ML Email Action Prediction and Prioritization
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Solution Overview
Problem
The increasing volume of electronic communications, such as emails, makes it difficult for users to identify and manage important messages amidst unwanted communications, leading to time-consuming sorting and potential oversight of important emails.
Innovation Solution
A computer-implemented method using machine learning models trained on historical email features and user actions to predict and recommend actions for new emails, prioritizing them and automatically handling actions that do not require user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually sort through electronic communications to identify important messages, then they can ensure accurate identification of important emails, but it consumes significant time and effort
Solution Approach 1:
The email system automatically classifies and prioritizes incoming messages using machine learning models without requiring user intervention. The system learns from historical user actions and autonomously organizes emails by predicted importance and required actions, enabling the system to serve itself in the email sorting task.
Solution Approach 2:
The system performs preliminary classification and prioritization of emails before users review them. By pre-processing incoming messages and organizing them based on predicted user actions and importance, the system prepares the email list in advance, reducing the time users need to spend sorting through messages.
2Reliability
If users review all received electronic communications, then they can ensure no important messages are overlooked, but the volume of communications makes this task increasingly difficult and time-consuming
Solution Approach 1:
The system segments the large volume of incoming emails into distinct groups based on predicted user actions (e.g., delete, archive, respond, forward) and importance levels. This segmentation divides the monolithic email list into manageable categories, allowing users to efficiently process different types of messages separately while ensuring comprehensive coverage.
Solution Approach 2:
The machine learning model acts as an intermediary between incoming emails and user review. It analyzes email features and predicts user actions, then presents a prioritized and categorized view of emails to users. This intermediary processing layer filters and organizes information before user interaction, maintaining reliability while improving productivity.
3Ease of manufacture
If traditional email filtering rules are used to manage unwanted communications, then simple spam can be blocked, but they cannot effectively prioritize important messages among legitimate emails
Solution Approach 1:
The system moves beyond simple binary filtering (spam/legitimate) by introducing multiple classification parameters including predicted user actions, importance scores, and priority levels. These parameter changes enable nuanced email management that adapts to different message types and user preferences while maintaining ease of use through automated decision-making.
Solution Approach 2:
The email management system transitions from static filtering rules to dynamic, adaptive classification. The machine learning models continuously learn from user actions and adjust predictions accordingly, enabling the system to adapt to changing email patterns and user preferences over time while maintaining simple user interaction.
Data Source
AI summary
Mechanisms are provided which generate an action recommendation for a received electronic communication. A machine learning (ML) computer model is trained at least by executing a ML training operation based on electronic communication features extracted from a plurality of historical electronic communications and actions taken by a user in response to each historical electronic communication. The ML computer model is trained to predict an action classification that specifies a predicted action that the user will take in response to receiving electronic communications. A new electronic communication is received and electronic communication features are extracted from the new electronic communication. The extracted features are processed by the trained ML computer model to generate a predicted action classification and an action recommendation output specifying a recommended action to take corresponding to the predicted action classification for the new electronic communication.