PMA: A Mobile Context-Aware Personal Messaging Assistant

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PMA A Mobile Context-Aware Personal Messaging
Assistant
Senaka Buthpitiya Deepthi Madamanchi Sumalatha
Kommaraju Martin Griss CyLab Mobility Research
Center
Mobility Research Center Carnegie Mellon Silicon
Valley
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Agenda

• Introduction to Email Sorting
• Related Work
• PMA Design and Architecture
• Experiments Results
• Conclusion
• Future Work

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What is a Mobile Context-Aware Personal
Messaging Assistant?

• An advanced rule-based email management system
  which uses the mobile users context and email
  content to
• classify emails
• prioritize emails
• selectively deliver key messages to mobile phone
• Uses real-time context information from
• hard sensors (GPS, accelerometer, etc.) on
  Mobile phone
• soft sensors (calendar, )

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Email Flooding in the Real World

• Busy professionals receive in excess of 50 emails
  per day,
• 23 require immediate attention
• 13 require attention later
• 64 are unimportant
• Problem is even worse for mobile
• professionals
• Difficult to sort through emails on mobile
  devices
• Wastes precious bandwidth and battery life
• End Result
• Wastes time sorting through unwanted emails
• Drastic reduction in productivity!

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Problems

• Most email sorting/classification programs take
  only email-content into account
• Depending on users contexts, the emails
  thatthey wish to see vary
• Depending on the users contexts the number of
  emails they can scan through varies
• Email sorting/classification programs consider
  importance only
• Importance and urgency are
• orthogonal yet affects
• email sorting equally

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Related Work

• A Personal Email Assistant, Bergman et. al., 2002
• CoolAgent Intelligent Digital Assistants for
  Mobile Professionals, Griss et. al., 2002
• Combining Bayesian and Rule Score Learning
  Automated Tuning for SpamAssassin, Seewald, 2004

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PMA Architecture
PMA separately rates emails according importance
and urgency using context information and email
content e.g. email from the users boss about
present meeting is important and very urgent
PMA decides on what-to deliver, how-to-deliver
and where-to-deliveraccording to users
context e.g. deliver as SMS, text-to-voice SMS,
forward to co-worker Uses a rule-based system for
decision making
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Context Information

• Gathered from hard sensors on a Nokia N95 (which
  also doubles as a delivery point for selected
  emails)
• Gathered from soft sensors such as Google
  Calendar
• Context includes all information
• related to user including,
• Static context such as name and
• family details
• Dynamic context such as meeting
• topic, driving speed
• User preferences

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Experiment - 1

• AIM Test effectiveness of PMAs urgency and
  importance classifiers
• For various user contexts,
• PMA classifies a test set of emails separately
  for importance and urgency
• compared against ratings for the same emails by
  user

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Results
Summary of precision and recall of importance
classification Summary of precision and
recall of urgency classification
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Experiment - 2

• AIM Test effectiveness of PMAs delivery agent
  and overall system
• For various user contexts,
• PMA decides on what action to perform with a
  given email
• SMS to user
• Send to users as text-to-voice SMS
• Folder for later viewing
• Take no action
• compared against users expected action on each
  email

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Results
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Conclusions

• PMA sorts and delivers messages that are relevant
  to the user in his current context, effectively
• Uses emails content and users context
  information for decision making
• PMA uses separate scales to measure urgency and
  importance of an email
• PMA is scalable for all inbox sizes
• PMA is easily personalized to suit the
  requirements of any user for better accuracy

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Future Work

• Performance of PMA
• Machine learning schemes to automate the learning
  from user feedback
• Improve run-time
• Generalization of PMA
• Support for various email accounts Yahoo! mail,
  Hotmail, etc.
• Support for additional message types (SMS, IM,
  RSS, etc.)
• Personalization of PMA
• User interface to create/edit custom rules
• Mobile device interface for feedback and usability

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Thank You