公开(公告)号：US12020706B2

公开(公告)日：2024-06-25

申请号：US17899162

申请日：2022-08-30

Applicant: GOOGLE LLC

Inventor： Arvind Neelakantan ,  Daniel Duckworth ,  Ben Goodrich ,  Vishaal Prasad ,  Chinnadhurai Sankar ,  Semih Yavuz

IPC: G10L15/22 ,  G06N5/04

CPC classification number: G10L15/22 ,  G06N5/04 ,  G10L2015/225

Abstract: Training and/or utilizing a single neural network model to generate, at each of a plurality of assistant turns of a dialog session between a user and an automated assistant, a corresponding automated assistant natural language response and/or a corresponding automated assistant action. For example, at a given assistant turn of a dialog session, both a corresponding natural language response and a corresponding action can be generated jointly and based directly on output generated using the single neural network model. The corresponding response and/or corresponding action can be generated based on processing, using the neural network model, dialog history and a plurality of discrete resources. For example, the neural network model can be used to generate a response and/or action on a token-by-token basis.

公开(公告)号：US10324993B2

公开(公告)日：2019-06-18

申请号：US15369849

申请日：2016-12-05

Applicant: Google LLC

Inventor： Javier Spagnolo Arrizabalaga ,  Malte Nuhn ,  Quoc V. Le ,  Daniel Duckworth ,  Matthias Heiler

IPC: G06N3/08 ,  G06F16/95 ,  G06F16/958 ,  G06F16/9535

Abstract: Methods, systems, and apparatus including computer programs encoded on a computer storage medium, for augmenting search engine index that indexes resources from a collection of resources. In one aspect, a method of augmenting a first search engine index that indexes resources from a first collection of resources includes the actions of identifying a first resource, in the first collection of resources, that is indexed in the first search engine index for which a value of a search engine ranking signal is not available, wherein a search engine uses values of the search engine ranking signal in ranking resources in response to received search queries; processing text from the first resource using a machine learning model, the machine learning model being configured to: process the text to predict a value of the search engine ranking signal for the first resource; and updating the first search engine index by associating the predicted value of the search engine ranking signal with the first resource in the first search engine index.

公开(公告)号：US20240347061A1

公开(公告)日：2024-10-17

申请号：US18751911

申请日：2024-06-24

Applicant: GOOGLE LLC

Inventor： Arvind Neelakantan ,  Daniel Duckworth ,  Ben Goodrich ,  Vishaal Prasad ,  Chinnadhurai Sankar ,  Semih Yavuz

IPC: G10L15/22 ,  G06N5/04

CPC classification number: G10L15/22 ,  G06N5/04 ,  G10L2015/225

Abstract: Training and/or utilizing a single neural network model to generate, at each of a plurality of assistant turns of a dialog session between a user and an automated assistant, a corresponding automated assistant natural language response and/or a corresponding automated assistant action. For example, at a given assistant turn of a dialog session, both a corresponding natural language response and a corresponding action can be generated jointly and based directly on output generated using the single neural network model. The corresponding response and/or corresponding action can be generated based on processing, using the neural network model, dialog history and a plurality of discrete resources. For example, the neural network model can be used to generate a response and/or action on a token-by-token basis.

公开(公告)号：US20220415324A1

公开(公告)日：2022-12-29

申请号：US17899162

申请日：2022-08-30

Applicant: GOOGLE LLC

Inventor： Arvind Neelakantan ,  Daniel Duckworth ,  Ben Goodrich ,  Vishaal Prasad ,  Chinnadhurai Sankar ,  Semih Yavuz

IPC: G10L15/22 ,  G06N5/04

Abstract: Training and/or utilizing a single neural network model to generate, at each of a plurality of assistant turns of a dialog session between a user and an automated assistant, a corresponding automated assistant natural language response and/or a corresponding automated assistant action. For example, at a given assistant turn of a dialog session, both a corresponding natural language response and a corresponding action can be generated jointly and based directly on output generated using the single neural network model. The corresponding response and/or corresponding action can be generated based on processing, using the neural network model, dialog history and a plurality of discrete resources. For example, the neural network model can be used to generate a response and/or action on a token-by-token basis.

公开(公告)号：US11475890B2

公开(公告)日：2022-10-18

申请号：US16910435

申请日：2020-06-24

Applicant: Google LLC

Inventor： Arvind Neelakantan ,  Daniel Duckworth ,  Ben Goodrich ,  Vishaal Prasad ,  Chinnadhurai Sankar ,  Semih Yavuz

IPC: G10L15/22 ,  G06N5/04

Abstract: Training and/or utilizing a single neural network model to generate, at each of a plurality of assistant turns of a dialog session between a user and an automated assistant, a corresponding automated assistant natural language response and/or a corresponding automated assistant action. For example, at a given assistant turn of a dialog session, both a corresponding natural language response and a corresponding action can be generated jointly and based directly on output generated using the single neural network model. The corresponding response and/or corresponding action can be generated based on processing, using the neural network model, dialog history and a plurality of discrete resources. For example, the neural network model can be used to generate a response and/or action on a token-by-token basis.