IP Phone Service State Detection Using Multi-Source Machine Learning
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Solution Overview
Problem
Current IP phone management systems struggle to accurately determine the service state of IP phones in large, diverse VoIP networks, especially when devices are from different manufacturers and technologies, leading to uncertainty in monitoring and managing network outages.
Innovation Solution
A system using machine learning algorithms trained on data from various network monitoring devices, including asset inventories, VoIP Call Control Servers, routers, and intrusion protection systems, to predict and diagnose the online/offline status of IP phones and potential outages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static monitoring methods (ICMP, SNMP, TR-069) are used to determine IP phone service state, then resource utilization is reduced and system complexity is lowered, but measurement precision and reliability of service state determination deteriorate
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between raw monitoring data and service state determination. The model processes multiple data sources (ICMP responses, SNMP information, TR-069 status, network flow data) and transforms them into accurate service state predictions, resolving the contradiction by achieving high precision without requiring complex rule-based processing logic
Solution Approach 2:
The system changes the parameter representation by using machine learning features and probability scores instead of simple binary status indicators. The model outputs confidence levels and probability distributions for service states, enabling more nuanced and accurate determination while maintaining manageable system complexity through standardized ML interfaces
2Productivity
If manual review of monitoring information is performed, then system complexity is reduced, but productivity and response time deteriorate
Solution Approach 1:
The system implements self-service automation where the machine learning model autonomously analyzes monitoring data, detects outages, and determines service states without human intervention. The model continuously processes incoming data and automatically updates service state determinations, achieving both high productivity and extensive automation simultaneously
Solution Approach 2:
The system incorporates feedback loops where the machine learning model's predictions are continuously refined based on actual network outcomes and new monitoring data. This feedback mechanism enables the automated system to improve its accuracy over time while maintaining high-speed automated operation without manual review
3Adaptability or versatility
If diverse VoIP technologies and multi-manufacturer devices are supported, then adaptability improves, but measurement precision and reliability of service state monitoring deteriorate
Solution Approach 1:
The machine learning model serves as a universal processing layer that handles multiple VoIP technologies (SIP, H.323, proprietary protocols) and multi-manufacturer devices through a single unified framework. The model ingests standardized features from diverse sources and produces consistent service state determinations, achieving both broad adaptability and high measurement precision simultaneously
Data Source
AI summary
A method of determining whether an end user device coupled to a voice over Internet Protocol (VOIP) network is online or offline assembling source data from a variety of network monitoring devices and services including at least one selected from the group consisting of: of asset inventories, VoIP Call Control Servers, routers, firewalls, and intrusion protection systems (IPSs) concerning an online and offline status of a plurality of end user device in the network. The method also comprises training a machine learning algorithm using the source data to determine whether the end user devices coupled to the VoIP network are online or offline, and after training, executing the machine learning algorithm to determine whether a specific end user device currently coupled to the VoIP network is online or offline.