Notification Manager Using Learned User Preferences
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Solution Overview
Problem
The increasing number of notifications from various applications and services on computing devices makes it impractical for users to act on each notification, leading to important information being missed as users often ignore or delete notifications without fully considering their content, and conventional notification filtering techniques fail to accurately reflect users' preferences.
Innovation Solution
Implementing learned user preference- and behavior-based notification filtering, where interactions with notifications are monitored to learn user preferences and behaviors in different contexts, allowing for the computation of importance scores for new notifications, and presenting only the most important ones to the user based on their current context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional notification filtering techniques are used, then users can control which notifications are shown, but the filtering process is cumbersome and does not accurately reflect user preferences
Solution Approach 1:
The system automatically learns and adapts to user notification preferences by monitoring interactions without requiring manual configuration. The notification manager autonomously analyzes user behavior patterns and adjusts filtering criteria, eliminating the need for users to manually configure complex filtering rules while accurately capturing their preferences.
Solution Approach 2:
The system continuously monitors user interactions with notifications (such as dismissing, acknowledging, or acting on notifications) and uses this feedback to refine its understanding of user preferences. This closed-loop feedback mechanism enables the system to progressively improve its filtering accuracy based on actual user behavior rather than relying on manual settings.
2Loss of information
If all notifications are presented to users, then users receive complete information, but the sheer number of notifications causes users to ignore or delete them without consideration
Solution Approach 1:
The notification manager extracts and prioritizes only the most important notifications based on learned user preferences and contextual factors. By separating important notifications from less important ones and selectively presenting only the critical subset, the system prevents information loss while eliminating notification overload that reduces user productivity.
Solution Approach 2:
The system applies different filtering and prioritization strategies to different types of notifications based on their characteristics and user preferences. Rather than treating all notifications uniformly, it tailors the presentation strategy to each notification's importance, type, and context, ensuring that important information receives appropriate attention while less important notifications are filtered or deferred.
3Adaptability or versatility
If users manually configure notification settings for each application, then notification delivery can be customized, but the process is time-consuming and users cannot fully develop accurate preferences
Solution Approach 1:
The notification manager autonomously performs the configuration task by automatically learning user preferences through monitoring interactions with notifications and the applications that generate them. This self-service approach eliminates the time-consuming manual configuration process while achieving comprehensive and accurate notification customization that reflects actual user behavior patterns.
Solution Approach 2:
The system proactively analyzes user interactions and pre-configures notification filtering preferences before users would need to manually set them. By performing the configuration action in advance based on observed behavior patterns, the system saves user time while ensuring that notification delivery is customized to accurately reflect user preferences.
Data Source
AI summary
Techniques for learned user preference- and behavior-based notification filtering are described herein. In one or more implementations, notifications obtained from computer applications are filtered for presentation to a user. Example notifications include notifications about emails, text messages, phone calls, web-page specific messages, antivirus application messages, and so forth. As part of filtering the notifications, interactions of a user with the notifications and with events for which the notifications can be generated are monitored. The monitored interactions are used to learn user preferences and behaviors for notifications in different contexts of user interaction with computing devices. Data is collected that describes characteristics of a current context. Based on the current context, importance scores are computed for new notifications using the learned user preferences and behaviors. The importance scores can then be used to determine which of the new notifications to present to the user.