Personalized VR Content Branch Prediction via User Classification
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Solution Overview
Problem
Virtual reality systems face challenges with latency and lack of personalization in cloud VR rendering, leading to suboptimal user experiences due to network and compression latencies, as well as the inability to tailor content to individual users' emotional, physical, and biological responses.
Innovation Solution
A system that aggregates sensory data from multiple users to perform crowd-sourced predictive processes, reducing latency by processing data locally and sending control information to a remote server for adaptive content rendering, using machine learning to personalize VR experiences based on user data such as emotional and physical states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If cloud VR rendering is used, then device complexity is reduced, but latency increases due to network and compression
Solution Approach 1:
The system segments the VR rendering pipeline into local processing components (sensor data aggregation, user classification, content selection) and remote rendering components. Local processing of sensor data and machine learning inference reduces the amount of data transmitted over the network, thereby reducing compression and network latency while maintaining cloud-based rendering capabilities.
Solution Approach 2:
The system performs preliminary actions by pre-classifying users into categories (e.g., motion-sensitive, motion-tolerant) and pre-selecting appropriate content branches before the actual VR experience begins. This preliminary classification and content selection based on user profiles reduces real-time processing delays and network transmission requirements during the VR session.
2Ease of manufacture
If cloud VR rendering is used, then manufacturing complexity is reduced, but content personalization is lost
Solution Approach 1:
The system applies local quality by tailoring specific content attributes and parameters to individual users based on their classified characteristics. While the overall rendering infrastructure remains cloud-based, the content delivery is customized locally for each user's needs, such as adjusting motion intensity, content complexity, and experience parameters according to user profiles.
Solution Approach 2:
The system implements feedback mechanisms where sensor data from users during VR experiences is continuously collected and fed back into the machine learning classification models. This feedback loop enables the system to refine user classifications and adjust content personalization dynamically, improving adaptability while maintaining the simplified cloud rendering architecture.
Data Source
AI summary
Methods, apparatus, and machine-readable mediums are described for doing predictive content branch selection for rendering environments such as virtual reality systems. User data is aggregated from multiple users. Each user is classified based upon the user data. Personalization parameters are identified for each of the plurality of users. Content to be presented is determined and modified with a modification for a user based upon the personalization parameters for the user. The modified content is sent to the user.