公开(公告)号：US20230136983A1

公开(公告)日：2023-05-04

申请号：US18091235

申请日：2022-12-29

Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

Inventor： Ryan Knuffman

IPC: G06T5/00 ,  G06T7/579 ,  G06N3/088 ,  G06N3/045

Abstract: A method for using a trained generative adversarial network to improve underwriting, claim handling and retail operations includes receiving a 3D point cloud; and generating a gap-filled semantically-segmented 3D point cloud using a trained generative adversarial network. A computing system for using a trained generative adversarial network to improve vehicle orientation and navigation includes one or more processors, and one or more memories having stored thereon computer-executable instructions that, when executed, cause the computing system to: receive a 3D point cloud; and generate a gap-filled semantically-segmented 3D point cloud using the trained generative adversarial network. A non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed, cause a computer to: receive a 3D point cloud; and generate a gap-filled semantically-segmented 3D point cloud using a trained generative adversarial network.

公开(公告)号：US20230060097A1

公开(公告)日：2023-02-23

申请号：US17982174

申请日：2022-11-07

Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

Inventor： Ryan Knuffman

IPC: G06T5/00 ,  G06N3/04 ,  G06T7/579 ,  G06N3/08

Abstract: A non-transitory computer readable storage medium includes instructions that, when executed by one or more processors, cause a computer to: generate a loss value; update one or more weights of a generative adversarial network; and store the updated weights on a non-transitory computer readable storage medium. A computer-implemented method includes generating a loss value; updating one or more weights of a generative adversarial network; and storing the updated weights on a non-transitory computer readable storage medium. A computing system for training a generative adversarial network includes generating a loss value; updating one or more weights of a generative adversarial network; and storing the updated weights on a non-transitory computer readable storage medium.

公开(公告)号：US11508042B1

公开(公告)日：2022-11-22

申请号：US17031580

申请日：2020-09-24

Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

Inventor： Ryan Knuffman

IPC: G06T5/00 ,  G06T7/579 ,  G06N3/04 ,  G06N3/08

Abstract: A generative adversarial network (GAN) is manufactured by a process including obtaining a three-dimensional (3D) point cloud, extracting a region from the 3D point cloud, the region corresponding to a gap, analyzing the extracted region to generate a loss, backpropagating the loss, and updating weights of the GAN. A computer-implemented method for training a GAN includes obtaining a 3D point cloud, extracting a region from the 3D point cloud, the region corresponding to a gap, analyzing the extracted region to generate a loss, backpropagating the loss, and updating weights of the GAN. A server includes a processor and a memory storing instructions that, when executed by the processor, cause the server to obtain a 3D point cloud, extract a region from the 3D point cloud, the region corresponding to a gap, analyze the extracted region to generate a loss, backpropagate the loss, and update weights of the GAN.

公开(公告)号：US11210851B1

公开(公告)日：2021-12-28

申请号：US16543127

申请日：2019-08-16

Applicant: State Farm Mutual Automobile Insurance Company

Inventor： Bryan Nussbaum ,  Jeremy Carnahan ,  Ryan Knuffman

IPC: G06T17/20 ,  G06K9/00 ,  G06T19/00 ,  H04W4/021 ,  G06F16/58

Abstract: A virtual reality (VR) labeling computer system configured to receive a 3D model, process the 3D model using object recognition, identify at least one environmental feature within the 3D model, generate a processed 3D model including the at least one environmental feature, display a VR environment based upon the processed 3D model; receive user input including labeling data associated with the environmental feature; generate a labeled 3D model by embedding the labeling data into the processed 3D model; and generate training data based upon the labeled 3D model.

公开(公告)号：US20210027390A1

公开(公告)日：2021-01-28

申请号：US17070318

申请日：2020-10-14

Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

Inventor： Ryan Knuffman ,  Bradley A. Sliz ,  Lucas Allen

IPC: G06Q40/08 ,  B64C39/02 ,  G05D1/00 ,  G07C5/00 ,  G06Q10/00 ,  G06Q30/02

Abstract: A remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed based upon data indicative of at least one of vehicle damage or a vehicle defect being shown in the one or more sets of imaging data. Based upon the analyzing of the one or more sets of imaging data, damage to the vehicle or a defect of the vehicle may be identified. The identified damage or defect may be compared to a claimed damage or defect to determine whether the claimed damage or defect occurred.