STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY (20240338803). METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR VEHICLES AND TRANSPORTATION simplified abstract

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METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR VEHICLES AND TRANSPORTATION

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 VEHICLES AND TRANSPORTATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240338803 titled 'METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR VEHICLES AND TRANSPORTATION

The abstract of this patent application describes a method involving receiving a navigation data set, generating a combined data set using a trained generative adversarial network, and generating a high-resolution map that includes spatial data.

  • The method involves utilizing a generative adversarial network to combine data sets and create high-resolution maps.
  • The computing system includes processors and memories with computer-executable instructions to carry out the method.
  • The non-transitory computer-readable medium contains instructions for a computer to perform the method.
  • The innovation focuses on enhancing map generation using advanced technology.
  • The technology aims to improve the accuracy and detail of high-resolution maps.
  • This innovation could have applications in navigation systems, urban planning, and geographic information systems.
  • The method addresses the need for more detailed and accurate spatial data in mapping applications.
  • Benefits include improved navigation accuracy, better urban planning, and enhanced geographic data analysis.

Potential Applications: This technology could be applied in navigation systems, urban planning, geographic information systems, and other mapping applications.

Problems Solved: This technology addresses the need for more detailed and accurate spatial data in map generation.

Benefits: The benefits of this technology include improved navigation accuracy, better urban planning, and enhanced geographic data analysis.

Commercial Applications: This technology could be used in commercial mapping services, urban development projects, and geographic data analysis tools.

Questions about the technology: 1. How does this technology improve the accuracy of high-resolution maps? 2. What are the potential commercial uses of this advanced mapping technology?


Original Abstract Submitted

a method includes receiving a navigation data set; generating a combined data set using a trained generative adversarial network; and generating a high resolution map that includes spatial data. a computing system 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 navigation data set; generate a combined data set using a trained generative adversarial network; and generate a high resolution map that includes spatial data. a non-transitory computer-readable medium includes computer-executable instructions that, when executed, cause a computer to: receive a navigation data set; generate a combined data set using a trained generative adversarial network; and generate a high resolution map that includes spatial data.

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