STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY (20240242316). METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR CONSTRUCTION AND URBAN PLANNING simplified abstract

METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR CONSTRUCTION AND URBAN PLANNING

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 CONSTRUCTION AND URBAN PLANNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240242316 titled 'METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR CONSTRUCTION AND URBAN PLANNING

Simplified Explanation:

This patent application describes a computer-implemented method for utilizing a trained generative adversarial network to enhance construction and urban planning processes by analyzing point cloud data from a construction site, determining soil measurements, and generating cost estimates.

• The method involves receiving semantically-segmented point cloud data from a construction site.
• It then calculates volumetric soil measurements based on the received data.
• Finally, the system generates a cost estimate for the construction project.

Key Features and Innovation:

• Utilization of a trained generative adversarial network for construction and urban planning.
• Analysis of semantically-segmented point cloud data for accurate measurements.
• Cost estimation based on the analyzed data.

Potential Applications:

This technology can be applied in various construction and urban planning projects to streamline processes, improve accuracy in soil measurements, and enhance cost estimation.

Problems Solved:

This technology addresses the challenges of accurate soil measurement, cost estimation, and efficient planning in construction projects.

Benefits:

• Improved accuracy in soil measurements.
• Enhanced cost estimation for construction projects.
• Streamlined processes in construction and urban planning.

Commercial Applications:

Title: Enhanced Construction and Urban Planning with Generative Adversarial Networks

This technology can be utilized by construction companies, urban planners, and developers to optimize project planning, improve cost estimation accuracy, and enhance overall efficiency in construction processes.

Questions about Enhanced Construction and Urban Planning with Generative Adversarial Networks:

1. How does this technology improve cost estimation accuracy in construction projects? 2. What are the potential benefits of utilizing a trained generative adversarial network in urban planning and construction processes?

Original Abstract Submitted

a computer-implemented method for using a trained generative adversarial network to improve construction and urban planning includes receiving a semantically-segmented point cloud corresponding to a construction site; determining a volumetric soil measurement; and generating a cost estimate. 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 semantically-segmented point cloud corresponding to a construction site; determine a volumetric soil measurement; and generate a cost estimate. a non-transitory computer-readable medium includes computer-executable instructions that, when executed, cause a computer to: receive a semantically-segmented point cloud corresponding to a construction site; determine a volumetric soil measurement; and generate a cost estimate.