[ad_1]
2022 JUL 07 (NewsRx) — By a
The patent’s assignee for patent number 11368491 is
News editors obtained the following quote from the background information supplied by the inventors: “Insurance companies gather information regarding insured parties and use such data to determine pricing for insurance policies. For example, an auto insurance company may use data regarding driver’s location, age, vehicle, driving history, or other information to determine a premium or a deductible of an auto insurance policy for the driver. Traditionally, data regarding a policy holder or a vehicle may be self-reported by policy holder, or may be reported by third parties such as repair shops, government agencies, or other drivers. Because such data may be infrequently or inconsistently reported, the data may become out-of-date over time. Accordingly, an insurance company may have difficulty developing and maintaining an accurate, current evaluation of the risk associated with insuring an individual.”
As a supplement to the background information on this patent, NewsRx correspondents also obtained the inventors’ summary information for this patent: “Implementations of the present disclosure are generally directed to adjusting characteristic(s) of a policy based on analysis of dynamic data collected during trip(s) taken by a vehicle and/or static data associated with the trip(s). More particularly, implementations of the present disclosure are directed to determining similarity metric(s) indicating the similarity between routes taken in a vehicle, and adjusting policy characteristic(s) based on the similarity metric(s). Implementations are also directed to determining recommended route(s) based on the similarity metric(s) indicating similarity between previous trip(s) and future, or current, trip(s).
“In general, innovative aspects of the subject matter described in this specification can be embodied in methods that includes actions of: based on data collected by one or more sensors during trips by one or more vehicles, determining changes in movement of the one or more vehicles during the trips; determining a similarity metric for at least one pair of the trips that includes a first trip and a second trip, the similarity metric based at least partly on a similarity between the changes in movement during the first trip and the changes in movement during the second trip; based at least partly on the similarity metric, determining at least one characteristic of at least one policy associated with one of the one or more vehicles; and providing the at least one characteristic of the at least one policy for presentation in a user interface on a computing device.
“Implementations can include one or more of the following features: accessing risk data describing a risk of the first trip; determining a risk of the second trip based on: the risk of the first trip; and the similarity metric for the first trip and the second trip; and based at least partly on the similarity metric for the first trip and the second trip, determining the at least one characteristic of the at least one policy associated with the second trip based at least partly on the risk of the second trip; the risk data describes a history of incidents that occurred on a route corresponding to the first trip; accessing static data describing one or more of a road characteristic, a terrain characteristic, or a neighborhood characteristic of a route corresponding to the second trip; and the risk of the second trip is further based on the static data; the first trip and the second trip have different starting locations; the first trip and the second trip have different ending locations; or the first trip and the second trip have different starting locations and different ending locations; the data indicates one or more of: changes in speed of the vehicle; or changes in orientation of the vehicle; the one or more sensors include one or more of: a sensor incorporated into the vehicle; a sensor incorporated into at least one other vehicle; a sensor included in a mobile computing device of a driver of the vehicle; a sensor included in a mobile computing device of a driver of the at least one other vehicle; or an external sensor in proximity to the vehicle during at least one of the plurality of times; the at least one characteristic of the policy includes one or more of: a coverage type; a coverage amount; a deductible amount; a premium amount; or a price; the at least one policy includes one or more of: a vehicle insurance policy for a vehicle on the second trip; a life insurance policy for a driver of the vehicle; or a health insurance policy for the driver of the vehicle; the data further indicates one or more dynamic conditions present during the first trip and the second trip; the one or more dynamic conditions include one or more of: a time of day; a day of the week or month; a weather condition; a road condition; a number other vehicles; a traffic condition (e.g., an accident or absence thereof, congestion or absence thereof) independent of or dependent on a number of other vehicles in the vicinity; and/or a presence of a repair crew; and the similarity metric is further based on a similarity between the one or more dynamic conditions present during the first trip and the second trip.
“Other implementations of any of the above aspects include corresponding systems, apparatus, and computer programs that are configured to perform the actions of the methods, encoded on computer storage devices.
“Implementations of the present disclosure provide one or more of the following technical advantages and/or improvements compared to traditional systems. Dynamic sensor data describing the movement, location, condition, or other operational characteristics of a vehicle may be received and analyzed (e.g., in real time) to determine a complexity of a trip or a similarity between the trip and other trips. One or both of the complexity or similarity may be employed to (e.g., dynamically) set insurance premiums or other policy characteristics for a driver, and (e.g., dynamically) recommend routes that minimize accident risk for the driver. By employing dynamic sensor data to generate outputs such as route recommendations and/or policy characteristic determinations, implementations provide route recommendations and/or policy characteristic determinations that are more accurate and/or more up-to-date compared to traditional systems that may employ static and/or out-of-date information. Accordingly, traditional systems may need to recalculate their outputs frequently if they are to remain current and accurate, thus consuming more processing power, storage space, network capacity, active memory, and/or other computing resources compared systems according to the implementations described herein.”
The claims supplied by the inventors are:
“1. A computer-implemented method performed by at least one processor, the method comprising: based on data collected by one or more sensors during trips by one or more vehicles, determining, by the at least one processor, changes in movement of the one or more vehicles during the trips; selecting, by the at least one processor, at least one pair of trips that includes a first trip driven by a first vehicle and a second trip driven by a second vehicle; determining, by the at least one processor, a first sequence of movements by the first vehicle during the first trip based on changes in orientation, speed, and acceleration of the first vehicle during the first trip; determining, by the at least one processor, a second sequence of movements by the second vehicle during the second trip based on changes in orientation, speed, and acceleration of the second vehicle during the second trip; determining, by the at least one processor, a risk level of the second trip by: comparing the first sequence of the changes in movement during the first trip in the selected pair and the second sequence of the changes in movement during the second trip in the selected pair to determine a similarity metric between the first trip and the second trip, and determining, by the at least one processor, a risk of the second trip based on a predetermined risk level of the first trip and the similarity metric for the first trip and the second trip; determining, by the at least one processor and based at least partly on the risk level of the second trip, at least one characteristic of at least one policy associated with the second vehicle, wherein the at least one characteristic of the policy includes one or more of: a deductible amount, a premium amount, or a price; providing, by the at least one processor, the at least one characteristic of the at least one policy for presentation in a user interface on a computing device.
“2. The method of claim 1, wherein the predetermined risk level of the first trip is determined based on a history of incidents that occurred on a route corresponding to the first trip.
“3. The method of claim 1, further comprising: accessing, by the at least one processor, static data describing one or more of a road characteristic, a terrain characteristic, or a neighborhood characteristic of a route corresponding to the second trip; wherein the risk level of the second trip is further based on the static data.
“4. The method of claim 1, wherein: the first trip and the second trip have different starting locations; the first trip and the second trip have different ending locations; or the first trip and the second trip have different starting locations and different ending locations.
“5. The method of claim 1, wherein the one or more sensors include one or more of: a sensor incorporated into the vehicle; a sensor incorporated into at least one other vehicle; a sensor included in a mobile computing device of a driver of the vehicle; a sensor included in a mobile computing device of a driver of the at least one other vehicle; or an external sensor in proximity to the vehicle during at least one of the trips.
“6. The method of claim 1, wherein the at least one policy includes one or more of: a vehicle insurance policy for a vehicle on the second trip; a life insurance policy for a driver of the vehicle; or a health insurance policy for the driver of the vehicle.
“7. The method of claim 1, wherein: the data further indicates one or more dynamic conditions present during the first trip and the second trip; the one or more dynamic conditions include one or more of: a weather condition; a road condition; a number other vehicles; or a presence of a repair crew; and the similarity metric is further based on a similarity between the one or more dynamic conditions present during the first trip and the second trip.
“8. The method of claim 1, further comprising: receiving data indicating a proposed trip to be taken by one of the second vehicle, wherein the proposed trip has and ending location that is different from and ending location of the first trip and an ending location of the second trip; identifying at least a first route for the proposed trip and a second route for the proposed trip; determining a first similarity metric between the first route and the second trip, the first similarity metric indicating a similarity in a sequence of expected vehicle movements along the first route with the second sequence of movements during the second trip; determining a second similarity metric between the second route and the second trip, the second similarity metric indicating a similarity in a sequence of expected vehicle movements along the second route with the second sequence of movements during the second trip; selecting, based on the first similarity metric and the second similarity metric, the first route as a recommended route for the proposed trip; and providing the recommended route for presentation on the computing device.
“9. The method of claim 8, wherein selecting the first route as the recommended route for the proposed trip comprises determining that the first similarity metric is greater than the second similarity metric.
“10. A system comprising: at least one processor; and a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed, cause the at least one processor to perform operations comprising: based on data collected by one or more sensors during trips by one or more vehicles, determining changes in movement of the one or more vehicles during the trips; selecting at least one pair of trips that includes a first trip driven by a first vehicle and a second trip driven by a second vehicle; determining a first sequence of movements by the first vehicle during the first trip based on changes in orientation, speed, and acceleration of the first vehicle during the first trip; determining a second sequence of movements by the second vehicle during the second trip based on changes in orientation, speed, and acceleration of the second vehicle during the second trip; determining a risk of the second trip by: comparing the first sequence of the changes in movement during the first trip in the selected pair and the second sequence of the changes in movement during the second trip in the selected pair to determine a similarity metric between the first trip and the second trip, and determining a risk of the second trip based on a predetermined risk of the first trip and the similarity metric for the first trip and the second trip; determining, based at least partly on the risk of the second trip, at least one characteristic of at least one policy associated with the second vehicle, wherein the at least one characteristic of the policy includes one or more of: a deductible amount, a premium amount, or a price; providing the at least one characteristic of the at least one policy for presentation in a user interface on a computing device.
“11. The system of claim 10, wherein the predetermined risk of the first trip is determined based on a history of incidents that occurred on a route corresponding to the first trip.
“12. The system of claim 10, the operations further comprising: accessing static data describing one or more of a road characteristic, a terrain characteristic, or a neighborhood characteristic of a route corresponding to the second trip; wherein the risk of the second trip is further based on the static data.
“13. The system of claim 10, wherein: the first trip and the second trip have different starting locations; the first trip and the second trip have different ending locations; or the first trip and the second trip have different starting locations and different ending locations.
“14. The system of claim 10, wherein the one or more sensors include one or more of: a sensor incorporated into the vehicle; a sensor incorporated into at least one other vehicle; a sensor included in a mobile computing device of a driver of the vehicle; a sensor included in a mobile computing device of a driver of the at least one other vehicle; or an external sensor in proximity to the vehicle during at least one of the trips.
“15. The system of claim 10, wherein the at least one policy includes one or more of: a vehicle insurance policy for a vehicle on the second trip; a life insurance policy for a driver of the vehicle; or a health insurance policy for the driver of the vehicle.
“16. The system of claim 10, wherein: the data further indicates one or more dynamic conditions present during the first trip and the second trip; the one or more dynamic conditions include one or more of: a weather condition; a road condition; a number other vehicles; or a presence of a repair crew; and the similarity metric is further based on a similarity between the one or more dynamic conditions present during the first trip and the second trip.”
There are additional claims. Please visit full patent to read further.
For additional information on this patent, see:
(Our reports deliver fact-based news of research and discoveries from around the world.)
[ad_2]
Source link
