Google Inc.

Locu, Inc.

Go Daddy Operating Company, LLC

Keir Banks Mierle

Tushar Udeshi

Pablo Bellver

Guang Yu Yang

Andrew Robert Kirmse

Emil Constantin Praun

James Anthony Guggemos

Dale Kris Hawkins

Gokay Baris Gultekin

Tom Carl Stamm

Bill Connor

Brian Edmond Brewington

David Singleton

Thomas Carl Stamm

Marek Olszewski

Jason Ansel

Steve Okamoto

Gregory Michael Blevins

Adam Marcus

Erez Louider

Matthias Grundmann

Taliver Heath

Cheng Yong Yang

John Paul Bates

Jianing Wei

Bryan Woods

Keith Schaefer

Sundeep Vaddadi

Konstantine Tsotsos

Leon Wong

Guang Yang

James A Guggemos

A computing system is described that obtains, based at least in part on information included in at least one previous communication associated with a user of a computing device, an indication of a future location and a future time and event information associated with the future location and the future time. The computing system obtains a duration of time for the user to travel from a current location of the computing device to the future location, and, based at least in part on the duration of time, obtains a departure time at which the user is predicted to need to depart from the current location in order to arrive at the future location by an arrival time based on the event. The computing system outputs, for transmission to the computing device, an indication associated with the event and including information indicative of the departure time.

Presenting information for a current location or time

In a general aspect, a method can include receiving data defining an augmented reality (AR) environment including a representation of a physical environment, and changing tracking of an AR object within the AR environment between region-tracking mode and plane-tracking mode.

Hybrid placement of objects in an augmented reality environment

An example technique may include performing a first time-warping of a scene of virtual reality content based on head pose information received during or after a rendering of the scene to obtain a time-warped scene, performing a second time-warping of an object based at least on object pose information received during or after a rendering of the object to obtain a time-warped object, and displaying on a display device a composite image based on the time-warped scene and the time-warped object.

Separate time-warping for a scene and an object for display of virtual reality content

In some examples, systems and techniques can determine a respective visit likelihood for each respective destination of a plurality of destinations based at least in part on a respective distance between the respective destination and a geographic location from a location history associated with a user and a comparison between a time associated with the geographic location and a visit likelihood distribution across time. The systems and techniques can then sort at least some of the plurality of destinations. In other examples, systems and techniques can determine whether a user is likely to visit a place during a future instance of a timeslot based at least in part on a location history associated with the user. The systems and techniques can then output information relating to the place prior to the beginning of the future instance of the timeslot.

Ranking nearby destinations based on visit likelihoods and predicting future visits to places from location history

Computer-implemented methods for generating and executing user-based standing queries are provided. In one aspect, a method includes determining whether to generate a notification based on a user-based query and geographical context. The method also includes filtering the notification through explicit or inferred filter criteria. Systems and machine-readable media are also provided.

Automatic user-based query generation and execution

A system and method for automatically submitting an online order from a customer to a restaurant. Input data and customer data is used by an order engine to select a deployment platform, such as social media networks, search engines, mobile applications, and related websites, for a user interface. The order engine provides a unique identifier for each restaurant. The unique identifier can be used by third parties, such as online content providers, to return the menu for the restaurant included in the third party content. The user interface automatically populates the restaurant's menu options and business data, allowing the customer to build an order. The order engine submits the order to the restaurant via a non-verbal communication platform.

System and method for enabling online ordering using unique identifiers

A system and method for document classification are presented. An input document is received (e.g., by at least one server communicatively coupled to a network). A plurality of features are identified in the input document. The plurality of features include sequences of text extracted from the input document. A feature vector of the input document is generated based upon the sequences of text, and the feature vector of the input document is compared to each of a plurality of signature vectors to determine a primary type of the input document. The primary type of the input document is stored into a storage system in communication with the at least one server.

Identifying and processing a number of features identified in a document to determine a type of the document

A system and method for data classification are presented. A plurality of training tokens are identified by at least one server communicatively coupled to a network. Each training token includes a token retrieved from a content source and a classification of the token. For each training token in the plurality of training tokens, a plurality of n-gram sequences are identified, a plurality of features for the plurality of n-gram sequences are generated, and first training data is generated using the token retrieved from the content source, the plurality of features, and the classification of the token. A first classifier is trained with the first training data, and the first classifier is stored into a storage system in communication with the at least one server.

Automated data classification

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for graphical overlay object translation. One of the methods includes receiving a first plurality of first feature points, each first feature point being associated with a plurality of feature descriptors and a respective location on the first digital image. The method includes receiving a second plurality of second feature points, each second feature point being associated with a plurality of feature descriptors and a respective location on the second digital image. The method includes determining the translation offset for the second digital image from the first digital image by matching locations of the first feature points with locations of the second feature points without regard to the feature descriptors.

Image alignment

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Mountain View, CA, United States of America

Keir Banks Mierle