Person Profile Spam Filtering Reduces False Positives
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Solution Overview
Problem
Existing spam filtering systems often incorrectly identify legitimate messages as spam, leading to false positives and the loss of important communications.
Innovation Solution
A method and system that uses person profiles generated from scanning messages across different accounts and sources to identify false positive spam messages by comparing sender and recipient addresses with stored profiles, allowing for automated rescuing of misclassified messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If spam filtering is applied to identify and block unsolicited messages, then spam messages are filtered out, but legitimate messages are incorrectly identified as spam (false positives)
Solution Approach 1:
The system pre-processes incoming messages by extracting sender information, message content, and metadata before applying spam filtering rules. This preliminary analysis includes identifying the sender's domain, checking against known spam patterns, and gathering contextual information that will be used in subsequent filtering stages, allowing for more accurate classification
Solution Approach 2:
The patent introduces an intermediary verification layer between the spam filter and the message delivery system. This intermediary component analyzes messages that are borderline cases or have low confidence scores, using additional heuristics and contextual information to make final determination, thereby reducing false positives while maintaining spam blocking effectiveness
2Measurement precision
If multiple message attributes are analyzed to improve spam detection accuracy, then spam identification improves, but system complexity increases
Solution Approach 1:
The spam filtering system is divided into multiple independent modules, each responsible for analyzing specific message attributes such as sender information, subject line, body content, and metadata. Each module produces a score or classification that is then combined by a decision-making component, allowing for comprehensive analysis while maintaining manageable system complexity through modular design
Solution Approach 2:
Different levels of analysis are applied to different message attributes based on their importance and reliability. Critical attributes like sender domain and message headers receive more rigorous analysis, while less important attributes receive lighter processing. This localized quality approach optimizes resource allocation and reduces unnecessary computational complexity
Data Source
AI summary
Systems and methods to reduce false positives in spam filtering. In one aspect, a method includes automatically generating profiles for persons identified in messages, such as incoming and outgoing e-mails. Messages flagged as spam can be checked against the profile to identify false positives.