公开(公告)号：US20070143099A1

公开(公告)日：2007-06-21

申请号：US11300799

申请日：2005-12-15

Applicant: Rajesh Balchandran ,  Linda Boyer ,  James Lewis ,  Brent Metz

Inventor： Rajesh Balchandran ,  Linda Boyer ,  James Lewis ,  Brent Metz

IPC: G06F17/27

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：US20050075878A1

公开(公告)日：2005-04-07

申请号：US10676524

申请日：2003-10-01

Applicant: Rajesh Balchandran ,  Linda Boyer

Inventor： Rajesh Balchandran ,  Linda Boyer

IPC: G10L15/18 ,  G10L15/22

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：US07707027B2

公开(公告)日：2010-04-27

申请号：US11279577

申请日：2006-04-13

Applicant: Rajesh Balchandran ,  Linda Boyer

Inventor： Rajesh Balchandran ,  Linda Boyer

IPC: G06F17/27

CPC classification number: G06F17/2715 ,  G06F17/30707 ,  G10L15/183

Abstract: A method for identifying data that is meaningless and generating a natural language statistical model which can reject meaningless input. The method can include identifying unigrams that are individually meaningless from a set of training data. At least a portion of the unigrams identified as being meaningless can be assigned to a first n-gram class. The method also can include identifying bigrams that are entirely composed of meaningless unigrams and determining whether the identified bigrams are individually meaningless. At least a portion of the bigrams identified as being individually meaningless can be assigned to the first n-gram class.

Abstract translation: 用于识别无意义的数据并产生可以拒绝无意义输入的自然语言统计模型的方法。 该方法可以包括从一组训练数据中识别单独无意义的单字组。 识别为无意义的unigrams的至少一部分可以分配给第一个n-gram类。 该方法还可以包括识别完全由无意义单元组成的二进制，并且确定所识别的两字组是否是单独无意义的。 识别为单独无意义的二进制组的至少一部分可以分配给第一个n-gram类。

公开(公告)号：US20080300865A1

公开(公告)日：2008-12-04

申请号：US12112768

申请日：2008-04-30

Applicant: Rajesh Balchandran ,  Linda Boyer

Inventor： Rajesh Balchandran ,  Linda Boyer

IPC: G06F17/27 ,  G10L21/00 ,  G10L11/00

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：US20070156392A1

公开(公告)日：2007-07-05

申请号：US11324057

申请日：2005-12-30

Applicant: Rajesh Balchandran ,  Linda Boyer

Inventor： Rajesh Balchandran ,  Linda Boyer

IPC: G06F17/27

CPC classification number: G06F17/27

Abstract: The invention disclosed herein concerns a system (100) and method (600) for building a language model representation of an NLU application. The method 500 can include categorizing an NLU application domain (602), classifying a corpus in view of the categorization (604), and training at least one language model in view of the classification (606). The categorization produces a hierarchical tree of categories, sub-categories and end targets across one or more features for interpreting one or more natural language input requests. During development of an NLU application, a developer assigns sentences of the NLU application to categories, sub-categories or end targets across one or more features for associating each sentence with desire interpretations. A language model builder (140) iteratively builds multiple language models for this sentence data, and iteratively evaluating them against a test corpus, partitioning the data based on the categorization and rebuilding models, so as to produce an optimal configuration of language models to interpret and respond to language input requests for the NLU application.

Abstract translation: 本文公开的发明涉及用于构建NLU应用的语言模型表示的系统（100）和方法（600）。 方法500可以包括将NLU应用域（602）分类，鉴于分类（604）对语料库进行分类，并且考虑到分类（606）训练至少一种语言模型。 分类产生跨越一个或多个特征的类别，子类别和结束目标的分层树，用于解释一个或多个自然语言输入请求。 在开发NLU应用程序期间，开发人员将NLU应用程序的句子分配给一个或多个功能的类别，子类别或终端目标，以将每个句子与欲望解释相关联。 语言模型构建器（140）迭代地构建用于该句子数据的多个语言模型，并且针对测试语料库迭代地对它们进行评估，基于分类和重建模型分割数据，以便产生语言模型的最佳配置来解释和 响应NLU应用程序的语言输入请求。

公开(公告)号：US07974835B2

公开(公告)日：2011-07-05

申请号：US12112768

申请日：2008-04-30

Applicant: Rajesh Balchandran ,  Linda Boyer

Inventor： Rajesh Balchandran ,  Linda 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：US20070244692A1

公开(公告)日：2007-10-18

申请号：US11279577

申请日：2006-04-13

Applicant: Rajesh Balchandran ,  Linda Boyer

Inventor： Rajesh Balchandran ,  Linda Boyer

IPC: G06F17/27

CPC classification number: G06F17/2715 ,  G06F17/30707 ,  G10L15/183

Abstract: A method for identifying data that is meaningless and generating a natural language statistical model which can reject meaningless input. The method can include identifying unigrams that are individually meaningless from a set of training data. At least a portion of the unigrams identified as being meaningless can be assigned to a first n-gram class. The method also can include identifying bigrams that are entirely composed of meaningless unigrams and determining whether the identified bigrams are individually meaningless. At least a portion of the bigrams identified as being individually meaningless can be assigned to the first n-gram class.

Abstract translation: 用于识别无意义的数据并产生可以拒绝无意义输入的自然语言统计模型的方法。 该方法可以包括从一组训练数据中识别单独无意义的单字组。 识别为无意义的unigrams的至少一部分可以分配给第一个n-gram类。 该方法还可以包括识别完全由无意义单元组成的二进制，并且确定所识别的两字组是否是单独无意义的。 识别为单独无意义的二进制组的至少一部分可以分配给第一个n-gram类。

公开(公告)号：US20050075874A1

公开(公告)日：2005-04-07

申请号：US10677044

申请日：2003-10-01

Applicant: Rajesh Balchandran ,  Linda Boyer

Inventor： Rajesh Balchandran ,  Linda Boyer

IPC: G06F17/27 ,  G10L15/26 ,  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：US20130086059A1

公开(公告)日：2013-04-04

申请号：US13251322

申请日：2011-10-03

Applicant: Rajesh Balchandran ,  Leonid Rachevsky ,  Bhuvana Ramabhadran

Inventor： Rajesh Balchandran ,  Leonid Rachevsky ,  Bhuvana Ramabhadran

IPC: G06F17/30

CPC classification number: G10L15/1815 ,  G06F17/2775 ,  G06F17/2785

Abstract: A method of automatically processing text data is described. An initial set of data tags is developed that characterize text data in a text database. Higher order entities are determined which are characteristic of patterns in the data tags. Then the text data is automatically tagged based on the higher order entities.

Abstract translation: 描述了一种自动处理文本数据的方法。 开发了一组数据标签，用于表征文本数据库中的文本数据。 确定哪些是数据标签中的图案特征的高阶实体。 然后，文本数据将根据较高阶实体自动标记。

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。