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1. 发明申请

US20110225192A1 AUTO-DETECTION OF HISTORICAL SEARCH CONTEXT 有权
标题翻译：自动检测历史搜索条件
公开(公告)号：US20110225192A1
公开(公告)日：2011-09-15
申请号：US12721565
申请日：2010-03-11
申请人： Scott K. Imig , Johnson T. Apacible , Aravind Bala , Peter R. Bailey , Gayathri Ravichandran Geetha , Robert L. Rounthwaite , Hung-chih Yang
发明人： Scott K. Imig , Johnson T. Apacible , Aravind Bala , Peter R. Bailey , Gayathri Ravichandran Geetha , Robert L. Rounthwaite , Hung-chih Yang
IPC分类号： G06F17/30 , G06F15/18 , G06N5/02
CPC分类号： G06F17/30646 , G06F17/30867
摘要： Architecture that automatically detects historical search contexts as well as behaviors related to a search query. Machine learning and hand-authored rules are employed to automatically identify search contexts. Historical information likely to be useful in the current context is surfaced. When a user enters a search query or executes another search behavior, past behaviors are exposed which are contextually related to the current behavior. The architecture also provides automatic discovery of historical contexts, features related to the contexts, and training or authoring of a system for classifying behavior into contexts, using some combination of the machine learning and/or hand-authored rules. A runtime system classifies the current user behavior into a context and surfaces contextual information to the user.
摘要翻译：自动检测历史搜索上下文以及与搜索查询相关的行为的体系结构。采用机器学习和手写规则来自动识别搜索上下文。在当前情况下可能有用的历史信息浮出水面。当用户输入搜索查询或执行其他搜索行为时，会暴露与当前行为上下文相关的过去行为。该架构还提供自动发现历史背景，与上下文有关的功能，以及使用机器学习和/或手工制作规则的某些组合来将行为分类到上下文中的系统的训练或创作。运行时系统将当前用户行为分类为上下文，并向用户显示上下文信息。

2. 发明授权

US08972397B2 Auto-detection of historical search context 有权
标题翻译：自动检测历史搜索语境
公开(公告)号：US08972397B2
公开(公告)日：2015-03-03
申请号：US12721565
申请日：2010-03-11
申请人： Scott K. Imig , Johnson T. Apacible , Aravind Bala , Peter R. Bailey , Gayathri Ravichandran Geetha , Robert L. Rounthwaite , Hung-chih Yang
发明人： Scott K. Imig , Johnson T. Apacible , Aravind Bala , Peter R. Bailey , Gayathri Ravichandran Geetha , Robert L. Rounthwaite , Hung-chih Yang
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06F17/30646 , G06F17/30867
摘要： Architecture that automatically detects historical search contexts as well as behaviors related to a search query. Machine learning and hand-authored rules are employed to automatically identify search contexts. Historical information likely to be useful in the current context is surfaced. When a user enters a search query or executes another search behavior, past behaviors are exposed which are contextually related to the current behavior. The architecture also provides automatic discovery of historical contexts, features related to the contexts, and training or authoring of a system for classifying behavior into contexts, using some combination of the machine learning and/or hand-authored rules. A runtime system classifies the current user behavior into a context and surfaces contextual information to the user.
摘要翻译：自动检测历史搜索上下文以及与搜索查询相关的行为的体系结构。采用机器学习和手写规则来自动识别搜索上下文。在当前情况下可能有用的历史信息浮出水面。当用户输入搜索查询或执行其他搜索行为时，会暴露与当前行为上下文相关的过去行为。该架构还提供自动发现历史背景，与上下文有关的功能，以及使用机器学习和/或手工制作规则的某些组合来将行为分类到上下文中的系统的训练或创作。运行时系统将当前用户行为分类为上下文，并向用户显示上下文信息。

3. 发明申请

US20130339000A1 IDENTIFYING COLLOCATIONS IN A CORPUS OF TEXT IN A DISTRIBUTED COMPUTING ENVIRONMENT 有权
标题翻译：在分布式计算环境中识别文本集合中的集合
公开(公告)号：US20130339000A1
公开(公告)日：2013-12-19
申请号：US13526527
申请日：2012-06-19
申请人： Xiong Zhang , Hung-chih Yang , Danny Lange
发明人： Xiong Zhang , Hung-chih Yang , Danny Lange
IPC分类号： G06F17/27
CPC分类号： G06F17/277 , G06Q50/01
摘要： Technologies pertaining to computing a metric that is indicative of whether an n-gram in a large corpus of text is a collocation are described herein. The metric is computed in connection with a distributed computing framework, wherein n-grams of varying lengths can be analyzed in a single input data pass, and wherein secondary sorting functionality of the distributed computing framework need not be invoked.
摘要翻译：本文描述了关于计算指示文本的大语料库中的n-gram是否为搭配的度量的技术。该度量与分布式计算框架结合计算，其中可以在单个输入数据遍中分析不同长度的n克，并且其中不需要调用分布式计算框架的辅助分类功能。

4. 发明授权

US08356086B2 Distributed non-negative matrix factorization 有权
标题翻译：分布式非负矩阵分解
公开(公告)号：US08356086B2
公开(公告)日：2013-01-15
申请号：US12750772
申请日：2010-03-31
申请人： Chao Liu , Hung-chih Yang , Jinliang Fan , Li-wei He , Yi-Min Wang
发明人： Chao Liu , Hung-chih Yang , Jinliang Fan , Li-wei He , Yi-Min Wang
IPC分类号： G06F15/16 , G06F15/173 , G06F17/30
CPC分类号： G06F17/16
摘要： Architecture that scales up the non-negative matrix factorization (NMF) technique to a distributed NMF (denoted DNMF) to handle large matrices, for example, on a web scale that can include millions and billions of data points. To analyze web-scale data, DNMF is applied through parallelism on distributed computer clusters, for example, with thousands of machines. In order to maximize the parallelism and data locality, matrices are partitioned in the short dimension. The probabilistic DNMF can employ not only Gaussian and Poisson NMF techniques, but also exponential NMF for modeling web dyadic data (e.g., dwell time of a user on browsed web pages).
摘要翻译：将非负矩阵分解（NMF）技术扩展到分布式NMF（表示为DNMF）以处理大型矩阵的架构，例如，可以包括数百万和数十亿个数据点的网络规模。为了分析网络规模数据，DNMF通过并行性应用于分布式计算机集群，例如数千台机器。为了最大化并行度和数据局部性，矩阵在短维中被划分。概率DNMF不仅可以采用高斯和泊松NMF技术，还可以采用指数NMF来建模网络二进制数据（例如，用户在浏览的网页上的停留时间）。

5. 发明授权

US09239827B2 Identifying collocations in a corpus of text in a distributed computing environment 有权
标题翻译：在分布式计算环境中识别文本语料库中的并置
公开(公告)号：US09239827B2
公开(公告)日：2016-01-19
申请号：US13526527
申请日：2012-06-19
申请人： Xiong Zhang , Hung-chih Yang , Danny Lange
发明人： Xiong Zhang , Hung-chih Yang , Danny Lange
IPC分类号： G06F17/27 , G06Q50/00
CPC分类号： G06F17/277 , G06Q50/01
摘要： Technologies pertaining to computing a metric that is indicative of whether an n-gram in a large corpus of text is a collocation are described herein. The metric is computed in connection with a distributed computing framework, wherein n-grams of varying lengths can be analyzed in a single input data pass, and wherein secondary sorting functionality of the distributed computing framework need not be invoked.
摘要翻译：本文描述了关于计算指示文本的大语料库中的n-gram是否为搭配的度量的技术。该度量与分布式计算框架结合计算，其中可以在单个输入数据遍中分析不同长度的n克，并且其中不需要调用分布式计算框架的辅助分类功能。

6. 发明申请

US20130346466A1 IDENTIFYING OUTLIERS IN A LARGE SET OF OBJECTS 审中-公开
标题翻译：在大型对象中识别出来的人
公开(公告)号：US20130346466A1
公开(公告)日：2013-12-26
申请号：US13530140
申请日：2012-06-22
申请人： Xiong Zhang , Hung-chih Yang , Danny Lange
发明人： Xiong Zhang , Hung-chih Yang , Danny Lange
IPC分类号： G06F15/16
CPC分类号： G06F16/35
摘要： Described herein are various technologies pertaining to identifying global outlier candidates from a relatively large collection of computer-readable objects in a distributed computing environment. The collection of computer-readable objects is partitioned into a plurality of sets of objects, and local outlier candidates are identified from each set of objects in the plurality of sets of objects. The local outlier candidates are updated through a hierarchical pairwise similarity analysis until global outlier candidates are identified. Thereafter, a pairwise similarity analysis is undertaken with respect to the global outlier candidates and the sets of objects in the plurality of sets of objects to identify true global outliers.
摘要翻译：这里描述的是关于从分布式计算环境中的相对较大的计算机可读对象集合中识别全局异常值候选的各种技术。计算机可读对象的集合被分割成多组对象，并且从多组对象中的每组对象中识别局部离群候选。通过分层成对相似性分析更新本地离群候选者，直到识别全球离群候选者。此后，对于全局异常值候选和多组对象中的对象集合进行成对相似性分析，以识别真正的全局异常值。

7. 发明申请

US20130346424A1 COMPUTING TF-IDF VALUES FOR TERMS IN DOCUMENTS IN A LARGE DOCUMENT CORPUS 审中-公开
标题翻译：在大型文件公司的文件中计算TF-IDF值
公开(公告)号：US20130346424A1
公开(公告)日：2013-12-26
申请号：US13528874
申请日：2012-06-21
申请人： Xiong Zhang , Hung-chih Yang , Danny Lange
发明人： Xiong Zhang , Hung-chih Yang , Danny Lange
IPC分类号： G06F17/30
CPC分类号： G06F16/313
摘要： Technologies pertaining to computing a respective TF-IDF value for each term in each document of a relative large document corpus are described herein. TF-IDF values are computed with respect to terms in documents of a large document corpus by in a single pass over the document corpus. Secondary sorting functionality of a distributed computing framework is exploited to compute TF-IDF values efficiently.
摘要翻译：这里描述了关于计算相对大型文档语料库的每个文档中的每个术语的相应TF-IDF值的技术。 TF-IDF值是通过在单个文档语料库中通过大型文档语料库的文档中的术语来计算的。利用分布式计算框架的二次排序功能有效地计算TF-IDF值。

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