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    DETERMINING INTENTS AND RESPONSES USING MACHINE LEARNING IN CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
    1.
    发明公开
    DETERMINING INTENTS AND RESPONSES USING MACHINE LEARNING IN CONVERSATIONAL AI SYSTEMS AND APPLICATIONS 审中-公开

    公开(公告)号：US20240184814A1

    公开(公告)日：2024-06-06

    申请号：US18173622

    申请日：2023-02-23

    Applicant: NVIDIA Corporation

    Inventor： Shubhadeep Das ,  Sumit Kumar Bhattacharya ,  Oluwatobi Olabiyi

    IPC: G06F16/332

    CPC classification number: G06F16/3329

    Abstract: In various examples, hybrid models for determining intents in conversational AI systems and applications are disclosed. Systems and methods are disclosed that use a machine learning model(s) and a data file(s) that associates requests (e.g., questions) with responses (e.g., answers) in order to generate final responses to requests. For instance, the machine learning model(s) may determine confidence scores that indicate similarities between the requests from the data file(s) and an input request represented by text data. The data file(s) is then used to determine, based on the confidence scores, one of the responses that is associated with one of the requests that is related to the input request. Additionally, the response may then used to generate a final response to the input request.

    DETERMINING INTENTS AND RESPONSES USING MACHINE LEARNING IN CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
    2.
    发明公开
    DETERMINING INTENTS AND RESPONSES USING MACHINE LEARNING IN CONVERSATIONAL AI SYSTEMS AND APPLICATIONS 审中-公开

    公开(公告)号：US20230205797A1

    公开(公告)日：2023-06-29

    申请号：US18173610

    申请日：2023-02-23

    Applicant: NVIDIA Corporation

    Inventor： Shubhadeep Das ,  Sumit Kumar Bhattacharya ,  Oluwatobi Olabiyi

    IPC: G06F16/332

    CPC classification number: G06F16/3329

    Abstract: In various examples, hybrid models for determining intents in conversational AI systems and applications are disclosed. Systems and methods are disclosed that use a machine learning model(s) and a data file(s) that associates intents with one another (e.g., using a tree-like structure) in order to determine a final intent associated with text. For example, the text may initially be processed using the machine learning model(s) (e.g., a first machine learning model) in order to determine a first intent associated with the text. The data file(s) may then be used to determine information (e.g., anchors) for one or more second intents (e.g., one or more sub-intents) that are related to the first intent. The text and the information may then be processed using the machine learning model(s) (e.g., a second machine learning model) to determine a second intent, from the one or more second intents, that is associated with the text.

    ENTITY LINKING FOR RESPONSE GENERATION IN CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
    5.
    发明申请
    ENTITY LINKING FOR RESPONSE GENERATION IN CONVERSATIONAL AI SYSTEMS AND APPLICATIONS 有权

    公开(公告)号：US20240370690A1

    公开(公告)日：2024-11-07

    申请号：US18309890

    申请日：2023-05-01

    Applicant: NVIDIA Corporation

    Inventor： Sagar Bogadi Manjunath ,  Shubhadeep Das ,  Sumit Kumar Bhattacharya ,  Oluwatobi Olabiyi

    IPC: G06N3/006 ,  G06N3/045

    Abstract: In various examples, query response generation using entity linking for conversational AI systems and applications is described herein. Systems and methods are disclosed that generate embeddings associated with entities that a dialogue system is trained to interpret. The systems and methods may then use the embeddings to interpret requests. For instance, when receiving a request, the systems and methods may generate at least an embedding for an entity included in the request and compare the embedding to the stored embeddings in order to determine that the entity from the request is related to one of the stored entities. The systems and methods may then use this relationship to generate the response to the query. This way, even if the entity is not an exact match to a stored entity, the systems and methods are still able to interpret the query from the user.

    QUERY RESPONSE GENERATION USING STRUCTURED AND UNSTRUCTURED DATA FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
    6.
    发明公开
    QUERY RESPONSE GENERATION USING STRUCTURED AND UNSTRUCTURED DATA FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS 审中-公开

    公开(公告)号：US20240176808A1

    公开(公告)日：2024-05-30

    申请号：US18172571

    申请日：2023-02-22

    Applicant: NVIDIA Corporation

    Inventor： Shubhadeep Das ,  Sumit Kumar Bhattacharya ,  Oluwatobi Olabiyi

    IPC: G06F16/33 ,  G06F16/338

    CPC classification number: G06F16/3344 ,  G06F16/338

    Abstract: In various examples, contextual data may be generated using structured and unstructured data for conversational AI systems and applications. Systems and methods are disclosed that use structured data (converted to unstructured form) and unstructured data, such as from a knowledge database(s), to generate contextual data. For instance, the contextual data may represent text (e.g., narratives), where a first portion of the text is generated using the structured data and a second portion of the text is generated using the unstructured data. The systems and methods may then use a neural network(s), such as a neural network(s) associated with a dialogue manager, to process input data representing a request (e.g., a query) and the contextual data in order to generate a response to the request. For instance, if the request includes a query for information associated with a topic, the neural network(s) may generate a response that includes the requested information.

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