Adaptive Reality Layer System for Real-Time User Context
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current digital experiences are predictable and impersonal, failing to adapt in real-time to user preferences, behaviors, or environmental contexts, leading to stagnated content delivery and limited user engagement.
Innovation Solution
The Celeste Opera system, which integrates multiple reality layers (AR, MR, VR, SR, DR) through a comprehensive adaptive framework. This system uses real-time data collection and analysis from biometric, environmental, and physical interaction data to tailor digital experiences dynamically, leveraging AI, machine learning, and quantum processing for seamless adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current digital experiences use static content delivery, then system complexity is reduced, but user engagement and personalization are worsened
Solution Approach 1:
The patent implements dynamic content delivery by continuously adjusting digital experiences based on real-time user data from multiple sources (biometric sensors, environmental sensors, device usage patterns). The system transitions from static to dynamic by using machine learning models that update user profiles and preferences continuously, allowing content to adapt automatically to changing user states and contexts without requiring manual reconfiguration.
Solution Approach 2:
The system incorporates multiple feedback loops where user interactions, biometric responses, and environmental conditions are continuously monitored and fed back into the content delivery mechanism. This feedback enables the system to learn from user behavior patterns and adjust content delivery in real-time, creating a closed-loop system that improves personalization over time while managing complexity through automated feedback processing.
2Adaptability or versatility
If real-time data collection and analysis is implemented, then user experience personalization is improved, but data processing requirements and system complexity are worsened
Solution Approach 1:
The patent segments data processing into distinct functional modules: data collection from multiple sensor types, data cleaning and validation, feature extraction, machine learning analysis, and content generation. This segmentation allows each component to handle specific processing tasks independently, making the overall complex system more manageable and maintainable while enabling real-time processing through parallel operation of these modular components.
Solution Approach 2:
The system performs preliminary data processing and feature extraction in advance to prepare data for analysis. By pre-processing and structuring data from multiple sources before it enters the machine learning models, the system reduces the computational burden during real-time decision-making, enabling faster personalization responses without requiring equally complex real-time processing.
3Ease of operation
If multiple reality layers are integrated, then user immersion and engagement are improved, but system complexity and integration difficulty are worsened
Solution Approach 1:
The patent merges multiple reality layers (augmented reality, virtual reality, mixed reality, and physical environment) into a unified system that coordinates content across different layers. By combining these layers through a common framework that shares data and processing resources, the system achieves enhanced user immersion while reducing the complexity that would result from implementing separate independent systems for each reality type.
Data Source
AI summary
Disclosed herein is a method for facilitating tailoring experiences of users, in accordance with some embodiments. Accordingly, the method includes receiving, using a communication device, a data from a device. Further, the method includes analyzing, using a processing device, the data. Further, the method includes determining, using the processing device, a context associated with a user based on the analyzing of the data. Further, the method includes adjusting, using the processing device, at least one of a plurality of reality layers of an artificial environment based on the context. Further, the method includes provisioning, using the processing device, a content corresponding to at least one of the plurality of reality layers of the artificial environment based on the adjusting. Further, the method includes storing, using a storage device, the data and the context.