“Methods Of Determining Accident Cause And/Or Fault Using Telematics Data” in Patent Application Approval Process (USPTO 20210407015): State Farm Mutual Automobile Insurance Company
2022 JAN 13 (NewsRx) -- By a
This patent application is assigned to
The following quote was obtained by the news editors from the background information supplied by the inventors: “During the claims process, insurance providers typically rely heavily on eyewitness accounts to determine how an accident occurred (e.g., to determine the cause and the individual(s) at fault). For example, an employee of the insurance provider may learn about the sequence of events leading to an accident by talking to the insured and/or other participants in the accident. As another example, the insurance provider employee may review a police report that, by its nature, typically reflects information garnered by another eyewitness (police officer) observing the accident scene well after the accident occurred, if at all. As a result, the insurance provider may obtain inaccurate information, which may in turn cause the insurance provider to incorrectly determine cause/fault, and/or fail to appropriately reflect that cause/fault in future actions (e.g., when adjusting premium levels for an insured involved in the accident, etc.).
“The present embodiments may overcome these and/or other deficiencies.”
In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventors’ summary information for this patent application: “The present embodiments disclose systems and methods that may relate to the intersection of telematics and insurance. In some embodiments, for example, telematics and/or other data may be collected and used to determine cause and/or fault of a vehicle accident. The data may be gathered from one or more sources, such as mobile devices (e.g., smart phones, smart glasses, smart watches, smart wearable devices, smart contact lenses, and/or other devices capable of wireless communication); smart vehicles; smart vehicle or smart home mounted sensors; third party sensors or sources of data (e.g., other vehicles, public transportation systems, government entities, and/or the Internet); and/or other sources of information. The cause and/or fault may be used to handle an insurance claim, for example. More generally, insurance claims, policies, premiums, rates, discounts, rewards, programs, and/or other insurance-related items may be adjusted, generated and/or updated based upon the cause and/or fault as determined from the telematics and/or other collected data.
“In one aspect, a computer-implemented method for facilitating fault determination may comprise (1) collecting, by one or more remote servers associated with an insurance provider, accident data associated with a vehicle accident involving a driver. The accident data may include vehicle telematics data, and/or the driver may be associated with an insurance policy issued by the insurance provider. The method may also include (2) analyzing, by the one or more remote servers, the accident data; (3) determining, by the one or more remote servers and based upon the analysis of the accident data, fault of the driver for the vehicle accident; (4) using the determined fault of the driver to handle, at the one or more remote servers, an insurance claim associated with the vehicle accident; and/or (5) using the determined fault of the driver to adjust, generate or update, at the one or more remote servers, one or more insurance-related items. The one or more insurance-related items may include one or more of (i) parameters of the insurance policy; (ii) a premium; (iii) a rate; (iv) a discount; and/or (v) a reward. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.
“In another aspect, a system for facilitating fault determination may comprise one or more processors and one or more memories. The one or more memories may store instructions that, when executed by the one or more processors, cause the one or more processors to (1) collect accident data associated with a vehicle accident involving a driver. The accident data may include vehicle telematics data, and/or the driver may be associated with an insurance policy issued by an insurance provider. The instructions may also cause the one or more processors to (2) analyze the accident data; (3) determine, based upon the analysis of the accident data, fault of the driver for the vehicle accident; (4) use the determined fault of the driver to handle an insurance claim associated with the vehicle accident; and/or (5) use the determined fault of the driver to adjust, generate or update one or more insurance-related items. The one or more insurance-related items may include one or more of (i) parameters of the insurance policy; (ii) a premium; (iii) a rate; (iv) a discount; or (v) a reward. The system may include additional, less, or alternate functionality, including that discussed elsewhere herein.
“In yet another aspect, a computer-implemented method for facilitating accident cause determination may comprise (1) collecting, by one or more remote servers associated with an insurance provider, accident data associated with a vehicle accident involving a driver. The accident data may include vehicle telematics data, and/or the driver may be associated with an insurance policy issued by the insurance provider. The method may also include (2) analyzing, by the one or more remote servers, the accident data; and/or (3) determining, by the one or more remote servers and based upon the analysis of the accident data, one or more causes of the vehicle accident. At least one of the one or more causes may be assigned or attributed to the driver or an external factor. The method may further include (4) using the one or more causes of the vehicle accident to handle, at the one or more remote servers, an insurance claim associated with the vehicle accident; and/or (5) using the one or more causes of the vehicle accident to adjust, generate or update, at the one or more remote servers, one or more insurance-related items. The one or more insurance-related items may include one or more of (i) parameters of the insurance policy; (ii) a premium; (iii) a rate; (iv) a discount; or (v) a reward. The method 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 claims supplied by the inventors are:
“1.-20. (canceled)
“21. A method implemented by a system comprising one or more processors and one or more memories, the method comprising: collecting, by one or more sensors, accident data associated with a vehicle accident involving a driver of a vehicle, the accident data including driver acuity data and vehicle telematics data before, during, and after the vehicle accident, the driver acuity data comprising at least one selected from a group consisting of phone usage data, audio data, image data, and video data, the vehicle telematics data comprising data indicating movement of the vehicle; time-stamping at least a part of the driver acuity data by associating the driver acuity data with time information; determining a time of the vehicle accident; selecting the driver acuity data during the vehicle accident based on the time information associated with the driver acuity data and the time of the vehicle accident; analyzing, by the one or more processors, the selected driver acuity data to determine a driver acuity of the driver during the vehicle accident; analyzing, by the one or more processors, the vehicle telematics data to determine a sequence of movements of the vehicle preceding and during the vehicle accident; and determining, by the one or more processors, fault of the driver for the vehicle accident based upon the determined sequence of movements of the vehicle preceding and during the accident and the determined driver acuity of the driver during the vehicle accident.
“22. The method of claim 21, wherein the vehicle telematics data further comprises data indicating at least one selected from a group consisting of operation status of the vehicle, braking, a brake light status, turning, a turn signal status, and air bag status.
“23. The method of claim 22, further comprising: analyzing, by the one or more processors, the accident data to determine driver behavior of the driver before, during or after the vehicle accident.
“24. The method of claim 23, further comprising: analyzing the accident data to determine driver behavior of another driver involved in the vehicle accident before, during or after the vehicle accident.
“25. The method of claim 21, wherein the accident data further comprises environmental condition data, the environmental condition data including data indicating at least one selected from a group consisting of a road condition, a weather condition, and a traffic condition.
“26. The method of claim 25, further comprising: analyzing, by the one or more processors, the environmental condition data to determine a condition that is associated with a location of the vehicle accident before, during or after the vehicle accident, the conditions including at least one selected from a group consisting of road conditions, weather conditions, traffic conditions, and construction conditions.
“27. The method of claim 25, further comprising: analyzing, by the one or more processors, the environmental condition data and data associated with other vehicle accidents that occurred at the location of the vehicle accident to determine the fault of the driver, wherein the fault of the driver includes a percentage representing the fault of the driver.
“28. The method of claim 21, wherein collecting accident data further includes collecting data generated by the vehicle or a computer system of the vehicle.
“29. The method of claim 28, wherein the accident data further at least one selected from a group consisting of data associated with a vehicle other than the insured vehicle, data received via vehicle-to-vehicle (V2V) communication, and data collected from roadside equipment or infrastructure located near a location of the vehicle accident.
“30. The method of claim 21, wherein the one or more sensors include one or more sensors mounted on the vehicle.
“31. A system comprising: one or more processors; one or more sensors configured to collect accident data associated with a vehicle accident involving a driver of a vehicle, the accident data including driver acuity data and vehicle telematics data before, during, and after the vehicle accident, the driver acuity data comprising at least one selected from a group consisting of phone usage data, audio data, image data, and video data, the vehicle telematics data comprising data indicating movement of the vehicle; and one or more memories storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: time-stamping at least a part of the driver acuity data by associating the driver acuity data with time information; determining a time of the vehicle accident; selecting the driver acuity data during the vehicle accident based on the time information associated with the driver acuity data and the time of the vehicle accident; analyzing the selected driver acuity data to determine a driver acuity of the driver during the vehicle accident; analyzing the vehicle telematics data to determine a sequence of movements of the vehicle preceding and during the vehicle accident; and determining fault of the driver for the vehicle accident based upon the determined sequence of movements of the vehicle preceding and during the accident and the determined driver acuity of the driver during the vehicle accident.
“32. The system of claim 31, wherein the vehicle telematics data further comprises data indicating at least one selected from a group consisting of operation status of the vehicle, braking, a brake light status, turning, a turn signal status, and air bag status.
“33. The system of claim 32, wherein the operations further comprise: analyzing the accident data to determine driver behavior of the driver before, during or after the vehicle accident.
“34. The system of claim 33, wherein the operations further comprise: analyzing the accident data to determine driver behavior of another driver involved in the vehicle accident before, during or after the vehicle accident.
“35. The system of claim 31, wherein the accident data further comprises environmental condition data, the environmental condition data including data indicating at least one selected from a group consisting of a road condition, a weather condition, and a traffic condition.
“36. The system of claim 35, wherein the operations further comprise: analyzing the environmental condition data to determine a condition that is associated with a location of the vehicle accident before, during or after the vehicle accident, the conditions including at least one selected from a group consisting of road conditions, weather conditions, traffic conditions, and construction conditions.
“37. The system of claim 35, wherein the operations further comprise: analyzing the environmental condition data and data associated with other vehicle accidents that occurred at the location of the vehicle accident to determine the fault of the driver, wherein the fault of the driver includes a percentage representing the fault of the driver.
“38. A method implemented by a system comprising one or more processors and one or more memories, the method comprising: collecting, by one or more sensors, accident data associated with a vehicle accident involving a driver of a vehicle, the accident data including driver acuity data and vehicle telematics data before, during, and after the vehicle accident, the driver acuity data comprising at least one selected from a group consisting of phone usage data, audio data, image data, and video data, the vehicle telematics data comprising data indicating movement of the vehicle; time-stamping at least a part of the driver acuity data by associating the driver acuity data with time information; determining a time of the vehicle accident; selecting the driver acuity data during the vehicle accident based on the time information associated with the driver acuity data and the time of the vehicle accident; analyzing, by the one or more processors, the selected driver acuity data to determine a driver acuity of the driver during the vehicle accident; analyzing, by the one or more processors, the vehicle telematics data to determine a sequence of movements of the vehicle preceding and during the vehicle accident; and determining, by the one or more processors, one or more causes of the vehicle accident based upon the determined sequence of movements of the vehicle preceding and during the accident and the determined driver acuity of the driver during the vehicle accident, at least one of the one or more causes being attributed to the driver.
“39. The method of claim 38, wherein the vehicle telematics data further comprises data indicating at least one selected from a group consisting of operation status of the vehicle, braking, a brake light status, turning, a turn signal status, and air bag status.
“40. The method of claim 39, further comprising: analyzing, by the one or more processors, the accident data to determine driver behavior of the driver before, during or after the vehicle accident.”
URL and more information on this patent application, see: Baumann,
(Our reports deliver fact-based news of research and discoveries from around the world.)
University of Birmingham Reports Findings in Risk Management (Understanding the potential of emerging digital technologies for improving road safety): Risk Management
Patent Issued for Utilizing a protected server environment to protect data used to train a machine learning system (USPTO 11210424): Deepintent Inc.
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