Email Structure Analysis Device Using Image Processing for Spam Detection
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Solution Overview
Problem
Conventional methods for detecting and classifying spam emails are inefficient in real-time due to the dynamic nature of spam content and the need for frequent updates, making it difficult to accurately identify and filter out spam emails.
Innovation Solution
A document structure analysis device using image processing that converts email data into n-value or n-dimensional representations for rapid comparison and classification, allowing for high-speed processing and improved accuracy in determining spam emails by calculating the degree of similarity between sample and input data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional spam detection methods process lots of spam mails to extract determination information, then detection accuracy improves, but processing time increases and real-time detection becomes difficult
Solution Approach 1:
The patent extracts only the essential structural features of emails (header presence, attachment indicators, formatting patterns) rather than processing entire email contents. This extraction approach maintains detection accuracy by focusing on spam-indicative structural elements while dramatically reducing processing time compared to analyzing full email bodies.
Solution Approach 2:
The email analysis is segmented into discrete structural components (header sections, body formatting, attachment markers) that can be independently evaluated. This segmentation allows the system to quickly assess multiple structural features in parallel, improving processing speed while maintaining comprehensive detection capability.
2Measurement precision
If spam detection processes entire email contents including body text, then detection accuracy improves, but processing load increases
Solution Approach 1:
The system extracts and analyzes only structural metadata and formatting patterns from emails rather than processing complete email contents. This includes header field structures, attachment presence indicators, and body formatting patterns, which significantly reduces processing load while maintaining detection accuracy through focus on spam-characteristic structural elements.
Solution Approach 2:
The patent applies partial action by analyzing only the necessary structural portions of emails that are sufficient for spam detection. Rather than processing entire email bodies, the system evaluates specific structural features (header organization, attachment markers, formatting patterns) that provide adequate discrimination between spam and legitimate emails.
3Measurement precision
If determination information is updated frequently to match changing spam content, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The system uses parameter changes by monitoring variations in structural feature patterns (header formats, attachment structures, body organization) rather than requiring complete reconfiguration of detection rules. This allows adaptive response to evolving spam techniques through incremental parameter adjustments, maintaining detection accuracy while limiting system complexity growth.
Data Source
AI summary
A mail processing apparatus includes a data retrieving section for retrieving sample data from e-mail and/or a network NW, a signalizing section for converting the sample data form the data retrieving section into n-value, a sample storage section for storing n-value data converted by the signalizing section, a signal processing section for comparing the n-value sample data stored in the sample storage section with an inputted e-mail to judge whether or not the e-mail is spam mail based on the degree of similarity, and a spam storing section for storing the spam mail based on the judgment result.