Google Inc.

Farhana Bandukwala

John Patrick Flynn

Hartwig Adam

Robert C Boman

Arcot J Preetham

Marco Andreetto

Krzysztof Sikora

Samuel Paul Ruth

Jonathan Jesse Halcrow

Georg Matthias Goerg

A method of identifying a contributing cause of an anomaly including receiving a set of timeseries data representing metric values over time, wherein the timeseries data has at least two dimensions, for each of two or more of the timestamps in the set of timeseries data, generating a first and second graph representing (i) the metric values at that timestamp, (ii) the at least two dimensions at that timestamp, and (iii) associations between the metric values at that timestamp, analyzing the first and second graphs associated with each timestamp to identify a particular timestamp including an anomaly, and analyzing the first and second graphs associated with the identified particular timestamp to identify a node that contributed in causing the anomaly.

Systems and methods of anomaly detection

Implementations relate to verifying labels for images using image recognition. In some implementations, a method includes obtaining an image associated with location information, obtaining one or more descriptor labels associated with the location information, determining one or more recognized image features depicted in the image, comparing the recognized image features with the descriptor labels, and determining one or more verified labels from the one or more descriptor labels. The verified labels are determined to describe at least one of the one or more recognized image features depicted in the image based on the comparing of the recognized image features with the descriptor labels.

Verifying labels for images using image recognition

Methods, systems, and apparatus are provided for determining location information for images. In one aspect, a method includes obtaining landmark location data from content depicted in an image and corresponding confidence scores. Also, the method includes obtaining caption location data from user input and corresponding confidence scores, and obtaining metadata location data from data provided by an image capturing device. Further, the method includes identifying location pairs from the landmark, caption, and metadata location data, and generating, for each location pair, a geographic consistency score. Additionally, the method includes selecting a location pair based on the geographic consistency scores, and selecting an image location for the image from the selected location pair. Moreover, the method includes determining an image location score based on a confidence score for one of the locations in the selected location pair, and associating the image location and image location score with the image.

Determining location information for images using landmark, caption, and metadata location data

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San Clemente, CA, United States of America

Farhana Bandukwala