Patent Application Titled “Systems And Methods For Modeling Telematics, Positioning, And Environmental Data” Published Online (USPTO 20240005411): Patent Application
2024 JAN 23 (NewsRx) -- By a
No assignee for this patent application has been made.
Reporters obtained the following quote from the background information supplied by the inventors: “Vehicle insurance provides financial protection against physical damage and/or bodily injury caused by a vehicular accident. Other financial protections may be provided, such as vehicle theft protection or weather-related damage protection. Conventionally, vehicle insurance rates or premiums may be typically determined based upon a driver’s age and driving history, a vehicle make, model, and year, among a myriad of other factors.
“Some insurance policies (e.g., vehicle insurance, rental insurance, homeowners insurance, and/or property insurance) provide coverage for loss or damage to personal possessions of a policyholder during a policy claim (e.g., a formal request by the policyholder to an insurance provider for reimbursement for one or more personal possessions covered under an insurance policy). Loss events may include vehicle damage, residential fires, theft, vandalism and/or other events that cause partial or complete loss of the personal possessions of the policyholder.
“Policy coverage may typically be associated with the amount of risk or liability that is covered by the insurance provider for the policyholder’s possessions during these loss events. Insurance providers may typically set policy premiums based upon a number of factors including an amount of coverage that the policy provides (e.g., policy coverage or insurance coverage). An insurance policy may have different limits, such as coverage limits (e.g., limits of liability) and aggregate limits. Different types of insurance policies limits may typically include payout limits to a policy holder with respect to payouts over time, the maximum amount the insurer will pay, or a combination thereof.
“Insurance premiums and coverage rates may depend on, at least in part, coverage limits or limits of liability, also referred to as liability limits. At least some applications may benefit from accurately predicting the likelihood of insurance claims being made by policyholders. In such applications, insurance claim costs may be anticipated. Further, based upon the likelihood of insurance claims being made and their respective costs, insurance policy premium prices may be determined appropriately.
“However, current solutions may lack the ability to provide accurate predictions of liability limits for users. Current solutions may also be inefficient, cumbersome, untimely, burdensome, and/or have other drawbacks.”
In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventors’ summary information for this patent application: “The present embodiments may relate to, inter alia, systems and methods for building a model to analyze collected data. The model may be built using historical data (e.g., historical user data and/or historical liability limit data) to analyze collected data including telematics, positioning, and/or environmental data. In some embodiments, the model may use the historical data to relate historical liability limit data to historical user data (e.g., personal data including filed claim data and natural loss data and telematics, positioning, and/or environmental data). Accordingly, the collected data may be input into the model to determine a liability limit for a user associated with the collected data. In some embodiments, the liability limit may be used to generate an insurance policy for the user, and the liability limit may be associated with a maximum amount for which an insurance company associated with the insurance policy is accountable.
“In an exemplary embodiment, the model may be created through the gathering of established user data records and historical data associated with a plurality of users. The user data records may include user driving history and insurance data (e.g., claims data, premium cost data, etc.). User data and historical data may also include positional data and/or telematics data reported from one or more measurement sensory devices, such as a GPS device, an accelerometer, a gyroscope, or other sensors mounted within user computing devices (e.g., mobile devices or tablets) or integrated into vehicles operated by the users. Historical user data may also include environmental data associated with the users or a surrounding area of the users (e.g., traffic data, pedestrian data, etc.). The model may be built by relating one or more sets of the historical data. In some embodiments, the model may be built by relating one or more of the historical vehicle positional, telematics data, and/or environmental data with the historical insurance data. For example, the model may be used to determine and/or predict an insurance liability amount based upon historical position, telematics, and/or environmental data.
“In another exemplary embodiment, systems and methods may provide feedback to users with respect to driving conditions, intersections, or the like. For example, a user may be provided with feedback with respect to their traveling speed when operating a vehicle in view of a posted speed limit. In another example, a user may be provided with data pertaining to a planned driving route. Data with respect to a certain route may indicate the number of traffic incidents that have occurred along the route over a certain time period (e.g., the past six months). Additionally or alternatively, users may be notified of dangerous areas (e.g., intersections) along a certain route. An optimal route may be suggested that is considered to be the lowest risk, or safest route. The optimal route may be determined using a combination of location data, historical telematics data, among other factors, such as weather data.
“In one aspect, a modeling computing device including at least one processor in communication with a memory device may be provided. The at least one processor may be configured to: (i) retrieve, from the at least one memory device, historical data associated with a plurality of users, wherein the historical data includes historical liability limit data and historical user data, and wherein the historical user data includes at least one of historical personal information, historical vehicle telematics data, and historical environmental data, (ii) generate a model that relates the historical liability limits data and the historical user data, (iii) store the model in the at least one memory device, (iv) collect current user data associated with a candidate user, wherein the current user data includes current personal information, current vehicle telematics data, and current environmental data, and/or (v) analyze the collected current user data using the generated model. The modeling computing device may include additional, less, or alternate actions, including those discussed elsewhere herein.
“In another aspect, a computer-implemented method implemented by a modeling computing device including at least one processor in communication with at least one memory device may be provided. The computer-implemented method may include (i) retrieving, from the at least one memory device, historical data associated with a plurality of users, wherein the historical data includes historical liability limit data and historical user data, and wherein the historical user data includes at least one of historical personal information, historical vehicle telematics data, and historical environmental data, (ii) generating a model that relates the historical liability limits data and the historical user data, (iii) storing the model in the at least one memory device, (iv) collecting current user data associated with a candidate user, wherein the current user data includes current personal information, current vehicle telematics data, and current environmental data, and/or (v) analyzing the collected current user data using the generated model. The computer-implemented method may include additional, less, or alternate actions, including those discussed elsewhere herein.
“In yet another aspect, a computer-readable storage medium having computer-executable instructions embodied thereon may be provided. The computer-executable instructions, when executed by at least one processor, may cause the at least one processor to: (i) retrieve, from the at least one memory device, historical data associated with a plurality of users, wherein the historical data includes historical liability limit data and historical user data, and wherein the historical user data includes at least one of historical personal information, historical vehicle telematics data, and historical environmental data, (ii) generate a model that relates the historical liability limits data and the historical user data, (iii) store the model in the at least one memory device, (iv) collect current user data associated with a candidate user, wherein the current user data includes current personal information, current vehicle telematics data, and current environmental data, and/or (v) analyze the collected current user data using the generated model. The computer-readable storage medium may include additional, less, or alternate actions, including those 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 Figures depict preferred embodiments for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the systems and methods illustrated herein may be employed without departing from the principles of the invention described herein.”
The claims supplied by the inventors are:
“1. A modeling computing device comprising at least one processor in communication with at least one memory device, the at least one processor configured to: retrieve, from the at least one memory device, historical data associated with a plurality of users, wherein the historical data includes historical liability data and historical user data; create a first plurality of training datasets including the historical liability data and the historical user data for training a model; collect current user data of a candidate user associated with a vehicle, wherein the current user data includes current vehicle telematics data associated with the vehicle, wherein the current vehicle telematics data is gathered by one or more sensors during operation of the vehicle, and wherein the one or more sensors include at least one of a GPS device, an accelerometer, a gyroscope, a camera, or a sensor installed within the vehicle or located remotely from the vehicle; create a second plurality of training datasets by updating the first plurality of training datasets to include the collected current user data; update the model by applying the second plurality of training datasets to the model; and execute the updated model to determine a current liability level for the candidate user.
“2. The modeling computing device of claim 1, wherein the historical user data includes at least one of historical personal information, historical vehicle telematics data, or historical environmental data.
“3. The modeling computing device of claim 1, wherein the at least one processor is further configured to: build, using one or more machine learning programs, the model based upon the first plurality of training datasets; and store the model in the at least one memory device.
“4. The modeling computing device of claim 3, wherein the one or more machine learning programs include machine learning, artificial intelligence, or a combination thereof, and wherein the at least one processor is further configured to build the first plurality of training datasets using the historical data associated with the plurality of users, the historical data including historical insurance data.
“5. The modeling computing device of claim 1, wherein the current user data further includes current personal information and current environmental data.
“6. The modeling computing device of claim 1, wherein the at least one processor is further configured to: transmit the determined current liability level to at least one third party computing device, wherein the at least one third party computing device includes an insurance computing device.
“7. The modeling computing device of claim 1, wherein the at least one processor is further configured to: generate an insurance policy for the candidate user based upon the determined current liability level; and enroll the candidate user with an insurance provider of the insurance policy.
“8. A computer-implemented method implemented by a modeling computing device including at least one processor in communication with at least one memory device, the computer-implemented method comprising: retrieving, from the at least one memory device, historical data associated with a plurality of users, wherein the historical data includes historical liability data and historical user data; creating a first plurality of training datasets including the historical liability data and the historical user data for training a model; collecting current user data of a candidate user associated with a vehicle, wherein the current user data includes current vehicle telematics data associated with the vehicle, wherein the current vehicle telematics data is gathered by one or more sensors during operation of the vehicle, and wherein the one or more sensors include at least one of a GPS device, an accelerometer, a gyroscope, a camera, or a sensor installed within the vehicle or located remotely from the vehicle; creating a second plurality of training datasets by updating the first plurality of training datasets to include the collected current user data; updating the model by applying the second plurality of training datasets to the model; and executing the updated model to determine a current liability level for the candidate user.
“9. The computer-implemented method of claim 8, wherein the historical user data includes at least one of historical personal information, historical vehicle telematics data, or historical environmental data.
“10. The computer-implemented method of claim 8 further comprising: building, using one or more machine learning programs, the model based upon the first plurality of training datasets; and storing the model in the at least one memory device.
“11. The computer-implemented method of claim 10, wherein the one or more machine learning programs include machine learning, artificial intelligence, or a combination thereof, and wherein the method further comprises building the first plurality of training datasets using the historical data associated with the plurality of users, the historical data including historical insurance data.
“12. The computer-implemented method of claim 8, wherein the current user data further includes current personal information and current environmental data.
“13. The computer-implemented method of claim 8 further comprising: transmitting the determined current liability level to at least one third party computing device, wherein the at least one third party computing device includes an insurance computing device.
“14. The computer-implemented method of claim 8 further comprising: generating an insurance policy for the candidate user based upon the determined current liability level; and enrolling the candidate user with an insurance provider of the insurance policy.
“15. At least one non-transitory computer-readable medium having computer-executable instructions embodied thereon, wherein when executed by a modeling computing device including at least one processor in communication with at least one memory device, the computer-executable instructions cause the at least one processor to: retrieve, from the at least one memory device, historical data associated with a plurality of users, wherein the historical data includes historical liability data and historical user data; create a first plurality of training datasets including the historical liability data and the historical user data for training a model; collect current user data of a candidate user associated with a vehicle, wherein the current user data includes current vehicle telematics data associated with the vehicle, wherein the current vehicle telematics data is gathered by one or more sensors during operation of the vehicle, and wherein the one or more sensors include at least one of a GPS device, an accelerometer, a gyroscope, a camera, or a sensor installed within the vehicle or located remotely from the vehicle; create a second plurality of training datasets by updating the first plurality of training datasets to include the collected current user data; update the model by applying the second plurality of training datasets to the model; and execute the updated model to determine a current liability level for the candidate user.
“16. The at least one non-transitory computer-readable medium of claim 15, wherein the historical user data includes at least one of historical personal information, historical vehicle telematics data, or historical environmental data.
“17. The at least one non-transitory computer-readable medium of claim 15, wherein the computer-executable instructions further cause the at least one processor to: build, using one or more machine learning programs, the model based upon the first plurality of training datasets; and store the model in the at least one memory device.
“18. The at least one non-transitory computer-readable medium of claim 15, wherein the current user data further includes current personal information and current environmental data.
“19. The at least one non-transitory computer-readable medium of claim 15, wherein the computer-executable instructions further cause the at least one processor to: transmit the determined current liability level to at least one third party computing device, wherein the at least one third party computing device includes an insurance computing device.
“20. The at least one non-transitory computer-readable medium of claim 15, wherein the computer-executable instructions further cause the at least one processor to: generate an insurance policy for the candidate user based upon the determined current liability level; and enroll the candidate user with an insurance provider of the insurance policy.”
For more information, see this patent application: Bracero, Bernardo; Fogg,
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