Determining threats from anomalous events based on artificial intelligence models

    公开(公告)号:US12238119B1

    公开(公告)日:2025-02-25

    申请号:US17544538

    申请日:2021-12-07

    Abstract: Threats can be determined from anomalous events based on artificial intelligence (AI) models. For example, a computer system stores, based on an output of a first AI model, first information indicating that a first event cluster of the first event clusters is associated with a threat classification and that a second event cluster of the first event clusters is associated with a non-threat classification. The computer system receives a first dataset representing first events and generate a first input to the first AI model based on the first dataset. The computer system determines, based on the first input to the first AI model, second event clusters and that a third event cluster of the second event clusters has no correspondence in the first event clusters and is associated with an unknown classification. The computer system generates second information indicating that the third event cluster is associated with the unknown classification.

    Associating an event with an agent

    公开(公告)号:US12236715B1

    公开(公告)日:2025-02-25

    申请号:US18302723

    申请日:2023-04-18

    Inventor: Gang Hua

    Abstract: Described are systems and methods for determining an agent that performed an event within a materials handling facility. A series of overhead images that include representations of the event location and one or more agents are processed to determine a motion or movement of the agent over a period of time. For example, a motion model representative of a motion of the agent over a period of time is generated from the images. A distance between the motion model and the event location is also determined. An association between the agent and the event may be determined based on the motion model and the distance between the motion model and the event location.

    Enhanced dynamic last-mile modeling for delivery assistants

    公开(公告)号:US12236374B1

    公开(公告)日:2025-02-25

    申请号:US18110265

    申请日:2023-02-15

    Abstract: A method of optimizing a delivery route for a delivery vehicle driver and a delivery assistant includes: identifying a delivery route for a delivery vehicle; determining, for each of the packages to be delivered using the delivery route, a first delivery time for a delivery vehicle driver of the delivery vehicle to deliver the respective package without the delivery assistant, a second delivery time for a delivery assistant of the delivery vehicle to deliver the respective package without the delivery vehicle driver, and a third delivery time for both the delivery assistant and the delivery vehicle driver to deliver the respective package; determining minimum times needed by the delivery vehicle driver and the delivery assistant to deliver the packages using the delivery route; and determining that a minimum of the estimated total delivery times corresponds to a first subset and a second subset of the packages.

    Configurable address decoder
    6.
    发明授权

    公开(公告)号:US12236260B1

    公开(公告)日:2025-02-25

    申请号:US17643572

    申请日:2021-12-09

    Abstract: An address decoder for a system is disclosed that can be used for different source nodes in the system. Each address decoder can be configured to perform a plurality of decode methods that can be customized for each source node. A first decode method can be used to determine a target node from a plurality of target nodes based on a destination address of the transaction. A second decode method can be used to assign a dedicated target node as the target node irrespective of the destination address of the transaction. The second decode method can be used to route the transaction to the dedicated target node for testing and verification operations.

    Autonomous navigation correction system

    公开(公告)号:US12235651B1

    公开(公告)日:2025-02-25

    申请号:US17215594

    申请日:2021-03-29

    Abstract: Systems, methods, and computer-readable media are disclosed for determining obstacle data based on sensor data such as greyscale data and depth data. Based on the obstacle data navigation data may be determined and used by an autonomous vehicle to navigate an environment such as a warehouse or storage facility. The obstacle data may be determined by determining three-dimensional representations of the greyscale data and the depth data and segmented and combining or fusing the three-dimensional representations of the greyscale data and the depth data. The system used to determine the obstacle data may be trained to avoid false obstructions and omitted obstructions.

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