Contextual Message Augmentation via Predictive User Action Modeling

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing network services lack the ability to provide relevant 'next action' content to users, instead offering generally relevant content that does not cater to individual user actions.

Innovation Solution

A per-user persona model is developed to predict the next action a user will take after interacting with a message, generating augmented content items based on these predictions and embedding them into a message display interface.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If bulk analyses are performed on messaging content to provide value-added content, then generally relevant content can be provided to users, but relevant 'next action' content cannot be provided to users

Engineering Contradiction:

Improvecontent relevance to user actionsVSAvoidprediction model complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary analysis by training a prediction model on historical user actions and message content. This pre-trained model is then used to predict next actions for new messages, enabling relevant content provision without complex real-time analysis. The model captures user behavior patterns in advance, allowing the system to adapt to individual user actions efficiently.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If per-user persona models are used to predict next actions, then relevant 'next action' content can be provided to users, but system complexity increases

Engineering Contradiction:

Improveuser action prediction accuracyVSAvoidpersona model implementation complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system implements local quality by creating personalized persona models for each user based on their individual historical actions and message interactions. Each user receives tailored content recommendations based on their specific behavior patterns rather than generic bulk analysis. This localized approach achieves high prediction accuracy for individual users while managing complexity through modular model implementation.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system uses copying by replicating the persona model structure across multiple users. Once a prediction model is trained and validated, it can be copied and applied to numerous users with minimal additional complexity. The model captures universal user behavior patterns that can be reused, reducing the overall system complexity while maintaining per-user customization through the copying mechanism.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS12309108B2Method and system for supplementing messages with contextual content

Publication Date: 2025.05.20 YAHOO ASSETS LLC

AI summary

The application relates to a method for supplementing messages with contextual content. A processor can receive a message from a sender to a user that includes a content item. The processor can then predict a next action of the user based on the message by inputting the content item into a predictive model trained using historical actions performed by the user. Based on the output of the model, the processor can generate an augmented content item and embed the augmented content item and the content item into a user interface which is then transmitted to a client device of the user.