Dynamic Infection Map for Real-Time Pathogen Viability Tracking
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Solution Overview
Problem
Current methods lack an effective solution for tracking and mitigating the spread of pathogens through contact tracing, as they do not provide real-time data on pathogen viability and infectiousness, making it difficult to identify and avoid infected areas and objects.
Innovation Solution
A network architecture that receives situational and participant data to generate a likelihood of infectiousness, displaying this information to users through a dynamic infection map, utilizing AI and IoT devices to track movement and environmental factors to determine pathogen viability and safe interaction zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time tracking of pathogen viability and infectiousness is implemented, then the ability to identify and avoid infected areas and objects is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system divides the infection tracking function into separate modules: IoT devices for data collection, network architecture for data transmission, AI system for analysis, and mobile application for user interface. This segmentation allows each component to specialize in specific tasks, improving measurement precision while managing overall system complexity through modular design.
Solution Approach 2:
The patent introduces an AI-based intermediary system that acts as a mediator between raw sensor data from IoT devices and actionable infection risk information for users. This intermediary processes complex pathogen viability data, environmental factors, and transmission models to generate simplified risk assessments, thereby improving detection accuracy without directly increasing end-user device complexity.
2Reliability
If comprehensive data collection from IoT devices and environmental sensors is performed, then the reliability of infection risk assessment is improved, but the loss of time for data processing and system response increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring AI models with pathogen decay rates, environmental impact factors, and transmission parameters before actual infection tracking begins. This allows the AI system to rapidly process real-time sensor data without extensive computation during critical response periods, thereby maintaining high reliability while reducing data processing time.
Solution Approach 2:
The patent implements continuous monitoring and real-time data processing through the network architecture and AI system, ensuring that infection risk assessments are continuously updated as new sensor data arrives. This continuous action eliminates gaps in monitoring and reduces overall response time while maintaining reliable assessments through constant data flow and processing.
3Ease of operation
If dynamic infection maps with real-time updates are displayed to users, then the ease of operation for avoiding infection is improved, but the use of energy for data transmission and display updates increases
Solution Approach 1:
The system implements partial updates to the dynamic infection map by only refreshing data for specific geographic zones or objects that have changed infection risk status, rather than continuously updating the entire map. This approach maintains ease of operation by providing real-time information where needed while significantly reducing the energy consumption associated with constant full-map transmissions and display updates.
Solution Approach 2:
The mobile application incorporates feedback mechanisms that allow users to adjust their information needs and receive targeted updates based on their specific interests and locations. The system learns from user interactions to prioritize data transmission for relevant areas only, thereby maintaining ease of operation while optimizing energy usage by avoiding unnecessary updates to unrelated geographic zones.
Data Source
AI summary
A network architecture may receive situational data. The network architecture may acquire participant data associated with a participant. The network architecture may accept object data associated with an object. The network architecture may generate likelihood of infectiousness of the object based on the participant data and the object data. The network architecture may display the likelihood of infectiousness to one or more users.