Message Classification System for Telecommunications Routing
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Solution Overview
Problem
Current telecommunications network architecture lacks the ability to accurately account for mislabeled, misclassified, or misidentified messages, leading to improper routing, revenue loss, and security concerns, as it cannot monitor and verify message classification effectively.
Innovation Solution
A system utilizing a computing device that applies a deep learning algorithm to classify messages based on their characteristics, such as payload, source, and timestamps, and compares the determined class to the service category provided by the source, enabling accurate routing and rating, and updating the trustworthiness rating of the source element.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing network architecture is used for message routing and rating, then system simplicity is maintained, but message classification accuracy and reliability deteriorate
Solution Approach 1:
The patent introduces an intermediary message classification system that sits between the message source and the telecommunications network. This intermediary component analyzes messages using multiple classification methods (rule-based, machine learning, deep learning) to determine accurate message classes, then provides this classification information to the network for routing and rating decisions. This mediator resolves the contradiction by enabling high classification accuracy without requiring the core network architecture to become complex.
Solution Approach 2:
The patent segments the message classification function into separate, independent components: rule-based classification, machine learning classification, and deep learning classification. Each component handles specific aspects of message analysis, and their results are combined to determine the final message class. This segmentation allows each component to be optimized independently while maintaining overall system manageability, thus achieving high accuracy without excessive complexity.
2Reliability
If message classification is not verified, then processing speed is maintained, but revenue loss and security risks increase
Solution Approach 1:
The patent implements preliminary message classification and verification before messages enter the main telecommunications network. By pre-classifying messages and verifying their accuracy in advance, the system ensures reliable routing and rating decisions are made based on accurate classification information. This preliminary action prevents revenue loss and security issues downstream while allowing the main network to process messages efficiently without repeated verification delays.
Solution Approach 2:
The patent replaces traditional mechanical verification methods with automated machine learning and deep learning algorithms. These intelligent systems automatically analyze message characteristics, compare them against classification rules, and verify message classes without manual intervention. This substitution maintains high processing throughput while ensuring reliable classification accuracy, as the automated systems can verify messages at speeds comparable to the original processing rate.
3Object-affected harmful factors
If source element trustworthiness is not evaluated, then system operation is simple, but improper and misclassified messages reach end users
Solution Approach 1:
The patent implements a feedback mechanism where the message classification system evaluates source element trustworthiness based on the accuracy of their message classifications. When source elements provide accurate service category information that matches the actual message class, their trust score increases. When mismatches occur, their trust score decreases. This feedback loop enables the system to identify and prioritize messages from trustworthy sources while filtering out potentially harmful messages from untrustworthy sources, reducing security risks without requiring complex external verification systems.
Data Source
AI summary
Systems and methods for automated routing and rating of communication data.