Dynamic Mobile Network Selection Rules via Machine Learning
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Solution Overview
Problem
Existing methods for selecting mobile networks using static rules fail to adapt to changes in network quality, reliability, and availability, often connecting users to unsuitable networks despite the presence of more suitable options.
Innovation Solution
The implementation of a computer-implemented method using machine learning to dynamically modify rules for selecting mobile networks by gathering training data through user feedback and network characteristic analysis, allowing for real-time adaptation to changes in network conditions and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static rules are used for selecting mobile networks, then user preference for certain networks is maintained, but the system cannot adapt to changes in network quality, reliability, and availability
Solution Approach 1:
The patent transforms static selection rules into dynamic rules that automatically adapt to changing network conditions. The system continuously monitors network quality, reliability, and availability metrics, and adjusts selection criteria in real-time based on observed performance patterns, enabling the system to respond to network changes without manual intervention
Solution Approach 2:
The patent implements a feedback mechanism where the system collects performance data from selected networks, analyzes the results, and uses this information to refine future selection decisions. The feedback loop continuously improves selection accuracy by learning from past performance patterns and adjusting rules accordingly
2Reliability
If simple static rules are used for network selection, then the system is easy to implement, but it continues to select unsuitable networks even when better options are available
Solution Approach 1:
The patent performs preliminary analysis of network characteristics and performance patterns before making selection decisions. By pre-processing and evaluating multiple network options against established criteria, the system identifies suitable networks in advance, ensuring more reliable selections while maintaining manageable complexity through structured evaluation frameworks
3Adaptability or versatility
If machine learning is used to dynamically adjust rules, then the system adapts to current conditions and user preferences, but the complexity of the selection system increases
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically monitors its own performance, identifies improvement opportunities, and adjusts selection rules without external intervention. The machine learning components autonomously learn from collected data and refine selection strategies, reducing the need for manual configuration while managing complexity through automated self-optimization
Data Source
AI summary
A computer-implemented method for dynamically modifying rules for selecting suitable mobile networks. The method may include (1) identifying a set of predefined rules for selecting suitable mobile networks with which to connect, (2) obtaining a training data set that includes data about at least one candidate mobile network, (3) using machine learning to dynamically adjust, based at least in part on the training data set, the set of predefined rules, and (4) connecting to a suitable mobile network identified by the dynamically adjusted set of predefined rules. Various other methods, systems, and computer-readable media are also disclosed.