Computing Device Association via Spatial-Temporal Correlation
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Solution Overview
Problem
Existing systems fail to accurately associate multiple computing devices on a network to a single user, especially in dynamic environments where devices may be out of proximity or changed frequently, leading to difficulties in monitoring user behavior and network management.
Innovation Solution
A system that extracts spatial and temporal data from network communication data of multiple computing devices, correlates this data using geometric distances and correlation functions, and identifies devices as belonging to a common user based on threshold values, utilizing a database, processing system, and correlator to establish associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional proximity-based association methods are used, then device-user association is simple, but accuracy deteriorates in dynamic environments where devices are frequently added, removed, or moved
Solution Approach 1:
The patent transforms the association problem from simple proximity detection to multi-parameter correlation analysis. It extracts spatial coordinates, temporal patterns, and geometric distance metrics from network communication data, then correlates these parameters across multiple devices to identify common users. This parameter-based approach maintains accuracy in dynamic environments while managing complexity through systematic data processing.
Solution Approach 2:
The patent introduces a correlation analysis mechanism as an intermediary between raw network data and user association results. The correlator computes geometric distances and correlates spatial-temporal data sets, acting as a mediator that transforms complex multi-device data into reliable user association information without requiring direct proximity detection between all device pairs.
2Reliability
If multiple computing devices are monitored continuously, then user behavior monitoring improves, but network traffic and processing load increase
Solution Approach 1:
The patent extracts only the necessary spatial and temporal parameters from complete network communication data sets. Instead of processing all network traffic, it selectively extracts spatial coordinates, timestamps, and communication patterns relevant to user association, reducing processing load while maintaining monitoring reliability through targeted data extraction.
Solution Approach 2:
The patent implements partial monitoring by focusing correlation analysis on devices that exhibit potential user associations based on initial parameter extraction. Rather than continuously analyzing all possible device pairs, it applies correlation computation selectively to promising candidates, reducing overall processing energy while maintaining reliable user behavior monitoring.
3Measurement precision
If geometric distance correlation is computed for all device pairs, then association accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the computational task into distinct phases: initial parameter extraction from network data, preliminary spatial-temporal data set creation, and then correlation computation. This segmentation allows the system to prepare data structures in advance and perform geometric distance computations only on pre-processed data sets, reducing peak computational power requirements while maintaining association accuracy.
Data Source
AI summary
Systems and methods for computing device association are described. One aspect includes receiving first and second network communication data for a first and second computing device over a communication network, respectively. For each computing device, a first and second data set are extracted from the first and second network communication data, respectively. The first data set includes first spatial data and first temporal data associated with the first computing device. The second data set includes second spatial data and second temporal data associated with the second computing device. The first and second data sets are correlated. A first geometric distance between the first temporal data and the second temporal data and a second geometric distance between the first spatial data and the second spatial data are computed. The method identifies that the first computing device and the second computing device belong to a common user.