Urgency Prediction for Electronic Messages
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Solution Overview
Problem
The challenge lies in efficiently distinguishing urgent electronic messages from routine ones within the high volume of communications, as existing methods lack effective automation for determining message urgency and corresponding response actions.
Innovation Solution
A data processing system predicts an urgency level for electronic messages by detecting pre-defined features using machine learning algorithms, such as Discriminant Function or Naive Bayes Classifier, and maps these messages to specific response actions, with the ability to learn and adapt through user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of electronic messages is used to determine urgency, then accuracy of urgency determination is improved, but productivity deteriorates due to high message volumes
Solution Approach 1:
The system enables messages to self-classify by automatically analyzing their own content, sender, and metadata to determine urgency levels without requiring external human review. The machine learning model processes messages autonomously, assigning urgency classifications based on learned patterns from training data.
Solution Approach 2:
The patent replaces the mechanical human review process with an automated machine learning system. The cognitive computing system uses algorithms to analyze message content, extract features, and determine urgency levels, substituting human cognitive labor with computational processing that can handle high volumes efficiently.
2Productivity
If automated classification systems are implemented, then productivity is improved, but measurement precision deteriorates due to inability to accurately determine message urgency
Solution Approach 1:
The system performs preliminary classification of messages into urgency levels before they reach the user. By pre-processing and categorizing messages based on their content, sender, and other features, the system prepares organized information that can be quickly reviewed, maintaining both speed and accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where users can review and correct the automated urgency classifications. This feedback loop allows the machine learning model to learn from user corrections and improve its classification accuracy over time, while maintaining high processing speeds through the initial automated filtering.
3Ease of operation
If all messages are treated equally, then ease of operation is improved, but loss of time increases due to inability to prioritize urgent messages
Solution Approach 1:
The system applies different quality levels of attention to different messages based on their urgency classification. Urgent messages receive priority handling and are highlighted for immediate attention, while routine messages are processed normally. This local differentiation allows users to focus their effort where it matters most without complicating the overall system.
Data Source
AI summary
Providing cognitive responses with an electronic message system includes predicting an urgency level corresponding to an electronic message received by the system based on detecting pre-defined features within the message and assigning a classification to the message based on the detected features. The message and corresponding urgency level can be mapped to a response action based on the classification assigned to the message. A notification of the response action and corresponding urgency level can be generated.