Regulatory Data Crawling for Dynamic Course Module Updates
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Solution Overview
Problem
Current systems for generating educational courses, particularly with large language models, lack the capability to efficiently filter out irrelevant data and require significant computational resources, leading to inefficient and outdated course content.
Innovation Solution
A system and method utilizing a web crawler to identify predetermined HTML elements, isolate relevant data, compare it to a modification baseline, and generate regulatory data, which is then classified and matched to course modules for dynamic updates, with notifications sent to end users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current systems are used for course generation, then courses can be created, but the systems require a vast amount of computational power resulting in less efficiency
Solution Approach 1:
The patent extracts and isolates only the relevant data elements from web pages using predetermined HTML element identification. By taking out only the necessary regulatory data and comparing it to a modification baseline, the system avoids processing entire web pages or irrelevant content, significantly reducing computational power requirements while maintaining course generation effectiveness.
Solution Approach 2:
The system segments the course content into modular units (course modules) that can be independently updated. By dividing the course generation process into discrete modules associated with specific guidelines, the system can update only affected portions rather than regenerating entire courses, improving productivity and reducing energy consumption.
2Reliability
If data is not properly filtered in course generation systems, then all available data can be processed, but irrelevant information is included making courses useless
Solution Approach 1:
The patent applies local quality by identifying and processing only specific predetermined HTML elements that contain relevant regulatory data. Rather than uniformly processing all data, the system selectively extracts information from designated HTML structures, ensuring that only high-quality, relevant data is included in course generation while filtering out irrelevant content.
Solution Approach 2:
The system performs preliminary filtering by pre-defining which HTML elements contain relevant data before processing begins. By establishing modification baselines and predetermined element structures in advance, the system ensures that only relevant information is extracted and processed, preventing irrelevant data from entering the course generation pipeline.
Data Source
AI summary
A system for dynamically updating data for course generation, the system including a web crawler operating on a server, wherein the web crawler is configured to identify one or more predetermined HTML elements on a plurality of web pages, identify isolated data as a function of the one or more predetermined HTML elements, compare the isolated data to a modification baseline and generate regulatory data as a function of the isolated data and the comparison, and a memory the memory containing instructions configuring at least a processor to receive the regulatory data, classify the regulatory data to one or more guideline categorizations, identify a plurality of course modules, modify the one or more course modules as a function of the regulatory data and transmit a notification associated with the one or more modified course modules to an end user.