SCALABLE ARCHITECTURE FOR AUTOMATIC GENERATION OF CONTENT DISTRIBUTION IMAGES

Smart overview of the Invention

Methods and systems are proposed for the automatic generation of content distribution images. The process begins with receiving user input related to a content-distribution operation, which is then analyzed to identify relevant keywords. Based on these keywords, corresponding image data is sourced and processed. A generative adversarial network (GAN) is employed to create images that align with the keywords, ensuring the images appear authentic and not machine-generated. These images are then presented alongside previously utilized images in content-distribution operations.

Field and Background

This technology is focused on automating the creation of content for distribution operations, specifically through scalable architectures leveraging neural networks. Traditionally, generating content for distribution is labor-intensive, requiring significant resources and time. Companies often spend weeks crafting content, which may become outdated by the time it is deployed. The invention addresses the need for more efficient, timely, and resource-effective content generation solutions.

Methodology

The method involves several key steps: receiving and parsing user input to extract keywords, sourcing image data related to these keywords, and processing this data. A GAN is used to generate images, with a focus on creating outputs that are indistinguishable from real images. The GAN consists of a generator neural network and a discriminator neural network, which work in tandem to refine image authenticity. The generated images are displayed with existing content-distribution images for user selection and deployment.

Generative Adversarial Network

The GAN operates by training the generator network to produce images that the discriminator network cannot distinguish from real ones. Initially, the generator creates images based on keywords, which are then evaluated by the discriminator. Feedback from the discriminator helps the generator improve its outputs, aiming to create images that the discriminator misclassifies as real. This iterative process enhances the quality and realism of the generated images.

User Input and Keyword Identification

User input for content distribution can be provided through natural language, commands, or pre-defined options. This input is processed to extract keywords using rule-based systems or machine-learning models. These models are trained on extensive datasets to accurately identify keywords, even assigning confidence levels to their predictions. This ensures that the generated images are closely aligned with the user's intended content-distribution goals.