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    PREDICTION ENGINE FOR A NETWORK-BASED SERVICE
    3.
    发明申请
    PREDICTION ENGINE FOR A NETWORK-BASED SERVICE 有权

    公开(公告)号：US20220030086A1

    公开(公告)日：2022-01-27

    申请号：US17391828

    申请日：2021-08-02

    申请人： Uber Technologies, Inc.

    发明人： Shicong Meng ,  Noah Harold Shaw ,  Joshua K. Hellerstein ,  Juan Pemberthy ,  Zhi Li ,  Jacob Edison

    IPC分类号： H04L29/08 ,  H04L12/24

    摘要： Service providers can be identified to fulfill service requests of a network-based service. A network system is configured to generate, based on historical data associated with the network-based service, a machine-learned service provider optimization (MLSPO) model for generating service provider optimizations. The optimizations can include action recommendations that optimize one or more service metrics. The MLSPO model can be a reinforcement learning model generated by performing a plurality of simulations utilizing one or more virtual agents. A provider device of a service provider can transmit a set of data to the network system that indicates a current location of the service provider. Based on the current location and the MLSPO model, the network system can generate service provider optimizations. Optimization data can be transmitted to the provider device so that the provider device can display information corresponding to the service provider optimizations.

    Prediction engine for a network-based service
    4.
    发明授权
    Prediction engine for a network-based service 有权

    公开(公告)号：US11082529B2

    公开(公告)日：2021-08-03

    申请号：US16654365

    申请日：2019-10-16

    申请人： Uber Technologies, Inc.

    发明人： Shicong Meng ,  Noah Harold Shaw ,  Joshua K. Hellerstein ,  Juan Pemberthy ,  Zhi Li ,  Jacob Edison

    IPC分类号： G06F15/173 ,  H04L29/08 ,  H04L12/24

    摘要： Service providers can be identified to fulfill service requests of a network-based service. A network system is configured to generate, based on historical data associated with the network-based service, a machine-learned service provider optimization (MLSPO) model for generating service provider optimizations. The optimizations can include action recommendations that optimize one or more service metrics. The MLSPO model can be a reinforcement learning model generated by performing a plurality of simulations utilizing one or more virtual agents. A provider device of a service provider can transmit a set of data to the network system that indicates a current location of the service provider. Based on the current location and the MLSPO model, the network system can generate service provider optimizations. Optimization data can be transmitted to the provider device so that the provider device can display information corresponding to the service provider optimizations.