Scalable GAN Image Generation for Content Distribution

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Solution Overview

Problem

Content-distribution operations are resource-intensive and time-consuming, often resulting in outdated content due to the lengthy process of image generation and distribution, which involves significant human effort and may not meet the relevance criteria of target devices.

Innovation Solution

A scalable architecture utilizing generative adversarial networks (GANs) for automatic image generation, where user input is parsed to identify keywords, image data is processed, and GANs are trained to produce relevant images, with a discriminator network refining the output to ensure realism and relevance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional manual content-distribution operations are used with teams of photographers, designers, and artists to create and modify images, then content quality and relevance can be maintained, but resource expenditure and time consumption increase significantly

Engineering Contradiction:

Improvecontent relevanceVSAvoidcontent generation time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical processes (photographers taking photos, designers editing images) with an automated neural network system. The generator network automatically creates distribution images from input images, eliminating the need for human teams while maintaining content quality and relevance through intelligent algorithms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service content generation where the neural network automatically processes input images and generates distribution-ready images without requiring human intervention. The generator network learns from training data and autonomously creates relevant content, reducing dependency on human resources.

Inventive Principle:

Principle #25Self-service

2Reliability

If traditional manual content-distribution operations are used with multiple professionals to create and format images, then content quality can be maintained, but resource expenditure increases

Engineering Contradiction:

Improvecontent qualityVSAvoidresource expenditure

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent replaces resource-intensive manual operations with a computational neural network system. Instead of paying multiple professionals for image creation, editing, and formatting, the system uses the generator network to automatically produce high-quality distribution images, significantly reducing human resource expenditure.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system creates synthetic copies of input images through the generator network, producing distribution-ready images that replicate the quality and relevance of manually created content. The neural network learns from training data to generate realistic and relevant image copies without requiring original human creation for each distribution instance.

Inventive Principle:

Principle #26Copying

3Adaptability or versatility

If traditional content-distribution operations are used with manual image creation and formatting processes, then content can be customized for target devices, but the process becomes overly complex

Engineering Contradiction:

Improvecontent customizationVSAvoiddistribution process complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent replaces complex manual customization processes with an automated neural network system. The generator network automatically adapts input images to create distribution-ready content suitable for target devices, eliminating the need for complex manual formatting and customization workflows while maintaining adaptability.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Reliability

If traditional manual content-distribution operations are used to create and transmit formatted images, then content relevance can be maintained, but productivity decreases

Engineering Contradiction:

Improvecontent relevanceVSAvoidcontent distribution speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces slow manual content creation and distribution processes with automated neural network processing. The generator network rapidly generates distribution images from input images, significantly increasing productivity while maintaining content relevance through intelligent image generation and adaptation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12418706B2Scalable architecture for automatic generation of content distribution images

Publication Date: 2025.09.16 ORACLE INT CORP

AI summary

Methods and systems are disclosed for automatic generation of content distribution images that include receiving user input corresponding to a content-distribution operation. The user input may be parsed to identify keywords. Image data corresponding to the keywords can be identified. Image-processing operations may be executed on the image data. Executing a generative adversarial network on the processed image data, which includes: executing a first neural network on the processed-image data to generate first images that correspond to the keywords, the first images generated based on a likelihood that each image of the first images would not be detected as having been generated by the first neural network. A user interface can display the first images with second images that include images that were previously part of content-distribution operations or images that were designated by an entity as being available for content-distribution operations.