公开(公告)号：WO2015006632A2

公开(公告)日：2015-01-15

申请号：PCT/US2014/046258

申请日：2014-07-11

Applicant: MICROSOFT CORPORATION

Inventor： SIMARD, Patrice Y. ,  CHICKERING, David Max ,  GRANGIER, David G. ,  CHARLES, Denis X. ,  BOTTOU, Leon ,  GARCIA JURADO SUAREZ, Carlos

IPC: G06N99/00

CPC classification number: G06F17/2735 ,  G06F3/0482 ,  G06F17/2785 ,  G06F17/30864 ,  G06N7/005 ,  G06N99/005 ,  H04L1/0072 ,  H04L1/0079

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract translation:

非常大的数据集合可能难以搜索和/或分析。 通过将查询和网页自动分类为有用类别，并将这些分类分数用作相关性特征，相关性可能会得到显着改善。 彻底的方法可能需要建立大量的分类器，对应于各种类型的信息，活动和产品。 在大数据集上提供分类器和模式化器的创建。 通过添加可用的元数据，在数以亿计的项目上运行分类器和构造器可能会暴露数据固有的价值。 一些方面包括主动标签探索，自动规则化和冷启动，根据项目数量和分类器数量进行缩放，主动特征化，分割和模式化。

公开(公告)号：WO2009002835A2

公开(公告)日：2008-12-31

申请号：PCT/US2008/067647

申请日：2008-06-20

Applicant: MICROSOFT CORPORATION

Inventor： SIMARD, Patrice Y. ,  VIOLA, Paul Alfred ,  LI, Jin

IPC: G06F15/16

CPC classification number: G06F9/5016 ,  G06F9/5072 ,  H04L67/1002 ,  H04L67/1008 ,  H04L67/104 ,  H04L67/1082

Abstract: Distributed computing devices comprising a system for sharing computing resources can provide shared computing resources to users having sufficient resource credits. A user can earn resource credits by reliably offering a computing resource for sharing for a predetermined amount of time. The conversion rate between the amount of credits awarded, and the computing resources provided by a user can be varied to maintain balance within the system, and to foster beneficial user behavior. Once earned, the credits can be used to fund the user's account, joint accounts which include the user and others, or others' accounts that do not provide any access to the user. Computing resources can be exchanged on a peer-to-peer basis, though a centralized mechanism can link relevant peers together. To verify integrity, and protect against maliciousness, offered resources can be periodically tested.

Abstract translation: 包括用于共享计算资源的系统的分布式计算设备可以向具有足够资源信用的用户提供共享的计算资源。 用户可以通过可靠地提供用于共享预定时间量的计算资源来获得资源信用。 可以改变授予的学分数量和用户提供的计算资源之间的转换率，以保持系统内的平衡，并促进有益的用户行为。 一旦获得，信用额可以用于为用户的帐户，包括用户和其他人的联合账户或不提供对用户的访问的其他账户提供资金。 计算资源可以在对等的基础上交换，尽管集中的机制可以将相关的对等体链接在一起。 为了验证完整性，并防止恶意，提供的资源可以定期测试。

公开(公告)号：WO2015006631A2

公开(公告)日：2015-01-15

申请号：PCT/US2014/046257

申请日：2014-07-11

Applicant: MICROSOFT CORPORATION

Inventor： SIMARD, Patrice Y. ,  CHICKERING, David Max ,  LAKSHMIRATAN, Aparna ,  CHARLES, Denis X. ,  BOTTOU, Leon

IPC: G06N99/00

CPC classification number: G06F17/2735 ,  G06F3/0482 ,  G06F17/2785 ,  G06F17/30864 ,  G06N7/005 ,  G06N99/005 ,  H04L1/0072 ,  H04L1/0079

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract translation: 非常大的数据的集合可能难以搜索和/或分析。 通过自动将查询和网页分类为有用类别，并将这些分类分数作为相关特征，可以显着提高相关性。 彻底的方法可能需要构建大量的分类器，对应于各种类型的信息，活动和产品。 在大数据集上提供了分类器和示意图的创建。 在数亿个项目上执行分类器和示意图可能会通过添加可用的元数据来揭示数据固有的值。 一些方面包括主动标签勘探，自动正规化和冷启动，扩展项目数量和分类数量，主动特征，细分和图解。

公开(公告)号：WO2015006254A3

公开(公告)日：2015-01-15

申请号：PCT/US2014/045639

申请日：2014-07-08

Applicant: MICROSOFT CORPORATION

Inventor： SIMARD, Patrice Y. ,  CHICKERING, David Max ,  GRANGIER, David G. ,  CHARLES, Denis X. ,  BOTTOU, Leon ,  AMERSHI, Saleema A. ,  LAKSHMIRATAN, Aparna ,  SUAREZ, Carlos Garcia Jurado

IPC: G06N99/00 ,  G06F17/27

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

公开(公告)号：WO2011163098A2

公开(公告)日：2011-12-29

申请号：PCT/US2011/041016

申请日：2011-06-19

Applicant: MICROSOFT CORPORATION

Inventor： HULTEN, Geoffrey J. ,  SIMARD, Patrice Y. ,  KIROVSKI, Darko ,  LIND, Jesper B. ,  MEEK, Christopher A.

IPC: G06F21/20 ,  H04L9/32 ,  G06F9/44

CPC classification number: G06F21/30 ,  G06F9/44 ,  G06F21/31 ,  G06F21/577 ,  G06F2221/2133 ,  H04L9/32

Abstract: Human Interaction Proofs ("HIPs", sometimes referred to as "captchas"), may be generated automatically. An captcha specification language may be defined, which allows a captcha scheme to be defined in terms of how symbols are to be chosen and drawn, and how those symbols are obscured. The language may provide mechanisms to specify the various ways in which to obscure symbols. New captcha schemes may be generated from existing specifications, by using genetic algorithms that combine features from existing captcha schemes that have been successful. Moreover, the likelihood that a captcha scheme has been broken by attackers may be estimated by collecting data on the time that it takes existing captcha schemes to be broken, and using regression to estimate the time to breakage as a function of either the captcha's features or its measured quality.

Abstract translation: 人工交互证明（“HIP”，有时也称为“验证码”）可能会自动生成。 可以定义验证码规范语言，这允许根据如何选择和绘制符号以及这些符号如何被遮蔽来定义验证码方案。 该语言可以提供机制来指定模糊符号的各种方式。 可以通过使用遗传算法从现有规范中生成新的验证码方案，该遗传算法将已经获得成功的现有验证码方案的功能相结合。 此外，验证码计划已被攻击者破坏的可能性可以通过在现有验证码计划被破坏的时间收集数据来估计，并且使用回归来估计作为验证码的特征或函数的破坏时间 其测量质量。

公开(公告)号：WO2015006630A2

公开(公告)日：2015-01-15

申请号：PCT/US2014/046256

申请日：2014-07-11

Applicant: MICROSOFT CORPORATION

Inventor： SIMARD, Patrice Y. ,  GRANGIER, David G. ,  BOTTOU, Leon ,  AMERSHI, Saleema A.

IPC: G06N99/00

CPC classification number: G06F17/2735 ,  G06F3/0482 ,  G06F17/2785 ,  G06F17/30864 ,  G06N7/005 ,  G06N99/005 ,  H04L1/0072 ,  H04L1/0079

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract translation: 非常大的数据的集合可能难以搜索和/或分析。 通过自动将查询和网页分类为有用类别，并将这些分类分数作为相关特征，可以显着提高相关性。 彻底的方法可能需要构建大量的分类器，对应于各种类型的信息，活动和产品。 在大数据集上提供了分类器和示意图的创建。 在数亿个项目上执行分类器和示意图可能会通过添加可用的元数据来揭示数据固有的值。 一些方面包括主动标签勘探，自动正规化和冷启动，扩展项目数量和分类数量，主动特征，细分和图解。

公开(公告)号：WO2007070489A1

公开(公告)日：2007-06-21

申请号：PCT/US2006/047291

申请日：2006-12-11

Applicant: MICROSOFT CORPORATION

Inventor： CHELLAPILLA, Kumar H. ,  SIMARD, Patrice Y.

IPC: G06F17/00

CPC classification number: G06K9/80

Abstract: A method and system for implementing character recognition is described herein. An input character is received. The input character is composed of one or more logical structures in a particular layout. The layout of the one or more logical structures is identified. One or more of a plurality of classifiers are selected based on the layout of the one or more logical structures in the input character. The entire character is input into the selected classifiers. The selected classifiers classify the logical structures. The outputs from the selected classifiers are then combined to form an output character vector.

Abstract translation: 本文描述了用于实现字符识别的方法和系统。 接收到一个输入字符。 输入字符由特定布局中的一个或多个逻辑结构组成。 识别一个或多个逻辑结构的布局。 基于输入字符中的一个或多个逻辑结构的布局来选择多个分类器中的一个或多个。 整个字符被输入到所选择的分类器中。 所选分类器对逻辑结构进行分类。 然后将所选分类器的输出组合以形成输出字符向量。

公开(公告)号：WO2007005775A3

公开(公告)日：2007-01-11

申请号：PCT/US2006/025884

申请日：2006-06-29

Applicant: MICROSOFT CORPORATION

Inventor： SIMARD, Patrice Y. ,  AGRAWALA, Maneesh ,  STEINKRAUS, David W.

IPC: G06K9/00

Abstract: Systems and methods are disclosed that facilitate normalizing and beautifying digitally generated handwriting, such as can be generated on a tablet PC or via scanning a handwritten document. A classifier can identify extrema in the digital handwriting and label such extrema according to predefined categories (e.g., bottom, baseline, midline, top, other, …) . Multi-linear regression, polynomial regression, etc., can be performed to align labeled extrema to respective and corresponding desired points as indicated by the labels. Additionally, displacement techniques can be applied to the regressed handwriting to optimize legibility for reading by a human viewer and/or for character recognition by a handwriting recognition application. The displacement techniques can comprise a "rubber sheet" displacement algorithm in conjunction with a "rubber rod" displacement algorithm, which can collectively preserve spatial features of the handwriting during warping thereof.

公开(公告)号：EP2585971A2

公开(公告)日：2013-05-01

申请号：EP11798691.9

申请日：2011-06-19

Applicant: Microsoft Corporation

Inventor： HULTEN, Geoffrey J. ,  SIMARD, Patrice Y. ,  KIROVSKI, Darko ,  LIND, Jesper B. ,  MEEK, Christopher A.

IPC: G06F21/20 ,  H04L9/32 ,  G06F9/44

CPC classification number: G06F21/30 ,  G06F9/44 ,  G06F21/31 ,  G06F21/577 ,  G06F2221/2133 ,  H04L9/32

Abstract: Human Interaction Proofs (“HIPs”, sometimes referred to as “captchas”), may be generated automatically. An captcha specification language may be defined, which allows a captcha scheme to be defined in terms of how symbols are to be chosen and drawn, and how those symbols are obscured. The language may provide mechanisms to specify the various ways in which to obscure symbols. New captcha schemes may be generated from existing specifications, by using genetic algorithms that combine features from existing captcha schemes that have been successful. Moreover, the likelihood that a captcha scheme has been broken by attackers may be estimated by collecting data on the time that it takes existing captcha schemes to be broken, and using regression to estimate the time to breakage as a function of either the captcha's features or its measured quality.

公开(公告)号：EP1899895A2

公开(公告)日：2008-03-19

申请号：EP06786164.1

申请日：2006-06-29

Applicant: Microsoft Corporation

Inventor： STEINKRAUS, David W. ,  AGRAWALA, Maneesh ,  SIMARD, Patrice Y.

IPC: G06K9/00

CPC classification number: G06K9/00416

Abstract: Systems and methods are disclosed that facilitate normalizing and beautifying digitally generated handwriting, such as can be generated on a tablet PC or via scanning a handwritten document. A classifier can identify extrema in the digital handwriting and label such extrema according to predefined categories (e.g., bottom, baseline, midline, top, other, …) . Multi-linear regression, polynomial regression, etc., can be performed to align labeled extrema to respective and corresponding desired points as indicated by the labels. Additionally, displacement techniques can be applied to the regressed handwriting to optimize legibility for reading by a human viewer and/or for character recognition by a handwriting recognition application. The displacement techniques can comprise a 'rubber sheet' displacement algorithm in conjunction with a 'rubber rod' displacement algorithm, which can collectively preserve spatial features of the handwriting during warping thereof.