18462071. ERROR CHECKING FOR CODE simplified abstract (State Farm Mutual Automobile Insurance Company)

ERROR CHECKING FOR CODE

Organization Name

State Farm Mutual Automobile Insurance Company

Inventor(s)

Brian Mark Fields of Phoenix AZ (US)

Nathan L. Tofte of Downs IL (US)

Joseph Robert Brannan of Bloomington IL (US)

Vicki King of Bloomington IL (US)

Justin Davis of Bloomington IL (US)

ERROR CHECKING FOR CODE - A simplified explanation of the abstract

This abstract first appeared for US patent application 18462071 titled 'ERROR CHECKING FOR CODE

Simplified Explanation: The patent application describes a system that uses a machine learning chatbot to check code for errors, provide solutions, and implement fixes if needed.

Key Features and Innovation:

• Utilizes a machine learning chatbot to check code for errors
• Provides solutions to fix errors in the code
• Implements fixes automatically if errors are detected
• Analyzes the solution to determine the steps required for implementation

Potential Applications: This technology can be used in software development, coding education, and quality assurance processes.

Problems Solved: The system addresses the need for efficient code checking, error detection, and automated error fixing in programming tasks.

Benefits:

• Saves time and effort in identifying and fixing code errors
• Improves code quality and reduces the risk of bugs in software applications
• Enhances the learning experience for coding students by providing instant feedback and solutions

Commercial Applications: The technology can be applied in software development companies, coding bootcamps, online coding platforms, and educational institutions to streamline code checking processes and improve code quality.

Prior Art: Researchers can explore existing literature on machine learning in code analysis, automated error detection, and chatbot-based solutions in software development.

Frequently Updated Research: Stay informed about advancements in machine learning algorithms for code analysis, chatbot technologies, and automated error fixing in programming.

Questions about Code Checking System: 1. How does the machine learning chatbot determine errors in the code? 2. What are the potential limitations of using a chatbot for code checking?

Original Abstract Submitted

Apparatuses, systems and methods are provided for checking code for errors. The apparatuses, systems and methods may send a target code and a prompt for code checking to a machine learning (ML) chatbot to cause the ML chatbot to check the target code for errors. The apparatuses, systems and methods may determine whether there is an error in the target code based at least partially on a response from the ML chatbot. The apparatuses, systems and methods may, responsive to determining that there is an error in the target code, determine, via an interaction with the ML chatbot, a solution to fix the error. The apparatuses, systems and methods may analyze the solution to determine a number of at least one of (i) a set of steps or (ii) a set of interactions required by the solution. The apparatuses, systems and methods may, responsive to determining that the number exceeds a predetermined threshold, fix the error by implementing the solution with respect to the target code.