Adaptive Query Rendering via AI Context Detection
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Solution Overview
Problem
Existing interactive query-based network communication systems are inflexible and non-adaptive, often requiring users to respond in rigid settings, which can be disadvantageous for visually impaired individuals and do not adapt to user context, limiting their accessibility and usability.
Innovation Solution
A method and system that determine the user's context of interaction through a server, fetch and load queries on a media device in audio, video, or gesture formats, and refine them based on user responses using AI and ML engines, allowing for adaptive and context-aware communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If preset questions are used in the survey, then the survey structure is simple and easy to implement, but the survey becomes non-adaptive and cannot adjust to user context
Solution Approach 1:
The survey system dynamically adjusts its structure based on user context and responses. The server receives user responses and modifies subsequent queries accordingly, transforming a static preset survey into a dynamic adaptive survey that evolves based on real-time user interactions and contextual information.
Solution Approach 2:
The system implements feedback mechanisms where user responses to queries are processed by the server to generate refined subsequent queries. This feedback loop enables the survey to adapt to user preferences, knowledge levels, and contextual information, making each query responsive to previous interactions.
2Adaptability or versatility
If rigid response settings are required, then the survey process is straightforward and controlled, but accessibility is limited for users with disabilities or in different settings
Solution Approach 1:
The system provides multiple response channels including text input, voice commands, and gesture recognition, allowing users to interact with the survey in the manner that best suits their abilities and preferences. This multi-functional approach ensures accessibility for users with disabilities while maintaining ease of operation across different contexts.
Solution Approach 2:
The system changes the parameters of query presentation and response collection based on detected user context and capabilities. By adjusting these parameters dynamically, the system can accommodate users with disabilities and adapt to different operational settings without compromising survey integrity.
3Adaptability or versatility
If queries are rendered only in text format, then the interface is simple and processing is efficient, but usability is limited for visually impaired users and diverse media preferences
Solution Approach 1:
The query rendering system segments the query presentation into different media formats (text, audio, video, gestures) that can be independently delivered through appropriate channels. This segmentation allows visually impaired users to receive queries through audio or tactile means while maintaining the core survey functionality across different media preferences.
Solution Approach 2:
The system introduces media devices as intermediaries between the survey system and the user. These devices translate queries into appropriate formats (speech synthesis for audio, gesture recognition for visual input) making the survey accessible to users with different abilities and preferences without requiring complex direct interfaces.
4Adaptability or versatility
If the system does not refine queries based on user responses, then the survey process is simple and fast, but the survey cannot adapt to user knowledge level or preferences
Solution Approach 1:
The system performs preliminary analysis of user responses and context information to pre-determine the most effective subsequent queries. By processing user feedback in advance and using it to generate refined queries, the system avoids unnecessary time-consuming iterations while maintaining adaptability to user knowledge levels and preferences.
Data Source
AI summary
A method includes, through a server, determining a context of interaction between a user of a data processing device communicatively coupled to the server through a computer network and the server, fetching a set of queries from a database associated with the server in accordance with determining the context of interaction, and loading the set of queries one by one on a first media device configured to render the set of queries in an audio, a video and/or a gesture format. The method also includes, through the server, receiving a response to a query of the set of queries from the user via the first media device and/or the data processing device, and refining the set of queries based on the response received to the query from the user in accordance with an Artificial Intelligence (AI) and/or a Machine Learning (ML) engine executing on the server.