Email Classification via Recipient Metadata Extraction
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Solution Overview
Problem
Existing methods for classifying emails as official or private struggle to balance efficiency with employee privacy, as monitoring email contents is often required to determine purpose, leading to resource strain and privacy concerns.
Innovation Solution
A method that generates feature values based on recipient email accounts using domain names, social network relations, and keyword analysis, employing classification algorithms like Naïve Bayes or SVM to classify emails without monitoring their contents, allowing ARM-based embedded systems to implement this classification in network environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If email contents are monitored to determine official or private purpose, then classification accuracy is improved, but employee privacy is violated
Solution Approach 1:
The patent extracts only the recipient email account information from the email content, separating this necessary classification data from the private email body. By taking out only the essential metadata (recipient accounts) while leaving the content untouched, the system achieves classification functionality without violating employee privacy.
2Measurement precision
If email monitoring is implemented to classify emails, then official email identification is improved, but email transmission efficiency deteriorates
Solution Approach 1:
The system extracts only the recipient email account metadata from emails, avoiding full content monitoring. This selective extraction of necessary classification information maintains classification accuracy while minimizing processing overhead and preserving email transmission efficiency.
3Measurement precision
If comprehensive email analysis is performed to improve classification, then classification accuracy is improved, but system resource consumption increases
Solution Approach 1:
The patent extracts only the essential recipient email account information needed for classification, avoiding comprehensive analysis of email contents. This selective extraction approach achieves sufficient classification accuracy while significantly reducing system resource consumption compared to full content analysis.
Data Source
AI summary
A method for classifying email includes receiving an email. Several recipient email accounts of the email are extracted. Several email feature values of the email are generated according to the recipient email accounts. A classification algorithm is utilized to classify the email as an official email or a private email according to the email feature values of the email.