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1. 发明授权

US08484253B2 Variational mode seeking 有权
标题翻译：变化模式寻求
公开(公告)号：US08484253B2
公开(公告)日：2013-07-09
申请号：US12982915
申请日：2010-12-31
申请人： Bo Thiesson , Jingu Kim
发明人： Bo Thiesson , Jingu Kim
IPC分类号： G06F17/30 , G06K9/62
CPC分类号： G06F17/30705 , G06K9/6226
摘要： A mode-seeking clustering mechanism identifies clusters within a data set based on the location of individual data point according to modes in a kernel density estimate. For large-scale applications the clustering mechanism may utilize rough hierarchical kernel and data partitions in a computationally efficient manner. A variational approach to the clustering mechanism may take into account variational probabilities, which are restricted in certain ways according to hierarchical kernel and data partition trees, and the mechanism may store certain statistics within these trees in order to compute the variational probabilities in a computational efficient way. The clustering mechanism may use a two-step variational expectation and maximization algorithm and generalizations hereof, where the maximization step may be performed in different ways in order to accommodate different mode-seeking algorithms, such as the mean shift, mediod shift, and quick shift algorithms.
摘要翻译：寻找模式的聚类机制根据核密度估计中的模式，根据各个数据点的位置来识别数据集内的簇。对于大规模应用，聚类机制可以以计算有效的方式利用粗略的分级内核和数据分区。聚类机制的变分方法可以考虑到根据分层内核和数据分区树在某些方面受到限制的变分概率，并且该机制可以在这些树中存储某些统计量，以便计算有效率的变分概率办法。聚类机制可以使用两步变化期望和最大化算法及其概括，其中最大化步骤可以以不同的方式执行，以便适应不同的寻呼算法，例如平均偏移，中间偏移和快速移位算法。

2. 发明申请

US20090287626A1 MULTI-MODAL QUERY GENERATION 审中-公开
标题翻译：多模式查询生成
公开(公告)号：US20090287626A1
公开(公告)日：2009-11-19
申请号：US12200648
申请日：2008-08-28
申请人： Timothy Seung Yoon Paek , Bo Thiesson , Yun-Cheng Ju , Bongshin Lee , Christopher A. Meek
发明人： Timothy Seung Yoon Paek , Bo Thiesson , Yun-Cheng Ju , Bongshin Lee , Christopher A. Meek
IPC分类号： G06F7/06 , G06F17/30 , G06N5/02
CPC分类号： G06F16/3322 , G10L15/26
摘要： A multi-modal search system (and corresponding methodology) is provided. The system employs text, speech, touch and gesture input to establish a search query. Additionally, a subset of the modalities can be used to obtain search results based upon exact or approximate matches to a search result. For example, wildcards, which can either be triggered by the user or inferred by the system, can be employed in the search.
摘要翻译：提供了一种多模式搜索系统（及相应的方法）。系统采用文字，语音，触摸和手势输入建立搜索查询。此外，模态的子集可以用于基于与搜索结果的精确或近似匹配来获得搜索结果。例如，可以由用户触发或由系统推断的通配符可用于搜索。

3. 发明授权

US07580813B2 Systems and methods for new time series model probabilistic ARMA 有权
标题翻译：新时间序列模型概率ARMA的系统和方法
公开(公告)号：US07580813B2
公开(公告)日：2009-08-25
申请号：US10463145
申请日：2003-06-17
申请人： Bo Thiesson , Christopher A. Meek , David M. Chickering , David E. Heckerman
发明人： Bo Thiesson , Christopher A. Meek , David M. Chickering , David E. Heckerman
IPC分类号： G06F17/50 , G05B23/02
CPC分类号： G06F17/18
摘要： The present invention utilizes a cross-prediction scheme to predict values of discrete and continuous time observation data, wherein conditional variance of each continuous time tube variable is fixed to a small positive value. By allowing cross-predictions in an ARMA based model, values of continuous and discrete observations in a time series are accurately predicted. The present invention accomplishes this by extending an ARMA model such that a first time series “tube” is utilized to facilitate or “cross-predict” values in a second time series tube to form an “ARMAxp” model. In general, in the ARMAxp model, the distribution of each continuous variable is a decision graph having splits only on discrete variables and having linear regressions with continuous regressors at all leaves, and the distribution of each discrete variable is a decision graph having splits only on discrete variables and having additional distributions at all leaves.
摘要翻译：本发明利用交叉预测方案来预测离散和连续时间观测数据的值，其中每个连续时间管变量的条件方差固定为小的正值。通过在基于ARMA的模型中允许交叉预测，可以准确预测时间序列中连续和离散观测值。本发明通过扩展ARMA模型来实现这一目的，使得第一时间序列“管”用于促进或“交叉预测”第二时间序列管中的值以形成“ARMAxp”模型。一般来说，在ARMAxp模型中，每个连续变量的分布是仅在离散变量上分裂并具有在所有叶上具有连续回归的线性回归的决策图，并且每个离散变量的分布是仅分解为离散变量，并在所有叶子上具有额外的分布。

4. 发明授权

US07548856B2 Systems and methods for discriminative density model selection 有权
标题翻译：用于区分密度模型选择的系统和方法
公开(公告)号：US07548856B2
公开(公告)日：2009-06-16
申请号：US10441470
申请日：2003-05-20
申请人： Bo Thiesson , Christopher A. Meek
发明人： Bo Thiesson , Christopher A. Meek
IPC分类号： G10L15/06
CPC分类号： G06K9/6226 , G06K9/6296 , G10L15/08
摘要： The present invention utilizes a discriminative density model selection method to provide an optimized density model subset employable in constructing a classifier. By allowing multiple alternative density models to be considered for each class in a multi-class classification system and then developing an optimal configuration comprised of a single density model for each class, the classifier can be tuned to exhibit a desired characteristic such as, for example, high classification accuracy, low cost, and/or a balance of both. In one instance of the present invention, error graph, junction tree, and min-sum propagation algorithms are utilized to obtain an optimization from discriminatively selected density models.
摘要翻译：本发明利用鉴别密度模型选择方法来提供可用于构建分类器的优化密度模型子集。通过允许在多类分类系统中为每个类别考虑多个替代密度模型，然后开发由每个类别的单个密度模型组成的最佳配置，分类器可以被调谐以呈现期望的特性，例如，分类精度高，成本低，和/或两者的平衡。在本发明的一个实例中，使用误差图，结树和最小和传播算法来从区分选择的密度模型中获得优化。

5. 发明授权

US07460712B2 Systems and methods for adaptive handwriting recognition 失效
标题翻译：自适应手写识别的系统和方法
公开(公告)号：US07460712B2
公开(公告)日：2008-12-02
申请号：US11672458
申请日：2007-02-07
申请人： Bo Thiesson , Christopher A. Meek
发明人： Bo Thiesson , Christopher A. Meek
IPC分类号： G06K9/00 , G06K9/62
CPC分类号： G06K9/6292 , G06K9/222
摘要： The present invention utilizes generic and user-specific features of handwriting samples to provide adaptive handwriting recognition with a minimum level of user-specific enrollment data. By allowing generic and user-specific classifiers to facilitate in a recognition process, the features of a specific user's handwriting can be exploited to quickly ascertain characteristics of handwriting characters not yet entered by the user. Thus, new characters can be recognized without requiring a user to first enter that character as enrollment or “training” data. In one instance of the present invention, processing of generic features is accomplished by a generic classifier trained on multiple users. In another instance of the present invention, a user-specific classifier is employed to modify a generic classifier's classification as required to provide user-specific handwriting recognition.
摘要翻译：本发明利用手写样本的通用和用户特定的特征来提供具有最低级别的用户特定注册数据的自适应手写识别。通过允许通用和用户特定的分类器便于识别过程，可以利用特定用户手写的特征来快速确定用户尚未输入的手写字符的特征。因此，可以识别新的字符，而不需要用户首先将该字符输入作为注册或“训练”数据。在本发明的一个实例中，通用特征的处理由对多个用户进行训练的通用分类器来完成。在本发明的另一个实例中，使用用户特定的分类器根据需要修改通用分类器的分类以提供用户特定的手写识别。

6. 发明授权

US07430633B2 Pre-storage of data to pre-cached system memory 有权
标题翻译：将数据预存储到预缓存的系统内存中
公开(公告)号：US07430633B2
公开(公告)日：2008-09-30
申请号：US11298218
申请日：2005-12-09
申请人： Kenneth W. Church , Robert J. Ragno , Bo Thiesson
发明人： Kenneth W. Church , Robert J. Ragno , Bo Thiesson
IPC分类号： G06F12/00 , G06F13/00 , G06F13/28 , G06Q30/00 , G07G1/14
CPC分类号： G06Q30/0277 , H04L67/28 , H04L67/2847 , H04L67/2852 , H04L67/306
摘要： System(s) and method(s) that facilitate utilizing pre-cached disk space. Pre-cached memory space within a storage device is identified, and a subset of the pre-cached memory space is pre-populated with data so that the data can be selectively and dynamically accessed. During use of a computer (e.g., in a web-browsing session) a subset of the pre-stored data can be dynamically and selectively exposed to the user as a function of user and/or computer application state. Pre-storage of the data on pre-cached memory of the computer mitigates delayed data access (e.g., due to insufficient transmission bandwidth) thereby enhancing user computing experience. The user can utilize the device without having to distinguish between pre-cached and free memory. In other words, the operating system can present the cached memory to the user so that it appears as free memory without the user having to direct the system to do so.
摘要翻译：有利于利用预先存储的磁盘空间的系统和方法。识别存储设备内的预缓存的存储器空间，并且用数据预填充预先存储的存储器空间的子集，使得可以选择性地和动态地访问数据。在使用计算机期间（例如，在网络浏览会话中），可以根据用户和/或计算机应用状态来动态地和选择性地暴露给用户预先存储的数据的子集。预先存储计算机的预先存储的存储器上的数据缓解了延迟的数据访问（例如，由于传输带宽不足），从而增强了用户计算体验。用户可以利用该设备，而不必区分预先缓存和空闲内存。换句话说，操作系统可以将高速缓存的存储器呈现给用户，使得它显示为空闲存储器，而用户不必指示系统这样做。

7. 发明授权

US07397948B1 System and method for image and video segmentation by anisotropic kernel mean shift 有权
标题翻译：通过各向异性核平均偏移的图像和视频分割的系统和方法
公开(公告)号：US07397948B1
公开(公告)日：2008-07-08
申请号：US10796736
申请日：2004-03-08
申请人： Michael Cohen , Bo Thiesson , Ying-Qing Xu , Jue Wang
发明人： Michael Cohen , Bo Thiesson , Ying-Qing Xu , Jue Wang
IPC分类号： G06K9/00
CPC分类号： G06K9/4652 , G06T7/11
摘要： Mean shift is a nonparametric estimator of density which has been applied to image and video segmentation. Traditional mean shift based segmentation uses a radially symmetric kernel to estimate local density, which is not optimal in view of the often structured nature of image and more particularly video data. The system and method of the invention employs an anisotropic kernel mean shift in which the shape, scale, and orientation of the kernels adapt to the local structure of the image or video. The anisotropic kernel is decomposed to provide handles for modifying the segmentation based on simple heuristics. Experimental results show that the anisotropic kernel mean shift outperforms the original mean shift on image and video segmentation in the following aspects: 1) it gets better results on general images and video in a smoothness sense; 2) the segmented results are more consistent with human visual saliency; and 3) the system and method is robust to initial parameters.
摘要翻译：平均偏移是已经应用于图像和视频分割的密度的非参数估计器。传统的基于平均移位的分割使用径向对称的核来估计局部密度，鉴于图像的经常结构化的特性，更特别是视频数据，这是非最优的。本发明的系统和方法采用各向异性核平均移位，其中内核的形状，尺度和取向适应于图像或视频的局部结构。各向异性核被分解以提供用于基于简单启发式修改分割的句柄。实验结果表明，各向异性核平均偏移在以下几个方面优于原始平均偏移图像和视频分割：1）在平滑度方面对一般图像和视频获得更好的结果; 2）分段结果与人类视觉显着性更为一致; 和3）系统和方法对初始参数是鲁棒的。

8. 发明申请

US20080140519A1 ADVERTISING BASED ON SIMPLIFIED INPUT EXPANSION 审中-公开
标题翻译：基于简化输入扩展的广告
公开(公告)号：US20080140519A1
公开(公告)日：2008-06-12
申请号：US11608631
申请日：2006-12-08
申请人： Bo Thiesson , Christopher A. Meek , Kenneth W. Church , Timothy D. Sharpe
发明人： Bo Thiesson , Christopher A. Meek , Kenneth W. Church , Timothy D. Sharpe
IPC分类号： G06Q30/00 , G06F7/06 , G06F17/30
CPC分类号： G06Q30/0222 , G06F16/81 , G06Q30/02
摘要： Match criteria are provided to specify when advertisements will be shown, for instance in a search environment. Input such as search queries can be represented in a simplified form such as an implicit and/or explicit wildcard expression. Advertisers or other entities can bid on terms such that advertisements or similar content are presented when the terms match an expansion of a simplified input. Matching ads can subsequently be displayed alone or in combination with query expansion suggestions and/or query results.
摘要翻译：提供匹配标准以指定何时显示广告，例如在搜索环境中。诸如搜索查询之类的输入可以以简单的形式表示，例如隐式和/或显式通配符表达式。广告商或其他实体可以按照条款进行出价，使得当术语与简化输入的扩展相匹配时，呈现广告或类似内容。随后可以单独显示匹配的广告，或与查询扩展建议和/或查询结果相结合。

9. 发明授权

US07246048B2 Determining near-optimal block size for incremental-type expectation maximization (EM) algorithms 有权
标题翻译：确定增量型期望最大化（EM）算法的近似最优块大小
公开(公告)号：US07246048B2
公开(公告)日：2007-07-17
申请号：US11177734
申请日：2005-07-08
申请人： Bo Thiesson , Christopher A. Meek , David E. Heckerman
发明人： Bo Thiesson , Christopher A. Meek , David E. Heckerman
IPC分类号： G06F7/60
CPC分类号： G06K9/6226 , G06F17/10 , G06F17/18 , Y10S707/99942
摘要： Determining the near-optimal block size for incremental-type expectation maximization (EM) algorithms is disclosed. Block size is determined based on the novel insight that the speed increase resulting from using an incremental-type EM algorithm as opposed to the standard EM algorithm is roughly the same for a given range of block sizes. Furthermore, this block size can be determined by an initial version of the EM algorithm that does not reach convergence. For a current block size, the speed increase is determined, and if the speed increase is the greatest determined so far, the current block size is set as the target block size. This process is repeated for new block sizes, until no new block sizes can be determined.
摘要翻译：公开了确定增量型期望最大化（EM）算法的近似最小块大小。基于新的认识来确定块大小，即对于给定的块大小范围，使用增量型EM算法而不是标准EM算法导致的速度增加大致相同。此外，该块大小可以由未达到收敛的EM算法的初始版本来确定。对于当前块大小，确定速度增加，并且如果到目前为止确定的速度增加最大，则将当前块大小设置为目标块大小。对于新的块大小重复此过程，直到不能确定新的块大小。

10. 发明申请

US20060129395A1 Gradient learning for probabilistic ARMA time-series models 有权
标题翻译：概率ARMA时间序列模型的梯度学习
公开(公告)号：US20060129395A1
公开(公告)日：2006-06-15
申请号：US11011864
申请日：2004-12-14
申请人： Bo Thiesson , Christopher Meek
发明人： Bo Thiesson , Christopher Meek
IPC分类号： G10L15/12
CPC分类号： G06K9/00523
摘要： The subject invention leverages the conditional Gaussian (CG) nature of a continuous variable stochastic ARMAxp time series model to efficiently determine its parametric gradients. The determined gradients permit an easy means to construct a parametric structure for the time series model. This provides a gradient-based alternative to the expectation maximization (EM) process for learning parameters of the stochastic ARMAxp time series model. Thus, gradients for parameters can be computed and utilized with a gradient-based learning method for estimating the parameters. This allows values of continuous observations in a time series to be predicted utilizing the stochastic ARMAxp time series model, providing efficient and accurate predictions.
摘要翻译：本发明利用连续可变随机ARMA 时间序列模型的条件高斯（CG）特性来有效地确定其参数梯度。确定的梯度允许构建时间序列模型的参数结构的简单方法。这提供了用于学习随机ARMA时间序列模型的参数的期望最大化（EM）过程的基于梯度的替代。因此，可以使用基于梯度的学习方法来计算和利用参数梯度来估计参数。这允许使用随机ARMA 时间序列模型来预测时间序列中的连续观测值，从而提供有效和准确的预测。

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