Facebook, Inc.

Microsoft Technology Licensing, LLC

Meta Platforms Technologies, LLC

Facebook Technologies, LLC

Amazon Technologies, Inc.

Meta Platforms, Inc.

Francislav Petrov Penov

Damian Kowalewski

Xiaohu Liu

Wissam S Kazan

Aidymar Bigio

Siyin Yang

Mateusz Marek Niewczas

Rajen Subba

Anuj Kumar

Paul Anthony Crook

Eran Tal

Martin Rehwald

Georgiy Yakovlev

Dung Nguyen Tien

William Crosby Presant

Anupma Chhabra

Zachary Chee-Ping Lawrence

Rizwan Ahmad

Angelica Estefania Escareno

Christopher Chun-Ning Pan

Daniel Bibireata

Geoffrey Heller

Mona Akmal

Shazaan Bahrainwala

Aaron Michael Donsbach

Yael Gregory Maguire

Kenneth Mark Karakotsios

Shizhe Shen

Benjamin N Truelove

Timothy Youngjin Sohn

Kah Kuen Fu

Felipe Luis Naranjo

Vasudha Chandrasekaran

Patrick Jakubowski

Kavita K Kamani

Richard Howard Suplee, Iii

Amanda Font

Eric Xiao

Viktor Passichenko

Lisa Xiaoyi Huang

Sridhar Rao

Samuel DeKoven Grossberg

Samuel D Patton, Iii

Arthur Beatte, Iv

Randy Alan Chapman

Gaurav Arora

Subodh Iyengar

Leo Benedict Baldwin

Louis LeRoi LeGrand, Iii

Dean Kenneth Jackson

Chang Yuan

Baiyang Liu

Emre Mehmet Kiciman

Michael Marucheck

James F Geist, Jr

John Stephen Ketchpaw

Chun-Kai Wang

Daniel Colascione

Georges Edouard Maurice Berenger

Aaron C Hoff

Christopher S A MacGregor

Honglei Liu

Charles P Vaughn

Alexander Charles Granieri

Eric Erkon Hsin

Sujay Sahni

Austin Longino

Christopher E Balmes

George Siqiao Wang

György K Schadt

Michael Greenberg

Leif Haven Martinson

Yiming Pu

Matteo Visentin

Amandeep S Ghag

Swati Goel

Alexis Hope Gottlieb

Daniel Joshua Steinbock

Clark Scheff

Upendra Shardanand

Xinlei Wang

Michael J Marucheck

Austin Randolph Longino

Wissam Kazan

Arthur Beatte

In one embodiment, a method includes receiving a user request from a first user from a client system associated with a first user, wherein the user request comprise a gesture-input from the first user and a speech-input from the first user, determining an intent corresponding to the user request based on the gesture-input by a personalized gesture-classification model associated with the first user, executing one or more tasks based on the determined intent and the speech-input, and sending instructions for presenting execution results of the one or more tasks to the client system responsive the user request.

Personalized gesture recognition for user interaction with assistant systems

A method includes detecting a user input comprising an incomplete three-dimensional (3D) gesture performed by one or more hands of a first user by a virtual-reality (VR) headset, selecting candidate 3D gestures from pre-defined 3D gestures based on a personalized gesture-recognition model, wherein each of the candidate 3D gestures is associated with a confidence score representing a likelihood the first user intended to input the respective candidate 3D gesture, and presenting one or more suggested inputs corresponding to one or more of the candidate 3D gestures at the VR headset.

Auto-completion for gesture-input in assistant systems

In one embodiment, a method includes receiving an initial input in a first modality from a first user from a client system associated with the first user, determining one or more intents corresponding to the initial input by an intent-understanding module, generating one or more candidate continuation-inputs based on the one or more intents, where the one or more candidate continuation-inputs are in one or more candidate modalities, respectively, and wherein the candidate modalities are different from the first modality, and sending instructions for presenting one or more suggested inputs corresponding to one or more of the candidate continuation-inputs to the client system.

In one embodiment, a method includes maintaining a reminder store comprising one or more reminders for a first user, wherein each reminder is associated with an initial activation-condition, determining a context associated with the first user based on signals from a client system associated with the first user, determining a suppression-condition associated with at least one of the reminders is met based on the context, and suppressing the at least one reminder for the first user.

Suppressing reminders for assistant systems

In accordance with one or more aspects of selecting between client-side and server-side market detection, a determination is made at a device as to which of a client-side detected market and a server-side detected market is to have priority for a service. An application of the device is configured in accordance with a client-side market configuration setting if the client-side detected market has priority, and is configured in accordance with a server-side market configuration setting if the server-side detected market has priority.

Selecting between client-side and server-side market detection

In one embodiment, a method includes receiving a user request including an ambiguous mention to create a reminder from a client system associated with a user, disambiguating the mention to identify a first entity referenced in the mention, wherein the first entity is identified based on user profile data associated with the user, determining an activation condition associated with the user request, wherein the activation condition is based on one or more of a time or a location referenced in the user request, wherein the time and/or location are determined based on contextual information associated with the user request, generating the reminder based on the first entity and the activation condition, and sending instructions to the client system for presenting the reminder when the activation condition is satisfied.

Generating smart reminders by assistant systems

In one embodiment, a method includes receiving a media content for delivery to a first user, wherein the media content is associated with a priority level, accessing context information associated with the first user, wherein the context information is determined based on one or more multimodal signals from a client device associated with the first user, determining a delivery level for the media content based on the priority level of the media content and the context information, and instructing the client device to present a notification associated with the media content to the first user, wherein the notification is rendered in one or more modalities determined based on the delivery level for the media content.

Generating context-aware rendering of media contents for assistant systems

In one embodiment, a method includes sending, to a client system of a first user, a notification for presentation to the first user. The notification may include a reference to a media-content item and an activatable element for accessing the media-content item. A user input selecting the activatable element may be received. Responsive to the user input, a list of proximate media-player devices to the client system may be determined. The list of proximate media-player devices may be sent to the client system for presentation to the first user. Each of the proximate media-player devices may correspond to a deep link being selectable by the first user. Each deep link may include instructions for presenting the media-content item on a display device coupled to the respective media-player device.

Content notification on online social networks for media-player devices

The present disclosure is directed towards systems and method for determining whether a scannable code displayed on a client device has been scanned. In particular, the systems and methods described herein involve activating one or more on-board sensors of the client device to capture output signals. Based on the output signals, the systems and methods include identifying discrete signals that indicate whether the scannable code has been scanned by a scanning device. Further, based on an analysis of the identified discrete signals, the systems and methods include determining whether the scannable code has been scanned by the scanning device.

Detecting a scan using on-device sensors

In one embodiment, a method includes detecting a user input comprising an incomplete gesture performed by one or more hands of a first user by a client system associated with the first user; selecting one or more candidate gestures from a plurality of pre-defined gestures by the client system based on a personalized gesture-recognition model, wherein each of the candidate gestures is associated with a confidence score representing a likelihood the first user intended to input the respective candidate gesture, and presenting one or more suggested inputs corresponding to one or more of the candidate gestures at the client system.

Auto-completion for multi-modal user input in assistant systems

In one embodiment, a method includes receiving, from a device via a wireless signal, a request to access a WLAN of a router, the request including a device identifier. The provisioning agent may be within wireless ranges of the device and of the router. The provisioning agent may request, from an authentication server, a password for the device. The password may be uniquely generated by the authentication server for the device. The provisioning agent may receive the password from the authentication server. The provisioning agent may send, to the router, the password and the device identifier. The provisioning agent may receive, from the router, an indication that the router has verified the password with the authentication server. The router may store the device identifier in association with the password. The provisioning agent may send, to the device, the password. The password may authenticate the device to the router.

Authenticating devices to a network

Growing community of inventors

Kirkland, WA, United States of America

Francislav Petrov Penov