Patent Issued for Systems and methods for dynamically generating optimal routes for vehicle operation management (USPTO 11466997): State Fram Mutual Automobile Insurance Company
2022 OCT 28 (NewsRx) -- By a
Patent number 11466997 is assigned to
The following quote was obtained by the news editors from the background information supplied by the inventors: “It is well known to use vehicles for delivery of cargo, including persons and items. Newer methods of transporting such cargo are being implemented, such as ride-sharing and businesses contracting with third parties for package delivery. However, current systems require users to pick and choose discrete jobs. For example, a driver for a ride-share service seeks individual persons (or groups thereof) to provide rides to. Likewise, third-party delivery contractors accept individual delivery jobs. Current systems do not provide users with any strategies for maximizing revenue. Accordingly, it is left up to users to attempt to maximize their revenue, time, and/or other resources as they choose individual jobs or tasks. Moreover, many known systems may limit or place restrictions on concurrent jobs, such that users accept consecutive jobs and may be deprived of the advantages (in time and revenue) of concurrent jobs.”
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, inter alia, systems and methods for optimizing delivery routing for a plurality of vehicles. Vehicle users (e.g., owners, lessors, fleet managers, etc.) may register with a vehicle routing system to receive vehicle routing services therefrom. The vehicle users may submit “definitions” of their vehicle that describe how they want their vehicle to be used (e.g., the times and locations that the vehicle is available to make deliveries). The vehicle routing system may receive a plurality of tasks (e.g., delivering cargo including person(s) and/or object(s)) to be completed, and may analyze the vehicle definitions and the plurality of tasks to generate an optimal route for each vehicle that maximizes the vehicle’s profit generation. In the exemplary embodiment, the vehicle routing system may include artificial intelligence and/or deep learning functionality to generate the optimal routes.
“In addition, the vehicle routing system may collect and process sensor data from the plurality of vehicles, as the vehicle operate according the optimal routes (e.g., performing delivery tasks). The sensor data may characterize use/performance of the vehicle, an ambient environment around the vehicle (e.g., weather, traffic), local infrastructure, and more. The vehicle routing system may process the sensor data (e.g., using the artificial intelligence and/or deep learning functionality) to generate vehicle analytics for each vehicle, identify trends, update optimal routes, and/or make recommendations.
“In the exemplary embodiment, the vehicle routing system maintains a management hub software application (“app”) that enables users to view vehicle analytics and recommendations made by the vehicle routing system. The management hub may additionally enable users to update their user preferences or vehicle definitions of registered vehicles. Additionally or alternatively, the management hub app provides users access to a plurality of other services provided by the vehicle routing system, including maintenance/repair services, financial services, marketing services, and the like.
“In one aspect, a vehicle routing and analytics (VRA) computing device for generating an optimal route for a vehicle that maximizes potential revenue for operation of the vehicle may be provided. The VRA computing device may include at least one processor in communication with a memory, wherein the at least one processor may be programmed to retrieve a vehicle definition for the vehicle, the vehicle definition including availability parameters and delivery preferences associated with the vehicle. The at least one processor may also be programmed to retrieve, based in part on the vehicle definition, a plurality of task definitions defining a respective plurality of tasks, each task including a respective cargo to be delivered, pick-up time, delivery time, pick-up location, delivery location, and task value. The at least one processor may be further programmed to generate, by executing at least one of artificial intelligence and deep learning functionality using the vehicle definition and the plurality of task definitions, an optimal route for the vehicle that includes a scheduled list of a subset of the plurality of tasks for the vehicle to perform, wherein the optimal route maximizes the potential revenue for operation of the vehicle within a period of time associated with the optimal route, and to transmit the optimal route to the vehicle for operation of the vehicle according to the optimal route. The VRA computing device may include additional, less, and/or alternative functionality, including that described herein.
“In another aspect, a computer-implemented method for generating an optimal route for a vehicle to travel that maximizes potential revenue for operation of the vehicle may be provided. The method may be implemented by a vehicle routing and analytics (VRA) computing device including at least one processor. The method may include retrieving a vehicle definition for the vehicle, the vehicle definition including availability parameters and delivery preferences associated with the vehicle. The method may also include retrieving, based in part on the vehicle definition, a plurality of task definitions defining a respective plurality of tasks, each task including a respective cargo to be delivered, pick-up time, delivery time, pick-up location, delivery location, and task value. The method may further include generating, by executing at least one of artificial intelligence and deep learning functionality using the vehicle definition and the plurality of task definitions, an optimal route for the vehicle that includes a scheduled list of a subset of the plurality of tasks for the vehicle to perform, wherein the optimal route maximizes the potential revenue for operation of the vehicle within a period of time associated with the optimal route, and transmitting the optimal route to the vehicle for operation of the vehicle according to the optimal route. The method may include additional, fewer, and/or alternative steps, including those described herein.”
The claims supplied by the inventors are:
“1. A vehicle routing and analytics (VRA) computing device for generating an optimal route for a vehicle that maximizes potential revenue for operation of the vehicle, the VRA computing device comprising at least one processor in communication with a memory, wherein the at least one processor is programmed to: retrieve a vehicle definition for the vehicle, the vehicle definition including availability parameters and delivery preferences associated with the vehicle; retrieve, based in part on the vehicle definition, a plurality of task definitions defining a respective plurality of tasks, each task including a respective cargo to be delivered, pick-up time, delivery time, pick-up location, delivery location, and task value; generate, by executing at least one of artificial intelligence and deep learning functionality using the vehicle definition and the plurality of task definitions, the optimal route for the vehicle that includes a scheduled list of a subset of the plurality of tasks for the vehicle to perform, wherein the optimal route maximizes the potential revenue for operation of the vehicle within a period of time associated with the optimal route; and transmit the optimal route to the vehicle for operation of the vehicle according to the optimal route.
“2. The VRA computing device of claim 1, wherein the vehicle is an autonomous vehicle, and wherein the optimal route, when received by the autonomous vehicle, causes the autonomous vehicle to travel to pick-up and delivery locations associated with the subset of tasks according to the optimal route.
“3. The VRA computing device of claim 1, wherein the VRA computing device is communicatively coupled to a task definition database that stores available tasks, and wherein to retrieve the plurality of task definitions, the at least one processor is further programmed to: identify, based upon the vehicle definition, an availability radius for the vehicle, the availability radius defining a bounded geographical area to which the vehicle is available to travel and a period of time over which the vehicle is available to travel; generate a query including the availability radius; and transmit, to the task definition database, the query including the availability radius, wherein the query causes the task definition database to identify and retrieve the plurality of tasks that can be completed within the availability radius of the vehicle.
“4. The VRA computing device of claim 1, wherein the VRA computing device is communicatively coupled to a vehicle definition database that stores vehicle definitions, wherein the vehicle definition database is populated by vehicle users associated with vehicles for which the vehicle definitions are stored, and wherein the vehicle definition includes data elements include a period of time over which the vehicle is available to complete tasks, a geographic range over which the vehicle is available to complete tasks, cargo preferences set by the associated vehicle user, a capacity of the vehicle for cargo including persons, a capacity of the vehicle for cargo including objects, a make of the vehicle, a model of the vehicle, a manufacturing year of the vehicle, an identifier of the vehicle, a vehicle type, vehicle features, a vehicle class, current vehicle location, current vehicle capacity, a performance rating for the vehicle, and historical claim data for the vehicle.
“5. The VRA computing device of claim 1, wherein a first task of the subset of tasks is associated with a high-value cargo, wherein the at least one processor is further programmed to generate the optimal route to schedule no additional tasks until completion of the first task.
“6. The VRA computing device of claim 5, wherein the at least one processor is further programmed to generate the optimal route to include a first sub-route associated with the first task that has a risk level at or below a risk level threshold associated with the first task.
“7. The VRA computing device of claim 1, wherein the at least one processor is further programmed to: retrieve a plurality of historical routes travelled by a plurality of vehicles; retrieve a plurality of completed tasks completed by the plurality of vehicles; identify one or more patterns in the plurality of historical routes and the plurality of completed tasks; and update the at least one of the artificial intelligence and deep learning functionality based upon the identified patterns.
“8. The VRA computing device of claim 7, wherein the at least one processor is further programmed to: identify, from the identified patterns, at least one underserved location that has fewer than a threshold numbers of completed tasks associated therewith; and generate the optimal route to prioritize tasks associated with the underserved location.
“9. The VRA computing device of claim 7, wherein the at least one processor is further configured to: identify, from the identified patterns, a lull period of time over which the plurality of vehicles completed a lowest number of completed tasks; generate a service schedule including one or more services associated with the vehicle, a respective service time associated with each service, and a respective service vendor associated with each service, wherein each service time is within the lull period of time; and transmit the service schedule to the vehicle for operation of the vehicle according to the service schedule.
“10. The VRA computing device of claim 9, wherein the vehicle is an autonomous vehicle, and wherein the service schedule, when received by the autonomous vehicle, causes the autonomous vehicle to travel to locations associated with the respective service vendors according to the service schedule.
“11. The VRA computing device of claim 1, wherein the at least one processor is further programmed to: identify a first event occurring at a first location at first time, wherein the first event is likely to be associated with a plurality of potential tasks; and generate the optimal route to schedule operation of the vehicle at the first location at the first time to make the vehicle available to accommodate at least one potential task.
“12. The VRA computing device of claim 1, wherein the delivery preferences for the vehicle include a person-only cargo preference, wherein the at least one processor is further programmed to: filter the plurality of tasks based upon the person-only cargo preference to identify a plurality of person-only cargo tasks from the plurality of tasks; and generate the optimal route for the vehicle that includes the scheduled list of a subset of the plurality of person-only cargo tasks for the vehicle to perform.
“13. The VRA computing device of claim 12, wherein the vehicle includes a first vehicle and the optimal route includes a first optimal route, and wherein the at least one processor is further programmed to: identify a second vehicle operating according to a second optimal route, wherein the second vehicle is associated with a vehicle definition that does not include the person-only cargo preference and operates within an availability radius similar to an availability radius of the first vehicle; compare a predicted revenue associated with the second optimal route to a predicted revenue associated with the first optimal route; calculate, based upon the comparing, a predicted loss associated with the first optimal route; and transmit, to a vehicle user associated with the first vehicle, a recommendation message including the predicted loss and a recommendation that the vehicle user remove the person-only cargo preference from the vehicle definition of the first vehicle.
“14. The VRA computing device of claim 1, wherein the delivery preferences for the vehicle include an object-only cargo preference, wherein the at least one processor is further programmed to: filter the plurality of tasks based upon the object-only cargo preference to identify a plurality of object-only cargo tasks from the plurality of tasks; and generate the optimal route for the vehicle that includes the scheduled list of a subset of the plurality of object-only cargo tasks for the vehicle to perform.
“15. The VRA computing device of claim 14, wherein the vehicle includes a first vehicle and the optimal route includes a first optimal route, and wherein the at least one processor is further programmed to: identify a second vehicle operating according to a second optimal route, wherein the second vehicle is associated with a vehicle definition that does not include the object-only cargo preference and operates within an availability radius similar to an availability radius of the first vehicle; compare a predicted revenue associated with the second optimal route to a predicted revenue associated with the first optimal route; calculate, based upon the comparing, a predicted loss associated with the first optimal route; and transmit, to a vehicle user associated with the first vehicle, a recommendation message including the predicted loss and a recommendation that the vehicle user remove the object-only cargo preference from the vehicle definition of the first vehicle.”
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URL and more information on this patent, see: Brannan,
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