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1. 发明申请

US20070282538A1 CONTINUOUS INFERENCE FOR SEQUENCE DATA 有权
标题翻译：序列数据的连续干扰
公开(公告)号：US20070282538A1
公开(公告)日：2007-12-06
申请号：US11421585
申请日：2006-06-01
申请人： Mukund Narasimhan , Paul Viola , Michael Shilman
发明人： Mukund Narasimhan , Paul Viola , Michael Shilman
IPC分类号： G06F19/00
CPC分类号： G06F19/22
摘要： Dynamic inference is leveraged to provide online sequence data labeling. This provides real-time alternatives to current methods of inference for sequence data. Instances estimate an amount of uncertainty in a prediction of labels of sequence data and then dynamically predict a label when an uncertainty in the prediction is deemed acceptable. The techniques utilized to determine when the label can be generated are tunable and can be personalized for a given user and/or a system. Employed decoding techniques can be dynamically adjusted to tradeoff system resources for accuracy. This allows for fine tuning of a system based on available system resources. Instances also allow for online inference because the inference does not require knowledge of a complete set of sequence data.
摘要翻译：利用动态推理来提供在线序列数据标签。这提供了对序列数据的推理的当前方法的实时替代。实例估计序列数据标签的预测中的不确定性量，然后当预测中的不确定性被认为是可接受的时候动态地预测标签。用于确定何时可以生成标签的技术是可调谐的，并且可以针对给定的用户和/或系统进行个性化。采用解码技术可以动态调整，以便对系统资源进行权衡以获得准确性。这允许基于可用的系统资源对系统进行微调。实例还允许在线推理，因为推理不需要知道一套完整的序列数据。

2. 发明申请

US20060245654A1 Utilizing grammatical parsing for structured layout analysis 审中-公开
标题翻译：利用语法解析进行结构化布局分析
公开(公告)号：US20060245654A1
公开(公告)日：2006-11-02
申请号：US11119451
申请日：2005-04-29
申请人： Paul Viola , Michael Shilman , Mukund Narasimhan , Percy Liang
发明人： Paul Viola , Michael Shilman , Mukund Narasimhan , Percy Liang
IPC分类号： G06K9/72 , G06F7/00
CPC分类号： G06K9/00463 , G06K9/726 , G06K2209/01
摘要： Grammatical parsing is utilized to parse structured layouts that are modeled as grammars. This type of parsing provides an optimal parse tree for the structured layout based on a grammatical cost function associated with a global search. Machine learning techniques facilitate in discriminatively selecting features and setting parameters in the grammatical parsing process. In one instance, labeled examples are parsed and a chart is generated. The chart is then converted into a subsequent set of labeled learning examples. Classifiers are then trained utilizing conventional machine learning and the subsequent example set. The classifiers are then employed to facilitate scoring of succedent sub-parses. A global reference grammar can also be established to facilitate in completing varying tasks without requiring additional grammar learning, substantially increasing the efficiency of the structured layout analysis techniques.
摘要翻译：语法解析用于分析模拟为语法的结构化布局。这种类型的解析为基于与全局搜索相关联的语法成本函数的结构化布局提供了最佳解析树。机器学习技术有助于在语法解析过程中区分性地选择特征和设置参数。在一个实例中，已分析标记的示例并生成图表。然后将该图转换成随后的一组标记的学习示例。然后使用常规机器学习和随后的示例集训练分类器。然后使用分类器来方便后续子解析的得分。还可以建立全局参考语法，以便于完成各种任务，而不需要额外的语法学习，从而大大提高结构化布局分析技术的效率。

3. 发明申请

US20060245641A1 Extracting data from semi-structured information utilizing a discriminative context free grammar 审中-公开
标题翻译：使用歧视性上下文无关语法从半结构化信息中提取数据
公开(公告)号：US20060245641A1
公开(公告)日：2006-11-02
申请号：US11119467
申请日：2005-04-29
申请人： Paul Viola , Mukund Narasimhan , Michael Shilman
发明人： Paul Viola , Mukund Narasimhan , Michael Shilman
IPC分类号： G06K9/62 , G06F17/27 , G06K9/46
CPC分类号： G06F17/2715 , G06F17/278 , G06K9/00469
摘要： A discriminative grammar framework utilizing a machine learning algorithm is employed to facilitate in learning scoring functions for parsing of unstructured information. The framework includes a discriminative context free grammar that is trained based on features of an example input. The flexibility of the framework allows information features and/or features output by arbitrary processes to be utilized as the example input as well. Myopic inside scoring is circumvented in the parsing process because contextual information is utilized to facilitate scoring function training.
摘要翻译：采用利用机器学习算法的歧视性语法框架来促进用于解析非结构化信息的学习评分功能。该框架包括基于示例输入的特征进行训练的歧视上下文无关语法。框架的灵活性允许通过任意进程输出的信息特征和/或特征作为示例输入。在分析过程中绕过了近视的内分，因为利用上下文信息来促进评分功能训练。

4. 发明授权

US07551784B2 Continuous inference for sequence data 有权
标题翻译：序列数据的连续推断
公开(公告)号：US07551784B2
公开(公告)日：2009-06-23
申请号：US11421585
申请日：2006-06-01
申请人： Mukund Narasimhan , Paul A. Viola , Michael Shilman
发明人： Mukund Narasimhan , Paul A. Viola , Michael Shilman
IPC分类号： G06K9/00 , G06K9/62
CPC分类号： G06F19/22
摘要： Dynamic inference is leveraged to provide online sequence data labeling. This provides real-time alternatives to current methods of inference for sequence data. Instances estimate an amount of uncertainty in a prediction of labels of sequence data and then dynamically predict a label when an uncertainty in the prediction is deemed acceptable. The techniques utilized to determine when the label can be generated are tunable and can be personalized for a given user and/or a system. Employed decoding techniques can be dynamically adjusted to tradeoff system resources for accuracy. This allows for fine tuning of a system based on available system resources. Instances also allow for online inference because the inference does not require knowledge of a complete set of sequence data.
摘要翻译：利用动态推理来提供在线序列数据标签。这提供了对序列数据的推理的当前方法的实时替代。实例估计序列数据标签的预测中的不确定性量，然后当预测中的不确定性被认为是可接受的时候动态地预测标签。用于确定何时可以生成标签的技术是可调谐的，并且可以针对给定的用户和/或系统进行个性化。采用解码技术可以动态调整，以便对系统资源进行权衡以获得准确性。这允许基于可用的系统资源对系统进行微调。实例还允许在线推理，因为推理不需要知道一套完整的序列数据。

5. 发明申请

US20060280370A1 Application of grammatical parsing to visual recognition tasks 有权
公开(公告)号：US20060280370A1
公开(公告)日：2006-12-14
申请号：US11151708
申请日：2005-06-13
申请人： Paul Viola , Michael Shilman
发明人： Paul Viola , Michael Shilman
IPC分类号： G06K9/72
CPC分类号： G06K9/726 , G06F17/271 , G06K2209/01
摘要： Image recognition is utilized to facilitate in scoring parse trees for two-dimensional recognition tasks. Trees and subtrees are rendered as images and then utilized to determine parsing scores. Other instances of the subject invention can incorporate additional features such as stroke curvature and/or nearby white space as rendered images as well. Geometric constraints can also be employed to increase performance of a parsing process, substantially improving parsing speed, some even resolvable in polynomial time. Additional performance enhancements can be achieved in yet other instances of the subject invention by employing constellations of integral images and/or integral images of document features.

6. 发明申请

US20060045337A1 Spatial recognition and grouping of text and graphics 失效
标题翻译：文本和图形的空间识别和分组
公开(公告)号：US20060045337A1
公开(公告)日：2006-03-02
申请号：US10927452
申请日：2004-08-26
申请人： Michael Shilman , Paul Viola , Kumar Chellapilla
发明人： Michael Shilman , Paul Viola , Kumar Chellapilla
IPC分类号： G06K9/00
CPC分类号： G06K9/726 , G06K9/00402 , G06K9/344 , G06K9/4614 , G06K2209/01
摘要： The present invention leverages spatial relationships to provide a systematic means to recognize text and/or graphics. This allows augmentation of a sketched shape with its symbolic meaning, enabling numerous features including smart editing, beautification, and interactive simulation of visual languages. The spatial recognition method obtains a search-based optimization over a large space of possible groupings from simultaneously grouped and recognized sketched shapes. The optimization utilizes a classifier that assigns a class label to a collection of strokes. The overall grouping optimization assumes the properties of the classifier so that if the classifier is scale and rotation invariant the optimization will be as well. Instances of the present invention employ a variant of AdaBoost to facilitate in recognizing/classifying symbols. Instances of the present invention employ dynamic programming and/or A-star search to perform optimization. The present invention applies to both hand-sketched shapes and printed handwritten text, and even heterogeneous mixtures of the two.
摘要翻译：本发明利用空间关系来提供识别文本和/或图形的系统手段。这允许以其符号意义来增加草图形状，实现许多功能，包括智能编辑，美化和视觉语言的交互式模拟。空间识别方法从同时分组和识别的草图形状的可能分组的大空间中获得基于搜索的优化。优化利用了将类标签分配给笔画集合的分类器。整体分组优化假设分类器的属性，以便如果分类器是缩放和旋转不变量，则优化将同样如此。本发明的实施例采用AdaBoost的变体来促进识别/分类符号。本发明的实施例采用动态规划和/或A星搜索来执行优化。本发明适用于手绘形状和印刷手写文本，甚至适用于两者的异构混合物。

7. 发明申请

US20070003147A1 Grammatical parsing of document visual structures 有权
标题翻译：文字视觉结构的语法解析
公开(公告)号：US20070003147A1
公开(公告)日：2007-01-04
申请号：US11173280
申请日：2005-07-01
申请人： Paul Viola , Michael Shilman
发明人： Paul Viola , Michael Shilman
IPC分类号： G06K9/72 , G06K9/34 , G06F17/20
CPC分类号： G06K9/726 , G06F17/271 , G06K2209/01
摘要： A two-dimensional representation of a document is leveraged to extract a hierarchical structure that facilitates recognition of the document. The visual structure is grammatically parsed utilizing two-dimensional adaptations of statistical parsing algorithms. This allows recognition of layout structures (e.g., columns, authors, titles, footnotes, etc.) and the like such that structural components of the document can be accurately interpreted. Additional techniques can also be employed to facilitate document layout recognition. For example, grammatical parsing techniques that utilize machine learning, parse scoring based on image representations, boosting techniques, and/or “fast features” and the like can be employed to facilitate in document recognition.
摘要翻译：利用文档的二维表示来提取便于识别文档的层次结构。使用统计解析算法的二维适应来语法解析视觉结构。这允许识别布局结构（例如，列，作者，标题，脚注等）等，使得可以准确地解释文档的结构组件。还可以采用附加技术来促进文档布局识别。例如，可以采用利用机器学习，基于图像表示的分析评分，增强技术和/或“快速特征”等的语法解析技术，以促进文档识别。

8. 发明授权

US07941382B2 Method of classifying and active learning that ranks entries based on multiple scores, presents entries to human analysts, and detects and/or prevents malicious behavior 有权
标题翻译：基于多个分数对条目进行分类和主动学习的方法，向人类分析人员提供条目，并检测和/或防止恶意行为
公开(公告)号：US07941382B2
公开(公告)日：2011-05-10
申请号：US11871587
申请日：2007-10-12
申请人： Jack W. Stokes , John C. Platt , Michael Shilman , Joseph L. Kravis
发明人： Jack W. Stokes , John C. Platt , Michael Shilman , Joseph L. Kravis
IPC分类号： G06E1/00
CPC分类号： G06F15/16
摘要： A malicious behavior detection/prevention system, such as an intrusion detection system, is provided that uses active learning to classify entries into multiple classes. A single entry can correspond to either the occurrence of one or more events or the non-occurrence of one or more events. During a training phase, entries are automatically classified into one of multiple classes. After classifying the entry, a generated model for the determined class is utilized to determine how well an entry corresponds to the model. Ambiguous classifications along with entries that do not fit the model well for the determined class are selected for labeling by a human analyst. The selected entries are presented to a human analyst for labeling. These labels are used to further train the classifier and the models. During an evaluation phase, entries are automatically classified using the trained classifier and a policy associated with determined class is applied.
摘要翻译：提供了一种恶意行为检测/预防系统，例如入侵检测系统，其使用主动学习将条目分类到多个类中。单个条目可以对应于一个或多个事件的发生或一个或多个事件的不发生。在训练阶段，条目自动分为多个类别之一。在对条目进行分类之后，使用所确定的类的生成模型来确定条目对应于模型的良好程度。选择不确定的分类以及不符合确定类别的模型的条目，由人类分析师进行标签。选定的条目提交给人类分析人员进行标签。这些标签用于进一步训练分类器和型号。在评估阶段，使用训练有素的分类器对条目进行自动分类，并应用与确定类相关联的策略。

9. 发明申请

US20080195931A1 Parsing of ink annotations 审中-公开
标题翻译：解析墨迹注释
公开(公告)号：US20080195931A1
公开(公告)日：2008-08-14
申请号：US11589028
申请日：2006-10-27
申请人： Sashi Raghupathy , Paul A. Viola , Michael Shilman , Xin Wang
发明人： Sashi Raghupathy , Paul A. Viola , Michael Shilman , Xin Wang
IPC分类号： G06F17/00
CPC分类号： G06K9/00402
摘要： Annotation recognition and parsing is accomplished by first recognizing and grouping shapes such that relationships between the annotations and the underlying text and/or images can be determined. The recognition and grouping is followed by categorization of recognized annotations according to predefined types. The classification may be according to functionality, relation to content, and the like. In a third phase, the annotations are anchored to the underlying text or images they are found to be related to.
摘要翻译：注释识别和解析是通过首先识别和分组形状来实现的，使得可以确定注释和底层文本和/或图像之间的关系。识别和分组之后是根据预定义类型对所识别的注释进行分类。分类可以根据功能，与内容的关系等。在第三阶段，注释被锚定到被发现与之相关的底层文本或图像。

10. 发明申请

US20050177783A1 Systems and methods that utilize a dynamic digital zooming interface in connection with digital inking 有权
标题翻译：利用与数字墨迹相关的动态数字缩放界面的系统和方法
公开(公告)号：US20050177783A1
公开(公告)日：2005-08-11
申请号：US10775710
申请日：2004-02-10
申请人： Maneesh Agrawala , Michael Shilman
发明人： Maneesh Agrawala , Michael Shilman
IPC分类号： G06F17/21 , G06F3/048 , G06F17/00
CPC分类号： G06F3/04883 , G06F17/241 , G06F17/242 , G06F2203/04806
摘要： The present invention relates to systems and methods that facilitate annotating digital documents (e.g., digital inking) with devices such as Tablet PCs, PDAs, cell phones, and the like. The systems and methods provide for multi-scale navigation during document annotating via a space-scale framework that fluidly generates and moves a zoom region relative to a document and writing utensil. A user can employ this zoom region to annotate various portions of the document at a size comfortable to the user and suitably scaled to the device display. The space-scale framework enables dynamic navigation, wherein the zoom region location, size, and shape, for example, can automatically adjust as the user annotates. When the user finishes annotating the document, the annotations scale back with the zoom region to original page size. These novel features provide advantages over conventional techniques that do not contemplate multi-scale navigation during document annotating.
摘要翻译：本发明涉及利用诸如Tablet PC，PDA，手机等的装置来便于注释数字文档（例如，数字墨迹）的系统和方法。系统和方法通过空间尺度框架提供文档注释期间的多尺度导航，该框架流体地生成并相对于文档和书写工具移动缩放区域。用户可以使用该缩放区域以用户舒适的尺寸来标注文档的各个部分，并适当地缩放到设备显示。空间尺度框架能够实现动态导航，其中例如，缩放区域位置，大小和形状可以随着用户注释而自动调整。当用户完成对文档的注释时，注释随缩放区域缩小到原始页面大小。这些新颖特征提供了优于在文件注释期间不考虑多尺度导航的常规技术的优点。

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