Mixed Reality Experience Orchestrator for User State Adaptation
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Solution Overview
Problem
Traditional mixed reality experiences, such as those found in entertainment systems, lack individualized customization and on-the-fly adjustments, leading to a one-size-fits-all approach that fails to cater to the unique preferences and experiences of each user, resulting in suboptimal enjoyment.
Innovation Solution
A system that models the experiential state of users participating in mixed reality experiences, predicts alterations to experience parameters based on user preferences, and executes remedial actions to enhance individual user experiences, utilizing sensors and data analysis to tailor virtual and physical elements in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional mixed reality experiences use a one-size-fits-all approach, then device complexity is reduced and ease of operation is improved, but adaptability to individual user preferences deteriorates and user enjoyment is suboptimal
Solution Approach 1:
The system performs preliminary actions by modeling user preferences and experience parameters before the mixed reality experience begins. Preference models are created in advance based on user data, and prediction models are prepared to anticipate user reactions to various experience parameters, allowing customization to be ready before the user actually engages with the content
Solution Approach 2:
The system implements self-service by automatically modeling user preferences and predicting optimal experience parameters without requiring manual user configuration. The system serves itself by using machine learning models to autonomously adjust experience parameters based on inferred user preferences, eliminating the need for complex manual setup while achieving high adaptability
Solution Approach 3:
The system applies parameter changes by dynamically adjusting mixed reality experience parameters (such as visual effects, audio levels, interaction complexity) based on predicted user preferences. The prediction model generates specific parameter adjustments tailored to each user, transforming the experience from a fixed configuration to a dynamically adapted one without requiring complete system redesign
2Ease of operation
If real-time customization is implemented based on user experiential state, then user enjoyment is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary modeling of user preferences before the actual experience begins, so that when real-time customization is needed, the heavy computational work of preference analysis has already been completed. The prediction model is prepared in advance, allowing rapid parameter adjustments during the experience without significant processing delays
Solution Approach 2:
The system implements feedback loops that continuously monitor user experiential state and adjust experience parameters in real-time. By using established prediction models that have been pre-trained on user data, the system can quickly process feedback signals and make appropriate parameter adjustments without requiring extensive computational resources during the actual experience delivery
3Measurement precision
If detailed modeling of experiential state is performed, then prediction accuracy is improved, but measurement precision requirements and data processing complexity increase
Solution Approach 1:
The system extracts only the most relevant features from user data for modeling experiential state, rather than processing all possible data points. By identifying and extracting key indicators of user preference and experience quality, the system achieves high measurement precision while reducing the complexity of data collection and processing infrastructure required
Solution Approach 2:
The system applies different levels of modeling detail to different aspects of user experience based on their importance. Rather than uniformly high-resolution modeling of all experience parameters, the system focuses computational resources on the most critical experience factors that significantly impact user enjoyment, achieving effective precision where it matters most while reducing overall system complexity
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for customizing a mixed reality experience based on the experiential state of users in a queue to join the mixed reality experience or immersed in the mixed reality experience is provided. The present invention may include modeling the experiential state of the at least one user participating in the mixed-reality experience; modeling one or more relationships between the experiential state of the at least one user and one or more physical or virtual experience parameters comprising the mixed-reality experience; based on the one or more modeled relationships, predicting one or more alterations to the one or more physical or virtual experience parameters to enhance the experiential state of the at least one user; and operating a mixed reality system to perform one or more remedial actions to execute the one or more predicted alterations.