AI Subscriber Profile Configuration for Accurate Network Personalization
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Solution Overview
Problem
Existing methods for generating subscriber profiles in mobile communication networks result in faulty configurations due to manual processes that fail to align with customer-specific requirements, leading to inefficiencies and potential loss of secure elements.
Innovation Solution
Utilizing an artificial intelligence system trained on initialization structures and personalization specifications to generate and validate subscriber profiles, ensuring alignment with customer-specific requirements and reducing the likelihood of errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual processes are used to generate subscriber profiles, then flexibility in customization is improved, but manufacturing precision deteriorates due to misalignment with customer-specific requirements
Solution Approach 1:
The patent replaces manual mechanical processes with an artificial intelligence-based automated system. The AI model processes initialization structures and personalization specifications to generate subscriber profiles, eliminating human error while maintaining customization capabilities through programmatic parameter adjustment.
Solution Approach 2:
The AI system performs self-validation of generated subscriber profiles against the initialization structure and personalization specifications. The system automatically detects and corrects configuration errors without requiring manual review, enabling self-service error prevention and correction.
2Manufacturing precision
If experienced human operators generate subscriber profiles, then manufacturing precision is improved through expertise, but loss of information deteriorates when operators are replaced
Solution Approach 1:
The patent creates a digital copy of expert knowledge by training the AI model on existing subscriber profile configurations and personalization specifications. The AI learns from historical data and replicates expert-level configuration quality without relying on individual human operators, preventing knowledge loss when personnel change.
3Ease of operation
If validation is performed based on general specification only, then ease of operation is improved, but reliability deteriorates due to undetected customer-specific requirement violations
Solution Approach 1:
The AI system performs multiple functions simultaneously: it validates against the general initialization structure while also checking compliance with customer-specific personalization specifications. This multi-functional validation approach maintains operational simplicity while significantly improving configuration correctness.
Solution Approach 2:
The system implements automated feedback loops where the AI model generates profiles, validates them against both general and customer-specific requirements, and iteratively corrects any violations. This feedback mechanism ensures high reliability while maintaining ease of operation through automation.
Data Source
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AI summary
Provided is a method of generating a subscriber profile configuration, preferably for a mobile communication network, in a data processing system. The method comprises: receiving configuration input data from at least one computer system external to the data processing system; processing the configuration input data by the artificial intelligence system, wherein the artificial intelligence system has been trained based on training data comprising an initialization structure, a plurality of personalization specifications and existing subscriber profile configurations generated based on the initialization structure and the plurality of personalization specifications; and generating the subscriber profile configuration based on the artificial intelligence system, wherein an output of the artificial intelligence system comprises the generated subscriber profile configuration.