Web Accessibility Remediation Using ML and Voice Command Routing
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Solution Overview
Problem
Existing assistive technologies, such as screen readers and voice command systems, are limited in their ability to remediate websites that do not comply with industry standards like WCAG 2.0 and WAI-ARIA, leading to inadequate accessibility for users with diverse abilities.
Innovation Solution
A system and method for programmatically detecting and remediating compliance issues in web pages using an administrator portal for form-based creation and deployment of remediation code, incorporating a machine learning platform to identify applicable remediations, and dynamically routing voice commands for enhanced accessibility features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional screen readers and voice command systems are used, then basic accessibility functions are provided, but the ability to remediate non-compliant websites is limited
Solution Approach 1:
The patent introduces an intermediary accessibility remediation system that sits between the assistive technology and the non-compliant website. This mediator automatically detects accessibility violations and applies corrective code transformations to the website content before it reaches the user's assistive technology, enabling reliable remediation without requiring the website to be pre-compliant.
Solution Approach 2:
The system dynamically changes parameters of the website content by injecting and executing remediation code that modifies DOM structures, adds missing accessibility attributes, and transforms non-compliant elements into compliant ones, thereby adapting the website to work with standard assistive technologies.
2Manufacturing precision
If manual remediation of each web page is performed, then compliance issues are addressed, but the process is time-consuming and inefficient
Solution Approach 1:
The patent implements a self-service automated remediation system that autonomously crawls websites, identifies accessibility violations, generates appropriate remediation code, and applies fixes without human intervention. The system uses machine learning models trained on accessibility guidelines to automatically determine the correct remediation actions, achieving both high accuracy and rapid processing of multiple web pages.
Solution Approach 2:
The system performs preliminary actions by pre-generating and storing remediation code templates for common accessibility violations. When violations are detected during website crawling, the system retrieves and applies these pre-prepared remediation snippets, significantly accelerating the remediation process while maintaining consistent quality across different pages.
3Measurement precision
If comprehensive accessibility testing is performed on all web pages, then compliance is ensured, but the time and resources required increase significantly
Solution Approach 1:
The patent applies partial action by focusing testing and remediation efforts on the most critical accessibility violations and high-priority web pages. The system uses heuristic analysis to identify and remediate the most impactful compliance issues first, achieving sufficient accessibility improvement without exhaustively testing every single element on every page, thereby reducing time loss while maintaining effective compliance.
Data Source
AI summary
Systems and methods are disclosed for manually and programmatically remediating websites to thereby facilitate website navigation by people with diverse abilities. For example, an administrator portal is provided for simplified, form-based creation and deployment of remediation code, and a machine learning system is utilized to create and suggest remediations based on past remediation history. Voice command systems and portable document format (PDF) remediation techniques are also provided for improving the accessibility of such websites.