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    Peer-based, angular distance detection of anomalous rights
    2.
    发明授权
    Peer-based, angular distance detection of anomalous rights 有权

    公开(公告)号：US12124559B1

    公开(公告)日：2024-10-22

    申请号：US17357306

    申请日：2021-06-24

    申请人： Amazon Technologies, Inc.

    发明人： Brendan Cruz Colon ,  Matthew Michael Sommer ,  Christopher Miller

    IPC分类号： G06F21/45 ,  G06F18/21 ,  G06F18/2413 ,  G06F21/31

    CPC分类号： G06F21/45 ,  G06F18/2193 ,  G06F18/24147 ,  G06F21/31 ,  G06F2221/2141

    摘要： Devices and techniques are generally described for peer-based anomalous rights detection. In various examples, a rights vector may be determined for a first individual, the rights vector representing rights held by the first individual. A nearest neighbor algorithm may be used to determine a set of individuals having similar rights to the first individual. In various examples, a category label associated with the first individual may be determined. In some examples, a number of individuals of the set of individuals having the category label may be determined. In some examples, a determination may be made that the rights held by the first individual are anomalous based at least in part on the number. In some cases, alert data indicating that the rights held by the first individual are anomalous may be generated.

    LEARNING SYSTEM OF MACHINE LEARNING MODEL FOR PREDICTION OF PEDESTRIAN TRAFFIC
    8.
    发明公开
    LEARNING SYSTEM OF MACHINE LEARNING MODEL FOR PREDICTION OF PEDESTRIAN TRAFFIC 审中-公开

    公开(公告)号：US20240338610A1

    公开(公告)日：2024-10-10

    申请号：US18749824

    申请日：2024-06-21

    申请人： NEC Corporation

    发明人： Mischa SCHMIDT ,  Julia Gastinger

    IPC分类号： G06N20/00 ,  G06F9/38 ,  G06F18/20 ,  G06F18/21

    CPC分类号： G06N20/00 ,  G06F9/3836 ,  G06F18/217 ,  G06F18/285

    摘要： A method for automated machine learning includes controlling execution of a plurality of instantiations of different automated machine learning frameworks on a machine learning task each as a separate arm in consideration of available computational resources and time budget. During the execution by the separate arms, a plurality of machine learning models are trained and performance scores of the plurality of trained machine learning models are computed such that one or more of the plurality of trained machine learning models are selectable for the machine learning task based on the performance scores. This invention can be used for predicting patient discharge, predictive control in buildings for energy optimization, and so on.

    Machine learning system and method for determining or inferring user action and intent based on screen image analysis
    9.
    发明授权
    Machine learning system and method for determining or inferring user action and intent based on screen image analysis 有权

    公开(公告)号：US12112526B2

    公开(公告)日：2024-10-08

    申请号：US18326756

    申请日：2023-05-31

    申请人： M37 Inc.

    发明人： Ali Jelveh

    IPC分类号： G06V10/778 ,  G06F18/21 ,  G06F18/40 ,  G06N3/084 ,  G06N20/00 ,  G06N3/006 ,  G06N7/01 ,  G06T13/40

    CPC分类号： G06V10/7784 ,  G06F18/2178 ,  G06F18/41 ,  G06N3/084 ,  G06N20/00 ,  G06N3/006 ,  G06N7/01 ,  G06T13/40 ,  G06V2201/02

    摘要： System(s) and method(s) that analyze image data associated with a computing screen operated by a user, and learns the image data (e.g., using pattern recognition, historical information analysis, user implicit and explicit training data, optical character recognition (OCR), video information, 360°/panoramic recordings, and so on) to concurrently glean information regarding multiple states of user interaction (e.g., analyzing data associated with multiple applications open on a desktop, mobile phone or tablet). A machine learning model is trained on analysis of graphical image data associated with screen display to determine or infer user intent. An input component receives image data regarding a screen display associated with user interaction with a computing device. An analysis component employs the model to determine or infer user intent based on the image data analysis; and an action component provisions services to the user as a function of the determined or inferred user intent. In an implementation, a gaming component gamifies interaction with the user in connection with explicitly training the model.