Lost Detection for Paired Mobile Devices Using Behavior Models
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Solution Overview
Problem
Existing solutions for detecting lost paired mobile devices are limited, especially when multiple devices are involved, as they rely on single metrics like Bluetooth connectivity and motion sensors, which are not sufficient to accurately determine if a device is lost, especially in complex scenarios such as during sports activities.
Innovation Solution
A method that utilizes embedded sensors like G-sensors and gyroscopes to collect data from paired devices, which is then processed on a backend server to compare with human behavior models, combining common human behavior and personal human behavior data to determine if a device is lost, allowing for timely and accurate detection of lost devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single metrics like Bluetooth connectivity and motion sensors are used for lost detection, then the detection system is simple, but the detection accuracy is insufficient especially in complex scenarios
Solution Approach 1:
The patent segments the detection system into multiple independent sensor components (Bluetooth connectivity sensor, motion sensor, accelerometer, gyroscope, GPS module) that each collect specific types of data. These segmented sensors work together to provide comprehensive detection information, improving accuracy while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent merges data from multiple different sensor types (Bluetooth connectivity status, motion detection data, acceleration data, gyroscope data, GPS location) into a unified detection framework. By combining these diverse data sources and analyzing them collectively against behavior models, the system achieves high detection accuracy in complex scenarios while using established sensor technologies.
2Measurement precision
If multiple sensors and behavior models are used for accurate detection, then the detection accuracy improves, but the processing complexity and computational requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-establishing common human behavior models and personal behavior models before actual lost device detection. These models are trained and stored in advance, containing expected behavior patterns for paired devices. During detection, the system only needs to compare current sensor data against these pre-computed models, significantly reducing real-time processing complexity while maintaining high detection accuracy.
Solution Approach 2:
The system implements feedback mechanisms where detection results and sensor data are continuously analyzed against behavior models. The comparison between expected behavior (from models) and actual sensor readings provides feedback that enables accurate determination of lost device status. This feedback loop allows the system to handle complex multi-sensor data efficiently by constantly referencing pre-established behavioral expectations.
3Reliability
If real-time monitoring of multiple paired devices is implemented, then the ability to detect lost devices improves, but the energy consumption increases
Solution Approach 1:
The patent implements periodic action by monitoring sensor data at defined intervals rather than continuously. The system collects behavior data from sensors and compares it against behavior models at periodic checkpoints, enabling reliable lost device detection while significantly reducing energy consumption compared to continuous monitoring. This periodic sampling approach maintains detection effectiveness while being energy-efficient.
Data Source
AI summary
A computer implemented method, computer system and computer program product are provided for lost detection for paired mobile devices. According to the method, a processor receives behavior data of paired mobile devices from one or more sensors of the paired mobile devices, wherein the behavior data comprising one or more parameters that reflect current status of the paired mobile devices. And the processor compares the received behavior data with human behavior data model. And, in response to the received behavior data being not matched with the human behavior data model, the processor determines that at least one of the paired mobile devices is lost.