Digital Twin Environment Monitoring With AI User Interaction Tracking
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Solution Overview
Problem
Existing systems rely on visual observation and computerized approximations to monitor environments, leading to inefficiencies and inaccuracies in identifying bottlenecks, as these methods are not real-time and do not accurately represent user interactions, resulting in suboptimal optimizations.
Innovation Solution
A system and method for electronic duplication and simulation of environments via a hardware device network, utilizing a graphical user interface to replicate environments in real-time, incorporating predictive artificial intelligence and self-learning algorithms to monitor user locations and engagement activities, and store data for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual observation and computerized approximations are used to monitor environments, then monitoring coverage is achieved, but real-time accuracy and representation of user interactions deteriorate
Solution Approach 1:
The system creates a digital twin - a virtual copy of the physical environment that replicates user locations, engagement activities, and environmental characteristics in real-time. This digital replica allows accurate monitoring and analysis without interfering with the actual environment, solving the contradiction by providing both precision and real-time capability through the virtual model.
Solution Approach 2:
The patent replaces traditional visual observation methods and basic computerized approximations with an AI-driven system that uses machine learning algorithms to automatically detect, classify, and analyze user interactions. This substitution transforms manual or simple automated monitoring into an intelligent system that achieves high accuracy while operating in real-time.
2Loss of information
If computerized approximations are used to represent user movements and engagement activities, then data collection is achieved, but accuracy of optimization insights deteriorates
Solution Approach 1:
The system implements continuous feedback loops where AI algorithms analyze user interaction data, generate optimization insights, and these insights are validated against actual environmental outcomes. The system learns from the feedback to improve its measurements and representations of user behavior, ensuring both completeness and accuracy of optimization insights.
Solution Approach 2:
The AI system automatically detects, categorizes, and analyzes user engagement activities without requiring manual intervention or approximation. The system self-calibrates and improves its measurement accuracy by continuously learning from observed user behaviors, eliminating the need for imperfect computerized approximations.
3Productivity
If real-time monitoring of user locations and engagement activities is implemented, then environmental efficiency optimization is improved, but system complexity increases
Solution Approach 1:
The system employs a multi-functional AI platform that simultaneously performs multiple tasks: tracking user locations, analyzing engagement activities, generating optimization insights, and providing recommendations. This universal system consolidates what would otherwise require multiple separate complex systems, achieving high productivity while managing overall system complexity through integration.
Solution Approach 2:
The AI system automatically performs data collection, analysis, and optimization without requiring complex manual configuration or intervention. The self-learning algorithms adapt to different environments and user behaviors autonomously, reducing the operational complexity despite the advanced capabilities of the monitoring system.
Data Source
AI summary
Embodiments of the invention are directed to a system, computer program product, and computer-implemented method for electronic duplication and simulation of environments via a hardware device network. An environment and the conditions therein are represented in an electronic layout. A hardware device network comprising user devices and/or IoT devices continuously relay user locations within the environment via a link to the electronic layout. Predictive artificial intelligence and self-learning algorithms are implemented in the electronic layout to provide optimized conditions within the environment.