Google Inc.

Alex Gontmakher

Mikhal Shemer

Gal Elidan

Benjamin Dov Kessler

Roee Engelberg

Yonit Tova Halperin Worzel

Alexander Goldshtein

Yossi Matias

Jack Wright Menzel

Michael Fink

Frederick Peter Brewin

Eyal Fink

Niv Efron

Noam Ben Haim

Andrin Von Rechenberg

Anatoly Vorobey

Asaph Arnon

Noam Ben-Haim

A cloud network is a complex environment in which hundreds and thousands of users or entities can each host, create, modify, and develop multiple virtual machines. Each virtual machine can have complex behavior unknown to the provider or maintainer of the cloud. Technologies disclosed include methods, systems, and apparatuses to monitor the complex environment to detect network anomalies using machine learning techniques. In addition, techniques to modify and adapt to user feedback are provided allowing the developed models to be tuned for specific use cases, virtual machine types, and users.

Network anomaly detection

Systems and methods offer a search system with third-party provided search applications that are triggered in response to specified queries and run at the search system. For example, a method may include determining that a query triggers a third party search application hosted at the search system, executing the third party search application at the search system using computer-instructions obtained from the third party to generate and format a third-party formatted answer for the query, and providing the third-party formatted answer as a search result for the query. The third party may provide the query template, parameter attributes, if any, and the third party formatted answer. The third party search application is stored at the search system and may include the query template, a data store, the parameter attributes, and computer-instructions for accessing the data store using the parameter.

Third party search applications for a search system

Methods and systems for fractional level of detail assignment are described herein. A method embodiment for fractional level of detail (LOD) assignment includes obtaining a set of features and image data at a range of LOD values, assigning one or more fractional LOD values to the obtained features and providing the features and the image data at the fractional LOD values. The embodiment also includes hashing an identifier associated with each feature and computing a hash cutoff value by mapping the range of LOD levels onto a range of integers. A system embodiment includes a LOD assigner to assign fractional LOD values to features in image data and to provide the features and the image data at the fractional LOD values. The system embodiment further includes a retrieval engine to return features with a range of LOD values that include the fractional LOD values to the LOD assigner.

Methods and systems for fractional level of detail assignment

Systems and methods offer a search system with third-party provided search applications that are triggered in response to specified queries and run at the search system. For example, a method may include determining that a query triggers a third party search application hosted at the search system, extracting a parameter from the query based on a query template, executing the third party search application with the parameter in a sandboxed manner at the search system to generate a third-party formatted answer for the query, and providing the third-party formatted answer as a search result for the query. The third party may provide the query template, parameter attributes, and the third party formatted answer. The third party search application may be stored at the search system and include the query template, a data store, the parameter attributes, and instructions for accessing the data store using the parameter.

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

Methods, systems, and apparatus, including executable instructions encoded on one or more machine-readable storage media, for correlating transportation data are described. A transportation schedule that includes planned records for first trips and associations between the first trips and corresponding first trip IDs may be accessed. A transportation status update that includes actual records for second trips and associations between the second trips and corresponding second trip IDs may be received. Information relating one or more of the first trip IDs to one or more of the second trip IDs may be obtained. The information may be used to identify a trip that is common to both the transportation schedule and the transportation status update. An update for the trip using the transportation status update may be provided.

Correlating transportation data

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.

Growing community of inventors

Ness Ziona, Israel

Alex Gontmakher