Predictive Accessibility System for component-based user interfaces

Abstract

A system (100) for predictively adapting accessibility in component-based user interfaces, the system comprising: a. a component monitoring module configured to continuously monitor and record properties, states, and metadata of user interface components in real time; b. a user profiling and behavior module configured to analyze user interactions with the components and generate dynamic user profiles based on behavior patterns indicative of accessibility needs; c. a context awareness module configured to detect environmental and device-specific context factors that affect user accessibility, the factors including at least ambient lighting, device orientation, and input device types; d. a Predictive Engine module configured to synthesize data from the Component Monitoring Module, the User Profiling and Behavior Module, and the Context Awareness Module to predict accessibility requirements and assign an Accessibility Adjustment Score to each component; e. an Accessibility Adjustment Module configured to dynamically modify the properties, behavior, or appearance of the components based on the Accessibility Adjustment Score without requiring an application reload; and f. a feedback and continuous learning module configured to collect implicit and explicit feedback from users and refine the predictive models based on the feedback, g. wherein the system dynamically adapts user interface components in real time to improve accessibility based on user behavior, environmental context, and predictive modeling.

Description

[0001] The present invention relates to the field of user interface (UI) design and accessibility, and more particularly to systems that predict and dynamically improve accessibility features in component-based user interfaces based on context, behavior, and environmental data.

[0002] As digital interfaces evolve, usability is becoming increasingly important. Component-based user interfaces (UIs), commonly used in modern web and mobile applications, allow developers to create flexible, dynamic, and reusable elements. However, ensuring accessibility across such diverse components presents a challenge. Traditional accessibility solutions, which are often static or manually configured, cannot adapt in real time to changing user needs. This static approach becomes inadequate as user interfaces become increasingly complex and user behaviors become more diverse, especially for users with disabilities or unique accessibility concerns.

[0003] The need for a more intelligent and adaptive system capable of predicting and dynamically adapting accessibility features based on user interactions and environmental context has grown. Current systems do not utilize machine learning or contextual data to automatically improve the accessibility of individual components without compromising the user experience. Therefore, there is a pressing need for a solution that can not only detect when accessibility changes are needed, but also make them in real time to ensure a more inclusive and efficient user interface. The present invention solves these problems by providing a predictive accessibility system capable of dynamically adapting user interface components to ensure optimal accessibility for all users.

[0004] A goal of the present disclosure is to dynamically adapt UI components in real time based on user behavior and environmental context.

[0005] Another objective of the present disclosure is to improve accessibility without requiring manual configuration or static design rules.

[0006] Another goal of the present disclosure is to enable seamless integration with various UI frameworks and technologies.

[0007] Another goal of this disclosure is to have machine learning-powered predictions that enable highly personalized accessibility adjustments.

[0008] Another goal of this disclosure is continuous self-improvement through feedback-driven learning and adaptation.

[0009] Another objective of the present disclosure is to enable a seamless user experience with real-time adjustments without reloading the application.

[0010] Another objective of the present disclosure is to provide configurability for developers to prioritize, restrict, or customize accessibility changes.

[0011] Another objective of this disclosure is privacy-aware profiling, which ensures that user data is treated securely and ethically.

[0012] The present invention relates to a predictive accessibility system (PAS) for dynamically adapting component-based user interfaces to meet individual user accessibility requirements. The system continuously monitors UI components through a component monitoring module that captures metadata such as type, role, and state transitions in real time. A user profiling and behavior module creates dynamic user profiles based on interaction patterns and detects accessibility issues such as motor impairments or visual impairments.

[0013] Another embodiment of the present invention is the Context Awareness Module, which captures environmental and device context such as ambient lighting, device orientation, and active assistive technologies to adjust accessibility predictions. The Predictive Engine Module synthesizes component, user, and context data to assign an accessibility adjustment score to each UI component and intelligently prioritize adjustments. An Accessibility Adjustment Module dynamically modifies component attributes, such as increasing button size, adjusting contrast, or adding alternative feedback mechanisms.

[0014] Another embodiment of the present invention is the feedback and continuous learning module, which captures interaction outcomes and user feedback and refines the predictive models over time for improved personalization. The system operates non-intrusively, supports modular integration with various UI frameworks, and ensures continuous optimization of user experiences without manual configuration, creating a truly adaptive and inclusive digital interface environment.

[0015] The present invention discloses a predictive accessibility system (PAS) specifically designed for component-based user interfaces (UIs). The system dynamically adapts accessibility features for individual user interface components by leveraging real-time monitoring, user behavior analysis, contextual data collection, predictive modeling, and continuous learning. Thus, it provides an optimized and inclusive user experience without the need for manual configuration or static rules.

[0016] At the heart of the invention is the Component Monitoring Module (CMM), which is responsible for real-time monitoring and logging of UI component properties and states. When components are created, updated, or removed within a dynamic interface, the CMM captures relevant metadata, including component types, roles, labels, focus behavior, event listeners, and state transitions. This module ensures that the system maintains a current understanding of UI structure and component behavior, which provides an important foundation for accessibility analysis.

[0017] Complementing this is the User Profiling and Behavior Module (UPBM), which continuously analyzes user interactions with the interface. By examining parameters such as navigation patterns, interaction time, input device usage, and error rates, the UPBM creates detailed user profiles. From these profiles, potential accessibility requirements can be derived, such as requirements for larger touchpoints, greater spacing between elements, support for voice feedback, or alternative text representations. The profiling mechanism can operate anonymously, session-based, or via authenticated profiles, with privacy-preserving techniques used to protect sensitive user data.

[0018] In addition, the Context Awareness Module (CAM) collects environmental and device-specific data that can influence accessibility requirements. By integrating device sensors such as ambient light detectors, orientation sensors, and screen readers, the CAM detects external conditions such as lighting quality, device posture, input methods, and other environmental factors. This module ensures that accessibility adjustments remain context-dependent, such as improving color contrast in bright environments or adapting layout modes for one-handed operation.

[0019] The Predictive Engine Module (PEM) is the analytical heart of the system. It synthesizes inputs from the CMM, UPBM, and CAM to predict the accessibility requirements of each component. The PEM assigns each UI element a dynamic Accessibility Adjustment Score (AAS), which represents the urgency and type of adjustment required. Predictive algorithms can include decision trees, ensemble learning models, or neural networks, depending on the implementation needs. The PEM ensures that adjustments are proactively and intelligently prioritized according to user and environmental requirements.

[0020] When necessary adjustments are identified, the Accessibility Adjustment Module (AAM) intervenes to make changes in real time. These adjustments can range from resizing buttons, changing text appearances, changing color schemes, adding audio or haptic feedback, or restructuring interaction flows for simplified accessibility. All changes are designed to be non-disruptive, providing a seamless user experience without the need to reload the application or manually refresh it. Additionally, developers can configure policies to prioritize or restrict certain types of adjustments based on application-specific requirements.

[0021] Finally, the Feedback and Continuous Learning Module (FCLM) closes the loop by monitoring the results of accessibility adjustments. Through implicit signals such as improved task completion rates and reduced interaction errors, as well as explicit user feedback, the FCLM updates the predictive models. Over time, the system becomes increasingly customized and accurate for each user and application context, using adaptive learning techniques such as reinforcement learning to refine its predictions.

[0022] Overall, the Predictive Accessibility System offers a holistic and intelligent approach to making modern, component-based user interfaces more inclusive and adaptable. Its modular design ensures easy integration into various development frameworks while maintaining developer flexibility and ensuring a high standard of user-centric accessibility.

[0023] The invention is explained again below with reference to the figure. It shows: Fig. : an illustration of the Predictive Accessibility System (100) for component-based user interfaces

[0024] Fig.Shows the Predictive Accessibility System (PAS), which works by continuously monitoring and analyzing various data streams from the user interface, user behavior, and environmental context to dynamically adapt accessibility features. First, the Component Monitoring module collects real-time data on the properties, states, and metadata of each UI component to ensure an up-to-date understanding of the user interface. The User Profiling and Behavior Module then processes user interaction patterns and creates adaptive profiles that identify specific accessibility needs, such as frequent errors or slow navigation, based on the user's actions and preferences.Meanwhile, the Context Awareness module detects environment- and device-specific factors, including ambient lighting conditions, screen orientation, and input methods, that can affect the visibility and usability of UI components. The Predictive Engine module synthesizes this data and applies machine learning algorithms to predict necessary adjustments and assign each component an Accessibility Adjustment Score that reflects the urgency and relevance of the change. Based on these predictions, the Accessibility Adjustment module automatically implements real-time changes to the components, such as resizing buttons, adjusting contrast, or adding alternative text or auditory cues. These changes occur seamlessly and without interrupting the user experience.As the user interacts with the system, the feedback and continuous learning module collects implicit and explicit feedback, refining the system's predictive capabilities over time, thus continuously improving the accuracy and effectiveness of accessibility adjustments. This closed-loop system ensures that the interface remains optimally accessible to users, regardless of their individual needs or environmental conditions.

Claims

[1] A system (100) for predictively adapting accessibility in component-based user interfaces, the system comprising: a. a component monitoring module configured to continuously monitor and record properties, states, and metadata of user interface components in real time; b. a user profiling and behavior module configured to analyze user interactions with the components and generate dynamic user profiles based on behavior patterns indicative of accessibility needs; c. a context awareness module configured to detect environmental and device-specific context factors that affect user accessibility, the factors including at least ambient lighting, device orientation, and input device types; d. a Predictive Engine module configured to synthesize data from the Component Monitoring Module, the User Profiling and Behavior Module, and the Context Awareness Module to predict accessibility requirements and assign an Accessibility Adjustment Score to each component; e. an Accessibility Adjustment Module configured to dynamically modify the properties, behavior, or appearance of the components based on the Accessibility Adjustment Score without requiring an application reload; and f. a feedback and continuous learning module configured to collect implicit and explicit feedback from users and refine the predictive models based on the feedback, g. wherein the system dynamically adapts user interface components in real time to improve accessibility based on user behavior, environmental context, and predictive modeling. [2] The system (100) of claim 1, wherein the component monitoring module captures at least one of the following: component type, role, label, event listener, and dynamic state transitions. [3] The system (100) of claim 1, wherein the user profiling and behavior module uses machine learning techniques including clustering, decision trees, or anomaly detection to infer users' accessibility needs. [4] The system (100) of claim 1, wherein the context awareness module is integrated with device sensors to detect at least ambient light conditions, screen orientation, and the presence of assistive technologies. [5] The system (100) of claim 1, wherein the prediction module uses ensemble learning techniques to generate the accessibility adaptation score for each component. [6] The system (100) of claim 1, wherein the accessibility adaptation module modifies at least one of the following: component size, color contrast, interaction flows, spacing, font sizes, or adding audio feedback. [7] The system (100) of claim 1, wherein the accessibility adaptation module supports developer-configurable policies to prioritize, restrict, or override certain accessibility adaptations. [8] The system (100) of claim 1, wherein the feedback and continuous learning module uses reinforcement learning techniques to continuously adapt and improve the predictive models over time based on user feedback.

Images