Mobile Notification Content Analysis for Dynamic Alert Prioritization

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Solution Overview

Problem

Current mobile devices lack an effective mechanism to dynamically adjust notification levels based on the content of notifications, often relying solely on the sender or originating application, which may not accurately reflect the importance of the notification to the user.

Innovation Solution

The notification library in mobile devices analyzes notification content to apply customized notification levels by matching the content with predefined rules, adjusting visual, auditory, and tactile alerts based on the importance of the notification, such as using escalated or de-escalated notification levels for specific keywords, names, or types of content.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If notification filtering is based solely on sender or application identity, then the notification system is simple to implement, but it cannot accurately reflect the importance of the notification content to the user

Engineering Contradiction:

Improvenotification importance assessment accuracyVSAvoidnotification analysis system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system performs preliminary analysis of notification content by extracting keywords and comparing them against predefined importance criteria before displaying the notification. This advance processing allows the system to determine notification importance levels based on content relevance rather than just sender identity, improving assessment accuracy while maintaining manageable complexity through pre-defined rule sets

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The notification system applies different analysis depths and processing intensities to different notification types and sources. High-priority notifications receive more thorough content analysis while lower-priority ones use simpler filtering, allowing accurate importance assessment for critical notifications without uniformly increasing system complexity across all notifications

Inventive Principle:

Principle #3Local quality

2Reliability

If all notifications are treated with equal alert levels, then the notification system is easy to manage, but important notifications may be missed or delayed

Engineering Contradiction:

Improveimportant notification delivery reliabilityVSAvoidnotification management ease

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The notification system dynamically adjusts alert levels based on real-time content analysis rather than using static treatment for all notifications. Keywords and content patterns trigger different notification priorities, ensuring important notifications receive enhanced attention while maintaining simple user-side management through automated dynamic adjustment

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system incorporates feedback mechanisms where user interactions with notifications (such as dismissing or engaging with certain types) inform future notification prioritization. This feedback loop improves reliability for important notifications by learning from user behavior patterns while keeping the system easy to operate as users don't need to manually configure complex rules

Inventive Principle:

Principle #23Feedback

3Measurement precision

If content analysis is performed on all notifications, then notification prioritization accuracy is improved, but processing time and energy consumption increase

Engineering Contradiction:

Improvenotification content relevance precisionVSAvoidnotification processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs partial content analysis by extracting only key keywords and phrases rather than analyzing entire notification texts. This selective approach maintains high precision in determining notification relevance while significantly reducing processing time and energy consumption compared to full-text analysis of every notification

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The notification processing system segments content analysis into hierarchical levels: first analyzing sender identity and basic metadata, then selectively performing deeper keyword extraction only on notifications that meet certain criteria. This segmented approach improves precision for important notifications while minimizing time loss on routine notifications

Inventive Principle:

Principle #1Segmentation

4Reliability

If notification alerts are increased for all messages, then no important notification is missed, but user distraction and annoyance increase

Engineering Contradiction:

Improvenotification detection reliabilityVSAvoiduser distraction and annoyance

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The notification system applies different alert intensities to different notification content types and priority levels. High-priority notifications with relevant keywords trigger strong alerts ensuring reliable detection, while lower-priority notifications use subdued alerts that minimize user distraction and annoyance, achieving both reliability and user comfort simultaneously

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS9813882B1Mobile notifications based upon notification content

Publication Date: 2017.11.07 AMAZON TECH INC

AI summary

Disclosed are various embodiments employed to generate device notifications based upon notification content. To this end, a request to generate a device notification is obtained from a user application. Notification content is extracted from the request and an escalated or de-escalated notification level is applied when the content corresponds to a notification rule stored in the device.