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公开(公告)号：US20250078823A1
公开(公告)日：2025-03-06
申请号：US18456949
申请日：2023-08-28
Applicant: Amazon Technologies, Inc.
Inventor： Xing Fan , Chenlei Guo , Narendra Gyanchandani , Hyungseo Park
IPC: G10L15/183 , G10L15/22
Abstract: Techniques for determining one or more responses associated with one or more components that are responsive to a user input are described. The system receives a user input and causes one or more components to generate one or more responses associated with the user input. The system determines one or more of the responses are responsive to the user input, causes one or more actions associated with the responses to be performed, and outputs a natural language summary of the one or more responses. If the system determines that none of the responses are responsive to the user input and/or an ambiguity exists with respect to the user input, the system can generate a request for additional information usable to resolve the ambiguity, which may be sent to another component of the system and/or output to the user that provided the user input.
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公开(公告)号：US20230119954A1
公开(公告)日：2023-04-20
申请号：US17974677
申请日：2022-10-27
Applicant: Amazon Technologies, Inc.
Inventor： Isaac Joseph Madwed , Julia Kennedy Nemer , Joo-Kyung Kim , Nikko Strom , Steven Mack Saunders , Laura Maggia Panfili , Anna Caitlin Jentoft , Sungjin Lee , David Thomas , Young-Bum Kim , Pablo Cesar Ganga , Chenlei Guo , Shuting Tang , Zhenyu Yao
Abstract: Described herein is a system for responding to a frustrated user with a response determined based on spoken language understanding (SLU) processing of a user input. The system detects user frustration and responds to a repeated user input by confirming an action to be performed or presenting an alternative action, instead of performing the action responsive to the user input. The system also detects poor audio quality of the captured user input, and responds by requesting the user to repeat the user input. The system processes sentiment data and signal quality data to respond to user inputs.
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公开(公告)号：US20210375272A1
公开(公告)日：2021-12-02
申请号：US16889420
申请日：2020-06-01
Applicant: Amazon Technologies, Inc.
Inventor： Isaac Joseph Madwed , Julia Kennedy Nemer , Joo-Kyung Kim , Nikko Strom , Steven Mack Saunders , Laura Maggia Panfili , Anna Caitlin Jentoft , Sungjin Lee , David Thomas , Young-Bum Kim , Pablo Cesar Ganga , Chenlei Guo , Shuting Tang , Zhenyu Yao
Abstract: Described herein is a system for responding to a frustrated user with a response determined based on spoken language understanding (SLU) processing of a user input. The system detects user frustration and responds to a repeated user input by confirming an action to be performed or presenting an alternative action, instead of performing the action responsive to the user input. The system also detects poor audio quality of the captured user input, and responds by requesting the user to repeat the user input. The system processes sentiment data and signal quality data to respond to user inputs.
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公开(公告)号：US11929070B1
公开(公告)日：2024-03-12
申请号：US17461124
申请日：2021-08-30
Applicant: Amazon Technologies, Inc.
Inventor： Ruhi Sarikaya , Zheng Du , Xiaohu Liu , Kai Liu , Sriharsha Venkata Chintalapati , Chenlei Guo , Hung Tuan Pham , Joe Pemberton , Zhenyu Yao , Bigyan Rajbhandari
CPC classification number: G10L15/22 , G06N20/20 , G10L15/02 , G10L15/063 , G10L2015/225
Abstract: Techniques for performing centralized unsuperivised learning in a multi-domain system are described. A user may request labeled data for an ML task, where the request includes a prompt for obtaining relevant explicit user feedback. The system may use the prompt to collect explicit user feedback for relevant runtime user inputs. After a duration of time (in the user's request for labeled data) has elapsed, the system determines whether collected user feedback indicates processing of the user input was defective and, if so, determines a cause of the defective processing. The system then uses one or more label generators to generate labeled data using the collected user feedback, whether the processing was defective, and the determined defect cause.
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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
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.
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Patent Agency Ranking
公开(公告)号：US11646035B1
公开(公告)日：2023-05-09
申请号：US17027903
申请日：2020-09-22
Applicant: Amazon Technologies, Inc.
Inventor： Xing Fan , Chenlei Guo
IPC: G10L15/32 , G10L15/07 , G10L15/18 , G06F16/9032
CPC classification number: G10L15/32 , G06F16/90332 , G10L15/075 , G10L15/1815
Abstract: Techniques for determining an intent for a user input in a dialog are described. The system processes historic interaction data that is structured based skills and intents, with each skill-intent pair being associated with one or more past user inputs received by the system, one or more sample inputs, and one or more alternative representations of the user inputs. Based on processing of the historic interaction data and dialog data of previous turns of the dialog, the system determines potential intents for the user input of the current turn of the dialog. The potential intents may correspond to a presently active skill or another skill, enabling the user to interact with another skill during the dialog.
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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
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.
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公开(公告)号：US11158307B1
公开(公告)日：2021-10-26
申请号：US16363814
申请日：2019-03-25
Applicant: Amazon Technologies, Inc.
Inventor： Alireza Roshan Ghias , Sean William Jewell , Chenlei Guo
Abstract: A system for handling errors during automatic speech recognition by processing a potentially defective utterance to determine an alternative, potentially successful utterance. The system processes the N-best ASR hypotheses corresponding to the defective utterance using a trained model to generate a word-level feature vector. The word-level feature vector is processed using a sequence-to-sequence architecture to determine the alternate utterance.
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公开(公告)号：US11837229B1
公开(公告)日：2023-12-05
申请号：US17363387
申请日：2021-06-30
Applicant: Amazon Technologies, Inc.
Inventor： Xing Fan , Saurabh Gupta , Chenlei Guo , Eunah Cho
CPC classification number: G10L15/22 , G06N5/02 , G10L15/144 , G06F16/3338 , G06F16/367 , G10L2015/223
Abstract: Techniques for determining and using interaction affinity data are described. Interaction affinity data may indicate a latent affinity between information corresponding to an interaction, such as, intents, entities, device type from which a user input is received, domain, etc. A system may use the interaction affinity data to determine an alternative input representation for a spoken input to cause output of a desired response to the spoken input. The system may also use the interaction affinity data to recommend an action to a user.
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公开(公告)号：US20230110205A1
公开(公告)日：2023-04-13
申请号：US17901209
申请日：2022-09-01
Applicant: Amazon Technologies, Inc.
Inventor： Chenlei Guo , Xing Fan , Jin Hock Ong , Kai Wei
IPC: G10L15/197 , G10L15/22 , G10L15/18
Abstract: Techniques for handling errors during processing of natural language inputs are described. A system may process a natural language input to generate an ASR hypothesis or NLU hypothesis. The system may use more than one data searching technique (e.g., deep neural network searching, convolutional neural network searching, etc.) to generate an alternate ASR hypothesis or NLU hypothesis, depending on the type of hypothesis input for alternate hypothesis processing.
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