Abstract: The present method and system are for providing a graphical user interface using machine learning and movement of the user or user device. In an example, the method and system comprise, training a context analyzer machine learning model with training motion data for a training activity context, the context analyzer for predicting a predicted activity context from new motion data; training a response analyzer machine learning model with training usage data and training user responses for the training activity context for a user interface, the response analyzer for predicting the predicted user response from the predicted activity context and new usage data; predicting the predicted user response from motion data and usage data using both the context analyzer and the response analyzer; and determining a preferred variation for the user interface using a predetermined performance metric and the predicted user response.

Abstract: The present method and system provides for recognizing user intent and updating a graphical user interface. In an example, the method and system comprise collecting usage data from users, grouping users based on usage data, assigning a user intent to each group of users, training an intent prediction model using machine learning, providing access to the intent prediction model, assigning an intent to a new user using the intent prediction model, and, modifying the graphical user interface to facilitate the assigned intent of the user.

Abstract: A system and method of user interface modification, and more particularly a system and method of automatically modifying a user interface to improve the quantitative evaluation of the user interface.