Automated Record Header Tag Identification via Scoring
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Solution Overview
Problem
Current methods for processing information from websites, such as identifying record headers, are inefficient and impractical at scale, requiring human intervention and being unable to address websites programmatically on-the-fly.
Innovation Solution
A system that parses Uniform Resource Locator (URL) documents to identify potential record header tags by scoring them against pre-defined and dynamically updated criteria, selecting the highest-scoring tag, and using machine learning to refine criteria for accurate identification and parsing of records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human operators manually identify record headers, then accuracy can be maintained, but efficiency and scalability deteriorate
Solution Approach 1:
The system enables automated identification of record header tags through machine learning models that self-train on website content without human intervention. The model automatically parses HTML, identifies potential header tags, scores them using multiple criteria, and selects the best match, eliminating the need for manual human operators while maintaining high accuracy through self-learning mechanisms.
Solution Approach 2:
The patent replaces the mechanical human operator system with an automated computational system comprising machine learning models, scoring algorithms, and HTML parsing mechanisms. This substitution transforms manual identification into an automated process that can handle large-scale website data processing efficiently while maintaining identification accuracy through multiple scoring criteria and model training.
2Adaptability or versatility
If human operators are used for identification, then flexibility in handling diverse websites is maintained, but the system cannot address websites programmatically on-the-fly
Solution Approach 1:
The system employs dynamic machine learning models that adapt to different website structures in real-time. The model scores potential header tags using multiple dynamic criteria including text string priority, pattern repetition, and positional information, allowing it to programmatically adjust to diverse website formats on-the-fly without requiring pre-programming for each specific site structure.
Solution Approach 2:
The patent changes parameters such as text string priority weights, pattern matching thresholds, and positional criteria based on the specific website being analyzed. The machine learning model adjusts these parameters dynamically during processing to optimize identification accuracy for different website types, enabling both adaptability and full automation.
3Measurement precision
If multiple criteria are used for scoring tags, then identification accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the identification process into distinct modular components: HTML parsing, potential tag extraction, multiple scoring criteria evaluation, and final selection. Each criterion (text string priority, pattern repetition, positional information) is evaluated separately and independently, allowing the system to maintain high accuracy through comprehensive evaluation while managing complexity through modular architecture.
Solution Approach 2:
The machine learning model serves multiple functions simultaneously: it parses HTML, extracts potential tags, evaluates multiple scoring criteria, and selects the best match. This multi-functional approach consolidates what would otherwise require separate systems into a single unified model, improving accuracy through comprehensive analysis while reducing overall system complexity.
Data Source
AI summary
Methods, devices and apparatuses pertaining to identifying record header tags are described. A method may involve parsing a URL document to identify multiple candidate record header tags and determining scores of an individual candidate record header tag of the multiple candidate record header tags based on record header tag criteria. The method may also involve cumulating the scores to obtain a total score for the individual candidate record header tag. The method may further involve selecting a candidate record header tag of the multiple candidate record header tags as a record header tag for the URL document based on the total score of the individual candidate record header tag.