Google Inc.

Pablo Bellver

Tushar Udeshi

Andrew Robert Kirmse

Keir Banks Mierle

Guang Yu Yang

Emil Constantin Praun

Dale Kris Hawkins

James Anthony Guggemos

Gokay Baris Gultekin

Tom Carl Stamm

Bill Connor

Ramanathan V Guha

Vivek Raghunathan

Ramakrishnan Srikant

Kiran S Panesar

Andrew M Dai

Vineet Gupta

Aparna Chennapragada

Amay Nitin Champaneria

Frederick Peter Brewin

Mahesh Keralapura Manjunatha

Carolyn F Au

Karthik Lakshminarayanan

Ravi Kolluri

Carl Lischeske

Brian Edmond Brewington

David Singleton

Thomas Carl Stamm

Matthieu Devin

James J Shuma

Steve Okamoto

Gregory Michael Blevins

Erez Louider

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

Methods and apparatus for providing travel-related information for a location to a user based on activity indications of the user that are related to the location. The location may be determined based on a set of one or more related activity indications and a travel-related score may be determined for the location that is indicative of likelihood that the user has interest in travelling to the location. The user may be provided the travel-related information for the location based on the travel-related score.

Automatic continued search

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

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

Growing community of inventors

Mountain View, CA, United States of America

Pablo Bellver