Uber Technologies, Inc.

Vmware, Inc.

Apteligent, Inc.

Kevin Su

Jon David Petersen

Pan Pan

Ronak Trivedi

Paul Lappas

Andrew Levy

Andrew Yousef

Sean Hermany

Robert Kwok

Keith Dreibelbis

A system can receive location data from a computing device of a requesting user, where the location data indicates a current position of the requesting user. The system can determine a rendezvous location for the requesting user prior to the requesting user transmitting a service request to the network computer system. The system may then transmit data corresponding to the rendezvous location to the computing device of the requesting user.

Predictive location selection system

A system can receive, over one or more networks, location data from a computing device of a requesting user, where the location data indicates a current position of the requesting user. The system can determine a rendezvous location for the requesting user prior to the requesting user transmitting a service request to the network computer system. The system may then transmit, over the one or more networks, data corresponding to the rendezvous location to the computing device of the requesting user.

A system can receive, over one or more networks, location data from a computing device of a requesting user, where the location data indicates a current position of the requesting user. The system can repeatedly determine, based at least in part on location data corresponding to a directional heading of a proximate transport provider in relation to the current position of the requesting user, an optimal rendezvous location for the requesting user prior to the requesting user transmitting a service request to the network computer system. The system may then transmit, over the one or more networks, data corresponding to the optimal rendezvous location to the computing device of the requesting user.

Predictive location selection optimization system

A method and system for predictive location selection are described. A network computer system can preselect a service area, prior to receiving a service request from a user, based on the position of the user, a destination, and proximate available service providers, among other factors. In response to the user inputting the destination, the network computer system determines probability scores for predefined service areas based on likelihoods of the proximate available service providers being available at a time when the user submits a service request. The network computer system uses the probability scores to select an appropriate service area and transmits data corresponding to the optimal service area to the user.

Predictive location selection transportation optimization system

Some embodiments provide a system that allows application developers to track and monitor crashes that are occurring with their applications on various mobile devices. In some embodiments, crash logs are received from the mobile devices. Hash codes are generated based on the crash logs. A report to summarize the crash logs is generated based on the hash codes.

System for monitoring and tracking application crashes occurring on different mobile devices

Some embodiments provide a system that allows application developers to track and monitor crashes that are occurring with their applications on various mobile devices. In some embodiments, the system includes an application programing interface (API) server that receives crash reports with raw crash data from the mobile devices and facilitates in assigning each crash report to one of several different crash processing servers. The crash processing server of some embodiments receives the raw crash data and translates obscure data in the raw crash data into a human or developer readable form.

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San Francisco, CA, United States of America

Kevin Su