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1. 发明授权

US08890651B2 Interchangeable lock core and opening method thereof 有权
标题翻译：可互换锁芯及其打开方法
公开(公告)号：US08890651B2
公开(公告)日：2014-11-18
申请号：US13475451
申请日：2012-05-18
申请人： Shuqiang Jin , Qing Chang , Hongwei Qiao , Lei Ji , Shuiping Liao
发明人： Shuqiang Jin , Qing Chang , Hongwei Qiao , Lei Ji , Shuiping Liao
IPC分类号： G06F1/26 , E05B9/08 , E05B27/08 , E05B67/24 , E05B35/00 , G07C9/00
CPC分类号： E05B27/086 , E05B9/084 , E05B35/001 , E05B67/24 , G07C9/00309 , G07C9/00857 , G07C2009/00769 , Y10T70/625
摘要： One embodiment of the present invention provides a lock-and-key system for an electrical power plant. The system includes a plurality of locks and a smart key. A respective lock is installed with a standardized lock core, and the lock is associated with a lock identifier (ID). The smart key includes a key head that matches the standardized lock core; a lock-ID detector configured to detect the lock ID; a rotation stopper which, when enabled, is configured to prevent rotation of the key head while the key head is inserted into the standardized lock core; and a control module configured to disable the rotation stopper based on the detected lock ID, thereby facilitating the smart key to unlock the lock by rotating the key head.
摘要翻译：本发明的一个实施例提供了一种用于电力设备的锁定和关键系统。该系统包括多个锁和智能钥匙。相应的锁安装有标准化的锁芯，并且锁与锁定标识符（ID）相关联。智能钥匙包括与标准化锁芯匹配的钥匙头; 锁定ID检测器，被配置为检测锁定ID; 旋转止动器，当启用时，被构造成在钥匙头插入到标准化锁芯中时防止钥匙头旋转; 以及控制模块，其被配置为基于检测到的锁定ID来禁止旋转止动器，由此便于智能钥匙通过旋转钥匙头来解锁锁定。

2. 发明申请

US20130024038A1 INTERCHANGEABLE LOCK CORE AND OPENING METHOD THEREOF 有权
标题翻译：可交换锁芯及其开启方法
公开(公告)号：US20130024038A1
公开(公告)日：2013-01-24
申请号：US13475451
申请日：2012-05-18
申请人： Shuqiang Jin , Qing Chang , Hongwei Qiao , Lei Ji , Shuiping Liao
发明人： Shuqiang Jin , Qing Chang , Hongwei Qiao , Lei Ji , Shuiping Liao
IPC分类号： G06F1/26 , E05B65/00
CPC分类号： E05B27/086 , E05B9/084 , E05B35/001 , E05B67/24 , G07C9/00309 , G07C9/00857 , G07C2009/00769 , Y10T70/625
摘要： One embodiment of the present invention provides a lock-and-key system for an electrical power plant. The system includes a plurality of locks and a smart key. A respective lock is installed with a standardized lock core, and the lock is associated with a lock identifier (ID). The smart key includes a key head that matches the standardized lock core; a lock-ID detector configured to detect the lock ID; a rotation stopper which, when enabled, is configured to prevent rotation of the key head while the key head is inserted into the standardized lock core; and a control module configured to disable the rotation stopper based on the detected lock ID, thereby facilitating the smart key to unlock the lock by rotating the key head.
摘要翻译：本发明的一个实施例提供了一种用于电力设备的锁定和关键系统。该系统包括多个锁和智能钥匙。相应的锁安装有标准化的锁芯，并且锁与锁定标识符（ID）相关联。智能钥匙包括与标准化锁芯匹配的钥匙头; 锁定ID检测器，被配置为检测锁定ID; 旋转止动器，当启用时，被构造成在钥匙头插入到标准化锁芯中时防止钥匙头旋转; 以及控制模块，其被配置为基于检测到的锁ID来禁止旋转止动器，由此便于智能钥匙通过旋转钥匙头来解锁锁定。

3. 发明授权

US08838512B2 Random walk on query pattern graph for query task classification 有权
标题翻译：随机走在查询模式图上进行查询任务分类
公开(公告)号：US08838512B2
公开(公告)日：2014-09-16
申请号：US13089103
申请日：2011-04-18
申请人： Jun Yan , Ning Liu , Lei Ji , Zheng Chen
发明人： Jun Yan , Ning Liu , Lei Ji , Zheng Chen
IPC分类号： G06F7/00 , G06N5/02 , G06F17/30
CPC分类号： G06F17/30864
摘要： A classification process may reduce the computational resources and time required to collect and classify training data utilized to enable a user to effectively access online information. According to some implementations, training data is established by defining one or more seed queries and query patterns. A bi-partite graph may be constructed using the seed query and query pattern information. A traversal of the bi-partite graph can be performed to expand the training data to encompass sufficient data to perform classification of the present search task.
摘要翻译：分类过程可以减少收集和分类用于使用户有效访问在线信息的训练数据所需的计算资源和时间。根据一些实施方式，通过定义一个或多个种子查询和查询模式来建立训练数据。可以使用种子查询和查询模式信息来构建双分图。可以执行双分图的遍历以扩展训练数据以包括足够的数据来执行本次搜索任务的分类。

4. 发明申请

US20130246435A1 FRAMEWORK FOR DOCUMENT KNOWLEDGE EXTRACTION 审中-公开
标题翻译：文件知识提取框架
公开(公告)号：US20130246435A1
公开(公告)日：2013-09-19
申请号：US13419690
申请日：2012-03-14
申请人： Jun Yan , Lei Ji , Edward W. Wild , Yi Li , Ning Liu , Zheng Chen
发明人： Jun Yan , Lei Ji , Edward W. Wild , Yi Li , Ning Liu , Zheng Chen
IPC分类号： G06F17/30
CPC分类号： G06F16/355
摘要： A knowledge extraction framework may iteratively enrich an ontology that is used to classify structured knowledge obtained from web pages based on structured knowledge previously acquired from other web pages. The framework may enable a user to define the ontology for extracting structured knowledge from a plurality of web pages. The framework applies the ontology using a supervised extraction algorithm to extract seed information from a set of web pages. The framework further applies an unsupervised extraction algorithm to extract the structured knowledge from an additional set of web pages. The framework subsequently maps the structured knowledge to the ontology based on the seed information to enrich the ontology.
摘要翻译：知识提取框架可以迭代地丰富用于基于先前从其他网页获取的结构化知识对从网页获得的结构化知识进行分类的本体。框架可以使用户能够定义用于从多个网页提取结构化知识的本体。该框架使用监督提取算法应用本体，从一组网页中提取种子信息。该框架进一步应用无监督提取算法从一组额外的网页提取结构化知识。该框架随后基于种子信息将结构化知识映射到本体，以丰富本体。

5. 发明申请

US20120284224A1 BUILD OF WEBSITE KNOWLEDGE TABLES 审中-公开
标题翻译：建立网站知识表
公开(公告)号：US20120284224A1
公开(公告)日：2012-11-08
申请号：US13100305
申请日：2011-05-04
申请人： Jun Yan , Lei Ji , Ning Liu , Zhimin Zhang , Zheng Chen
发明人： Jun Yan , Lei Ji , Ning Liu , Zhimin Zhang , Zheng Chen
IPC分类号： G06F17/30
CPC分类号： G06F16/951
摘要： Architecture that defines domain knowledge on networks, such as the Internet, as tables where each row is an entity in the target domain and each column is an attribute of these entities. The corresponding values for entity-attribute pairs are the domain knowledge. The architecture provides semi-automatic and systematic ways to extract network knowledge from at least an unstructured and semi-structured network (the Internet), structuralizes the knowledge in table format, and uses the domain tables to build the online updated knowledge base.
摘要翻译：在网络上定义领域知识（例如Internet）的架构，作为每个行是目标域中的实体的表，每列是这些实体的属性。实体 - 属性对的相应值是域知识。该架构提供半自动和系统的方法，从至少一个非结构化和半结构化网络（Internet）中提取网络知识，将表格格式的知识结构化，并使用域表构建在线更新的知识库。

6. 发明授权

US08239334B2 Learning latent semantic space for ranking 有权
标题翻译：学习潜在语义空间进行排名
公开(公告)号：US08239334B2
公开(公告)日：2012-08-07
申请号：US12344093
申请日：2008-12-24
申请人： Jun Yan , Ning Liu , Lei Ji , Zheng Chen
发明人： Jun Yan , Ning Liu , Lei Ji , Zheng Chen
IPC分类号： G06E1/00 , G06E3/00 , G06F15/18 , G06F7/00 , G06F17/00 , G06G7/00
CPC分类号： G06F17/30675
摘要： A tool facilitating learning latent semantics for ranking (LLSR) tailored to the ranking task via leveraging relevance information of query-document pairs to learn a tailored latent semantic space such that other documents are better ranked for the queries in the subspace. The tool applying a learning latent semantics for ranking algorithm integrating LLSR, thereby enabling learning an optimal latent semantic space (LSS) for ranking by utilizing relevance information in the training process of subspace learning. The tool enabling an optimization of the LSS as a closed form solution and facilitating reporting the learned LSS.
摘要翻译：一种通过利用查询文档对的相关性信息来学习定制的潜在语义空间，使其他文档更好地排列在子空间中的查询的方法，帮助学习用于排名任务的潜在语义（LLSR）。该工具应用学习潜在语义用于整合LLSR的排序算法，从而通过在子空间学习的训练过程中利用相关性信息来学习优化潜在语义空间（LSS）进行排名。该工具可以将LSS优化为封闭式解决方案，并有助于报告所学习的LSS。

7. 发明申请

US20110077998A1 CATEGORIZING ONLINE USER BEHAVIOR DATA 审中-公开
标题翻译：分类在线用户行为数据
公开(公告)号：US20110077998A1
公开(公告)日：2011-03-31
申请号：US12568707
申请日：2009-09-29
申请人： Jun Yan , Ning Liu , Lei Ji , Dong Zhuang , Zheng Chen
发明人： Jun Yan , Ning Liu , Lei Ji , Dong Zhuang , Zheng Chen
IPC分类号： G06Q10/00 , G06Q30/00 , G06F17/30
CPC分类号： G06Q30/02
摘要： A method for categorizing online user behavior data, including creating a target set of users based on an advertiser query, identifying two or more users in the target set having one or more first similar behavior attributes using a Minhash algorithm; and modifying the target set according to the two or more identified users.
摘要翻译：一种用于对在线用户行为数据进行分类的方法，包括基于广告商查询创建目标用户集合，使用Minhash算法识别具有一个或多个第一相似行为属性的目标集合中的两个或多个用户; 以及根据所述两个或多个识别的用户修改所述目标集合。

8. 发明申请

US20080288483A1 Efficient retrieval algorithm by query term discrimination 有权
标题翻译：通过查询词辨别的有效检索算法
公开(公告)号：US20080288483A1
公开(公告)日：2008-11-20
申请号：US11804627
申请日：2007-05-18
申请人： Chenxi Lin , Lei Ji , Huajun Zeng , Benyu Zhang , Zheng Chen , Jian Wang
发明人： Chenxi Lin , Lei Ji , Huajun Zeng , Benyu Zhang , Zheng Chen , Jian Wang
IPC分类号： G06F17/30
CPC分类号： G06F17/30675
摘要： Described is an efficient retrieval mechanism that quickly locates documents (e.g., corresponding to online advertisements) based on query term discrimination. A topmost subset (e.g., two) of search terms is selected according to their ranked importance, e.g., as ranked by inverted document frequency. The topmost terms are then used to narrow the number of rows of an inverted query index that are searched to find document identifiers and associated scores, such as computed offline by a BM25 algorithm. For example, for each document identifier of each important term, a fast search within each of the narrowed subset of rows (that also contain that document identifier) may be performed by comparing document identifiers to jump a pointer within each other row, followed by a binary search to locate a particular document. The scores of the set of particular documents may then be used to rank their relative importance for returning as results.
摘要翻译：描述了一种有效的检索机制，其基于查询词辨别快速定位文档（例如，对应于在线广告）。根据其排序的重要性来选择搜索项的最顶层子集（例如，两个），例如按照倒排的文档频率排序。然后使用最上面的术语来缩小被搜索以查找文档标识符和相关分数的反向查询索引的行数，例如通过BM25算法离线计算。例如，对于每个重要术语的每个文档标识符，可以通过比较文档标识符来跳过每个其他行中的指针，然后是一个指针，来执行每个狭窄的行子集（也包含该文档标识符）的快速搜索二进制搜索查找特定文档。然后可以使用该组特定文件的分数来排列其作为结果返回的相对重要性。

9. 发明授权

US08719192B2 Transfer of learning for query classification 有权
标题翻译：转移学习查询分类
公开(公告)号：US08719192B2
公开(公告)日：2014-05-06
申请号：US13081391
申请日：2011-04-06
申请人： Lei Ji , Jun Yan , Ning Liu , Zheng Chen
发明人： Lei Ji , Jun Yan , Ning Liu , Zheng Chen
IPC分类号： G06N5/02 , G06F17/30
CPC分类号： G06N99/005
摘要： Transfer of learning trains a new domain for the classification of search queries according to different tasks, as well as the generation of a corresponding domain-specific query classifier that may be used to classify the search queries according to the different tasks in the new domain. The transfer of learning may include preparing a new domain to receive classification knowledge from one or more source domains by populating the new domain with preliminary query patterns extracted for a search engine log. The transfer of learning may further include preparing the classification knowledge in each source domain for transfer to the new domain. The classification knowledge in each source domain may then be transferred to the new domain.
摘要翻译：学习的转移为根据不同任务对搜索查询进行分类的新领域提供了新的领域，以及生成可用于根据新域中的不同任务对搜索查询进行分类的相应的域特定查询分类器。学习的转移可能包括准备一个新的域，以通过用搜索引擎日志提取的初步查询模式填充新域来从一个或多个源域接收分类知识。学习的转移还可以包括准备每个源域中的分类知识以转移到新的域。然后可以将每个源域中的分类知识转移到新域。

10. 发明授权

US08412726B2 Related links recommendation 有权
标题翻译：相关链接推荐
公开(公告)号：US08412726B2
公开(公告)日：2013-04-02
申请号：US12793047
申请日：2010-06-03
申请人： Jun Yan , Ning Liu , Zheng Chen , Lei Ji , Jiulong Wang , Xiao Liang
发明人： Jun Yan , Ning Liu , Zheng Chen , Lei Ji , Jiulong Wang , Xiao Liang
IPC分类号： G06F17/30
CPC分类号： G06F17/30867
摘要： The related links recommendation technique described herein employs combined collaborative filtering to recommend related web pages to users. The technique creates multiple collaborative filters which are combined in order to create a combined collaborative filter to recommend web pages similar to a given web page to a user. One query-based collaborative filter is created by using query search clicks (e.g., user input device selection actions on search results returned in response to a search query). Another user-behavior-based collaborative filter is created by using query search clicks and user clicks while browsing websites (e.g., user input device selection actions while a user is browsing websites). Lastly, another content-based collaborative filter based on similar content of web pages is created by finding web pages with similar content.
摘要翻译：本文描述的相关链接推荐技术采用组合协同过滤来向用户推荐相关网页。该技术创建了多个协作过滤器，这些过滤器被组合以便创建组合的协同过滤器以向用户推荐类似于给定网页的网页。通过使用查询搜索点击创建一个基于查询的协作过滤器（例如，响应于搜索查询返回的搜索结果上的用户输入设备选择动作）。通过在浏览网站时使用查询搜索点击和用户点击创建另一个基于用户行为的协作过滤器（例如，用户浏览网站时的用户输入设备选择动作）。最后，通过查找具有相似内容的网页来创建基于类似内容的网页的另一基于内容的协作过滤器。

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