Snap inc. (20240354555). XR EXPERIENCE BASED ON GENERATIVE MODEL OUTPUT simplified abstract
Contents
- 1 XR EXPERIENCE BASED ON GENERATIVE MODEL OUTPUT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 XR EXPERIENCE BASED ON GENERATIVE MODEL OUTPUT - A simplified explanation of the abstract
- 1.4 Potential Applications
- 1.5 Problems Solved
- 1.6 Benefits
- 1.7 Commercial Applications
- 1.8 Questions about Machine Learning in XR Experience Generation
- 1.9 Original Abstract Submitted
XR EXPERIENCE BASED ON GENERATIVE MODEL OUTPUT
Organization Name
Inventor(s)
Jacob Knipfing of Culver City CA (US)
Alex Meckes of Medford MA (US)
Jonathan Solichin of Arcadia CA (US)
XR EXPERIENCE BASED ON GENERATIVE MODEL OUTPUT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240354555 titled 'XR EXPERIENCE BASED ON GENERATIVE MODEL OUTPUT
The abstract describes methods and systems for operating an extended reality (XR) experience using machine learning models. An interaction application accesses an XR application, receives a query defining attributes of the XR application, generates a prompt for a generative machine learning model, processes the prompt to generate data objects matching the query, and generates XR objects based on the data objects.
- XR experience operated using machine learning models
- Interaction application accesses XR application and receives query defining attributes
- Prompt generated for generative machine learning model
- Data objects matching query generated by the model
- XR objects generated based on data objects
Potential Applications
This technology can be applied in various industries such as gaming, virtual reality training simulations, architectural visualization, and virtual product prototyping.
Problems Solved
This technology streamlines the process of creating XR experiences by automating the generation of XR objects based on user-defined attributes, saving time and resources.
Benefits
The benefits of this technology include improved efficiency in creating XR experiences, enhanced user customization, and the ability to quickly adapt XR applications to changing requirements.
Commercial Applications
"Machine Learning in XR Experience Generation" can be utilized in industries such as entertainment, education, design, and engineering for creating immersive and interactive XR content that meets specific user needs.
Questions about Machine Learning in XR Experience Generation
How does this technology impact the development of XR applications?
This technology accelerates the development process by automating the generation of XR objects based on user-defined attributes, increasing efficiency and customization.
What are the potential limitations of using machine learning models in XR experience generation?
One potential limitation could be the need for high-quality training data to ensure accurate and relevant generation of XR objects.
Original Abstract Submitted
methods and systems are disclosed for operating an extended reality (xr) experience using one or more machine learning models. the methods and systems accessing, by an interaction application, an xr application and receive, by the interaction application, a query that defines one or more attributes of the xr application. the methods and systems generate a prompt for a generative machine learning model using the query and process the prompt using the generative machine learning model to generate one or more data objects that match the one or more attributes defined by the query. the methods and systems generate, using the xr application, one or more xr objects based on the one or more data objects generated by the generative machine learning model responsive to the prompt.
