Google Inc.

Frederick Peter Brewin

Amay Nitin Champaneria

Ramanathan V Guha

Vivek Raghunathan

Ramakrishnan Srikant

Kiran S Panesar

Andrew M Dai

Pablo Bellver

Vineet Gupta

Aparna Chennapragada

Carolyn F Au

Mahesh Keralapura Manjunatha

Karthik Lakshminarayanan

Carl Lischeske

Ravi Kolluri

Andrew Theodore Wansley

Alex Gontmakher

Jason Luther Smart

Noam Ben Haim

Jakob Nicolaus Foerster

Jakob Foerster

Methods and apparatus related to determining an inquiry to provide to a user based on deficient information related to a plan of the user. Deficient information may be determined based on an insufficient association between a desired information item of the plan and a set of information items that are determined from one or more sources associated with the user. In some implementations, the user may provide, responsive to the inquiry, additional information related to the deficient information and a suggestion may be provided to the user based on the additional information.

Providing an inquiry based on deficient plan information

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

A system is described that, after executing a parameterless search query to identify results that are determined to be relevant to a user of a device, determines a respective target property, for each of the results, that indicates whether a result is associated with audio content, visual content, audio and visual content, haptic content, or textual content. The system also determines one or more dynamic properties, of the device, that indicate one or more of a network connection strength, connection status to an audio output device, battery level, or connection status to a multimedia streaming device. Based on the one or more dynamic properties of the computing device and the respective target property of each of the results, the system determines a dynamic ranking of the results and transmits the results and the dynamic ranking to the device.

Information ranking based on properties of a computing device

A computing system is described that predicts a future action to be taken by a user of a computing device and determines, based on contextual information associated with the computing device, a current action being taken by the user. The computing system determines, based on the current action, a degree of likelihood of whether the user will be able to take the future action and predicts, based on the degree of likelihood, that the user will not be able to take the future action. The computing system sends information to the computing device indicating that the current action being taken by the user will lead to the user not being able to take the future action.

Detecting and correcting potential errors in user behavior

Providing an inquiry based on deficient plan information using email/chat and confidence levels

Methods and apparatus are disclosed for determining supervised training data, such as travel-related supervised training data, for training a machine learning system. In some implementations, supervised training data may be determined that includes input features based on information items of users and desired output features based on one or more physical locations visited by users.

Determining supervised training data including features pertaining to a class/type of physical location and time location was visited

An exemplary method includes determining whether real-time vehicle location information deviates from at least one of historical real-time vehicle location information or scheduled vehicle location information. The exemplary method further includes generating scheduling information for a user based on user data and the public transportation data, determining that the real-time vehicle location information deviates from at least one of the historical real-time vehicle location information or the scheduled vehicle location information by more than a threshold deviation, and updating the scheduling information for the user based on the user data, the public transportation data, and the determined deviation.

Transportation information systems and methods

An exemplary method includes determining whether real-time vehicle location information deviates from at least one of historical real-time vehicle location information or scheduled vehicle location information. The exemplary method further includes generating a schedule degradation mode based on a deviation of real-time vehicle location information from at least one of historical real-time vehicle location information or scheduled vehicle location information. The exemplary method also includes generating first scheduling information for a user based on the user data and public transportation data when the deviation does not exceed a threshold. The exemplary method farther includes generating second scheduling information, different from the first scheduling information, that includes an indication of the schedule degradation mode for the user based on user data, public transportation data, and the deviation when the deviation exceeds the threshold.

Transportation information systems and methods associated with degradation modes

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San Francisco, CA, United States of America

Frederick Peter Brewin