Application Usage Profiling via Network Signal Algorithm Selection
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Solution Overview
Problem
Existing techniques for application usage profiling, particularly in 5G networks, face challenges such as insufficient coverage, sample bias, and computational costs, making it difficult to accurately detect and predict application usage across diverse devices and applications.
Innovation Solution
A system that selects the best predictive algorithm from a variety of algorithms based on network signals, specifically Server Name Indication (SNI) signals, to accurately profile applications, using machine learning models trained on user device data to dynamically allocate network resources and provide insights for commercial purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep packet inspection (DPI) is used to detect application packets, then measurement precision is improved, but use of energy and computational cost increase
Solution Approach 1:
The patent extracts and analyzes specific network signal features (SNI signals, packet headers, metadata) without performing full deep packet inspection. This selective extraction of relevant information maintains application detection accuracy while significantly reducing computational overhead and energy consumption compared to comprehensive DPI analysis.
Solution Approach 2:
The patent replaces the computationally intensive mechanical process of deep packet inspection with machine learning models that process network signals more efficiently. These models learn patterns from training data and can identify applications based on lighter-weight signal features, substituting the heavy DPI mechanism with a more energy-efficient computational approach.
2Measurement precision
If existing application profiling techniques are used, then ease of operation is maintained, but measurement precision and coverage are insufficient
Solution Approach 1:
The patent performs preliminary training of machine learning models using labeled network signal data before deployment. This preliminary action creates pre-trained models that can accurately profile applications without requiring complex real-time analysis configurations. The upfront training work simplifies subsequent operation while maintaining high measurement precision.
Solution Approach 2:
The patent changes the parameters and features used for application profiling from traditional DPI-based approaches to machine learning-based signal特征 analysis. By transforming the input parameters and using learned feature representations, the system achieves better accuracy while managing complexity through automated model-based processing.
3Adaptability or versatility
If 5G network capabilities are utilized to support diverse applications, then adaptability is improved, but difficulty of detecting and measuring application usage increases
Solution Approach 1:
The patent employs dynamic machine learning models that can adapt to different application types and network conditions in 5G environments. The models are trained on diverse data and can dynamically adjust their analysis based on the specific application being detected, enabling the system to handle the variety of 5G applications without increasing detection difficulty for each individual case.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between the complex 5G network signals and the application detection process. These models serve as mediators that translate diverse, complex network signals into identifiable application patterns, simplifying the detection task while maintaining adaptability to support various application types across the 5G network.
Data Source
AI summary
The disclosed technique incudes a method for dynamically selecting an algorithm that predicts usage of a mobile application based on network signals. In one example, an application profile includes known network signals and designates a best predictive algorithm. When network signals of user devices are subsequently captured, the best algorithms of sufficiently matching profiles are used to estimate application usage. As such, for example, the popularity of a particular application or relationships among applications can be determined for managing the network or for commercial purposes.