公开(公告)号：US20080147390A1

公开(公告)日：2008-06-19

申请号：US12035944

申请日：2008-02-22

Applicant: Rajesh Balchandran ,  Linda M. Boyer

Inventor： Rajesh Balchandran ,  Linda M. Boyer

IPC: G10L15/00

CPC classification number: G06F17/2715 ,  G06F17/2785 ,  G10L15/26

Abstract: A method for processing language input can include the step of determining at least two possible meanings for a language input. For each possible meaning, a probability that the possible meaning is a correct interpretation of the language input can be determined. At least one relative data computation can be computed based at least in part upon the probabilities. At least one irregularity within the language input can be detected based upon the relative delta computation. The irregularity can include mumble, ambiguous input, and/or compound input. At least one programmatic action can be performed responsive to the detection of the irregularity.

Abstract translation: 用于处理语言输入的方法可以包括为语言输入确定至少两个可能的含义的步骤。 对于每个可能的含义，可以确定可能的含义是语言输入的正确解释的概率。 可以至少部分地基于概率来计算至少一个相对数据计算。 可以基于相对增量计算来检测语言输入内的至少一个不规则性。 不规则性可以包括嘟嘟声，模糊输入和/或复合输入。 响应于不规则性的检测可以执行至少一个编程动作。

公开(公告)号：US09342588B2

公开(公告)日：2016-05-17

申请号：US11764291

申请日：2007-06-18

Applicant: Rajesh Balchandran ,  Linda M. Boyer ,  Gregory Purdy

Inventor： Rajesh Balchandran ,  Linda M. Boyer ,  Gregory Purdy

IPC: G06F17/27 ,  G06F17/30

CPC classification number: G06F17/30705

Abstract: A method of creating a statistical classification model for a classifier within a natural language understanding system can include processing training data using an existing statistical classification model. Sentences of the training data correctly classified into a selected class of the statistical classification model can be selected. The selected sentences of the training data can be assigned to a fringe group or a core group according to confidence score. The training data can be updated by associating the fringe group with a fringe subclass of the selected class and the core group with a core subclass of the selected class. A new statistical classification model can be built from the updated training data. The new statistical classification model can be output.

Abstract translation: 在自然语言理解系统内创建用于分类器的统计分类模型的方法可以包括使用现有的统计分类模型处理训练数据。 可以选择正确分类为所选类别的统计分类模型的训练数据句子。 训练数据的所选句子可以根据置信度得分分配给边缘组或核心组。 可以通过将边缘组与所选类的边缘子类和具有所选类的核心子类的核心组相关联来更新训练数据。 可以从更新的训练数据构建新的统计分类模型。 可以输出新的统计分类模型。

公开(公告)号：US09058319B2

公开(公告)日：2015-06-16

申请号：US11764274

申请日：2007-06-18

Applicant: Rajesh Balchandran ,  Linda M. Boyer ,  Gregory Purdy

Inventor： Rajesh Balchandran ,  Linda M. Boyer ,  Gregory Purdy

IPC: G06F17/27

CPC classification number: G06F17/2715

Abstract: A method of classifying text input for use with a natural language understanding system can include determining classification information including a primary classification and one or more secondary classifications for a received text input using a statistical classification model (statistical model). A statistical classification sub-model (statistical sub-model) can be selectively built according to a model generation criterion applied to the classification information. The method further can include selecting the primary classification or the secondary classification for the text input as a final classification according to the statistical sub-model and outputting the final classification for the text input.

Abstract translation: 用于分类文本输入以与自然语言理解系统一起使用的方法可以包括使用统计分类模型（统计模型）确定包括主分类的分类信息和用于所接收的文本输入的一个或多个次分类。 可以根据应用于分类信息的模型生成准则选择性地建立统计分类子模型（统计子模型）。 该方法还可以包括根据统计子模型选择文本输入的主分类或次级分类作为最终分类，并输出文本输入的最终分类。

公开(公告)号：US08612229B2

公开(公告)日：2013-12-17

申请号：US11300799

申请日：2005-12-15

Applicant: Rajesh Balchandran ,  Linda M. Boyer ,  James R. Lewis ,  Brent D. Metz

Inventor： Rajesh Balchandran ,  Linda M. Boyer ,  James R. Lewis ,  Brent D. Metz

IPC: G10L15/00 ,  G10L15/18 ,  G10L21/00 ,  G10L25/00

CPC classification number: G10L15/063 ,  G06F17/28 ,  G10L15/18 ,  G10L15/183 ,  G10L2015/0638 ,  H04M3/4936 ,  H04M2203/2061

Abstract: A method (300) and system (100) is provided to add the creation of examples at a developer level in the generation of Natural Language Understanding (NLU) models, tying the examples into a NLU sentence database (130), automatically validating (310) a correct outcome of using the examples, and automatically resolving (316) problems the user has using the examples. The method (300) can convey examples of what a caller can say to a Natural Language Understanding (NLU) application. The method includes entering at least one example associated with an existing routing destination, and ensuring an NLU model correctly interprets the example unambiguously for correctly routing a call to the routing destination. The method can include presenting the example sentence in a help message (126) within an NLU dialogue as an example of what a caller can say for connecting the caller to a desired routing destination. The method can also include presented a failure dialogue for displaying at least one example that failed to be properly interpreted to ensure that ambiguous or incorrect examples are not presented in a help message.

Abstract translation: 提供了一种方法（300）和系统（100），用于在生成自然语言理解（NLU）模型时在开发者级别添加示例的创建，将示例绑定到NLU句子数据库（130）中，自动验证（310 ）使用示例的正确结果，并自动解析（316）用户使用示例的问题。 方法（300）可以传达呼叫者可以对自然语言理解（NLU）应用程序说什么的示例。 该方法包括输入与现有路由目的地相关联的至少一个示例，并且确保NLU模型明确地解释该示例以正确路由呼叫到路由目的地。 该方法可以包括将NLU对话内的帮助消息（126）中的示例呈现为呼叫者可以将呼叫者连接到期望的路由目的地的示例。 该方法还可以包括呈现失败对话，用于显示未被正确解释的至少一个示例，以确保在帮助消息中不呈现模糊或不正确的示例。

公开(公告)号：US08630856B2

公开(公告)日：2014-01-14

申请号：US12499544

申请日：2009-07-08

Applicant: Rajesh Balchandran ,  Linda M. Boyer

Inventor： Rajesh Balchandran ,  Linda M. Boyer

IPC: G10L15/14 ,  G10L15/18

CPC classification number: G06F17/2715 ,  G06F17/2785 ,  G10L15/26

Abstract: A method for processing language input can include the step of determining at least two possible meanings for a language input. For each possible meaning, a probability that the possible meaning is a correct interpretation of the language input can be determined. At least one relative data computation can be computed based at least in part upon the probabilities. At least one irregularity within the language input can be detected based upon the relative delta computation. The irregularity can include mumble, ambiguous input, and/or compound input. At least one programmatic action can be performed responsive to the detection of the irregularity.

Abstract translation: 用于处理语言输入的方法可以包括为语言输入确定至少两个可能的含义的步骤。 对于每个可能的含义，可以确定可能的含义是语言输入的正确解释的概率。 可以至少部分地基于概率来计算至少一个相对数据计算。 可以基于相对增量计算来检测语言输入内的至少一个不规则性。 不规则性可以包括嘟嘟声，模糊输入和/或复合输入。 响应于不规则性的检测可以执行至少一个编程动作。

公开(公告)号：US20100010805A1

公开(公告)日：2010-01-14

申请号：US12499544

申请日：2009-07-08

Applicant: Rajesh Balchandran ,  Linda M. Boyer

Inventor： Rajesh Balchandran ,  Linda M. Boyer

IPC: G06F17/27

CPC classification number: G06F17/2715 ,  G06F17/2785 ,  G10L15/26

Abstract: A method for processing language input can include the step of determining at least two possible meanings for a language input. For each possible meaning, a probability that the possible meaning is a correct interpretation of the language input can be determined. At least one relative data computation can be computed based at least in part upon the probabilities. At least one irregularity within the language input can be detected based upon the relative delta computation. The irregularity can include mumble, ambiguous input, and/or compound input. At least one programmatic action can be performed responsive to the detection of the irregularity.

Abstract translation: 用于处理语言输入的方法可以包括为语言输入确定至少两个可能的含义的步骤。 对于每个可能的含义，可以确定可能的含义是语言输入的正确解释的概率。 可以至少部分地基于概率来计算至少一个相对数据计算。 可以基于相对增量计算来检测语言输入内的至少一个不规则性。 不规则性可以包括嘟嘟声，模糊输入和/或复合输入。 响应于不规则性的检测可以执行至少一个编程动作。

公开(公告)号：US07562016B2

公开(公告)日：2009-07-14

申请号：US12035944

申请日：2008-02-22

Applicant: Rajesh Balchandran ,  Linda M. Boyer

Inventor： Rajesh Balchandran ,  Linda M. Boyer

IPC: G10L15/14 ,  G10L15/18

CPC classification number: G06F17/2715 ,  G06F17/2785 ,  G10L15/26

Abstract: A method for processing language input can include the step of determining at least two possible meanings for a language input. For each possible meaning, a probability that the possible meaning is a correct interpretation of the language input can be determined. At least one relative data computation can be computed based at least in part upon the probabilities. At least one irregularity within the language input can be detected based upon the relative delta computation. The irregularity can include mumble, ambiguous input, and/or compound input. At least one programmatic action can be performed responsive to the detection of the irregularity.

Abstract translation: 用于处理语言输入的方法可以包括为语言输入确定至少两个可能的含义的步骤。 对于每个可能的含义，可以确定可能的含义是语言输入的正确解释的概率。 可以至少部分地基于概率来计算至少一个相对数据计算。 可以基于相对增量计算来检测语言输入内的至少一个不规则性。 不规则性可以包括嘟嘟声，模糊输入和/或复合输入。 响应于不规则性的检测可以执行至少一个编程动作。

公开(公告)号：US07386440B2

公开(公告)日：2008-06-10

申请号：US10676524

申请日：2003-10-01

Applicant: Rajesh Balchandran ,  Linda M. Boyer

Inventor： Rajesh Balchandran ,  Linda M. Boyer

IPC: G06F17/27

CPC classification number: G10L15/1822 ,  G10L15/22

Abstract: In a natural language, mixed-initiative system, a method of processing user dialogue can include receiving a user input and determining whether the user input specifies an action to be performed or a token of an action. The user input can be selectively routed to an action interpreter or a token interpreter according to the determining step.

Abstract translation: 以自然语言，混合主动系统，处理用户对话的方法可以包括接收用户输入并确定用户输入是指定要执行的动作还是动作的标记。 可以根据确定步骤将用户输入选择性地路由到动作解释器或令牌解释器。

公开(公告)号：US08521511B2

公开(公告)日：2013-08-27

申请号：US11764294

申请日：2007-06-18

Applicant: Rajesh Balchandran ,  Linda M. Boyer ,  Gregory Purdy

Inventor： Rajesh Balchandran ,  Linda M. Boyer ,  Gregory Purdy

IPC: G06F17/27

CPC classification number: G06F17/278 ,  G06F17/2715 ,  G06F17/2725 ,  G06F17/2785 ,  G06F17/28

Abstract: A method of extracting information from text within a natural language understanding system can include processing a text input through at least one statistical model for each of a plurality of features to be extracted from the text input. For each feature, at least one value can be determined, at least in part, using the statistical model associated with the feature. One value for each feature can be combined to create a complex information target. The complex information target can be output.

Abstract translation: 从自然语言理解系统中的文本提取信息的方法可以包括通过至少一个统计模型处理文本输入，以便从文本输入中提取多个要素中的每一个。 对于每个特征，至少可以使用与特征相关联的统计模型至少部分地确定至少一个值。 可以组合每个功能的一个值来创建复杂的信息目标。 可以输出复杂的信息目标。

公开(公告)号：US20080312905A1

公开(公告)日：2008-12-18

申请号：US11764285

申请日：2007-06-18

Applicant: Rajesh Balchandran ,  Linda M. Boyer ,  Gregory Purdy

Inventor： Rajesh Balchandran ,  Linda M. Boyer ,  Gregory Purdy

IPC: G06F17/27

CPC classification number: G06F17/277 ,  G06F17/278

Abstract: A method of processing text within a natural language understanding system can include applying a first tokenization technique to a sentence using a statistical tokenization model. A second tokenization technique using a named entity can be applied to the sentence when the first tokenization technique does not extract a needed token according to a class of the sentence. A token determined according to at least one of the tokenization techniques can be output.

Abstract translation: 一种在自然语言理解系统内处理文本的方法可以包括使用统计标记化模型对句子应用第一标记化技术。 当第一个标记化技术不能根据句子的类提取所需的标记时，使用命名实体的第二个标记化技术可以应用于该句子。 可以输出根据令牌化技术中的至少一个所确定的令牌。