Context-Aware Mobile Notification Filtering
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Solution Overview
Problem
Mobile push notifications often result in user annoyance due to clutter and poor timing, leading to user disengagement and app uninstalls, as existing systems fail to effectively select the right context for notification delivery.
Innovation Solution
A method that utilizes sensor data from mobile devices to determine the likelihood of user interaction with notifications, employing a rule-based or learning-based decision model to selectively display notifications based on context, timing, and user behavior, allowing for personalized and relevant content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If notifications are displayed frequently to increase user engagement, then user engagement may improve, but user annoyance and notification clutter increase
Solution Approach 1:
The system changes the parameter of notification timing by using sensor data (accelerometer, gyroscope, proximity sensor) to determine optimal moments for display. Notifications are delayed or cancelled based on real-time context parameters such as device orientation, user activity state, and environmental conditions, transforming a static notification system into a dynamic one that adapts to user situation.
Solution Approach 2:
The system implements feedback loops where sensor data continuously monitors user context and feeds back to the notification decision engine. User interactions with notifications (clicks, ignores, dismissals) are tracked and used to refine future notification timing predictions. This closed-loop system learns from user behavior patterns to optimize engagement while minimizing annoyance.
2Loss of information
If notifications are displayed at any time to ensure information delivery, then information delivery is guaranteed, but user satisfaction decreases due to poor timing
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data and establishing baseline user behavior patterns before notification delivery decisions are made. Contextual parameters are continuously monitored and stored in advance, allowing the system to predict optimal notification times rather than reacting in the moment. This preliminary preparation enables timely information delivery that aligns with user expectations.
Solution Approach 2:
The notification system transitions from a static, rule-based approach to a dynamic, adaptive system. Decision parameters such as display timing, notification type, and target screen are adjusted in real-time based on changing user context. The system dynamically modifies notification behavior according to device state, user activity, and environmental factors, making information delivery contextually appropriate rather than uniformly applied.
3Ease of operation
If context-based filtering is implemented to reduce notification clutter, then user satisfaction improves, but system complexity increases
Solution Approach 1:
The system segments the complex decision-making process into distinct modular components: sensor data acquisition module, context analysis module, decision engine module, and notification delivery module. Each component handles a specific aspect of context-based filtering, processing sensor inputs independently and passing results through the decision pipeline. This segmentation reduces overall system complexity by making each component manageable and independently optimizable.
Solution Approach 2:
The system introduces intermediary layers between raw sensor data and notification decisions. A context abstraction layer translates diverse sensor inputs (accelerometer, gyroscope, proximity sensor) into standardized context parameters. A decision model intermediary then processes these parameters using machine learning algorithms to generate notification recommendations. These intermediaries simplify the complexity by providing structured interfaces between different system components.
Data Source
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Figure 3(A)~3(B)
AI summary
This disclosure relates to displaying notifications for content items on a mobile device to a user. A processor receives meta-data associated with the content item and receives context data indicative of a current context of the mobile device. The processor then determines a first value based on the meta-data, the context data and historical data indicative of historical interactions with historical notifications that are based on the meta-data, the first value being indicative of a current likelihood of the user interacting with a notification that is based on the meta-data. Finally, the processor sends first output data based on the first value to an output module to allow selectively outputting the notification based on the current likelihood. By selectively outputting the notification based on the current likelihood the notification can be withheld until the user is likely to interact with the notification.