Capital One Services, LLC (20240354489). METHODS AND SYSTEMS FOR GENERATING ALTERNATIVE CONTENT USING GENERATIVE ADVERSARIAL NETWORKS IMPLEMENTED IN AN APPLICATION PROGRAMMING INTERFACE LAYER simplified abstract

METHODS AND SYSTEMS FOR GENERATING ALTERNATIVE CONTENT USING GENERATIVE ADVERSARIAL NETWORKS IMPLEMENTED IN AN APPLICATION PROGRAMMING INTERFACE LAYER

Organization Name

Capital One Services, LLC

Inventor(s)

Austin Walters of Savoy IL (US)

Vincent Pham of Seattle WA (US)

Galen Rafferty of Mahomet IL (US)

Alvin Hua of McLean VA (US)

Anh Truong of Champaign IL (US)

Ernest Kwak of Urbana IL (US)

Jeremy Goodsitt of Champaign IL (US)

METHODS AND SYSTEMS FOR GENERATING ALTERNATIVE CONTENT USING GENERATIVE ADVERSARIAL NETWORKS IMPLEMENTED IN AN APPLICATION PROGRAMMING INTERFACE LAYER - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240354489 titled 'METHODS AND SYSTEMS FOR GENERATING ALTERNATIVE CONTENT USING GENERATIVE ADVERSARIAL NETWORKS IMPLEMENTED IN AN APPLICATION PROGRAMMING INTERFACE LAYER

The abstract describes methods and systems for using a generative adversarial network to generate personalized content in real-time as a user accesses original content through an API layer.

• The innovation involves generating alternative content for users accessing original content like websites, videos, or documents.
• The API layer accesses the generative adversarial network to create personalized alternative content based on the original content.
• This technology enables real-time personalization of content for users as they interact with various types of media.
• The use of generative adversarial networks allows for dynamic and customized content creation tailored to individual user preferences.
• By leveraging AI technology, this system enhances user engagement and provides a unique and personalized experience.

Potential Applications:

• Personalized content delivery in e-commerce platforms.
• Customized news feeds and articles for online media outlets.
• Tailored recommendations for streaming services based on user preferences.
• Dynamic content creation for social media platforms to increase user interaction.
• Enhanced user experience on websites through personalized content generation.

Problems Solved:

• Addressing the need for personalized content delivery in real-time.
• Improving user engagement by offering customized content.
• Streamlining content creation processes through AI technology.
• Enhancing user satisfaction by providing relevant and personalized information.
• Increasing user retention and loyalty through personalized experiences.

Benefits:

• Improved user engagement and interaction with content.
• Enhanced user experience through personalized recommendations.
• Increased user satisfaction and loyalty to platforms.
• Streamlined content creation processes for businesses.
• Dynamic and real-time content delivery based on user preferences.

Commercial Applications:

• "Real-Time Personalized Content Generation Using Generative Adversarial Networks" - A technology that revolutionizes content delivery by offering personalized experiences to users in real-time. This innovation has significant implications for e-commerce, media, and social platforms, enhancing user engagement and satisfaction.

Questions about Real-Time Personalized Content Generation Using Generative Adversarial Networks: 1. How does this technology impact user engagement on digital platforms?

  - This technology significantly improves user engagement by providing personalized content tailored to individual preferences, leading to increased interaction and satisfaction.

2. What are the potential challenges in implementing real-time personalized content generation using generative adversarial networks?

  - The challenges may include data privacy concerns, algorithm accuracy, and the need for continuous training of the AI model to ensure relevant and accurate content generation.

Original Abstract Submitted

methods and systems for using a generative adversarial network to generate personalized content in real-time as a user accesses original content. the methods and systems perform the generation through the use of an application programming interface (“api”) layer. using the api layer, the methods and systems may generate alternative content as a user accesses original content (e.g., a website, video, document, etc.). upon receiving this original content, the api layer access the generative adversarial network to create personalized alternative content.