AI Subscriber Profile Configuration for Mobile Network Validation
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Solution Overview
Problem
Existing methods for generating subscriber profiles in mobile communication networks lead to faulty configurations due to manual input and lack of customer-specific validation, resulting in failed connections and inefficiencies, with no effective learning from previous profiles and high risk of misalignment with customer requirements.
Innovation Solution
A computer-implemented method using artificial intelligence to generate and validate subscriber profiles, trained on initialization structures and personalization specifications, to ensure alignment with customer-specific requirements and reduce errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual input methods are used to generate subscriber profiles, then human operators can create configurations, but the likelihood of faulty configurations increases due to manual errors and lack of customer-specific validation
Solution Approach 1:
The patent replaces the manual mechanical input process with an artificial intelligence system that automatically generates subscriber profiles. The AI system processes initialization structures and personalization specifications to produce configurations, eliminating human operators from the direct configuration creation process and thereby reducing manual errors while maintaining ease of operation through automated systems
Solution Approach 2:
The AI system performs self-validation by automatically checking generated configurations against customer-specific requirements and general specifications. The system validates its own output without requiring external manual review, enabling self-correction and ensuring configuration accuracy while maintaining operational efficiency
2Device complexity
If validation is performed only based on general specification, then validation process is simple, but customer-specific requirements are not verified leading to faulty profiles
Solution Approach 1:
The validation process is segmented into two distinct layers: validation against general specifications and validation against customer-specific requirements. The AI system independently checks each layer, ensuring that configurations meet both universal standards and personalized customer needs, thereby improving compliance precision without excessive complexity
Solution Approach 2:
The system performs preliminary validation by checking configurations against both general specifications and customer-specific requirements before final deployment. This advance validation prevents faulty profiles from being delivered, ensuring compliance precision while keeping the overall process manageable through structured preliminary checks
3Reliability
If experienced human operators generate profiles, then quality may be maintained through experience, but knowledge is lost when operators are replaced
Solution Approach 1:
The AI system captures and replicates the knowledge embedded in experienced operators by training on historical subscriber profile data and validation rules. This creates a digital copy of operational expertise that can be consistently applied without degradation when personnel change, maintaining profile quality while preventing knowledge loss
Solution Approach 2:
The system transforms qualitative operational knowledge into quantitative parameters and validation rules that can be processed by the AI. By converting experienced operators' judgment into structured validation criteria and configuration parameters, the knowledge becomes codified and transferable, maintaining reliability without dependency on specific individuals
4Ease of manufacture
If compile-time switches are used for parameter configuration, then configuration is simplified at build time, but parameters cannot be reconfigured at runtime
Solution Approach 1:
The system transitions from static compile-time configuration to dynamic runtime configuration capabilities. The AI-generated profiles include parameters that can be adjusted at runtime based on customer-specific requirements, enabling the system to adapt to changing needs while maintaining the simplicity of initial setup through automated generation
Data Source
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.