Wireless Service Provisioning Anomaly Detection With Machine Learning

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Solution Overview

Problem

Wireless communication networks face challenges in efficiently detecting and responding to provisioning errors, which can lead to service discrepancies and interruptions due to the complex one-to-many relationship between customer-facing service descriptors and network attributes, exacerbated by the high volume of provisioning transactions.

Innovation Solution

A method and system utilizing a machine learning model to detect discrepancies between customer-facing services and implemented service attributes by converting service descriptors into network attributes, querying network elements, and transferring provisioning updates to correct these discrepancies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If the network provisioning engine processes a large number of provisioning transactions to enable comprehensive service provisioning, then the service coverage and functionality are improved, but the complexity of detecting and managing provisioning errors increases

Engineering Contradiction:

Improveservice coverageVSAvoidprovisioning error detection complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary machine learning model that acts as a mediator between the provisioning engine and the service delivery system. This model receives provisioning transactions, predicts potential errors, and provides risk assessments without requiring the provisioning engine to directly manage complex error detection logic for each transaction, thus reducing operational complexity while maintaining comprehensive service coverage

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces traditional mechanical/error-based detection systems with a machine learning-based predictive system. Instead of relying on complex rule-based error detection mechanisms that scale linearly with transaction volume, the system uses trained neural networks to efficiently assess provisioning risk, substituting computational complexity with statistical pattern recognition

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Device complexity

If traditional provisioning error detection methods are used, then the system complexity is kept low, but the ability to detect and respond to provisioning errors efficiently is insufficient

Engineering Contradiction:

Improvesystem complexityVSAvoidprovisioning error detection efficiency

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The patent implements preliminary action by using the machine learning model to predict and identify potential provisioning errors before they are executed in the network. The model analyzes provisioning transactions in advance, assesses their risk levels, and enables preventive measures to be taken, thereby improving detection efficiency without requiring complex real-time monitoring systems

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system establishes a feedback loop where the machine learning model continuously learns from actual provisioning outcomes and adjusts its predictions. This feedback mechanism allows the system to improve its error detection accuracy over time without increasing operational complexity, as the model automatically adapts to changing network conditions and error patterns

Inventive Principle:

Principle #23Feedback

3Manufacturing precision

If manual provisioning verification is performed to ensure accuracy, then the precision of service provisioning is improved, but the processing time and productivity are reduced

Engineering Contradiction:

Improveservice provisioning accuracyVSAvoidprovisioning processing speed

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The patent implements self-service by enabling the machine learning model to automatically verify provisioning accuracy without requiring manual intervention. The model independently analyzes provisioning transactions, predicts potential issues, and provides verification assessments, thereby maintaining high provisioning accuracy while significantly reducing the time and resources that would otherwise be required for manual verification

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250337641A1Service provisioning anomaly detection in wireless communication networks

Publication Date: 2025.10.30 T MOBILE INNOVATIONS LLC

AI summary

Various embodiments include a wireless communication network that comprises provisioning circuitry. The provisioning circuitry converts customer facing services to network service attributes that define service provided to a user device on the network. The provisioning circuitry transfers a command to a network element to update existing service attributes stored in the device's subscriber profile using the network service attributes. The provisioning circuitry queries the network element to retrieve implemented service attributes from the subscriber profile. The provisioning circuitry provides the customer facing services, the network service attributes, and the implemented service attributes to a machine learning model trained to detect discrepancies between implemented service attributes and customer facing services. In response to detecting a discrepancy between the customer facing services and the implemented service attributes, the provisioning circuitry transfers an update to the network element to correct the discrepancy between the implemented service attributes and the customer facing services.