US 12,190,358 B2
Systems and methods for automatically determining associations between damaged parts and repair estimate information during damage appraisal
Jerry Gastineau, San Diego, CA (US); Niv Genchel, San Diego, CA (US); Julian Louis, San Diego, CA (US); and Beau Sullivan, San Diego, CA (US)
Assigned to Mitchell International, Inc., San Diego, CA (US)
Filed by Mitchell International, Inc., San Diego, CA (US)
Filed on Dec. 18, 2020, as Appl. No. 17/127,575.
Application 17/127,575 is a continuation in part of application No. 15/421,972, filed on Feb. 1, 2017.
Claims priority of provisional application 62/289,720, filed on Feb. 1, 2016.
Prior Publication US 2021/0150591 A1, May 20, 2021
Int. Cl. G06Q 40/00 (2023.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06Q 10/0875 (2023.01); G06Q 10/20 (2023.01); G06Q 30/018 (2023.01); G06Q 30/02 (2023.01)
CPC G06Q 30/0278 (2013.01) [G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06Q 10/0875 (2013.01); G06Q 10/20 (2013.01); G06Q 30/018 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for conducting an automatic appraisal of a vehicle damaged during an adverse incident, the system comprising:
a user computing device; and
an appraisal management computing apparatus comprising:
a processor; and
a memory storing computer-executable instructions that, when executed by the processor, cause the appraisal management computing apparatus to:
provide at least one damage evidence file for a damaged part of the vehicle as input to a deep neural network (DNN), wherein responsive to the input, the DNN generates output comprising a part code corresponding to the damaged part, wherein the DNN comprises multiple layers including an input layer, an output layer, and one or more hidden layers between the input layer and the output layer, and wherein the DNN is trained on historic damage data comprising a plurality of damage evidence files associated with corresponding damaged parts previously identified as damaged;
identify an Original Equipment Manufacturer (OEM) part number based on the part code and a vehicle identification number (VIN) associated with the vehicle; and
generate a repair estimate line comprising the OEM part number.