“Systems And Methods For Generating Mobility Insurance Products Using Ride-Sharing Telematics Data” in Patent Application Approval Process (USPTO 20230316418): Patent Application

2023 OCT 23 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent application by the inventors Brannan, Joseph Robert (Bloomington, IL, US); Gross, Ryan Michael (Normal, IL, US); Harvey, Brian N. (Bloomington, IL, US), filed on June 8, 2023, was made available online on October 5, 2023, according to news reporting originating from Washington, D.C., by NewsRx correspondents.

This patent application has not been assigned to a company or institution.

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 application, NewsRx journalists also obtained the inventors’ summary information for this patent application: “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.

“In a further aspect, a computer-implemented method for facilitating automatic insurance payments through a hybrid savings account (“HSA”) associated with a driver operating a vehicle for a transportation network company (“TNC”) 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, via the processor and/or associated transceiver, receiving, from the TNC, funds earned by a driver operating the vehicle for the TNC. The method may also include transmitting, via the processor and/or associated transceiver, the funds to a financial institution to be deposited into the hybrid savings account associated with the driver. The method may further include receiving, via the processor and/or associated transceiver, 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 method may also include transferring, via the processor and/or associated transceiver, from the hybrid savings account to an insurance provider, payment for the requested insurance coverage. 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”), funds earned by a driver operating a vehicle for the TNC. The computer-executable instructions may further cause the at least one processor and/or associated transceiver to transmit the funds to a financial institution to be deposited into a hybrid savings account (“HSA”) associated with the driver. The computer-executable instructions may further cause the at least one processor and/or associated transceiver 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 computer-executable instructions may further cause the at least one processor and/or associated transceiver to transfer, from the HSA to an insurance provider, payment for the requested insurance coverage. The instructions may direct additional, less, or alternate functionality, including that discussed elsewhere herein.”

There is additional summary information. Please visit full patent to read further.”

The claims supplied by the inventors are:

“1. A data analytics computing device in communication with a transportation network company (“TNC”) computing device that is associated with a TNC, and a vehicle computing device associated with a vehicle that at least periodically operates as part of the TNC, the data analytics computing device having at least one processor in communication with at least one memory, the at least one processor configured to: receive telematics data from the vehicle computing device, the telematics data associated with operation of the vehicle and collected by a plurality of sensors associated with the vehicle; generate, using one or more machine learning programs, a driving model of a driver of the vehicle trained using the telematics data collected while the driver is operating the vehicle and ratings of the driver while operating the vehicle as part of the TNC; receive additional telematics data and additional ratings of the driver for a more recent operation of the vehicle; receive, in real-time from the TNC computing device, vehicle usage data indicative of a current demand of vehicles as compared to a current supply of vehicles operating as part of the TNC; further train, using the one or more machine learning programs, the driving model by applying the additional telematics data, the additional ratings, and the vehicle usage data to the driving model; execute, in real-time, the further trained driving model to determine, in real-time, personalized coverage for the driver currently operate the vehicle as part of the TNC; and transmit, to a user computing device associated with the driver, the personalized coverage.

“2. The data analytics computing device of claim 1, wherein the ratings are associated with a condition of the vehicle.

“3. The data analytics computing device of claim 1, wherein the at least one processor is further configured to receive environment data indicative of current environmental conditions of an area where the vehicle is being operated.

“4. The data analytics computing device of claim 3, wherein the at least one processor is further configured to further train the driving model by applying the environment data.

“5. The data analytics computing device of claim 1, wherein the at least one processor is further configured to: receive, from the user computing device, a first message indicating an acceptance to obtain the personalized coverage; and transmit a second message to a financial institution computing device, the second message instructing the financial institution computing device to withdraw an amount of funds from a health savings account associated with the driver to purchase of the personalized coverage.

“6. The data analytics computing device of claim 1, wherein the at least one processor is further configured to generate the driving model by overlaying the ratings with data associated with a geographic region including a route for a ride provided to a passenger of the vehicle.

“7. The data analytics computing device of claim 1, wherein the ratings include a description inputted by a passenger of the vehicle during a ride provided by the driver, 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.

“8. A computer-implemented method using a data analytics computing device in communication with a transportation network company (“TNC”) computing device that is associated with a TNC, and a vehicle computing device associated with a vehicle that at least periodically operates as part of the TNC, the data analytics computing device having at least one processor in communication with at least one memory, method comprising: receiving telematics data from the vehicle computing device, the telematics data associated with operation of the vehicle and collected by a plurality of sensors associated with the vehicle; generating, using one or more machine learning programs, a driving model of a driver of the vehicle trained using the telematics data collected while the driver is operating the vehicle and ratings of the driver while operating the vehicle as part of the TNC; receiving additional telematics data and additional ratings of the driver for a more recent operation of the vehicle; receiving, in real-time from the TNC computing device, vehicle usage data indicative of a current demand of vehicles as compared to a current supply of vehicles operating as part of the TNC; further training, using the one or more machine learning programs, the driving model by applying the additional telematics data, the additional ratings, and the vehicle usage data to the driving model; executing, in real-time, the further trained driving model to determine, in real-time, personalized coverage for the driver currently operate the vehicle as part of the TNC; and transmitting, to a user computing device associated with the driver, the personalized coverage.

“9. The computer-implemented method of claim 8, wherein the ratings are associated with a condition of the vehicle.

“10. The computer-implemented method of claim 8 further comprising receiving environment data indicative of current environmental conditions of an area where the vehicle is being operated.

“11. The computer-implemented method of claim 10 further comprising further training the driving model by applying the environment data.

“12. The computer-implemented method of claim 8 further comprising: receiving, from the user computing device, a first message indicating an acceptance to obtain the personalized coverage; and transmitting a second message to a financial institution computing device, the second message instructing the financial institution computing device to withdraw an amount of funds from a health savings account associated with the driver to purchase of the personalized coverage.

“13. The computer-implemented method of claim 8 further comprising generating the driving model by overlaying the ratings with data associated with a geographic region including a route for a ride provided to a passenger of the vehicle.

“14. The computer-implemented method of claim 8, wherein the ratings include a description inputted by a passenger of the vehicle during a ride provided by the driver, and wherein the method further comprises: 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.

“15. At least one non-transitory computer-readable storage medium having computer-executable instructions embodied thereon, when executed by at least one processor of a data analytics computing device in communication with a transportation network company (“TNC”) computing device that is associated with a TNC, and a vehicle computing device associated with a vehicle that at least periodically operates as part of the TNC, the at least one processor in communication with at least one memory, the computer-executable instructions cause the at least one processor to: receive telematics data from the vehicle computing device, the telematics data associated with operation of the vehicle and collected by a plurality of sensors associated with the vehicle; generate, using one or more machine learning programs, a driving model of a driver of the vehicle trained using the telematics data collected while the driver is operating the vehicle and ratings of the driver while operating the vehicle as part of the TNC; receive additional telematics data and additional ratings of the driver for a more recent operation of the vehicle; receive, in real-time from the TNC computing device, vehicle usage data indicative of a current demand of vehicles as compared to a current supply of vehicles operating as part of the TNC; further train, using the one or more machine learning programs, the driving model by applying the additional telematics data, the additional ratings, and the vehicle usage data to the driving model; execute, in real-time, the further trained driving model to determine, in real-time, personalized coverage for the driver currently operate the vehicle as part of the TNC; and transmit, to a user computing device associated with the driver, the personalized coverage.

“16. The at least one non-transitory computer-readable storage medium of claim 15, wherein the ratings are associated with a condition of the vehicle.

“17. The at least one non-transitory computer-readable storage medium of claim 15, wherein the computer-executable instructions further cause the at least one processor to receive environment data indicative of current environmental conditions of an area where the vehicle is being operated.

“18. The at least one non-transitory computer-readable storage medium of claim 15, wherein the computer-executable instructions further cause the at least one processor to: receive, from the user computing device, a first message indicating an acceptance to obtain the personalized coverage; and transmit a second message to a financial institution computing device, the second message instructing the financial institution computing device to withdraw an amount of funds from a health savings account associated with the driver to purchase of the personalized coverage.

“19. The at least one non-transitory computer-readable storage medium of claim 15, wherein the computer-executable instructions further cause the at least one processor to generate the driving model by overlaying the ratings with data associated with a geographic region including a route for a ride provided to a passenger of the vehicle.”

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

URL and more information on this patent application, see: Brannan, Joseph Robert; Gross, Ryan Michael; Harvey, Brian N. Systems And Methods For Generating Mobility Insurance Products Using Ride-Sharing Telematics Data. U.S. Patent Application Number 20230316418, filed June 8, 2023 and posted October 5, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230316418)&db=US-PGPUB&type=ids

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