Abstract: A computerized method has the steps of: obtaining one or more first touches on a touch surface; and determining that the one or more first touches are resting on the touch surface if any of following first conditions is true: each first touch being contacting the touch surface without movement for longer than a first time duration, the one or more first touches being a plurality of first touches sequentially contacting the touch surface, the one or more first touches being a plurality of first touches contacting the touch surface substantially at a same time and remaining in contact with the touch surface for longer than a second time duration, the one or more first touches being in contact with the touch surface at one or more locations outside an input area or at one or more locations inside an resting area, or a combination thereof.

Abstract: Systems, apparatuses, methods, and computer-readable storage media are disclosed for facilitating user interaction with a virtual keyboard. A computerized method comprises: receiving contextual information associated with a virtual keyboard displayed on a user interface; determining, based on the contextual information, that a visual hint display condition is met; displaying a visual hint associated with one or more keys of the virtual keyboard based on the visual hint display condition; after displaying the visual hint, receiving additional contextual information associated with the virtual keyboard; determining, based on the additional contextual information, that a visual hint dismissal condition is met; and dismissing the visual hint from the virtual keyboard based on the visual hint dismissal condition.

Abstract: Embodiments of the present application provide methods for classifying a detected touch input on a capacitive interface. A sequence of capacitive images representing capacitive sensor data associated with touch inputs detected during a continuous touch gesture is obtained. The capacitive images are sequentially processed using a machine learning model. A touch region of the capacitive image that corresponds to an area of the capacitive interface associated with a current touch input for the capacitive image is identified. A set of features of the identified touch region is extracted using the machine learning model. A state of touch of the capacitive image is determined based on the extracted set of features and one or more previous capacitive images associated with previously detected touch inputs during the continuous touch gesture.

Abstract: Embodiments of the present application provide methods for classifying a detected touch input on a capacitive interface. A sequence of capacitive images representing capacitive sensor data associated with touch inputs detected during a continuous touch gesture is obtained. The capacitive images are sequentially processed using a machine learning model. A touch region of the capacitive image that corresponds to an area of the capacitive interface associated with a current touch input for the capacitive image is identified. A set of features of the identified touch region is extracted using the machine learning model. A state of touch of the capacitive image is determined based on the extracted set of features and one or more previous capacitive images associated with previously detected touch inputs during the continuous touch gesture.

Abstract: This application provides a touch module, an electronic device, a touch accessory, and a touch detection method. The touch module includes a touch capacitive layer and a pressure sensing layer. The touch capacitive layer and the pressure sensing layer are disposed opposite to each other. The touch capacitive layer is configured to output a capacitance signal, and the capacitance signal is used to determine a touch location of a touch operation. The pressure sensing layer is configured to change under an action of touch pressure, and a signal value of the capacitance signal is related to a variation of the pressure sensing layer.

Abstract: There are provided methods and systems for a user-adaptive virtual keyboard comprising a plurality of virtual adaptive keys. In examples, the systems, methods, and processor-readable media can be applied to dynamically configure a user interface of the user-adaptive virtual keyboard, to adapt to a user's needs and preferences, while the user is performing a typing action. In response to detecting a user touch input, the user-adaptive virtual keyboard may generate an updated virtual keyboard UI, by dynamically adjusting one or more properties of the plurality of virtual adaptive keys. The disclosed methods and systems may enable improved user interaction with a virtual keyboard on an electronic device, for example, while a user is performing blind typing actions.

Abstract: Techniques for using a multi-core processor for optimizing power consumption in a security and home automation system are described. The security and home automation system may include a multi-core processor having a first core and a second core. The first core may be partitioned from the second core to form an asymmetric multi-core processor. The first core is a master core assigned to execute a real time operating system (RTOS) and configured to periodically transition between a partial sleep state and an awake state during a power shortage condition. The second core is a slave core assigned to execute a standard operating system (OS) and configured to enter a deep sleep state during the power shortage condition.

Abstract: Techniques for using a multi-core processor for optimizing power consumption in a security and home automation system are described. The security and home automation system may include a multi-core processor having a first core and a second core. The first core may be partitioned from the second core to form an asymmetric multi-core processor. The first core is a master core assigned to execute a real time operating system (RTOS) and configured to periodically transition between a partial sleep state and an awake state during a power shortage condition. The second core is a slave core assigned to execute a standard operating system (OS) and configured to enter a deep sleep state during the power shortage condition.