Google Inc.

David Petrou

Matthew Nirvan Sharifi

Hartwig Adam

Hartmut Neven

John J Flynn

Matthew John Bridges

Matthew Ryan Casey

Andrew Logan Harp

Shailesh Nalawadi

Abhanshu Sharma

Adam L Berenzweig

Daniel Ramage

Andrews-Junior Kimbembe

Teresa Ko

Mikkel Crone Koser

Alexei Masterov

Paul Chang

Dominik Roblek

Blaise Hilary Aguera-Arcas

Ashok Chhabedia Popat

Andrew Rabinovich

Gabriel Taubman

Julian James Odell

Keith Allen Bonawitz

Jeffrey C Reynar

John Patrick Flynn

Jorge Pereira

Cong Li

Alfred Zalmon Spector

Kai Wang

Charles Joseph Rosenberg

Ulrich Franz Buddemeier

Fernando A Brucher

Laura Garcia-Barrio

Theodore Power

Samy Bengio

Andrew William Hogue

Krishnendu Chaudhury

Michael Gordon

David J Vespe

Daniel Loreto

Nikola Jevtic

Chiu-Ki Chan

Dragomir Dimitrov Anguelov

Yossi Matias

Brandon Charles Barbello

Pranav Khaitan

Nadav Bar

Noam Etzion-Rosenberg

Benjamin Schlesinger

Matthew Sharifi

Rafael Spring

Ori Kabeli

Shavit Matias

Lu Chen

Avi Flamholz

Jacob Smullyan

Sergiu Goschin

Matthew Dominic Sharifi

Andrew Harp

Systems and methods are provided for suggesting actions for selected text based on content displayed on a mobile device. An example method can include converting a selection made via a display device into a query, providing the query to an action suggestion model that is trained to predict an action given a query, each action being associated with a mobile application, receiving one or more predicted actions, and initiating display of the one or more predicted actions on the display device. Another example method can include identifying, from search records, queries where a website is highly ranked, the website being one of a plurality of websites in a mapping of websites to mobile applications. The method can also include generating positive training examples for an action suggestion model from the identified queries, and training the action suggestion model using the positive training examples.

Action suggestions for user-selected content

At least one aspect of the present disclosure is directed to systems and methods of generating on-device content items to improve security and network utilization. A client device can access content resources and category information from a data structure in the memory the memory of the client device. The client device can generate on-device content items based on the content resources and the category information. The client device can receive an indication to display a content item in an application executing on the client device. The client device can scan the client device for context information to create a relevant device context. The client device can select an on-device content item based on the relevant device context. The client device can provide the selected on-device item to the application for display, detect an interaction with the displayed content item, and update a content selection model or a content generation model.

Systems and methods for generating and selecting local content to improve security and utilization of network resources

A system and method of identifying objects is provided. In one aspect, the system and method includes a hand-held device with a display, camera and processor. As the camera captures images and displays them on the display, the processor compares the information retrieved in connection with one image with information retrieved in connection with subsequent images. The processor uses the result of such comparison to determine the object that is likely to be of greatest interest to the user. The display simultaneously displays the images the images as they are captured, the location of the object in an image, and information retrieved for the object.

System and method of identifying visual objects

Systems and methods are provided for a content-based security for computing devices. An example method includes identifying content rendered by a mobile application, the content being rendered during a session, generating feature vectors from the content and determining that the feature vectors do not match a classification model. The method also includes providing, in response to the determination that the feature vectors do not match the classification model, a challenge configured to authenticate a user of the mobile device. Another example method includes determining a computing device is located at a trusted location, capturing information from a session, the information coming from content rendered by a mobile application during the session, generating feature vectors for the session, and repeating this until a training criteria is met. The method also includes training a classification model using the feature vectors and authenticating a user of the device using the trained classification model.

Screen-analysis based device security

Methods, systems, and apparatus for receiving a query image, receiving one or more entities that are associated with the query image, identifying, for one or more of the entities, one or more candidate search queries that are pre-associated with the one or more entities, generating a respective relevance score for each of the candidate search queries, selecting, as a representative search query for the query image, a particular candidate search query based at least on the generated respective relevance scores and providing the representative search query for output in response to receiving the query image.

Mapping images to search queries

Systems and methods are provided for sharing a screen from a mobile device. For example, a method includes receiving, at a second mobile device, an image of a screen captured from a first mobile device and determining whether to trigger an automated action. The method may also include displaying, responsive to not triggering the automated action, annotation data generated for the image with the image on a display of the second mobile device, the annotation data including at least one visual cue corresponding to content in the image relevant to a user of the second mobile device. The method may further include, responsive to triggering the automated action, determining that a mobile application associated with the image is installed on the second mobile device and replaying user input actions received with the image on the second mobile device starting from a reference screen associated with the mobile application.

Annotating screen content in a mobile environment

Systems and methods are provided for a personalized entity repository. For example, a computing device comprises a personalized entity repository having fixed sets of entities from an entity repository stored at a server, a processor, and memory storing instructions that cause the computing device to identify fixed sets of entities that are relevant to a user based on context associated with the computing device, rank the fixed sets by relevancy, and update the personalized entity repository using selected sets determined based on the rank and on set usage parameters applicable to the user. In another example, a method includes generating fixed sets of entities from an entity repository, including location-based sets and topic-based sets, and providing a subset of the fixed sets to a client, the client requesting the subset based on the client's location and on items identified in content generated for display on the client.

Personalized entity repository

Systems and methods are shown for providing private local sponsored content selection and improving intelligence models through distribution among mobile devices. This allows greater data gathering capabilities through the use of the sensors of the mobile devices as well as data stored on data storage components of the mobile devices to create predicted models while offering better opportunities to preserve privacy. Locally stored profiles comprising machine intelligence models may also be used to determine the relevance of the data gathered and in improving an aggregated model for identifying the relevance of data and the selection of sponsored content items. Distributed optimization is used in conjunction with privacy techniques to create the improved machine intelligence models. Publishers may also benefit from the improved privacy by protecting the statistics of type or volume of sponsored content items shown with publisher content.

Systems and methods for private local sponsored content

Methods, systems, and apparatus for receiving a query image and a user tap location, processing the received query image based on the user tap location, identifying one or more entities associated with the processed query image and in response to receiving (i) the query image, and (ii) the user tap location, providing information about the identified one or more of the entities.

Visual recognition using user tap locations

Implementations of the present disclosure include actions of receiving image data of an image capturing a scene, receiving data describing one or more entities determined from the scene, the one or more entities being determined from the scene, determining one or more actions based on the one or more entities, each action being provided at least partly based on search results from searching the one or more entities, and providing instructions to display an action interface comprising one or more action elements, each action element being to induce execution of a respective action, the action interface being displayed in a viewfinder.

Smart camera user interface

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving, at a mobile computing device that is associated with a called user, a call from a calling computing device that is associated with a calling user; in response to receiving the call, determining, by the mobile computing device, that data associated with the called user indicates that the called user will not respond to the call; in response to determining that the called user will not respond to the call, inferring, by the mobile computing device, an informational need of the calling user; and automatically providing, from the mobile computing device to the calling computing device, information associated with the called user and that satisfies the inferred informational need of the calling user.

Automatically performing actions by a mobile computing device

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Brooklyn, NY, United States of America

David Petrou