AI Content Page Assembly with Global Constraint Optimization

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Solution Overview

Problem

Existing content page assembly systems face challenges in maximizing user engagement while adhering to global constraints, risking user frustration and potential loss of engagement due to irrelevant or overwhelming content options.

Innovation Solution

The use of artificial intelligence and machine learning to dynamically select and arrange content components on a content page, balancing user engagement with global constraints by optimizing the composition of content pages across multiple instances, ensuring relevant and diverse content is presented while meeting imposed metrics.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If content pages are tailored to user preferences to maximize engagement, then user engagement is improved, but the entity's other objectives may not be met

Engineering Contradiction:

Improveuser engagementVSAvoidcompliance with entity objectives

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system changes parameters by introducing constraint values that modify the optimization objective function. Instead of purely maximizing engagement, the system adjusts the parameter space to include compliance with entity objectives, transforming the single-objective optimization into a multi-parameter optimization problem that balances both engagement and constraint satisfaction

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system dynamically adjusts content page composition by iteratively modifying constraint values based on compliance metrics. The optimization process is dynamic, continuously adapting the balance between engagement maximization and constraint satisfaction through repeated adjustments of the objective function parameters

Inventive Principle:

Principle #15Dynamics

2Adaptability or versatility

If content constraints are imposed to meet entity objectives, then compliance with objectives is improved, but user engagement may be reduced

Engineering Contradiction:

Improvecompliance with entity objectivesVSAvoiduser engagement

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system implements feedback by measuring compliance with entity objectives and using this information to adjust constraint values in subsequent optimization iterations. The feedback loop allows the system to learn from compliance outcomes and dynamically adjust the balance between constraints and engagement, preventing excessive constraint imposition that would harm user experience

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system applies partial action by imposing constraints only to the extent necessary to meet entity objectives without过度 restricting content selection. The optimization process determines the minimum necessary constraint application that achieves compliance while preserving maximum user engagement, avoiding excessive constraint imposition

Inventive Principle:

Principle #16Partial or excessive action

3Productivity

If the system optimizes content pages for individual users, then user engagement is improved, but the complexity of managing multiple constraints increases

Engineering Contradiction:

Improveuser engagementVSAvoidoptimization system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system achieves universality by creating a multi-functional optimization framework that simultaneously handles engagement maximization, constraint compliance, and iterative adjustment. The same optimization engine performs multiple functions: initial engagement optimization, constraint compliance checking, constraint value adjustment, and re-optimization, eliminating the need for separate systems for each function

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Adaptability or versatility

If the system adjusts constraint values iteratively, then compliance with entity objectives is improved, but the time required for optimization increases

Engineering Contradiction:

Improvecompliance with entity objectivesVSAvoidoptimization time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary action by pre-calculating and storing engagement metrics and constraint evaluations during the initial optimization phase. This preliminary computation allows subsequent iterative adjustments to proceed more quickly, as the system builds upon pre-computed foundations rather than starting from scratch in each iteration

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS10915336B1Optimizing content engagement with imposed content constraints

Publication Date: 2021.02.09 AMAZON TECH INC

AI summary

Systems and methods are provided for selecting content components to include in portions of a content page under various constraints and according to various effectiveness considerations. Organizing the content components may include considering the expected user engagement with the content components, while simultaneously endeavoring to comply with certain content constraints imposed on the system. The content constraints may be complied with to the greatest extent possible before the expected user engagement with the page is adversely impacted to an unacceptable degree. The content constraints may be imposed across an entire set of content pages, such that only the content pages in the aggregate are expected to comply with a global constraint. This allows for maximizing the expected user engagement while assembling a content page without unduly constraining each content page, where those global constraints may be known to reduce expected user engagement in some instances.