State Farm Mutual Automobile Insurance Company

Hanpei Zhang

Brian Mark Fields

Audrey Schwartz

James P Rodriguez

Michael DiBenedetto

Diana Sherwood

Anna Zarkoob

Dar Beachy

Hannah Estes

Ben Tucker

Kristin Sellers

Elijah Abella

Emily Bryant

Systems and methods relating to improving the experience of gig-economy workers are disclosed, with particular reference to gig-economy work involving vehicle use. During or after performance of gig-economy work, data automatically collected may be used to generate and present recommendations or education points to gig-economy workers. Such recommendations may include targeted alerts to notify gig-economy workers of high-demand situations or to coordinate between gig-economy workers and gig-economy customers. Alerts may be generated based upon current excess demand for gig-economy services, as well as gig-economy worker availability and preferences. For example, alerts may be generated in response to detecting emergency conditions in an area in order to mobilize a large number of gig-economy transportation service providers.

Systems and methods of gig-economy fleet mobilization

Systems and methods relating to improving the experience of gig-economy workers are disclosed, with particular reference to gig-economy work involving vehicle use. Such systems and methods include automatically monitoring and evaluating activities such as driving during performance of gigs, as well as providing recommendations based thereupon. Transferable tokens may be used to apply information across various types of gig activities. Such a transferable token may represent one or more risk levels associated with a gig-economy worker to provide reputational information between gig-economy platforms or between types of gig-economy activities. Thus, the transferable token may enable a gig-economy worker to transfer high customer ratings across gig-economy platforms.

Systems and methods of using a transferrable token for gig-economy activity assessment

Systems and methods relating to improving the experience of gig-economy workers are disclosed, with particular reference to gig-economy work involving vehicle use. Such systems and methods include automatically monitoring and evaluating activities such as driving during performance of gigs, as well as providing recommendations based thereupon. Gig-related activities may be automatically detected based upon data provided by a mobile device associated with the gig-economy worker, thereby automatically generating a record of such activities for the worker. Telematics data indicative of movement of a vehicle may be collected and analyzed to detect gig-economy activities. During or after performance of gig-economy work, data automatically collected may be used to generate and present recommendations or education points to the gig-economy worker.

Systems and methods for automatic detection of gig-economy activity

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Mesa, AZ, United States of America

Hanpei Zhang