VEHICLE MOBILITY PATTERNS BASED ON USER LOCATION DATA

    公开(公告)号:WO2023023149A1

    公开(公告)日:2023-02-23

    申请号:PCT/US2022/040591

    申请日:2022-08-17

    Abstract: Methods, computer-readable media, software, and system may generally build and quantify mobility patterns based on user location data, both at an individual level and an aggregate level. The system may determine the origin and destination data for each trip taken by a user. The system may then define areas of mobility using a mobility graph built from the data. The graph may include nodes and edges. In some examples, the nodes are constructed from the origins and destinations of the trajectories using spatial clustering techniques. Further, the edges between nodes may be constructed based on the trips between them, such as two nodes are connected by an edge if there is at least one trip between them. The edges may be given different weights based on trip frequencies. The system may then determine a region of mobility using the generated mobility graph and data clustering techniques.

    DATA PROCESSING SYSTEMS WITH MACHINE LEARNING ENGINES FOR DYNAMICALLY GENERATING RISK INDEX DASHBOARDS

    公开(公告)号:WO2022178296A1

    公开(公告)日:2022-08-25

    申请号:PCT/US2022/017051

    申请日:2022-02-18

    Abstract: Methods, computer-readable media, software, and apparatuses include receiving, from a plurality of risk information sources, risk information associated with a user account, wherein the risk information includes a plurality of risk components, determining, for each of the plurality of risk components, an impact score and a risk probability by applying a machine learning model to risk information associated with the user account, generating an interactive risk index dashboard including a plurality of interactive risk index elements, wherein each of the plurality of interactive risk index elements is associated with a risk component of the plurality of risk components, and displaying, on the display of the apparatus, the interactive risk index dashboard, wherein each of the plurality of interactive risk index elements is displayed in a portion of the interactive risk index dashboard in accordance with a respective determined impact score and risk probability.

    CONTEXT BASED PRIVACY RISK FOOTPRINT AND INCIDENT PROTECTION

    公开(公告)号:WO2022125496A1

    公开(公告)日:2022-06-16

    申请号:PCT/US2021/062123

    申请日:2021-12-07

    Abstract: Methods, computer-readable media, software, systems and apparatuses may receive, from a user device, notification of a user enrolling in a privacy incident protection application, receive, from the user device, user account information associated with one or more user accounts of the user, where the user account information includes a plurality of contextual settings, determine a risk footprint associated with the user based on the user account information, monitor the one or more user accounts, receive an indication of an incident based on monitoring the one or more user accounts and based on the risk footprint, and transmit an incident notification to a data server provider associated with the incident. The incident notification may include instructions to perform a mitigation action associated with the incident.

    VEHICLE COMMUTE LOCATION PATTERNS BASED ON USER LOCATION DATA

    公开(公告)号:WO2023023140A1

    公开(公告)日:2023-02-23

    申请号:PCT/US2022/040577

    申请日:2022-08-17

    Abstract: Methods, computer-readable media, software, and system may generally identify, determine, and understand the significance of commute location data using telematics data. The system and methods may identify significant commute location data and points by analyzing telematics data and capturing GPS locations associated with the mobility of a user. The commute location data may be classified as data points including origin, destination, and waypoints. This commute location data may be used with metadata to identify significant locations associated with the user. The commute location data may also be used with metadata to understand mobility behavior of the user. Lastly, the commute location data may be used with metadata to determine risk associated with the user, such as based on a risk map.

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