Server Message Verification System for Spam Prevention
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Solution Overview
Problem
The existing communication networks face challenges in effectively identifying and preventing fraudulent or spam messages, which can negatively impact service providers and users by consuming bandwidth and resources, and compromising security.
Innovation Solution
A server computer performs a message verification process using a predictive model that analyzes historical data, user-defined rules, and machine-learned rules to determine the legitimacy of messages, intercepting and potentially blocking spam or fraudulent messages before they reach their intended destination, and modifying user accounts accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional message transmission is used without verification, then communication speed and simplicity are maintained, but fraudulent and spam messages consume bandwidth and resources
Solution Approach 1:
The patent implements preliminary verification of messages before they are transmitted across the network. The verification system checks messages against multiple criteria including sender reputation, message content analysis, and pattern recognition to identify and block fraudulent or spam messages before they consume network bandwidth and user resources.
Solution Approach 2:
The patent introduces an intermediary verification system that acts as a mediator between message senders and recipients. This intermediary service analyzes messages using machine learning models and rule-based systems to determine legitimacy, allowing legitimate messages to pass through while blocking fraudulent ones without requiring changes to end-user devices.
2Reliability
If message verification is performed on all messages, then security is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial verification by prioritizing analysis of messages that exhibit suspicious characteristics. The system uses quick initial filters to identify high-risk messages that require full verification, while allowing messages that clearly meet legitimacy criteria to pass through with minimal processing, thus reducing average processing time while maintaining security.
Solution Approach 2:
The patent dynamically adjusts verification parameters and thresholds based on learned patterns and current threat levels. The machine learning models continuously update their criteria for what constitutes suspicious behavior, allowing the system to adapt its processing intensity to match actual risk levels, thereby optimizing the balance between security and processing speed.
3Measurement precision
If strict verification rules are applied, then spam filtering accuracy is improved, but legitimate messages may be blocked increasing false positives
Solution Approach 1:
The patent implements feedback mechanisms where user responses to verified messages are fed back into the verification system. When users mark messages as legitimate or spam, this feedback is used to retrain and refine the machine learning models, improving accuracy over time while reducing false positives. The system continuously learns from real-world outcomes to adjust its verification criteria.
Solution Approach 2:
The patent employs dynamic verification thresholds that adjust based on contextual factors such as sender history, recipient preferences, and current communication patterns. Rather than applying static rules, the system adapts its strictness level dynamically, being more lenient with established trusted senders and more stringent with unknown sources, thereby maintaining high detection accuracy while minimizing false positives.
Data Source
AI summary
A server computer receives an indication of an interaction between a first user device of a first user and a second user device of a second user, where the interaction includes a message for transmission from the first user to the second user. The server computer performs a verification process on the message, including performing one or more binary checks on the message. The server computer then generates a response indicating whether the message is a legitimate message based on the verification process. When the response indicates that the message is a legitimate message, the server computer transmits the message to the second user device of the second user for display.