20240013673. Machine Learning Model-Guided Training and Development simplified abstract (Disney Enterprises, Inc.)

Machine Learning Model-Guided Training and Development

Organization Name

Disney Enterprises, Inc.

Inventor(s)

Anna T. Wolfe of San Francisco CA (US)

Alexandra Hernandez of San Diego CA (US)

Anthony M. Accardo of Los Angeles CA (US)

Shelly Henling of Pasadena CA (US)

Machine Learning Model-Guided Training and Development - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240013673 titled 'Machine Learning Model-Guided Training and Development

Simplified Explanation

The patent application describes a system for creating accessibility-enhanced content using machine learning. Here is a simplified explanation of the abstract:

• The system includes a computing platform with processing hardware and a system memory storing software code and a machine learning model.
• The software code provides a graphical user interface (GUI) for the system.
• The processing hardware executes the software code to identify a user and obtain their user profile.
• The system also obtains activity data from one or more applications used by the user.
• Using the user profile and activity data, the system modifies the node weights of the machine learning model to create a tuned model.
• The processing hardware then uses the tuned model to infer actions for advancing the user's development.
• Finally, the system outputs recommendations for performing the inferred actions to the user through the GUI.

Potential applications of this technology:

• Personalized learning platforms: The system can be used to provide tailored recommendations and guidance to users based on their profiles and activity data, enhancing their learning experience.
• Accessibility tools: By analyzing user profiles and activity data, the system can suggest accessibility features or modifications to improve the user's experience with digital content.

Problems solved by this technology:

• Lack of personalized recommendations: The system addresses the issue of generic recommendations by using machine learning to tailor suggestions based on user profiles and activity data.
• Accessibility barriers: By analyzing user data, the system can identify accessibility challenges and provide recommendations to overcome them, making digital content more accessible.

Benefits of this technology:

• Personalized user experience: Users receive recommendations and guidance that are specifically tailored to their needs and preferences.
• Improved accessibility: The system helps users overcome accessibility barriers by suggesting modifications or features that enhance their interaction with digital content.
• Efficient learning: By leveraging machine learning, the system can optimize the learning process by providing targeted recommendations for advancing the user's development.

Original Abstract Submitted

a system for creating accessibility enhanced content includes a computing platform having processing hardware and a system memory storing a software code and a machine learning (ml) model, the software code providing a graphical user interface (gui). the processing hardware executes the software code to identify a user of the system, obtain a user profile of the user, obtain, from one or more application(s) utilized by the user, activity data relating to use of the application(s) by the user, and modify, using the user profile and the activity data, one or more node weights of the ml model to provide a tuned ml model. the processing hardware further executes the software code to infer, using the tuned ml model, at least one action for advancing a development of the user and output to the user, using the ui, a recommendation for performing the at least one action by the user.