Email Prominence Calculation via Conversation Weight Analysis
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Solution Overview
Problem
Conventional methods for calculating email prominence rely on user surveys and feedback, which are labor-intensive and require significant resources for deriving social network features, making it difficult to automatically determine email importance and prioritize emails effectively.
Innovation Solution
A system calculates prominence values for emails and email participants by determining importance values based on conversation weights, which incorporate factors like recipient and contribution weights, temporal weights, and additional email features, using machine-learning techniques and user feedback for customization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user surveys and feedback are used to evaluate email importance, then perceived email importance can be measured, but the process becomes labor-intensive and resource-consuming
Solution Approach 1:
The system automatically calculates email prominence values by analyzing email metadata and conversation patterns without requiring manual user surveys or feedback. The prominence calculation is performed self-service by the system itself, eliminating the need for labor-intensive user input while maintaining measurement accuracy.
Solution Approach 2:
The patent replaces manual mechanical processes (user surveys, manual feedback collection) with automated computational processes. The system uses algorithms to analyze email metadata, sender-receiver relationships, and conversation patterns to automatically determine prominence values, substituting mechanical human evaluation with automated mechanical computation.
2Extent of automation
If social network features are automatically derived to prioritize emails, then email importance can be determined automatically, but significant calculation resources are required
Solution Approach 1:
The patent segments the complexity of email analysis into distinct components: email metadata analysis, conversation pattern analysis, and prominence calculation. By breaking down the complex task of automatic prioritization into manageable segments, the system reduces the computational resources required while maintaining automation.
Solution Approach 2:
Instead of analyzing all possible social network features and email attributes, the system applies partial action by focusing on specific key features such as sender-receiver relationships, conversation continuity, and email metadata. This selective approach achieves adequate automation without requiring excessive calculation resources.
3Measurement precision
If hand-labeled input factors are used in linear regression models, then correlations between factors and perceived importance can be shown, but the derivation is not automatic
Solution Approach 1:
The system automatically derives input factors for prominence calculation by analyzing email metadata and conversation patterns without requiring hand-labeling. The prominence calculation self-services by automatically extracting and processing features such as sender importance, recipient importance, conversation weight, and temporal factors, eliminating the need for manual factor derivation.
Data Source
AI summary
One embodiment of the present invention provides a system for calculating prominence of an email with regard to a user. During operation, the system determines an importance value associated with an email participant in the user's conversations, wherein the email participant is an email sender and/or recipient other than the user. Next, the system calculates a prominence value associated with a received email based upon at least the importance values associated with the email participants in the received email.