Non-Specific Location Determination via Mobility Pattern Matching
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Solution Overview
Problem
Current location-based service systems face challenges in providing contextually relevant services while protecting user privacy, particularly location privacy, as users often opt out of location services due to privacy concerns, leading to imprecise user positioning and inaccurate information.
Innovation Solution
A method and system that determines a non-specific device location using observed mobility patterns derived from non-positioning related sensor data, such as accelerometer and barometer signals, without requiring precise location information, allowing for the provision of contextually relevant services while preserving user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If precise location services are enabled to provide contextually relevant services, then service accuracy and relevance are improved, but user location privacy is compromised
Solution Approach 1:
The patent segments location information into two levels: precise location data (which remains private) and non-specific location characteristics (which are used for service delivery). By dividing the location concept into specific coordinates and general area characteristics, the system can provide contextually relevant services without exposing exact user position, thus resolving the contradiction between location precision and privacy protection.
Solution Approach 2:
The patent introduces mobility patterns as an intermediary between the user's precise location and the service provider's need for location context. Instead of directly using precise location data, the system extracts mobility patterns (movement characteristics, travel behavior) that serve as a mediator to deliver relevant services while preserving the actual location information, thereby balancing service accuracy with privacy protection.
2Object-affected harmful factors
If location services are disabled to protect user privacy, then privacy protection is improved, but service relevance and accuracy deteriorate
Solution Approach 1:
The patent creates copies of location-related information in the form of mobility patterns derived from sensor data. These pattern copies capture the essential characteristics needed for service relevance (movement behavior, location context) without containing the actual precise location data. This allows services to remain relevant and accurate while the original sensitive location information remains protected and undisclosed.
3Object-affected harmful factors
If non-positioning sensor data is used to determine location, then privacy protection is improved, but measurement precision of location deteriorates
Solution Approach 1:
The patent changes the parameters used for location determination from precise coordinates (latitude, longitude) to non-positioning sensor parameters (accelerometer, barometer, gyroscope data) that capture mobility patterns. This parameter transformation enables location inference at a lower precision level that still provides sufficient context for services while inherently protecting privacy, as the sensor data does not directly reveal exact location information.
Data Source
AI summary
An approach is disclosed for determining a non-specific device location according to an observed mobility pattern derived from non-positioning related sensors. The approach involves, for example, determining non-positioning related sensor data collected from one or more sensors of a device. The approach also involves processing the non-positioning related sensor data to determine an observed mobility pattern. The approach further involves making a determination that the observed mobility pattern corresponds to reference data associated with a non-specific location, the non-specific location being at a designated location specificity level. Based at least on the determination, the approach further involves providing an output indicating that the device is located at the non-specific location.