Natural-Language Code Generation for Text Editing and Grammar Rules
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Solution Overview
Problem
Existing rule-based text processing systems require users to have programming expertise, making them inaccessible to those without coding knowledge and inefficient in handling large datasets or complex processing tasks.
Innovation Solution
A method using machine-learning language models to generate executable code from natural language descriptions, enabling efficient and adaptable text data processing, allowing users to input instructions intuitively and facilitating dynamic rule adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rule-based text processing systems are used, then text manipulation and analysis can be performed, but users must have programming expertise and it becomes inaccessible to those without coding knowledge
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user-friendly natural language instructions into executable code. This mediator component allows users without programming knowledge to interact with the text processing system by simply describing their needs in natural language, which is then converted into the appropriate processing rules and code by the system.
2Productivity
If rule-based processing is implemented for text data, then specific rules can be defined for text manipulation, but it becomes inefficient in handling large datasets or complex processing tasks
Solution Approach 1:
The patent implements dynamic rule generation and adjustment capabilities where processing rules are not static but can be automatically adapted and optimized based on the characteristics of the input data. The system dynamically generates appropriate processing rules for different data types and complexities, and can automatically adjust rule parameters to handle varying dataset sizes and complexities efficiently.
Solution Approach 2:
The system automatically adjusts processing parameters such as rule complexity, matching thresholds, and processing depth based on the characteristics of the input data. For large datasets, the system modifies parameters to optimize performance, such as adjusting memory allocation, processing batch sizes, and rule evaluation strategies to maintain efficiency across different data scales.
3Ease of operation
If natural language processing is used to generate code, then accessibility is improved, but additional processing time and computational resources are required
Solution Approach 1:
The system performs preliminary processing of natural language inputs by pre-compiling and caching commonly used processing patterns and rules. When users submit natural language requests, the system checks against pre-compiled templates and cached rules first, significantly reducing the time required to generate code for common text processing tasks while maintaining full natural language input capability.
Data Source
AI summary
The computer-implemented method for processing text data includes receiving text data, receiving a set of descriptions for editing the text data, and generating a set of functions corresponding to the set of descriptions. The process of generating the set of functions for each description in the set of descriptions includes generating a computer code representing a function from the set of functions for processing the text data using a machine learning language model. Further, the method includes applying each one of the set of functions to the text data to generate output text data.