Apple Inc.

Keith P Avery

Julie A Arney

Jay Kriz Blahnik

David S Clark

Aaron P Thompson

Brian R Drell

Kevin M Lynch

Timothy S Paek

Minwoo Jeong

Brandt M Westing

Jamil Dhanani

Varun Maudgalya

Harveen Kaur

Stephen Olivier Lemay

Pedro Mari

Christopher Brian Fleizach

Aled Hywel Williams

Molly Pray Wiebe

Xiaojin Shi

Eamon Francis Gilravi

Aswath Manoharan

Garrett L Weinberg

Dmytro Rudchenko

Jay Moon

Francesco Rossi

Sreeneel K Maddika

Anthony D'Auria

Chaitanya Mannemala

Heriberto Nieto

Lynn I Streja

Myra C Lukens

Karan M Daryanani

Madhu Chinthakunta

Abhishek Rawat

Muhammad Amir Shafiq

John M Nefulda

William Caruso

Xinyi Yan

Kenneth S Friedman

Viet Huy Le

Michal K Wegrzynski

Sommer E Panage

Rishabh Yadav

An example process includes: while displaying a portion of an extended reality (XR) environment representing a current field of view of a user: detecting a user gaze at a first object displayed in the XR environment, where the first object is persistent in the current field of view of the XR environment; in response to detecting the user gaze at the first object, expanding the first object into a list of objects including a second object representing a digital assistant; detecting a user gaze at the second object; in accordance with detecting the user gaze at the second object, displaying a first animation of the second object indicating that a digital assistant session is initiated; receiving a first audio input from the user; and displaying a second animation of the second object indicating that the digital assistant is actively listening to the user.

Extended reality based digital assistant interactions

Notifications for instructing user behavior can be determined and presented when appropriate. Current fitness data are accessed. This data is used to determine whether a user fitness goal is likely to be achieved. If not, a notification may be determined and presented at a user device.

Determination and presentation of customized notifications

Notifications for instructing user behavior can be determined and presented when appropriate. Historical fitness data and current fitness data are accessed. This data is used to determine whether a user fitness goal is likely to be achieved. If not, a notification may be determined and presented at a user device.

The subject technology receives, from a first sensor of a device, first sensor output of a first type. The subject technology receives, from a second sensor of the device, second sensor output of a second type, the first and second sensors being non-touch sensors. The subject technology provides 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 touch-based gesture based on sensor output of the first type and sensor output of the second type. The subject technology provides a predicted touch-based gesture based on output from the machine learning model. Further, the subject technology adjusts an audio output level of the device based on the predicted gesture, and where the device is an audio output device.

Machine-learning based gesture recognition

The present disclosure generally relates to assigning tasks to various user inputs, and detecting and responding to user inputs. In some embodiments, the present disclosure relates to assigning tasks to various user inputs received on a back surface of a device, and detecting and responding to user inputs on the back surface of the device.

User interfaces for assigning and responding to user inputs

Systems, methods, and computer-readable media for monitoring and motivating physical activity of a user with an electronic device are provided.

Physical activity monitoring and motivating with an electronic device

The subject technology receives a neural network model in a model format, the model format including information for a set of layers of the neural network model, each layer of the set of layers including a set of respective operations. The subject technology generates neural network (NN) code from the neural network model, the NN code being in a programming language distinct from the model format, and the NN code comprising a respective memory allocation for each respective layer of the set of layers of the neural network model, where the generating comprises determining the respective memory allocation for each respective layer based at least in part on a resource constraint of a target device. The subject technology compiles the NN code into a binary format. The subject technology generates a package for deploying the compiled NN code on the target device.

Compiling code for a machine learning model for execution on a specialized processor

Notifications for instructing user behavior can be determined and presented when appropriate. The timing of when to present the notification can be based on a device being in a worn state, configuration data, and/or an open time on a user calendar.

Coaching notifications can be determined based on current user fitness data and historical user fitness data. The coaching notifications may include information that encourages or otherwise coaches users towards established fitness goals. To this end, the timing for presenting along with the substance of the coaching notifications can be customized to the users.

Growing community of inventors

Sunnyvale, CA, United States of America

Keith P Avery