Google Inc.

Sri International

Hrishikesh Balkrishna Aradhye

Huazhong Ning

Ruei-Sung Lin

Wei Hua

Jay Yagnik

Gal Chechik

Sanketh Shetty

Douglas Eck

Luciano Sbaiz

Tomas Izo

George Dan Toderici

Daniel Ramage

Rodrigo Lima Carceroni

David A Ross

Pannag Raghunath Sanketi

Wolfgang Grieskamp

Gregory Kent Myers

Wei Chai

Reto Strobl

Xiaoquan Yi

Rich Gossweiler

Zhen Li

Christopher Tim Althoff

Bilson Campana

Juan Carlos Niebles Duque

Richard Carl Gossweiler, Iii

Gabriel Aaron Cohen

Gregory Sean Corrado

Anand P Rangarajan

Jonathon Shlens

Amarnag Subramanya

Tushar Deepak Chandra

Tal Shaked

Alexandru Marius Pasca

Jeremiah Harmsen

Marius Renn

Fernando Pereira

Glen Anderson

Mustafa Ispir

Mehmet Emre Sargin

Charles DuHadway

Robert C Bolles

Mehmet Levent Koc

Heng-Tze Cheng

Rohan Anil

Xiaobing Liu

Mohammed Saberian

Jasper Snoek

Emre Sargin

Mohammad Saberian

Yifan Peng

Martin A Fischler

Rajhans Samdani

Zile Wei

Bilson Jake Libres Campana

Shiyu Hu

Pannag Sanketi

The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

On-device machine learning platform

A system and method for identifying and predicting subjective attributes for entities (e.g., media clips, movies, television shows, images, newspaper articles, blog entries, persons, organizations, commercial businesses, etc.) are disclosed. In one aspect, subjective attributes for a first media item are identified based on a reaction to the first media item, and relevancy scores for the subjective attributes with respect to the first media item are determined. A classifier is trained using (i) a training input comprising a set of features for the first media item, and a target output for the training input, the target output comprising the respective relevancy scores for the subjective attributes with respect to the first media item.

Identifying subjective attributes by analysis of curation signals

A processor determines metadata associated with an audio track. The processor identifies categories that are related to the audio track based on the metadata. The processor determines rankings for the categories that are related to the audio track. The ranking is indicative of a relevance of a particular category to the audio track. The processor performs a query to identify visual media for one or more of ranked categories. The visual media is related to the audio track. The processor generates a visual presentation for the audio track by selecting at least some of the visual media to include in the visual presentation.

Creating a video for an audio file

A system and method to profile an application use and identify data used for application execution, map the identified data for application execution to a virtual memory associated with application execution, including execution beginning at specific times, states or stages of the application, and transmit the virtual memory to an end user wishing to demonstrate the application on an end user device. The end user device can emulate the application from any desired application start time, state or stage using data at the end user device identified by the virtual memory.

Transmitting application data for on-device demos

A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.

Wide and deep machine learning models

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing app store search results. An example method includes responsive to a first search query directed to an app store: revising the first search query to produce a second search query different from the first search query; obtaining, from an Internet search engine, second search results responsive to the second search query; analyzing the second search results to identify apps available on the app store that are relevant to the second search query; obtaining, from the app store, first search results responsive to the first search query that identify apps available in the app store; and modifying the first search results based on analyzing the second search results.

Providing app store search results

A system and method of recommending a bundle of content items to a user, including storing a plurality of content items in a computer system, determining a respective co-selection score for each pair of content items among the plurality of content items, the co-selection score indicating a probability that a given pair of content items among the plurality of content items will both be downloaded by a user of the computer system, and outputting, to a first user, a plurality of content items comprising a sub-set of the plurality of content items.

System and method to recommend a bundle of items based on item/user tagging and co-install graph

The present disclosure provides a system and method for automatic clustering and recognition of software applications using metadata. The system selects and extracts visual features from software applications which are then classified, analyzed using a cluster analysis, and then used to assign the software application to a cluster group.

Clustering applications using visual metadata

A demographics analysis trains classifier models for predicting demographic attribute values of videos and users not already having known demographics. In one embodiment, the demographics analysis system trains classifier models for predicting demographics of videos using video features such as demographics of video uploaders, textual metadata, and/or audiovisual content of videos. In one embodiment, the demographics analysis system trains classifier models for predicting demographics of users (e.g., anonymous users) using user features based on prior video viewing periods of users. For example, viewing-period based user features can include individual viewing period statistics such as total videos viewed. Further, the viewing-period based features can include distributions of values over the viewing period, such as distributions in demographic attribute values of video uploaders, and/or distributions of viewings over hours of the day, days of the week, and the like.

Video content analysis for automatic demographics recognition of users and videos

Systems and methods described herein relate to automation of video creation for an associated audio file or musical composition. In particular, a video can be generated for the audio file that includes images and videos that are compelling and contextually relevant to, and technically compatible with, the audio file.

A system and method for associating videos with geographic locations is disclosed. The system comprises a communication module, a location module, a tagging module and a database association module. The communication module receives a video uploaded by a content provider and a set of video data describing the video. The location module determines that the video describes a geographic location included in a geographic map based at least in part on the video data. The tagging module determines one or more travelling tags for the video based at least in part on the video data. The database association module associates the video and the one or more travelling tags with the geographic location so that the video with the one or more travelling tags is included in the geographic map.

Associating video content with geographic maps

Certain embodiments of the disclosed technology include systems and methods for determining the priority of a notification on a mobile device using machine learning. Other aspects of the disclosed technology include selectively displaying notifications based on the priority of a notification. According to an embodiment of the disclosed technology, a computer-implement method is provided that comprises outputting, to a display device operatively coupled to a mobile device, a plurality of notifications, wherein each respective notification from the plurality of notifications is associated with a respective priority score; modifying, by the mobile device, a ranking model based on a user input received responsive to a first notification from the plurality of notifications and a characteristic of a second notification from the plurality of notifications; determining, by the mobile device, a priority score associated with a third notification based on the modified ranking model; and outputting, to the display device, the third notification based on the priority score associated with the third notification, wherein the third notification is graphically emphasized responsive to the priority score associated with the third notification being greater than at least one respective priority score associated with a corresponding respective notification from the plurality of notifications.

Systems and methods for prioritizing notifications on mobile devices

A system and method for identifying and predicting subjective attributes for entities (e.g., media clips, images, newspaper articles, blog entries, persons, organizations, commercial businesses, etc.) are disclosed. In one aspect, a first set of subjective attributes for a first entity is identified based on a reaction to the first entity. A classifier is trained on a set of input-output mappings, wherein the set of input-output mappings comprises an input-output mapping whose input is based on a feature vector for the first entity and whose output is based on the first set of subjective attributes. A feature vector for a second entity is then provided to the trained classifier to obtain a second set of subjective attributes for the second entity.

Growing community of inventors

Mountain View, CA, United States of America

Hrishikesh Balkrishna Aradhye