Machine Learning Calendar Event Classification for Meeting Categories
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Solution Overview
Problem
Analyzing and tracking enterprise calendar data using manual or conventional methods is resource-intensive and prone to errors due to the vast amount of information stored in big data systems.
Innovation Solution
Implementing machine learning-based systems and methods to automatically classify and predict digital calendar events using XGBoost-based multiclass classification and hard-coded rules, which are fully automated and executed periodically, allowing for efficient classification and prediction of meeting categories with high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual or conventional methods are used to analyze and track enterprise calendar data, then the process can be performed with simple technology, but it becomes resource-intensive and error-prone
Solution Approach 1:
The patent replaces manual analysis methods with an automated machine learning system that uses trained models to classify calendar events. The mechanical/manual process of analyzing calendar data is substituted with an automated computational system that retrieves data, generates features, applies trained models, and outputs classifications without human intervention.
Solution Approach 2:
The system performs self-service by automatically retrieving calendar data, generating feature data, applying machine learning models, and producing classification results without requiring manual operation. The machine learning model autonomously processes the calendar events and generates insights about time utilization patterns.
2Measurement precision
If machine learning models are implemented to automatically classify calendar events, then processing efficiency and accuracy improve, but computational resources and system complexity increase
Solution Approach 1:
The system performs preliminary action by training machine learning models in advance using historical calendar data and feature engineering. The trained models are then deployed to automatically classify new calendar events, eliminating the need for real-time manual analysis and ensuring consistent high accuracy across all predictions.
3Loss of information
If all calendar data is processed and stored, then complete information is available for analysis, but processing time and memory requirements increase
Solution Approach 1:
The system extracts only the essential features from calendar event data that are relevant for classification, such as participant information, event type, and time patterns. By extracting and processing only these critical features rather than all raw data, the system maintains classification accuracy while significantly reducing processing time and computational resources.
Data Source
AI summary
Machine learning based systems and methods are disclosed herein for automatically classifying digital calendar events. A calendar tracking application (app) receives a first set of digital calendar data of a first period. A machine learning model may generate classification data comprising the first set of digital calendar data classified according to predefined meeting categorie(s). The machine learning based system and methods provide various benefits, including: (1) categorizing one or more user digital calendars into multiple pre-defined categories and reporting these in a GUI based dashboard; (2) using a machine learning based model trained on categorized data (as opposed to mechanistic rules only) to improve the predictive accuracy of the categorization; and (3) using the combination of machine learning based model and mechanistic rules to automate the categorization.