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1. 发明授权

US08700592B2 Shopping search engines 有权
标题翻译：购物搜索引擎
公开(公告)号：US08700592B2
公开(公告)日：2014-04-15
申请号：US12757095
申请日：2010-04-09
申请人： Satya Pradeep Kanduri , Marcelo De Barros , Mikhail Parakhin , Cynthia Yu , Qiang Wu
发明人： Satya Pradeep Kanduri , Marcelo De Barros , Mikhail Parakhin , Cynthia Yu , Qiang Wu
IPC分类号： G06F17/30
CPC分类号： G06F17/30867
摘要： A web search system uses humans to rank the relevance of results returned for various sample search queries. The search results may be divided into groups allowing training and validation with the ranked results. Consistent guidelines for human evaluation allow consistent results across a number of people performing the ranking. After a machine learning categorization tool, such as MART, has been programmed and validated, it may be used to provide an absolute rank of relevance for documents returned, rather than a simple relative ranking, based, for example, on key word matches and click counts. Documents with lower relevance rankings may be excluded from consideration when developing related refinements, such as category and price sorting.
摘要翻译：网络搜索系统使用人类对各种样本搜索查询返回的结果的相关性进行排名。搜索结果可以分为允许训练和验证与排名结果的组。一致的人类评估指南可以让许多执行排名的人员获得一致的结果。在机器学习分类工具（例如MART）已经被编程和验证之后，它可以用于提供返回的文档的绝对等级，而不是基于例如关键词匹配和点击的简单的相对排名计数具有较低相关性排名的文件可能在开发相关改进（例如类别和价格排序）时被排除在考虑之外。

2. 发明申请

US20120163709A1 AUTOMATED IDENTIFICATION OF IMAGE OUTLIERS 有权
标题翻译：自动识别图像显示器
公开(公告)号：US20120163709A1
公开(公告)日：2012-06-28
申请号：US12975684
申请日：2010-12-22
申请人： Marcelo De Barros , Satya Pradeep Kanduri , Nabeel Kaushal , Mikhail Parakhin , Manish Mittal , Adam Edlavitch
发明人： Marcelo De Barros , Satya Pradeep Kanduri , Nabeel Kaushal , Mikhail Parakhin , Manish Mittal , Adam Edlavitch
IPC分类号： G06K9/00
CPC分类号： G06K9/6265 , G06F17/30247 , G06F17/30864 , G06K9/6284
摘要： Outlier images—those images that differ substantially from other images in a set—can be automatically identified. One or more penalty values can be assigned to each image that quantifies how different that image is from others in the set. A threshold can be determined based on the set of penalty values. Each image whose penalty values are above the threshold is an outlier image. The penalty values can be the sum of per-pixel penalty values multiplied by the number of pixels with nonzero penalty values. A per-pixel penalty value can be the difference between a color value for that pixel and a predetermined range of color values, based on corresponding pixels in other images. The per-pixel penalty value can be determined for each component color and then optionally summed together. The threshold penalty values can be adjusted to provide for greater, or less, sensitivity to differences among the images.
摘要翻译：离群图像 - 可以自动识别那些与集合中的其他图像显着不同的图像。可以为每个图像分配一个或多个惩罚值，以量化该图像与该组中的其他图像的不同。可以基于惩罚值集合来确定阈值。惩罚值高于阈值的每个图像是异常值图像。惩罚值可以是每像素惩罚值乘以具有非零惩罚值的像素数的总和。基于其他图像中的相应像素，每像素惩罚值可以是该像素的颜色值与颜色值的预定范围之间的差。可以为每个分量颜色确定每像素罚分值，然后可选地将其相加。可以调整阈值惩罚值以对图像之间的差异提供更大或更小的灵敏度。

3. 发明授权

US08463026B2 Automated identification of image outliers 有权
标题翻译：自动识别图像异常值
公开(公告)号：US08463026B2
公开(公告)日：2013-06-11
申请号：US12975684
申请日：2010-12-22
申请人： Marcelo De Barros , Satya Pradeep Kanduri , Nabeel Kaushal , Mikhail Parakhin , Manish Mittal , Adam Edlavitch
发明人： Marcelo De Barros , Satya Pradeep Kanduri , Nabeel Kaushal , Mikhail Parakhin , Manish Mittal , Adam Edlavitch
IPC分类号： G06K9/00
CPC分类号： G06K9/6265 , G06F17/30247 , G06F17/30864 , G06K9/6284
摘要： Outlier images—those images that differ substantially from other images in a set—can be automatically identified. One or more penalty values can be assigned to each image that quantifies how different that image is from others in the set. A threshold can be determined based on the set of penalty values. Each image whose penalty values are above the threshold is an outlier image. The penalty values can be the sum of per-pixel penalty values multiplied by the number of pixels with nonzero penalty values. A per-pixel penalty value can be the difference between a color value for that pixel and a predetermined range of color values, based on corresponding pixels in other images. The per-pixel penalty value can be determined for each component color and then optionally summed together. The threshold penalty values can be adjusted to provide for greater, or less, sensitivity to differences among the images.
摘要翻译：离群图像 - 可以自动识别那些与集合中的其他图像显着不同的图像。可以为每个图像分配一个或多个惩罚值，以量化该图像与该组中的其他图像的不同。可以基于惩罚值集合来确定阈值。惩罚值高于阈值的每个图像是异常值图像。惩罚值可以是每像素惩罚值乘以具有非零惩罚值的像素数的总和。基于其他图像中的相应像素，每像素惩罚值可以是该像素的颜色值与颜色值的预定范围之间的差。可以为每个分量颜色确定每像素罚分值，然后可选地将其相加。可以调整阈值惩罚值以对图像之间的差异提供更大或更小的灵敏度。

4. 发明申请

US20110252012A1 Shopping Search Engines 有权
标题翻译：购物搜索引擎
公开(公告)号：US20110252012A1
公开(公告)日：2011-10-13
申请号：US12757095
申请日：2010-04-09
申请人： Satya Pradeep Kanduri , Marcelo De Barros , Mikhail Parakhin , Cynthia Yu , Qiang Wu
发明人： Satya Pradeep Kanduri , Marcelo De Barros , Mikhail Parakhin , Cynthia Yu , Qiang Wu
IPC分类号： G06F17/30 , G06F15/18
CPC分类号： G06F17/30867
摘要： A web search system uses humans to rank the relevance of results returned for various sample search queries. The search results may be divided into groups allowing training and validation with the ranked results. Consistent guidelines for human evaluation allow consistent results across a number of people performing the ranking. After a machine learning categorization tool, such as MART, has been programmed and validated, it may be used to provide an absolute rank of relevance for documents returned, rather than a simple relative ranking, based, for example, on key word matches and click counts. Documents with lower relevance rankings may be excluded from consideration when developing related refinements, such as category and price sorting.
摘要翻译：网络搜索系统使用人类对各种样本搜索查询返回的结果的相关性进行排名。搜索结果可以分为允许训练和验证与排名结果的组。一致的人类评估指南可以让许多执行排名的人员获得一致的结果。在机器学习分类工具（例如MART）已经被编程和验证之后，它可以用于提供返回的文档的绝对等级，而不是基于例如关键词匹配和点击的简单的相对排名计数具有较低相关性排名的文件可能在开发相关改进（例如类别和价格排序）时被排除在考虑之外。

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