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.

公开(公告)号：US20240321261A1

公开(公告)日：2024-09-26

申请号：US18670819

申请日：2024-05-22

Applicant: Amazon Technologies, Inc.

Inventor： Vasiliy Radostev ,  Ruhi Sarikaya ,  Rekha Seshadrinathan ,  Abhinav Sethy ,  Chetan Nagaraj Naik ,  Anjishnu Kumar

IPC: G10L13/08 ,  G10L15/06 ,  G10L15/08 ,  G10L15/183

CPC classification number: G10L13/08 ,  G10L15/063 ,  G10L15/083 ,  G10L15/183

Abstract: Techniques for generating a visual response to a user input are described. A system may receive input data corresponding to a user input, determining a first skill component is to determine a response to the user input, and determine a second skill component is to determine supplemental content related to the user input. The system may also determine a template for presenting a visual response to the user input, where the template is configured for presenting the response and the supplemental content. The system may receive, from the first skill component, first image data corresponding to the first response. The system may also receive, from the second skill component, second image data corresponding to the first supplemental content. The system may send, to a device including a display, a command to present the first image data and the second image data using the template.

公开(公告)号：US11862149B2

公开(公告)日：2024-01-02

申请号：US17464755

申请日：2021-09-02

Applicant: Amazon Technologies, Inc.

Inventor： Bigyan Rajbhandari ,  Praveen Kumar Bodigutla ,  Zhenxiang Zhou ,  Karen Catelyn Stabile ,  Chenlei Guo ,  Abhinav Sethy ,  Alireza Roshan Ghias ,  Pragaash Ponnusamy ,  Kevin Quinn

IPC: G10L15/00 ,  G10L15/18 ,  G10L15/30 ,  G10L15/22

CPC classification number: G10L15/1815 ,  G10L15/22 ,  G10L15/30 ,  G10L2015/223

Abstract: Techniques for decreasing (or eliminating) the possibility of a skill performing an action that is not responsive to a corresponding user input are described. A system may train one or more machine learning models with respect to user inputs, which resulted in incorrect actions being performed by skills, and corresponding user inputs, which resulted in the correct action being performed. The system may use the trained machine learning model(s) to rewrite user inputs that, if not rewritten, may result in incorrect actions being performed. The system may implement the trained machine learning model(s) with respect to ASR output text data to determine if the ASR output text data corresponds (or substantially corresponds) to previous ASR output text data that resulted in an incorrect action being performed. If the trained machine learning model(s) indicates the present ASR output text data corresponds (or substantially corresponds) to such previous ASR output text data, the system may rewrite the present ASR output text data to correspond to text data representing a rephrase of the user input that will (or is more likely to) result in a correct action being performed.

公开(公告)号：US20220059086A1

公开(公告)日：2022-02-24

申请号：US17464755

申请日：2021-09-02

Applicant: Amazon Technologies, Inc.

Inventor： Bigyan Rajbhandari ,  Praveen Kumar Bodigutla ,  Zhenxiang Zhou ,  Karen Catelyn Stabile ,  Chenlei Guo ,  Abhinav Sethy ,  Alireza Roshan Ghias ,  Pragaash Ponnusamy ,  Kevin Quinn

IPC: G10L15/18 ,  G10L15/30 ,  G10L15/22

Abstract: Techniques for decreasing (or eliminating) the possibility of a skill performing an action that is not responsive to a corresponding user input are described. A system may train one or more machine learning models with respect to user inputs, which resulted in incorrect actions being performed by skills, and corresponding user inputs, which resulted in the correct action being performed. The system may use the trained machine learning model(s) to rewrite user inputs that, if not rewritten, may result in incorrect actions being performed. The system may implement the trained machine learning model(s) with respect to ASR output text data to determine if the ASR output text data corresponds (or substantially corresponds) to previous ASR output text data that resulted in an incorrect action being performed. If the trained machine learning model(s) indicates the present ASR output text data corresponds (or substantially corresponds) to such previous ASR output text data, the system may rewrite the present ASR output text data to correspond to text data representing a rephrase of the user input that will (or is more likely to) result in a correct action being performed.

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：US20240029708A1

公开(公告)日：2024-01-25

申请号：US18324234

申请日：2023-05-26

Applicant: Amazon Technologies, Inc.

Inventor： Vasiliy Radostev ,  Ruhi Sarikaya ,  Rekha Seshadrinathan ,  Abhinav Sethy ,  Chetan Nagaraj Naik ,  Anjishnu Kumar

IPC: G10L13/08 ,  G10L15/183 ,  G10L15/06 ,  G10L15/08

CPC classification number: G10L13/08 ,  G10L15/183 ,  G10L15/063 ,  G10L15/083

Abstract: Techniques for generating a visual response to a user input are described. A system may receive input data corresponding to a user input, determining a first skill component is to determine a response to the user input, and determine a second skill component is to determine supplemental content related to the user input. The system may also determine a template for presenting a visual response to the user input, where the template is configured for presenting the response and the supplemental content. The system may receive, from the first skill component, first image data corresponding to the first response. The system may also receive, from the second skill component, second image data corresponding to the first supplemental content. The system may send, to a device including a display, a command to present the first image data and the second image data using the template.

公开(公告)号：US11996081B2

公开(公告)日：2024-05-28

申请号：US18324234

申请日：2023-05-26

Applicant: Amazon Technologies, Inc.

Inventor： Vasiliy Radostev ,  Ruhi Sarikaya ,  Rekha Seshadrinathan ,  Abhinav Sethy ,  Chetan Nagaraj Naik ,  Anjishnu Kumar

IPC: G10L13/08 ,  G10L15/06 ,  G10L15/08 ,  G10L15/183

CPC classification number: G10L13/08 ,  G10L15/063 ,  G10L15/083 ,  G10L15/183

Abstract: Techniques for generating a visual response to a user input are described. A system may receive a natural language input and use a machine learning model to determine a first component is to determine a response to the natural language input while a second component is to determine supplemental content related to the natural language input. The system may receive, from the first component, first image data corresponding to the response. The system may also receive, from the second component, second image data corresponding to the supplemental content. The system may send, to a display, a command to present the first image data and the second image data.

公开(公告)号：US11705108B1

公开(公告)日：2023-07-18

申请号：US17547586

申请日：2021-12-10

Applicant: Amazon Technologies, Inc.

Inventor： Vasiliy Radostev ,  Ruhi Sarikaya ,  Rekha Seshadrinathan ,  Abhinav Sethy ,  Chetan Nagaraj Naik ,  Anjishnu Kumar

IPC: G10L13/08 ,  G10L15/183 ,  G10L15/06 ,  G10L15/08

CPC classification number: G10L13/08 ,  G10L15/063 ,  G10L15/083 ,  G10L15/183

Abstract: Techniques for generating a visual response to a user input are described. A system may receive input data corresponding to a user input, determining a first skill component is to determine a response to the user input, and determine a second skill component is to determine supplemental content related to the user input. The system may also determine a template for presenting a visual response to the user input, where the template is configured for presenting the response and the supplemental content. The system may receive, from the first skill component, first image data corresponding to the first response. The system may also receive, from the second skill component, second image data corresponding to the first supplemental content. The system may send, to a device including a display, a command to present the first image data and the second image data using the template.

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.