View source for 20230088881. METHODS AND SYSTEMS FOR GENERATING ALTERNATIVE CONTENT USING GENERATIVE ADVERSARIAL NETWORKS IMPLEMENTED IN AN APPLICATION PROGRAMMING INTERFACE LAYER simplified abstract (Capital One Services, LLC)

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


==Organization Name==

[[:Category:Capital One Services, LLC|Capital One Services, LLC]]


[[Category:Capital One Services, LLC]]

==Inventor(s)==

[[:Category:Austin Walters of Savoy IL (US)|Austin Walters of Savoy IL (US)]][[Category:Austin Walters of Savoy IL (US)]]

[[:Category:Vincent Pham of Seattle WA (US)|Vincent Pham of Seattle WA (US)]][[Category:Vincent Pham of Seattle WA (US)]]

[[:Category:Galen Rafferty of Mahomet IL (US)|Galen Rafferty of Mahomet IL (US)]][[Category:Galen Rafferty of Mahomet IL (US)]]

[[:Category:Alvin Hua of McLean VA (US)|Alvin Hua of McLean VA (US)]][[Category:Alvin Hua of McLean VA (US)]]

[[:Category:Anh Truong of Champaign IL (US)|Anh Truong of Champaign IL (US)]][[Category:Anh Truong of Champaign IL (US)]]

[[:Category:Ernest Kwak of Urbana IL (US)|Ernest Kwak of Urbana IL (US)]][[Category:Ernest Kwak of Urbana IL (US)]]

[[:Category:Jeremy Goodsitt of Champaign IL (US)|Jeremy Goodsitt of Champaign IL (US)]][[Category: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 20230088881 titled 'METHODS AND SYSTEMS FOR GENERATING ALTERNATIVE CONTENT USING GENERATIVE ADVERSARIAL NETWORKS IMPLEMENTED IN AN APPLICATION PROGRAMMING INTERFACE LAYER

==Simplified Explanation==

The patent application describes methods and systems for using a generative adversarial network (GAN) to generate personalized content in real-time as a user accesses original content. This is achieved through an application programming interface (API) layer.

* The innovation involves using a GAN to generate alternative content as a user interacts with original content.
* The API layer is responsible for accessing the GAN and creating personalized alternative content based on the original content.
* The GAN is a type of machine learning model that consists of a generator and a discriminator, which work together to generate realistic content.
* The generator creates alternative content based on the original content, while the discriminator evaluates the generated content for realism.
* The API layer receives the original content and uses the GAN to generate personalized alternative content, which can be presented to the user in real-time.
* This technology can be applied to various types of content, such as websites, videos, documents, etc.
* It allows for the creation of personalized content tailored to each user's preferences and interests.
* The generated alternative content can enhance the user experience by providing relevant and engaging information.
* This technology solves the problem of static and generic content by dynamically generating personalized content in real-time.
* It addresses the challenge of delivering content that is relevant and engaging to individual users.
* The use of GANs enables the creation of realistic and high-quality alternative content.
* The real-time generation of personalized content enhances user engagement and satisfaction.
* It can lead to increased user retention and conversion rates.
* The technology has potential applications in various industries, including e-commerce, advertising, entertainment, and education.
* It can be used to personalize product recommendations, advertisements, video suggestions, and educational materials.
* The ability to generate personalized content in real-time can improve user interactions with websites, apps, and other digital platforms.
* It can also be used to create personalized news articles, blog posts, and social media content.


==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.



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