公开(公告)号：US12210567B2

公开(公告)日：2025-01-28

申请号：US18071986

申请日：2022-11-30

Applicant: Google LLC

Inventor： Ben Goodrich ,  Kumar Chippala

IPC: G06F16/63 ,  G06F16/638 ,  G06F40/263 ,  G06N20/00

Abstract: Methods, systems, and media for determining playlist title coherence and quality are provided. In some embodiments, a method for generating playlist recommendations includes: determining, using a hardware processor, a title of a playlist; generating, using the hardware processor, a byte-level representation of the title based on the title of the playlist; determining, using the hardware processor, an embedded representation of the title based on the byte-level representation; determining, using the hardware processor, a perplexity score of the title by inputting the embedded representation of the title into a trained language model, wherein the perplexity score is an output of the trained language model; and causing, using the hardware processor, a recommendation based on the perplexity score of the title to be presented.

公开(公告)号：US20240176816A1

公开(公告)日：2024-05-30

申请号：US18071986

申请日：2022-11-30

Applicant: Google LLC

Inventor： Ben Goodrich ,  Kumar Chippala

IPC: G06F16/638 ,  G06F40/263 ,  G06N20/00

CPC classification number: G06F16/639 ,  G06F40/263 ,  G06N20/00

Abstract: Methods, systems, and media for determining playlist title coherence and quality are provided. In some embodiments, a method for generating playlist recommendations includes: determining, using a hardware processor, a title of a playlist; generating, using the hardware processor, a byte-level representation of the title based on the title of the playlist; determining, using the hardware processor, an embedded representation of the title based on the byte-level representation; determining, using the hardware processor, a perplexity score of the title by inputting the embedded representation of the title into a trained language model, wherein the perplexity score is an output of the trained language model; and causing, using the hardware processor, a recommendation based on the perplexity score of the title to be presented.

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：US11886998B2

公开(公告)日：2024-01-30

申请号：US18096946

申请日：2023-01-13

Applicant: Google LLC

Inventor： Noam M. Shazeer ,  Lukasz Mieczyslaw Kaiser ,  Etienne Pot ,  Mohammad Saleh ,  Ben Goodrich ,  Peter J. Liu ,  Ryan Sepassi

IPC: G06N3/08 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.

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：US20230153613A1

公开(公告)日：2023-05-18

申请号：US18096946

申请日：2023-01-13

Applicant: Google LLC

Inventor： Noam M. Shazeer ,  Lukasz Mieczyslaw Kaiser ,  Etienne Pot ,  Mohammad Saleh ,  Ben Goodrich ,  Peter J. Liu ,  Ryan Sepassi

IPC: G06N3/08 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.

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.