Google Inc.

Tushar Udeshi

Andrew Robert Kirmse

Emil Constantin Praun

Dale Kris Hawkins

Pablo Bellver

Guang Yu Yang

Keir Banks Mierle

James Anthony Guggemos

Tom Carl Stamm

Gokay Baris Gultekin

Thomas Carl Stamm

Bill Connor

Paul Steven Strauss

Costa Touma

Brian Edmond Brewington

David Singleton

Matthieu Devin

James J Shuma

Swaminathan Krishnamurthy

Steve Okamoto

Gregory Michael Blevins

Ivan Ordonez

Erez Louider

David Paul

Igor V Guskov

Aiyou Chen

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

Systems and methods for assigning word fragments to lines of text in optical character recognition (OCR) extracted data can include at least one processor obtaining a plurality of word fragments from OCR generated data associated with an image. The at least one processor can determine vertical coordinates of each of the word fragments in the image. The at least one processor can cluster the plurality of word fragments into one or more clusters of word fragments based on the vertical coordinates of the plurality of word fragments. The at least one processor can assign each word fragment of a respective cluster to a corresponding text line based on the clustering.

System and methods for assigning word fragments to text lines in optical character recognition-extracted data

Systems and methods for merging adjacent word fragments in outputs of optical character recognition (OCR) systems can include a processor obtaining word fragments associated with OCR data generated from an image. Each word fragment can be associated with a respective text line of a plurality of text lines. The at least one processor can determine, for each pair of adjacent word fragments in a text line, a respective normalized horizontal distance between the pair of adjacent word fragments. The processor can identify one or more pairs of adjacent word fragments that are candidates for merging based on the determined normalized horizontal distances. The processor can determine that a pair of adjacent word fragments, among the one or more pairs of adjacent word fragments that are candidates for merging, matches a predefined expression of a plurality of predefined expressions, and merge that pair of adjacent word fragments into a single word.

Systems and methods for merging word fragments in optical character recognition-extracted data

The systems and techniques disclosed herein provide the ability to present, via a user interface, an indication of a geographic location to a user, determining that the geographic location corresponds to an address included in contact information for one of several contacts of the user, the contact information further including a name; receive, via the user interface, an indication that the geographic location is to be associated with the name included in the contact information; generate a location label for the geographic location, using the name included in the contact information; and outputting an indication of the location label.

Labeling locations based on contact information

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

The systems and techniques disclosed herein provide the ability to determine locations visited by a user and associate relevant location labels with the locations visited based on contact information. In some examples, a location label can be applied based on a match between a location visited and information stored in a user's contact list. In other examples, a user can efficiently designate a contact and location label to be associated with a location visited. In still other examples, if a location visited by a user is not listed in the user's contact list, but is otherwise known to the system, the location visited can be appropriately labeled and the corresponding contact in the user's contact list can be updated to include the location visited.

Labeling visited locations based on contact information

Embodiments relate to determining commute routes and clustering commute routes from a user's location history. Points in the user's location history may be clustered to find the user's home and work locations. Additionally, points along the user's commute may be identified to determine the user's typical commute. Similar commutes can be clustered together, and used to suggest various services to the user.

Extracting patterns from location history

A method and system for searching for data covered by nodes in a quadtree are disclosed. Nodes at a selected level of the quadtree are encoded with an index. The index nodes store data related to the level of descendants and ancestors of each index node. To search for data covered by a particular desired node, the index node ancestor or descendant of the desired node is determined. Based on the data in the index node, the desired node can be located without a full traversal of the quadtree. The quadtree nodes store elevation data for points on the Earth.

Methods and systems for encoding the maximum resolution data level for a quadtree

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Broomfield, CO, United States of America

Tushar Udeshi