Machine Learning Interface Generation for Dynamic Content Layouts

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Solution Overview

Problem

Existing interface generation methods require manual specification of content organization and navigation, which can be cumbersome and inflexible, especially when dealing with diverse and dynamic content sets.

Innovation Solution

A system utilizing a machine learning model to determine hierarchical relationships and layouts of content sets, automatically generating a multipage navigable interface without user input, by analyzing content types and organizational structures.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual specification of content organization is used, then user control over interface structure is improved, but development time and complexity increase

Engineering Contradiction:

Improveuser control over interface structureVSAvoiddevelopment time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system automatically generates interface structure by analyzing content relationships without requiring manual specification from users. The machine learning model processes content sets and autonomously determines hierarchical relationships, organization, and navigation paths, eliminating the need for manual interface design while maintaining user control through system-determined optimizations

Inventive Principle:

Principle #25Self-service

2Ease of operation

If manual specification of navigation is used, then user experience is improved, but interface flexibility decreases

Engineering Contradiction:

Improveuser experienceVSAvoidinterface flexibility

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The interface structure and navigation paths are dynamically determined by the machine learning model based on the specific content relationships rather than being fixed by manual design. The system adapts the hierarchical organization and navigation elements to the actual content structure, enabling flexible interfaces that automatically adjust to different content sets while maintaining optimal user experience

Inventive Principle:

Principle #15Dynamics

3Productivity

If automatic interface generation is used, then development efficiency is improved, but system complexity increases

Engineering Contradiction:

Improvedevelopment efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

A machine learning model serves as an intermediary between raw content sets and the final interface structure. The model processes content relationships, determines hierarchical organization, and generates navigation paths automatically. This intermediary component handles the complexity of interface generation, enabling high development efficiency while managing system complexity through automated intelligence rather than manual processes

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11593127B2Generating a navigable interface based on system-determined relationships between sets of content

Publication Date: 2023.02.28 ORACLE INT CORP

AI summary

In one or more embodiments, a system generates a navigable interface for traversing sets of content based on system-determined relationships between the sets of content. The system uses a trained machine learning model to determine characteristics, such as layout, for sets of content. The characteristics are mapped to a content type. The system organizes the sets of content, based on respective content type, into a set of pages of a multipage navigable interface. Furthermore, the system selects navigational relationships between the sets of content based on respective content type. The navigational relationships are implemented via interface elements that allow for navigation between the pages of the navigable interface including corresponding sets of content.