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：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：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：US20180174578A1

公开(公告)日：2018-06-21

申请号：US15384275

申请日：2016-12-19

Applicant: Interactions LLC

Inventor： SRINIVAS BANGALORE ,  John Chen

IPC: G10L15/18 ,  G10L15/19

CPC classification number: G06F17/30654 ,  G06F17/2241 ,  G06F17/2785

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.