Intelligent Network Service Provisioning via ML-Based MNO Selection
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Solution Overview
Problem
Existing network service provisioning methods lack the ability to efficiently provision mobile devices with wireless network services from multiple mobile network operators (MNOs), leading to suboptimal service quality and resource management.
Innovation Solution
A method for intelligent network service provisioning using a trained machine-learning model to identify the most suitable MNO from a pool of operators based on target attributes, generating a SIM profile request, and provisioning SIMs on mobile devices to provide granular network services from multiple MNOs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional network service provisioning methods are used, then the provisioning process is simple, but the service quality is suboptimal and cannot leverage multiple MNOs
Solution Approach 1:
The patent segments network services into granular components that can be independently provisioned from different MNOs. Each service component (e.g., voice, data, messaging) can be selected and configured separately, allowing the system to leverage the strengths of individual operators for specific services while maintaining manageable complexity through modular configuration.
Solution Approach 2:
The patent introduces an intermediary provisioning system that manages the complexity of multi-MNO service composition. This intermediary layer handles the selection, configuration, and coordination of services from multiple operators, shielding end users from the underlying complexity while enabling access to optimized multi-operator service bundles.
2Reliability
If multiple MNOs are provisioned for a single mobile device, then service quality improves, but the provisioning process becomes more complex
Solution Approach 1:
The patent implements self-service capabilities that enable automated selection and configuration of services from multiple MNOs based on predefined criteria, user profiles, and real-time network conditions. The system automatically provisions appropriate service combinations without requiring manual intervention, making the complex multi-operator provisioning process as easy as traditional single-operator setup.
3Adaptability or versatility
If granular network services are selected from multiple MNOs, then customized services tailored to user needs are provided, but the system complexity increases
Solution Approach 1:
The patent applies local quality by allowing different service components to be optimized independently according to their specific requirements and the strengths of individual MNOs. Each granular service element (voice, data, messaging, etc.) can be configured with specific parameters and sourced from the most appropriate operator, enabling high customization without requiring the entire system to become uniformly complex.
Data Source
AI summary
This disclosure relates to intelligent network service provisioning. In some aspects, a method includes receiving, via one or more application programming interfaces (APIs), a request for provisioning wireless network services on a mobile device, the request identifying target attributes associated with the wireless network services. The method includes identifying, using a trained machine-learning model, at least one mobile network operator (MNO) satisfying corresponding target attributes associated with at least a portion of the wireless network services. The method includes generating a subscriber identification module (SIM) profile request corresponding to the at least one MNO, which is transmitted to retrieve a corresponding SIM profile associated with the at least one MNO. The method includes receiving the corresponding SIM profile associated with the at least one MNO. The method includes provisioning one or more SIMs of the mobile device in accordance with the corresponding SIM profile.