利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

148 条记录 (耗时 0.035 秒)

    IDENTIFYING INFLUENTIAL PERSONS IN A SOCIAL NETWORK
    72.
    发明申请
    IDENTIFYING INFLUENTIAL PERSONS IN A SOCIAL NETWORK 有权
    在社会网络中识别受影响人

    公开(公告)号：US20080070209A1

    公开(公告)日：2008-03-20

    申请号：US11533742

    申请日：2006-09-20

    申请人： Dong Zhuang ,  Benyu Zhang ,  Heng Zhang ,  Jeremy Tantrum ,  Teresa Mah ,  Hua-Jun Zeng ,  Zheng Chen ,  Jian Wang

    发明人： Dong Zhuang ,  Benyu Zhang ,  Heng Zhang ,  Jeremy Tantrum ,  Teresa Mah ,  Hua-Jun Zeng ,  Zheng Chen ,  Jian Wang

    IPC分类号： G09B19/00

    CPC分类号： G06Q30/02 ,  G06Q10/10

    摘要： An influential persons identification system and method for identifying a set of influential persons (or influencers) in a social network (such as an online social network). The influential persons set is generated such that by sending a message to the set the message will be propagated through the network at the greatest speed and coverage. A ranking of users is generated, and a pruning process is performed starting with the top-ranked user and working down the list. For each user on the list, the user is identified as an influencer and then the user and each of his friends are deleted from the social network users list. Next, the same process is performed for the second-ranked user, the third-ranked user, and so forth. The process terminates when the list of users of the social network is exhausted or the desired number of influencers on the influential person set is reached.

    摘要翻译： 在社交网络（如在线社交网络）中识别一组有影响力的人（或影响者）的有影响力的人员识别系统和方法。 产生有影响力的人员，通过发送消息给消息集，消息将以最大的速度和覆盖率通过网络传播。 生成用户排名，并从顶级用户开始执行修剪过程，并在列表中执行操作。 对于列表中的每个用户，用户被识别为影响者，然后从社交网络用户列表中删除用户和他的每个朋友。 接下来，对于第二等级的用户，第三等级的用户等执行相同的处理。 当社交网络的用户列表用完或者达到期望数量的有影响力的人集合的影响者时，该过程终止。

    Key phrase navigation map for document navigation
    73.
    发明申请
    Key phrase navigation map for document navigation 失效
    关键短语导航地图文件导航

    公开(公告)号：US20070219945A1

    公开(公告)日：2007-09-20

    申请号：US11372365

    申请日：2006-03-09

    申请人： Min Wang ,  Benyu Zhang ,  Hua-Jun Zeng ,  Jian Wang ,  Shiguang Liu ,  Zheng Chen

    发明人： Min Wang ,  Benyu Zhang ,  Hua-Jun Zeng ,  Jian Wang ,  Shiguang Liu ,  Zheng Chen

    IPC分类号： G06F17/30 ,  G06F3/048

    CPC分类号： G06F17/30713 ,  G06F17/272 ,  G06F17/2775 ,  G06F17/30643 ,  Y10S707/92

    摘要： Computer-readable media having computer-executable instructions and apparatuses provide a keyphrase navigation map (KNM) for a document page. Keyphrases are extracted from the document page. Keyphrase clusters are subsequently formed by a measure of relevancy, and a salient keyphrase is determined for each cluster. A thumbnail is formed with tags corresponding to the salient keyphrases. A selected tag is expanded with associated keyphrases. An associated keyphrase may be further selected in order to facilitate the navigation of the document page. The displayed tags on the thumbnail are positioned in accordance with locations of associated keyphrases in the document page.

    摘要翻译： 具有计算机可执行指令和装置的计算机可读介质为文档页面提供关键词导航映射（KNM）。 从文档页面提取关键短语。 随后通过相关性的量度形成关键词组，并且为每个簇确定显着的关键短语。 使用与突出关键短语相对应的标签形成缩略图。 所选标签用相关的关键短语展开。 可以进一步选择相关联的关键短语，以便于文档页面的导航。 缩略图上显示的标签根据文档页面中相关联的关键短语的位置进行定位。

    Predicting demographic attributes based on online behavior
    74.
    发明申请
    Predicting demographic attributes based on online behavior 审中-公开
    基于在线行为预测人口统计特征

    公开(公告)号：US20070208728A1

    公开(公告)日：2007-09-06

    申请号：US11366526

    申请日：2006-03-03

    申请人： Benyu Zhang ,  Honghua Dai ,  Hua-Jun Zeng ,  Li Qi ,  Tarek Najm ,  Teresa Mah ,  Vladimir Shipunov ,  Ying Li ,  Zheng Chen

    发明人： Benyu Zhang ,  Honghua Dai ,  Hua-Jun Zeng ,  Li Qi ,  Tarek Najm ,  Teresa Mah ,  Vladimir Shipunov ,  Ying Li ,  Zheng Chen

    IPC分类号： G06F17/30

    CPC分类号： G06F16/337 ,  G06F16/951

    摘要： This invention provides a system and method for predicting user demographic attributes for non-registered users and users with incomplete profiles. The invention uses demographic information from registered users and user search history logs to create a database of information that associates the users' search history habits with their demographic attributes. The invention creates a first database that associates users' search query history with their demographic attributes, and also creates a second database that associates web pages that users have visited frequently along with the users' demographic attributes. The invention can compare the searching and browsing habits of non-registered users and users with incomplete profiles to the searching and browsing habits of registered users. Through the comparison, the invention can use the corresponding demographic attributes of the registered users to predict the demographic attributes of the non-registered users and the registered users with incomplete profiles.

    摘要翻译： 本发明提供一种用于预测非注册用户和具有不完整简档的用户的用户人口统计属性的系统和方法。 本发明使用来自注册用户和用户搜索历史日志的人口统计信息来创建将用户的搜索历史习惯与其人口统计属性相关联的信息数据库。 本发明创建了将用户的搜索查询历史与其人口统计属性相关联的第一数据库，并且还创建了将用户经常访问的网页与用户的人口统计属性一起关联的第二数据库。 本发明可以将注册用户和注册用户不完整的用户的搜索和浏览习惯与注册用户的搜索和浏览习惯进行比较。 通过比较，本发明可以使用注册用户的相应人口统计特性来预测非注册用户和具有不完整简档的注册用户的人口统计属性。

    Generation of contextual image-containing advertisements
    75.
    发明申请
    Generation of contextual image-containing advertisements 有权
    生成含上下文图像的广告

    公开(公告)号：US20070192164A1

    公开(公告)日：2007-08-16

    申请号：US11355634

    申请日：2006-02-15

    申请人： Shuzhen Nong ,  Ying Li ,  Tarek Najm ,  Li Li ,  Zheng Chen ,  Hua-Jun Zeng ,  Benyu Zhang ,  Yin Li ,  Dean Carignan ,  Ying-Qing Xu

    发明人： Shuzhen Nong ,  Ying Li ,  Tarek Najm ,  Li Li ,  Zheng Chen ,  Hua-Jun Zeng ,  Benyu Zhang ,  Yin Li ,  Dean Carignan ,  Ying-Qing Xu

    IPC分类号： G07G1/00 ,  H04N7/10

    CPC分类号： G06Q30/02

    摘要： According to embodiments of the invention, an advertisement-generation system generates image-containing advertisements. The advertisement-generation system includes: at least one feature-selection guideline that specifies at least one recommended feature for image-containing advertisements based on advertiser inputs that specify at least one of advertisement-target-audience information, cost information, and advertiser-industry information; an image-clip library from which images are selected for inclusion in the image-containing advertisements; and at least one advertisement template that is based on the at least one feature-selection guideline; wherein the system automatically generates image-containing advertisements that contain one or more suggested colors that are automatically suggested based on one or more colors present on a web page that will host the image-containing advertisement.

    摘要翻译： 根据本发明的实施例，广告生成系统生成包含图像的广告。 广告生成系统包括：至少一个特征选择指南，其基于广告商输入指定至少一个推荐的用于图像的广告的特征，其指定广告目标受众信息，成本信息和广告商行业中的至少一个 信息; 选择图像以包含在包含图像的广告中的图像剪辑库; 以及至少一个基于所述至少一个特征选择指南的广告模板; 其中所述系统自动生成包含一个或多个建议的颜色的图像包含广告，所述一个或多个建议的颜色是基于存在于将承载含图像的广告的网页上的一种或多种颜色而自动建议的。

    Advertising keyword cross-selling
    76.
    发明授权
    Advertising keyword cross-selling 有权
    广告关键字交叉销售

    公开(公告)号：US07788131B2

    公开(公告)日：2010-08-31

    申请号：US11300918

    申请日：2005-12-15

    申请人： Shuzhen Nong ,  Ying Li ,  Tarek Najm ,  Li Li ,  Hua-Jun Zeng ,  Zheng Chen ,  Benyu Zhang

    发明人： Shuzhen Nong ,  Ying Li ,  Tarek Najm ,  Li Li ,  Hua-Jun Zeng ,  Zheng Chen ,  Benyu Zhang

    IPC分类号： G06Q30/00

    CPC分类号： G06Q30/02 ,  G06F17/30864 ,  G06Q30/0251 ,  G06Q30/0275

    摘要： Seed keywords are leveraged to provide expanded keywords that are then associated with relevant advertisers. Instances can also include locating potential advertisers based on the expanded keywords. Inverse lookup techniques are employed to determine which keywords are associated with an advertiser. Filtering can then be employed to eliminate inappropriate keywords for that advertiser. The keywords are then automatically revealed to the advertiser for consideration as relevant search terms for their advertisements. In this manner, revenue for a search engine and/or for an advertiser can be substantially enhanced through the automatic expansion of relevant search terms. Advertisers also benefit by having larger and more relevant search term selections automatically available to them, saving them both time and money.

    摘要翻译： 使用种子关键字来提供扩展的关键字，然后与相关的广告商相关联。 实例还可以包括根据扩展的关键字定位潜在的广告客户。 采用反向查找技术来确定哪些关键字与广告商相关联。 然后可以使用过滤来消除该广告客户的不合适的关键字。 然后，这些关键字会自动向广告客户显示，作为其广告的相关搜索字词。 以这种方式，可以通过自动扩展相关搜索词来大大增强搜索引擎和/或广告商的收入。 广告商也可以通过自动获得更大更多相关的搜索词选项来获益，从而节省时间和金钱。

    Text classification by weighted proximal support vector machine based on positive and negative sample sizes and weights
    77.
    发明授权
    Text classification by weighted proximal support vector machine based on positive and negative sample sizes and weights 有权
    基于正，负样本大小和权重的加权近端支持向量机进行文本分类

    公开(公告)号：US07707129B2

    公开(公告)日：2010-04-27

    申请号：US11384889

    申请日：2006-03-20

    申请人： Dong Zhuang ,  Benyu Zhang ,  Zheng Chen ,  Hua-Jun Zeng ,  Jian Wang

    发明人： Dong Zhuang ,  Benyu Zhang ,  Zheng Chen ,  Hua-Jun Zeng ,  Jian Wang

    IPC分类号： G06F15/18 ,  G06E1/00 ,  G06E3/00

    CPC分类号： G06F17/30707 ,  G06K9/6269

    摘要： Embodiments of the invention relate to improvements to the support vector machine (SVM) classification model. When text data is significantly unbalanced (i.e., positive and negative labeled data are in disproportion), the classification quality of standard SVM deteriorates. Embodiments of the invention are directed to a weighted proximal SVM (WPSVM) model that achieves substantially the same accuracy as the traditional SVM model while requiring significantly less computational time. A weighted proximal SVM (WPSVM) model in accordance with embodiments of the invention may include a weight for each training error and a method for estimating the weights, which automatically solves the unbalanced data problem. And, instead of solving the optimization problem via the KKT (Karush-Kuhn-Tucker) conditions and the Sherman-Morrison-Woodbury formula, embodiments of the invention use an iterative algorithm to solve an unconstrained optimization problem, which makes WPSVM suitable for classifying relatively high dimensional data.

    摘要翻译： 本发明的实施例涉及对支持向量机（SVM）分类模型的改进。 当文本数据显着不平衡（即正负标签数据不成比例）时，标准SVM的分类质量恶化。 本发明的实施例涉及一种加权近端SVM（WPSVM）模型，其实现与传统SVM模型基本相同的精度，同时需要显着更少的计算时间。 根据本发明的实施例的加权近端SVM（WPSVM）模型可以包括每个训练误差的权重和用于估计权重的方法，其自动地解决不平衡数据问题。 而且，不是通过KKT（Karush-Kuhn-Tucker）条件和Sherman-Morrison-Woodbury公式来解决优化问题，而是本发明的实施例使用迭代算法来解决无约束优化问题，这使得WPSVM适合于相对分类 高维数据。

    Text classification by weighted proximal support vector machine
    78.
    发明申请
    Text classification by weighted proximal support vector machine 有权
    通过加权近端支持向量机进行文本分类

    公开(公告)号：US20070239638A1

    公开(公告)日：2007-10-11

    申请号：US11384889

    申请日：2006-03-20

    申请人： Dong Zhuang ,  Benyu Zhang ,  Zheng Chen ,  Hua-Jun Zeng ,  Jian Wang

    发明人： Dong Zhuang ,  Benyu Zhang ,  Zheng Chen ,  Hua-Jun Zeng ,  Jian Wang

    IPC分类号： G06F15/18

    CPC分类号： G06F17/30707 ,  G06K9/6269

    摘要： Embodiments of the invention relate to improvements to the support vector machine (SVM) classification model. When text data is significantly unbalanced (i.e., positive and negative labeled data are in disproportion), the classification quality of standard SVM deteriorates. Embodiments of the invention are directed to a weighted proximal SVM (WPSVM) model that achieves substantially the same accuracy as the traditional SVM model while requiring significantly less computational time. A weighted proximal SVM (WPSVM) model in accordance with embodiments of the invention may include a weight for each training error and a method for estimating the weights, which automatically solves the unbalanced data problem. And, instead of solving the optimization problem via the KKT (Karush-Kuhn-Tucker) conditions and the Sherman-Morrison-Woodbury formula, embodiments of the invention use an iterative algorithm to solve an unconstrained optimization problem, which makes WPSVM suitable for classifying relatively high dimensional data.

    摘要翻译： 本发明的实施例涉及对支持向量机（SVM）分类模型的改进。 当文本数据显着不平衡（即正负标签数据不成比例）时，标准SVM的分类质量恶化。 本发明的实施例涉及一种加权近端SVM（WPSVM）模型，其实现与传统SVM模型基本相同的精度，同时需要显着更少的计算时间。 根据本发明的实施例的加权近端SVM（WPSVM）模型可以包括每个训练误差的权重以及用于估计权重的方法，其自动地解决不平衡数据问题。 而且，不是通过KKT（Karush-Kuhn-Tucker）条件和Sherman-Morrison-Woodbury公式来解决优化问题，而是本发明的实施例使用迭代算法来解决无约束优化问题，这使得WPSVM适合于相对分类 高维数据。

    Document characterization using a tensor space model
    80.
    发明授权
    Document characterization using a tensor space model 失效
    文档表征使用张量空间模型

    公开(公告)号：US07529719B2

    公开(公告)日：2009-05-05

    申请号：US11378095

    申请日：2006-03-17

    申请人： Ning Liu ,  Benyu Zhang ,  Jun Yan ,  Zheng Chen ,  Hua-Jun Zeng ,  Jian Wang

    发明人： Ning Liu ,  Benyu Zhang ,  Jun Yan ,  Zheng Chen ,  Hua-Jun Zeng ,  Jian Wang

    IPC分类号： G06N5/00

    CPC分类号： G06N5/02 ,  G06F17/30705

    摘要： Computer-readable media having computer-executable instructions and apparatuses categorize documents or corpus of documents. A Tensor Space Model (TSM), which models the text by a higher-order tensor, represents a document or a corpus of documents. Supported by techniques of multilinear algebra, TSM provides a framework for analyzing the multifactor structures. TSM is further supported by operations and presented tools, such as the High-Order Singular Value Decomposition (HOSVD) for a reduction of the dimensions of the higher-order tensor. The dimensionally reduced tensor is compared with tensors that represent possible categories. Consequently, a category is selected for the document or corpus of documents. Experimental results on the dataset for 20 Newsgroups suggest that TSM is advantageous to a Vector Space Model (VSM) for text classification.

    摘要翻译： 具有计算机可执行指令和设备的计算机可读介质将文档或语料库分类。 张量空间模型（TSM），其通过高阶张量对文本进行建模，表示文档或文档语料库。 由多线代数技术支持，TSM为多因素结构分析提供了框架。 TSM还受到操作和提出的工具的支持，例如用于降低高阶张量尺寸的高阶奇异值分解（HOSVD）。 将尺寸减小的张量与表示可能类别的张量进行比较。 因此，文档或文档的语料库选择一个类别。 20个新闻组的数据集的实验结果表明，TSM对于文本分类的向量空间模型（VSM）是有利的。