L C S

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 (aoh@ai.mit.edu)

Stephen Peters (slp@ai.mit.edu)