Email Rewriting System for Inbox Prioritization
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Solution Overview
Problem
Users are overwhelmed by the sheer volume of emails, making it difficult to identify critical or valuable commercial emails amidst spam and unsolicited messages, even with the use of spam filters.
Innovation Solution
An email rewriting system that uses machine learning models to identify commercial emails, extract salient facts, and generate informative subject lines, while prioritizing emails based on user transaction history and content analysis, thereby simplifying inbox organization and user understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If spam filters are used to remove unsolicited messages, then the volume of relevant emails increases, but commercial emails that are not spam still feel overwhelming to users
Solution Approach 1:
The patent segments commercial emails into different categories based on their content and importance. By dividing the email stream into distinct segments (e.g., high-priority commercial emails vs. standard commercial emails), the system makes it easier for users to process and respond to relevant messages without being overwhelmed by the entire volume.
Solution Approach 2:
The patent applies local quality by differentiating the treatment of commercial emails based on their specific characteristics. High-priority commercial emails receive enhanced visibility and prioritization, while standard commercial emails are handled differently. This localized differentiation improves user experience by tailoring the presentation to the specific needs and importance of each email type.
2Measurement precision
If users manually review each email, then understanding of critical emails improves, but time consumption increases significantly
Solution Approach 1:
The patent performs preliminary action by automatically analyzing and categorizing commercial emails before users need to review them. The system pre-processes emails to identify priority levels and extract key information, so when users access their inbox, the most critical emails are already highlighted and ready for immediate attention, reducing the time needed for manual review.
Solution Approach 2:
The patent implements self-service by enabling the email system to automatically prioritize and organize commercial emails based on their content and importance. The system serves itself by using machine learning models to analyze email characteristics and assign priority levels, reducing the burden on users to manually evaluate each email's importance.
3Productivity
If machine learning models are used to classify and prioritize emails, then email processing efficiency improves, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer in the form of machine learning models that sit between the raw email stream and the user interface. These models act as mediators that automatically analyze email content, extract key information, and assign priority levels, thereby improving processing efficiency while hiding the underlying complexity from the user.
Solution Approach 2:
The patent replaces manual mechanical processing (users manually reading and evaluating each email) with automated machine learning-based processing. The system uses neural networks and other AI techniques to automatically categorize and prioritize emails, substituting human cognitive processing with computational algorithms that can scale efficiently.
Data Source
AI summary
Commercial emails are rewritten to aid user understanding and usability. A commercial email is identified from a set of email messages received by an email client for a user. The content of the commercial email is analyzed to identify salient facts associated with the terms of a sale specified by the content. A simplified subject line for the commercial email is generated based on the salient facts and used to replace the original subject line. Priority scores can also be computed based on user transaction history, time, or other factors. The priority scores can then be utilized to organize commercial emails. Further, commercial emails can be segmented from other emails, such as personal or work emails.