Messaging Campaign Drift Detection Through Semantic Vectors
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Solution Overview
Problem
The telecommunications ecosystem faces challenges with registered SMS and MMS campaigns being abused, as businesses often diverge from their registered message content, leading to inappropriate content transmission and financial discrepancies due to variable fees based on campaign types, despite existing vetting processes.
Innovation Solution
A computer-readable media (CRM) employs machine learning techniques to analyze and compare incoming messages against registered campaign samples in real-time, representing text and multimedia content in a high-dimensional vector space, calculating angular distances to detect deviations and reclassify messages, interfacing with a CSP Registry API for real-time updates, and using configurable thresholds for sensitivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text-based matching is used to monitor message campaigns, then the system is simple to implement, but it cannot detect semantic deviations or inappropriate content effectively
Solution Approach 1:
The patent replaces traditional mechanical text-based matching with machine learning models that process messages in high-dimensional vector spaces. These models compute angular distances between message vectors and campaign sample vectors to detect semantic deviations, enabling nuanced content analysis beyond keyword matching while maintaining system manageability through automated processing.
Solution Approach 2:
The patent transforms message content from one-dimensional text into high-dimensional vector representations. By calculating angular distances in this multi-dimensional space, the system can detect semantic similarities and deviations that would be imperceptible in traditional text-based approaches, significantly improving measurement precision for message compliance.
2Reliability
If real-time message monitoring is implemented, then compliance detection is improved, but processing time and computational resources increase
Solution Approach 1:
The patent pre-processes and vectorizes campaign sample messages during campaign registration, storing them ready for comparison. When messages arrive, the system only needs to compute angular distances against pre-existing vectors rather than performing full text analysis, enabling real-time monitoring with minimal processing delay.
Solution Approach 2:
The machine learning models automatically process and compare messages without requiring manual review or intervention. The system self-manages the compliance detection process by computing vector similarities and flagging deviations autonomously, maintaining high reliability while minimizing processing time through automated decision-making.
3Measurement precision
If vector-based semantic analysis is used, then message drift detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential semantic features of messages by representing them as vectors in a high-dimensional space. By focusing on angular distance calculations rather than analyzing every word or character, the system achieves accurate drift detection while minimizing computational energy consumption through feature extraction.
4Reliability
If manual review of deviant messages is required, then false positives can be reduced, but processing speed and productivity decrease
Solution Approach 1:
The machine learning models autonomously detect message drift by computing angular distances between message vectors and campaign sample vectors. The system automatically flags deviant messages without requiring manual review, maintaining high compliance monitoring accuracy while maximizing processing throughput through automated decision-making capabilities.
Data Source
AI summary
The invention provides computer-readable media for detecting message drift within registered message campaigns to ensure compliance with predefined use case parameters. Utilizing a messaging compliance registry (MCR), the media includes instructions for downloading campaign data and employing control vectors derived from sample texts, representing intended communications within specific use cases. These vectors enable real-time comparison of incoming messages against expected messaging, employing methods such as cosine similarity measures and various vectorization models. If an incoming message's vector significantly deviates from the established threshold, indicating message drift, the media can delay, quarantine, or tag the message, while notifying the MCR. The process ensures that messages adhere to their registered campaign's guidelines, enhancing regulatory compliance and content integrity across messaging platforms.