Mobile Device Analytics Using Space-Time Boxes
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Solution Overview
Problem
Current location-based services are unable to scale effectively with large volumes of mobile device data, limiting their ability to provide detailed insights into mobile subscriber characteristics and behaviors.
Innovation Solution
A system and method that generate mobility, hangout, and buddy profiles by analyzing mobile device data, using space-time boxes to categorize and process location data, enabling the creation of lifestyle profiles and predictive analytics for personalized marketing and network optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current location-based services process mobile device data, then location information can be provided to users, but the system cannot scale to large volumes of mobile device data
Solution Approach 1:
The patent divides the mobile device data processing system into multiple components: data collection modules at device level, aggregation servers at network level, and analytics platforms at processing level. This segmentation allows each component to handle specific tasks independently, enabling the system to scale horizontally by adding more components rather than increasing individual component complexity
Solution Approach 2:
The patent introduces intermediary aggregation servers that collect and pre-process data from multiple mobile devices before forwarding to analytics platforms. These intermediaries buffer the data flow, reducing the direct processing burden on central systems and enabling scalable architecture where intermediaries can be replicated to handle increasing data volumes
2Loss of information
If detailed analytics are performed on mobile device records, then subscriber characteristics and behaviors can be understood, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary data cleaning, validation, and aggregation operations during data collection and ingestion phases. By pre-processing data before detailed analytics, the system reduces the computational burden during actual analysis while maintaining complete information about subscriber characteristics and behaviors
Solution Approach 2:
The patent implements dynamic analytics processing that adjusts the depth and granularity of analysis based on query requirements and data characteristics. This allows the system to provide detailed information when needed while using optimized, faster processing paths for routine queries, thereby reducing overall processing time without sacrificing information completeness
3Loss of information
If mobile device data is collected and stored, then analytics can be performed, but data privacy and security concerns increase
Solution Approach 1:
The patent extracts and separates personally identifiable information (PII) from mobile device data during the collection and processing phases. Sensitive information is extracted, stored separately with enhanced security controls, while the main analytics processing uses anonymized or aggregated data, thereby maintaining data availability for analytics while reducing privacy risks
Solution Approach 2:
The patent applies different security and privacy protection measures to different portions of the data based on their sensitivity. Highly sensitive PII receives stronger encryption and access controls, while less sensitive aggregated data uses standard protection measures. This localized approach to data protection maintains data availability where possible while applying privacy safeguards only where necessary
Data Source
AI summary
A computing device generates profiles based on mobile device data. The computing device receives a plurality of mobile device records of a plurality of mobile devices in a region, that each include a timestamp, a location data, and an activity data, and assigns each of the plurality of mobile device records to one of a plurality of space-time boxes. The computing device performs analytics on the mobile device records assigned to the plurality of space-time boxes to yield a resulting plurality of profiles, which can include a mobility profile indicating the number of locations in the region that each mobile device occupies during a span, a hangout profile indicating the number of mobile devices that occupy each location in the region during a span, and a buddy profile indicating the mobile devices that occupy the same location in the region as a given mobile device.