Intelligent Inclusive Design System for Accessibility Contradictions
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Solution Overview
Problem
Current ICT products and services are not adequately accessible and usable for persons with disabilities, despite compliance with universal design standards and accessibility guidelines, as they fail to address the unique usability and interaction needs of differently abled users.
Innovation Solution
A processor-implemented method and system for intelligent generation of inclusive system designs, which dynamically captures user interactions, identifies functional limitations, and generates multi-modal designs based on pre-defined scenarios and transactions stored in a knowledge bank, allowing for real-time adaptation to user needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If universal design standards and accessibility guidelines are complied with, then basic accessibility is improved, but unique usability and interaction needs of differently abled users are not addressed
Solution Approach 1:
The system dynamically generates personalized interaction patterns and micro-interactions based on captured user behaviors and identified functional limitations. Instead of static design compliance, the system adapts design solutions in real-time to address unique usability needs of differently abled users while maintaining accessibility standards.
Solution Approach 2:
The system changes design parameters by capturing user behaviors, identifying functional limitations, and generating customized interaction patterns. This transforms generic accessibility compliance into personalized usability solutions by modifying interaction parameters based on individual user needs and assistive technology requirements.
2Manufacturing precision
If design considerations are dependent on designers' competency and knowledge, then design quality may be improved, but the learning curve and accessibility to design principles deteriorates
Solution Approach 1:
The system performs automated behavioral pattern capture, functional limitation identification, and personalized design generation without requiring deep expert knowledge from designers. The automated system serves itself by learning from user interactions and generating appropriate design solutions, reducing the burden on designers while maintaining high design quality.
Solution Approach 2:
The system replaces the manual mechanical process of designer expertise with an automated intelligent system. Instead of relying on designers' knowledge and experience, the system uses automated behavioral analysis and machine learning to generate personalized designs, eliminating the need for extensive learning curves while maintaining design quality.
3Ease of operation
If assistive technology tools are employed to interact with technology, then accessibility for persons with disabilities is improved, but interaction patterns change thereby impacting design considerations
Solution Approach 1:
The system continuously captures user behaviors and assistive technology usage patterns, using this feedback to identify functional limitations and generate personalized interaction patterns. This closed-loop feedback mechanism allows the system to adapt to changing interaction patterns automatically, maintaining accessibility while adjusting design considerations based on actual user behavior with assistive technologies.
Data Source
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AI summary
In spite of complying with accessibility standards and guidelines, it is noted that persons with disabilities (PwD) continue to face usability challenges when accessing content. The present disclosure addresses these challenges by firstly identifying design considerations for each type of disability and facilitates intelligent generation of inclusive system designs that addresses usability and accessibility challenges based on persona, scenario and modality associated with users of all abilities, hence being inclusive. Systems and methods of the present disclosure aide designers with a comprehensive knowledge bank of captured challenges, needs and effectiveness of modalities, the outcomes and existing design considerations that may be prompted at appropriate state of development of system designs to help designers make informed design choices without curtailing their creativity thus digitizing the end-to-end design process. An Artificial Intelligence-Machine Learning module facilitates automated generation of run-time interface using cognitive inputs from designers.