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100 条记录 (耗时 0.021 秒)

    Constraint-based correction of handwriting recognition errors
    72.
    发明授权
    Constraint-based correction of handwriting recognition errors 有权
    手写识别错误的基于约束的校正

    公开(公告)号：US07720316B2

    公开(公告)日：2010-05-18

    申请号：US11470076

    申请日：2006-09-05

    申请人： Michael Shilman ,  Desney S. Tan ,  Patrice Y. Simard

    发明人： Michael Shilman ,  Desney S. Tan ,  Patrice Y. Simard

    IPC分类号： G06K9/34 ,  G06K9/03 ,  G06K9/00

    CPC分类号： G06K9/00436 ,  G06K9/6255 ,  G06K9/626

    摘要： A handwriting recognition system interprets handwritten text and produces a typed interpretation of that text. When the initial interpretation of the handwritten text is inaccurate, the handwriting recognition system alters the initial recognition by reinterpreting the handwritten text in view of a correction made by a user and constraints (e.g., derived by assumptions in user behavior). The handwriting recognition system intelligently reinterprets and renews its text recognition each time the user implements a correction. In effect, a single correction can trigger multiple adjustments to the text recognition. Therefore, with the use of a reinterpretation algorithm, the handwriting recognition system helps the user obtain the desired result in fewer correction steps.

    摘要翻译： 手写识别系统解释手写文本，并产生对该文本的类型解释。 当手写文本的初始解释不准确时，鉴于用户进行的修正和约束（例如，通过用户行为的假设导出），手写识别系统通过重新解读手写文本来改变初始识别。 手写识别系统在每次用户执行校正时智能地重新诠释和更新其文本识别。 实际上，单次校正可以触发文本识别的多个调整。 因此，通过使用重新解释算法，手写识别系统帮助用户在更少的校正步骤中获得期望的结果。

    Using electroencephalograph signals for task classification and activity recognition
    74.
    发明授权
    Using electroencephalograph signals for task classification and activity recognition 有权
    使用脑电图信号进行任务分类和活动识别

    公开(公告)号：US07580742B2

    公开(公告)日：2009-08-25

    申请号：US11349859

    申请日：2006-02-07

    申请人： Desney S Tan ,  Johnny C Lee

    发明人： Desney S Tan ,  Johnny C Lee

    IPC分类号： A61B5/04

    CPC分类号： G06F3/015 ,  A61B5/0476 ,  A61B5/16 ,  A61B5/7207 ,  A61B5/7267 ,  G06K9/00496

    摘要： A method for classifying brain states in electroencephalograph (EEG) signals comprising building a classifier model and classifying brain states using the classifier model is described. Brain states are determined. Labeled EEG data is collected and divided into overlapping time windows. The time dimension is removed from each time window. Features are generated by computing the base features; combining the base features to form a larger feature set; pruning the large feature set; and further pruning the feature set for a particular machine learning technique. Brain states in unlabeled EEG data are classified using the classifier model by dividing the unlabeled EEG data into overlapping time windows and removing the time dimension from each time window. Features required by the classifier model are generated. Artifacts in the labeled and unlabeled EEG data comprise cognitive artifacts and non-cognitive artifacts.

    摘要翻译： 描述了一种在脑电图（EEG）信号中分类脑状态的方法，包括建立分类器模型并使用分类器模型对脑状态进行分类。 大脑状态是确定的。 标记的EEG数据被收集并分为重叠的时间窗口。 时间维度从每个时间窗口被删除。 通过计算基本特征生成特征; 组合基本特征以形成较大的特征集; 修剪大功能集; 并进一步修剪特定机器学习技术的特征集。 未标记的EEG数据中的脑状态通过将未标记的EEG数据划分为重叠时间窗口并从每个时间窗口去除时间维度，使用分类器模型进行分类。 生成分类器模型所需的特征。 标记和未标记的脑电图数据中的伪像包括认知伪像和非认知伪像。

    INTERACTIVE SCENARIO EXPLORATION FOR TOURNAMENT-STYLE GAMING
    75.
    发明申请
    INTERACTIVE SCENARIO EXPLORATION FOR TOURNAMENT-STYLE GAMING 审中-公开
    交互式场景探索游戏风格

    公开(公告)号：US20090170584A1

    公开(公告)日：2009-07-02

    申请号：US11965772

    申请日：2007-12-28

    申请人： Desney S. Tan ,  Gregory R. Smith ,  Yuval Peres ,  Joseph Yossi Azar ,  Eyal Lubetzky

    发明人： Desney S. Tan ,  Gregory R. Smith ,  Yuval Peres ,  Joseph Yossi Azar ,  Eyal Lubetzky

    IPC分类号： A63F9/24

    CPC分类号： G07F17/3276 ,  G07F17/32

    摘要： A tournament-style gaming scenario exploration system and method for interactively exploring current and future scenarios of a tournament and associated pick'em pool. The system and method include a prediction module (including a game constraint sub-module), and a key event detection module. Embodiments of the prediction module include a binary integer that represents tournament outcomes. The prediction module generates predictions of tournament outcomes using an exhaustive or a sampling technique. The sampling technique includes random sampling, where the tournament bracket is randomly sampled, and a weighted sampling technique, which sample portions of the tournament bracket more densely than others areas. Embodiments of the game constraint sub-module allow real-world results constraints and user-supplied constraints to be imposed on the tournament outcomes. Embodiments of the key event detection module identify key games in the tournament that affect a user's placement in the pick'em pool, a competitor's placement in the tournament standings, or both.

    摘要翻译： 一种比赛风格的游戏场景探索系统和方法，用于交互式探索比赛和相关选择队列的当前和未来场景。 该系统和方法包括预测模块（包括游戏约束子模块）和键事件检测模块。 预测模块的实施例包括表示赛事结果的二进制整数。 预测模块使用穷举或抽样技术来产生比赛成果的预测。 采样技术包括随机采样，其中随机采样比赛支架，以及加权采样技术，其中比赛部分比其他地区更加密集。 游戏约束子模块的实施例允许实际结果约束和用户提供的约束被施加在比赛结果上。 关键事件检测模块的实施例识别比赛中的关键游戏，影响用户在选择池中的位置，竞争对手在比赛积分中的位置，或两者兼而有之。

    INTERACTIVE CONCEPT LEARNING IN IMAGE SEARCH
    76.
    发明申请
    INTERACTIVE CONCEPT LEARNING IN IMAGE SEARCH 有权
    图像搜索中的交互式概念学习

    公开(公告)号：US20090154795A1

    公开(公告)日：2009-06-18

    申请号：US11954246

    申请日：2007-12-12

    申请人： Desney S. Tan ,  Ashish Kapoor ,  Simon A. J. Winder ,  James A. Fogarty

    发明人： Desney S. Tan ,  Ashish Kapoor ,  Simon A. J. Winder ,  James A. Fogarty

    IPC分类号： G06K9/62

    CPC分类号： G06F17/30247 ,  G06K9/6215

    摘要： An interactive concept learning image search technique that allows end-users to quickly create their own rules for re-ranking images based on the image characteristics of the images. The image characteristics can include visual characteristics as well as semantic features or characteristics, or may include a combination of both. End-users can then rank or re-rank any current or future image search results according to their rule or rules. End-users provide examples of images each rule should match and examples of images the rule should reject. The technique learns the common image characteristics of the examples, and any current or future image search results can then be ranked or re-ranked according to the learned rules.

    摘要翻译： 一种交互式概念学习图像搜索技术，允许最终用户基于图像的图像特征快速创建自己的重新排序图像的规则。 图像特征可以包括视觉特征以及语义特征或特征，或者可以包括两者的组合。 然后，最终用户可以根据其规则或规则对当前或将来的图像搜索结果进行排名或重新排序。 最终用户提供每个规则应该匹配的图像的示例以及规则应该拒绝的图像的示例。 该技术学习示例的常见图像特征，然后可以根据学习的规则对任何当前或将来的图像搜索结果进行排名或重新排序。