公开(公告)号：US10796100B2

公开(公告)日：2020-10-06

申请号：US16248433

申请日：2019-01-15

Applicant: Interactions LLC

Inventor： Srinivas Bangalore ,  John Chen

IPC: G06F17/28 ,  G06F40/30 ,  G06F40/137 ,  G06F16/332 ,  G10L15/18 ,  G10L15/183

Abstract: A natural language processing system has a hierarchy of user intents related to a domain of interest, the hierarchy having specific intents corresponding to leaf nodes of the hierarchy, and more general intents corresponding to ancestor nodes of the leaf nodes. The system also has a trained understanding model that can classify natural language utterances according to user intent. When the understanding model cannot determine with sufficient confidence that a natural language utterance corresponds to one of the specific intents, the natural language processing system traverses the hierarchy of intents to find a more general user intent that is related to the most applicable specific intent of the utterance and for which there is sufficient confidence. The general intent can then be used to prompt the user with questions applicable to the general intent to obtain the missing information needed for a specific intent.

公开(公告)号：US20190147044A1

公开(公告)日：2019-05-16

申请号：US16248433

申请日：2019-01-15

Applicant: Interactions LLC

Inventor： Srinivas Bangalore ,  John Chen

IPC: G06F17/27 ,  G06F17/22 ,  G06F16/332

Abstract: A natural language processing system has a hierarchy of user intents related to a domain of interest, the hierarchy having specific intents corresponding to leaf nodes of the hierarchy, and more general intents corresponding to ancestor nodes of the leaf nodes. The system also has a trained understanding model that can classify natural language utterances according to user intent. When the understanding model cannot determine with sufficient confidence that a natural language utterance corresponds to one of the specific intents, the natural language processing system traverses the hierarchy of intents to find a more general user intent that is related to the most applicable specific intent of the utterance and for which there is sufficient confidence. The general intent can then be used to prompt the user with questions applicable to the general intent to obtain the missing information needed for a specific intent.

公开(公告)号：US10891435B1

公开(公告)日：2021-01-12

申请号：US15900687

申请日：2018-02-20

Applicant: Interactions LLC

Inventor： Nicholas Ruiz ,  John Chen ,  Srinivas Bangalore

IPC: G06F40/30 ,  G06F40/58

Abstract: Machine translation is used to leverage the semantic properties (e.g., intent) already known for one natural language for use in another natural language. In a first embodiment, the corpus of a first language is translated to each other language of interest using machine translation, and the corresponding semantic properties are transferred to the translated corpuses. Semantic models can then be generated from the translated corpuses and the transferred semantic properties. In a second embodiment, given a first language for which there is a semantic model, if a query is received in a second, different language lacking its own semantic model, machine translation is used to translate the query into the first language. Then, the semantic model for the first language is applied to the translated query, thereby obtaining the semantic properties for the query, even though no semantic model existed for the language in which the query was specified.

公开(公告)号：US20160026627A1

公开(公告)日：2016-01-28

申请号：US14877709

申请日：2015-10-07

Applicant: Interactions LLC

Inventor： Michael J. Johnston ,  Srinivas Bangalore ,  Junlan Feng ,  Taniya Mishra

IPC: G06F17/30 ,  G10L15/22

CPC classification number: G06F17/30026 ,  G06F17/30976 ,  G06F17/30979 ,  G10L15/22 ,  G10L2015/227

Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for approximating responses to a user speech query in voice-enabled search based on metadata that include demographic features of the speaker. A system practicing the method recognizes received speech from a speaker to generate recognized speech, identifies metadata about the speaker from the received speech, and feeds the recognized speech and the metadata to a question-answering engine. Identifying the metadata about the speaker is based on voice characteristics of the received speech. The demographic features can include age, gender, socio-economic group, nationality, and/or region. The metadata identified about the speaker from the received speech can be combined with or override self-reported speaker demographic information.

Abstract translation: 本文公开的是基于包括说话者的人口统计特征的元数据的用于在基于语音的搜索中近似对用户语音查询的响应的系统，方法和非暂时计算机可读存储介质。 实施该方法的系统识别来自扬声器的接收到的语音以产生识别的语音，从接收到的语音识别关于说话者的元数据，并将识别的语音和元数据馈送到问答引擎。 识别关于扬声器的元数据是基于所接收语音的语音特征。 人口特征可以包括年龄，性别，社会经济群体，国籍和/或地区。 从接收到的语音中识别的关于说话者的元数据可以与自报告的说话者人口统计信息进行组合或覆盖。

公开(公告)号：US12243517B1

公开(公告)日：2025-03-04

申请号：US17500834

申请日：2021-10-13

Applicant: Interactions LLC

Inventor： Mahnoosh Mehrabani ,  Srinivas Bangalore

IPC: G10L15/18 ,  G10L15/02 ,  G10L15/06 ,  G10L15/08

Abstract: A task-oriented dialog system determines an endpoint in a user utterance by receiving incremental portions of a user utterance that is provided in real time during a task-oriented communication session between a user and a virtual agent (VA). The task-oriented dialog system recognizes words in the incremental portions using an automated speech recognition (ASR) model and generates semantic information for the incremental portions of the utterance by applying a natural language processing (NLP) model to the recognized words. An acoustic-prosodic signature of the incremental portions of the utterance is generated using an acoustic-prosodic model. The task-oriented dialog system can generate a feature vector that represents the incrementally recognized words, the semantic information, the acoustic-prosodic signature, and corresponding confidence scores of the model outputs. A model is applied to the feature vector to identify a likely endpoint in the user utterance.

公开(公告)号：US12154552B1

公开(公告)日：2024-11-26

申请号：US17462889

申请日：2021-08-31

Applicant: Interactions LLC

Inventor： Brian David Lester ,  Srinivas Bangalore

IPC: G10L15/18 ,  G10L15/06 ,  G10L15/16

Abstract: A natural language understanding (NLU) system generates in-place annotations for natural language utterances or other types of time-based media based on stand-off annotations. The in-place annotations are associated with particular sub-sequences of an annotation, which provides richer information than stand-off annotations, which are associated only with an utterance as a whole. To generate the in-place annotations for an utterance, the NLU system applies an encoder network and a decoder network to obtain attention weights for the various tokens within the utterance. The NLU system disqualifies tokens of the utterance based on their corresponding attention weights, and selects highest-scoring contiguous sequences of tokens between the disqualified tokens. In-place annotations are associated with the selected sequences.

公开(公告)号：US11508355B1

公开(公告)日：2022-11-22

申请号：US16172115

申请日：2018-10-26

Applicant: Interactions LLC

Inventor： Ryan Price ,  Srinivas Bangalore

IPC: G10L15/06 ,  G10L15/22 ,  G10L15/02 ,  G10L21/0232 ,  G10L15/18

Abstract: Systems and methods are disclosed herein for discerning aspects of user speech to determine user intent and/or other acoustic features of a sound input without the use of an ASR engine. To this end, a processor may receive a sound signal comprising raw acoustic data from a client device, and divides the data into acoustic units. The processor feeds the acoustic units through a first machine learning model to obtain a first output and determines a first mapping, using the first output, of each respective acoustic unit to a plurality of candidate representations of the respective acoustic unit. The processor feeds each candidate representation of the plurality through a second machine learning model to obtain a second output, determines a second mapping, using the second output, of each candidate representation to a known condition, and determines a label for the sound signal based on the second mapping.

公开(公告)号：US10216832B2

公开(公告)日：2019-02-26

申请号：US15384275

申请日：2016-12-19

Applicant: Interactions LLC

Inventor： Srinivas Bangalore ,  John Chen

IPC: G06F17/27 ,  G06F17/30 ,  G06F17/22

Abstract: A natural language processing system has a hierarchy of user intents related to a domain of interest, the hierarchy having specific intents corresponding to leaf nodes of the hierarchy, and more general intents corresponding to ancestor nodes of the leaf nodes. The system also has a trained understanding model that can classify natural language utterances according to user intent. When the understanding model cannot determine with sufficient confidence that a natural language utterance corresponds to one of the specific intents, the natural language processing system traverses the hierarchy of intents to find a more general user intent that is related to the most applicable specific intent of the utterance and for which there is sufficient confidence. The general intent can then be used to prompt the user with questions applicable to the general intent to obtain the missing information needed for a specific intent.

公开(公告)号：US09697206B2

公开(公告)日：2017-07-04

申请号：US14877709

申请日：2015-10-07

Applicant: Interactions LLC

Inventor： Michael J. Johnston ,  Srinivas Bangalore ,  Junlan Feng ,  Taniya Mishra

IPC: G10L15/00 ,  G06F17/30 ,  G10L15/22

CPC classification number: G06F17/30026 ,  G06F17/30976 ,  G06F17/30979 ,  G10L15/22 ,  G10L2015/227

Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for approximating responses to a user speech query in voice-enabled search based on metadata that include demographic features of the speaker. A system practicing the method recognizes received speech from a speaker to generate recognized speech, identifies metadata about the speaker from the received speech, and feeds the recognized speech and the metadata to a question-answering engine. Identifying the metadata about the speaker is based on voice characteristics of the received speech. The demographic features can include age, gender, socio-economic group, nationality, and/or region. The metadata identified about the speaker from the received speech can be combined with or override self-reported speaker demographic information.

公开(公告)号：US09189483B2

公开(公告)日：2015-11-17

申请号：US13847173

申请日：2013-03-19

Applicant: Interactions LLC.

Inventor： Michael Johnston ,  Srinivas Bangalore ,  Junlan Feng ,  Taniya Mishra

IPC: G10L17/00 ,  G06F17/30 ,  G10L15/22

CPC classification number: G06F17/30026 ,  G06F17/30976 ,  G06F17/30979 ,  G10L15/22 ,  G10L2015/227

Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for approximating responses to a user speech query in voice-enabled search based on metadata that include demographic features of the speaker. A system practicing the method recognizes received speech from a speaker to generate recognized speech, identifies metadata about the speaker from the received speech, and feeds the recognized speech and the metadata to a question-answering engine. Identifying the metadata about the speaker is based on voice characteristics of the received speech. The demographic features can include age, gender, socio-economic group, nationality, and/or region. The metadata identified about the speaker from the received speech can be combined with or override self-reported speaker demographic information.

Abstract translation: 本文公开的是基于包括说话者的人口统计特征的元数据的用于在基于语音的搜索中近似对用户语音查询的响应的系统，方法和非暂时计算机可读存储介质。 实施该方法的系统识别来自扬声器的接收到的语音以产生识别的语音，从接收到的语音识别关于说话者的元数据，并将识别的语音和元数据馈送到问答引擎。 识别关于扬声器的元数据是基于所接收语音的语音特征。 人口特征可以包括年龄，性别，社会经济群体，国籍和/或地区。 从接收到的语音中识别的关于说话者的元数据可以与自报告的说话者人口统计信息进行组合或覆盖。