Abstract: A method, system and apparatus for touch gesture recognition are provided. A device generates a trajectory corresponding to a two-dimensional touch gesture. The device generates a plurality of variations of the trajectory in one or more of two dimensions. The device extracts one or more features of the trajectory and the plurality of variations of the trajectory. The device generates, from the one or more features, one or more machine learning classifiers. The device stores, at a memory, the one or more machine learning classifiers, such that a machine learning algorithm uses the one or more machine learning classifiers to recognize the two-dimensional touch gesture when receiving touch gesture input.

Abstract: A method of gesture detection in a controller includes: storing, in a memory connected with the controller, inference model data defining inference model parameters for a plurality of gestures; obtaining, at the controller, motion sensor data; extracting an inference feature from the motion sensor data; selecting, based on the inference feature and the inference model data, a detected gesture from the plurality of gestures; and presenting the detected gesture

Abstract: A graphical user interface of a web-based toolkit application for a cloud-based NLU engine is a drag-and-drop toolkit application for building NLU contextual recognition models. Using the graphical user interface, users who do not have expertise in NLU and text interpretation can focus on building customized NLU contextual recognition models to enable human (end-user) interaction with electronics and software applications without requiring expert programming skills or prior in-depth knowledge of NLU. The NLU models provide an intelligent, natural conversational speech and/or text interface for end-users of electronic devices and software applications.