AI-Based Message Priority Scoring Across Email, SMS, and Instant Messaging
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Solution Overview
Problem
There is a disconnect between what electronic messages are deemed important by the sender and the recipient, with manual flagging mechanisms being inaccurate and limited to email systems, and no effective prioritization in other messaging platforms like SMS and instant messaging.
Innovation Solution
A system that analyzes electronic messages using machine learning and artificial intelligence to determine importance based on sender identity, relationship with the recipient, urgent terms, and message content, automatically flagging messages and sending reminders if they remain unread.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual flagging is used to indicate important messages, then the sender can mark messages as high priority, but the flagging becomes inaccurate when senders flag virtually every message as high-importance
Solution Approach 1:
The system automatically determines message priority by analyzing message content, sender-recipient relationships, and communication history without requiring manual intervention from the sender. The machine learning model self-evaluates and assigns priority scores, eliminating the need for senders to manually flag messages while improving accuracy.
Solution Approach 2:
The manual mechanical flagging system is replaced with an automated machine learning-based priority determination system. The system uses algorithms to analyze message metadata, content, and communication patterns to automatically assign priority levels, substituting human judgment with computational analysis.
2Adaptability or versatility
If manual flagging is used for email priority indication, then high-priority messages can be identified, but the mechanism is not available in other messaging platforms like SMS and instant messaging
Solution Approach 1:
The priority determination system is designed to work across multiple messaging platforms including email, SMS, and instant messaging applications. The machine learning model can process different message formats and communication channels universally, making the priority indication feature available across all platforms without requiring platform-specific implementations.
3Reliability
If senders manually flag every message as high-priority, then all messages are marked as important, but the value and effectiveness of the flag is lost
Solution Approach 1:
The system replaces manual flagging with automated machine learning-based priority assessment. The model analyzes message content, sender-recipient relationships, and communication history to objectively determine priority, eliminating the problem of senders marking all messages as high-priority while maintaining reliable and effective priority indication.
4Measurement precision
If automated priority determination is implemented, then accurate priority scoring can be achieved, but the system complexity increases due to machine learning components
Solution Approach 1:
The machine learning model serves as an intermediary component that processes raw message data and transforms it into priority scores. The system uses pre-trained models that analyze message metadata, content, and communication patterns to generate priority indications, balancing accuracy with manageable system complexity through established AI techniques.
Data Source
AI summary
A method includes analyzing a content of an electronic message associated with a sender. A score associated with the electronic message is generated. The score is indicative of an importance of the electronic message to the sender. The electronic message is automatically flagging based on the score. The flagged electronic message is transmitted to a recipient.