AR Content Management via Stage Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current augmented reality (AR) systems lack efficient content management solutions that enable live user interaction with virtual environments, real-time data analytics, and flexible content structures to accommodate diverse user interactions and updates within holographic MR environments, limiting their scalability and interactivity.
Innovation Solution
A content management system that structures AR content into independent stages, allowing real-time modification and user-generated content incorporation, using artificial intelligence and data analytics to provide dynamic, interactive experiences by integrating user interactions, real-life images, and analytics data, while maintaining access restrictions and efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AR content is structured into independent stages for real-time modification, then adaptability and ease of operation are improved, but device complexity increases
Solution Approach 1:
The AR content is divided into multiple independent stages, where each stage represents a discrete content unit that can be individually modified, added, or removed. This segmentation enables real-time content updates without affecting the entire AR experience, allowing flexible content management while maintaining system organization through clear stage boundaries and sequential structure.
2Adaptability or versatility
If real-time data analytics are incorporated to modify AR content, then adaptability is improved, but computational resource requirements increase
Solution Approach 1:
The system performs data analytics selectively on specific stages of AR content rather than processing the entire content set. This partial action approach allows the system to analyze and modify only the relevant content portions based on user interactions and contextual data, reducing overall computational overhead while maintaining real-time adaptability for personalized content delivery.
3Ease of operation
If user-generated content is integrated in real-time, then ease of operation and adaptability are improved, but system reliability may deteriorate due to unverified content
Solution Approach 1:
The system establishes predetermined content validation rules and filtering mechanisms before user-generated content is integrated into the AR experience. These preliminary actions include automated content type verification, format validation, and adherence checks against established guidelines, ensuring that user contributions meet quality standards while maintaining real-time interaction capabilities.
4Reliability
If access restrictions are implemented for AR content, then reliability is improved, but ease of operation worsens
Solution Approach 1:
The access control system implements a universal permission framework that automatically applies different access levels based on user profiles, content sensitivity, and contextual factors. This multi-functional approach consolidates multiple access control mechanisms into a single system that handles authentication, authorization, and content filtering uniformly across all AR stages, simplifying user interaction while maintaining security and reliability.
Data Source
AI summary
Described is a real-time content management and data analytics system for AR-based platforms. The data management system described herein manages AR content and user interactions with the AR content. Additionally, a new multiple-stage information augmentation design based on real-time data analysis and live AR interaction is described. In this design, AR content design is very flexible and may be organized into one or more stages containing pre-defined content, on-line searched content, user generated content, other user generated content, real-time user interactively generated content, or some combination. The flexibility content structure brings allows for a high customizable AR experience for each user to maximize user relevance and stickiness. An example application of the system described herein is for interactive advertisements.


