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The Intelligent Room’s MeetingManager: A Look Forward
Alice Oh
Stephen Peters
Oxygen Workshop, January, 2002
L C S The Existing MeetingManager
• Organizes Agenda
• Allows for raising of issues and assignment of commitments
• Timestamps all in-meeting activities
• Coordinates with video recordings
L C SMeetingManager in Action
• Needs better data model, and a better UI.
L C S
Meeting
Data Model Issues
• Rigid structure– All items stored based on meeting
– Can’t capture elements of discussion except through issues and commitments
– No capacity for external data, such as documents, presentations, etc.
Agenda 1
Agenda 2
Agenda 3
Commitment
Issue 1
Issue 2
Movie
L C SProblems with Queries
• Collating information across meetings– “Show me all my open commitments.”
– “Review all the issues raised in opposition to this proposal”
• Correlations of agendas and discussions – “In which meeting last October did we decide to change the database
design?”
– “Are we going to be discussing the budget at any upcoming meetings this month?”
• Lack of data storage– “What was that document Joe was talking about last week?”
– “Who was absent from the last meeting?”
L C SNew Data Model
• Meetings are an organizational tool for discussions, not the primary piece of information– People can review and even attach more information about the
discussion offline
• Sometimes the important piece isn’t the data, but how the different pieces inter-relate.– Support and opposition
– Implication and contradiction
• Simplify the storage, so queries are easier to do – Use tuples for all information: <node, attribute, value>
– Similar to Semantic Web (RDF)
– Databases are well indexed, fast, and loosely organized
L C SNew Data Model (p2)
Could even augment the links between nodes!
Budget
Meeting
agenda item
More Computers?Joe owner
raised
PricingDocument
attach Julie
owner
Affordable
supports
owner
ResearchAlternatives
Bill
committed
agrees
L C SCurrent Work on Data Model
• Editing of information outside of meeting– Leveraging the WorldWideWeb
• Better meeting client– Needs to capture the links within the discussions
– Better examination of existing data
– Exploring new user interface technologies
L C SUI in E21
• Reasons to move beyond the current UI technologies– Keyboard and mouse not usable in most situations
– Speech recognition difficult to use with multiple (unknown) users
– Modalities not fully integrated
• Toward a natural UI for collaboration applications– Integrate modalities including state-of-the-art vision technologies
– Enable seamless transitions between human-computer and human-human interactions
– Provide a natural and efficient means for multi-user, multi-agent collaboration
L C SLook-to-Talk: a Natural UI
• Gaze-based UI for directing utterances to agents– A natural way to get the agent to listen
– Extensible to support multi-user multi-agent conversations
– Integrates vision technologies with Metaglue
– Other applications include switching context, protecting private information, switching language/acoustic models
L C SEvaluating Look-to-Talk
• User study with 13 users
• Prototype and Wizard-of-Oz experiments
• Setup: – Camera for head-pose tracking
– Two displays: • SAM (an animated character representing the Room)
• The task (quiz containing trivia questions)
– Two subjects and a software agent to simulating collaboration
• Confirmed our hypothesis that Look-to-Talk would be natural to use
L C SUsing Look-to-Talk to turn on Speech Recognition
Subject not looking at SAMASR turned off
Subject looking at SAMASR turned on
L C SFuture Plans for User-Centered Interfaces
• More usability studies– Experiment with various settings and applications
– Build user models
• Flexible multimodal UI– Give users choice of modality
– Easy-to-learn UI for novice users
– Shortcuts for expert users
• Integration with collaboration applications
L C SSummary
For more information, contact
The Intelligent Room Project
http://www.ai.mit.edu/projects/iroom/
Alice Oh ([email protected])
Stephen Peters ([email protected])
