural network model to the separate input applied to the subsequent content neural network model.