US 11,887,063 B2
Automated vehicle repair estimation by random ensembling of multiple artificial intelligence functions
Abhijeet Gulati, San Diego, CA (US); Joseph Hyland, San Diego, CA (US); and Chenlei Zhang, San Diego, CA (US)
Assigned to Mitchell International, Inc., San Diego, CA (US)
Filed by Mitchell International, Inc., San Diego, CA (US)
Filed on Sep. 30, 2020, as Appl. No. 17/039,311.
Claims priority of provisional application 62/908,348, filed on Sep. 30, 2019.
Claims priority of provisional application 62/908,354, filed on Sep. 30, 2019.
Claims priority of provisional application 62/908,361, filed on Sep. 30, 2019.
Prior Publication US 2021/0097622 A1, Apr. 1, 2021
Int. Cl. G06Q 10/20 (2023.01); G06N 5/04 (2023.01); G06Q 10/10 (2023.01); G06Q 40/08 (2012.01); G06T 7/00 (2017.01); G06N 20/20 (2019.01); G06N 20/00 (2019.01); G06F 18/214 (2023.01)
CPC G06Q 10/20 (2013.01) [G06F 18/2148 (2023.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06Q 10/10 (2013.01); G06Q 40/08 (2013.01); G06T 7/0002 (2013.01); G06T 7/0004 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30248 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A system, comprising:
a hardware processor; and
a non-transitory machine-readable storage medium encoded with instructions executable by the hardware processor to perform a method comprising:
receiving a vehicle damage object for a damaged vehicle, wherein the vehicle damage object includes a plurality of metadata objects of the damaged vehicle;
fragmenting the object into a plurality of vehicle damage object fragments, wherein each vehicle damage object fragment includes at least one of the metadata objects of the damaged vehicle;
providing each of the vehicle damage object fragments to a respective one of a plurality of artificial intelligence functions by:
generating random associations between the vehicle damage object fragments and the artificial intelligence functions, and
providing the vehicle damage object fragments to the artificial intelligence functions according to the generated random associations;
receiving a respective vehicle repair recommendation set from each of the artificial intelligence functions, wherein each vehicle repair recommendation set is based on a respective one of the vehicle damage object fragments, and wherein each of the vehicle repair recommendation sets identifies a recommended vehicle repair operation of a plurality of the vehicle repair operations for the damaged vehicle;
selecting a plurality of the recommended vehicle repair operations;
generating a composite vehicle repair recommendation set that identifies the selected recommended vehicle repair operations; and
providing the composite vehicle repair recommendation set to one or more vehicle repair insurance claims management systems.