18183346. INTELLIGENT MEETING TIMESLOT ANALYSIS AND RECOMMENDATION simplified abstract (Dell Products L.P.)
Contents
INTELLIGENT MEETING TIMESLOT ANALYSIS AND RECOMMENDATION
Organization Name
Inventor(s)
Ajay Maikhuri of Bangalore (IN)
Dhilip Kumar of Bangalore (IN)
INTELLIGENT MEETING TIMESLOT ANALYSIS AND RECOMMENDATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18183346 titled 'INTELLIGENT MEETING TIMESLOT ANALYSIS AND RECOMMENDATION
The abstract of the patent application describes a methodology for analyzing a timeslot of a meeting, predicting attendee meeting acceptance levels, and determining the acceptance level of the meeting based on the likelihood of attendees joining.
- Receiving a request to analyze a timeslot of a meeting
- Determining meeting details including organizer, date, time, subject, and attendees
- Predicting attendee meeting acceptance levels using a machine learning model
- Determining the acceptance level of the meeting based on predicted attendee acceptance levels
- Sending information about the acceptance level of the meeting in response to the request
Potential Applications: - Meeting scheduling optimization - Event planning efficiency - Resource allocation in organizations
Problems Solved: - Uncertainty in attendee meeting acceptance - Time wasted on scheduling conflicts - Lack of visibility into meeting attendance likelihood
Benefits: - Improved meeting attendance rates - Enhanced scheduling accuracy - Time and resource savings for organizations
Commercial Applications: Title: "Meeting Optimization Technology for Enhanced Productivity" This technology can be utilized by companies to streamline meeting scheduling processes, increase productivity, and improve overall operational efficiency.
Prior Art: Readers can explore prior research on machine learning models for predicting meeting attendance and scheduling optimization algorithms in the field of organizational behavior and management.
Frequently Updated Research: Stay informed about advancements in machine learning algorithms for predicting human behavior in scheduling and attendance management systems.
Questions about Meeting Optimization Technology: 1. How does this technology contribute to reducing scheduling conflicts in organizations? 2. What are the key factors considered in predicting attendee meeting acceptance levels?
Original Abstract Submitted
An example methodology includes receiving a request to analyze a timeslot of a meeting and determining meeting details, wherein the meeting details include an organizer of the meeting, a date of the meeting, a time of the meeting, a subject of the meeting, and one or more attendees of the meeting. The method also includes predicting, using a machine learning model, an attendee meeting acceptance level for each attendee of the one or more attendees. The method further includes determining an acceptance level of the meeting based on likelihood of the one or more attendees joining the meeting, wherein the likelihood of the one or more attendees joining the meeting is based at least on the predicted attendee meeting acceptance levels for the one or more attendees, and sending information about the acceptance level of the meeting in a response to the request to analyze the timeslot of the meeting.
