Mobile Device Location Determination Using Environmental Fingerprinting
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Solution Overview
Problem
Current mobile device location tracking technologies rely solely on GPS data, lacking granularity and failing to effectively determine the location when a device is lost, stolen, or misplaced, especially in unauthorized possession.
Innovation Solution
Implementing machine learning models that utilize environmental and location data captured by sensors, such as image, temperature, and sound data, to determine the device's location and communicate it to authorized users, with the option to disable the device if unauthorized possession is confirmed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS data is used for location tracking, then location information can be obtained, but the granularity and precision of location determination is insufficient
Solution Approach 1:
The patent combines GPS location data with environmental sensor data (image, temperature, sound, etc.) to create a comprehensive location determination system. This merging of multiple data sources enables precise location identification by matching environmental fingerprints with stored reference data, thereby resolving the contradiction between obtaining location information and achieving sufficient granularity.
Solution Approach 2:
The patent transitions from traditional two-dimensional GPS coordinates to a multi-dimensional location identification approach by incorporating environmental parameters (visual, thermal, acoustic dimensions). This dimensional expansion allows for much finer location granularity by capturing the unique environmental characteristics of specific places beyond what GPS coordinates alone can provide.
2Measurement precision
If multiple sensors are used to capture environmental data, then location determination precision is improved, but device complexity increases
Solution Approach 1:
The patent employs existing multi-functional sensors in mobile devices (camera, microphone, temperature sensor) that serve both their primary purposes and environmental data collection for location determination. This multi-functionality approach allows the system to gather comprehensive environmental data without significantly increasing device complexity, as these sensors are already integrated into modern mobile devices.
Solution Approach 2:
The system utilizes the mobile device's own existing sensors and processing capabilities to perform environmental data collection and analysis. By leveraging the device's self-contained resources rather than requiring external specialized equipment, the patent achieves high location precision without proportionally increasing device complexity.
3Reliability
If machine learning models are implemented to analyze environmental data, then unauthorized possession detection is improved, but processing time and energy consumption increase
Solution Approach 1:
The patent pre-processes environmental data and creates environmental fingerprints during normal device operation, storing them for later comparison. This preliminary action allows the machine learning model to perform rapid matching operations when location determination is needed, rather than performing complex analysis in real-time, thereby reducing energy consumption while maintaining high detection accuracy.
4Measurement precision
If environmental data is captured and stored, then location identification accuracy is improved, but data loss and privacy concerns increase
Solution Approach 1:
The patent extracts and stores only the essential environmental fingerprint characteristics needed for location identification, rather than storing complete environmental datasets. This extraction approach maintains location identification accuracy by preserving the unique identifying features of locations while minimizing the storage of unnecessary detailed information, thereby reducing privacy concerns and data management burdens.
Data Source
AI summary
Methods and apparatuses associated with determining a location of a mobile device are described. Examples can include receiving, at a mobile device in response to a triggering event, signaling that indicates the mobile device is in an unauthorized location, in possession of an unauthorized user, or both. Examples can include prompting an input representative of authorized user verification and enabling one or more circuits or power supplies of the mobile device based at least in part on determining that a value of the input satisfies. In response and based at least in part on determining that the value of the input fails to satisfy the threshold, examples can include capturing environmental data and location data associated with the mobile device and communicating the environmental data and location data and a location determination to an authorized user. In some examples, a mobile device can be deactivated responsive to unconfirmed authorized verification.