Neural Network Anomaly Detection for Immersive User Safety
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Solution Overview
Problem
Users engaged in immersive activities like VR or autonomous vehicle rides may be unaware of external occurrences due to immersion, posing safety risks and inconvenience.
Innovation Solution
A system that uses sensors and machine learning models to detect anomalies in audio and video data, providing notifications or actions based on predefined events of interest, such as speech recognition, acoustic novelty detection, and visual anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users engage in immersive activities (VR, autonomous vehicle rides), then user immersion and engagement are improved, but user awareness of external occurrences deteriorates
Solution Approach 1:
The patent introduces an intermediary system consisting of sensors, neural networks, and notification mechanisms that mediate between the external environment and the immersed user. Sensors capture audio and video data from the external environment, neural networks analyze this data to detect events of interest, and notifications alert users to these events without requiring them to break immersion voluntarily. This intermediary system resolves the contradiction by maintaining immersion while compensating for the loss of external awareness.
2Reliability
If the system provides notifications to alert users of external events, then user safety and awareness are improved, but the immersive experience is disrupted
Solution Approach 1:
The notification system applies local quality by providing targeted, selective notifications only for specific events of interest rather than continuously disrupting the immersive experience. The neural networks analyze sensor data to identify particular events (such as unusual sounds, visual anomalies, or safety-critical occurrences) and trigger notifications only for these localized events. This approach maintains the overall immersive experience while providing safety-critical information where needed.
Solution Approach 2:
The system uses partial action by providing notifications for only a subset of external events rather than alerting users to all possible occurrences. The neural networks filter sensor data to identify events that meet predefined criteria for notification, thereby providing sufficient safety information without excessive interruptions that would completely break immersion. This partial notification approach balances safety requirements with immersion preservation.
3Measurement precision
If the system continuously monitors audio and video data for anomalies, then detection accuracy of external events is improved, but computational resource consumption increases
Solution Approach 1:
The system applies preliminary action by pre-training neural networks with large datasets to recognize patterns and events of interest. During actual operation, these pre-trained models can quickly analyze sensor data with high accuracy without requiring continuous heavy computational resources for training. The neural networks are prepared in advance to efficiently detect anomalies, thereby achieving high detection accuracy while reducing real-time computational burden.
Solution Approach 2:
The monitoring system is segmented into multiple specialized neural networks that process different types of data (audio, video, sensor inputs) independently. Each neural network is optimized for specific detection tasks, allowing the system to achieve high overall detection accuracy while distributing computational load across multiple specialized models rather than requiring one monolithic high-resource system. This segmentation enables efficient resource utilization.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to determine actions to be taken for data anomalies. In at least one embodiment, audio and video data captured for an environment of a user can be analyzed to detect one or more data anomalies and determine whether to notify this user depending on whether the anomalies are applicable to this user.