Infection Risk Alerting via Adaptive Location Tracking
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Solution Overview
Problem
Current Mobile Positional Social Media (MPSM) systems face challenges in reducing energy consumption due to reliance on GPS, require active user input for location tracking, and provide limited informational guidance, lacking proactive notification of relevant activities based on location and context.
Innovation Solution
A method and system that automatically determines infection risks and medical concerns by monitoring user locations and activities through mobile devices, using machine learning to assign risk assessments and provide proactive notifications, while reducing GPS reliance through geofence and WiFi triangulation methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS is used for location tracking in MPSM systems, then location accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent segments location tracking into multiple methods: GPS for high-accuracy requirements, WiFi triangulation for moderate accuracy with lower energy consumption, and geofence for basic area-based tracking. This segmentation allows the system to select appropriate tracking methods based on specific needs, resolving the contradiction between accuracy and energy consumption.
Solution Approach 2:
The system dynamically adjusts the location tracking method based on current conditions, switching between GPS, WiFi triangulation, and geofence approaches. This dynamic adaptation allows the system to optimize energy consumption while maintaining adequate location accuracy for different scenarios.
2Measurement precision
If active user input is required for location tracking, then tracking accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service location tracking by automatically collecting location data through multiple passive methods including GPS, WiFi triangulation, and geofence monitoring without requiring active user input. The machine learning component automatically processes this data to generate risk assessments, eliminating the need for users to manually provide location information while maintaining tracking accuracy.
Solution Approach 2:
The system performs preliminary actions by continuously monitoring location data and pre-processing information through machine learning algorithms before user interaction is needed. This allows the system to have location tracking and risk assessment ready when users need information, eliminating the need for active user input during critical moments.
3Reliability
If comprehensive location and activity monitoring is implemented, then infection risk detection capability is improved, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional machine learning system that handles multiple tasks: collecting location data from various sources, performing WiFi triangulation, monitoring activities, assessing infection risks, and generating notifications. This universal system consolidates multiple functions into a single integrated platform, improving reliability without proportionally increasing device complexity.
Solution Approach 2:
The machine learning component acts as an intermediary that processes raw location and activity data, transforming it into meaningful infection risk assessments. This intermediary layer simplifies the overall system architecture by centralizing complex processing logic, making the monitoring system more manageable despite its comprehensive capabilities.
4Loss of information
If proactive notifications are provided to users, then informational guidance is improved, but loss of time for processing information increases
Solution Approach 1:
The system performs preliminary actions by continuously analyzing location and activity data through machine learning to identify potential infection risks before users need information. Proactive notifications are generated in advance based on this pre-processing, providing users with timely guidance without requiring them to spend time querying the system.
Solution Approach 2:
The system implements feedback mechanisms where machine learning algorithms continuously process user responses and notification effectiveness, adjusting the timing and content of proactive notifications. This feedback loop optimizes information delivery to provide comprehensive guidance while minimizing unnecessary processing time for users.
Data Source
AI summary
A method, system, and/or apparatus for automatically monitoring for possible infection or other physical health concerns, such as from Covid-19. The method or implementing software application uses or relies upon location information available on the mobile device from any source, such as cell phone usage and/or other device applications. The method and system automatically uses and/or learns user location and activity patterns and determines and infection risk or illness-based deviation that can be communicated as a warning to community members.