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    APPARATUS AND METHOD FOR VERIFYING CONTEXT
    1.
    发明申请
    APPARATUS AND METHOD FOR VERIFYING CONTEXT 审中-公开
    用于验证语境的装置和方法

    公开(公告)号：US20140172411A1

    公开(公告)日：2014-06-19

    申请号：US14040036

    申请日：2013-09-27

    Applicant: Electronics and Telecommunications Research Institute

    Inventor： Chang Hyun KIM ,  Oh Woog Kwon ,  Seung Hoon Na ,  Yoon Hyung Roh ,  Young Ae Seo ,  Seong Il Yang ,  Ki Young Lee ,  Sang Keun Jung ,  Sung Kwon Choi ,  Yun Jin ,  Eun Jin Park ,  Jong Hun Shin ,  Jinxia Huang ,  Sang Kyu Park

    IPC: G06F17/28

    CPC classification number: G06F17/2854

    Abstract: Provided are an apparatus and a method for verifying a context that verify an ambiguous expression of an input text through a user's intention and utilize the verified expression as a context in interpretation and translation. The apparatus includes: an ambiguous expression verifying unit verifying an ambiguous expression in a first input text or a back translation text for the first input text in accordance with user's input; a context generating unit generating the verified ambiguous expression as a context; and a context controlling unit controlling the generated context to be applied to translate or interpret the first input text or second input texts input after the first input text.

    Abstract translation: 提供了一种用于验证通过用户意图验证输入文本的模糊表达的上下文的装置和方法，并且将所验证的表达式用作解释和翻译中的上下文。 该装置包括：模糊表达式验证单元，用于根据用户的输入验证第一输入文本或第一输入文本的后翻译文本中的模糊表达; 上下文生成单元，生成经验证的模糊表达作为上下文; 以及上下文控制单元，其控制要应用的生成的上下文来翻译或解释在第一输入文本之后输入的第一输入文本或第二输入文本。

    SYSTEM AND METHOD FOR PROBLEM INFERENCE BASED ON MULTI-MODAL GENERATIVE ARTIFICIAL INTELLIGENCE
    2.
    发明申请
    SYSTEM AND METHOD FOR PROBLEM INFERENCE BASED ON MULTI-MODAL GENERATIVE ARTIFICIAL INTELLIGENCE 有权

    公开(公告)号：US20250139188A1

    公开(公告)日：2025-05-01

    申请号：US18889622

    申请日：2024-09-19

    Applicant: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE

    Inventor： Jeong Heo ,  Oh Woog Kwon ,  Jihee Ryu ,  Young-Ae Seo ,  Jin SEONG ,  Jong Hun Shin ,  Ki Young Lee ,  Yo Han Lee ,  Soojong Lim

    IPC: G06F17/11 ,  G06F40/205 ,  G06F40/40 ,  G06V10/86

    Abstract: A method for problem inference based on multi-modal generative artificial intelligence includes receiving question information including an image and text, generating formal languages by parsing the image and text of the question information, respectively, based on a pre-constructed problem solving template, generating text-based intermediate inference information for the question information by inputting the generated formal language to a formal language inference unit, generating image-based inference information by inputting the text-based intermediate inference information, the text included in the question information (hereinafter referred to as “text question information”), and the image included in the question information (hereinafter referred to as “image question information”) to a multi-modal image generation model, and generating text-based inference information by inputting the text-based intermediate inference information, the image-based inference information, and the text question information to a multi-modal text generation model.

    Self-learning based dialogue apparatus and method for incremental dialogue knowledge
    3.
    发明授权
    Self-learning based dialogue apparatus and method for incremental dialogue knowledge 有权

    公开(公告)号：US10332033B2

    公开(公告)日：2019-06-25

    申请号：US15405425

    申请日：2017-01-13

    Applicant: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE

    Inventor： Oh Woog Kwon ,  Young Kil Kim ,  Yun Keun Lee

    IPC: G10L15/01 ,  G10L15/22 ,  G06N20/00 ,  G10L15/18 ,  G06F17/24 ,  G06F17/27

    Abstract: An incremental self-learning based dialogue apparatus for dialogue knowledge includes a dialogue processing unit configured to determine a intention of a user utterance by using a knowledge base and perform processing or a response suitable for the user intention, a dialogue establishment unit configured to automatically learn a user intention stored in a intention annotated learning corpus, store information about the learned user intention in the knowledge base, and edit and manage the knowledge base and the intention annotated learning corpus, and a self-knowledge augmentation unit configured to store a log of a dialogue performed by the dialogue processing unit, detect and classify an error in the stored dialogue log, automatically tag a user intention for the detected and classified error, and store the tagged user intention in the intention annotated learning corpus.

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