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71. 发明申请

US20070208728A1 Predicting demographic attributes based on online behavior 审中-公开
标题翻译：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。

72. 发明申请

US20070192164A1 Generation of contextual image-containing advertisements 有权
标题翻译：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。

73. 发明授权

US07788131B2 Advertising keyword cross-selling 有权
标题翻译：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。

74. 发明授权

US07707129B2 Text classification by weighted proximal support vector machine based on positive and negative sample sizes and weights 有权
标题翻译：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。

75. 发明申请

US20070239638A1 Text classification by weighted proximal support vector machine 有权
标题翻译：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。

76. 发明申请

US20070239553A1 Collaborative filtering using cluster-based smoothing 审中-公开
标题翻译：使用基于群集的平滑的协同过滤
公开(公告)号：US20070239553A1
公开(公告)日：2007-10-11
申请号：US11377130
申请日：2006-03-16
申请人： Chenxi Lin , Gui-Rong Xue , Hua-Jun Zeng , Zheng Chen , Benyu Zhang , Jian Wang
发明人： Chenxi Lin , Gui-Rong Xue , Hua-Jun Zeng , Zheng Chen , Benyu Zhang , Jian Wang
IPC分类号： G06Q30/00
CPC分类号： G06Q30/0623 , G06F16/9535 , G06Q30/0242 , G06Q30/0274
摘要： In an embodiment, a method of predicting an active user's rating for an item is disclosed. A database of users may be sorted into clusters. The data associated with the users in each cluster may be smoothed to filling in ratings for items that the users have not personally rated. An active user may then be compared to a set of users, where the set may be all or some portion of the database, to determine the K users that are most similar to the active user. The ratings of the K users regarding the item may be used to predict the active user's rating for the item. In an embodiment, the rating of each of the K users is assigned a confidence value associated with whether the user personally rated the item or if the rating was generated by the data smoothing process.
摘要翻译：在一个实施例中，公开了一种用于预测项目的活跃用户评级的方法。可以将用户的数据库分类为群集。可以平滑与每个群集中的用户相关联的数据，以填充用户未被评估的项目的评级。然后可以将活动用户与一组用户进行比较，其中该集合可以是数据库的全部或部分，以确定与活动用户最相似的K个用户。关于该项目的K个用户的评级可以用于预测该项目的活动用户的评级。在一个实施例中，每个K个用户的评级被分配与用户个人评价该项目相关联的置信度值，或者如果该评级是由数据平滑处理产生的。

77. 发明申请

US20060259473A1 System and method for utilizing the content of an online conversation to select advertising content and/or other relevant information for display 有权
标题翻译：用于利用在线对话的内容来选择广告内容和/或其他相关信息进行显示的系统和方法
公开(公告)号：US20060259473A1
公开(公告)日：2006-11-16
申请号：US11128788
申请日：2005-05-13
申请人： Ying Li , Li Li , Tarek Najm , Xianfang Wang , Hongbin Gao , Hua-Jun Zeng , Benyu Zhang , Zheng Chen
发明人： Ying Li , Li Li , Tarek Najm , Xianfang Wang , Hongbin Gao , Hua-Jun Zeng , Benyu Zhang , Zheng Chen
IPC分类号： G06F17/30
CPC分类号： G06Q30/02 , Y10S707/99937
摘要： Systems and methods for analyzing the content of online conversations using data mining technologies are provided. Methods and systems for utilizing the results of data mining technology implementation to retrieve relevant advertising content and/or other relevant information for display in association with the content of an online conversation are also provided.
摘要翻译：提供了使用数据挖掘技术分析在线对话内容的系统和方法。还提供了利用数据挖掘技术实现结果来检索相关广告内容和/或与在线对话内容相关联地显示的其他相关信息的方法和系统。

78. 发明授权

US07529719B2 Document characterization using a tensor space model 失效
标题翻译：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。

79. 发明申请

US20080016087A1 INTERACTIVELY CRAWLING DATA RECORDS ON WEB PAGES 失效
标题翻译：互联网络数据记录在网页上
公开(公告)号：US20080016087A1
公开(公告)日：2008-01-17
申请号：US11456753
申请日：2006-07-11
申请人： Benyu Zhang , Chenxi Lin , Hua-Jun Zeng , Jian Wang , Ke Tang , Zheng Chen
发明人： Benyu Zhang , Chenxi Lin , Hua-Jun Zeng , Jian Wang , Ke Tang , Zheng Chen
IPC分类号： G06F7/00
CPC分类号： G06F17/30864 , Y10S707/99935
摘要： The invention provides a method of interactively crawling data records on a web page. Users may select various data records of interest on a web page to generate templates to search for similar data items on the same web page or on different web pages. A tree matching algorithm may be used to compare and extract data matching the generated template.
摘要翻译：本发明提供了一种在网页上交互地爬行数据记录的方法。用户可以在网页上选择感兴趣的各种数据记录，以生成在同一网页或不同网页上搜索类似数据项的模板。可以使用树匹配算法来比较和提取与生成的模板匹配的数据。

80. 发明申请

US20070022134A1 Cross-language related keyword suggestion 审中-公开
标题翻译：跨语言相关的关键字建议
公开(公告)号：US20070022134A1
公开(公告)日：2007-01-25
申请号：US11187289
申请日：2005-07-22
申请人： Ming Zhou , Hua-Jun Zeng , Zheng Chen , Yajuan Lv , Benyu Zhang , Ying Li , Li Li , Jeffrey Hartin
发明人： Ming Zhou , Hua-Jun Zeng , Zheng Chen , Yajuan Lv , Benyu Zhang , Ying Li , Li Li , Jeffrey Hartin
IPC分类号： G06F7/00
CPC分类号： G06F17/2795 , G06F16/3322 , G06F16/3337 , G06F17/28
摘要： Identifying and selecting keywords in a second language based on an input keyword from a user in a first language. Translation candidates in the second language are determined from the input keyword. Keywords in the second language related to the translation candidates are identified and included with the translation candidates. The translation candidates are ranked and presented to the user for selection.
摘要翻译：基于来自第一语言的用户的输入关键字来识别和选择第二语言中的关键字。根据输入的关键词确定第二语言的翻译候选者。与翻译候选人相关的第二语言中的关键词被识别并包括在翻译候选者中。翻译候选人被排序并呈现给用户进行选择。

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