公开(公告)号：US11527237B1

公开(公告)日：2022-12-13

申请号：US17024959

申请日：2020-09-18

Applicant: Amazon Technologies, Inc.

Inventor： Ruhi Sarikaya ,  Hung Tuan Pham ,  Savas Parastatidis ,  Dean Curtis ,  Pushpendre Rastogi ,  Nitin Ashok Jain ,  John Arland Nave ,  Abhinav Sethy ,  Arpit Gupta ,  Mayank Kumar ,  Nakul Dahiwade ,  Arshdeep Singh ,  Nikhil Reddy Kortha ,  Rohit Prasad

IPC: G10L13/00 ,  G10L15/16 ,  G06F16/9032 ,  G10L13/08

Abstract: Techniques for recommending a skill experience to a user after a user-system dialog session has ended are described. Upon a dialog session ending, the system uses a first machine learning model to determine potential intents to recommend to a user. The system then uses a second machine learning model to determine a particular skill and intent to recommend. The system then prompts the user to accept the recommended skill and intent. If the user accepts, the system calls the recommended skill to execute. As part of calling the skill, the system sends to the skill at least one entity provided in a natural language user input of the ended dialog session. This enables the skill to skip welcome prompts, and initiate processing to output a response based on the intent and the at least one entity of the ended dialog session.

公开(公告)号：US11908463B1

公开(公告)日：2024-02-20

申请号：US17361761

申请日：2021-06-29

Applicant: Amazon Technologies, Inc.

Inventor： Arjit Biswas ,  Shishir Bharathi ,  Anushree Venkatesh ,  Yun Lei ,  Ashish Kumar Agrawal ,  Siddhartha Reddy Jonnalagadda ,  Prakash Krishnan ,  Arindam Mandal ,  Raefer Christopher Gabriel ,  Abhay Kumar Jha ,  David Chi-Wai Tang ,  Savas Parastatidis

IPC: G10L15/22 ,  G06F40/35 ,  G10L15/183 ,  G10L15/18 ,  G06F40/279 ,  G06F40/295 ,  G10L15/19 ,  G06F40/30

CPC classification number: G10L15/183 ,  G06F40/279 ,  G10L15/1815 ,  G10L15/22 ,  G06F40/295 ,  G06F40/30 ,  G06F40/35 ,  G10L15/1822 ,  G10L15/19 ,  G10L2015/228

Abstract: Techniques for storing and using multi-session context are described. A system may store context data corresponding to a first interaction, where the context data may include action data, entity data and a profile identifier for a user. Later the stored context data may be retrieved during a second interaction corresponding to the entity of the second interaction. The second interaction may take place at a system different than the first interaction. The system may generate a response during the second interaction using the stored context data of the prior interaction.

公开(公告)号：US20240347050A1

公开(公告)日：2024-10-17

申请号：US18663237

申请日：2024-05-14

Applicant: Amazon Technologies, Inc.

Inventor： Ruhi Sarikaya ,  Hung Tuan Pham ,  Savas Parastatidis ,  Dean Curtis ,  Pushpendre Rastogi ,  Nitin Ashok Jain ,  John Arland Nave ,  Abhinav Sethy ,  Arpit Gupta ,  Mayank Kumar ,  Nakul Dahiwade ,  Arshdeep Singh ,  Nikhil Reddy Kortha ,  Rohit Prasad

IPC: G10L15/16 ,  G06F16/9032 ,  G10L13/08

CPC classification number: G10L15/16 ,  G06F16/90332 ,  G10L13/08

Abstract: Techniques for recommending a skill experience to a user after a user-system dialog session has ended are described. Upon a dialog session ending, the system uses a first machine learning model to determine potential intents to recommend to a user. The system then uses a second machine learning model to determine a particular skill and intent to recommend. The system then prompts the user to accept the recommended skill and intent. If the user accepts, the system calls the recommended skill to execute. As part of calling the skill, the system sends to the skill at least one entity provided in a natural language user input of the ended dialog session. This enables the skill to skip welcome prompts, and initiate processing to output a response based on the intent and the at least one entity of the ended dialog session.

公开(公告)号：US20240185846A1

公开(公告)日：2024-06-06

申请号：US18439166

申请日：2024-02-12

Applicant: Amazon Technologies, Inc.

Inventor： Arjit Biswas ,  Shishir Bharathi ,  Anushree Venkatesh ,  Yun Lei ,  Ashish Kumar Agrawal ,  Siddhartha Reddy Jonnalagadda ,  Prakash Krishnan ,  Arindam Mandal ,  Raefer Christopher Gabriel ,  Abhay Kumar Jha ,  David Chi-Wai Tang ,  Savas Parastatidis

IPC: G10L15/183 ,  G06F40/279 ,  G06F40/295 ,  G06F40/30 ,  G06F40/35 ,  G10L15/18 ,  G10L15/19 ,  G10L15/22

CPC classification number: G10L15/183 ,  G06F40/279 ,  G10L15/1815 ,  G10L15/22 ,  G06F40/295 ,  G06F40/30 ,  G06F40/35 ,  G10L15/1822 ,  G10L15/19 ,  G10L2015/228

Abstract: Techniques for storing and using multi-session context are described. A system may store context data corresponding to a first interaction, where the context data may include action data, entity data and a profile identifier for a user. Later the stored context data may be retrieved during a second interaction corresponding to the entity of the second interaction. The second interaction may take place at a system different than the first interaction. The system may generate a response during the second interaction using the stored context data of the prior interaction.

公开(公告)号：US11990122B2

公开(公告)日：2024-05-21

申请号：US18076987

申请日：2022-12-07

Applicant: Amazon Technologies, Inc.

Inventor： Ruhi Sarikaya ,  Hung Tuan Pham ,  Savas Parastatidis ,  Dean Curtis ,  Pushpendre Rastogi ,  Nitin Ashok Jain ,  John Arland Nave ,  Abhinav Sethy ,  Arpit Gupta ,  Mayank Kumar ,  Nakul Dahiwade ,  Arshdeep Singh ,  Nikhil Reddy Kortha ,  Rohit Prasad

IPC: G10L15/26 ,  G06F16/9032 ,  G10L13/08 ,  G10L15/16

CPC classification number: G10L15/16 ,  G06F16/90332 ,  G10L13/08

Abstract: Techniques for recommending a skill experience to a user after a user-system dialog session has ended are described. Upon a dialog session ending, the system uses a first machine learning model to determine potential intents to recommend to a user. The system then uses a second machine learning model to determine a particular skill and intent to recommend. The system then prompts the user to accept the recommended skill and intent. If the user accepts, the system calls the recommended skill to execute. As part of calling the skill, the system sends to the skill at least one entity provided in a natural language user input of the ended dialog session. This enables the skill to skip welcome prompts, and initiate processing to output a response based on the intent and the at least one entity of the ended dialog session.

公开(公告)号：US20230215425A1

公开(公告)日：2023-07-06

申请号：US18076987

申请日：2022-12-07

Applicant: Amazon Technologies, Inc.

Inventor： Ruhi Sarikaya ,  Hung Tuan Pham ,  Savas Parastatidis ,  Dean Curtis ,  Pushpendre Rastogi ,  Nitin Ashok Jain ,  John Arland Nave ,  Abhinav Sethy ,  Arpit Gupta ,  Mayank Kumar ,  Nakul Dahiwade ,  Arshdeep Singh ,  Nikhil Reddy Kortha ,  Rohit Prasad

IPC: G10L15/16 ,  G06F16/9032 ,  G10L13/08

CPC classification number: G10L15/16 ,  G06F16/90332 ,  G10L13/08

Abstract: Techniques for recommending a skill experience to a user after a user-system dialog session has ended are described. Upon a dialog session ending, the system uses a first machine learning model to determine potential intents to recommend to a user. The system then uses a second machine learning model to determine a particular skill and intent to recommend. The system then prompts the user to accept the recommended skill and intent. If the user accepts, the system calls the recommended skill to execute. As part of calling the skill, the system sends to the skill at least one entity provided in a natural language user input of the ended dialog session. This enables the skill to skip welcome prompts, and initiate processing to output a response based on the intent and the at least one entity of the ended dialog session.