Network Usage Analysis System with Dynamic User Clustering
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Solution Overview
Problem
Current telecommunications networks face challenges in dynamically analyzing and forecasting network usage patterns, leading to inefficient resource deployment and promotional efforts, as existing methods lack the ability to effectively group users with similar usage characteristics.
Innovation Solution
The implementation of a dynamic mechanism that groups users with similar network usage patterns using feature generation and clustering techniques, analyzing network access data, user data, and network resource data to determine feature sets and form clusters for improved resource forecasting and deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If network usage data is analyzed using traditional methods, then resource deployment can be performed, but the analysis precision and forecasting accuracy are insufficient
Solution Approach 1:
The patent segments network usage data into multiple feature dimensions including temporal patterns, spatial distribution, user behavior characteristics, and service types. This segmentation enables precise analysis of different aspects of network usage independently, improving measurement precision while organizing complexity through structured categorization
Solution Approach 2:
The patent transforms raw network usage data into standardized feature parameters with consistent formats and scales. By changing the parameter representation from raw data to normalized features, the system achieves higher analysis precision while making the complexity manageable through parameter standardization
2Adaptability or versatility
If users are grouped using static methods, then promotional efforts can be made, but the ability to identify users with similar usage characteristics is insufficient
Solution Approach 1:
The patent implements dynamic user grouping that adapts to changing usage patterns over time. The system continuously updates user profiles and reassigns users to clusters based on their current behavior, enabling the grouping to adapt to evolving patterns while preserving detailed usage information through continuous tracking
Solution Approach 2:
The system incorporates feedback loops where clustering results are used to generate insights that refine the feature extraction and grouping criteria. This feedback mechanism improves adaptability by learning from previous grouping outcomes while preventing information loss through iterative refinement of the clustering algorithm
3Productivity
If network resources are deployed without precise forecasting, then deployment can proceed, but resource allocation efficiency is reduced
Solution Approach 1:
The patent performs preliminary analysis of network usage patterns and pre-computes feature sets and cluster assignments before resource deployment decisions are made. This preliminary action captures temporal patterns and user characteristics in advance, enabling efficient resource allocation while reducing the time required during actual deployment
Solution Approach 2:
The system transforms forecasting into a parameter optimization problem where resource allocation is determined by matching predefined resource templates with user cluster characteristics. By changing the forecasting approach from predictive modeling to parameter matching, the system achieves high efficiency while maintaining accuracy
Data Source
AI summary
Techniques for network usage analysis and forecasting are disclosed. In one embodiment, a computerized method is disclosed comprising obtaining network access data identifying network access of a plurality of users, obtaining user data for the plurality of users, analyzing the obtained network access data, the analyzing comprising determining a network usage pattern for each user, determining a plurality of feature sets corresponding to the plurality of users, a feature set determined for a respective user comprising network usage features and user features determined using the user data obtained for the respective user, determining a number of clusters formed using the plurality of feature sets, each cluster grouping a number of users, of the plurality of users, having similar feature sets, and communicating information about at least one cluster in response to a network analysis request.