Automated Program Generation from Procedure Manuals
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Solution Overview
Problem
Professionals in public safety occupations face challenges in memorizing and retaining complex procedures, leading to potential legal and operational risks, and current methods for converting documented procedures into automated programs are costly and inefficient.
Innovation Solution
A computing device is used to convert natural language documents into automated programs by accessing models from a labeled database, decoding information, and generating executable code, allowing for the creation of models based on concepts and steps within the documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a trained computer science professional manually programs procedures into a computer language, then the robot can execute procedures accurately, but the programming cost and time increase significantly
Solution Approach 1:
The system enables self-service by allowing the robot to automatically generate its own program code from natural language procedure manuals without requiring manual programming by computer science professionals. The robot processes the manual, extracts procedural steps, and generates executable code autonomously, eliminating the need for human programmers while maintaining execution accuracy.
Solution Approach 2:
The patent replaces the mechanical process of manual programming with an automated natural language processing system. Instead of programmers manually translating procedures into code, the system uses AI models to automatically convert natural language text into executable program code, substituting human cognitive labor with computational processes.
2Reliability
If a trained computer science professional manually updates procedures when manuals are updated, then the program remains accurate, but the updating cost and time increase
Solution Approach 1:
The system enables automatic self-updating by reprocessing the natural language procedure manual whenever it is updated. The robot independently generates new program code from the updated manual without requiring human intervention, automatically synchronizing the program with the latest procedural information and eliminating manual update efforts.
Solution Approach 2:
The system performs preliminary action by establishing an automated workflow where procedure updates are automatically detected and processed. When a manual is updated, the system proactively reprocesses the entire manual to generate updated program code, preventing accuracy issues before they occur rather than reacting to problems after they arise.
3Reliability
If detailed procedures are documented in manuals, then procedure accuracy can be maintained, but the complexity of converting them to automated programs increases
Solution Approach 1:
The patent replaces complex manual analysis and programming tasks with automated natural language processing models. The system uses AI to automatically parse, understand, and convert detailed procedural text into executable code, substituting human expert analysis with computational intelligence that handles the complexity of translating detailed manuals into program logic.
Solution Approach 2:
The system introduces an intermediary natural language processing layer between the procedure manual and the executable program. This intermediary layer automatically translates natural language descriptions into program code, mediating the conversion process and eliminating the need for direct manual programming while preserving procedure fidelity through accurate language understanding.
Data Source
AI summary
A computing device receives a query about at least one concept and at least one document associated with the at least one concept. The computing device accesses a plurality of models created based on information in a labeled database. The computing device decodes information in the at least one document using the plurality of models. Responsive to the decoding, the computing device generate a program with steps associated with the at least one concept.