Real-Time AR/VR Preference Clustering and Model Generation
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Solution Overview
Problem
Existing personalization mechanisms in AR/VR systems rely heavily on past user behavior and interactions, which limits their ability to capture real-time user preferences and adapt to individual user interests in immersive environments.
Innovation Solution
A method that captures user preferences in real-time through interactions with AR/VR elements, maps these preferences to clusters, generates a multidimensional user preference model, and displays it within the AR/VR space, allowing for personalized and dynamic content presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing personalization mechanisms rely on past user behavior, then system simplicity is maintained, but the ability to capture real-time user preferences and adapt to individual user interests deteriorates
Solution Approach 1:
The system performs preliminary actions by capturing user feedback and interactions during the AR/VR experience itself, rather than relying solely on historical data. The feedback capture mechanism is activated in advance to collect real-time preferences, enabling dynamic personalization without requiring complex post-processing of past behavior data.
Solution Approach 2:
The system introduces continuous feedback loops where user interactions and feedback during the AR/VR session are immediately processed to update personalization models. This real-time feedback mechanism allows the system to adapt to user preferences dynamically, transforming the static personalization approach into an adaptive system that responds to current user interests.
2Measurement precision
If real-time user feedback is captured and processed, then personalization accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by focusing on capturing and processing only the most relevant user feedback and interactions that directly indicate preferences. Rather than analyzing all possible data points, the system selectively processes key feedback signals, achieving adequate personalization accuracy with reduced computational overhead and faster processing times.
3Ease of operation
If user preferences are mapped to clusters and models are generated, then user engagement is improved, but system complexity increases
Solution Approach 1:
The system introduces clustering as an intermediary layer between raw user feedback and the final personalization model. User preferences are first grouped into clusters based on similarity, which simplifies the subsequent model generation process. This intermediary step reduces the complexity of direct model construction from individual preference points while maintaining the ability to deliver personalized experiences that enhance user engagement.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for augmented reality/virtual reality (AR/VR) preference mapping is provided. The embodiment may include capturing a plurality of user preferences from a plurality of user feedback while a user interacts with elements of an AR/VR space. The embodiment may also include mapping one or more user preferences from the plurality of captured user preferences to one or more clusters. The embodiment may further include generating a user preference model of the elements based on the one or more mapped user preferences. The embodiment may also include displaying the user preference model in the AR/VR space.