Apple Inc.

Jamil Dhanani

Timothy S Paek

Minwoo Jeong

Keith P Avery

Brandt M Westing

Harveen Kaur

Varun Maudgalya

Muhammad Amir Shafiq

Christopher Brian Fleizach

Xiaojin Shi

Garrett L Weinberg

Dmytro Rudchenko

Francesco Rossi

Juan Carlos Garcia

Heriberto Nieto

Naga Rama Abhishek Pratapa

John M Nefulda

Madhu Chinthakunta

Xinyi Yan

Kenneth S Friedman

Viet Huy Le

Geoffrey Nagy

SeyedMehdi Mohaimenianpour

Richard T Vaughan

Michal K Wegrzynski

Rishabh Yadav

Sommer E Panage

William Caruso

Systems and processes for an integrated sensor framework are provided. For example, a first electronic device receives at least one input including sensor data from a second device. A representation of a physical environment associated with the first electronic device is obtained based on sensor data from the first electronic device and the sensor data from the second device. Movement information corresponding to movement of an object within the physical environment is identified. Event information is determined corresponding to activity within the physical environment, wherein the event information is determined based on the identified movement information and the representation of the physical environment. Accordingly, an output is provided to the user based on the event information.

Integrated sensor framework for multi-device communication and interoperability

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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San Francisco, CA, United States of America

Jamil Dhanani