SDK Contact Tracing via Wi-Fi Signal Analysis
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Solution Overview
Problem
Existing contact tracing methods are costly, inefficient, and difficult to scale, particularly in environments with public Wi-Fi hotspots, leading to false positives and reduced accuracy due to reliance on GPS data.
Innovation Solution
A Software Development Kit (SDK) integrated into client devices uses AI to analyze Wi-Fi signal data to track human mobility patterns and contact points, enabling effective contact tracing by identifying proximity and transmitting information securely to authorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS data is used for contact tracing, then location tracking capability is improved, but false positives increase and efficiency decreases
Solution Approach 1:
The patent extracts the contact tracing function from GPS-dependent systems and implements it using Wi-Fi signal measurements instead. The SDK records Wi-Fi access point signal strengths and uses them to determine proximity between devices, completely removing the dependency on GPS data while maintaining contact tracing capability.
Solution Approach 2:
The patent changes the measurement parameter from GPS coordinates to Wi-Fi signal strength (RSSI). By using signal strength measurements of Wi-Fi access points encountered by devices, the system achieves accurate proximity detection without the false positives associated with GPS, thereby improving both accuracy and efficiency.
2Measurement precision
If manual contact tracing is used, then contact identification accuracy is improved, but cost and time consumption increase
Solution Approach 1:
The patent implements self-service contact tracing where the SDK automatically runs in the background of client devices, continuously recording Wi-Fi signal measurements and identifying contacts without manual intervention. The system autonomously performs contact identification, notification, and reporting functions, eliminating the need for manual tracing while maintaining high accuracy.
Solution Approach 2:
The patent replaces the mechanical manual tracing process with an automated software-based system. The SDK uses algorithmic processing of Wi-Fi signal data to automatically identify contacts, replace manual review and verification steps, thereby dramatically reducing time loss while preserving identification accuracy through systematic data analysis.
3Reliability
If extensive setup is implemented for contact tracing, then system reliability is improved, but ease of deployment deteriorates
Solution Approach 1:
The patent creates a universal SDK that can be integrated into any client device application regardless of platform or device type. The SDK provides multi-functional capabilities including contact detection, tracking, notification, and reporting within a single integrated package, eliminating the need for extensive separate setups and enabling easy deployment across diverse infrastructures.
Solution Approach 2:
The patent segments the contact tracing functionality into a modular SDK that can be independently deployed and integrated. The SDK is divided into discrete components that can be configured and activated as needed, allowing flexible deployment without requiring extensive system-wide setup, thereby maintaining reliability through modular architecture while improving ease of implementation.
Data Source
AI summary
The invention generally relates to a method and system for utilizing an Artificial Intelligence (AI)-based technology for tracking human mobility patterns and contact points, via a Software Development Kit (SDK) integrated into client device applications. The SDK performs contact tracing by recording past and current signal data/measurements of Wi-Fi access points encountered by a client device and stores these measurements in a local memory of the client device. The SDK further analyses the recorded past and current signal data/measurements of the Wi-Fi access points using an AI module to derive mobility patterns of user of the client device. The AI module of the SDK then identifies other client devices that the client device may have encountered based on analyzing the mobility patterns of the user of the client device and the recorded past and current signal data/measurements and via performing Wi-Fi network sniffing.