Meeting Agenda Time Estimation Using Historical Speaking Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional conferencing systems lack intelligence for estimating time allotments for conference topics, leading to scheduling errors due to unpredictable discussion durations and participant interactions.
Innovation Solution
Implementing a system that uses a learning model to determine time allotments for conference topics based on historical data, including participant and topic speaking times, and updates the conference schedule accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional conferencing systems are used without intelligence for time estimation, then the system is simple and easy to operate, but scheduling errors occur due to unpredictable discussion durations
Solution Approach 1:
The system performs preliminary analysis of historical conference data before the actual conference scheduling. A learning model is trained in advance on historical speaking time data, and this pre-trained model is then used to automatically estimate time allotments for new conference topics, eliminating the need for manual estimation and reducing scheduling errors.
Solution Approach 2:
The conferencing system serves itself by automatically generating time allotment estimates using its own historical data. The learning model processes historical conference transcripts and speaking time information that the system itself has collected, and uses this self-generated knowledge base to improve future scheduling accuracy without external intervention.
2Productivity
If manual time estimation is used for conference topics, then the system remains simple, but scheduling efficiency decreases due to unpredictable discussion durations
Solution Approach 1:
The system implements a feedback loop where historical conference data is continuously analyzed to improve future time estimations. The learning model processes outcomes from past conferences (actual speaking times vs. estimated times) and uses this feedback to refine its predictions, progressively improving scheduling efficiency and reducing time allocation errors.
Solution Approach 2:
The manual mechanical process of estimating time allotments is replaced with an automated intelligent system. Instead of relying on human judgment and experience, the system uses a learning model that automatically processes historical data and generates time estimates, significantly improving scheduling efficiency and consistency.
3Measurement precision
If historical conference data is processed using a learning model, then time allotment accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The system performs computationally intensive learning model training in advance, before actual conference scheduling is needed. By pre-processing historical data and training the model beforehand, the system avoids repeated heavy computational tasks during active scheduling, reducing real-time energy consumption while maintaining high estimation accuracy.
Data Source
AI summary
An input to schedule a future conference is received. The input identifies a list of topics and a total time scheduled for the future conference. Time allotments for the list of topics are determined based on historical conference data. The time allotments are scaled based on a total time weight indicative of the total time to generate scaled time allotments within that total time. A schedule item for the future conference is then updated to include the scaled time allotments for the list of topics