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PMA: A Mobile Context-Aware Personal Messaging Assistant

The Mobile Context-Aware Personal Messaging Assistant (PMA) is an innovative rule-based email management system designed to enhance communication for busy professionals. By leveraging real-time context information, including GPS and calendar data, PMA classifies and prioritizes emails to deliver only the most relevant messages to users’ mobile devices. This minimizes distractions, optimizes bandwidth usage, and significantly boosts productivity. Future enhancements include machine learning for user feedback integration, broader email compatibility, and personalized user interfaces.

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PMA: A Mobile Context-Aware Personal Messaging Assistant

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  1. 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

  2. Agenda • Introduction to Email Sorting • Related Work • PMA – Design and Architecture • Experiments & Results • Conclusion • Future Work

  3. What is a “Mobile Context-Aware Personal Messaging Assistant”? • An advanced rule-based email management system which uses the mobile user’s 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, …)

  4. 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!

  5. 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

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

  7. 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

  8. Experiment - 1 • AIM – Test effectiveness of PMA’s 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 Number of type X emails correctly classified by PMA Recall = Total number of emails selected by users as type X Number of type X emails correctly classified by PMA Precision = Number of emails classified by PMA as X

  9. Results Summary of precision and recall of importance classification Summary of precision and recall of urgency classification

  10. Experiment - 2 • AIM – Test effectiveness of PMA’s 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 user’s expected action on each email

  11. Results

  12. Conclusions • PMA sorts and delivers messages that are relevant to the user in his current context, effectively • Uses emails content and user’s 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

  13. 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

  14. Thank You

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