Microsoft Technology Licensing, LLC

Apple Inc.

Microsoft Corporation

Timothy S Paek

Eric Norman Badger

Asela J Gunawardana

Dmytro Rudchenko

Bongshin Lee

David Maxwell Chickering

Eric J Horvitz

Johnson T Apacible

Minwoo Jeong

Vishwas Kulkarni

Brandt M Westing

Paramvir Bahl

Itai Almog

Parthasarathy Sundararajan

Drew Elliott Linerud

Heriberto Nieto

Stephen Olivier Lemay

Brandon J Newendorp

Srikrishna Sridhar

Shawn R Scully

Juan Carlos Garcia

Charles Maalouf

Joanna Peterson

Shiwen Zhao

Michael Reese Bastian

Jamil Dhanani

Roman Holenstein

Anoop Gupta

Shawna Julie Davis

Moises Goldszmidt

Daniel Allen Rosenfeld

Tirthankar Sengupta

Keith P Avery

Anatoly D Adamov

Andrew Douglas Peterson

Kenneth R Walters

Julia R Reisler

Yu-Chung Hsiao

Pavan Chitta

Gretchen Nash

Nate M Landes

Olivia Janik

Dmitry Rudchenko

Marcel Van Os

Christopher A Meek

Bo Thiesson

Amish Patel

Jason Anthony Grieves

Daniel Edward Ostrowski

Madhusudan Chinthakunta

Lauren Elise Tappana

Ronald Keith Logan

Sreenivas Addagatla

Max Chickering

Ping Wen Ong

Asela Jeevaka Ranaweera Gunawardana

Muhammad Amir Shafiq

Varun Maudgalya

Zach Oligschlaeger

Harveen Kaur

Qiang Wu

Stephen Edward Hodges

Christopher Brian Fleizach

Susan Theresa Dumais

Paul Nathan Bennett

Xiaojin Shi

Garrett L Weinberg

Anton Mityagin

John Helmes

Francesco Rossi

Bo-June Hsu

Yutaka Suzue

Juan Dai

Aparna Lakshmiratan

Hong Z Tan

Nikhil Devanur Rangarajan

Devlin David Bentley

David Lee Nelson

Madhu Chinthakunta

William Caruso

Naga Rama Abhishek Pratapa

Drew Elliot Linerud

John M Nefulda

Norman Timo Pohl

Nicolas Villar Martinez

Sommer E Panage

Kenneth S Friedman

Xinyi Yan

Geoffrey Nagy

SeyedMehdi Mohaimenianpour

Richard T Vaughan

Viet Huy Le

Cole R Benson

Michal K Wegrzynski

Rishabh Yadav

Zachary D Oligschlaeger

Alisher Saydalikhodjayev

Gleb G Krivosheev

Systems and methods are disclosed for triggering an update to a machine-learning model upon detecting that a distribution of particular (e.g., recently collected) input data set is sufficiently different from a distribution training input data set used to train the model. The distributions may be determined to be sufficiently different when a classifier can identify to which distribution individual data elements belong (e.g., to at least a predetermined degree). An update to the machine-learning model can include morphing weights used by the model and/or retraining the model.

Automated input-data monitoring to dynamically adapt machine-learning techniques

Systems and processes for integrating a digital assistant with telephony are provided. For example, an incoming call may be received, from a caller, at an electronic device. A communication session may be established between the caller and the digital assistant of the electronic device. In accordance with a determination that the identification of the caller is unknown, determination is made whether the caller corresponds to an automated calling system. In accordance with a determination that the identification of the caller is known, a response is provided by the digital assistant to the caller. An output including information corresponding to the communication is provided at the electronic device.

Digital assistant integration with telephony

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

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

Described herein is a split virtual keyboard that is displayed on a tablet (slate) computing device. The split virtual keyboard includes a first portion and a second portion, the first portion being separated from the second portion. The first portion includes a plurality of character keys that are representative at least one respective character. The tablet computing device is configured to support text generation by way of a continuous sequence of strokes over the plurality of character keys in the first portion of the split virtual keyboard.

Split virtual keyboard on a mobile computing device

Techniques for providing an arc layout for a soft input panel are described. In some examples, an electronic device can transition from providing a substantially straight and/or horizontal layout for the soft input panel to an arc layout for the soft input panel. The electronic device can display a graphical element at a location on the soft input panel. The electronic device can receive input corresponding to, for example, a swipe of the graphical element in a curved manner across the soft input panel. Based on the input, the electronic device can transition from displaying the soft input panel using the substantially straight layout to displaying the soft input panel using the arc layout.

Arc keyboard layout

Growing community of inventors

Sammamish, WA, United States of America

Timothy S Paek