Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Steven Scott Noble

Liang Zhang

Deepak Agarwal

Yang Yang

David Q He

Sanjay Sureshchandra Dubey

Ruixuan Hou

Ritesh Maheshwari

Jieying Chen

Toon Sripatanaskul

Matthew Paul Bell

Yi Ding

Paul Aksenti Savastinuk

Stephen Michael Polansky

Alexander L Honda

Ross David Roessler

Peter Cheng

Michael Dillon

A system for processing data is provided. During operation, the system obtains a current window of one or more intervals of timeseries data collected from a monitored system. Next, the system continuously performs a statistical hypothesis test that compares the one or more intervals of the time-series data with baseline values from historic time-series data associated with the monitored system. When the statistical hypothesis test indicates a deviation of the time-series data from the baseline values, the system outputs an alert of an anomaly represented by the deviation.

Statistical detection of site speed performance anomalies

The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of components of a time-series performance metric associated with an anomaly in a performance of one or more monitored systems. For each component in the set of components, the system performs a statistical hypothesis test on the component to assess a deviation of the component from a baseline value of the component. When the statistical hypothesis test identifies a statistically significant deviation of the component from the baseline value, the system outputs an alert comprising a root cause of the anomaly that is represented by the statistically significant deviation of the component from the baseline value.

Root cause investigation of site speed performance anomalies

The disclosed embodiments provide a system for processing data. During operation, the system obtains a component of a time-series performance metric associated with a server-side root cause of an anomaly in the time-series performance metric. Next, the system obtains a call graph representation of the component, wherein the call graph representation includes a parent node having a parent value of the component and a set of child nodes of the parent node, each child node having a corresponding child value of the component. The system then analyzes the call graph representation to identify one or more of the child nodes as sources of the anomaly. Finally, the system outputs an alert that identifies the sources of the anomaly.

Analysis of site speed performance anomalies caused by server-side issues

The hand which a user is using to hold an electronic device can be determined by analyzing data captured by one or more motion sensors on the device. The curvature to the motion can be indicative of handedness, and processing motion features using a classifier algorithm can enable the determination of handedness with a corresponding confidence. In some embodiments, motion data is collected over a monitoring window, and handedness values are accepted when the handedness value remains the same with at least a minimum confidence for at least a minimum number of window periods. A determination of handedness enables an operating system and/or applications executing on the device to adjust one or more operational or interface aspects in order to make it easier for the user to operate the device using the hand currently holding the device.

Handedness determinations for electronic devices

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.

Machine-learning based tap detection

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Soquel, CA, United States of America

Steven Scott Noble