TRANSACTION POLICY AUDIT
    1.
    发明申请

    公开(公告)号:US20250054070A1

    公开(公告)日:2025-02-13

    申请号:US18929850

    申请日:2024-10-29

    Applicant: SAP SE

    Abstract: The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes receiving receipt data associated with an entity. Policy questions associated with the entity are associated with at least one policy question answer that corresponds to a conformance or a violation of a policy selected by the entity. For each policy question, a machine learning policy model is identified for the policy question that includes, for each policy question answer, receipt data features that correspond to the policy question answer. The machine learning policy model is used to automatically determine a selected policy question answer to the policy question by comparing features of extracted tokens to respective receipt data features of the policy question answers that are included in the machine learning policy model. In response to determining that the selected policy question answer corresponds to a policy violation, an audit alert is generated.

    Document clusterization
    3.
    发明授权

    公开(公告)号:US12190622B2

    公开(公告)日:2025-01-07

    申请号:US16951485

    申请日:2020-11-18

    Abstract: A computer-implemented method for document clusterization, comprising: receiving an input document; determining, by evaluating a document similarity function, a plurality of similarity measures, wherein each similarity measure of the plurality of similarity measures reflects a degree of similarity between the input document and a corresponding cluster of documents of a plurality of clusters of documents; based on the plurality of similarity measures, determining that the input document does not belong to any of the clusters of documents of the plurality of clusters of documents; creating a new cluster of documents; and associating the input document with the new cluster of documents.

    ANOMALY DETECTION IN DOCUMENTS WITH VISUAL CUES

    公开(公告)号:US20250005953A1

    公开(公告)日:2025-01-02

    申请号:US18342165

    申请日:2023-06-27

    Abstract: Techniques are disclosed for understanding the visual structure and patterns of documents and detecting anomalies in data of the documents based on the understanding of the visual structure and patterns of the documents. In one aspect, a computer-implemented method is provided that includes accessing a set of documents, converting the set of documents to a set of images in a binary format, generating a common feature template based on the set of images, comparing each image from the set of images to the common feature template to identify images with at least one anomalous feature, and outputting the images with at least one anomalous feature.

    DOCUMENT SEARCH FOR DOCUMENT RETRIEVAL USING 3D MODEL

    公开(公告)号:US20240371191A1

    公开(公告)日:2024-11-07

    申请号:US18777315

    申请日:2024-07-18

    Abstract: Technologies are described for reconstructing physical objects which are preserved or represented in pictorial records. The reconstructed models can be three-dimensional (3D) point clouds and can be compared to existing physical models and/or other reconstructed models based on physical geometry. The 3D point cloud models can be encoded into one or more latent space feature vector representations which can allow both local and global geometric properties of the object to be described. The one or more feature vector representations of the object can be used individually or in combination with other descriptors for retrieval and classification tasks. Neural networks can be used in the encoding of the one or more feature vector representations.

    COUNTERFEIT DETECTION USING IMAGE ANALYSIS
    7.
    发明公开

    公开(公告)号:US20240346843A1

    公开(公告)日:2024-10-17

    申请号:US18301372

    申请日:2023-04-17

    CPC classification number: G06V30/42 G06V10/82 G06V30/418

    Abstract: In some implementations, a device may obtain a first image of a first note, and identify a first identifier associated with the first note and a first set of visual characteristics of the first note indicating an appearance of the first note. The device may obtain a second image of a second note, and identify a second identifier associated with the second note and a second set of visual characteristics of the second note indicating an appearance of the second note. The second identifier may correspond to the first identifier, indicating that the second note is purported to be the first note. The device may determine whether the second note is counterfeit based on the first set of visual characteristics of the first note and the second set of visual characteristics of the second note. The device may perform action(s) based on a determination that the second note is counterfeit.

    IMAGE BASED HUMAN-COMPUTER INTERACTION METHOD AND APPARATUS, DEVICE, AND STORAGE MEDIUM

    公开(公告)号:US20240338962A1

    公开(公告)日:2024-10-10

    申请号:US18747599

    申请日:2024-06-19

    CPC classification number: G06V30/414 G06V30/418

    Abstract: The present disclosure provides an image based human-computer interaction method, which includes: acquiring a to-be-analyzed image, and determining image layout information and image content information of the to-be-analyzed image, where the to-be-analyzed image includes a variety of modal data, the image layout information represents distribution of image elements with preset granularity in the to-be-analyzed image, and the image content information represents a content expressed by the modal data in the to-be-analyzed image; and determining, in response to acquiring question information, response information corresponding to the question information according to the image layout information and the image content information, where the question information represents a question proposed by a user for the to-be-analyzed image, and the response information represents a reply answer corresponding to the question information. By extracting layout information and content information from an image, the accuracy of answering a question and user experience of human-computer interaction are improved.

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