 The Future of Business Meetings
By Christine Perey
People, processes, and technologies we use in business meetings are continuously
changing in order to increase efficiency in the workplace or enhance meetingprod-
uctivity. How can the addition of more technology help more than it hurts? The goal
of this article is to take what is currently known about meetings and to overlay a
vision of the future, to see how the addition of these new technologies (based on
advanced signal processing and information analysis) can have a positive impact on
meetings.
Non-Verbal Counts
The AMI Project, an EU-funded research project involving dozens of scientists across a fifteen-member
consortium, focuses meetings in order to develop intelligent software algorithms and systems. The
algorithms and related technologies under development can become core building blocks on which
products and services may emerge for use by people in and between meetings.
One of the goals of the AMI Project is to explore beyond the current analysis of simple verbal communications -
adding non-verbal communications - which can reveal deeper trends and patterns giving people the ability to:
- Prepare better for upcoming meetings
- Review parts of meetings in progress or past meetings missed
- Analyze behaviors and positions taken by individuals or groups, and
- Attend multiple meetings without missing critical elements in any
At a management level, technologies which analyze verbal and non-verbal content and communications
could be integrated with other enterprise managements systems to:
The scientists in the AMI Project bring expertise from many disciplines, including speech processing, video and
vision processing, human-computer interfaces, sociology, psychology, and linguistics. The focus of their research
is on the human-to-human communication that occurs between people during product design interactions.
Statistical machine learning is used by the AMI Project in the context of improving our understanding of business meetings. Machine learning will produce software building blocks.
The process of developing these core technologies begins by extracting information from large numbers of multimedia meeting recordings. All the information of interest is labeled. Based on the labels, models are developed (trained) to recognize events, words, and other patterns of interest. Then, once the models are able to reliably recognize information from sources on which they were trained, a system deployed in a meeting environment can automatically recognize patterns based on new multimedia meeting data it receives.
Figure 1: Statistical machine learning is used by the AMI Project to develop algorithms and technologies that can recognize meeting elements automatically.
AMI technologies can also add value to participants between and during meetings.
Specific Search
Future knowledge workers will have the need to access multimedia assets from the corporate knowledge base for work between meetings through an indexed and specific search that lets them access the meeting segment they are interested in quickly and easily.
Meetings could be more effective if, prior to entering, each participant were better prepared. Between meetings a user of the multimedia meeting archive could:
- Review a summary of one or any number of past meetings,
- Search one or more past meetings to answer specific questions,
- Browse one or more past meetings to answer specific or general questions,
- View the entire meeting (or multiple meetings) in faster-than-real time, and
- Detect patterns exhibited by groups or individuals during past meetings that may provide insights into the upcoming meeting.
Meeting Summaries
Today if a person misses a meeting, they must go to others who attended and ask for a summary. Meeting summaries used in business today are:
- Verbal,
- Contain the biases of a participant’s point of view,
- Are not searchable, and
- Can be in the form of notes or minutes.
A summary should capture the essence of the content of a meeting. There are as many summary formats as types of meetings. One can imagine options such as:
- Bullet summary,
- Paragraph summary,
- Summary in audio, and
- Summary in audio, video, and with supporting media introduced during the meeting.
Regardless of their presentation media or their depth, those who rely on them need the content of summaries to be linked to the detailed contents (the multimedia record) of the meeting. In much the way one navigates a web site or any interactive application, a summary statement should be a “window” into the meeting at the particular time when an issue is discussed or a decision made. The idea of an intelligent meeting database architecture, which would be able to produce summaries of multiple meetings, is also part of the AMI vision. From the summary, the user of meeting archives must also be able to search, browse, and have flexible ways of accessing the contents of the meeting or multiple meetings in a database.
Searching, Browsing, and Skimming Archives
When unstructured media files from a meeting archive are indexed and stored in an appropriate repository, their contents are temporally associated with structured data, consisting of other relevant information in the database (time stamps, text transcripts of speech and all written additions or information projected on the screen, names of people in a meeting, the subject of the meeting, the agenda of the meeting, and any files introduced during the meeting).
A user interface for a multimedia meeting repository provides a search function. One can imagine a dialog box in which an inquiry is entered by the user (it might be typed in using a keyboard but in the future, the user might speak or point to designate what aspects are sought). A pop-up menu might have the most frequently asked search threads.
Questions which the AMI Project is using machine learning to answer on its database of meetings include:
- Who is in the meeting?
- What are the participants saying?
- When and how do they communicate?
- What are they doing?
- What are their emotional states?
- What are they looking at (focus of attention)?
Based on the details, higher order questions are asked, such as:
- What topics are discussed and when?
- What decisions are made and by whom?
- What roles do the participants play?
- What positions do they take on issues?
- What activities are completed?
- What tasks are assigned or reported done?
In some cases, the person using a meeting archive may not know exactly what they are looking for. This requires a different type of interaction with the archive or the repository, one that permits “skimming” in a linear fashion as well as non-linear browsing (through text).
Accelerating Meeting Playback
The user may also seek tools to experience the meeting in less time than it took to conduct the meeting. As illustrated in Figure 2, the user can accelerate the playback of a telephone conference by only asking to hear or “see” those sections attributable to a particular meeting participant, or can adjust the speed of the playback of all the meeting media.
Imagine being in a meeting and suddenly needing to step out to attend to an emergency or arriving after a meeting has already begun. Prior to entering or returning to the meeting, the essence of the segment missed could be obtained and permit continuing the meeting without interruption or loss of context. Figure 2 illustrates how the user would control the playback of a meeting in progress.
Figure 2: An AMI-assisted playback user interface, developed using JFerret, permits the user to control the variables and speed up the experience of a meeting archive or a meeting in progress. Source: AMI Project
Detecting Patterns
Summaries are, in some ways, the detection and compression of patterns into smaller, more accessible chunks. Patterns can come in any shape and size. They may consist of the utilization of a word or expression, a gesture or non-verbal type of communication such as nodding to indicate agreement or nodding when a person is drowsy. These are subtle differences that the human brain can distinguish and, in time, the AMI algorithms will also be able to detect and flag or enter in the database for use by meeting applications.
Some scenarios for this technology can use a meeting participant’s gestures or position relative to others, which can be the cue that causes a response in a virtual representation of a remote participant. The example illustrated in Figure 3 shows all the participants in the meeting are turned towards a white board; the virtual participant is expected to turn similarly.
Figure 3. In the AMI-assisted Virtual Meeting Room, the focus of attention of the meeting participants is detected and helps an agent (see below about meeting agents) to behave according to meeting norms.
Detecting patterns could also help decisions in rendering agent actions (body language). If during a meeting everyone has their arms folded, would the remote participant also seek to assume this posture as well? These are other examples of how using AMI technology to detect patterns will be potentially valuable during meetings.
Improving Meeting Management and Progress
There are many scenarios for improving the flow and dynamics of communication during a meeting. Since the AMI project technologies are able to measure the interactions and participation of people in a meeting, analyses could be summarized and presented to a chairperson during a meeting. Many of these focus on the meeting agenda, including:
- Comparing an (ideal) agenda to the current meeting progress and notifying the meeting facilitator of deviations.
- The directives or opinions of leaders or behaviors of participants in past meetings could be privately or publicly compared with the real time progress. The comparison could be used by the meeting chair to re-orient discussions to key issues which are known to cause delays in a project.
- Accessing past meeting repositories, or having an agent that automatically compares new action items with the past, allowing the meeting facilitator to introduce the relevant conclusions and accelerate both the meeting agenda and the project.
- Allowing the meeting facilitator to track participant monologues (measuring “time spoken,” “number of times grabbed the microphone,” “the number of people paying attention” all could be metrics used to manage meeting interactions. People who repeatedly grab the floor could receive automatically- generated notifications that others are finding their input valuable or irritating and permit the participant to adapt behaviors in real time.
Meeting Agents
Frequently it is necessary for the success of multiple projects for a person to be assigned responsibilities with overlapping time requirements. Having knowledge workers “attend” two or more meetings simultaneously is one way to solve this problem. The individual may participate in one meeting in person or by telephone, and request to have an agent monitor one or more additional meetings. Provided participants in another meeting agree to the participation of the meeting agent, this agent can be configured to detect real time events such as changes in the agenda, discussion of a particular item on the agenda which concerns the employee directly, a new person entering the meeting, or someone who is known to be important leaving a meeting. This could optimize the use of limited human resources.
Figure 4: The AMI technology-based Remote Meeting Assistant can help those who cannot attend a meeting to send their agents to monitor a meeting in progress.
In this scenario, the Remote Meeting Assistant (RMA) will detect events (e.g., keywords, entry or exit, change in dialog, debates) which it has been configured to monitor and alert the user. These could be real time alert (via a pop-up or toaster like an instant message) and they could be compiled for later review. Taking action based on information provided by a RMA would require first gaining the context for the alert, perhaps by way of an accelerated playback of recent remarks or discussion.
Future Meetings
Only time will tell how large an impact the AMI Project will have on people and processes in future meetings. Without very strong incentives, humans resist changing their behaviors, so many of the technologies talked about in this article will have to be introduced into meetings without requiring a steep learning curve from the participants or requiring large changes in their behavior. The only thing we can be certain of is that, in the future, there will continue to be business meetings, and they can be dramatically improved using new technologies.
Christine Perey is the principal of PEREY Research & Consulting, based in Montreux, Switzerland. Perey focuses on multimedia communications and offers technology or market-specific services, such as opportunity and risk analyses, business development, and strategic planning services, to video and visual communications technology vendors and service providers. She is responsible for technology transfer and manages the Community of Interest for the AMI Project. She can be reached at cperey@perey.com or cperey@amiproject.org
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