ML Meeting Optimizer Predicts Productivity Shifts
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Solution Overview
Problem
Current computer-based scheduling systems lack the ability to accurately recognize changes in user productivity, making it difficult to predict the impact of scheduling decisions on productivity levels.
Innovation Solution
A machine learning-based system that processes scheduling and productivity data to predict real-time productivity changes by training a meeting optimization model using historical data, user profiles, and natural language processing to analyze incoming scheduling requests and their potential impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computer-based scheduling systems are used to automate meeting scheduling, then scheduling efficiency is improved, but the ability to recognize and predict productivity changes deteriorates
Solution Approach 1:
The patent introduces machine learning models as an intermediary between the scheduling system and productivity measurement. These models process scheduling data and calendar information to predict productivity changes, serving as a mediator that translates scheduling decisions into productivity insights without requiring direct complex measurement of user productivity
Solution Approach 2:
The patent replaces traditional mechanical productivity measurement methods with machine learning-based predictive models. Instead of directly measuring productivity through complex surveys or time-tracking mechanisms, the system uses ML models to infer productivity changes from scheduling patterns and calendar data
2Measurement precision
If machine learning models are trained with historical scheduling and productivity data, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the machine learning system into multiple specialized models: a productivity prediction model that processes scheduling data and a calendar model that processes calendar information. Each model is trained on specific types of data and performs specific functions, reducing the overall complexity compared to a single monolithic model
Solution Approach 2:
The patent performs preliminary data processing and feature extraction during the training phase, preparing processed data that can be quickly queried during inference. Historical scheduling and productivity data are pre-processed and stored in optimized formats, reducing the computational complexity required during real-time prediction operations
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for machine learning systems to predict real-time productivity changes based on prospective scheduling. A productivity predictive model is trained by a machine learning engine with meeting and productivity training data, wherein the productivity predictive model includes one or more algorithms to select a future productivity change based on a future schedule. The system receives a current schedule and current productivity measurements of a user, receives a scheduling request for the user, predicts a future productivity change and displays a graphic to a user revealing the future productivity change of the user based on accepting the scheduling request.