Google Inc.

Arcot J Preetham

Matthew E Kulick

Hartmut Neven

Aparna Chennapragada

Albert Segars

Charles Joseph Rosenberg

Samy Bengio

Hyung-Jin Kim

Radhika Malpani

Yangli Hector Yee

Thomas J Duerig

Gabriel Wolosin

Omkar Mate

Hartwig Adam

John Patrick Flynn

Hyduke Noshadi

Prateek Sarkar

Randall G Keller

Farhana Bandukwala

Marco Andreetto

Krzysztof Sikora

Manish M Sambhu

Methods and apparatus related to determining a triggering event of a user, selecting media relevant to the triggering event, and providing the selected media to the user. Some implementations are directed to methods and apparatus for determining a past event of the user that is indicative of past interaction of the user with one or more past entities and the triggering event may be determined to be associated with the past event. The media selected to provide to the user may contain media that includes the one or more past entities associated with the past event and the media may be provided to the user in response to the triggering event.

Providing media to a user based on a triggering event

One or more computer devices may receive a document from private content that is accessible by a set of users, and may extract metadata from the document. The metadata may provide information relating to one or more aspects of the document. The one or more computer devices may determine, based on the metadata, whether a portion of the document includes information that is only to be accessible by a particular user from the set of users, and may append a restrict indicator to the metadata when the portion of the document includes information that is only to be accessible by the particular user. The restrict indicator may identify the particular user and the portion of the document. The one or more computer devices may store the metadata and the restrict indicator in a personal search index.

Indexing and searching documents with restricted portions

Methods, systems and apparatus for refining image relevance models. In general, one aspect of the subject matter described in this specification can be implemented in methods that include re-training an image relevance model by generating a first re-trained model based on content feature values of first images of a first portion of training images in a set of training images, receiving, from the first re-trained model, image relevance scores for second images of a second portion of the set of training images, removing, from the set of training images, some of the second images identified as outlier images for which the image relevance score received from the first re-trained model is below a threshold score, and generating a second re-trained model based on content feature values of the first images of the first portion and the second images of the second portion that remain following removal of the outlier images.

Refining image relevance models

Methods, systems, and apparatus for identifying landing pages for images. In one aspect, a method includes, for each image of a plurality of images, identifying web pages that each include the image, and, for each of the web pages, receiving a web score generated by a ranking algorithm. The web page having a web score indicating a highest importance relative to other web pages is selected, sets of similar images are determined from the images included in the selected web pages, and each set of similar images includes different images. For each set of similar images, an image is selected based on the selected web page that includes the selected image having a highest web score of the set, and the selected web page that includes the selected image is identified as a landing page for an image search result.

Identifying landing pages for images

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

Methods, and systems, including computer programs encoded on computer-readable mediums, including a method for determining codomain relationship measures between domains pairs. The method includes accessing domain data specifying first and second domains; for each first domain: determining a total number of image links on web pages on the first domain, each image link being a link to an image on a second domain; identifying second domains hosting images to which image links on web pages on the first domain link; for each second domain: determining a number of image links from web pages on the first domain linking to images on the second domain; determining a codomain relationship measure for the first and second domain based on the number of image links from web pages on the first domain linking to images on the second domain and the total number of image links on web pages on the first domain.

Determining codomain relationship measures

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting labels for a non-text content item. In one aspect, a method receives a set of initial labels for a non-text content item, wherein the set of initial labels specifies text that has been identified as descriptive of the non-text content item and a web page to which the text corresponds. Initial labels corresponding to sets of matching web pages are grouped into separate initial label groups that correspond to each set of matching web pages. Sets of matching labels are grouped into other separate initial label groups that correspond to the sets of matching labels. One or more words that are included in at least a threshold number of the separate label groups are selected as final labels for the non-text content item.

Non-text content item search

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Cupertino, CA, United States of America

Arcot J Preetham