公开(公告)号：US11373086B2

公开(公告)日：2022-06-28

申请号：US15476292

申请日：2017-03-31

Applicant: Google Inc.

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

IPC: G06N3/04 ,  H04L51/02 ,  G06N3/08 ,  H04L51/046

Abstract: Systems, methods, and computer readable media related to determining one or more responses to provide that are responsive to an electronic communication that is generated through interaction with a client computing device. For example, determining one or more responses to provide for presentation to a user as suggestions for inclusion in a reply to an electronic communication sent to the user. Some implementations are related to training and/or using separate input and response neural network models for determining responses for electronic communications. The input neural network model and the response neural network model can be separate, but trained and/or used cooperatively.

公开(公告)号：US20180240014A1

公开(公告)日：2018-08-23

申请号：US15476292

申请日：2017-03-31

Applicant: Google Inc.

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

IPC: G06N3/08 ,  H04L12/58

CPC classification number: G06N3/0454 ,  G06N3/084 ,  H04L51/02 ,  H04L51/046

Abstract: Systems, methods, and computer readable media related to determining one or more responses to provide that are responsive to an electronic communication that is generated through interaction with a client computing device. For example, determining one or more responses to provide for presentation to a user as suggestions for inclusion in a reply to an electronic communication sent to the user. Some implementations are related to training and/or using separate input and response neural network models for determining responses for electronic communications. The input neural network model and the response neural network model can be separate, but trained and/or used cooperatively.

公开(公告)号：US11188824B2

公开(公告)日：2021-11-30

申请号：US15476280

申请日：2017-03-31

Applicant: Google Inc.

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.

公开(公告)号：US20180240013A1

公开(公告)日：2018-08-23

申请号：US15476280

申请日：2017-03-31

Applicant: Google Inc.

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

IPC: G06N3/08 ,  G06F17/30

CPC classification number: G06N3/084 ,  G06F16/00 ,  G06F16/335 ,  G06N3/0445 ,  G06N3/0454 ,  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.