Google Inc.

Yahoo! Inc.

International Business Machines Corporation

Matsushita Electric Industrial Co., Ltd.

Panasonic Technologies, Inc.

Verizon Media Inc.

Matsushita Electronic Industrial Co., Ltd

Andrew Stephen Tomkins

Shanmugasundaram Ravikumar

Bo Pang

Shalini Agarwal

Mark Yinan Li

Prabhakar Raghavan

Timothy Youngjin Sohn

Erwin Can Sar

Justin Lewis Kosslyn

Sangsoo Sung

MyLinh Yang

Sridhar Rajagopalan

Daniel Gruhl

Robin Dua

Andre Rohe

Todd Christopher Jackson

Andrew Ames Bunner

Tristan Harris

Jasmine Novak

Can Sar

Angelo DiNardi

Dandapani Sivakumar

Sujith Ravi

Pavel Berkhin

Ramanathan V Guha

Fergus Gerard Hurley

Daniel P Lopresti

Luca De Alfaro

Cameron A Marlow

Ya Luo

Matthew K Gray

David Andrew Gibson

Michael Angelo

Russell J Ryan

Roni Fischman Zeiger

Ray Su

Namrata U Godbole

Usama M Fayyad

Matthew Denesuk

Byron Edward Dom

Joseph Andrew Magnani

Vijay Umapathy

Kevin Snow McCurley

John Anthony Tomlin

Abhijit Bose

Sergei Vassilvitskii

Soumen Chakrabarti

Raghu Ramakrishnan

Cameron Knox Adams

Mayur Dhondu Datar

Rajesh Girish Parekh

Jonathan Terleski

Vanja Josifovski

Marcus Felipe Fontoura

Yonatan Zunger

Boris Mazniker

Madhukar R Korupolu

Benjamin Clay Reed

Deepayan Chakrabarti

Mohammad Mahdian

Daniel Wesley Dulitz

Alexander Fabrikant

Thomas J Duerig

Michael Ching

Christopher Olston

Jiangying Zhou

Alexander Sobol

Robert Spiro

Joerg Meyer

Stephen Dill

Srinivasan Balasubramanian

Michael Schwarz

Zhen Li

Daniel Wyatt

Reiner Kraft

Nimrod Megiddo

Shivakumar Vaithyanathan

Jessica Nicola Staddon

Scott J Gaffney

Karon A Weber

Jignashu Parikh

Arvind Arasu

Tushar Deepak Chandra

Samantha Marie Tripodi

Tal Shaked

Sang Soo Sung

Alexander Johannes Smola

Jason Yeong Zien

Nadav Eiron

Yue Ma

Ronald Fagin

Daniel Joseph Clancy

Atish Das Sarma

Andrei Zary Broder

Saurabh Mathur

Jan Hendrik Pieper

Daniel Norin Meredith

Zach Yeskel

Randolph Preston McAfee

Christopher Wiggins

Methods and apparatus related to determining one or more user habits for a user. A group of one or more past user activity occurrences of a user may be determined based on similarity between the past user activity occurrences of the group. A user habit may be determined based on the past user activity occurrences of the group.

Determining a user habit

Methods and apparatus related to associating a task with a user based on the user selecting a task suggestion that is provided to the user in response to a user query. In some implementations, the task may be identified based on similarities between the words and/or phrases of the user query and a task suggestion that is associated with a task. In some implementations, the task may be identified based on user data associated with the user. In some implementations, the task may be associated with additional information related to completing the task.

Associating a task with a user based on user selection of a query suggestion

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for list accumulation and reminder triggering. In one aspect, a method includes receiving, at a data processing apparatus, a first input comprising a plurality of terms; determining, by the data processing apparatus, that one or more of the terms in the first input specify an item that belongs to a reminder category, the reminder category being associated with a collection of items that belong to the category and for which reminders may be generated; and in response to the determination, storing data that associates a reminder type with the item specified by the one or more terms.

List accumulation and reminder triggering

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an image embedding model. In one aspect, a method comprises: obtaining training data comprising a plurality of training examples, wherein each training example comprises: an image pair comprising a first image and a second image; and selection data indicating one or more of: (i) a co-click rate of the image pair, and (ii) a similar-image click rate of the image pair; and using the training data to train an image embedding model having a plurality of image embedding model parameters.

Training image and text embedding models

Implementations are directed to facilitating user device and/or agent device actions during a communication session. An interactive communications system provides outputs, as outlined below, that are tailored to enhance the functionality of the communication session, reduce the number of dialog 'turns' of the communications session and/or the number of user inputs to devices involved in the session, and/or otherwise mitigate consumption of network and/or hardware resources during the communication session. In various implementations, the communication session involves user device(s) of a user, agent device(s) of an agent, and the interactive communications system. The interactive communications system can analyze various communications from the user device(s) and/or agent device(s) during a communication session in which the user (via the user device(s)) directs various communications to the agent, and in which the agent (via the agent device(s)) optionally directs various communications to the user. The interactive communications system provides action performance element(s) and/or other output(s) that are each specific to a corresponding current intent and corresponding current action of the communication session.

Facilitating user device and/or agent device actions during a communication session

Methods and apparatus related to identifying one or more messages sent by a user, identifying two or more contacts that are associated with one or more of the messages, determining a strength of relationship score between identified contacts, and utilizing the strength of relationship scores to provide additional information related to the contacts. A strength of relationship score between a contact and one or more other contacts may be determined based on one or more properties of one or more of the messages. In some implementations, contacts groups may be determined based on the strength of relationship scores. In some implementations, contacts groups may be utilized to disambiguate references to contacts in messages. In some implementations, contacts group may be utilized to provide suggestions to the user of additional contacts of a contacts group that includes the indicated recipient contact of a message.

Determining strength of association between user contacts

Methods and apparatus related to determining additional features for a user task entry of a user based on a user habit of the user. For example, one or more aspects of a user task entry of a user may be compared to one or more aspects of a user habit of the user to determine a correlation measure between the task entry and the user habit. If the correlation measure satisfies a threshold, one or more additional features of the user task entry may be determined based on the user habit. For example, the user habit may include one or more trigger indicators that indicate an actual entry or anticipated entry of the user into the user habit, and one or more of the trigger indicators may be utilized to determine task trigger indicators for the user task entry.

Determining additional features for a task entry based on a user habit

Methods and apparatus related to determining a ranking of class members of a task component class. A task component class identifier may be identified, the task component class identifier having a plurality of associated class members. One or more task completion indicators having the identified at least one task component class identifier may be determined. Each of the task completion indicators may indicate which of the associated class members was utilized to complete a task. One or more of the associated class members for the task component class identifier may be ranked. The ranking may be based on frequency of occurrence of the given associated class member in the task completion indicators.

Selecting an action member in response to input that indicates an action class

Methods, systems, and apparatus, for detour based content selection. In one aspect, a method includes receiving, at a data processing apparatus, location data for a plurality of users, the location data describing, for each user, locations of a user device of the user; determining, from the location data, route data for the plurality of users, the route data describing, for each user, a route traveled by the user; determining, by the data processing apparatus, for at least one or more of the users, a detour from the route traveled by the user to a retail location of the offeror; determining, by the data processing apparatus, for each of the at least one or more of the users, whether the user is eligible for an offer of the offeror based on the detour; and for each user eligible for an offer, enabling the offeror to provide an offer to the user.

Detour based content selections

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San Jose, CA, United States of America

Andrew Stephen Tomkins