XR Motion Feedback Using Predictive Event Filtering
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Solution Overview
Problem
Existing extended reality systems face inefficiencies in processing and storage demands due to large amounts of sensor data, leading to inadequate feedback on future states and constrained real-time experiences.
Innovation Solution
Implementing a system that applies analytics and machine learning to predict user actions by filtering and reducing events using confidence thresholds, enhancing processing time and providing real-time feedback through a real-time platform and learning platform architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If sensor data is collected and processed in real-time to generate information about physical or virtual space changes, then user interaction enhancement is achieved, but processing and storage demands increase
Solution Approach 1:
The patent extracts only the most relevant features from sensor data using machine learning models, rather than processing all raw sensor data. The system identifies and extracts key motion parameters and user actions, discarding redundant information, thereby reducing processing and storage demands while maintaining user interaction enhancement
Solution Approach 2:
The system transforms raw sensor data into meaningful parameters through machine learning processing. By changing the representation of data from raw sensor readings to extracted motion features and predicted user actions, the system reduces data complexity while preserving essential interaction information
2Measurement precision
If machine learning models process all sensor data to predict user actions, then prediction accuracy improves, but processing time increases
Solution Approach 1:
The patent segments the data processing pipeline into distinct stages: raw sensor data collection, feature extraction using machine learning models, and action prediction. By dividing the processing into segments, the system can apply complex ML models only to extracted features rather than all raw data, reducing overall processing time while maintaining prediction accuracy
Solution Approach 2:
The system applies machine learning processing selectively to extract only the most relevant features needed for accurate prediction, rather than processing all possible sensor data. This partial processing approach maintains prediction accuracy while significantly reducing computation time and resource requirements
3Reliability
If confidence thresholds are applied to filter predicted events, then feedback reliability improves, but event detection coverage decreases
Solution Approach 1:
The system dynamically adjusts the confidence threshold parameter based on the specific application context and event type. By changing this parameter, the system can optimize the balance between feedback reliability and event detection coverage for different scenarios, allowing high reliability for critical events while maintaining broader detection coverage for less critical events
Data Source
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AI summary
Systems and methods for predictively responding to real-time sensor data from an computer-augmented environment are provided. A system may receive sensor data from the computer-augmented environment. The system may obtain a feature model, a prediction model, and a filter criteria. The system may derive, based on the feature model and the sensor data, a first event. The system may determine the first event is associated with an authorized event identifier in the filter criteria. The system may forecast, based on the authorized event identifier and the prediction model that a second event occurs after the first event. The system may transmit an action message to the computer-augmented environment, the action message indicative of the second event. Updates to the feature model, the prediction model, and/or the filter criteria may occur in batch using machine learning and statistical analytics while the real-time platform predictively responds to the sensor data.