Apple Inc.

Charles Maalouf

Shawn R Scully

Heriberto Nieto

Shiwen Zhao

Christopher Brian Fleizach

Tu K Nguyen

Stephen Olivier Lemay

Timothy S Paek

Brandon J Newendorp

Srikrishna Sridhar

Minwoo Jeong

Joanna Peterson

Brandt M Westing

Michael Reese Bastian

Roman Holenstein

Jennifer D Patton

Behrooz Shahsavari

Michael J Beyhs

Hojjat Seyed Mousavi

Ibrahim G Yusuf

Julian Quintana

Lilian H Liang

Warren J Seto

Joanna Arreaza-taylor

Hannah G Coleman

Jeremiah R Johnson

Yoonju Han

Jared L Zerbe

Elizabeth Caroline Cranfill

Kevin M Lynch

William C Athas

Andreas Edgar Schobel

Gierad Laput

James Nathan Cartwright

Brian H Tsang

Joshua R Ford

Hojat Seyed Mousavi

The present disclosure generally relates to navigating user interfaces using a cursor.

Navigating user interfaces using a cursor

The present disclosure generally relates to displaying user interfaces with device controls.

User interfaces for device controls

Individual health related events (e.g., handwashing events) can be detected based on multiple sensors including motion and audio sensors. Detecting a qualifying handwashing event can include detecting a qualifying scrubbing event based on motion data (e.g., accelerometer data) and a qualifying rinsing event based on audio data. In some examples, power consumption can be reduced by implementing one or more power saving mitigations.

Methods and apparatus for detecting individual health related events

The present disclosure generally relates to navigating user interfaces using hand gestures.

Navigating user interfaces using hand gestures

A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

Machine-learning based gesture recognition using multiple sensors

Systems and processes for operating an intelligent automated assistant are provided. An example process includes detecting input representing motion of an electronic device and sampling an audio input with a microphone of the electronic device. The example process further includes determining, based on the audio input and the input representing motion of the electronic device, whether to initiate a virtual assistant session. In accordance with a determination to initiate the virtual assistant session, the example process includes initiating the virtual assistant session. In accordance with a determination not to initiate the virtual assistant session, the example process includes forgoing initiating the virtual assistant session.

Raise to speak

Growing community of inventors

Seattle, WA, United States of America

Charles Maalouf