VR Session Context Preservation via AI Monitoring and Blockchain Storage
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Solution Overview
Problem
Existing VR systems fail to maintain session context after connection losses or interruptions, leading to frustrating experiences for customers and inefficiencies for support companies, as prior art systems cannot track VR conversations, identify, or restore connection failures effectively.
Innovation Solution
Implementing a self-correction layer in VR systems that uses AI/ML to capture, store, and monitor VR sessions, allowing for real-time detection of connection anomalies and automatic recreation of sessions with preserved context, utilizing blockchain technology for conversation tracking and AI/ML for context identification and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If VR session context is not captured and stored, then system complexity is reduced, but customer service quality deteriorates due to loss of conversation context after interruptions
Solution Approach 1:
The system captures and stores VR session context data (audio, video, messages, screen shares) in real-time during the interaction, preparing it for potential restoration before any interruption occurs. This preliminary capture ensures that when disconnections happen, the context is already preserved and ready for immediate restoration without requiring complex post-interruption analysis
Solution Approach 2:
A context capture and storage module acts as an intermediary between the VR session and the restoration mechanism. This module continuously records session data and stores it in a retrievable format, serving as a buffer that decouples the complexity of real-time capture from the restoration process, thereby improving reliability without proportionally increasing overall system complexity
2Productivity
If manual analysis is used to restore failed transactions, then implementation simplicity is maintained, but productivity deteriorates due to extended turn-around periods
Solution Approach 1:
The system automatically detects connection failures, retrieves stored context data, and restores VR sessions without requiring manual intervention. The automated failure detection module monitors session status, and when an interruption is detected, the system autonomously fetches the captured context and reestablishes the conversation, dramatically improving productivity while the modular design keeps complexity manageable
Solution Approach 2:
The system implements continuous monitoring of VR session connectivity with automatic feedback loops. When disconnections are detected, the system automatically triggers context retrieval and session restoration processes. This closed-loop feedback mechanism eliminates manual analysis needs and accelerates transaction processing while maintaining controlled system complexity through automated decision-making
3Reliability
If VR session data is captured and stored in real-time, then context restoration capability is improved, but energy consumption increases
Solution Approach 1:
The system extracts only the essential context data needed for session restoration (key conversation points, critical information exchanged) rather than storing complete high-fidelity recordings of all session elements. This selective extraction maintains sufficient context for effective restoration while significantly reducing the energy required for data capture, storage, and processing
Data Source
AI summary
A real-time process for establishing and reestablishing virtual reality (VR) sessions between automated agent and a customer with customer VR equipment without losing session context after a connection loss is disclosed. VR sessions are dynamically created/recreated using artificial intelligence/machine learning (AI/ML) for engaging virtual agents and customers. Real-time capturing and processing of the VR interaction is captured for session context and stored via blockchain ledgers for secure tracking. VR session connectivity is monitored in real-time. If any anomaly or interruption occurs, retry notifications are sent to establish a second session. Prior session context is used for the second VR session to allow the virtual agent and customer interaction to continue without loss of context.