Semantic Script Parsing for Executable Natural-Language Code
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Solution Overview
Problem
Existing computer programming languages are complex and technical, requiring formal syntax that non-programmers find difficult to understand, and prior art systems fail to allow pseudo-code-based coding or effectively lower the complexity for non-programmers.
Innovation Solution
The Semantic Script Programming Language (SSPL) allows users to provide instructions and queries in a human-readable format, using a stack-oriented, concatenative and procedural programming language with minimal symbology, leveraging machine learning to interpret expressions and adapt to user style, and supports natural language interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional computer programming languages are used, then the computer can execute instructions accurately, but the language complexity and difficulty for non-programmers increases
Solution Approach 1:
The patent introduces a natural language processing layer as an intermediary between the user and the computer system. This layer translates human-readable natural language queries into executable code or database queries, eliminating the need for users to learn complex programming syntax while maintaining accurate computer execution. The intermediary translator handles the complexity internally while presenting a simple interface to users.
Solution Approach 2:
The patent replaces the mechanical system of learning and applying formal programming syntax with a natural language processing system. Instead of requiring users to manually construct code according to strict syntax rules, the system uses AI and natural language understanding to interpret and translate user intent into executable instructions, substituting the mechanical coding process with a more intuitive linguistic interface.
2Ease of operation
If pseudo-code is used to simplify programming, then the expressiveness for non-programmers improves, but the ability to actually execute the code deteriorates
Solution Approach 1:
The patent introduces a translation layer that acts as an intermediary between pseudo-code/natural language expressions and executable code. This translator converts the simplified expressions into actual executable instructions that the computer can run, bridging the gap between human-readable pseudo-code and machine-executable code while maintaining both expressiveness and reliability.
Solution Approach 2:
The patent replaces the traditional mechanical compilation process that requires strict syntax with an AI-driven natural language processing system. This system understands the intent behind pseudo-code and natural language expressions, translating them into executable code without requiring users to follow rigid syntax rules, thereby maintaining execution capability while improving expressiveness.
3Adaptability or versatility
If a human-readable language interface is provided, then the accessibility for domain experts improves, but the processing complexity for the system increases
Solution Approach 1:
The patent replaces traditional mechanical parsing and compilation systems with AI-based natural language processing. This substitution enables the system to handle human-readable language interfaces with greater flexibility and understanding, improving accessibility for domain experts while the AI system manages the processing complexity through contextual understanding and intent recognition rather than rigid syntax analysis.
Data Source
AI summary
A method and system provide the ability to process source computer instructions. The source computer instructions are obtained and include input statements that consist of two functions and one or more arguments. The two functions are competing with each other for consumption of the one or more arguments. For an input statement, an inherent numeric precedence weight is determined for each function and argument. All possible legal configurations of the functions and arguments are determined and consist of different groupings of the functions and arguments. A score is assigned to each grouping and consists of a sum of the weights within each different grouping. The grouping and legal configuration having the highest score is selected. The input statements are compiled into executable code using the selected different grouping.