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Method and system for meeting schedule Disclosure Number: IPCOM000249522D
Publication Date: 2017-Mar-02
Document File: 4 page(s) / 79K

Publishing Venue

The Prior Art Database


In this article we propose a system and method to come up with an optimal meeting schedule after analyzing user's actual and predicted activities , behavioral patters inlcuding other such factors and thereby improve user's productivity

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Method and system for meeting schedule


Electronic calendar is being used for tracking our engagement on daily basis. Various meeting, activities and also holidays are tracked in the electronic calendar. At the same time, one person can be engaged with various teams. The participants for each meeting are often different and we often experienced with overlapping meeting. Many times it becomes very difficult to manage our calendar schedule and sending meeting invite to a group of people.

Shortcomings in the existing solutions

As per current system, while scheduling any meeting, we can check the availability status of each participants in electronic calendar. It is not possible to know if any participant can change his other meeting if there is an overlap or the meeting server can predict other possible priority meetings might be scheduled during that time in a short notice. In this scenario, if we can predict each user’s engagement in a pro-active manner then the identified information about the user’s engagement can be shown during meeting invite. This will help to create effective meeting schedule.

Proposed Solution

Find below the proposed system and method to accomplish an optimal meeting schedule 1. Software installed in meeting server will identify information from various information

sources to predict possible free time of the user with a confidence. Software will analyze following activities of the user to predict possible free time.

a. Various predicted activities of any user (Actual work, personal work, meeting participation, pre and post discussion of any meeting, vacation, conference schedules etc.), For example, before any meeting user prepare for 30 min, so even though the 30 min slot is available, but does not mean user is available, he will prepare for the meeting. This can be based on machine learning. System learns about the participant's behaviour based on the previous experience on the type of meeting, importance, criticality, agenda, whether the user is the presenter or listener,

b. Predicted meeting timing of different other meetings (User might be involved in various activities, team, and there is a predicted escalation so user might not be available etc.)

c. Behavior based engagement (User takes 15 minutes break between any two meetings)

d. User’s involvement in the meeting, (Like - mandatory, optional, degree of involvement, key participants etc., and it is identified user skips all the meeting when he is optional)

e. Historical pattern (Predicted ad-hoc meeting, if there is any customer complaint, then an emergency ad-hoc mandatory meeting is called). eg) some escalation has


come and it is predicted that manager would call for a meeting and can also predict the people whom the manager would include in the invite and it is predicted that such a meeting can happen immediately based on the criticality or he might schedule based on the next availability of him and certain critical recipients eg) 10 AM tomorro...