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    • 5. 发明授权
    • Machine-learning based tap detection
    • 基于机器学习的抽头检测
    • US09235278B1
    • 2016-01-12
    • US14340455
    • 2014-07-24
    • Amazon Technologies, Inc.
    • Peter ChengSteven Scott NobleMatthew Paul BellYi DingStephen Michael PolanskyAlexander Li Honda
    • G06F3/045G06F3/038G06F3/0354G06F3/0346G06K9/00G06F3/0487
    • G06F3/0487G06F3/0346G06K9/00355
    • An electronic device can be configured to enable a user to provide input via a tap of the device without the use of touch sensors (e.g., resistive, capacitive, ultrasonic or other acoustic, infrared or other optical, or piezoelectric touch technologies) and/or mechanical switches. Such a device can include other sensors, including inertial sensors (e.g., accelerometers, gyroscopes, or a combination thereof), microphones, proximity sensors, ambient light sensors, and/or cameras, among others, that can be used to capture respective sensor data. Feature values with respect to the respective sensor data can be extracted, and the feature values can be analyzed using machine learning to determine when the user has tapped on the electronic device. Detection of a single tap or multiple taps performed on the electronic device can be utilized to control the device.
    • 电子设备可以被配置为使得用户能够通过设备的抽头来提供输入,而不需要使用触摸传感器(例如,电阻,电容,超声波或其他声学,红外或其它光学或压电触摸技术)和/或 机械开关 这样的装置可以包括其他传感器,包括惯性传感器(例如,加速度计,陀螺仪或其组合),麦克风,接近传感器,环境光传感器和/或相机等,其可用于捕获相应的传感器数据 。 可以提取关于各个传感器数据的特征值,并且可以使用机器学习来分析特征值,以确定用户何时利用电子设备。 可以利用在电子设备上执行的单个抽头或多个抽头的检测来控制该设备。
    • 7. 发明申请
    • MACHINE-LEARNING BASED TAP DETECTION
    • 基于机器学习的TAP检测
    • US20160026261A1
    • 2016-01-28
    • US14340455
    • 2014-07-24
    • Amazon Technologies, Inc.
    • Peter ChengSteven Scott NobleMatthew Paul BellYi DingStephen Michael PolanskyAlexander Li Honda
    • G06F3/038G06F3/0346G06K9/00G06F3/0354
    • G06F3/0487G06F3/0346G06K9/00355
    • An electronic device can be configured to enable a user to provide input via a tap of the device without the use of touch sensors (e.g., resistive, capacitive, ultrasonic or other acoustic, infrared or other optical, or piezoelectric touch technologies) and/or mechanical switches. Such a device can include other sensors, including inertial sensors (e.g., accelerometers, gyroscopes, or a combination thereof), microphones, proximity sensors, ambient light sensors, and/or cameras, among others, that can be used to capture respective sensor data. Feature values with respect to the respective sensor data can be extracted, and the feature values can be analyzed using machine learning to determine when the user has tapped on the electronic device. Detection of a single tap or multiple taps performed on the electronic device can be utilized to control the device.
    • 电子设备可以被配置为使得用户能够通过设备的抽头来提供输入,而不需要使用触摸传感器(例如,电阻,电容,超声波或其他声学,红外或其它光学或压电触摸技术)和/或 机械开关 这样的装置可以包括其他传感器,包括惯性传感器(例如,加速度计,陀螺仪或其组合),麦克风,接近传感器,环境光传感器和/或相机等,其可用于捕获相应的传感器数据 。 可以提取关于各个传感器数据的特征值,并且可以使用机器学习来分析特征值,以确定用户何时在电子设备上点击。 可以利用在电子设备上执行的单个抽头或多个抽头的检测来控制该设备。