Automated Calendar Management with ML Task Prediction
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Solution Overview
Problem
Current computer-based systems lack efficient methods for automating electronic calendar management and work task scheduling, particularly in predicting unavailability periods and dynamically securing meeting times, which can lead to inefficiencies and conflicts in scheduling meetings.
Innovation Solution
The system utilizes machine learning models, specifically a task estimation model and a meeting scheduling model, to predict work parameters and unavailability periods based on attendee work history, schedule information, and location data, displaying these predictions to attendees and dynamically securing selected unavailability periods before meetings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated calendar management systems are implemented, then scheduling efficiency is improved, but system complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: a task estimation machine learning model that predicts work parameters, and a meeting scheduling machine learning model that determines unavailability periods. Each module operates independently with specific responsibilities, reducing overall system complexity while maintaining automation capabilities.
Solution Approach 2:
Machine learning models serve as intermediaries between raw input data (meeting requests, attendee information, work history) and scheduling decisions. These models process and transform data automatically, reducing the need for complex manual scheduling logic while improving efficiency.
2Measurement precision
If machine learning models predict work parameters and unavailability periods, then scheduling accuracy is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary computations by training machine learning models on historical work data in advance. Once trained, the models can quickly predict work parameters and unavailability periods during actual scheduling operations, reducing real-time computational requirements while maintaining high accuracy.
Solution Approach 2:
The system utilizes historical work data and meeting patterns as feedback to continuously improve model predictions. By learning from past performance data, the models become more accurate over time, allowing for reduced computational resources while maintaining or improving scheduling precision.
3Reliability
If the system dynamically secures unavailability periods, then meeting conflicts are reduced, but automation extent increases
Solution Approach 1:
The system proactively identifies and secures unavailability periods before meetings are scheduled by predicting when attendees need to complete work tasks. By reserving these time blocks in advance, the system prevents scheduling conflicts before they occur, improving reliability while managing automation through structured prediction algorithms.
Data Source
AI summary
In order to facilitate automated electronic calendar task management with automatic task scheduling, systems and methods are described including receiving and electronic meeting request to schedule a meeting. Work task data items identifying work tasks associated with the attendees are determined. A task estimation machine learning model predicts work parameters of meeting task objects based on the work task data items, and a work history data identifying work history of each attendee. A meeting scheduling machine learning model predicts parameters of unavailability period objects representing unavailability periods required to complete the work tasks based on the meeting task object, schedule information and location information. An indication of the at least one unavailability period is displayed on a screen of a computing device associated with each attendee. Selections of the unavailability period is received from the attendees, and the unavailability period is dynamically secured prior to the meeting.