Industrial Metaverse AI Analytics for Real-Time User Experience Testing
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Solution Overview
Problem
Existing methods for enhancing user interaction experience in industrial metaverse environments are inefficient and impractical, particularly in multiuser scenarios, as they rely on manual data collection and analysis, which becomes unfeasible with increasing interaction touch points and multiple users solving different problems.
Innovation Solution
A system and method utilizing AI models to automatically collect and analyze data on user interactions in real-time, determining parameters like learnability, proficiency, and efficiency, and providing cues such as sensory feedback to enhance user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection and analysis is performed at each interaction touch point, then user experience can be evaluated, but the time consumption increases significantly
Solution Approach 1:
The patent replaces manual data collection and analysis (mechanical human effort) with automated AI-based systems. Machine learning models automatically analyze user interaction data, track user behavior patterns, and evaluate experience metrics without human intervention, thereby maintaining measurement precision while dramatically reducing time consumption.
Solution Approach 2:
The patent introduces AI models and automated analytics systems as intermediaries between raw user interaction data and experience evaluation results. These intermediary systems process and interpret data automatically, serving as a bridge that eliminates the need for manual analysis while preserving evaluation accuracy.
2Measurement precision
If regular experience testing is performed at every interaction touch point for multiple users, then comprehensive user experience data can be collected, but the complexity of the system becomes unmanageable
Solution Approach 1:
The patent implements a universal automated analytics platform that serves multiple users and multiple interaction touch points simultaneously. The system uses general-purpose AI models that can analyze various types of user interactions (navigation, transactions, communications) across different users, eliminating the need for separate testing systems for each user or touch point and thereby managing complexity while maintaining comprehensive data collection.
Solution Approach 2:
The patent uses automated systems to replicate and analyze user interactions at scale. Instead of manual testing for each user, the system creates digital representations of user behavior patterns and analyzes these copies automatically, allowing comprehensive data collection from multiple users without proportionally increasing system complexity.
3Measurement precision
If the number of interaction touch points increases, then more comprehensive user experience coverage is achieved, but manual analysis becomes impractical
Solution Approach 1:
The patent replaces manual analysis processes with automated AI-based analytics systems that can handle increasing numbers of interaction touch points. The automated systems track and analyze user behavior across numerous touch points (navigation, search, transactions, communications) without the diminishing returns that plague manual analysis, thereby maintaining ease of analysis while achieving comprehensive coverage.
Data Source
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AI summary
Disclosed herein is a computer-implemented method (400) for enhancing user interaction experience. The method (400) includes obtaining (404), for each user of a plurality of users, data associated with a corresponding user while a corresponding avatar associated with the corresponding user is performing a plurality of interactions in a computer-simulated environment. Further, the method (400) includes determining (406), using a first AI model for each user, a plurality of parameters associated with the plurality of interactions. Further, the method (400) includes analysing (408), for each user using a second AI model, the plurality of parameters to determine a user experience of the corresponding user. Furthermore, the method (400) includes determining (410), for each user using a third AI model, one or more cues when the determined user experience is negative. Furthermore, the method (400) includes providing (412), to each user, the one or more cues to enhance the user experience.