Personal Content Analysis for Customized Artificial Reality Environments
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Solution Overview
Problem
Conventional artificial reality environments are standardized across all users, failing to fully engage users by not appealing to their individual preferences.
Innovation Solution
Auto-generating artificial reality environments based on personal user content, including digital media such as photos, videos, and calendar events, to create customized experiences tailored to individual user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standardized artificial reality environments are used for all users, then device complexity is reduced and ease of manufacture is improved, but user engagement and satisfaction deteriorate due to lack of personalization
Solution Approach 1:
The system performs preliminary analysis of user content (photos, videos, messages, calendar events) to extract preferences and contextual information before environment generation. This pre-processing enables automated personalization without requiring complex real-time decision-making during environment creation.
Solution Approach 2:
The system autonomously generates personalized artificial reality environments by automatically analyzing user content and extracting preferences without requiring manual user configuration. The environment generation process serves itself by using the extracted user profile data to automatically configure personalized settings, themes, and content recommendations.
2Reliability
If personal user content is accessed to generate customized environments, then user engagement and relevance are improved, but privacy concerns and security risks worsen
Solution Approach 1:
The system introduces an intermediary processing layer that analyzes user content to extract preferences and contextual information without storing or exposing the original personal data. This intermediary layer acts as a buffer between the user's private content and the environment generation process, ensuring that only aggregated, anonymized preference data is used for personalization.
Solution Approach 2:
The system extracts only the necessary preference information and contextual data from user content while leaving the original personal data intact and private. By taking out only the essential elements needed for personalization (such as preferred colors, themes, activity types) and discarding or protecting the rest, the system minimizes privacy exposure while maintaining personalization effectiveness.
3Measurement precision
If comprehensive user content analysis is performed to determine preferences, then personalization accuracy is improved, but processing time and computational resources worsen
Solution Approach 1:
The system performs partial analysis of user content by focusing on the most informative and relevant data sources first (such as frequently accessed apps, recent calendar events, prominent photos) rather than analyzing every piece of user data equally. This selective approach achieves sufficient personalization accuracy while significantly reducing processing time and computational resource requirements.
Solution Approach 2:
The system applies different levels of analysis depth to different types of user content based on their informational value. High-value content sources (such as calendar events, frequently used apps) receive more thorough analysis, while lower-value content receives lighter processing. This localized quality approach optimizes the balance between personalization accuracy and processing efficiency.
Data Source
AI summary
Methods, systems, and storage media for auto-generating an artificial reality environment based on access to personal user content are disclosed. Exemplary implementations may: receive consent from a user to access user content on a user device, the user content comprising digital media; generate a user profile based at least in part on the user content; determine user preferences based at least in part on the user profile; generate an artificial reality environment based at least in part on the user preferences; and share the artificial reality environment with contacts of the user.