Automobile Monitoring Systems and Methods for Detecting Damage and Other Conditions

Abstract

A method of determining damage to property includes inputting historical data into a machine learning model to identify an insured type, features, and/or characteristics. The method may include identifying a peril, repair and/or replacement cost of the vehicle by analyzing a digital image from a device of an insured, the digital image depicting damage to the vehicle, The method may include inputting the digital image into the trained machine learning model to identify a type, feature, and/or characteristic of the vehicle, and may include identifying a peril, repair, and/or replacement cost associated with the vehicle. A method may include receiving and/or retrieving free-form text associated with an insurance claim and/or a vehicle, identifying at least one key word composing the free-form text, and determining based on the at least one key word a cause of loss and/or peril that caused damage to the vehicle.

1 . A computer-implemented method of determining damage to personal property, the method comprising:
 inputting, via one or more processors, historical claim data into a machine learning algorithm to train the algorithm to identify an insured vehicle, a respective type of the insured vehicle, respective insured vehicle features or characteristics, a peril associated with the insured vehicle, and/or a repair or replacement cost associated with the insured vehicle;   receiving, via the one or more processors and/or the one or more transceivers, a digital image depicting damage to the insured vehicle, the digital image submitted by an insured entity via a webpage, website, and/or mobile device; and   inputting, via the one or more processors, the digital image of the damaged insured vehicle into a processor having the trained machine learning algorithm installed in a memory unit, the trained machine learning algorithm identifying a type of the damaged insured vehicle, a respective feature or characteristic of the damaged insured vehicle, a peril associated with the damaged insured vehicle, and/or a repair or replacement cost associated with the damaged insured vehicle to facilitate handling an insurance claim associated with the damaged insured vehicle or enhancing an online customer experience.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein the respective features or characteristics of the damaged insured vehicle include one or more autonomous or semi-autonomous technologies or systems. 
     
     
         3 . The computer-implemented method of  claim 2 , wherein the peril associated with the damaged insured vehicle comprises collision, comprehensive, the or water. 
     
     
         4 . The computer-implemented method of  claim 1 , the method further comprising:
 retrieving, via the one or more processors and/or the one or more transceivers, an insurance policy associated with the damaged insured vehicle; and   determining, via the one or more processors, whether or not the peril associated with the damaged insured vehicle is a covered peril under the insurance policy.   
     
     
         5 . The computer-implemented method of  claim 1 , wherein the peril associated with the damaged insured vehicle comprises fire, smoke, water, hail, wind, or storm surge. 
     
     
         6 .- 17 . (canceled) 
     
     
         18 . A non-transitory computer readable medium containing program instructions that when executed, cause a computer to:
 input historical claim data into a machine learning algorithm to operate the algorithm to identify a damaged insured vehicle, respective damaged insured vehicle type, respective damaged insured vehicle features or characteristics, a peril associated with the damaged insured vehicle, and/or a repair or replacement cost associated with the damaged insured vehicle;   receive a digital image depicting damage to the insured vehicle, the digital image being submitted by an insured entity via a webpage, webpage, or mobile device: and   input the image of the damaged insured vehicle into a processor having the trained machine learning algorithm installed in a memory unit, the trained machine learning algorithm identifying a type of the damaged insured vehicle, a feature or characteristic of the damaged insured vehicle, a peril associated with the damaged insured vehicle, and/or a repair or replacement cost associated with the damaged insured vehicle to facilitate handling an insurance claim associated with the damaged insured vehicle.   
     
     
         19 . The non-transitory computer readable medium of  claim 18 , wherein the features or characteristics of the damaged insured vehicle include geographical area, make, model, transmission, tire, engine, autonomous or semi-autonomous features, air conditioning, power brakes, power windows, and/or color of the vehicle. 
     
     
         20 . The non-transitory computer readable medium of  claim 18 , containing further program instructions that when executed, cause the computer to:
 retrieve an insurance policy associated with the damaged insured vehicle; and   determine whether or not the peril associated with the damaged insured vehicle is a covered peril under the insurance policy.