George Krasadakis

@gkrasadakis

Better, more effective meetings using tech and AI

The right participants in the right meetings with a continuous optimization framework: a system to evaluate meetings and make smart recommendations.

Meetings can be a valuable tool for planning, sharing information, decision-making, etc. Meeting quality, however, can greatly depend on the skill, expertise, experience, temperament, etc., of those in attendance. Having the right people involved can make for a highly productive and satisfying meeting. On the other hand, meetings can be unpleasant and frustrating, or simply unproductive, when meeting participants lack relevant experience, are disinterested, have poor leadership/organizational skills, are not well-prepared, have no access to related resources, when the meeting is not well-defined (e.g., no topic/agenda set, no provided resources), etc.

Identifying, discovering, and recommending relevant and/or valuable participants for a meeting, project, or other collaborative engagement, is also very important. Such a collaborative engagement may be organized through a computerized meeting service — e.g., an email/calendar client — which may create the collaborative engagement based on user input, and automatically recommend one or more candidate participants for the collaborative engagement.

In one example, these candidate participants may be recommended based on a relevance score calculated for each candidate participant. A candidate relevance-to-the-meeting score may be calculated based upon a variety of factors, including (1) the particular candidate participant’s relevance to the meeting topic/objective/agenda, (2) the extent to which the particular candidate participant has positively contributed to previous meetings, and/or (3) whether the candidate participant has attended meetings that are particularly relevant to the topic.

Recommending meeting participants in this manner can reduce the potential for inviting low-value participants to meetings, as well as identifying and recommending potentially valuable meeting participants who would not otherwise attend.

FIG. 1 schematically depicts an example collaborative engagement 100, which in many examples will be referred to as a “meeting.” It will be appreciated that “collaborative engagement” as used herein may refer to any meeting, project, or other coordinated interaction/communication between two or more individuals. A “meeting” may be any previously-scheduled interaction between two or more participants for the purpose of sharing information, planning, decision-making, brainstorming, or other forms of collaboration. Specific reference to “meetings” may occur throughout the disclosure, though it will be appreciated that the candidate participant identification and recommendation techniques described herein may be applied to any variety of collaborative engagements where appropriate, and not exclusively to interactions characterized as meetings.

As indicated, collaborative engagement 100 may include several participants 102 meeting in the same physical location. It will be appreciated that the meeting may include one or more other participants who are attending virtually, e.g., via remote participation using various devices, sensors and communication channels.

As shown, collaborative engagement 100 has an associated topic 104. As will be described below, the topic 104 may be defined in advance of the meeting by a meeting organizer, and/or automatically inferred by a computing system based on a user input creating the meeting, meeting materials provided to the computing system, etc. Furthermore, candidate meeting participants may be recommended at least in part based on their relevance to/particular expertise regarding the meeting topic. In some implementations, a single collaborative engagement may have several distinct topics. Furthermore, a single topic may refer to multiple meetings, projects, products, strategies, etc.

Check the patent application here

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