公开(公告)号：US11166000B1

公开(公告)日：2021-11-02

申请号：US16180449

申请日：2018-11-05

Applicant: Google LLC

Inventor： David Ross ,  Hrishikesh Aradhye ,  Douglas Eck ,  Christopher Tim Althoff

IPC: H04N9/802 ,  G11B27/031 ,  G11B27/022 ,  G11B27/00 ,  G06F16/60 ,  G06F16/68 ,  G06F16/683

Abstract: 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.

公开(公告)号：US10474688B2

公开(公告)日：2019-11-12

申请号：US15727384

申请日：2017-10-06

Applicant: Google LLC

Inventor： Huazhong Ning ,  Wei Chai ,  Hrishikesh Aradhye

IPC: G06F17/30 ,  G06F16/2457 ,  G06N7/00 ,  G06F16/954

Abstract: 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.

公开(公告)号：US10210462B2

公开(公告)日：2019-02-19

申请号：US14552001

申请日：2014-11-24

Applicant: Google LLC

Inventor： Juan Carlos Niebles Duque ,  Hrishikesh Aradhye ,  Luciano Sbaiz ,  Jay Yagnik ,  Reto Strobl

IPC: G06F15/18 ,  G06N99/00 ,  G06F17/30 ,  G06K9/00 ,  H04N21/234 ,  H04N21/258 ,  H04L29/06 ,  H04N21/25

Abstract: 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.

公开(公告)号：US20180032529A1

公开(公告)日：2018-02-01

申请号：US15727088

申请日：2017-10-06

Applicant: Google LLC

Inventor： Hrishikesh Aradhye ,  Wei Hua ,  Ruei-Sung Lin ,  Mohammad Saberian

IPC: G06F17/30 ,  G06Q10/10 ,  H04L12/58

CPC classification number: G06F16/24578 ,  G06F3/0481 ,  G06F3/0482 ,  G06Q10/107 ,  H04L51/24 ,  H04L51/26 ,  H04L51/38

Abstract: 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 or emphasizing notifications based on the priority of a notification.

公开(公告)号：US20220358385A1

公开(公告)日：2022-11-10

申请号：US17874967

申请日：2022-07-27

Applicant: Google LLC

Inventor： Pannag Sanketi ,  Wolfgang Grieskamp ,  Daniel Ramage ,  Hrishikesh Aradhye

IPC: G06N5/04 ,  G06N20/00

Abstract: 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.