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Method and System of Optimizing Meeting Schedules using Cognitive Meeting Scheduler

IP.com Disclosure Number: IPCOM000247240D
Publication Date: 2016-Aug-17
Document File: 3 page(s) / 52K

Publishing Venue

The IP.com Prior Art Database

Abstract

A method and system is disclosed for optimizing meeting schedules using cognitive meeting scheduler. The cognitive meeting scheduler automatically detects, analyzes and learns meeting pattern information to propose a list of ranked meeting time.

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Method and System of Optimizing Meeting Schedules using Cognitive Meeting Scheduler

Scheduling meetings with multiple parties is always a challenging task in which one has to take into consideration personal daily agenda or work habits as well as other parties invited to the meeting. In addition, as enterprise goes global, some of the meeting participants may live in a very different time zone with very different work habits and work schedules. Therefore, the method and system is necessitated to automatically schedule meetings with the assistance of a cognitive meeting scheduler.

Disclosed is a method and system for optimizing meeting schedules using a cognitive meeting scheduler.

The following Figure illustrates a cognitive meeting scheduler in accordance with the method and system disclosed herein.

Figure

As illustrated, the cognitive meeting scheduler automatically detects, analyzes and learns from the potential data sources such as, but not limited to, email, calendar data, personal contact list, organizational data, shared documents, location data and user profiles. The email can be searched for particular phrases within the body of the text or subject line that match the topic of the meeting subject. If identified, the To: and CC: lists can be used to identify potential attendees for a meeting and to pro-actively suggest meeting subject, time, date, and attendees based on the email content. The Cognitive meeting scheduler can directly engage with the emails by use of key words. The calendar data can be used for searching for free time, analyzing patterns in a person's scheduling habits and determine if any invitee is traveling due to entries on the

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calendar or out of office messages enabled, and pro-actively taken into account time zone considerations.

In addition, the personal contact list can be used as back-up information that can be used to include an alternate attendee for a meeting and to identify additional attendees (e.g. subject matter experts) who may get left out accidentally based on the subject and description of the meeting. The organizational data can be used to suggest attendees for particular meetings and based on the participant's job description/title, estimate the investment required for this meeting. The shared documents or agendas can be used to suggest invitees for a particular meeting. The real-time location data from mobile phone or from calendar entry can be used to determine possible and recommended optimal meeting times for attendees. Real-time location data can also be used at the time of the meeting to call an attendee directly, if it is determined that the attendee is 'on the move'. (e.g. The attendee is in their car traveling to a destination and not able to dial in to the call.) The user profile provides information such as work location (either home or office) and work patterns can be used to determine availability and need f...