Markup Request Generation for Accurate Custom Script Creation
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Solution Overview
Problem
Users of database management systems face challenges in configuring customized scripts due to the need for technical expertise and lack of insight into script restrictions, making it difficult to express their unique requirements effectively.
Innovation Solution
A system utilizing a generative AI model to convert natural language prompts into markup language-based requests for a script generator, enabling users to describe their rules in plain English and generating customized scripts without requiring advanced programming knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users directly input natural language to generate custom scripts, then ease of operation is improved, but manufacturing precision deteriorates because the script generator cannot accurately interpret unstructured language requirements
Solution Approach 1:
The patent introduces a markup language as an intermediary between natural language input and script generation. Users write requirements in markup language (e.g., XML-like syntax with tags such as <script>, <condition>, <action>), which serves as a structured intermediate representation that bridges the gap between human-readable requirements and machine-executable scripts. This intermediary structure enables the script generator to accurately parse and interpret requirements while maintaining ease of use through template-based markup.
Solution Approach 2:
The patent segments the script generation process into distinct phases: requirement specification in markup language, markup validation and parsing, and script generation from structured markup. This segmentation allows each component to be optimized independently - the markup language provides structure for accurate interpretation, while the script generator focuses on translating validated markup into executable code, thereby improving both ease of operation and manufacturing precision.
2Manufacturing precision
If users are required to learn programming languages and script syntax, then manufacturing precision is improved, but ease of operation deteriorates due to the technical expertise required
Solution Approach 1:
The patent employs disposable markup templates that users can fill in without needing to understand underlying programming concepts. These markup templates provide pre-defined structures with placeholders for common script elements (conditions, actions, parameters), allowing users to generate accurate scripts through simple template instantiation rather than learning complex programming syntax. The templates are discarded after use, and users can request new templates as needed.
3Adaptability or versatility
If the system provides extensive customization options for scripts, then adaptability is improved, but device complexity increases making it harder for users to manage
Solution Approach 1:
The patent creates a universal markup language structure that can represent multiple types of script requirements through a consistent set of tags and attributes. The same markup framework handles diverse operations (data retrieval, transformation, validation, execution) using unified syntax patterns. This universality allows extensive adaptability for different script types while maintaining consistent, manageable complexity through standardized markup conventions rather than requiring separate configuration mechanisms for each script type.
Data Source
AI summary
Techniques for generating a markup language-based request for script generation are disclosed. In some embodiments, a system receives user input comprising a target natural language prompt. The system may generate a supplemented prompt comprising the target natural language prompt and example prompt data, the example prompt data comprising (a) a set of example natural language prompts and (b) a set of example markup language-based requests that correspond to the set of example natural language prompts. Next, the system may submit the supplemented prompt to a generative artificial intelligence (AI) model, wherein the generative AI model generates a target markup language-based request based on the supplemented prompt. The system may then receive the target markup language-based request from the generative AI model. Next, the system may submit the target markup language-based request to a script generator, wherein the script generator generates a customized script based on the target markup language-based request.