US 12,008,658 B2
Deep learning image processing method for determining vehicle damage
He Yang, The Colony, TX (US); Bradley A. Sliz, Normal, IL (US); Carlee A. Clymer, Atlanta, GA (US); and Jennifer Malia Andrus, Champaign, 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 Feb. 16, 2023, as Appl. No. 18/110,510.
Application 18/110,510 is a continuation of application No. 16/937,318, filed on Jul. 23, 2020, granted, now 11,610,074.
Application 16/937,318 is a continuation of application No. 16/023,414, filed on Jun. 29, 2018, granted, now 10,762,385, issued on Sep. 1, 2020.
Claims priority of provisional application 62/526,879, filed on Jun. 29, 2017.
Prior Publication US 2023/0222179 A1, Jul. 13, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 40/08 (2012.01); G06F 16/583 (2019.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06N 3/08 (2023.01); G06V 10/74 (2022.01); G06V 10/82 (2022.01)
CPC G06Q 40/08 (2013.01) [G06F 16/583 (2019.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06N 3/08 (2013.01); G06V 10/761 (2022.01); G06V 10/82 (2022.01); G06V 2201/08 (2022.01)] 20 Claims
OG exemplary drawing
 
15. A computer system comprising:
one or more processors; and
a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to:
determine, using a predictive similarity model, a similarity score for a given two digital images of damaged vehicles, wherein the predictive similarity model is trained using a historical training dataset populated with digital images of damaged vehicles and historical claim data related to the damaged vehicles, to identify damaged vehicle image characteristics that are predictive of one or more of damage level, repair time, or repair cost; and
based on the similarity score for the given two digital images,
predict a damage level, repair time, or repair cost for a first damaged vehicle depicted in a first digital image of the given two digital images based on a known damage level, repair time, or repair cost for a second damaged vehicle depicted in a second digital image of the given two digital images.