Natural Language Macro Expansion With Context-Filtered AI Generation
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Solution Overview
Problem
Existing macro expansion methods for programming languages suffer from hardware limitations, high coding costs, and vulnerability to typos and human errors.
Innovation Solution
A method and system utilizing an API server and a backend server with AI capabilities to generate macros or strings based on natural language input and context filters, reducing the need for manual coding and improving precision and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional macro expansion methods are used for programming languages, then manual coding is required, but this leads to high coding costs and vulnerability to human errors
Solution Approach 1:
The system enables self-service by allowing developers to automatically generate macros through natural language input. The AI model processes the natural language description and automatically creates the corresponding macro code, eliminating the need for manual macro programming and reducing human error while lowering development costs.
Solution Approach 2:
The patent replaces the mechanical process of manual macro coding with an intelligent system using natural language processing and AI models. Instead of requiring developers to manually write and maintain macro code, the system uses language models to automatically generate macros from natural language descriptions, substituting human mechanical coding with automated intelligent processing.
2Measurement precision
If comprehensive domain knowledge is processed without context filtering, then more accurate macro expansion is achieved, but processing time and resource consumption increase
Solution Approach 1:
The system performs preliminary action by extracting and storing context information from source code before the actual macro expansion process. The context extractor identifies relevant variables, data types, and code structures in advance, preparing filtered context data that can be quickly retrieved during macro generation, thus reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent applies extraction by separating and filtering only the relevant context information from the complete domain knowledge base. The context extractor extracts specific variables, data types, and code structures that are relevant to the current macro expansion task, discarding unnecessary information. This reduces the amount of data the AI model needs to process while maintaining macro expansion accuracy.
3Productivity
If natural language processing is used for macro generation, then coding efficiency improves, but processing complexity increases
Solution Approach 1:
The system segments the macro generation process into distinct modular components: a context extractor module that analyzes source code and extracts relevant information, a natural language processing module that processes the user's natural language input, and an AI model module that generates the macro code. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining high coding efficiency.
Data Source
AI summary
A method is provided including, receiving, at an API server, a natural language string and a context filter defined by automatically generated information about a terminal; selecting, at the API server, one or more domain knowledge files based on the natural language string and the context filter; transmitting, from the API server to a backend server having artificial intelligence capabilities, the one or more domain knowledge files, the natural language string, and the context filter; generating, at the backend server via AI processing, one or more of: one or more macros or one or more strings, based on the one or more domain knowledge files, the natural language string, and the context filter; and receiving, at the API server from the backend server, the one or more of: one or more macros or one or more strings. A system and computer-readable medium are also included.