Patent Issued for Systems and methods of using a transferrable token for gig-economy activity assessment (USPTO 11783257): State Farm Mutual Automobile Insurance Company

2023 OCT 31 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Alexandria, Virginia, NewsRx journalists report that a patent by the inventors Abella, Elijah (Chandler, AZ, US), Beachy, Dar (Hopedale, IL, US), Bryant, Emily (Mesa, AZ, US), DiBenedetto, Michael (Mesa, AZ, US), Estes, Hannah (Queen Creek, AZ, US), Fields, Brian M. (Normal, IL, US), Rodriguez, James P. (Avondale, AZ, US), Schwartz, Audrey (Mesa, AZ, US), Sellers, Kristin (Gilbert, AZ, US), Sherwood, Diana (Scottsdale, AZ, US), Tucker, Ben (Bloomington, IL, US), Zarkoob, Anna (Tempe, AZ, US), Zhang, Hanpei (Mesa, AZ, US), filed on February 8, 2021, was published online on October 10, 2023.

The patent’s assignee for patent number 11783257 is State Farm Mutual Automobile Insurance Company (Bloomington, Illinois, United States).

News editors obtained the following quote from the background information supplied by the inventors: “Gig-economy work has grown significantly in recent years due to the coordination power of mobile computing networks. Millions of gig-economy workers provide a broad array of gig-economy services, such as on-demand transportation services (e.g., ridesharing or transportation network company (“TNC”) services), distributed goods delivery services, project-based home and office assistance services, and other services on an ad hoc or transactional basis.

“The rapid increase in both supply and demand for such services has drawn in many new service providers, but information resources are lacking. The differences between traditional work and gig-economy work have left gaps in areas such as risk assessment and gig optimization. This results in an increased record-keeping burden on individual gig-economy workers to attempt to track their own activities. Additionally, by the distributed nature of gig-economy work, the supply of gig-economy is the result of many unrelated individual decisions, making it difficult for individual gig-economy workers to determine whether it is worthwhile to offer their services at any given time.

“Certain costs with significant impacts on gig-economy work profitability are also unobservable by even the most sophisticated gig-economy workers. For example, on-demand transportation services are typically in high demand at times and places where risk levels of vehicle accidents are elevated (e.g., during inclement weather, in crowded business districts, and late at night on weekends). However, risk levels associated with on-demand transportation gigs may not be directly observable. Thus, gig-economy workers are left without much-needed information relating to costs of providing gig services relative to the revenue that may be obtained from offering such services. Thus, inefficient use of gig-economy worker effort results from a lack of relevant data. Conventional techniques may have other drawbacks as well.”

As a supplement to the background information on this patent, NewsRx correspondents also obtained the inventors’ summary information for this patent: “The present embodiments relate to, inter alia, detecting, monitoring, and optimizing gig-economy work based upon data associated with a plurality of gig-economy workers and relating to a plurality of gigs performed by such gig-economy workers. Additional, fewer, or alternative features described herein below may be included in some aspects.

“In one aspect, a computer-implemented method for monitoring and evaluating gig-economy work (e.g., commercial driving activity) may be provided. The method may include, via one or more processors, servers, transceivers, and/or sensors, (i) receiving (and/or generating) availability data corresponding to a gig-economy worker; (ii) responsive to receiving the availability data, collecting a set of data indicative of one or more gig-related behaviors (e.g., driving behaviors) of the gig-economy worker; (iii) determining a risk score for each gig-related behavior indicated in the set of data; and/or (iv) determining a gig-economy worker profile (e.g., a commercial driving profile) corresponding to the gig-economy worker by evaluating each risk score. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

“In another aspect, a computer-implemented method for detecting gig-economy work (e.g., commercial driving activity) may be provided. The method may include, via one or more processors, servers, transceivers, and/or sensors, (i) receiving (and/or generating) movement data representing movement (e.g., movement of a vehicle) associated with a gig-economy worker; (ii) responsive to receiving the movement data, determining likelihoods that portions of the movement data are attributable to gig-economy work (e.g., commercial driving activities) based upon the movement data; and/or (iii) determining an aspect of an insurance policy for the gig-economy work or the gig-economy worker (e.g., insurance for a vehicle) based upon the likelihoods. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

“In yet another aspect, a computer-implemented method for optimizing gig-economy work (e.g., commercial driving activity) may be provided. The method may include, via one or more processors, servers, transceivers, and/or sensors, (i) receiving, at one or more processors, one or more gig optimization criteria indicating one or more outcome gig metrics to optimize; (ii) obtaining condition data indicating a plurality of conditional values for gig metrics; (iii) selecting one or more gig-economy data models associated with the one or more outcome gig metrics; (iv) generating one or more gig optimization recommendations associated with the one or more gig optimization criteria by applying at least some of the condition data to the one or more gig-economy data models; and/or (v) causing at least one of the one or more gig optimization recommendations to be presented to a gig-economy worker by a display of a mobile computing device associated with the gig-economy worker. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

“In a still a further aspect, a computer-implemented method for generating a transferable token for a gig-economy worker may be provided. Generating such a transferable token may include: (1) receiving data representing at least one of an activity, a behavior or a work of the gig-economy worker; (2) responsive to receipt of the data, determining at least one of a risk level or a risk profile for the gig-economy worker based upon the data; (3) forming a transferable token that includes the at least one of the risk level or the risk profile; and/or (4) when requested by a third party, providing the transferable token to the third party. The transferable token may be used by the third party to offer a new or updated policy, service, agreement, or account. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.”

The claims supplied by the inventors are:

“1. A computer-implemented method for generating a transferable token for a gig-economy worker, the method comprising: training, by one or more processors, a machine-learning model based upon a first set of data associated with a first geographic area and representing activities, behaviors, or work of the gig-economy worker; determining, by the one or more processors, occurrence of a subsequent event associated with the machine-learning model or the first set of data, wherein the subsequent event comprises determining a second set of data representing the activities, behaviors, or work of the gig-economy worker contains sufficient data for training the machine-learning model as a model specific to a second geographic area, which second geographic area is contained within the first geographic area; retraining, by the one or more processors, the machine-learning model using the second set of data; collecting, by the one or more processors, telematics data indicating a plurality of driving behaviors of the gig-economy worker during performance of one or more gigs, wherein the plurality of driving behaviors comprise control actions performed by the gig-economy worker to control operation of one or more vehicles during vehicle trips; receiving, at the one or more processors, data representing at least one of an activity, behavior or work of the gig-economy worker, the data including the telematics data; responsive to receipt of the data, determining, by the one or more processors, at least one of a risk level or a risk profile for the gig-economy worker based upon the data by applying the machine-learning model to the data, wherein determining the at least one of the risk level or the risk profile includes determining a risk score for each of the plurality of driving behaviors indicated by the telematics data; forming, by the one or more processors, the transferable token that includes a plurality of data entries, including the at least one of the risk level or the risk profile for the gig-economy worker and a baseline risk level indicating average risk for gig-economy workers performing similar gig-economy work as the gig-economy worker; receiving, by the one or more processors, a request for the transferable token from a third party token consumer via an application programming interface (API); and providing, by the one or more processors via a network interface, the transferable token to the third party token consumer in response to the request.

“2. The computer-implemented method of claim 1, further comprising determining aspects of the transferable token to indicate information related to least one of a second risk level or a second risk profile for use in offering a new or updated policy, service, agreement, or account.

“3. The computer-implemented method of claim 1, wherein forming the transferable token includes determining the at least one of the risk level or the risk profile to indicate a general risk level associated with the gig-economy worker based upon performance of a first type of gig-economy work by the gig-economy worker and indicative of risk relating to a second type of gig-economy work by the gig-economy worker.

“4. The computer-implemented method of claim 1, wherein the data includes an indication of at least one of the following with respect to performance of gig-economy work by the gig-economy worker: a quality of work performed, a rating associated with the gig-economy worker, or a reputation of the gig-economy worker.

“5. The computer-implemented method of claim 1, further comprising: receiving, by the one or more processors, additional data indicating aspects of performance of one or more gigs by the gig-economy worker; and updating, by the one or more processors, the transferable token by adjusting the at least one of the risk level or the risk profile based upon the additional data.

“6. The computer-implemented method of claim 1, wherein the at least one of the risk level or the risk profile is further based upon at least one of an activity, a behavior or a work of another gig-economy worker.

“7. The computer-implemented method of claim 1, wherein the transferable token is a first transferable token, the gig-economy worker is a first gig-economy worker, and further comprising: receiving a second transferable token including at least one of a risk level or a risk profile for a second gig-economy worker based upon data the representing at least one of an activity, behavior or work of the second gig-economy worker; and offering a new or updated at least one of a policy, a service, an agreement, or an account to the second gig-economy worker based upon the second transferable token.

“8. A system for generating a transferable token for a gig-economy worker, the system comprising: a data collector implemented as a module by one or more processors configured to (i) collect telematics data indicating a plurality of driving behaviors of the gig-economy worker during performance of one or more gigs, wherein the plurality of driving behaviors comprise control actions performed by the gig-economy worker to control operation of one or more vehicles during vehicle trips, and (ii) receive data representing at least one of an activity, behavior or work of the gig-economy worker; a machine-learning module implemented by one or more processors configured to: (i) train a machine-learning model based upon a first set of data associated with a first geographic area and representing activities, behaviors, or work of the gig-economy worker, (ii) determine occurrence of a subsequent event associated with the machine-learning model or the first set of data, wherein the subsequent event comprises determining a second set of data representing the activities, behaviors, or work of the gig-economy worker contains sufficient data for training the machine-learning model as a model specific to a second geographic area, which second geographic area is contained within the first geographic area, (iii) retrain the machine-learning model using the second set of data, and (iv) determine at least one of a risk level or a risk profile for the gig-economy worker based upon the telematics data and the data by applying the machine-learning model to the data, wherein determining the at least one of the risk level or the risk profile includes determining a risk score for each of the plurality of driving behaviors indicated by the telematics data; a token generator implemented as a module by one or more processors configured to form the transferable token that includes a plurality of data entries, including the at least one of the risk level or the risk profile for the gig-economy worker and a baseline risk level indicating average risk for gig-economy workers performing similar gig-economy work as the gig-economy worker; an application programming interface (API) implemented by one or more processors and configured to receive a request for the transferable token from a third party token consumer; and a network interface component configured to provide the transferable token to the third party token consumer via an electronic communication network in response to the request.

“9. The system of claim 8, wherein the token generator is configured to determine aspects of the transferable token to indicate information representing at least one of a second risk level or a second risk profile for use in offering a new or updated policy, service, agreement, or account.

“10. The system of claim 8, wherein the token generator is configured to determine the at least one of the risk level or the risk profile to indicate a general risk level associated with the gig-economy worker based upon performance of a first type of gig-economy work by the gig-economy worker and indicative of risk relating to a second type of gig-economy work by the gig-economy worker.

“11. The system of claim 8, wherein the data includes an indication of at least one of the following with respect to performance of gig-economy work by the gig-economy worker: a quality of work performed, a rating associated with the gig-economy worker, or a reputation of the gig-economy worker.

“12. The system of claim 8, wherein: the data collector is configured to receive additional data indicating aspects of performance of one or more gigs by the gig-economy worker; and the token generator is configured to update the transferable token by adjusting the at least one of the risk level or the risk profile based upon the additional data.

“13. The system of claim 8, wherein the at least one of the risk level or the risk profile is further based upon at least one of an activity, a behavior or a work of another gig-economy worker.”

There are additional claims. Please visit full patent to read further.

For additional information on this patent, see: Abella, Elijah. Systems and methods of using a transferrable token for gig-economy activity assessment. U.S. Patent Number 11783257, filed February 8, 2021, and published online on October 10, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11783257)&db=USPAT&type=ids

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