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    DOCUMENT SIGNING AND STORAGE USING DATA MODELS AND DISTRIBUTED LEDGERS
    1.
    发明公开
    DOCUMENT SIGNING AND STORAGE USING DATA MODELS AND DISTRIBUTED LEDGERS 审中-公开

    公开(公告)号：US20240160791A1

    公开(公告)日：2024-05-16

    申请号：US18055673

    申请日：2022-11-15

    Applicant: ADOBE INC.

    Inventor： Songlin HE ,  Tong SUN ,  Rajiv JAIN ,  Nedim LIPKA ,  Curtis WIGINGTON ,  Anindo ROY

    IPC: G06F21/64

    CPC classification number: G06F21/64

    Abstract: A method includes populating a template database with templates associated with template identifiers (IDs) identifying the templates. The method also includes generating a data model that references a template within the template database, where the data model includes a template ID referencing the template in the template database, and where the template includes a parameter field. The data model further includes a template parameter to apply to the parameter field and a digital signature for at least the template ID and the template parameter. The method also includes deploying the data model within a distributed ledger.

    FACT CORRECTION OF NATURAL LANGUAGE SENTENCES USING DATA TABLES
    2.
    发明公开
    FACT CORRECTION OF NATURAL LANGUAGE SENTENCES USING DATA TABLES 审中-公开

    公开(公告)号：US20230334244A1

    公开(公告)日：2023-10-19

    申请号：US17724349

    申请日：2022-04-19

    Applicant: Adobe Inc.

    Inventor： Christopher TENSMEYER ,  Danilo Neves Ribeiro ,  Varun MANJUNATHA ,  Nedim LIPKA ,  Ani NENKOVA

    IPC: G06F40/284 ,  G06N20/20 ,  G06F16/2453 ,  G06F40/226

    CPC classification number: G06F40/284 ,  G06N20/20 ,  G06F16/24535 ,  G06F40/226

    Abstract: Embodiments are disclosed for performing fact correction of natural language sentences using data tables. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input sentence, tokenizing elements of the input sentence, and identifying, by a first machine learning model, a data table associated with the input sentence. The systems and methods further comprise a second machine learning model identifying a tokenized element of the input sentence that renders the input sentence false based on the data table and masking the tokenized element of the tokenized input sentence that renders the input sentence false. The systems and method further includes a third machine learning model predicting a new value for the masked tokenized element based on the input sentence with the masked tokenized element and the identified data table and providing an output including a modified input sentence with the new value.

    ASSOCIATING USER LOGS USING GEO-POINT DENSITY
    3.
    发明申请
    ASSOCIATING USER LOGS USING GEO-POINT DENSITY 审中-公开

    公开(公告)号：US20190155863A1

    公开(公告)日：2019-05-23

    申请号：US16254125

    申请日：2019-01-22

    Applicant: ADOBE INC.

    Inventor： Sungchul KIM ,  Nedim LIPKA ,  Eunyee KOH

    IPC: G06F16/9535 ,  G06F16/335

    Abstract: A method for clustering geolocations using geo-point density includes receiving a user log of geolocation data extracted from user interactions with at least one electronic device. A density is determined relative to other geo-points for each geo-point in a set of geo-points extracted from the user log. Lower density geo-points in the set are merged into higher density geo-points in the set to result in a merged set of geo-points, and clusters of geo-points are identified from the merged set. Merging the geo-points tends to preserve frequently occurring geo-points while reducing those that constitute noise, which improves the reliability of identifying the clusters. Core geo-points of the user log are selected from the clusters. The core geo-points of the user log can be compared to core geo-points of other use logs to identify associations between the user logs.

    EXECUTING AN ACTION USING EXTRACTED INFORMATION FROM A DOCUMENT
    5.
    发明申请
    EXECUTING AN ACTION USING EXTRACTED INFORMATION FROM A DOCUMENT 有权

    公开(公告)号：US20250005691A1

    公开(公告)日：2025-01-02

    申请号：US18344203

    申请日：2023-06-29

    Applicant: Adobe Inc.

    Inventor： Nedim LIPKA ,  Ryan ROSSI ,  Jianna Audrey Reyes SO ,  Franck DERNONCOURT ,  Alexa SIU

    IPC: G06Q50/18

    Abstract: A method includes extracting an action from a document using a machine learning model. The action is associated with an action parameter. The method further includes extracting a plurality of action events corresponding to the action from the document using the machine learning model. The method further includes generating a record associated with the document based on the extracted action. The method further includes populating the record with the action parameter. The method further includes executing an action event in the plurality of action events using the record.

    MACHINE LEARNING BASED MULTIPAGE SCANNING
    6.
    发明公开
    MACHINE LEARNING BASED MULTIPAGE SCANNING 审中-公开

    公开(公告)号：US20230377363A1

    公开(公告)日：2023-11-23

    申请号：US17663785

    申请日：2022-05-17

    Applicant: ADOBE INC.

    Inventor： Tong SUN ,  Nicholas Sergei REWKOWSKI ,  Nedim LIPKA ,  Jennifer Anne HEALEY ,  Curtis Michael WIGINGTON ,  Anshul MALIK

    IPC: G06V30/41 ,  G06V40/10 ,  G06T7/13

    CPC classification number: G06V30/41 ,  G06V40/107 ,  G06T7/13 ,  G06T2207/30176

    Abstract: Systems and methods for machine learning based multipage scanning are provided. In one embodiment, one or more processing devices perform operations that include receiving a video stream that includes image frames that capture a plurality of pages of a document. The operations further include detection, via a machine learning model that is trained to infer events from the video stream detects, a new page event. Detection of the new page event indicates that a page of the plurality of pages available for scanning has changed from a first page to a second page. Based on the detection of the new page event, the one or more processing devices capture an image frame of the page from the video stream. In some embodiments, the machine learning model detects events based on a weighted use of video data, inertial data, audio samples, image depth information, image statistics and/or other information.

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