Adaptive AI Feedback Prioritization for Multilingual Timeliness
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Solution Overview
Problem
Conventional AI conversational engines fail to deliver timely and language-adaptive feedback, impacting user experience and adaptability due to varying language processing times and neglecting ambient and user-specific factors.
Innovation Solution
An AI-based adaptive feedback system that determines a target language from user profiles, location, and interactions, identifies events requiring feedback, assesses ambient and user attributes, and assigns priorities based on criticality and rendering time windows to deliver feedback contemporaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI feedback is delivered in multiple languages with varying processing times, then language coverage is improved, but feedback timeliness deteriorates
Solution Approach 1:
The system pre-determines the target language based on user profile, location, and interaction history before feedback generation is needed. This preliminary language identification eliminates delays during actual feedback delivery, as the translation and rendering process can begin immediately when an event occurs, rather than determining the language at that moment.
Solution Approach 2:
The system dynamically adjusts the feedback rendering time window based on the criticality factor and language processing requirements. For high-criticality events in languages requiring longer processing time, the system expands the time window or prioritizes rendering, while for low-criticality events, it uses standard time windows, thus adapting to both language diversity and timeliness requirements.
2Measurement precision
If AI feedback delivery is delayed for accurate language processing, then language accuracy is improved, but user experience deteriorates
Solution Approach 1:
The system performs preliminary determination of the target language and prepares the translation pipeline in advance based on user attributes and event context. This allows accurate language processing to occur without noticeable delay to the user, as the system is already configured to render feedback in the correct language when the event occurs.
Solution Approach 2:
The system changes the parameter of feedback rendering time window based on the criticality factor and language processing requirements. By dynamically adjusting this time window parameter, the system ensures that language processing completes with sufficient accuracy while minimizing the perceived delay to the user, thus maintaining both language accuracy and user experience.
3Device complexity
If conventional AI engines provide feedback without considering ambient factors, then system simplicity is maintained, but feedback relevance deteriorates
Solution Approach 1:
The system segments the feedback generation process into distinct components: event identification, ambient factor determination, criticality factor calculation, and feedback rendering. This segmentation allows the system to incorporate complex ambient factor analysis while maintaining clear, manageable processing stages, thus balancing system complexity with feedback relevance.
Solution Approach 2:
The system preliminarily determines ambient factors and user attributes when an event is identified, before generating the actual feedback. This preliminary assessment of context enables the system to tailor feedback relevance to current conditions without adding significant complexity to the overall system architecture, as the contextual analysis is performed as a standard preparatory step.
Data Source
AI summary
The invention relates to system and method for providing language based adaptive feedback to users. The method includes receiving information related to a target language associated with a user; identifying occurrence of at least one event requiring rendering of automated feedback to the user; determining ambient factors at and proximate to the time of occurrence of each of the at least one event, and user attributes; determining a criticality factor associated with each of the at least one event; determining, for each of the at least one event, a feedback rendering time window; identifying, for each of the at least one event, feedback attributes of the associated feedback; assigning a priority to each of the at least one event; identifying one or more events from each of the at least one event based on the assigned priority; and rendering the feedback associated with each of the one or more events.