Markup Language Content Extraction for Accessibility
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Solution Overview
Problem
Websites are becoming increasingly cluttered with distracting elements, making them inaccessible to users with disabilities, and existing solutions like screen readers often read raw markup language text, failing to remove clutter effectively.
Innovation Solution
A method for extracting content from markup language text by parsing it into a hierarchical data model and applying filters to remove clutter, such as links, scripts, and advertisements, while maintaining essential information, with settings that can be determined automatically or by the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If markup language text includes guides, menus, advertisements, and other formatting elements to improve user efficiency and navigation, then the website becomes more functional and easier to navigate, but the text becomes cluttered and less accessible to users with disabilities
Solution Approach 1:
The patent segments the markup language text into hierarchical components using a data model that distinguishes between navigation elements (guides, menus) and content elements. This segmentation allows screen readers to selectively access only the relevant content portions while ignoring clutter elements, thereby resolving the contradiction between navigation functionality and accessibility.
Solution Approach 2:
The patent extracts and removes clutter elements (advertisements, pop-ups, excessive formatting) from the markup language text while preserving the essential content and navigation structure. This extraction process eliminates harmful factors that impede accessibility for users with disabilities while maintaining the website's functional navigation capabilities.
2Object-affected harmful factors
If screen readers read raw markup language text to provide access to web content, then all content is made accessible, but users with disabilities are overwhelmed by clutter and unnecessary elements
Solution Approach 1:
The patent applies preliminary action by preprocessing the markup language text before it reaches the screen reader. The system parses the markup into a structured data model, identifies and removes clutter elements, and organizes the remaining content in a hierarchical structure. This preliminary processing ensures that when screen readers access the content, it is already filtered and optimized for accessibility, preventing overwhelming users with unnecessary elements.
3Adaptability or versatility
If websites include more features like animations, pop-ups, and advertisements to enhance user experience, then the website becomes more engaging, but bandwidth usage increases and performance decreases on constrained devices
Solution Approach 1:
The patent extracts and removes resource-intensive elements such as advertisements, pop-ups, and excessive formatting from the markup language text. This extraction reduces the overall data volume that needs to be transmitted and processed, thereby decreasing bandwidth usage and energy consumption on constrained devices while retaining the essential content that provides user experience value.
Data Source
AI summary
Systems and methods are presented for content extraction from markup language text. The content extraction process may parse markup language text into a hierarchical data model and then apply one or more filters. Output filters may be used to make the process more versatile. The operation of the content extraction process and the one or more filters may be controlled by one or more settings set by a user, or automatically by a classifier. The classifier may automatically enter settings by classifying markup language text and entering settings based on this classification. Automatic classification may be performed by clustering unclassified markup language texts with previously classified markup language texts.