Detecting temporal anomalous data using dependency modeling

    公开(公告)号:US12248457B1

    公开(公告)日:2025-03-11

    申请号:US18454115

    申请日:2023-08-23

    Abstract: An embodiment for detecting anomalous data using dependency modeling. The embodiment may, within a target data environment, identify references between data contained in one or more data files. The embodiment may determine dependency relationships between data fields in the data contained in the one or more data files. The embodiment may construct computational graphs depicting the determined dependency relationships as series of related data fields. The embodiment may identify a series of associated computational graphs within the constructed computational graphs. The embodiment may calculate abnormality degree values for each of the data fields within the constructed computation graphs. The embodiment may, in response to detecting an anomalous data field having a calculated abnormality degree value above a threshold value, calculating contribution values for a series of associated component data fields to identify a root cause for the detected anomalous data field.

    DETECTING TEMPORAL ANOMALOUS DATA USING DEPENDENCY MODELING

    公开(公告)号:US20250068619A1

    公开(公告)日:2025-02-27

    申请号:US18454115

    申请日:2023-08-23

    Abstract: An embodiment for detecting anomalous data using dependency modeling. The embodiment may, within a target data environment, identify references between data contained in one or more data files. The embodiment may determine dependency relationships between data fields in the data contained in the one or more data files. The embodiment may construct computational graphs depicting the determined dependency relationships as series of related data fields. The embodiment may identify a series of associated computational graphs within the constructed computational graphs. The embodiment may calculate abnormality degree values for each of the data fields within the constructed computation graphs. The embodiment may, in response to detecting an anomalous data field having a calculated abnormality degree value above a threshold value, calculating contribution values for a series of associated component data fields to identify a root cause for the detected anomalous data field.

    INTERNET-OF-THINGS-BASED ALLERGEN POLLEN CONCENTRATION PREDICTION

    公开(公告)号:US20250089638A1

    公开(公告)日:2025-03-20

    申请号:US18469665

    申请日:2023-09-19

    Abstract: According to one embodiment, a method, computer system, and computer program product for pollen prediction is provided. The present invention may include identifying, by a classification model, plant species within a plurality of locatable images taken at a plurality of locations; creating growth cycle prediction models for the plant species; modelling pollen count mappings for the plant species; predicting pollen yields at the locations for the plant species based on the growth cycle prediction models and the pollen count mappings; and calculating a pollen distribution at the locations based on the predicted pollen yields and aerodynamic models.

    VIDEO PRESENTATION SYSTEM
    5.
    发明公开

    公开(公告)号:US20240323318A1

    公开(公告)日:2024-09-26

    申请号:US18188845

    申请日:2023-03-23

    CPC classification number: H04N7/152 G06V20/49 G10L15/1815

    Abstract: A computer hardware system includes a video analyzer configured to assist in a presentation of at least a portion of a video to a plurality of participants by a presenter on a presenter client and a hardware processor configured to perform the following executable operations. The video is analyzed to generate a plurality of segments. Presenter data is captured during the presentation, and presenter intent is determined based upon the presenter data. Based upon the presenter intent, forward-looking controls are presented only to the presenter client. One of the plurality of segments is identified using at least one of the presenter intent and the forward-looking controls. The identified one segment is presented to the plurality of participants.

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