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 includes 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: 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: 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: 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: An on-line thickness gauge (OLTG) and method are described herein that are capable of measuring a thickness of a moving glass substrate. In the preferred embodiment, the OLTG includes a Y-guide and a stabilizing unit that respectively captures and stabilizes the moving glass substrate. The OLTG also includes a laser instrument which contains a laser source and a detector. The laser source emits a beam at the front surface of the moving glass substrate. And, the detector receives two beams one of which was reflected by the front surface of the moving glass substrate and the other beam which was reflected by the back surface of the moving glass substrate. The OLTG further includes a processor that analyzes the two beams received by the detector to determine a distance between the two beams which is then used to determine the thickness of the moving glass substrate.