Patent Issued for Systems and methods for generating mobility insurance products using ride-sharing telematics data (USPTO 11599947): State Farm Mutual Automobile Insurance Company

2023 MAR 23 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Brannan, Joseph Robert (Bloomington, IL, US), Gross, Ryan Michael (Normal, IL, US), Harvey, Brian N. (Bloomington, IL, US), filed on February 3, 2020, was published online on March 7, 2023, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11599947 is assigned to State Farm Mutual Automobile Insurance Company (Bloomington, Illinois, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “Transportation network companies (“TNC”), such as UBER and LYFT, enable efficient transportation of passengers by providing “ride-sharing” systems that allow independent operators (e.g., drivers) the ability to offer transportation services to riders seeking transportation.

“Ride-sharing systems may use dynamic pricing models based upon passenger demand and the current supply of vehicles and drivers to determine a market rate. A higher price may be used to encourage drivers to offer rides during high demand or “peak” times when passenger demand is high and low driver supply occurs. By allowing drivers the ability to easily offer or discontinue transportation services, the demand for transportation may be more efficiently met by automatically adjusting the total number of vehicles in operation according to the number of passengers seeking transportation. Prices for transportation services using a flexible model may therefore be lower than if a fixed fleet of vehicles and drivers were deployed.

“Independent operators driving for a TNC may be incentivized to offer services to riders through a number of benefits offered by the TNC including flexible scheduling, freedom from substantial oversight, and potential for significant compensation in highly trafficked areas. However, these incentives may not be sufficient to motivate an adequate number of drivers.

“Drivers face a number of other difficulties that may deter offering transportation services. For example, driving during peak times may be extremely stressful when dealing with congested traffic conditions. In addition, offering driving services during peak times may also increase the possibility of accidents. Operating a vehicle in highly trafficked areas may also incur added maintenance costs further deterring drivers from offering such transportation services. Enhancing peace of mind and/or providing additional incentives may be desirable to encourage independent operators to offer these TNC transportation services.”

In addition to the background information obtained for this patent, NewsRx journalists also obtained the inventors’ summary information for this patent: “The present embodiments may relate to systems and methods for enhancing dynamic allocation of transportation services by improving ease of access to personalized insurance protection and rewards-based instruments. The system may include a personalized insurance (“PI”) computing device in communication with one or more transportation network companies (“TNC”), one or more financial service providers, one or more user computing devices, and/or one or more databases.

“In one aspect, a personalized insurance (“PI”) computing device may be provided. The PI computing device may be configured to determine an optimal usage-based insurance (“UBI”) product for a driver operating a vehicle for a transportation network company (“TNC”) during a period of increased demand for transportation services. The PI computing device may have at least one processor (and/or associated transceiver) in communication with at least one memory. The processor and/or associated transceiver may be configured to receive, from the TNC, data indicating the increased demand for transportation services. The processor may further be configured to retrieve driver data for a driver operating a vehicle for the transportation network company, wherein the driver data includes at least the driver history. The processor may be further configured to generate an optimal pricing model for the driver based upon the increased demand and the driver data. The processor may be further configured to execute the model to determine an optimal insurance product, where the optimal insurance product includes characteristics reflecting at least one risk factor associated with the increased demand for transportation services and a risk profile determined from analyzing the driver data. The processor and/or associated transceiver may be further configured to transmit, to a user computing device, an offer, to the driver, to provide transportation services at an increased earnings rate and with the determined optimal insurance product. The PI computing device may be configured to perform additional, less, or alternate functionality, including that discussed elsewhere herein.

“In another aspect, a computer-implemented method for determining an optimal usage-based insurance (“UBI”) product for a driver operating a vehicle for a transportation network company (“TNC”) during a period of increased demand for transportation services using a personal insurance (“PI”) computing device may be provided. The PI computing device may have at least one processor (and/or associated transceiver) in communication with at least one memory. The method may include receiving, via the processor and/or associated transceiver (such as via wireless communication or data transmission over one or more radio frequency links), data from the TNC indicating an increased demand for transportation services. The method may further include retrieving, via the processor, driver data for a driver operating a vehicle for the TNC, where the driver data includes at least the driver history. The method may further include generating, via the processor, an optimal pricing model for the driver based upon the increased demand and the driver data. The method may further include executing, via the processor, the model to determine an optimal insurance product, where the optimal insurance product includes characteristics reflecting at least one risk factor associated with the increased demand for transportation services and a risk profile determined from analyzing the driver data. The method may further include transmitting, via the processor and/or transceiver (such as via wireless communication or data transmission over one or more radio frequency links), to a user computing device, an offer, to the driver, to provide transportation services at an increased earnings rate and with the determined optimal insurance product. The method may include additional, less, or alternate functionality, including that discussed elsewhere herein.

“In another aspect, a non-transitory computer-readable storage media having computer-executable instructions embodied thereon may be provided. When the computer-executable instructions are executed by a personalized insurance (“PI”) computing device having at least one processor (and/or associated transceiver) in communication with at least one memory, the computer-executable instructions may cause the at least one processor and/or associated transceiver to receive, from a transportation network company (“TNC”), data indicating an increased demand for transportation services. The computer-executable instructions may also cause the at least one processor to retrieve driver data for a driver operating a vehicle for the TNC, where the driver data includes at least the driver history. The computer-executable instructions may further cause the at least one processor to generate an optimal pricing model for the driver based upon the increased demand and the driver data. The computer-executable instructions may also cause the at least one processor to execute the model to determine an optimal insurance product where the optimal insurance product includes characteristics reflecting at least one risk factor associated with the increased demand for transportation services and a risk profile determined from analyzing the driver data. The computer-executable instructions may further cause the at least one processor and/or associated transceiver to transmit, to a user computing device, an offer, to the driver, to provide transportation services at an increased earnings rate and with the determined optimal insurance product. The instructions may direct additional, less, or alternate functionality, including that discussed elsewhere herein.

“In yet another aspect, a personalized insurance (“PI”) computing device for facilitating automatic insurance payments through a hybrid savings account (“HSA”) associated with a driver operating a vehicle for a transportation network company (“TNC”) may be provided. The PI computing device may have at least one processor (and/or associated transceiver) in communication with at least one memory. The processor and/or associated transceiver may be configured to receive, from the TNC, funds earned by a driver operating the vehicle for the TNC. The processor and/or associated transceiver may also be configured to transmit the funds to a financial institution to be deposited into the HSA associated with the driver. The processor and/or associated transceiver may be further configured to receive, from a user computing device associated with the driver, a signal indicating (i) initiation of a ride for a passenger, and (ii) a request for insurance coverage for the ride. The processor and/or associated transceiver may also be configured to transfer, from the HSA to an insurance provider, payment for the requested insurance coverage. The PI computing device may be configured to perform additional, less, or alternate functionality, including that discussed elsewhere herein.”

The claims supplied by the inventors are:

“1. A personalized insurance (“PI”) computing device for determining an optimal usage-based insurance (“UBI”) product in real-time for a driver operating a vehicle for a transportation network company (“TNC”) during a period of increased demand for transportation services, the PI computing device having at least one processor in communication with at least one memory, the at least one processor configured to: generate, using one or more machine learning programs, an optimal pricing model for the driver based upon historical supply and demand data associated with a plurality of TNC vehicles and historical driver data associated with the driver; receive from the TNC in real-time supply and demand data associated with the plurality of TNC vehicles, wherein the supply and demand data includes increased demand data for the transportation services, and wherein the increased demand data represents a current increase in demand for the transportation services; retrieve driver data for the driver, wherein the driver data includes at least driver history associated with the driver; update, in real-time, by applying one or more machine learning programs to the increased demand data and the driver data, the optimal pricing model; determine, in real-time, the optimal UBI product by executing the updated optimal pricing model, wherein the optimal UBI product is personalized for the driver and includes current driving characteristics reflecting at least one risk factor associated with the increased demand for the transportation services and a risk profile associated with the driver; and transmit, in-real time, a message to a user computing device associated with the driver, the message including the determined optimal UBI product and an offer to provide the transportation services with the determined optimal UBI product and at an increased payment rate based upon the increased demand data.

“2. The PI computing device of claim 1, wherein the processor is further configured to retrieve event data from a third party, the event data indicating the increased demand for the transportation services and a time period for the increased demand.

“3. The PI computing device of claim 1, wherein the processor is further configured to retrieve weather data from a weather service, and wherein generating the optimal pricing model includes factoring in the weather data.

“4. The PI computing device of claim 3, wherein the weather data includes a time period for at least one predicted weather event, and wherein the optimal UBI product includes an expiration associated with the time period for the at least one predicted weather event.

“5. The PI computing device of claim 1, wherein the PI computing device is in communication with an insurance provider and the optimal UBI product is underwritten by the insurance provider.

“6. The PI computing device of claim 1, wherein the increased payment rate is based upon a payment rate offered by the TNC, wherein the increased payment rate is less than the payment rate offered by the TNC, and wherein a determined difference between the increased payment rate and the payment rate offered by the TNC is transferred to an insurance provider.

“7. The PI computing device of claim 6, wherein the processor is further configured to determine a price for the optimal UBI product, and wherein determining the price is based upon at least the difference between the increased payment rate and the payment rate offered by the TNC.

“8. A computer-implemented method for determining an optimal usage-based insurance (“UBI”) product in real-time for a driver operating a vehicle for a transportation network company (“TNC”) during a period of increased demand for transportation services using a personalized insurance (“PI”) computing device, the PI computing device having at least one processor in communication with at least one memory, the method comprising: generating, using one or more machine learning programs, an optimal pricing model for the driver based upon historical supply and demand data associated with a plurality of TNC vehicles and historical driver data associated with the driver; receiving from the TNC in real-time supply and demand data associated with a plurality of TNC vehicles, wherein the supply and demand data includes increased demand data for the transportation services, and wherein the increased demand data represents a current increase in demand for the transportation services; retrieving driver data for the driver, wherein the driver data includes at least driver history associated with the driver; updating, in real-time, by applying one or more machine learning programs to the increased demand data and the driver data, the optimal pricing model; determining, in real-time, the optimal UBI product by executing the updated optimal pricing model, wherein the optimal UBI product is personalized for the driver and includes current driving characteristics reflecting at least one risk factor associated with the increased demand for the transportation services and a risk profile associated with the driver; and transmitting, in real-time, a message to a user computing device associated with the driver, the message including the determined optimal UBI product and an offer to provide the transportation services with the determined optimal UBI product and at an increased payment rate based upon the increased demand data.

“9. The computer-implemented method of claim 8, the method further comprising retrieving event data from a third party, the event data indicating the increased demand for the transportation services and a time period for the increased demand.

“10. The computer-implemented method of claim 8, the method further comprising retrieving weather data from a weather service, and wherein generating the optimal pricing model includes factoring in the weather data.

“11. The computer-implemented method of claim 10, wherein the weather data includes a time period for at least one predicted weather event, and wherein the optimal UBI product includes an expiration associated with the time period for the at least one predicted weather event.

“12. The computer-implemented method of claim 8, wherein the PI computing device is in communication with an insurance provider and the optimal UBI product is underwritten by the insurance provider.

“13. The computer-implemented method of claim 8, wherein the increased payment rate is based upon a payment rate offered by the TNC, wherein the increased payment rate less than the payment rate offered by the TNC, and wherein a determined difference between the increased payment rate and the payment rate offered by the TNC is transferred to an insurance provider.

“14. The computer-implemented method of claim 13, the method further comprising determining a price for the optimal UBI product, wherein determining the price is based upon at least the difference between the increased payment rate and the payment rate offered by the TNC.

“15. One or more non-transitory computer-readable storage media having computer-executable instructions embodied thereon, when executed by a personalized insurance (“PI”) computing device having at least one processor in communication with at least one memory, the computer-executable instructions causing the at least one processor to: generate, using one or more machine learning programs, an optimal pricing model for the driver based upon historical supply and demand data associated with a plurality of TNC vehicles and historical driver data associated with the driver; receive from a transportation network company (“TNC”) in real-time supply and demand data associated with a plurality of TNC vehicles, wherein the supply and demand data includes increased demand data for transportation services, and wherein the increased demand data represents a current increase in demand for the transportation services; retrieve driver data for a driver operating a vehicle for the TNC, wherein the driver data includes at least driver history associated with the driver; update, in real-time, by applying one or more machine learning programs to the increased demand data and the driver data, the optimal pricing model; determine, in real-time, an optimal usage-based insurance (“UBI”) product by executing the updated optimal pricing model, wherein the optimal UBI product is personalized for the driver and includes current driving characteristics reflecting at least one risk factor associated with an increased demand for the transportation services and a risk profile associated with the driver; and transmit, in-real time, a message to a user computing device associated with the driver, the message including the determined optimal UBI product and an offer to provide the transportation services with the determined optimal UBI product and at an increased payment rate based upon the increased demand data.

“16. The non-transitory computer-readable storage media of claim 15, wherein the computer-executable instructions further cause the at least one processor to retrieve weather data from a weather service, and wherein generating the optimal pricing model includes factoring in the weather data.

“17. The non-transitory computer-readable storage media of claim 16, wherein the weather data includes a time period for at least one predicted weather event, and wherein the optimal UBI product includes an expiration associated with the time period for the at least one predicted weather event.

“18. The non-transitory computer-readable storage media of claim 15, wherein the PI computing device is in communication with an insurance provider and the optimal UBI product is underwritten by the insurance provider.”

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

URL and more information on this patent, see: Brannan, Joseph Robert. Systems and methods for generating mobility insurance products using ride-sharing telematics data. U.S. Patent Number 11599947, filed February 3, 2020, and published online on March 7, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11599947)&db=USPAT&type=ids

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