US 11,989,759 B2
Using machine learning techniques to calculate damage of vehicles involved in an accident
Brian Mark Fields, Phoenix, AZ (US); and Lee Marvin John Assam, El Paso, IL (US)
Assigned to STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY, Bloomington, IL (US)
Filed by STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY, Bloomington, IL (US)
Filed on May 27, 2022, as Appl. No. 17/826,250.
Application 17/826,250 is a continuation of application No. 15/675,077, filed on Aug. 11, 2017, granted, now 11,379,886.
Prior Publication US 2022/0284484 A1, Sep. 8, 2022
Int. Cl. G06Q 30/02 (2023.01); G06Q 40/08 (2012.01)
CPC G06Q 30/0278 (2013.01) [G06Q 40/08 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
accessing image data representing one or more digital images of damage to a vehicle;
selecting, using one or more processors, an applicable machine learning algorithm from a plurality of different trained machine learning algorithms, wherein the plurality of different machine learning algorithms are trained for respective different combinations of one or more of vehicle make, vehicle model, vehicle year, or area of damage;
processing the image data, with the applicable machine learning algorithm using one or more processors, to determine assessed damage for the vehicle;
accessing actual damage information for the vehicle;
determining, using one or more processors, differences between the actual damage and the assessed damage; and
iterating, using one or more processors, the applicable machine learning algorithm based on the differences to improve its damage assessment accuracy.