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    Preference judgements for relevance
    1.
    发明授权
    Preference judgements for relevance 有权
    相关性的偏好判断

    公开(公告)号：US08069179B2

    公开(公告)日：2011-11-29

    申请号：US12108531

    申请日：2008-04-24

    申请人： David M. Chickering ,  Paul N. Bennett ,  Susan T. Dumais ,  Benjamin Austin Carterette

    发明人： David M. Chickering ,  Paul N. Bennett ,  Susan T. Dumais ,  Benjamin Austin Carterette

    IPC分类号： G06F7/00 ,  G06F17/30

    CPC分类号： G06F17/30867

    摘要： The claimed subject matter provides a system that trains or evaluates ranking techniques by employing or obtaining relative preference judgments. The system can include mechanisms that retrieve a set of documents from a storage device, combine the set of documents with a query or judgment task received via an interface to form a comparative selection panel, and present the comparative selection panel for evaluation by an assessor. The system further requests the assessor to make a selection as to which document included in the set of documents and presented in the comparative selection panel most satisfies the query or judgment task, and thereafter produces a comparative assessment of the set of documents based on the selections elicited from the assessor and associated with the set of documents.

    摘要翻译： 所要求保护的主题提供了通过采用或获得相对偏好判断来训练或评估排名技术的系统。 该系统可以包括从存储装置检索一组文档的机构，将该组文件与通过界面接收到的查询或判断任务组合以形成比较选择面板，并呈现评估者进行评估的比较选择面板。 该系统进一步要求评估人员选择一组文件中包含的文件，并在比较选择面板中提供的文档最符合查询或判断任务，然后根据选择对文档集进行比较评估 从评估员处获得并与该组文件相关联。

    PREFERENCE JUDGEMENTS FOR RELEVANCE
    2.
    发明申请
    PREFERENCE JUDGEMENTS FOR RELEVANCE 有权
    相关判决

    公开(公告)号：US20090271389A1

    公开(公告)日：2009-10-29

    申请号：US12108531

    申请日：2008-04-24

    申请人： David M. Chickering ,  Paul N. Bennett ,  Susan T. Dumais ,  Benjamin Austin Carterette

    发明人： David M. Chickering ,  Paul N. Bennett ,  Susan T. Dumais ,  Benjamin Austin Carterette

    IPC分类号： G06F7/06

    CPC分类号： G06F17/30867

    摘要： The claimed subject matter provides a system that trains or evaluates ranking techniques by employing or obtaining relative preference judgments. The system can include mechanisms that retrieve a set of documents from a storage device, combine the set of documents with a query orjudgment task received via an interface to form a comparative selection panel, and present the comparative selection panel for evaluation by an assessor. The system further requests the assessor to make a selection as to which document included in the set of documents and presented in the comparative selection panel most satisfies the query or judgment task, and thereafter produces a comparative assessment of the set of documents based on the selections elicited from the assessor and associated with the set of documents.

    摘要翻译： 所要求保护的主题提供了通过采用或获得相对偏好判断来训练或评估排名技术的系统。 该系统可以包括从存储设备检索一组文档的机构，将该组文档与经由界面接收到的查询或判断任务组合以形成比较选择面板，并呈现评估者进行评估的比较选择面板。 该系统进一步要求评估人员选择一组文件中包含的文件，并在比较选择面板中提供的文档最符合查询或判断任务，然后根据选择对文档集进行比较评估 从评估员处获得并与该组文件相关联。

    Arrangement for building and operating human-computation and other games
    5.
    发明授权
    Arrangement for building and operating human-computation and other games 有权
    建设和运行人机计算等游戏的安排

    公开(公告)号：US09120017B2

    公开(公告)日：2015-09-01

    申请号：US13368848

    申请日：2012-02-08

    申请人： David M. Chickering ,  Edith Law ,  Anton Mityagin

    发明人： David M. Chickering ,  Edith Law ,  Anton Mityagin

    IPC分类号： G06F17/00 ,  A63F13/40

    CPC分类号： G06F17/272 ,  A63F13/10 ,  A63F13/60 ,  A63F2300/6009

    摘要： A game description language is provided for human computation games, as well as a game platform or generator component that can generate the code base for the game. The game description language and schema framework can be used to represent the game logic and synchronization patterns of a human computation game. The automated code generation tool takes a file, e.g., a file made from the above game description language, or the like, as an input and generates a code base for the corresponding human computation game. These tools allow a prototype of a human computation game to be generated within minutes.

    摘要翻译： 为人类计算游戏提供游戏描述语言，以及可以生成游戏代码库的游戏平台或生成器组件。 游戏描述语言和模式框架可用于表示人类计算游戏的游戏逻辑和同步模式。 自动代码生成工具将文件，例如，由上述游戏描述语言制成的文件等作为输入，并生成相应的人类计算游戏的代码库。 这些工具允许在几分钟内生成人类计算游戏的原型。

    Dialog repair based on discrepancies between user model predictions and speech recognition results
    6.
    发明授权
    Dialog repair based on discrepancies between user model predictions and speech recognition results 有权
    基于用户模型预测和语音识别结果之间的差异的对话框修复

    公开(公告)号：US08244545B2

    公开(公告)日：2012-08-14

    申请号：US11393321

    申请日：2006-03-30

    申请人： Timothy S. Paek ,  David M. Chickering

    发明人： Timothy S. Paek ,  David M. Chickering

    IPC分类号： G10L21/00

    CPC分类号： G10L15/22 ,  G10L2015/228

    摘要： An architecture is presented that leverages discrepancies between user model predictions and speech recognition results by identifying discrepancies between the predictive data and the speech recognition data and repairing the data based in part on the discrepancy. User model predictions predict what goal or action speech application users are likely to pursue based in part on past user behavior. Speech recognition results indicate what goal speech application users are likely to have spoken based in part on words spoken under specific constraints. Discrepancies between the predictive data and the speech recognition data are identified and a dialog repair is engaged for repairing these discrepancies. By engaging in repairs when there is a discrepancy between the predictive results and the speech recognition results, and utilizing feedback obtained via interaction with a user, the architecture can learn about the reliability of both user model predictions and speech recognition results for future processing.

    摘要翻译： 提出了一种通过识别预测数据和语音识别数据之间的差异以及部分地基于差异来修复数据来利用用户模型预测和语音识别结果之间的差异的架构。 用户模型预测部分地基于过去的用户行为来预测用户可能追求的目标或动作语音应用程序。 语音识别结果表明，目标语音应用程序用户可能部分地基于特定约束条件下所说的话语言。 识别预测数据和语音识别数据之间的差异，并进行对话修复以修复这些差异。 通过在预测结果和语音识别结果之间存在差异并利用通过与用户的交互获得的反馈来进行维修，架构可以了解用户模型预测和语音识别结果的可靠性以供将来处理。

    Dependency network based model (or pattern)
    8.
    发明授权
    Dependency network based model (or pattern) 有权
    基于依赖网络的模型（或模式）

    公开(公告)号：US08140569B2

    公开(公告)日：2012-03-20

    申请号：US10447462

    申请日：2003-05-29

    申请人： Geoffrey J. Hulten ,  David M. Chickering ,  David E. Heckerman

    发明人： Geoffrey J. Hulten ,  David M. Chickering ,  David E. Heckerman

    IPC分类号： G06F17/30 ,  G06F7/00

    CPC分类号： G06F17/30687 ,  G06F17/30536 ,  G06N7/005 ,  G06Q30/00

    摘要： A dependency network is created from a training data set utilizing a scalable method. A statistical model (or pattern), such as for example a Bayesian network, is then constructed to allow more convenient inferencing. The model (or pattern) is employed in lieu of the training data set for data access. The computational complexity of the method that produces the model (or pattern) is independent of the size of the original data set. The dependency network directly returns explicitly encoded data in the conditional probability distributions of the dependency network. Non-explicitly encoded data is generated via Gibbs sampling, approximated, or ignored.

    摘要翻译： 从使用可伸缩方法的训练数据集创建依赖网络。 然后构建统计模型（或模式），例如贝叶斯网络，以允许更方便的推论。 采用模型（或模式）代替用于数据访问的训练数据集。 产生模型（或模式）的方法的计算复杂度与原始数据集的大小无关。 依赖网络直接在依赖网络的条件概率分布中返回显式编码的数据。 通过Gibbs采样，近似或忽略来生成非显式编码数据。

    Systems and methods for new time series model probabilistic ARMA
    9.
    发明授权
    Systems and methods for new time series model probabilistic ARMA 有权
    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。