Uber Technologies, Inc.

Zhi Li

Shicong Meng

Rei Chiang

Ashley Quitoriano

Parijat Mazumdar

Kevin Spieser

Jacob Edison

Noah Harold Shaw

Joshua K Hellerstein

Juan Pemberthy

A network system can receive location data from a provider device of a service provider. Using at least the location data in an optimization model, the network system can determine one or more actions for the service provider to optimize one or more metrics. The one or more metrics correspond to at least one of (i) an expected wait time for the service provider over a future period of time, (ii) an expected travel distance between providing services over a future period of time, or (iii) an expected amount of earnings for the service provider over a future period of time. The network system may then transmit a dataset to the provider device to display information corresponding to the one or more actions for the service provider.

Prediction engine for a network-based service

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.

A network computer system receives request data from computing devices of requesting users in a sub-region of a service area. The system further receives location data from computing devices of drivers operating in the sub-region. Based on the request data and the location data, the system determines a service condition for the sub-region. Based on the service condition indicating that the sub-region is in a driver oversupply state, the system transmits a service instruction to computing devices of a plurality of drivers within the sub-region, the service instruction being associated with a target outside the sub-region and a set of progress conditions. The system then periodically determines, for each driver of the plurality of drivers, an estimated time of arrival (ETA) to the target from a current position of the driver to determine whether the driver is satisfying the set of progress conditions of the service instruction.

Real-time service provider progress monitoring

A network computer system provides a service instruction to a computing device. The service instruction can include offers, such as a service request to pick up and transport a user, and recommendations, such as a movement recommendation encouraging the service provider to relocate to another geographic area. The network computer system remotely monitors the computing device to receive a current position of the computing device as the service provider travels within a geographic area. The network computer system remotely monitors the computing device to receive a service state of the service provider. The network computer system periodically determines whether the service provider is making progress towards a target of the service instruction based on the current position of the computing device and a set of progress conditions, including determining whether the service provider satisfied the set of progress conditions in response to a change in the service state.

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Emeryville, CA, United States of America

Zhi Li