State Farm Mutual Automobile Insurance Company (20240265511). METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR UNDERWRITING, CLAIM HANDLING AND RETAIL OPERATIONS simplified abstract

METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR UNDERWRITING, CLAIM HANDLING AND RETAIL OPERATIONS

Organization Name

State Farm Mutual Automobile Insurance Company

Inventor(s)

Ryan Knuffman of Danvers IL (US)

METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR UNDERWRITING, CLAIM HANDLING AND RETAIL OPERATIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240265511 titled 'METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR UNDERWRITING, CLAIM HANDLING AND RETAIL OPERATIONS

The abstract describes a method for using a trained generative adversarial network to improve underwriting, claim handling, and retail operations by generating a gap-filled semantically-segmented 3D point cloud from a received 3D point cloud.

• Trained generative adversarial network used to improve various operations
• Gap-filled semantically-segmented 3D point cloud generated from received 3D point cloud
• Computing system with processors and memories executes the method

Potential Applications: - Insurance industry for underwriting and claim handling - Retail operations for inventory management - Navigation systems for improved vehicle orientation

Problems Solved: - Enhances efficiency in underwriting and claim handling processes - Improves accuracy in retail inventory management - Enhances precision in vehicle navigation systems

Benefits: - Streamlines operations in various industries - Increases accuracy and precision in data processing - Enhances decision-making processes

Commercial Applications: Title: "Enhancing Operations with Trained Generative Adversarial Networks" This technology can be applied in insurance companies, retail businesses, and navigation systems to improve efficiency, accuracy, and decision-making processes.

Questions about the technology: 1. How does the trained generative adversarial network improve underwriting processes? 2. What are the potential challenges in implementing this technology in retail operations?

Original Abstract Submitted

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.