Email Metadata Classification for High-Volume Shared Mailboxes
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Solution Overview
Problem
High volumes of incoming emails in shared mailboxes of organizations lead to significant human resource expenses due to manual processing, which is time-consuming and labor-intensive.
Innovation Solution
A system utilizing an event streaming platform and machine learning models to automate the processing and classification of emails, including metadata extraction, attachment handling, and workflow assignment, reducing the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human resources are assigned to monitor and process email messages in shared mailboxes, then email processing can be performed manually, but the cost of human resources increases significantly with high volumes of emails
Solution Approach 1:
The system enables email messages to process themselves through automated classification and routing. The workflow engine automatically analyzes email content, determines appropriate workflows, and routes messages without human intervention, allowing the system to serve itself rather than requiring human operators for each email
Solution Approach 2:
The patent replaces the mechanical system of manual human review with an automated computational system. The workflow engine uses text analysis, classification algorithms, and automated decision-making to substitute human cognitive and manual processing activities, thereby eliminating the need for human resources while maintaining or improving processing capacity
2Productivity
If multiple employees are assigned to monitor a single shared mailbox, then email processing capacity increases, but the expense of human resources increases considerably
Solution Approach 1:
The system changes the parameter of processing capacity from being dependent on human quantity to being dependent on system computational capacity. By implementing automated workflow analysis and classification, the system can process a variable number of emails based on computational resources rather than fixed human staffing levels, allowing flexible scaling without proportionally increasing employee numbers
3Ease of operation
If manual monitoring and processing of email messages is performed, then emails can be handled, but time consumption and labor intensity increase
Solution Approach 1:
The system performs preliminary classification and workflow determination automatically upon receiving each email. By pre-analyzing email content, extracting relevant information, and determining appropriate workflows before human review, the system eliminates the need for time-consuming manual analysis and reduces processing time while maintaining operational simplicity
Data Source
AI summary
In some aspects, the techniques described herein relate to a method including: receiving, by a capture module, a reception notification from an email server, wherein the reception notification is triggered by an event handler of the email server and the event handler is configured to generate the reception notification upon receipt of an email in a mailbox of the email server; generating, by the capture module, a reception event including metadata related to the email that is received at the mailbox of the email server; sending, by the capture module, the reception event to an event streaming platform; retrieving, by a workflow engine, email metadata, wherein the email metadata is related to the email that is received at the mailbox of the email server; and providing, by the workflow engine and as input to a rules-based procedure, the email metadata.