Screen Content Accessibility Evaluation via Audio Transcription
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Solution Overview
Problem
The repetitive validation of website accessibility across various user devices and platforms is time-consuming and costly, with a high risk of defect seepage due to the arduous evaluation process, which can be inefficient and resource-intensive.
Innovation Solution
A comprehensive screen readability evaluation system that utilizes a baseline document and a machine learning model to transcribe audio from screen readers, flagging mismatches and enabling simultaneous evaluation across multiple devices, thereby streamlining the validation process and reducing evaluation time by up to 70%.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If repetitive manual validation is performed across various user devices and platforms, then accessibility compliance is ensured, but evaluation time and resources increase significantly
Solution Approach 1:
The patent creates a digital twin or virtual representation of the screen content that can be evaluated repeatedly without physical re-testing on multiple devices. The system captures screen content and generates a digital model that simulates accessibility evaluation, eliminating the need for repetitive manual testing across devices while maintaining compliance verification reliability
Solution Approach 2:
The system performs preliminary accessibility evaluation by analyzing screen content against accessibility guidelines before actual deployment or final validation. By conducting preliminary checks on the digital representation, potential accessibility issues are identified early, reducing the need for repeated corrective validation cycles across multiple devices
2Measurement precision
If comprehensive evaluation across multiple devices is performed, then accessibility defects are detected, but the evaluation process becomes arduous and resource-intensive
Solution Approach 1:
The patent extracts the essential accessibility evaluation logic from the complex multi-device testing process. By isolating the key accessibility checkpoints and creating a standalone digital evaluation model, the system achieves comprehensive defect detection without requiring the full complexity of multi-device physical testing
Solution Approach 2:
The system introduces a digital intermediary layer between the screen content and the accessibility evaluation. This digital representation acts as a mediator that simplifies the evaluation process by providing a standardized, device-agnostic model that can be analyzed without directly interfacing with multiple physical devices
3Measurement precision
If manual reevaluation is performed after defect fixes, then validation accuracy is maintained, but the process remains time-consuming
Solution Approach 1:
The system uses digital copying of screen content to enable rapid reevaluation after defect fixes. Instead of manual re-testing, the updated screen content is captured and compared against the accessibility guidelines in the digital model, maintaining validation accuracy while dramatically improving reevaluation efficiency
Solution Approach 2:
The patent establishes continuous automated monitoring and evaluation capability that operates without interruption. The system continuously validates screen content against accessibility requirements, eliminating the stop-start nature of manual reevaluation and maintaining high validation accuracy throughout the development cycle
Data Source
AI summary
In one example, a method for evaluating screen content for accessibility with a screen reader device is disclosed. The method provides a baseline document including a script of expected screen content that conforms accessibility requirements. The method may generate an audio file based on screen content elements. For some implementations, the method uses a machine learning model to transcribe the audio file into an output transcription file. The method may determine whether output transcription file matches the baseline document and a corresponding output report is generated.