Apple Inc.

Brandt M Westing

Timothy S Paek

Minwoo Jeong

Heriberto Nieto

Stephen Olivier Lemay

Brandon J Newendorp

Srikrishna Sridhar

Shawn R Scully

Charles Maalouf

Joanna Peterson

Shiwen Zhao

Michael Reese Bastian

Roman Holenstein

Keith P Avery

Jamil Dhanani

Varun Maudgalya

Harveen Kaur

Christopher Brian Fleizach

Xiaojin Shi

Garrett L Weinberg

Dmytro Rudchenko

Francesco Rossi

Madhu Chinthakunta

Muhammad Amir Shafiq

John M Nefulda

Xinyi Yan

Kenneth S Friedman

Viet Huy Le

Michal K Wegrzynski

Rishabh Yadav

Sommer E Panage

William Caruso

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

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

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

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Seattle, WA, United States of America

Brandt M Westing