Tiered AI Spam Blocking for Regulated Message Screening
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Solution Overview
Problem
Existing spam blocking technologies are inadequate in effectively preventing fraudulent sign-ups and spam messages, particularly in SMS communications, due to the complexity of evolving fraud tactics and the need to comply with strict regulatory and carrier guidelines, requiring significant manual effort and resources.
Innovation Solution
A tiered software framework utilizing AI engines with machine learning models for real-time pattern recognition and semantic analysis, combined with verification processes, to enforce regulatory compliance and block spam and fraud, including keyword filtering, semantic search, and daily limits, while reducing manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional spam blocking methods are used, then implementation is simple, but effectiveness against evolving fraud tactics is insufficient
Solution Approach 1:
The system is divided into multiple tiers (tiered software framework) where different AI models and blocking strategies operate at different levels. Tier 1 handles basic keyword filtering, Tier 2 applies machine learning models for pattern recognition, and Tier 3 uses advanced semantic analysis. This segmentation allows the system to achieve high reliability through layered defense while managing complexity by distributing functions across tiers.
Solution Approach 2:
The patent combines multiple AI technologies (machine learning, natural language processing, semantic analysis) with traditional filtering methods to create a composite blocking system. This composite approach integrates the simplicity of rule-based filtering with the adaptability of AI models, achieving both high effectiveness against evolving fraud and manageable system complexity through modular architecture.
2Reliability
If manual verification processes are used, then accuracy in detecting fraud is maintained, but significant manual effort and resources are required
Solution Approach 1:
The system implements self-service fraud detection through automated AI-driven analysis that independently evaluates sign-up patterns, message content, and user behavior without requiring manual intervention. The machine learning models automatically adapt to new fraud patterns and make real-time blocking decisions, maintaining high detection accuracy while eliminating the need for continuous manual verification effort.
Solution Approach 2:
The patent replaces manual verification processes (mechanical human effort) with automated AI systems that use machine learning and natural language processing to detect fraud. This substitution maintains or improves detection accuracy through advanced pattern recognition while dramatically increasing verification efficiency by processing thousands of sign-ups and messages simultaneously without human intervention.
3Reliability
If AI engines with semantic analysis are implemented, then spam blocking effectiveness is improved, but computational resources and processing time increase
Solution Approach 1:
The system segments computational tasks across different tiers, with Tier 1 performing lightweight keyword matching that consumes minimal resources, Tier 2 applying machine learning models for moderate computational effort, and Tier 3 using semantic analysis only for suspicious cases. This segmentation improves spam detection capability through progressive analysis while controlling computational resource consumption by applying heavy processing only when necessary.
Solution Approach 2:
The patent implements partial semantic analysis by applying full AI-powered semantic analysis only to messages that pass through initial filtering tiers and exhibit suspicious characteristics. For clearly benign messages, the system performs only lightweight filtering. This partial action approach maintains high spam detection capability for problematic content while reducing overall computational resource consumption by avoiding exhaustive analysis of all messages.
Data Source
AI summary
Embodiments of a method for facilitating spam blocking in a tiered software framework include: providing instructions to a machine learning module (MLM) to generate a threshold for classifying spam in messages generated in a tiered software framework. The instructions include inputs comprising government regulations; carrier guidelines; feedback on previously sent messages; and previously flagged messages. The instructions specify that the threshold is to prevent false positives while allowing false negatives. The method further includes, receiving the threshold according to the instructions from the MLM; parsing a message; automatically performing a semantic search using natural language processing for regulated content in the parsed message by comparing semantics of text of the parsed message to the inputs to find matches; assigning a score to the message based on matches found; and responsive to the score being higher than the threshold, blocking sending the message from the tiered software framework.