AR Risk Prediction Model for Object Metrics Visualization
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Solution Overview
Problem
Existing augmented reality systems lack the capability to accurately predict and visualize the risks associated with physical objects in virtual environments, such as movement, impact, and force, which can pose dangers to users and their surroundings.
Innovation Solution
A computer-implemented method and system that receives object data, generates an optimal assistance model, and predicts object metrics to visualize potential risks and movements within virtual environments, using a combination of augmented reality and artificial intelligence for enhanced user safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If augmented reality systems visualize physical objects in virtual environments, then user awareness of surroundings is improved, but the systems cannot accurately predict risks such as movement, impact, and force
Solution Approach 1:
The system performs preliminary analysis of object data including physical characteristics, historical behavior, and environmental factors before visualization occurs. This allows the system to pre-calculate risk metrics such as movement probability, impact force, and potential hazards, ensuring accurate risk information is available when objects are visualized in the augmented reality environment.
Solution Approach 2:
The patent introduces an intermediary analysis layer between object detection and risk visualization. This intermediary component processes raw object data through multiple analysis dimensions (physical properties, behavioral patterns, environmental context) to generate accurate risk predictions, which are then transmitted to the visualization system.
2Measurement precision
If the system analyzes multiple object characteristics to improve risk prediction accuracy, then measurement precision is improved, but the complexity of the system increases
Solution Approach 1:
The system segments the risk analysis process into distinct modular components: object data acquisition module, physical characteristic analysis module, behavioral pattern recognition module, environmental factor assessment module, and risk calculation module. Each module handles a specific aspect of analysis, improving overall precision while maintaining manageable system complexity through clear separation of concerns.
Solution Approach 2:
The patent creates a universal analysis framework that can process multiple types of objects (vehicles, pedestrians, animals, inanimate objects) using the same multi-dimensional analysis approach. This universal system handles diverse object characteristics through standardized processing pipelines, reducing complexity compared to having separate specialized systems for each object type.
3Reliability
If the system provides comprehensive risk visualization, then user safety is improved, but the amount of information processed and displayed increases system complexity
Solution Approach 1:
The system applies local quality by tailoring the visualization details to the specific risk level and object type. High-risk objects receive detailed multi-parameter visualization showing movement trajectories, force vectors, and probability metrics, while lower-risk objects receive simplified indicators. This ensures comprehensive safety information is provided where needed without unnecessarily complicating the overall system.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors user interactions with visualized risk information and adjusts the level of detail and complexity of displayed data. This feedback loop ensures that comprehensive risk information is provided to improve safety while adapting the visualization complexity based on user needs and system performance.
Data Source
AI summary
Techniques are described with respect to a system, method, and computer program product for visualizing optimal augmented reality (AR) assistance. An associated method includes receiving a plurality of object data of at least one object associated with a user; generating an optimal assistance model based on analysis of the plurality of object data; predicting a plurality of object metrics of the object based on the optimal assistance model; and visualizing the plurality of object metrics in a virtual environment associated the user.