Abstract: Systems, methods, and instrumentalities are described herein related to online tuning of pen characterizations. Online tuning may be performed during use of a pen with a touch device. A digitizer may detect signals associated with the pen and noise. A touch controller may execute a signal characterization model that characterizes the detected signals and an online tuner that processes the detected signals to perform online tuning of the signal characterization model. Online testing may validate an online-tuned signal characterization model for online use. Tuning may be based on signal statistics, such as mean or average signal gradients in the detected signals. Signal characterization models may include positioning, signal locating, noise reduction, communication decoding, etc.

Abstract: A touch-sensitive display device comprises: a first touch-sensitive input device comprising touch-sensitive display including a first plurality of touch-sensing electrodes, and a second touch-sensitive input device including a second plurality of touch-sensing electrodes. A peripheral device uplink controller is configured to receive a command to transmit outgoing computer data from the touch-sensitive display device to a separate peripheral device via an electrostatic uplink. The outgoing computer data is transmitted via the first plurality of touch-sensing electrodes of the touch-sensitive display by driving the first plurality of touch-sensing electrodes with an encoded electrostatic drive signal. The outgoing computer data is transmitted via the second plurality of touch-sensing electrodes of the second touch-sensitive input device by driving the second plurality of touch-sensing electrodes with the encoded electrostatic drive signal.

Abstract: An earbud is configured to detect its location (e.g., in-ear and out-of-ear) based on an acoustical signature with and without user-specific customization. The earbud location may be indicated to a host, e.g., to determine playback. Location determinations are based on features extracted from acoustical samples taken by the earbud compared to features extracted from out-of-ear acoustical samples and non-user-specific and/or user-specific in-ear samples. A non-user-specific machine learning (ML) model trained on features extracted from non-user-specific in-ear and out-of-ear samples may be an initial/default locator. The non-user-specific model may be customized for specific users. A user-specific in-model may be created by training the non-user-specific model on features extracted from user-specific in-ear samples collected when the earbud is located in-ear for a specific user. The user-specific ML model may be selected to classify a location of the earbud for one or more associated hosts.

Abstract: A computing system for a plurality of classes of audio events is provided, including one or more processors configured to divide a run-time audio signal into a plurality of segments and process each segment of the run-time audio signal in a time domain to generate a normalized time domain representation of each segment. The processor is further configured to feed the normalized time domain representation of each segment to an input layer of a trained neural network. The processor is further configured to generate, by the neural network, a plurality of predicted classification scores and associated probabilities for each class of audio event contained in each segment of the run-time input audio signal. In post-processing, the processor is further configured to generate smoothed predicted classification scores, associated smoothed probabilities, and class window confidence values for each class for each of a plurality of candidate window sizes.

Abstract: A method to provide classified touch data to a computer program executing on a device comprises: (a) assembling a map of touch signal from a touch sensor arranged on an electronic display and including a plurality of crossings of row and column electrodes, the map including a corresponding touch-signal value for each of the plurality of crossings, and defining at least one touched region of the touch sensor; (b) serving context data relating to user-interface content currently presented on the electronic display; (c) computing classified touch data corresponding to the map of touch signal, based at least partly on the map and the context data; and (d) providing the classified touch data to an operating system of the device.

Abstract: A computing device is provided comprising a processor, a first display device having a first capacitive touch sensor, a second display device having a second capacitive touch sensor, and a hinge positioned between and coupled to each of the first display device and the second display device, the first display device and second display device being rotatable about the hinge and separated by a hinge angle. The processor is configured to detect the hinge angle at a first point in time, determine that the hinge angle at the first point in time is outside a first predetermined range, and upon at least determining that the hinge angle is outside the first predetermined range, perform run-time calibration of at least a plurality of rows of the capacitive touch sensor of the first display device and of the capacitive touch sensor of the second display device.

Abstract: An electronic device comprises a touch sensor, a movement sensor, and recalibration logic. The touch sensor provides input to the electronic device. The movement sensor has an output that varies in dependence on movement of the electronic device, and the recalibration logic is configured to vary a rate of successful recalibration of the touch sensor based at least partly on the output of the movement sensor. The successful recalibration comprises replacement of a stored calibration mapping of each of a plurality of (X, Y) coordinates of the touch sensor to a corresponding touch-free capacitance.

Abstract: Systems, methods, and instrumentalities are described herein related to online tuning of pen characterizations. Online tuning may be performed during use of a pen with a touch device. A digitizer may detect signals associated with the pen and noise. A touch controller may execute a signal characterization model that characterizes the detected signals and an online tuner that processes the detected signals to perform online tuning of the signal characterization model. Online testing may validate an online-tuned signal characterization model for online use. Tuning may be based on signal statistics, such as mean or average signal gradients in the detected signals. Signal characterization models may include positioning, signal locating, noise reduction, communication decoding, etc.

Abstract: Computing devices and methods for performing touch detection on a touch screen display and trackpad are disclosed. In one example, a trackpad input signal from a trackpad is received at a processor of the device. Using at least the trackpad input signal, a touch screen touch detection algorithm is modified. A touch screen input signal is then received at the processor from the touch screen display. The touch screen input signal is processed with the modified touch screen touch detection algorithm.

Abstract: Systems and methods are described for reducing the power consumption of a camera-based presence detection system of a user device. The camera-based presence detection system is used to transition the user device from a low-power mode to another power mode when the presence of a user is detected. To reduce the power consumption of the camera-based presence detection system, an operating parameter of the camera-based presence detection system may be modified based on contextual information received while the user device is in the low-power mode. For example, an operating parameter of the camera-based presence detection system is modified when the contextual information indicates that there is a reduced reliability or utility of the camera-based presence detection system.

Abstract: A method for electronic accessory pairing includes capturing an image of an external environment via a camera communicatively coupled to a host computing device. The image of the external environment is analyzed to detect presence of an imaged electronic accessory. After determining that the host computing device is not presently paired with the imaged electronic accessory, a pairing is established between the host computing device and the imaged electronic accessory.

Abstract: A method of using an earbud to maintain a user's authenticated session comprises, after establishing a user's first authenticated session in a first computing device, broadcasting an initial ultrasonic signal from a speaker of the earbud. At least a subsequent ultrasonic echo signal that is received at an in-ear microphone of the earbud is analyzed to determine if the earbud is located in an ear of the user. The method determines that the earbud is within a predetermined distance of the second computing device. At least on condition of determining that the earbud is located in the ear of the user and that the earbud is within the predetermined distance of the second computing device, a user's second authenticated session in a second computing device is established.

Abstract: A method for electronic accessory pairing includes capturing an image of an external environment via a camera communicatively coupled to a host computing device. The image of the external environment is analyzed to detect presence of an imaged electronic accessory. After determining that the host computing device is not presently paired with the imaged electronic accessory, a pairing is established between the host computing device and the imaged electronic accessory.

Abstract: Computing devices and methods for performing touch detection on a touch screen display and trackpad are disclosed. In one example, a trackpad input signal from a trackpad is received at a processor of the device. Using at least the trackpad input signal, a touch screen touch detection algorithm is modified. A touch screen input signal is then received at the processor from the touch screen display. The touch screen input signal is processed with the modified touch screen touch detection algorithm.

Abstract: A computing system for a plurality of classes of audio events is provided, including one or more processors configured to divide a run-time audio signal into a plurality of segments and process each segment of the run-time audio signal in a time domain to generate a normalized time domain representation of each segment. The processor is further configured to feed the normalized time domain representation of each segment to an input layer of a trained neural network. The processor is further configured to generate, by the neural network, a plurality of predicted classification scores and associated probabilities for each class of audio event contained in each segment of the run-time input audio signal. In post-processing, the processor is further configured to generate smoothed predicted classification scores, associated smoothed probabilities, and class window confidence values for each class for each of a plurality of candidate window sizes.

Abstract: Examples are disclosed relating to computing devices and methods for performing touch detection within a virtual trackpad area of a touch screen display. In one example, a non-trackpad touch input signal is received from outside the virtual trackpad area and processed with at least a jitter restrictor algorithm that applies a non-trackpad distance between reported touch locations. A virtual trackpad touch input signal is received from within the virtual trackpad area. On condition of determining that the virtual trackpad touch input signal is received from within the virtual trackpad area, the virtual trackpad touch input signal is processed with the jitter restrictor algorithm that applies a virtual trackpad distance between reported touch locations that is smaller than the non-trackpad distance between reported touch locations.

Abstract: Examples are disclosed relating to computing devices and methods for performing touch detection within a virtual trackpad area of a touch screen display. In one example, a non-trackpad touch input signal is received from outside the virtual trackpad area and processed with at least one touch detection algorithm applying a non-trackpad threshold value. A virtual trackpad touch input signal is determined to be received from within the virtual trackpad area. On condition of determining that the virtual trackpad touch input signal is received from within the virtual trackpad area, the virtual trackpad touch input signal is processed with the touch detection algorithm applying a virtual trackpad threshold value different from the non-trackpad threshold value.

Abstract: One example provides a computing device comprising a touch sensor, a camera, a logic subsystem, and a storage subsystem. The storage subsystem comprises instructions executable by the logic subsystem to receive image data from the camera; determine, based on the image data, information regarding one or more of a hand of a user or a stylus held by the user; and based at least on the information regarding one or more of the hand of the user or the stylus held by the user, control operation of the touch sensor.

Abstract: An electronic device comprises a touch sensor, a movement sensor, and recalibration logic. The touch sensor provides input to the electronic device. The movement sensor has an output that varies in dependence on movement of the electronic device, and the recalibration logic is configured to vary a rate of successful recalibration of the touch sensor based at least partly on the output of the movement sensor. The successful recalibration comprises replacement of a stored calibration mapping of each of a plurality of (X, Y) coordinates of the touch sensor to a corresponding touch-free capacitance.

Abstract: An earbud is configured to detect its location (e.g., in-ear and out-of-ear) based on an acoustical signature with and without user-specific customization. The earbud location may be indicated to a host, e.g., to determine playback. Location determinations are based on features extracted from acoustical samples taken by the earbud compared to features extracted from out-of-ear acoustical samples and non-user-specific and/or user-specific in-ear samples. A non-user-specific machine learning (ML) model trained on features extracted from non-user-specific in-ear and out-of-ear samples may be an initial/default locator. The non-user-specific model may be customized for specific users. A user-specific in-model may be created by training the non-user-specific model on features extracted from user-specific in-ear samples collected when the earbud is located in-ear for a specific user. The user-specific ML model may be selected to classify a location of the earbud for one or more associated hosts.