Predictive Virtual Reconstruction of Physical Events
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods struggle to accurately predict and reconstruct physical events, such as accidents or natural disasters, by determining precursor and post-events, leading to delays and decision errors due to the difficulty in analyzing and interpreting the surrounding environment.
Innovation Solution
An AI-based augmented reality system utilizing machine learning models, including Markov models, reinforcement learning, and recurrent neural networks, analyzes video feeds and historical data to predict and virtually reconstruct physical events, displaying potential scenarios through AR glasses or smartphones, mimicking environmental conditions and illustrating object movements and damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional video analysis methods are used to analyze physical events, then the analysis process is simple, but the prediction accuracy and reconstruction precision are insufficient
Solution Approach 1:
The patent replaces traditional mechanical video analysis methods with AI-based machine learning models including Markov models, reinforcement learning, and recurrent neural networks. These AI systems automatically process video feeds and historical data to predict precursor events and reconstruct physical events with high accuracy, eliminating the need for manual analysis while significantly improving prediction precision.
Solution Approach 2:
The patent creates virtual copies of physical environments and events through AI-generated simulations. By generating virtual reconstructions that mirror real-world physical events, the system enables accurate prediction and analysis without requiring complex physical experimentation or direct manipulation of the actual events.
2Measurement precision
If AI-based augmented reality system with machine learning models is used, then the prediction accuracy and reconstruction precision are improved, but the computational resources and processing time are increased
Solution Approach 1:
The patent implements preliminary action by continuously training and updating AI models with historical data before actual events occur. The system pre-processes and stores patterns from previous physical events, enabling faster and more energy-efficient real-time prediction when actual events happen, as the heavy computational lifting has already been done in advance.
Solution Approach 2:
The patent applies local quality by focusing computational resources on specific critical areas of analysis rather than uniformly processing all data. The AI models concentrate processing power on identifying key precursor events and critical patterns in video feeds, reducing overall energy consumption while maintaining high reconstruction precision for the most important event characteristics.
3Loss of information
If video feeds and historical data are collected and analyzed, then the information completeness is improved, but the data processing complexity and time consumption are increased
Solution Approach 1:
The patent extracts and isolates only the most relevant features and patterns from vast amounts of video feeds and historical data using AI-based image recognition and pattern matching. Instead of analyzing all data equally, the system identifies and focuses on critical indicators of precursor events, significantly reducing processing time while maintaining complete information about the physical events.
Solution Approach 2:
The patent implements continuous analysis of video feeds and historical data through recurrent neural networks that process information in real-time streams. This continuous processing allows the system to maintain up-to-date knowledge of the physical environment without requiring periodic batch processing, reducing overall analysis time while ensuring no critical information is lost.
Data Source
AI summary
Embodiments of the present invention describe predictively reconstructing a physical event using augmented reality. Embodiments describe, identifying relative states of objects located in a physical event area by using video analysis to analyze collected video feeds from the physical event area before and after a physical event involving at least one of the objects, creating a knowledge corpus including the video analysis and the collected video feeds associated with the physical event and historical information, and capturing data, by a computing device, of the physical event area. Additionally, embodiments describe identifying possible precursor events based on the captured data and the knowledge corpus, and generating a virtual reconstruction of the physical event using the possible precursor events, displaying, by the computing device, the generated virtual reconstruction of the predicted physical event, wherein the displayed virtual reconstruction of the predicted physical event overlays an image of the physical event area.