Google llc (20240126576). Conversational Interface for Content Creation and Editing Using Large Language Models simplified abstract

Conversational Interface for Content Creation and Editing Using Large Language Models

Organization Name

google llc

Inventor(s)

Sylvanus Garnet Bent, Iii of Palo Alto CA (US)

Xiaolan Zhou of Santa Clara CA (US)

Mehmet Levent Koc of Redwood City CA (US)

Wei Luo of Jersey City NJ (US)

Conversational Interface for Content Creation and Editing Using Large Language Models - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240126576 titled 'Conversational Interface for Content Creation and Editing Using Large Language Models

Simplified Explanation

The abstract describes a method involving a content assistant component, user input data, machine learned models, and content item components to provide an updated user interface with displayed content items based on user input and selections.

• The method involves generating an initial user interface with a content assistant component.
• User input data is obtained and processed by a machine learned model interfacing with the content assistant component.
• Output data indicative of content item components is obtained from the machine learned model.
• The data is transmitted to update the user interface with the content item components for display.
• User selection of approval of the content item components results in the generation of content items.

Potential Applications

This technology could be applied in content recommendation systems, personalized user interfaces, and interactive content creation platforms.

Problems Solved

This technology streamlines the process of content creation and recommendation by utilizing machine learning models to assist users in selecting and approving content items.

Benefits

The benefits of this technology include improved user experience, personalized content recommendations, and efficient content creation processes.

Potential Commercial Applications

Potential commercial applications of this technology include digital marketing platforms, e-commerce websites, and social media platforms for targeted content delivery.

Possible Prior Art

One possible prior art could be the use of machine learning models in content recommendation systems or user interface design tools.

How does this method handle user privacy and data security concerns?

The method described in the abstract involves obtaining user input data and processing it through machine learned models. It is important to consider how user privacy and data security are maintained throughout this process, especially when dealing with sensitive information.

What are the limitations of using machine learned models in generating content recommendations?

While machine learned models can be effective in providing personalized content recommendations, there may be limitations in terms of bias, accuracy, and scalability. It is essential to understand these limitations to ensure the reliability and effectiveness of the method described in the abstract.

Original Abstract Submitted

example embodiments of the present disclosure provide for an example method. the example method includes generating an initial user interface including a content assistant component. the example method include obtaining user input data. the example method includes processing, by a machine learned model interfacing with the content assistant component, the data indicative of the input received from the user. the method includes obtaining output data, from the machine learned model interfacing with the content assistant component, indicative of one or more content item components. the method includes transmitting data which causes the content item components to be provided for display via an updated user interface. the method includes obtaining data indicative of user selection of approval of the content item components. the method includes generating, in response to obtaining the data indicative of the user selection of the approval of the content item components, content items.