Mobile Device Tracking via MAC Address Filtering
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Solution Overview
Problem
Current physical security monitoring systems face challenges in efficiently analyzing and predicting visitor behavior in real-time due to excessive manpower requirements and the need for manual review of vast video footage, lacking integration of video, ID, and social media data for effective tracking and forensic analysis.
Innovation Solution
A system and method that integrates real-time tracking of mobile devices, video streams, and social media information, using query languages and data structures to analyze behavior and predict user actions, while automating the filtering and analytics process for improved security and marketing insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of video footage is used to identify and track individuals, then identification accuracy can be maintained, but excessive manpower requirements and time consumption occur
Solution Approach 1:
The patent replaces manual mechanical review of video footage with automated electronic systems including MAC address tracking, WiFi connectivity data analysis, and video management systems that automatically correlate digital identifiers with video streams. This substitution maintains identification accuracy while dramatically improving productivity by eliminating the need for manual frame-by-frame video review.
Solution Approach 2:
The system creates and utilizes digital copies of identification data (MAC addresses, device IDs) from mobile devices to track individuals across multiple cameras and time periods. Instead of manually analyzing original video footage, the system works with copied digital identifier data that can be automatically processed, filtered, and correlated with video streams to maintain accuracy while reducing manpower requirements.
2Loss of information
If all video information from all cameras is collected and manually reviewed to reconstruct event paths, then complete forensic analysis can be achieved, but the process is too slow to intercept individuals in real time
Solution Approach 1:
The system performs preliminary tracking and filtering of MAC addresses and device identifiers continuously before forensic events occur. By maintaining real-time logs of device locations, WiFi connectivity, and camera correlations in advance, the system prepares identification data structures that enable immediate retrieval and analysis when forensic events happen, achieving both completeness and real-time response.
Solution Approach 2:
The patent introduces digital intermediary data structures (MAC address logs, device identifier databases, correlation tables) that mediate between raw video footage and forensic analysis. These intermediaries pre-process and organize identification information, allowing the system to retrieve relevant video streams quickly without manually reviewing all camera footage, thus maintaining forensic completeness while enabling real-time intervention.
3Quantity of substance
If mobile device tracking data is collected without filtering, then comprehensive data availability is maintained, but false identifications and privacy concerns increase
Solution Approach 1:
The system extracts and isolates reliable identification signals from the broader mobile device tracking data stream. By filtering MAC addresses through connectivity validation (WiFi association status, signal strength thresholds, duration of detection), the system separates true device identifiers from false or transient signals, maintaining data availability while improving identification reliability through selective extraction of validated data.
4Loss of information
If integrated tracking of video, ID, and social media data is implemented, then comprehensive behavior analysis is achieved, but system complexity increases
Solution Approach 1:
The patent implements a universal data structure and processing framework that handles multiple data types (video streams, MAC addresses, social media data, WiFi connectivity information) through common methodologies. The system uses unified correlation tables, standardized timestamp formatting, and consistent filtering algorithms that work across all data sources, achieving comprehensive behavioral analysis while managing complexity through universal processing approaches rather than separate systems for each data type.
Data Source
AI summary
System and method for tracking a mobile device includes: receiving unique identifications for a mobile device; filtering out the unique identifications to obtain a true identifications for the mobile device; identifying cameras relevant to movement of the mobile device; receiving video streams; generating data structures for the video streams and tracking information of the mobile device, the data structure including time stamped videos, and viewpoints of the identified cameras; utilizing the data structures to retrieve, video and tracking information for the mobile device and the user, as the mobile moves in the site; and applying analytics to the retrieved video and tracking information to analyze behavior of the user and to predict what the user will do while on site.