L C SL C S The Intelligent Room’s MeetingManager: A Look Forward Alice Oh Stephen Peters Oxygen Workshop, January, 2002

L C SL 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 SL C S MeetingManager in Action Needs better data model, and a better UI.

L C SL 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 SL C S Problems 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 SL C S New 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: –Similar to Semantic Web (RDF) –Databases are well indexed, fast, and loosely organized

L C SL C S New Data Model (p2) Could even augment the links between nodes! Budget Meeting agenda item More Computers? Joe owner raised Pricing Document attach Juli e owne r Affordable supports owner Research Alternatives Bill committed agrees

L C SL C S Current 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 SL C S UI 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 SL C S Look-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 SL C S Evaluating 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 SL C S Using Look-to-Talk to turn on Speech Recognition Subject not looking at SAM ASR turned off Subject looking at SAM ASR turned on

L C SL C S Future 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 SL C S Summary For more information, contact The Intelligent Room Project Alice Oh Stephen Peters