Transaction Kiosk Eavesdropper Detection Using Positional Sensors
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Solution Overview
Problem
Current systems fail to dynamically detect the presence of potential eavesdroppers near transaction kiosks and alert users, exposing them to risks of inadvertently sharing private financial information in high-traffic areas.
Innovation Solution
A system utilizing positional sensors and machine learning models to detect objects within a predetermined proximity of a transaction kiosk, identifying humans as potential eavesdroppers, and triggering security measures such as alerts to notify users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If positional sensors and detection systems are deployed to detect eavesdroppers, then security is improved, but device complexity and cost increase
Solution Approach 1:
The patent applies multi-functionality by enabling mobile devices to serve dual purposes: their original communication functions plus security detection functions. The system utilizes the mobile device's existing sensors (camera, microphone, accelerometer, GPS) to detect eavesdroppers, eliminating the need for dedicated detection hardware and reducing overall system complexity.
Solution Approach 2:
The system implements self-service by allowing the user's own mobile device to perform security detection. The device uses its built-in sensors to detect the presence of eavesdroppers and triggers alerts independently, without requiring external specialized equipment. This approach reduces device complexity while maintaining security functionality.
2Reliability
If continuous monitoring for eavesdroppers is implemented, then security is improved, but energy consumption increases
Solution Approach 1:
The patent implements periodic action by triggering sensor activation and detection routines only during specific conditions - when a transaction is detected or when the device is in use. The system periodically checks for transaction states and activates monitoring only when needed, rather than continuously running sensors. This approach maintains security during critical moments while significantly reducing energy consumption during idle periods.
Solution Approach 2:
The system applies dynamics by making the monitoring state changeable - transitioning between active monitoring and idle states based on transaction detection. The system dynamically adjusts its behavior: when a transaction is detected, full monitoring activates; when no transaction is present, monitoring reduces to low-power mode. This dynamic approach balances security needs with energy conservation.
3Measurement precision
If multiple sensors and detection mechanisms are added, then detection precision is improved, but device complexity increases
Solution Approach 1:
The patent leverages the mobile device's existing multi-functional sensors (camera, microphone, accelerometer, GPS, biometric sensors) to achieve precise eavesdropper detection without adding specialized equipment. Each existing sensor is repurposed for security detection, maintaining detection precision while avoiding the complexity increase that would result from dedicated detection hardware.
Solution Approach 2:
The system substitutes mechanical/dedicated detection hardware with software-based processing of data from existing sensors. Instead of adding physical detection devices, the patent uses software algorithms to analyze data from the mobile device's existing sensors, achieving precise detection while minimizing additional hardware complexity.
Data Source
AI summary
Disclosed embodiments may include a system that may receive first level authentication data from a first user, identify a first user device associated with the first user, and determine whether a current location of the first user device is within a predetermined proximity of a first computing device. In response to the determination, the system may detect one or more objects within the predetermined proximity of the first computing device using the one or more positional sensors. The system may determine that at least one of the one or more objects is associated with a human, and in response, trigger a security measure. The system may transmit an indication of the triggered security measure to the first computing device, and may transmit instructions to the first user device configured to cause the first user device to provide an alert to the first user.