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公开(公告)号：US08924314B2
公开(公告)日：2014-12-30
申请号：US13198560
申请日：2011-08-04
Applicant: Parashuram Kulkarni , Abdelhalim Abbas , Michael Mathieson , Jingzhou Hua , Jon Degenhardt , Ramakrishnan Natarajan
Inventor： Parashuram Kulkarni , Abdelhalim Abbas , Michael Mathieson , Jingzhou Hua , Jon Degenhardt , Ramakrishnan Natarajan
CPC classification number: G06F17/3053 , G06F17/30864 , G06N99/005
Abstract: Various embodiments include systems and methods for search result ranking using machine learning. A goal model can be created using machine learning. Responsive to a search query, a plurality of data factors can be inputted into the goal model to create a model output. Search results can be presented to a user based on the model output.
Abstract translation: 各种实施例包括使用机器学习的搜索结果排序的系统和方法。 可以使用机器学习创建目标模型。 响应于搜索查询，可以将多个数据因子输入到目标模型中以创建模型输出。 搜索结果可以根据模型输出呈现给用户。
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公开(公告)号：US20120016873A1
公开(公告)日：2012-01-19
申请号：US12838307
申请日：2010-07-16
Applicant: Michael Mathieson , Sanjay Pundlkrao Ghatare , Vipul C. Dalal , Sameep Navin Solanki , Muhammad Faisal Rehman
Inventor： Michael Mathieson , Sanjay Pundlkrao Ghatare , Vipul C. Dalal , Sameep Navin Solanki , Muhammad Faisal Rehman
IPC: G06F17/30
CPC classification number: G06F17/3053
Abstract: Described herein are methods and systems for promoting item listings that satisfy a query based on the item listings being assigned to certain categories that have, based on historical click data, exhibited high demand characteristics for the query. Consistent with some embodiments, a certain number of leaf-level categories are identified based on demand data for those categories, and the item listings assigned to those categories are promoted through a weighting factor derived in part based on the click probability score associated with the category. In some embodiments, certain sub-categories may be selected when the demand associated with the child categories of the sub-category is well balanced.
Abstract translation: 这里描述的是用于促进项目列表的方法和系统，该项目列表基于分配给某些类别的项目列表来满足查询，该类别具有基于历史点击数据的查询的高需求特征。 根据一些实施例，基于这些类别的需求数据来识别一定数量的叶级别类别，并且通过基于与该类别相关联的点击概率得分部分导出的加权因子来促进分配给这些类别的项目列表 。 在一些实施例中，当与子类别的子类别相关联的需求良好平衡时，可以选择某些子类别。
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3.发明授权Methods and systems for assessing excessive accessory listings in search results 有权用于评估搜索结果中过多附件列表的方法和系统公开(公告)号：US09384278B2
公开(公告)日：2016-07-05
申请号：US13082226
申请日：2011-04-07
Applicant: Ted Tao Yuan , Michael Mathieson , Parashuram Kulkarni
Inventor： Ted Tao Yuan , Michael Mathieson , Parashuram Kulkarni
IPC: G06F17/30
CPC classification number: G06F17/30867 , G06F17/3053 , G06F17/30864
Abstract: A system and method for assessing excessive accessory listings in search results includes a processor-implemented textual mining module that parses a data field of a document and generates at least one token from the data field. A processor-implemented scoring module calculates a score for the at least one token, with the at least one token score representing a likelihood that the at least one token belongs to one of two binary classifications. The processor-implemented scoring module also calculates a score for the document based on the at least one token score, with the document score representing a probability of the document being in one of the two binary classifications. A processor-implemented decision tree module inputs the document score and document attribute values into a decision tree and generates an output representing a refined score based on the document score and at least one of the document attribute values.
Abstract translation: 用于评估搜索结果中的过多附件列表的系统和方法包括处理器实现的文本挖掘模块，其解析文档的数据字段并从数据字段生成至少一个令牌。 处理器实现的评分模块计算所述至少一个令牌的分数，所述至少一个令牌分数表示所述至少一个令牌属于两个二进制分类中的一个的可能性。 处理器实现的评分模块还基于至少一个令牌分数来计算文档的分数，文档分数表示文档处于两个二进制分类之一的概率。 处理器实施的决策树模块将文档分数和文档属性值输入到决策树中，并且基于文档分数和文档属性值中的至少一个生成表示精确分数的输出。
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公开(公告)号：US08515980B2
公开(公告)日：2013-08-20
申请号：US12838307
申请日：2010-07-16
Applicant: Michael Mathieson , Sanjay Pundlkrao Ghatare , Vipul C. Dalal , Sameep Navin Solanki , Muhammad Faisal Rehman
Inventor： Michael Mathieson , Sanjay Pundlkrao Ghatare , Vipul C. Dalal , Sameep Navin Solanki , Muhammad Faisal Rehman
CPC classification number: G06F17/3053
Abstract: Described herein are methods and systems for promoting item listings that satisfy a query based on the item listings being assigned to certain categories that have, based on historical click data, exhibited high demand characteristics for the query. Consistent with some embodiments, a certain number of leaf-level categories are identified based on demand data for those categories, and the item listings assigned to those categories are promoted through a weighting factor derived in part based on the click probability score associated with the category. In some embodiments, certain sub-categories may be selected when the demand associated with the child categories of the sub-category is well balanced.
Abstract translation: 这里描述的是用于促进项目列表的方法和系统，该项目列表基于分配给某些类别的项目列表来满足查询，该类别具有基于历史点击数据的查询的高需求特征。 根据一些实施例，基于这些类别的需求数据来识别一定数量的叶级别类别，并且通过基于与该类别相关联的点击概率得分部分导出的加权因子来促进分配给这些类别的项目列表 。 在一些实施例中，当与子类别的子类别相关联的需求良好平衡时，可以选择某些子类别。
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Patent Agency Ranking
公开(公告)号：US08382052B1
公开(公告)日：2013-02-26
申请号：US11973638
申请日：2007-10-09
Applicant: Michael Mathieson , Mark A. Krull
Inventor： Michael Mathieson , Mark A. Krull
CPC classification number: F16M11/046 , F16M11/048 , F16M13/02 , F16M2200/041
Abstract: A flat-screen television is movably mounted on a wall for movement between a first position, disposed above a fireplace, and a second position, disposed in front of a least part of the fireplace.
Abstract translation: 平板电视可移动地安装在墙壁上，用于在设置在壁炉之上的第一位置和设置在壁炉的至少一部分之前的第二位置之间移动。
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公开(公告)号：US20120078825A1
公开(公告)日：2012-03-29
申请号：US13198560
申请日：2011-08-04
Applicant: Parashuram Kulkarni , Abdelhalim Abbas , Michael Mathieson , Jingzhou Hua , Jon Degenhardt , Ramakrishnan Natarajan
Inventor： Parashuram Kulkarni , Abdelhalim Abbas , Michael Mathieson , Jingzhou Hua , Jon Degenhardt , Ramakrishnan Natarajan
IPC: G06F15/18
CPC classification number: G06F17/3053 , G06F17/30864 , G06N99/005
Abstract: Various embodiments include systems and methods for search result ranking using machine learning. A goal model can be created using machine learning. Responsive to a search query, a plurality of data factors can be inputted into the goal model to create a model output. Search results can be presented to a user based on the model output.
Abstract translation: 各种实施例包括使用机器学习的搜索结果排序的系统和方法。 可以使用机器学习创建目标模型。 响应于搜索查询，可以将多个数据因子输入到目标模型中以创建模型输出。 搜索结果可以根据模型输出呈现给用户。
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公开(公告)号：US20070214128A1
公开(公告)日：2007-09-13
申请号：US11370314
申请日：2006-03-07
Applicant: Michael Smith , Michael Wilkens , Deepa Joshi , Kalpana Ravinarayanan , Michael Mathieson
Inventor： Michael Smith , Michael Wilkens , Deepa Joshi , Kalpana Ravinarayanan , Michael Mathieson
IPC: G06F17/30
CPC classification number: A61J1/20 , G06F17/3064 , G06F17/30672
Abstract: A search query that includes a first spelling of a term is received. A second spelling of the term is generated. A first set of items that are associated with the first spelling of the term is accessed, and a second set of items that are associated with the second spelling of the term is accessed. A comparison is performed between the first set to the second set. A determination is made as to how to respond to the search query based, at least in part, on the comparison. The response may include a suggested search query that includes the second spelling of the term. The second spelling may be a suggested spelling correction or an alternative spelling. The response may include search results that are relevant to a search query that includes the first spelling of the term, the second spelling, or both the first and second spelling.
Abstract translation: 收到包含术语第一拼写的搜索查询。 产生该术语的第二个拼写。 访问与该术语的第一拼写相关联的第一组项目，并且访问与该术语的第二拼写相关联的第二组项目。 在第一组与第二组之间进行比较。 至少部分地基于比较来确定如何响应搜索查询。 响应可以包括建议的搜索查询，其包括该术语的第二拼写。 第二个拼写可能是建议的拼写更正或替代拼写。 响应可能包括与搜索查询相关的搜索结果，搜索查询包括术语的第一拼写，第二拼写，或第一和第二拼写两者。
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8.发明申请METHODS AND SYSTEMS FOR ASSESSING EXCESSIVE ACCESSORY LISTINGS IN SEARCH RESULTS 有权用于评估搜索结果中超额附件列表的方法和系统公开(公告)号：US20120259844A1
公开(公告)日：2012-10-11
申请号：US13082226
申请日：2011-04-07
Applicant: Ted Tao Yuan , Michael Mathieson , Parashuram Kulkarni
Inventor： Ted Tao Yuan , Michael Mathieson , Parashuram Kulkarni
IPC: G06F17/30
CPC classification number: G06F17/30867 , G06F17/3053 , G06F17/30864
Abstract: A system and method for assessing excessive accessory listings in search results includes a processor-implemented textual mining module that parses a data field of a document and generates at least one token from the data field. A processor-implemented scoring module calculates a score for the at least one token, with the at least one token score representing a likelihood that the at least one token belongs to one of two binary classifications. The processor-implemented scoring module also calculates a score for the document based on the at least one token score, with the document score representing a probability of the document being in one of the two binary classifications. A processor-implemented decision tree module inputs the document score and document attribute values into a decision tree and generates an output representing a refined score based on the document score and at least one of the document attribute values.
Abstract translation: 用于评估搜索结果中的过多附件列表的系统和方法包括处理器实现的文本挖掘模块，其解析文档的数据字段并从数据字段生成至少一个令牌。 处理器实现的评分模块计算所述至少一个令牌的分数，所述至少一个令牌分数表示所述至少一个令牌属于两个二进制分类中的一个的可能性。 处理器实现的评分模块还基于至少一个令牌分数来计算文档的分数，文档分数表示文档处于两个二进制分类之一的概率。 处理器实施的决策树模块将文档分数和文档属性值输入到决策树中，并且基于文档分数和文档属性值中的至少一个生成表示精确分数的输出。
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公开(公告)号：US07814097B2
公开(公告)日：2010-10-12
申请号：US11370314
申请日：2006-03-07
Applicant: Michael Smith , Michael J. Wilkens , Deepa Joshi , Kalpana Ravinarayanan , Michael Mathieson
Inventor： Michael Smith , Michael J. Wilkens , Deepa Joshi , Kalpana Ravinarayanan , Michael Mathieson
CPC classification number: A61J1/20 , G06F17/3064 , G06F17/30672
Abstract: A search query that includes a first spelling of a term is received. A second spelling of the term is generated. A first set of items that are associated with the first spelling of the term is accessed, and a second set of items that are associated with the second spelling of the term is accessed. A comparison is performed between the first set to the second set. A determination is made as to how to respond to the search query based, at least in part, on the comparison. The response may include a suggested search query that includes the second spelling of the term. The second spelling may be a suggested spelling correction or an alternative spelling. The response may include search results that are relevant to a search query that includes the first spelling of the term, the second spelling, or both the first and second spelling.
Abstract translation: 收到包含术语第一拼写的搜索查询。 产生该术语的第二个拼写。 访问与该术语的第一拼写相关联的第一组项目，并且访问与该术语的第二拼写相关联的第二组项目。 在第一组与第二组之间进行比较。 至少部分地基于比较来确定如何响应搜索查询。 响应可以包括建议的搜索查询，其包括该术语的第二拼写。 第二个拼写可能是建议的拼写更正或替代拼写。 响应可能包括与搜索查询相关的搜索结果，搜索查询包括术语的第一拼写，第二拼写，或第一和第二拼写两者。
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