Patent Issued for Systems and methods for adaptive route optimization for learned task planning (USPTO 11797931): State Farm Mutual Automobile Insurance Company

2023 NOV 13 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Alexandria, Virginia, NewsRx journalists report that a patent by the inventors Brannan, Joseph Robert (Bloomington, IL, US), Gross, Ryan Michael (Normal, IL, US), Moore, Eric R. (Heyworth, IL, US), Thoele, Jody Ann (Bloomington, IL, US), filed on December 18, 2020, was published online on October 24, 2023.

The patent’s assignee for patent number 11797931 is State Farm Mutual Automobile Insurance Company (Bloomington, Illinois, United States).

News editors obtained the following quote from the background information supplied by the inventors: “Social activities, running errands, and family events may often be very demanding on one’s time. The number of activities and events that are available and/or required may be so numerous that planning may not be possible by mere memory recall. Some planning and scheduling systems and methods, such as calendar reminders, task lists, and project management methods may be cumbersome and difficult to use. For example, a wide variety of desired activities may be added to a list, however, researching the availability of event times while coordinating with already planned activities may not be feasible. In particular, the total combination of possible schedules that may be generated from analyzing all the available activities and times may be of an order of magnitude beyond human capability for solving. Arranging tasks to achieve maximum completion under limited constraints may require time-consuming planning.

“For highly active individuals, travel between multiple activities may add to the complexity by requiring additional time and cost considerations. In cases where task locations span a wide geographic region, determining an optimal route may require highly sophisticated analysis. While use of a global positioning system (GPS) device and mapping data may aid in calculating distances between a current position and desired targets, determining optimal travel paths between activities may require retrieving and examining a wide variety of other information. Rapidly changing conditions, such as traffic and weather, may impede calculation of the most efficient travel path. Further, spontaneously adding additional waypoints to a planned route may substantially increase the complexity in determining minimal travel time and cost. Task scheduling and management for activities dispersed across a geographic region presents many challenges for busy people. Conventional techniques may include additional drawbacks as well.”

As a supplement to the background information on this patent, NewsRx correspondents also obtained the inventors’ summary information for this patent: “The present embodiments may relate to systems and methods for optimizing travel plans via adaptive waypoint and route scheduling. The system may include one or more servers, one or more user computing devices, one or more sensors, one or more insurance provider servers, third party computing systems, one or more client devices, and/or one or more databases.

“In one aspect, an adaptive mapping computing device having at least one processor and/or associated transceiver in communication with at least one memory device may be provided. The at least one processor may be programmed to retrieve a plurality of tasks associated with a user and to retrieve geographic mapping data. The at least one processor may also be programmed to generate a route model based upon the retrieved plurality of tasks and the retrieved geographic mapping data. The at least one processor may be further programmed to execute the route model to determine an optimal route. The at least one processor and/or associated transceiver may also be programmed to transmit, to the user, an optimized travel plan based upon the optimal route. The computing device may include additional, less, or alternate functionality, including that discussed elsewhere herein.

“In another aspect, a computer-implemented method for generating an optimal travel plan using an adaptive mapping computing device having at least one processor and/or associated transceiver in communication with at least one memory device may be provided. The computer-implemented method may include, via the at least one processor, retrieving a plurality of tasks associated with a user and retrieving geographic mapping data. The computer-implemented method may also include, via the at least one processor, generating a route model based upon the retrieved plurality of tasks and the retrieved geographic mapping data, and executing the route model to determine an optimal route. The computer-implemented method may also include, via the at least one processor and/or associated transceiver, transmitting, to the user, an optimized travel plan based upon the optimal route. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

“In yet another aspect, a non-transitory computer-readable storage medium having computer-executable instructions embodied thereon may be provided. When executed by an adaptive mapping computing device having at least one processor and/or associated transceiver in communication with at least one memory device, the computer-executable instructions may cause the at least one processor to retrieve a plurality of tasks associated with a user and retrieve geographic mapping data. The computer-executable instructions may also cause the at least one processor to generate a route model based upon the retrieved plurality of tasks and the retrieved geographic mapping data. The computer-executable instructions may further cause the at least one processor to execute the route model to determine an optimal route. The computer-executable instructions may also cause the at least one processor and/or associated transceiver to transmit, to the user, an optimized travel plan based upon the optimal route. The instructions may direct additional, less, or alternate functionality, including that discussed elsewhere herein.

“Advantages will become more apparent to those skilled in the art from the following description of the preferred embodiments, which have been shown and described by way of illustration. As will be realized, the present embodiments may be capable of other and different embodiments, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.”

The claims supplied by the inventors are:

“1. An adaptive mapping computing device comprising: at least one processor in communication with at least one non-transitory memory device, wherein the at least one processor is configured to: retrieve a plurality of tasks associated with a user and preferred modes of transportation of the user; retrieve geographic mapping data; generate a route model based upon the retrieved plurality of tasks, the preferred modes of transportation, and the geographic mapping data including a starting location of the user; execute the route model to determine an optimal route and one or more optimal modes of transportation for the optimal route, the one or more optimal modes of transportation available at the starting location of the user; determine an optimized travel plan for the user based upon the optimal route and the one or more optimal modes of transportation; receive, from a user computing device associated with the user, geolocation data of the user computing device, wherein the geolocation data indicates a real-time location of the user; continuously receive real-time information from one or more data sources and the geolocation data from the user computing device, wherein the real-time information includes at least one of real-time weather data or real-time traffic data; continuously analyze the real-time information and the geolocation data to update the optimized travel plan in real-time based upon the real-time information and the geolocation data of the user computing device, thereby enabling the adaptive mapping computing device to perform, in real-time, continuous and adaptive route pathing and transportation mode selection based upon the real-time location of the user and fluctuating conditions; transmit, to the user computing device, the updated optimized travel plan; provide a computer application configured to display, on a user interface of the user computing device, the updated optimized travel plan overlaid on a map; and execute the computer application, wherein executing the computer application comprises displaying the updated optimized travel plan on the user interface of the user computing device.

“2. The computing device of claim 1, wherein the at least one processor is further configured to generate a task model associated with the user, wherein the task model is based upon one of tasks input by the user and predicted tasks determined from historical data associated with the user, the historical data including previous tasks planned for the user, and wherein the at least one processor is further configured to generate the route model based upon risk data associated with at least one of the user or estimated routes associated with the retrieved plurality of tasks.

“3. The computing device of claim 1, wherein the at least one processor is further configured to retrieve relationship data including at least one relationship data point indicating a relationship and an activity recorded including a location, a date, and a time.

“4. The computing device of claim 1, wherein the optimal route includes at least one beginning waypoint and at least one destination waypoint, and wherein the at least one destination waypoint is associated with at least one task of the plurality of tasks.

“5. The computing device of claim 1, wherein determining the optimal route comprises at least comparing a first optimization parameter of a first route to a first optimization parameter of a second route.

“6. The computing device of claim 1, wherein the optimal route includes at least one path between a first waypoint and a second waypoint, and wherein the at least one processor is further configured to transmit an offer for personal mobility policy insurance to the user.

“7. The computing device of claim 1, wherein the at least one processor is further configured to generate the route model based upon traffic data and weather data.

“8. The computing device of claim 1, wherein the at least one processor is further configured to: receive contextual data including information associated with a route the user travels more than a threshold number of times within a period of time; train a machine learning model using the contextual data, such that the machine learning model learns a travel behavior of the user over the period of time and how the contextual data affects the travel behavior; and execute the machine learning model to determine one or more optimal routes.

“9. A computer-implemented method for generating an optimal travel plan using an adaptive mapping computing device having at least one processor in communication with at least one non-transitory memory device, the method comprising: retrieving a plurality of tasks associated with a user and preferred modes of transportation of the user; retrieving geographic mapping data; generating a route model based upon the retrieved plurality of tasks, the preferred modes of transportation, and the geographic mapping data including a starting location of the user; executing the route model to determine an optimal route and one or more optimal modes of transportation for the optimal route, the one or more optimal modes of transportation available at the starting location of the user; determining an optimized travel plan for the user based upon the optimal route and the one or more optimal modes of transportation; receiving, from a user computing device associated with the user, geolocation data of the user computing device, wherein the geolocation data indicates a real-time location of the user; continuously receiving real-time information from one or more data sources and the geolocation data from the user computing device, wherein the real-time information includes at least one of real-time weather data or real-time traffic data; continuously analyzing the real-time information and the geolocation data to updating the optimized travel plan in real-time based upon the real-time information and the geolocation data of the user computing device, thereby enabling the adaptive mapping computing device to perform, in real-time, continuous and adaptive route pathing and transportation mode selection based upon the real-time location of the user and fluctuating conditions; transmitting, to the user computing device, the updated optimized travel plan; providing a computer application configured to display, on a user interface of the user computing device, the updated optimized travel plan overlaid on a map; and executing the computer application, wherein executing the computer application comprises displaying the updated optimized travel plan on the user interface of the user computing device.

“10. The computer-implemented method of claim 9 further comprising generating a task model associated with the user, wherein the task model is based upon one of tasks input by the user and predicted tasks determined from historical data associated with the user, the historical data including previous tasks planned for the user, and wherein the at least one processor is further configured to generate the route model based upon risk data associated with at least one of the user or estimated routes associated with the retrieved plurality of tasks.

“11. The computer-implemented method of claim 9 further comprising retrieving relationship data including at least one relationship data point indicating a relationship and an activity recorded including a location, a date, and a time.

“12. The computer-implemented method of claim 9, wherein the optimal route includes at least one beginning waypoint and at least one destination waypoint, and wherein the at least one destination waypoint is associated with at least one task of the plurality of tasks.

“13. The computer-implemented method of claim 9, wherein determining the optimal route includes at least comparing a first optimization parameter of a first route to a first optimization parameter of a second route.

“14. The computer-implemented method of claim 9, wherein the optimal route includes at least one path between a first waypoint and a second waypoint, and wherein the method further comprises an offer for personal mobility policy insurance to the user.

“15. The computer-implemented method of claim 9 further comprising generating the route model based upon traffic data and weather data.”

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

For additional information on this patent, see: Brannan, Joseph Robert. Systems and methods for adaptive route optimization for learned task planning. U.S. Patent Number 11797931, filed December 18, 2020, and published online on October 24, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11797931)&db=USPAT&type=ids

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