US 11,983,851 B2
Methods and systems for using trained generative adversarial networks to impute 3D data for underwriting, claim handling and retail operations
Ryan Knuffman, Danvers, 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 Dec. 29, 2022, as Appl. No. 18/091,235.
Application 18/091,235 is a continuation of application No. 17/982,174, filed on Nov. 7, 2022.
Application 17/982,174 is a continuation of application No. 17/031,580, filed on Sep. 24, 2020, granted, now 11,508,042, issued on Nov. 22, 2022.
Claims priority of provisional application 62/967,315, filed on Jan. 29, 2020.
Prior Publication US 2023/0136983 A1, May 4, 2023
Int. Cl. G06T 5/00 (2006.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01); G06T 7/579 (2017.01)
CPC G06T 5/005 (2013.01) [G06N 3/045 (2023.01); G06N 3/088 (2013.01); G06T 7/579 (2017.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for using a trained generative adversarial network to improve underwriting, claim handling and retail operations comprising:
receiving a 3D point cloud; and
generating a gap-filled semantically-segmented 3D point cloud using the trained generative adversarial network to augment the semantically-segmented 3D point cloud using historical customer data,
wherein the augmenting includes filling at least one gap in the 3D point cloud using customer data.