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    System and methods for language processing of document sequences using a neural network
    2.
    发明授权
    System and methods for language processing of document sequences using a neural network 有权

    公开(公告)号：US11983498B2

    公开(公告)日：2024-05-14

    申请号：US17205153

    申请日：2021-03-18

    Applicant: Augmented Intelligence Technologies, Inc.

    Inventor： Martin Elisco ,  Jim Lindstrom ,  Logan Courtney

    IPC: G06F40/295 ,  G06F16/93 ,  G06F40/12 ,  G06F40/205 ,  G06N3/08

    CPC classification number: G06F40/295 ,  G06F16/93 ,  G06F40/12 ,  G06F40/205 ,  G06N3/08

    Abstract: A system and method for natural language processing for document sequences comprises a computing device configured to train a neural network as a function of a corpus of documents, wherein training comprises receiving the corpus of documents, identifying significant terms, and tuning, as a function of the corpus of documents, the neural network to generate a plurality of vectors for each significant term of the plurality of significant terms, a vector in a vector space representing semantic relationships between the significant terms and semantic units in the corpus of documents, receive a current document sequence including a plurality of documents in a sequential order, map a plurality of mapped terms of the plurality of significant terms to the plurality of documents as a function of the neural network and the plurality of vectors, and generate a plurality of timelines as a function of the sequential order and the mapped terms.

    Techniques for secure document management and verification
    5.
    发明授权
    Techniques for secure document management and verification 有权

    公开(公告)号：US11809582B2

    公开(公告)日：2023-11-07

    申请号：US17245069

    申请日：2021-04-30

    Applicant: Douglas Cobb

    Inventor： Douglas Cobb

    IPC: G06F21/62 ,  G06F16/93 ,  G06F40/20 ,  G06F40/12 ,  H04L67/06

    CPC classification number: G06F21/6218 ,  G06F16/93 ,  G06F40/12 ,  G06F40/20 ,  H04L67/06

    Abstract: The present innovative solution solves the problem of managing secure documents so that they can be verified, and protected from tampering and illegal printing. A legal document is converted to a secure document by embedding into the legal document one or more security codes that have been encrypted with a standard of proprietary cryptographic algorithm. The security codes are supplemented by a QR code associated with the archive location of each page of the secure document, and stored at a server or database. The security codes stored in the document and can be printed together with the document, as a form of watermark, using UV-sensitive ink or toner at a security printer. The security codes are encrypted and can be printed on varying locations in the secure document pages, which are defined in a geolocation template, separately transmitted in encrypted format.

    NATURAL LANGUAGE ANALYSIS OF A COMMAND LINE USING A MACHINE LEARNING MODEL TO GENERATE A NATURAL LANGUAGE DESCRIPTION OF THE COMMAND LINE
    6.
    发明公开
    NATURAL LANGUAGE ANALYSIS OF A COMMAND LINE USING A MACHINE LEARNING MODEL TO GENERATE A NATURAL LANGUAGE DESCRIPTION OF THE COMMAND LINE 审中-公开

    公开(公告)号：US20230316005A1

    公开(公告)日：2023-10-05

    申请号：US17709574

    申请日：2022-03-31

    Applicant: Sophos Limited

    Inventor： Joshua Daniel SAXE

    IPC: G06F40/56 ,  G06F40/12 ,  G06F9/455

    CPC classification number: G06F40/56 ,  G06F9/45512 ,  G06F40/12

    Abstract: In one or more embodiments, a command is repeatedly input a predetermined number of times into a machine learning model to generate a plurality of different natural language (NL) descriptions. The plurality of different NL descriptions are input into the machine learning model to generate a plurality of different check commands. A plurality of similarity metrics are determined by comparing each check command from the plurality of different check commands to the command. A check command from the plurality of different check commands that is most similar to the command is identified based on the plurality of similarity metrics. An NL description from the plurality of different NL descriptions is caused to be displayed, the NL description previously input into the machine learning model to generate the check command.

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