17972791. Graphical Neural Network for Error Identification simplified abstract (Bank of America Corporation)

Graphical Neural Network for Error Identification

Organization Name

Bank of America Corporation

Inventor(s)

Sakshi Bakshi of New Delhi (IN)

Srinivasa Dhanwada of Hyderabad (IN)

Sathya Thamilarasan of Tamilnadu (IN)

Siva Paini of Telangana (IN)

Shyamala Manoharan of Chennai (IN)

Nagalaxmi Sama of Telangana (IN)

Sri Lakshmi Priya Doraiswamy of Charlotte NC (US)

Graphical Neural Network for Error Identification - A simplified explanation of the abstract

This abstract first appeared for US patent application 17972791 titled 'Graphical Neural Network for Error Identification

The abstract of the patent application describes a method for upgrading an application within a simulated version of an enterprise system to detect and correct potential errors resulting from the upgrade.

• The computing platform creates a simulated version of the enterprise system by converting metadata into system parameters.
• Virtual parameters are generated based on the application upgrade within the simulated system.
• System nodes and virtual nodes are dynamically linked to identify errors caused by the upgrade.
• The computing platform determines actions to correct the errors and refines its accuracy over time using an AI engine.

Potential Applications: - Quality assurance testing for software upgrades - Error detection and correction in enterprise systems - Enhancing the reliability of computing platforms

Problems Solved: - Identifying errors in applications after upgrades - Improving the accuracy and reliability of computing systems - Streamlining the process of error detection and correction

Benefits: - Minimizing downtime due to errors in application upgrades - Enhancing the overall performance of enterprise systems - Increasing efficiency in software development processes

Commercial Applications: Title: "Enhanced Software Upgrade Testing System" This technology can be used in software development companies to streamline the process of testing and upgrading applications, leading to more reliable and efficient systems.

Questions about the technology: 1. How does this technology improve the accuracy of error detection in enterprise systems? 2. What are the potential long-term benefits of using this upgraded testing system in software development processes?

Frequently Updated Research: Stay updated on the latest advancements in software testing methodologies and AI integration to further enhance the accuracy and efficiency of error detection and correction processes.

Original Abstract Submitted

Aspects of the disclosure relate to upgrading an application within a simulated version of an enterprise system to detect and correct potential errors as a result of the upgrade. A computing platform may create a simulated version of the enterprise system by receiving metadata associated with the enterprise system, and converting the metadata into system parameters. Virtual parameters may be created by the computing system based on upgrading an application within the simulated version of the enterprise system. The computing system may create system nodes and virtual nodes. The system nodes and virtual nodes may be dynamically linked in order to determine errors caused by the application upgrade within the simulated version of the enterprise system. The computing platform may determine actions to correct the errors and input the results and feedback into an AI engine to further refine the accuracy and reliability of the computing platform over time.