ML Message Prioritization Using Calendar and Personnel Data
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Solution Overview
Problem
Current electronic messaging systems lack an efficient method to prioritize messages based on calendar events, sender personnel, and message subjects, leading to suboptimal organization and presentation of messages.
Innovation Solution
A computer-based method utilizing a message prioritization machine learning model that predicts the prioritized ordering of messages by considering calendar event, personnel, and message subject parameters, along with interaction history, to determine the current message priority and display them accordingly on a user's screen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If electronic messages are displayed in traditional order (e.g., chronological), then the system is simple and easy to implement, but important messages may be overlooked and user productivity decreases
Solution Approach 1:
The patent applies parameter changes by introducing multiple parameters (calendar event parameters, personnel parameters, message subject parameters) to determine message priority. The machine learning model dynamically adjusts the weighting of these parameters to predict message priority scores, transforming the simple chronological ordering into a multi-dimensional prioritization system that enhances productivity while managing complexity through automated parameter evaluation.
Solution Approach 2:
The system implements self-service by using a machine learning model that automatically analyzes message parameters, calendar events, and interaction history to generate priority predictions without requiring manual user intervention. The model continuously learns from user interactions and automatically refines its prioritization algorithm, enabling the system to serve itself in optimizing message ordering while improving productivity.
2Measurement precision
If message prioritization based on multiple parameters is implemented, then important messages are highlighted effectively, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and extracting relevant parameters from messages, calendar events, and interaction history before the prioritization decision is needed. The machine learning model is trained in advance on historical data to establish parameter weightings and prediction algorithms. This pre-computation and pre-training approach enables accurate message priority prediction while minimizing real-time processing delays when messages need to be displayed.
3Measurement precision
If interaction history is used to determine message priority, then the prioritization becomes more accurate and personalized, but data privacy concerns and system complexity increase
Solution Approach 1:
The patent applies the extraction principle by isolating and selectively using only the necessary interaction history parameters that contribute to message prioritization. The system extracts specific features from interaction history (such as response times, message frequency, and communication patterns) while excluding unnecessary personal data. This selective extraction maintains prioritization accuracy by focusing on relevant behavioral patterns while reducing data management complexity and privacy concerns through minimal data collection.
Data Source
AI summary
In order to facilitate automatic message prioritization, systems and methods are described including a processor that receives electronic messages, where each electronic message is associated with a sender and a recipient. The processor utilizes a message prioritization machine learning model to predict a current prioritized ordering of the electronic messages based on parameters associated with each electronic message, where the parameters include a calendar event parameter representing a calendar event associated with each electronic message, a personnel parameter associated with the sender of each electronic message, and a message subject parameter associated with a subject of each electronic message, where the current prioritized ordering includes an order of notification of each electronic message according to priority based on an interaction history of historical electronic messages associated with the sender The processor causes to display the electronic messages according to the current prioritized ordering.