Patent Issued for Systems and methods for generating mobility insurance products using ride-sharing telematics data (USPTO 11954736): State Farm Mutual Automobile Insurance Company

2024 APR 25 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Alexandria, Virginia, by NewsRx journalists, 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 April 9, 2024.

The assignee for this patent, patent number 11954736, is State Farm Mutual Automobile Insurance Company (Bloomington, Illinois, United States).

Reporters obtained the following quote 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 obtaining background information on this patent, NewsRx editors 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 developing a model to determine a ratings-based insurance product for a driver of a transportation network company (TNC) operating a vehicle for the TNC, the PI computing device in communication with a TNC computing device associated with the TNC, a plurality of sensors via a vehicle computing device associated with the vehicle, and a financial institution that manages a health savings account for the driver, the PI computing device having at least one processor in communication with at least one memory, the at least one processor configured to: retrieve ratings for the driver and the vehicle operated by the driver; receive telematics data from the vehicle computing device, the telematics data associated with operation of the vehicle and collected by the plurality of sensors; generate, using one or more machine learning programs, a risk model for the driver based upon the ratings and the telematics data; receive additional ratings for the driver and the vehicle, additional telematics data associated with current operation of the vehicle, and environment data indicative of current environmental conditions of an area where the vehicle is being operated; receive, in real-time from the TNC computing device, supply and demand data indicative of current supply and demand for the TNC; train, using the one or more machine learning programs, the risk model by applying the additional telematics data, the environment data, the supply and demand data, and the additional ratings to the risk model; determine, in real-time, a personalized optimal insurance product for the driver by executing the trained risk model, wherein the personalized optimal insurance product is personalized for the driver and vehicle; transmit, to a first user computing device associated with the driver, the personalized optimal insurance product to the driver; receive, from the first user computing device, a message indicating a purchase of the personalized optimal insurance product; and transmit a message to the financial institution to withdraw an amount of funds from the health savings account for the purchase of the personalized optimal insurance product.

“2. The PI computing device of claim 1, wherein the at least one processor is further configured to: receive the ratings from a second user computing device associated with a passenger, wherein the ratings are associated with a ride provided to the passenger, and wherein the ratings include at least one numerical score associated with the ride provided by the driver; and store the ratings in the memory.

“3. The PI computing device of claim 1, wherein the ratings include a description of a ride provided by the driver, wherein the description is inputted by a passenger of the ride, and wherein the at least one processor is further configured to: parse the description using natural language processing; and analyze the parsed description to determine a numerical score corresponding to a rating for the ride provided by the driver.

“4. The PI computing device of claim 1, wherein the ratings and the additional ratings include performance metrics for at least one ride provided to a passenger, the performance metrics retrieved from measurement devices on the vehicle, the performance metrics including the telematics data, the telematics data including at least one of acceleration data, braking data, and cornering data, and wherein the ratings and the additional ratings further include ratings associated with the vehicle, ratings associated with the driver, and location data associated with trips performed by the driver while operating the vehicle.

“5. The PI computing device of claim 1, wherein generating the risk model includes overlaying the retrieved ratings with data associated with a geographic region associated with a route for a ride provided to a passenger.

“6. The PI computing device of claim 1, wherein generating the risk model includes incorporating an optimal pricing model, and wherein the at least one processor is further configured to: retrieve, from the TNC, data indicating a level of demand for transportation services; and generate the optimal pricing model based upon the retrieved demand data.

“7. The PI computing device of claim 1, wherein the environment data is one of (a) detected by the plurality of sensors and received from the vehicle computing device, and (b) received from at least one environment data source.

“8. The PI computing device of claim 1, wherein the environment data includes at least one weather related data or traffic conditions.

“9. A computer-implemented method for developing a model to determine a ratings-based insurance product for a driver of a transportation network company (TNC) operating a vehicle for the TNC using a personalized insurance (“PI”) computing device, the PI computing device in communication with TNC computing device associated with the TNC, a plurality of sensors via a vehicle computing device associated with the vehicle, and a financial institution that manages a health savings account for the driver, the PI computing device having at least one processor in communication with at least one memory, the method comprising: retrieving ratings for the driver and the vehicle operated by the driver; receiving telematics data from the vehicle computing device, the telematics data associated with operation of the vehicle and collected by the plurality of sensors; generating, using one or more machine learning programs, a risk model for the driver based upon the ratings and the telematics data; receiving additional ratings for the driver and the vehicle, additional telematics data associated with current operation of the vehicle, and environment data indicative of current environmental conditions of an area where the vehicle is being operated; receiving, in real-time from the TNC computing device, supply and demand data indicative of current supply and demand for the TNC; training, using the one or more machine learning programs, the risk model by applying the additional telematics data, the environment data, the supply and demand data, and the additional ratings to the risk model; determining, in real-time, a personalized optimal insurance product for the driver by executing the trained risk model, wherein the personalized optimal insurance product is personalized for the driver and vehicle; transmitting, to a first user computing device associated with the driver, the personalized optimal insurance product to the driver; receiving, from the first user computing device, a message indicating a purchase of the personalized optimal insurance product; and transmitting a message to the financial institution to withdraw an amount of funds from the health savings account for the purchase of the personalized optimal insurance product.

“10. The computer-implemented method of claim 9, the method further comprising: receiving the ratings from a second user computing device associated with a passenger, wherein the ratings are associated with a ride provided to the passenger, and wherein the ratings include at least one numerical score associated with the ride provided by the driver; and storing the ratings in the memory.

“11. The computer-implemented method of claim 9, wherein the ratings include a description of a ride provided by the driver, the description inputted by a passenger of the ride provided, the method further comprising: parsing the description using natural language processing; and analyzing the parsed description to determine a numerical score corresponding to a rating for the ride provided by the driver.

“12. The computer-implemented method of claim 9, wherein the ratings and the additional ratings include performance metrics for at least one ride provided to a passenger, the performance metrics retrieved from measurement devices on the vehicle, the performance metrics including the telematics data, the telematics data including at least one of acceleration data, braking data, and cornering data, and wherein the ratings and the additional ratings further include ratings associated with the vehicle, ratings associated with the driver, and location data associated with trips performed by the driver while operating the vehicle.

“13. The computer-implemented method of claim 9, the method further comprising generating the risk model further based on overlaying the retrieved ratings with data associated with a geographic region associated with a route for a ride provided to a passenger.

“14. The computer-implemented method of claim 9, the method further comprising: retrieving, from the TNC, data indicating a level of demand for transportation services; generating an optimal pricing model based upon the retrieved demand data; and generating the risk model further based upon the generated optimal pricing model.”

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

For more information, see this patent: Brannan, Joseph Robert. Systems and methods for generating mobility insurance products using ride-sharing telematics data. U.S. Patent Number 11954736, filed February 3, 2020, and published online on April 9, 2024. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11954736)&db=USPAT&type=ids

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