Dynamic Notification Content Generation via Machine Learning

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Solution Overview

Problem

Conventional notification systems optimize content uniformly for all recipients, leading to suboptimal user engagement as they fail to account for individual user preferences, requiring extensive resource-intensive A/B testing and resulting in a one-size-fits-all approach that may improve engagement for some users while decreasing it for others.

Innovation Solution

A machine-learned model calculates relevance scores for headline and call-to-action combinations based on user relationships and interests, generating dynamic notification content tailored to each recipient, reducing the need for additional A/B testing and enhancing user engagement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If uniform optimization is applied to all recipients, then implementation simplicity is maintained, but user engagement effectiveness deteriorates due to ignoring individual user preferences

Engineering Contradiction:

Improveimplementation simplicityVSAvoiduser engagement effectiveness

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The notification system transitions from uniform optimization to personalized optimization by analyzing individual user preferences, behaviors, and characteristics. Each recipient receives notification content tailored to their specific preferences, with different headlines, call-to-action buttons, and content types selected based on their unique profile, thereby improving engagement effectiveness while maintaining systematic management through automated preference analysis.

Inventive Principle:

Principle #3Local quality

2Reliability

If A/B testing is performed to optimize notification content, then user engagement can be improved, but resource consumption and time requirements increase significantly

Engineering Contradiction:

Improveuser engagementVSAvoidtesting time and resources

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary analysis of user preferences, behaviors, and characteristics before generating notification content. By pre-processing and storing user preference data, the system can directly select optimized notification content without requiring extensive A/B testing at the time of notification delivery, thereby reducing time and resource consumption while maintaining high engagement effectiveness.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The notification system uses automated machine learning models to independently analyze user preferences and select optimal notification content without requiring manual A/B testing intervention. The system self-optimizes by continuously learning from user interactions and automatically adjusting notification content based on accumulated data, eliminating the need for resource-intensive manual testing processes.

Inventive Principle:

Principle #25Self-service

3Reliability

If personalized notification content is generated for each recipient, then user engagement effectiveness is improved, but system complexity increases

Engineering Contradiction:

Improveuser engagement effectivenessVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The notification system implements a universal preference analysis framework that handles multiple user characteristics, behaviors, and preferences through a single integrated machine learning model. This multi-functional approach allows the system to process diverse user data types and generate personalized notifications without requiring separate complex systems for each user attribute, thereby managing system complexity while delivering personalized content.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system dynamically adjusts notification parameters such as headline text, call-to-action buttons, content type, and timing based on user preference parameters. By changing these content parameters automatically based on analyzed user characteristics, the system achieves personalized optimization without requiring complex structural changes, maintaining system manageability while improving engagement effectiveness.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11475084B2Machine learning driven dynamic notification content

Publication Date: 2022.10.18 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Technologies for generating dynamic notification content for notification messages using a machine learned model are provided. The disclosed techniques include identifying an event related to a particular user, where the event has a particular notification type that represents a subject type of the event. Based on the particular notification type of the event, a set of candidate headline and call-to-action combinations corresponding to the particular notification type are identified. Using the machine learned model, scores are calculated for each headline and call-to-action combination in the set of candidate headline and call-to-action combinations. One or more particular headline and call-to-action combinations from the set of candidate headline and call-to-action combinations are selected based upon the scores calculated for each combination of the set of candidate headline and call-to-action combinations. A notification message is generated for the event that includes the one or more particular headline and call-to-action combinations selected.