Sender Reputation Calculation via Non-Spam Log Analysis
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Solution Overview
Problem
Current spam filtering technologies rely on email sending history to calculate sender reputation, making them vulnerable to cheating and unable to accurately differentiate between legitimate and spam emails without historical data.
Innovation Solution
A method to calculate sender reputation by analyzing non-spam logs within a specified period, using formulas to derive initial, transferred, and current reputation values based on sender and recipient identifications, and email counts, without relying on historical data, thereby preventing reputation manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sender reputation is calculated based on email sending history, then the reputation value can be improved through account maintenance and sending normal emails, but the system becomes vulnerable to cheating and manipulation
Solution Approach 1:
The patent introduces recipient feedback as an intermediary element in the reputation calculation system. Instead of relying solely on sender behavior history, the system uses recipient responses (complaints, deletions, replies) as a mediating factor to objectively evaluate sender reputation, making it difficult for senders to manipulate their reputation through controlled email boxes
Solution Approach 2:
The system implements a feedback mechanism where recipient actions (complaints, deletions, replies) are collected and fed back into the reputation calculation algorithm. This continuous feedback loop ensures that reputation values are dynamically adjusted based on actual recipient responses rather than sender self-reported metrics, preventing reputation inflation through manipulation
2Measurement precision
If massive logs are analyzed using machine learning to generate reputation database, then the accuracy of reputation value is improved, but the system complexity and computational resources increase
Solution Approach 1:
The patent segments the reputation calculation into distinct components: initial reputation based on sending behavior, adjusted reputation incorporating recipient feedback, and final reputation values. This segmentation allows the system to achieve high accuracy through multiple factors while maintaining manageable complexity by processing each segment separately rather than requiring a single complex machine learning model
Solution Approach 2:
The system changes parameters dynamically based on recipient feedback. Instead of using a fixed machine learning model trained on historical data, the reputation parameters (initial reputation, adjustment factors) are continuously updated based on real-time recipient responses, allowing the system to adapt to new spam tactics without requiring complete retraining of complex models
Data Source
AI summary
A method for generating a reputation value of a sender includes: obtaining non-spam logs in a specified period; calculating an initial reputation value of a target sender according to sender identifications and recipient identifications of the non-spam logs as well as the number of emails sent by senders of the non-spam logs; calculating a transferred reputation value of the target sender according to the sender identifications of the non-spam logs and the number of the emails sent by the senders of the non-spam logs; and calculating a current reputation value of the target sender according to the initial reputation value and the transferred reputation value of the target sender. The method can accurately calculate the reputation value of the sender without relying on an email sending history of the sender, thereby effectively preventing the reputation value of the sender from being increased by cheating.