XR Content Prioritization Using User Intent and Camera Context
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Solution Overview
Problem
Traditional XR systems lack the ability to interpret implicit user data and context, leading to mismatches between XR content and the real-world environment, requiring explicit user interactions, which can be unnatural and result in confusion and irrelevant content overload.
Innovation Solution
Utilizing machine learning and computer vision technologies to analyze user interaction data and environment, inferring user intent to provide personalized and relevant XR content, emphasizing certain content over others based on user context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional XR systems display all available content, then users receive comprehensive information, but users experience information overload and confusion
Solution Approach 1:
The system extracts and identifies only the most relevant XR content items based on user intent analysis, separating important content from less important content. This allows the system to display a subset of high-priority content rather than all available content, reducing information overload while maintaining comprehensiveness for relevant information.
Solution Approach 2:
The system applies different display qualities and priorities to different content items based on their relevance to user intent. High-priority content receives enhanced visualization and prominence, while lower-priority content is de-emphasized or hidden, creating local quality variations that guide user attention without overwhelming them.
2Ease of operation
If traditional XR systems require explicit user interactions to navigate content, then users can control information flow, but interactions become unnatural and interruptive
Solution Approach 1:
The system performs self-service by automatically analyzing user behavior patterns, environmental context, and interaction history to infer user intent without requiring explicit commands. The system autonomously determines which content to prioritize and display, eliminating the need for users to manually navigate or filter content, thus making interactions more natural and less interruptive.
Solution Approach 2:
The system continuously monitors and analyzes user interactions, environmental data, and content engagement patterns to provide feedback loops that refine intent understanding. This feedback mechanism enables the system to adapt content prioritization dynamically based on real-time user behavior, improving the accuracy of implicit intent recognition while reducing the need for explicit user control inputs.
3Adaptability or versatility
If traditional XR systems provide generic content, then content delivery is simple, but content relevance to user context is low
Solution Approach 1:
The system performs preliminary analysis of user intent, environmental context, and content relevance before content delivery. By pre-processing and prioritizing content based on inferred user needs and contextual factors, the system prepares personalized content recommendations in advance, enabling adaptive content delivery without requiring complex real-time processing during user interaction.
Data Source
AI summary
Described is a system for emphasizing XR content based on user intent by gathering interaction data from use of one or more interaction functions by a user, accessing a camera feed of a camera system from the XR device, analyzing a combination of data corresponding to the interaction data and the camera feed using a first machine learning model to identify a priority for individual media content items, and determining that a first subset of media content items are of a higher priority than a second subset of media content items. Then the system displays the media content items on the XR device of the user, the first subset of the media content items displayed differently than the second subset of the media content items based on the identified priority.