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1. 发明申请

US20100169605A1 ARBITRARY PRECISION FLOATING NUMBER PROCESSING 有权
标题翻译：仲裁精度浮数代码处理
公开(公告)号：US20100169605A1
公开(公告)日：2010-07-01
申请号：US12346061
申请日：2008-12-30
申请人： Xu Yang , Hao Wei , Gong Cheng , ZhangZhang Song , Dongmei Zhang , Jian Wang
发明人： Xu Yang , Hao Wei , Gong Cheng , ZhangZhang Song , Dongmei Zhang , Jian Wang
IPC分类号： G06F12/02 , G06F7/38
CPC分类号： G06F7/483 , G06F7/49957
摘要： Techniques for providing arbitrary precision floating number (APFN) processing are disclosed. In some aspects, an APFN store may be used to store a large number (i.e., an APFN) having many significant digits, which in turn may enable a high degree of precision in mathematical operations. An APFN module may be used to create and define the APFN store. The APFN module may enable a user to define a precision (significant digits) for the large number that corresponds to the size of an array of bytes in the APFN store that are allocated for storing the large number. In further aspects, the APFN store may be used to store additional intermediary data and a resultant.
摘要翻译：公开了提供任意精度浮动数（APFN）处理的技术。在一些方面，可以使用APFN存储来存储具有许多有效数字的大量（即，APFN），这又可以使数学运算中的高度精度。可以使用APFN模块来创建和定义APFN存储。 APFN模块可以使用户能够为与分配用于存储大数量的APFN存储中的字节数组的大小相对应的大数量定义精确度（有效数字）。在另外的方面，APFN存储可以用于存储附加的中间数据和结果。

2. 发明授权

US08095767B2 Arbitrary precision floating number processing 有权
标题翻译：任意精度浮点数处理
公开(公告)号：US08095767B2
公开(公告)日：2012-01-10
申请号：US12346061
申请日：2008-12-30
申请人： Xu Yang , Hao Wei , Gong Cheng , ZhangZhang Song , Dongmei Zhang , Jian Wang
发明人： Xu Yang , Hao Wei , Gong Cheng , ZhangZhang Song , Dongmei Zhang , Jian Wang
IPC分类号： G06F12/02 , G06F7/38
CPC分类号： G06F7/483 , G06F7/49957
摘要： Techniques for providing arbitrary precision floating number (APFN) processing are disclosed. In some aspects, an APFN store may be used to store a large number (i.e., an APFN) having many significant digits, which in turn may enable a high degree of precision in mathematical operations. An APFN module may be used to create and define the APFN store. The APFN module may enable a user to define a precision (significant digits) for the large number that corresponds to the size of an array of bytes in the APFN store that are allocated for storing the large number. In further aspects, the APFN store may be used to store additional intermediary data and a resultant.
摘要翻译：公开了提供任意精度浮动数（APFN）处理的技术。在一些方面，可以使用APFN存储来存储具有许多有效数字的大量（即，APFN），这又可以使数学运算中的高度精度。可以使用APFN模块来创建和定义APFN存储。 APFN模块可以使用户能够为与分配用于存储大数量的APFN存储中的字节数组的大小相对应的大数量定义精确度（有效数字）。在另外的方面，APFN存储可以用于存储附加的中间数据和结果。

3. 发明申请

US20100191793A1 Symbolic Computation Using Tree-Structured Mathematical Expressions 审中-公开
标题翻译：使用树结构数学表达式的符号计算
公开(公告)号：US20100191793A1
公开(公告)日：2010-07-29
申请号：US12360853
申请日：2009-01-28
申请人： Xu Yang , Xiaolin Quan , Dongmei Zhang , Gong Cheng , Jian Wang , Hao Wei
发明人： Xu Yang , Xiaolin Quan , Dongmei Zhang , Gong Cheng , Jian Wang , Hao Wei
IPC分类号： G06F7/38 , G06F17/10
CPC分类号： G06F17/10 , G06F7/38
摘要： A method for performing symbolic computations on a mathematical expression. The mathematical expression may be converted to a tree structure having one or more parent nodes and one or more child nodes. Each parent node may be a mathematical operation. Each child node may be a mathematical expression on which the mathematical operation is performed in a specified order. Each child node may be in a hierarchical relationship to one of the parent nodes. The parent nodes, the child nodes or both may be manipulated to perform a first symbolic computation on the mathematical expression.
摘要翻译：一种用于对数学表达式执行符号计算的方法。数学表达式可以转换为具有一个或多个父节点和一个或多个子节点的树结构。每个父节点可以是数学运算。每个子节点可以是以指定顺序执行数学运算的数学表达式。每个子节点可以与父节点中的一个分层关系。可以操纵父节点，子节点或两者以对数学表达式执行第一符号计算。

4. 发明授权

US08266078B2 Platform for learning based recognition research 有权
标题翻译：基于学习的平台识别研究
公开(公告)号：US08266078B2
公开(公告)日：2012-09-11
申请号：US12366655
申请日：2009-02-06
申请人： Yu Zou , Hao Wei , Gong Cheng , Dongmei Zhang , Jian Wang
发明人： Yu Zou , Hao Wei , Gong Cheng , Dongmei Zhang , Jian Wang
IPC分类号： G06F15/18 , G06K9/62 , G06K9/46
CPC分类号： G06K9/6253 , G10L15/063
摘要： A method for researching and developing a recognition model in a computing environment, including gathering one or more data samples from one or more users in the computing environment into a training data set used for creating the recognition model, receiving one or more training parameters defining a feature extraction algorithm configured to analyze one or more features of the training data set, a classifier algorithm configured to associate the features to a template set, a selection of a subset of the training data set, a type of the data samples, or combinations thereof, creating the recognition model based on the training parameters, and evaluating the recognition model.
摘要翻译：一种用于在计算环境中研究和开发识别模型的方法，包括将来自所述计算环境中的一个或多个用户的一个或多个数据样本收集到用于创建所述识别模型的训练数据集中，接收定义一个或多个训练参数的训练参数特征提取算法，其被配置为分析训练数据集的一个或多个特征，分类器算法，被配置为将特征与模板集合相关联，训练数据集的子集的选择，数据样本的类型或其组合，基于训练参数创建识别模型，并对识别模型进行评估。

5. 发明申请

US20100205120A1 PLATFORM FOR LEARNING BASED RECOGNITION RESEARCH 有权
标题翻译：基于学习的识别研究平台
公开(公告)号：US20100205120A1
公开(公告)日：2010-08-12
申请号：US12366655
申请日：2009-02-06
申请人： Yu Zou , Hao Wei , Gong Cheng , Dongmei Zhang , Jian Wang
发明人： Yu Zou , Hao Wei , Gong Cheng , Dongmei Zhang , Jian Wang
IPC分类号： G06F15/18 , G06K9/62 , G10L15/00 , G06K9/46
CPC分类号： G06K9/6253 , G10L15/063
摘要： A method for researching and developing a recognition model in a computing environment, including gathering one or more data samples from one or more users in the computing environment into a training data set used for creating the recognition model, receiving one or more training parameters defining a feature extraction algorithm configured to analyze one or more features of the training data set, a classifier algorithm configured to associate the features to a template set, a selection of a subset of the training data set, a type of the data samples, or combinations thereof, creating the recognition model based on the training parameters, and evaluating the recognition model.
摘要翻译：一种用于在计算环境中研究和开发识别模型的方法，包括将来自所述计算环境中的一个或多个用户的一个或多个数据样本收集到用于创建所述识别模型的训练数据集中，接收定义一个或多个训练参数的训练参数特征提取算法，其被配置为分析训练数据集的一个或多个特征，分类器算法，被配置为将特征与模板集合相关联，训练数据集的子集的选择，数据样本的类型或其组合，基于训练参数创建识别模型，并对识别模型进行评估。

6. 发明申请

US20080313211A1 Data Relationship Visualizer 失效
标题翻译：数据关系可视化器
公开(公告)号：US20080313211A1
公开(公告)日：2008-12-18
申请号：US11764354
申请日：2007-06-18
申请人： Yingnong Dang , Xu Yang , Dongmei Zhang , Min Wang , Jian Wang
发明人： Yingnong Dang , Xu Yang , Dongmei Zhang , Min Wang , Jian Wang
IPC分类号： G06F17/00
CPC分类号： G06F17/30994 , G06F17/30014
摘要： Data having express or implied relationships may be displayed by selecting a starting entity in a data structure, building a relationship tree, and building and optimizing a relationship matrix based on the relationship tree. The optimized relationship matrix may be used to layout and render a graphical image that positions various elements with respect to the starting entity based on the relationships. The distance matrix may be optimized by creating a first distance matrix based on the relationship tree, developing a dissimilarity matrix based on expressed or implied relationships, and multiplying the dissimilarity matrix by a weighting factor to determine a distance matrix that may be optimized by multi-dimensional scaling. An optimized weighting factor may be determined and used to select an optimized distance matrix.
摘要翻译：可以通过选择数据结构中的起始实体，建立关系树，以及基于关系树建立和优化关系矩阵来显示具有明确或隐含关系的数据。优化的关系矩阵可以用于基于关系来布局和渲染相对于起始实体定位各种元素的图形图像。距离矩阵可以通过基于关系树创建第一距离矩阵，基于表示或隐含的关系开发不相似矩阵，以及将不相似矩阵乘以加权因子来确定可以通过多重关系树优化的距离矩阵来优化，尺寸缩放。可以确定优化的加权因子并用于选择优化的距离矩阵。

7. 发明授权

US08060540B2 Data relationship visualizer 失效
标题翻译：数据关系可视化
公开(公告)号：US08060540B2
公开(公告)日：2011-11-15
申请号：US11764354
申请日：2007-06-18
申请人： Yingnong Dang , Xu Yang , Dongmei Zhang , Min Wang , Jian Wang
发明人： Yingnong Dang , Xu Yang , Dongmei Zhang , Min Wang , Jian Wang
IPC分类号： G06F17/30
CPC分类号： G06F17/30994 , G06F17/30014
摘要： Data having express or implied relationships may be displayed by selecting a starting entity in a data structure, building a relationship tree, and building and optimizing a relationship matrix based on the relationship tree. The optimized relationship matrix may be used to layout and render a graphical image that positions various elements with respect to the starting entity based on the relationships. The distance matrix may be optimized by creating a first distance matrix based on the relationship tree, developing a dissimilarity matrix based on expressed or implied relationships, and multiplying the dissimilarity matrix by a weighting factor to determine a distance matrix that may be optimized by multi-dimensional scaling. An optimized weighting factor may be determined and used to select an optimized distance matrix.
摘要翻译：可以通过选择数据结构中的起始实体，建立关系树，以及基于关系树建立和优化关系矩阵来显示具有明确或隐含关系的数据。优化的关系矩阵可以用于基于关系来布局和渲染相对于起始实体定位各种元素的图形图像。距离矩阵可以通过基于关系树创建第一距离矩阵，基于表示或隐含的关系开发不相似矩阵，以及将不相似矩阵乘以加权因子来确定可以通过多重关系树优化的距离矩阵来优化，尺寸缩放。可以确定优化的加权因子并用于选择优化的距离矩阵。

8. 发明授权

US09147271B2 Graphical representation of aggregated data 有权
标题翻译：聚合数据的图形表示
公开(公告)号：US09147271B2
公开(公告)日：2015-09-29
申请号：US11820636
申请日：2007-06-20
申请人： Yingnong Dang , Dongmei Zhang , Min Wang , Xiaohui Hou , Jian Wang
发明人： Yingnong Dang , Dongmei Zhang , Min Wang , Xiaohui Hou , Jian Wang
IPC分类号： G06F3/048 , G06T11/20 , G06F21/57
CPC分类号： G06T11/206 , G06F21/577
摘要： A method for enabling graphical representation of aggregated data is provided. The method includes accessing aggregated data retrieved from a plurality of on-line sources and receiving selection of a portion of the data through a graphical user interface. The method further includes identifying attributes associated with the portion of the data and generating a graphical representation of the portion of the data.
摘要翻译：提供了一种用于实现聚合数据的图形表示的方法。该方法包括访问从多个在线源检索的聚合数据，并通过图形用户界面接收对部分数据的选择。所述方法还包括识别与所述数据的所述部分相关联的属性并且生成所述数据的所述部分的图形表示。

9. 发明授权

US08363950B2 Combining online and offline recognizers in a handwriting recognition system 有权
公开(公告)号：US08363950B2
公开(公告)日：2013-01-29
申请号：US13426427
申请日：2012-03-21
申请人： Xinjian Chen , Dongmei Zhang , Yu Zou , Ming Chang , Shi Han , Jian Wang
发明人： Xinjian Chen , Dongmei Zhang , Yu Zou , Ming Chang , Shi Han , Jian Wang
IPC分类号： G06K9/00 , G06F17/00
CPC分类号： G06K9/00973 , G06K9/6292 , G06K9/6296
摘要： Described is a technology by which online recognition of handwritten input data is combined with offline recognition and processing to obtain a combined recognition result. In general, the combination improves overall recognition accuracy. In one aspect, online and offline recognition is separately performed to obtain online and offline character-level recognition scores for candidates (hypotheses). A statistical analysis-based combination algorithm, an AdaBoost algorithm, and/or a neural network-based combination may determine a combination function to combine the scores to produce a result set of one or more results. Online and offline radical-level recognition may be performed. For example, a HMM recognizer may generate online radical scores used to build a radical graph, which is then rescored using the offline radical recognition scores. Paths in the rescored graph are then searched to provide the combined recognition result, e.g., corresponding to the path with the highest score.

10. 发明授权

US08041120B2 Unified digital ink recognition 有权
标题翻译：统一数字墨水识别
公开(公告)号：US08041120B2
公开(公告)日：2011-10-18
申请号：US11821858
申请日：2007-06-26
申请人： Dongmei Zhang , Xiaohui Hou , Yingjun Qiu , Jian Wang
发明人： Dongmei Zhang , Xiaohui Hou , Yingjun Qiu , Jian Wang
IPC分类号： G06K9/46 , G06K9/66
CPC分类号： G06K9/00422
摘要： Described is a unified digital ink recognizer that recognizes various different types of digital ink data, such as handwritten character data and custom data, e.g., sketched shapes, handwritten gestures, and/or drawn pictures, without further participation by a user such as recognition mode selection or parameter input. For a custom item, the output may be a Unicode value from a private use area of Unicode. Building the unified digital ink recognizer may include defining the data set to be recognized, extracting features of training samples corresponding to the dataset items to build a recognizer model, evaluating the recognizer model using testing data, and modifying the recognizer model using tuning data. The extracted features may be processed into feature data for a multi-dimensional nearest neighbor recognizer approach; the extracted features for the samples of each class is calculated and combined into the feature set for this class in the resulting recognizer model.
摘要翻译：描述了一种统一的数字墨水识别器，其识别各种不同类型的数字墨水数据，例如手写字符数据和自定义数据，例如草图形状，手写手势和/或绘制的图像，而不需要诸如识别模式的用户的进一步参与选择或参数输入。对于自定义项目，输出可能是Unicode的私有使用区域的Unicode值。构建统一数字墨水识别器可以包括定义要识别的数据集，提取与数据集项目相对应的训练样本的特征以构建识别器模型，使用测试数据评估识别器模型，以及使用调谐数据修改识别器模型。提取的特征可以被处理成用于多维最近邻识别器方法的特征数据; 计算每个类的样本的提取特征，并将其组合到生成的识别器模型中的该类的特征集中。

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