Patent Issued for Telematics service detection and action messaging based on machine learning for assisting car sharing platform (USPTO 11887141): State Farm Mutual Automobile Insurance Company

2024 FEB 20 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- State Farm Mutual Automobile Insurance Company (Bloomington, Illinois, United States) has been issued patent number 11887141, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Brannan, Joseph Robert (Bloomington, IL, US), Gross, Ryan (Normal, IL, US).

This patent was filed on December 13, 2022 and was published online on January 30, 2024.

From the background information supplied by the inventors, news correspondents obtained the following quote: “The background description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.

“A car sharing company may operate a car sharing platform (CSP) including a fleet of one or more vehicles generally controlled and/or monitored by the car sharing company. The vehicles’ respective titles may be held by the car sharing company, and the vehicles may include any number of trucks, vans and/or other vehicles (e.g., motorcycles, buses, etc.). The vehicles may include autonomous and/or semi-autonomous features. Increasingly, telematics data related to the operation of motor vehicles of all types is captured by telematics systems that are built into vehicles, or which are carried into vehicles by drivers and passengers (e.g., mobile computing devices). Such telematics systems measure and capture data directly from vehicle computer systems as well as indirectly from the vehicle environment. In general, a car sharing company’s owner does not drive the fleet of vehicles operated by the car sharing company. Rather, a customer of the car sharing company (i.e., a vehicle operator) reserves a vehicle via the CSP to drive to a destination, and then leaves (i.e., abandons) the vehicle.

“In contrast, a ride sharing service may include a vehicle generally controlled and/or monitored by a ride sharing operator who owns the vehicle. The ride sharing company may provide a mechanism by which the ride sharing operator (i.e., the owner of the ride sharing vehicle) may offer rides to vehicle passengers.

“Several logistical challenges face car sharing companies. First, as CSP vehicle operators reserve vehicles to take trips and then abandon those vehicles, the CSP’s fleet of vehicles becomes dispersed in geographically inconvenient locations, whereupon the CSP cannot offer the abandoned vehicles to subsequent CSP customers without first relocating (e.g., retrieving) the vehicles. Retrieving the vehicles is currently performed by manually tracking the abandoned vehicles, and sending employee-drivers of the CSP to drive the abandoned vehicles back to a centralized location. The CSP may be required to employ full-time employees to relocate vehicles because the CSP lacks a means to require the customer to return the vehicle to a centralized location or to another return location that is remote from the customer’s final destination, or to verify the status of the vehicle.

“Second, surges in consumer demand may cause many vehicles to be needed at a particular location at a particular time, which cannot be accommodated by the CSP when the centralized location where the fleet of vehicles is kept is removed from the location wherein the consumer demand is surging.

“Third, the CSP may be constantly challenged to maintain and upkeep the vehicles composing the CSP’s fleet. However, the CSP may be unable to determine the state of vehicles that are abandoned because no technological mechanism currently exists to determine whether a vehicle is in need of routine service and/or service due to normal wear and tear. At present, the CSP must send a service technician to drive the vehicle to a service center, which may be remote from the customer’s final destination and/or the centralized location(s) of the CSP.”

Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “In one aspect, a computer-implemented method of causing a user of a car sharing platform to perform service or relocation actions with respect to a vehicle includes (i) training, by one or more processors, a plurality of artificial neural network models by analyzing historical data, wherein each model is trained to generate respective action values for actions corresponding to respective vehicle imperative types, at least one of the plurality of artificial neural network models trained to generate action values for actions corresponding to a non-relocation vehicle imperative type; (ii) analyzing, by the one or more processors, a first data set to determine one or more vehicle imperatives, each comprising one or more respective actions; and (iii) generating, by the one or more processors, a respective action value for each of the respective actions corresponding to the determined one or more vehicle imperatives by analyzing each of the respective actions using a respective one of the plurality of trained artificial neural network models according to a vehicle imperative type associated with each of the one or more respective actions.

“In another aspect, a non-transitory computer readable medium includes program instructions that when executed, cause a computer system to: (i) train, by one or more processors, a plurality of artificial neural network models by analyzing historical data, wherein each model is trained to generate respective action values for actions corresponding to respective vehicle imperative types, at least one of the plurality of artificial neural network models trained to generate action values for actions corresponding to a non-relocation vehicle imperative type; (ii) analyze, by the one or more processors, a first data set to determine one or more vehicle imperatives, each comprising one or more respective actions; and (iii) generate, by the one or more processors, a respective action value for each of the respective actions corresponding to the determined one or more vehicle imperatives by analyzing each of the respective actions using a respective one of the plurality of trained artificial neural network models according to a vehicle imperative type associated with each of the one or more respective actions.

“In yet another aspect, a computing system for causing a user of a car sharing platform to perform service or relocation actions with respect to a vehicle includes one or more processors; and one or more memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: (i) train, by one or more processors, a plurality of artificial neural network models by analyzing historical data, wherein each model is trained to generate respective action values for actions corresponding to respective vehicle imperative types, at least one of the plurality of artificial neural network models trained to generate action values for actions corresponding to a non-relocation vehicle imperative type; (ii) analyze, by the one or more processors, a first data set to determine one or more vehicle imperatives, each comprising one or more respective actions; and (iii) generate, by the one or more processors, a respective action value for each of the respective actions corresponding to the determined one or more vehicle imperatives by analyzing each of the respective actions using a respective one of the plurality of trained artificial neural network models according to a vehicle imperative type associated with each of the one or more respective actions.”

The claims supplied by the inventors are:

“1. A computer-implemented method of causing a user of a car sharing platform to perform service or relocation actions with respect to a vehicle, the method comprising: training, by one or more processors, a plurality of artificial neural network models by analyzing historical data, wherein each model is trained to generate respective action values for actions corresponding to respective vehicle imperative types, at least one of the plurality of artificial neural network models trained to generate action values for actions corresponding to a non-relocation vehicle imperative type; analyzing, by the one or more processors, a first data set to determine one or more vehicle imperatives, each comprising one or more respective actions; and generating, by the one or more processors, a respective action value for each of the respective actions corresponding to the determined one or more vehicle imperatives by analyzing each of the respective actions using a respective one of the plurality of trained artificial neural network models according to a vehicle imperative type associated with each of the one or more respective actions.

“2. The computer-implemented method of claim 1, wherein the first data set is a vehicle telematics data set.

“3. The computer-implemented method of claim 1, wherein the first data set is a user data set.

“4. The computer-implemented method of claim 1, wherein analyzing the first data set to determine one or more vehicle imperatives includes determining a vehicle service imperative.

“5. The computer-implemented method of claim 1, wherein analyzing the first data set to determine one or more vehicle imperatives includes determining a relocation service imperative.

“6. The computer-implemented method of claim 1, further comprising: applying a respective set of rules to each of the one or more determined vehicle imperatives.

“7. The computer-implemented method of claim 1, further comprising: analyzing historical action acceptances of the user.

“8. The computer-implemented method of claim 1, further comprising: displaying the one or more actions to the user while the user is operating the vehicle at rest.

“9. The computer-implemented method of claim 1, further comprising: transmitting the one or more actions to a mobile computing device of the user via a push message.

“10. The computer-implemented method of claim 1, wherein the user is a first user and further comprising: receiving a second data set from the vehicle when the vehicle is being operated by a second user.

“11. A non-transitory computer readable medium containing program instructions that when executed, cause a computer system to: train, by one or more processors, a plurality of artificial neural network models by analyzing historical data, wherein each model is trained to generate respective action values for actions corresponding to respective vehicle imperative types, at least one of the plurality of artificial neural network models trained to generate action values for actions corresponding to a non-relocation vehicle imperative type; analyze, by the one or more processors, a first data set to determine one or more vehicle imperatives, each comprising one or more respective actions; and generate, by the one or more processors, a respective action value for each of the respective actions corresponding to the determined one or more vehicle imperatives by analyzing each of the respective actions using a respective one of the plurality of trained artificial neural network models according to a vehicle imperative type associated with each of the one or more respective actions.

“12. The non-transitory computer readable medium of claim 11, wherein the first data set is a vehicle telematics data set.

“13. The non-transitory computer readable medium of claim 11, wherein the first data set is a user data set.

“14. The non-transitory computer readable medium of claim 11, containing further program instructions that when executed, cause a computer system to: analyze the first data set to determine a vehicle service imperative.

“15. The non-transitory computer readable medium of claim 11, containing further program instructions that when executed, cause a computer system to: analyze the first data set to determine a relocation service imperative.

“16. The non-transitory computer readable medium of claim 11, containing further program instructions that when executed, cause a computer system to: apply a respective set of rules to each of the one or more determined vehicle imperatives.

“17. The non-transitory computer readable medium of claim 11, containing further program instructions that when executed, cause a computer system to: analyze historical action acceptances of a user.

“18. The non-transitory computer readable medium of claim 11, containing further program instructions that when executed, cause a computer system to: display the one or more actions to the user while the user is operating the vehicle at rest.

“19. The non-transitory computer readable medium of claim 11, containing further program instructions that when executed, cause a computer system to: transmit the one or more actions to a mobile computing device of the user via a push message.

“20. A computing system for causing a user of a car sharing platform to perform service or relocation actions with respect to a vehicle, comprising: one or more processors; and one or more memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: train, by the one or more processors, a plurality of artificial neural network models by analyzing historical data, wherein each model is trained to generate respective action values for actions corresponding to respective vehicle imperative types, at least one of the plurality of artificial neural network models trained to generate action values for actions corresponding to a non-relocation vehicle imperative type; analyze, by the one or more processors, a first data set to determine one or more vehicle imperatives, each comprising one or more respective actions; and generate, by the one or more processors, a respective action value for each of the respective actions corresponding to the determined one or more vehicle imperatives by analyzing each of the respective actions using a respective one of the plurality of trained artificial neural network models according to a vehicle imperative type associated with each of the one or more respective actions.”

For the URL and additional information on this patent, see: Brannan, Joseph Robert. Telematics service detection and action messaging based on machine learning for assisting car sharing platform. U.S. Patent Number 11887141, filed December 13, 2022, and published online on January 30, 2024. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11887141)&db=USPAT&type=ids

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