公开(公告)号：US11645860B2

公开(公告)日：2023-05-09

申请号：US15463774

申请日：2017-03-20

Applicant: Oath Inc.

Inventor： Suleyman Cetintas ,  Kuang-chih Lee ,  Jia Li

IPC: G06K9/62 ,  G06F3/04842 ,  G06F16/58 ,  G06V10/44 ,  G06V20/10 ,  G06V30/413 ,  G06N3/08

CPC classification number: G06K9/6267 ,  G06F3/04842 ,  G06F16/58 ,  G06K9/6201 ,  G06K9/626 ,  G06K9/627 ,  G06N3/08 ,  G06V10/454 ,  G06V20/10 ,  G06V30/413

Abstract: Briefly, embodiments of methods and/or systems of generating preference indices for contiguous portions of digital images are disclosed. For one embodiment, as an example, parameters of a neural network may be developed to generate object labels for digital images. The developed parameters may be transferred to a neural network utilized to generate signal sample value levels corresponding to preference indices for contiguous portions of digital images.

公开(公告)号：US11269962B2

公开(公告)日：2022-03-08

申请号：US16724949

申请日：2019-12-23

Applicant: Oath Inc.

Inventor： Suleyman Cetintas ,  Kuang-chih Lee

IPC: G06F16/951 ,  G06F16/33

Abstract: Users may consume and/or share information through various types of content items. For example, user may post a family photo through a social network, create a running blog through a microblogging service, etc. Because users may be overwhelmed by the amount of available content items, it may be advantageous to recommend content items, such as blogs to follow, to users. Accordingly, inductive matrix completion is used to evaluate user interactions with content items (e.g., a user following a blog), content item features (e.g., text and/or images of a blog is evaluated to identify a topic of the blog), and/or user features (e.g., a user liking or reblogging a blog, user demographics, user interests, etc.) to determine whether to recommend a content item to a user. Additionally, graph proximity is used to recommend content items based upon weights of edges connecting user nodes to content item nodes within a directed graph.

公开(公告)号：US10672025B2

公开(公告)日：2020-06-02

申请号：US15064310

申请日：2016-03-08

Applicant: Oath Inc.

Inventor： Suleyman Cetintas ,  Pengyuan Wang ,  Jian Yang ,  Puneet Mohan Sangal

IPC: G06Q30/02

Abstract: Systems, devices, and methods are disclosed for determining the quality of traffic received from different web page publishers, and setting a pricing structure for the different traffic based on the determined quality of traffic. Accurately determining the quality of traffic and/or clicks from different publishers allows the network system described herein to offer a fair marketplace with just return on investments (ROI) for advertisers, and offer a robust and accurate traffic quality based pricing model for publishers. Internet based technology, and in particular deep learning techniques available through a neural network, are utilized to determine the pricing structure based on click and/or web page traffic quality measurements generated through the deep learning techniques.

公开(公告)号：US10515127B2

公开(公告)日：2019-12-24

申请号：US14682603

申请日：2015-04-09

Applicant: Oath Inc.

Inventor： Suleyman Cetintas ,  Kuang-chih Lee

IPC: G06F16/951 ,  G06F16/33

Abstract: Users may consume and/or share information through various types of content items. For example, user may post a family photo through a social network, create a running blog through a microblogging service, etc. Because users may be overwhelmed by the amount of available content items, it may be advantageous to recommend content items, such as blogs to follow, to users. Accordingly, inductive matrix completion is used to evaluate user interactions with content items (e.g., a user following a blog), content item features (e.g., text and/or images of a blog is evaluated to identify a topic of the blog), and/or user features (e.g., a user liking or reblogging a blog, user demographics, user interests, etc.) to determine whether to recommend a content item to a user. Additionally, graph proximity is used to recommend content items based upon weights of edges connecting user nodes to content item nodes within a directed graph.