公开(公告)号：US20170024406A1

公开(公告)日：2017-01-26

申请号：US15288567

申请日：2016-10-07

Applicant: Yahoo! Inc.

Inventor： Marco PENNACCHIOTTI ,  Siva GURUMURTHY

IPC: G06F17/30

CPC classification number: G06F16/90324 ,  G06F16/9535 ,  G06N20/00 ,  G06Q30/0255 ,  G06Q50/01

Abstract: Each user is represented by a mixture of topics, e.g., one or more topics, and a probability of interest in each topic in the mixture, and given the target user, one or more other users can be recommended, each user that is recommended to the target user is determined to have a topical interest similarity with the target user, e.g., the target user's interest in one or more topics of the mixtures of topics is determined to be similar to a recommended interest in the one or more topics of the mixture of topics. The target user and the one or more recommended users can be said to have similar topical interests. The target user can use the user recommendation to establish an interactive dialogue, for example, with one or more users identified in the user recommendation.

Abstract translation: 每个用户通过主题的混合来表示，例如一个或多个主题，以及混合中每个主题的兴趣概率，并且给予目标用户，可以推荐一个或多个其他用户，推荐使用每个用户 目标用户被确定为与目标用户具有主题兴趣相似性，例如，目标用户对主题混合物的一个或多个主题的兴趣被确定为与混合物的一个或多个主题中的推荐兴趣相似 的主题。 目标用户和一个或多个推荐用户可以说具有类似的主题兴趣。 目标用户可以使用用户建议来建立例如与用户推荐中标识的一个或多个用户的交互对话。

公开(公告)号：US09554030B2

公开(公告)日：2017-01-24

申请号：US14500911

申请日：2014-09-29

Applicant: Yahoo! Inc.

Inventor： Jia Li ,  Haojian Jin

IPC: H04N5/232 ,  G06K9/32

CPC classification number: H04N5/23212 ,  G06K9/3233 ,  H04N5/23216 ,  H04N5/23293

Abstract: An approach is provided for acquiring images with camera-enabled mobile devices using objects of interest recognition. A mobile device is configured to acquire an image represented by image data and process the image data to identify a plurality of candidate objects of interest in the image. The plurality of candidate objects of interest may be identified based upon a plurality of low level features or “cues” in the image data. Example cues include, without limitation, color contrast, edge density and superpixel straddling. A particular candidate object of interest is selected from the plurality of candidate objects of interest and a graphical symbol is displayed on a screen of the mobile device to identify the particular candidate object of interest. The particular candidate object of interest may be located anywhere on the image. Passive auto focusing is performed at the location of the particular candidate object of interest.

Abstract translation: 提供了一种用于使用感兴趣的对象识别摄像机的移动设备来获取图像的方法。 移动装置被配置为获取由图像数据表示的图像并处理图像数据以识别图像中的多个候选对象。 可以基于图像数据中的多个低级特征或“提示”来识别多个候选对象。 示例提示包括但不限于颜色对比度，边缘密度和超像素跨越。 从多个感兴趣的候选对象中选择特定的候选对象，并且在移动设备的屏幕上显示图形符号以识别特定的感兴趣的候选对象。 感兴趣的特定候选对象可以位于图像的任何地方。 在特定候选对象的位置执行被动自动对焦。

公开(公告)号：US20170011034A1

公开(公告)日：2017-01-12

申请号：US15272739

申请日：2016-09-22

Applicant: Yahoo! Inc.

Inventor： David Ayman SHAMMA

IPC: G06F17/30

CPC classification number: G06F16/435 ,  G06F16/438 ,  G06F16/9032 ,  G06F16/907 ,  G06F16/9535

Abstract: In accordance with embodiments of the present invention, a method for associating metadata with a media object is provided. The method provides the ability to tag, or bookmark, a point in time for future use. The method includes receiving the metadata, an associated time condition, and an associated user identification. The method further includes storing at least the time condition. The at least stored time condition is used, at least in part, for associating the metadata with the media object. The media object is then provided to the user. In some embodiments the media object is not available for association with the metadata at the time the metadata is received. In other embodiments, the media object is provided by an external application.

Abstract translation: 根据本发明的实施例，提供了一种用于将元数据与媒体对象相关联的方法。 该方法提供了标记或书签的能力，以供将来使用。 该方法包括接收元数据，相关联的时间条件和相关联的用户标识。 该方法还包括至少存储时间条件。 至少部分地使用至少存储的时间条件来将元数据与媒体对象相关联。 然后将媒体对象提供给用户。 在一些实施例中，在接收元数据时，媒体对象不可用于与元数据相关联。 在其他实施例中，媒体对象由外部应用提供。

公开(公告)号：US20170005962A1

公开(公告)日：2017-01-05

申请号：US14755518

申请日：2015-06-30

Applicant: YAHOO! INC.

Inventor： Liane Lewin-Eytan ,  Guy Halawi ,  Dotan Di Castro ,  Zohar Karnin ,  Yoelle Maarek ,  Michael Albers

IPC: H04L12/58

CPC classification number: H04L51/12

Abstract: As is disclosed herein, user behavior in connection with a number of electronic messages, such as electronic mail (email) messages, can be used to automatically learn from, and predict, whether a message is wanted or unwanted by the user, where an unwanted message is referred to herein as gray spam. A gray spam predictor is personalized for a given user in vertical learning that uses the user's electronic message behavior and horizontal learning that uses other users' message behavior. The gray spam predictor can be used to predict whether a new message for the user is, or is not, gray spam. A confidence in a prediction may be used in determining the disposition of the message, such as and without limitation placing the message in a spam folder, a gray spam folder and/or requesting input from the user regarding the disposition of the message, for example.

Abstract translation: 如本文所公开的，与诸如电子邮件（电子邮件）消息的多个电子消息相关联的用户行为可以用于自动学习和预测消息是否被用户想要或不想要的，其中不期望的 消息在本文中称为灰色垃圾邮件。 对于垂直学习中的给定用户，使用用户的电子消息行为和使用其他用户的消息行为的横向学习，灰色垃圾邮件预测器是个性化的。 灰色垃圾邮件预测器可用于预测用户的新消息是否为灰色垃圾邮件。 预测的置信度可以用于确定消息的布置，例如但不限于将消息放置在垃圾邮件文件夹，灰色垃圾邮件文件夹中和/或从用户请求关于消息的处理的请求输入，例如 。

公开(公告)号：US20170004524A1

公开(公告)日：2017-01-05

申请号：US14788227

申请日：2015-06-30

Applicant: Yahoo! Inc.

Inventor： Lin MA ,  Giovanni GARDELLI ,  Gaurav DHAGE

IPC: G06Q30/02

CPC classification number: G06Q30/0245 ,  G06Q30/0267 ,  G06Q30/0269

Abstract: Systems and methods are provided for mobile campaign optimization without knowing user identity. The system includes circuitry configured to obtain mobile application data about a mobile application from at least one mobile device. The system includes circuitry configured to generate a mobile application profile for the mobile application using the mobile application data. The system further includes circuitry configured to select at least one mobile application to show a mobile advertisement in the at least one mobile application at least partially using the mobile application profile.

Abstract translation: 提供系统和方法用于移动广告系列优化，而不需要了解用户身份。 该系统包括被配置为从至少一个移动设备获得关于移动应用的移动应用数据的电路。 该系统包括被配置为使用移动应用数据生成用于移动应用的移动应用简档的电路。 该系统还包括被配置为选择至少一个移动应用以至少部分地使用移动应用简档在至少一个移动应用中显示移动广告的电路。

公开(公告)号：US20160379257A1

公开(公告)日：2016-12-29

申请号：US15261686

申请日：2016-09-09

Applicant: Yahoo! Inc.

Inventor： Bharathi Shekar ,  Ashootosh Chand ,  Arnab Nandi

IPC: G06Q30/02 ,  G06F17/30 ,  G06Q50/00

CPC classification number: G06Q30/0255 ,  G06F16/24578 ,  G06F16/29 ,  G06F16/9535 ,  G06Q30/0261 ,  G06Q50/01 ,  H04L65/403

Abstract: Techniques are described for providing automated recommendations of real-world locations, such as businesses, for users to visit based at least in part on historical location-preference information. The historical location-preference information used by the recommendation system may include the historical location-preference information of the person that requests the recommendation, other people explicitly identified as participants by the requestor, and/or other people implicitly determined to be participants. The historical location-preference information may be explicit, such as “check-ins” or reviews, or implicit. Implicit participants may be identified in a variety of ways, including social network relationships and the context in which the recommendation request is submitted.

Abstract translation: 描述了技术，用于至少部分地基于历史位置偏好信息来提供真实世界地点（例如企业）的自动建议，供用户访问。 推荐系统使用的历史位置偏好信息可以包括请求推荐的人的历史位置偏好信息，被请求者明确地标识为参与者的其他人和/或隐含地被确定为参与者的其他人。 历史位置偏好信息可能是明确的，例如“签入”或评论，或隐含的。 隐性参与者可以以各种方式被识别，包括社交网络关系以及推荐请求的上下文。

公开(公告)号：US09529858B2

公开(公告)日：2016-12-27

申请号：US14199729

申请日：2014-03-06

Applicant: Yahoo! Inc.

Inventor： Asad Sheth ,  Ferras Hamad

IPC: G06F17/30

CPC classification number: G06F17/3053 ,  G06F17/3005 ,  G06F17/30905

Abstract: A method includes accessing a number of cards from a database. The cards are ranked in the database based on a test conducted on a number of users. The cards are associated with one or more rule states. The one or more rule states provide binary outcomes of one or more rules. Each rule is identified using a code. The test is conducted by presenting different random sequences of the cards to different users and receiving inputs from the number of users. The method further includes receiving a request for a presentation area from a client device operated by a user. The presentation area is used for displaying the number of cards in an order, which is determined based on the test. The method includes providing the number of cards for display in the order within the presentation area on the client device of the user in response to the request.

Abstract translation: 一种方法包括从数据库访问多个卡。 基于对许多用户进行的测试，这些卡在数据库中排名。 这些卡与一个或多个规则状态相关联。 一个或多个规则状态提供一个或多个规则的二进制结果。 每个规则都使用代码来标识。 通过向不同的用户呈现不同的随机序列并从用户数量接收输入来进行测试。 该方法还包括从用户操作的客户端设备接收对于呈现区域的请求。 显示区域用于显示按照测试确定的顺序的卡片数量。 该方法包括响应于该请求，在用户的客户端设备上以呈现区域内的顺序提供用于显示的卡片数量。

公开(公告)号：US20160371589A1

公开(公告)日：2016-12-22

申请号：US14748333

申请日：2015-06-24

Applicant: Yahoo! Inc.

Inventor： Nadav GOLBANDI ,  Chao Wang

IPC: G06N5/04 ,  G06N7/00 ,  G06N99/00

CPC classification number: G06N20/00 ,  G06F16/9535 ,  G06Q30/0241 ,  G06Q30/0269

Abstract: The present disclosure relates to computer systems implementing methods for online content recommendation. The computer systems may be configured to receive a training sample from a first client device corresponding to a predefined feedback interacting with online content displayed on the first client device; update a preexisting training database in real-time based on the received training sample to generate an updated training sample, wherein prior to being updated based on the training sample received from the first client, the training database includes a set of historical training samples; conduct a regression training to a computer learning model in real-time, using the updated training sample, to produce a set of trained parameters for an online content recommendation model; call the set of trained parameters in real-time to determine recommend online content for a second user with the online content recommendation model; and send the recommended online content to a second client device of the second user.

Abstract translation: 本公开涉及实现在线内容推荐方法的计算机系统。 计算机系统可以被配置为从第一客户端设备接收对应于与在第一客户端设备上显示的在线内容交互的预定义反馈的训练样本; 基于所接收的训练样本来实时更新预先存在的训练数据库以生成更新的训练样本，其中在根据从第一客户端接收到的训练样本进行更新之前，训练数据库包括一组历史训练样本; 对计算机学习模型实时进行回归训练，使用更新的训练样本，为在线内容推荐模型生成一组经过训练的参数; 通过在线内容推荐模型实时调用一组受过训练的参数，以确定第二个用户的推荐在线内容; 并将推荐的在线内容发送给第二个用户的第二个客户端设备。

公开(公告)号：US20160357770A1

公开(公告)日：2016-12-08

申请号：US14729512

申请日：2015-06-03

Applicant: YAHOO! INC.

Inventor： Xue Wu ,  Renhui Zhang ,  Qichu Lu

IPC: G06F17/30 ,  H04L29/08 ,  G06F17/22 ,  G06F17/27 ,  G06F7/08

Abstract: Disclosed are systems and methods for improving interactions with and between computers in a content system supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data across platforms, which data can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods determine a breaking news story and track breaking developments in such story. The present disclosure can construct a breaking news storyline from the developments in the detected breaking news story, whereby a user can view the storyline as individual breaking news messages or as a complete timeline of events displayed on a provided page.

Abstract translation: 公开了用于改善由个人计算设备，服务器和/或平台支持或配置的内容系统中的计算机之间和之间的交互的系统和方法。 系统进行交互以识别和检索跨平台的数据，哪些数据可用于提高用于处理这些系统中处理器之间的交互的数据的质量。 所披露的系统和方法决定了一个突发性的新闻故事，并跟踪了这样的故事中的突破性发展。 本公开可以从检测到的突发新闻故事的发展中构建突发新闻故事情节，由此用户可以将故事情节视为个人突发新闻消息或作为在所提供的页面上显示的事件的完整时间表。

公开(公告)号：US20160357748A1

公开(公告)日：2016-12-08

申请号：US14730476

申请日：2015-06-04

Applicant: Yahoo!, Inc.

Inventor： JenHao Hsiao

IPC: G06F17/30 ,  G06N3/08

CPC classification number: G06N3/08 ,  G06F17/30256 ,  G06N3/0427 ,  G06N3/0454 ,  G06N3/084

Abstract: As provided herein, a domain model, corresponding to a domain of an image, may be merged with a pre-trained fundamental model to generate a trained fundamental model. The trained fundamental model may comprise a feature description of the image converted into a binary code. Responsive to a user submitting a search query, a coarse image search may be performed, using a search query binary code derived from the search query, to identify a candidate group, comprising one or more images, having binary codes corresponding to the search query binary code. A fine image search may be performed on the candidate group utilizing a search query feature description derived from the search query. The fine image search may be used to rank images within the candidate group based upon a similarity between the search query feature description and feature descriptions of the one or more images within the candidate group.

Abstract translation: 如本文所提供的，对应于图像域的域模型可以与预训练的基本模型合并以产生经过训练的基本模型。 经过训练的基本模型可以包括转换成二进制码的图像的特征描述。 响应于提交搜索查询的用户，可以使用从搜索查询导出的搜索查询二进制代码来执行粗略图像搜索，以识别具有与搜索查询二进制对应的二进制代码的一个或多个图像的候选组 码。 可以利用从搜索查询导出的搜索查询特征描述对候选组执行精细图像搜索。 精细图像搜索可以用于基于候选组中的一个或多个图像的搜索查询特征描述和特征描述之间的相似度来对候选组内的图像进行排序。