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11.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
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。
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12.发明授权公开(公告)号：US08137201B2
公开(公告)日：2012-03-20
申请号：US12351564
申请日：2009-01-09
申请人： David M. Chickering , Edith Law , Anton Mityagin
发明人： David M. Chickering , Edith Law , Anton Mityagin
IPC分类号： A63F13/00
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。
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13.发明申请公开(公告)号：US20100178985A1
公开(公告)日：2010-07-15
申请号：US12351564
申请日：2009-01-09
申请人： David M. Chickering , Edith Law , Anton Mityagin
发明人： David M. Chickering , Edith Law , Anton Mityagin
IPC分类号： A63F9/24
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。
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公开(公告)号：US08032375B2
公开(公告)日：2011-10-04
申请号：US11378202
申请日：2006-03-17
CPC分类号： G06Q10/10
摘要： A generic predictive argument model that can be applied to a set of shot values to predict a target slot value is provided. The generic predictive argument model can predict whether or not a particular value or item is the intended target of the user command given various features. A prediction for each of the slot values can then be normalized to infer a distribution over all values or items. For any set of slot values (e.g., contacts), a number of binary variable s are created that indicate whether or not each specific slot value was the intended target. For each slot value, a set of input features can be employed to predict the corresponding binary variable. These input features are generic properties of the contact that are “instantiated” based o n properties of the contact (e.g., contact-specific features). These contact-specific features can be stored in a user data store.
摘要翻译： 提供了可应用于一组镜头值以预测目标时隙值的通用预测参数模型。 通用预测参数模型可以预测特定值或项目是否是给定各种特征的用户命令的预期目标。 然后可以对每个时隙值的预测进行归一化以推断所有值或项目上的分布。 对于任何一组时隙值（例如，联系人），创建指示每个特定时隙值是否为预期目标的多个二进制变量。 对于每个时隙值，可以采用一组输入特征来预测相应的二进制变量。 这些输入特征是基于联系人的“实例化”属性的联系人的通用属性（例如联系人特定的特征）。 这些联系人特定的功能可以存储在用户数据存储中。
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公开(公告)号：US07734471B2
公开(公告)日：2010-06-08
申请号：US11170999
申请日：2005-06-29
IPC分类号： G10L21/00
CPC分类号： G10L15/065
摘要： An online dialog system and method are provided. The dialog system receives speech input and outputs an action according to its models. After executing the action, the system receives feedback from the environment or user. The system immediately utilizes the feedback to update its models in an online fashion.
摘要翻译： 提供在线对话系统和方法。 对话系统接收语音输入，并根据其模型输出动作。 执行该操作后，系统会收到来自环境或用户的反馈。 系统立即利用反馈以在线方式更新其模型。
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16.发明授权Easy generation and automatic training of spoken dialog systems using text-to-speech 有权使用文字转语音轻松地生成和自动训练语音对话系统公开(公告)号：US07885817B2
公开(公告)日：2011-02-08
申请号：US11170584
申请日：2005-06-29
IPC分类号： G10L21/00
CPC分类号： G10L15/22 , G10L13/00 , G10L15/063
摘要： A dialog system training environment and method using text-to-speech (TTS) are provided. The only knowledge a designer requires is a simple specification of when the dialog system has failed or succeeded, and for any state of the dialog, a list of the possible actions the system can take.The training environment simulates a user using TTS varied at adjustable levels, a dialog action model of a dialog system responds to the produced utterance by trying out all possible actions until it has failed or succeeded. From the data accumulated in the training environment it is possible for the dialog action model to learn which states to go to when it observes the appropriate speech and dialog features so as to increase the likelihood of success. The data can also be used to improve the speech model.
摘要翻译： 提供了使用文本到语音（TTS）的对话系统训练环境和方法。 设计师需要的唯一知识是对话系统何时失败或成功的简单规范，对于对话框的任何状态，系统可能采取的行动的列表。 训练环境模拟用户使用可调节级别变化的TTS，对话系统的对话动作模型通过尝试所有可能的动作直到失败或成功来响应所产生的话语。 从训练环境中累积的数据可以看出，当对话动作模型观察适当的语音和对话特征时，对话动作模型可以了解哪些状态可以增加成功的可能性。 数据也可用于改进语音模型。
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17.发明授权Thompson strategy based online reinforcement learning system for action selection 失效基于Thompson战略的在线强化学习系统的行动选择公开(公告)号：US07707131B2
公开(公告)日：2010-04-27
申请号：US11169503
申请日：2005-06-29
CPC分类号： G06N99/005
摘要： A system and method for online reinforcement learning is provided. In particular, a method for performing the explore-vs.-exploit tradeoff is provided. Although the method is heuristic, it can be applied in a principled manner while simultaneously learning the parameters and/or structure of the model (e.g., Bayesian network model).The system includes a model which receives an input (e.g., from a user) and provides a probability distribution associated with uncertainty regarding parameters of the model to a decision engine. The decision engine can determine whether to exploit the information known to it or to explore to obtain additional information based, at least in part, upon the explore-vs.-exploit tradeoff (e.g., Thompson strategy). A reinforcement learning component can obtain additional information (e.g., feedback from a user) and update parameter(s) and/or the structure of the model. The system can be employed in scenarios in which an influence diagram is used to make repeated decisions and maximization of long-term expected utility is desired.
摘要翻译： 提供了一种在线强化学习的系统和方法。 特别地，提供了用于执行探索与利用的权衡的方法。 尽管该方法是启发式的，但是它可以以原则的方式应用，同时学习模型的参数和/或结构（例如，贝叶斯网络模型）。 该系统包括接收输入（例如，来自用户）并且向决策引擎提供与关于模型的参数的不确定性相关联的概率分布的模型。 决策引擎可以确定是否利用已知的信息，或者至少部分地基于探索与利用权衡（Thompson策略）来探索获取附加信息。 强化学习组件可以获得附加信息（例如，来自用户的反馈）和更新参数和/或模型的结构。 该系统可用于使用影响图进行重复决策的场景，并期望实现长期预期效用的最大化。
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18.发明授权公开(公告)号：US07689420B2
公开(公告)日：2010-03-30
申请号：US11278899
申请日：2006-04-06
CPC分类号： G10L15/19 , G10L2015/088
摘要： Architecture for integrating and generating back-off grammars (BOG) in a speech recognition application for recognizing out-of-grammar (OOG) utterances and updating the context-free grammars (CFG) with the results. A parsing component identifies keywords and/or slots from user utterances and a grammar generation component adds filler tags before and/or after the keywords and slots to create new grammar rules. The BOG can be generated from these new grammar rules and can be used to process the OOG user utterances. By processing the OOG user utterances through the BOG, the architecture can recognize and perform the intended task on behalf of the user.
摘要翻译： 用于在语音识别应用程序中集成和生成后退语法（BOG）的体系结构，用于识别语法（OOG）语音并更新无上下文语法（CFG）。 解析组件识别来自用户话语的关键字和/或时隙，并且语法生成组件在关键词和时隙之前和之后添加填充标签以创建新的语法规则。 BOG可以从这些新的语法规则生成，并可用于处理OOG用户的话语。 通过通过BOG处理OOG用户话语，架构可以代表用户识别并执行预期的任务。
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19.发明授权Using predictive user models for language modeling on a personal device with user behavior models based on statistical modeling 失效使用基于统计建模的用户行为模型对个人设备进行语言建模的预测用户模型公开(公告)号：US07752152B2
公开(公告)日：2010-07-06
申请号：US11378024
申请日：2006-03-17
CPC分类号： G06F17/276 , G06N99/005 , G10L15/183 , G10L15/22 , G10L2015/0631
摘要： A system and method for prediction of a user goal for command/control of a personal device (e.g., mobile phone) is provided. The system employs statistical model(s) that can predict a command based, at least in part, on past user behavior (e.g., probability distribution over a set of predicates, and, optionally arguments). Further, the system can be employed with a speech recognition component to facilitate language modeling for predicting the user goal.The system can include predictive user models (e.g., predicate model and argument model) that receive a user input (e.g., utterance) and employ statistical modeling to determine the likely command without regard to the actual content of the input (e.g., utterance). The system employs features for predicting the next user goal which can be stored in a user data store. Features can capture personal idiosyncrasies or systematic patterns of usage (e.g., device-related, time-related, predicate-related, contact-specific and/or periodic features).
摘要翻译： 提供了一种用于预测用于个人设备（例如，移动电话）的命令/控制的用户目标的系统和方法。 该系统使用至少部分地基于过去的用户行为（例如，一组谓词上的概率分布，以及可选的参数）来预测命令的统计模型。 此外，该系统可以与语音识别组件一起使用以便于用于预测用户目标的语言建模。 该系统可以包括接收用户输入（例如，话语）并且采用统计建模来确定可能的命令而不考虑输入的实际内容（例如，话语）的预测用户模型（例如谓词模型和参数模型）。 该系统采用用于预测可存储在用户数据存储中的下一个用户目标的特征。 特征可以捕获个人特征或系统的使用模式（例如，与设备相关的，与时间相关的，谓词相关的，特定于接触的和/或周期的特征）。
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公开(公告)号：US07580813B2
公开(公告)日：2009-08-25
申请号：US10463145
申请日：2003-06-17
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。
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