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    • 1. 发明授权
    • System and method for matching comment data to text data
    • 将注释数据与文本数据相匹配的系统和方法
    • US08972413B2
    • 2015-03-03
    • US13253157
    • 2011-10-05
    • Hyun Chul LeeLiqin XuKe Zeng
    • Hyun Chul LeeLiqin XuKe Zeng
    • G06F7/04G06F17/30G06F17/24
    • G06F17/30722G06F17/241G06F17/30011G06F17/30342G06F17/30616G06F17/30864
    • Methods and comment association systems for associating one or more comments with one or more primary electronic documents are described. In one aspect, the method comprises: identifying, at a comment association system, one or more key terms from at least a portion of the one or more primary electronic documents; identifying, at the comment association system, one or more comments associated with the identified key terms; determining, at the comment association system, whether an identified comment is sufficiently related to the one or more primary electronic documents by calculating one or more relation score for that identified comment and comparing the relation score to one or more threshold; and if the identified comment is sufficiently related to the one or more primary electronic documents, then associating the identified comment with the one or more primary electronic documents at the comment association system.
    • 描述用于将一个或多个注释与一个或多个主要电子文档相关联的方法和注释关联系统。 一方面,该方法包括:在注释关联系统处从一个或多个主要电子文档的至少一部分识别一个或多个关键术语; 在所述评论关联系统处识别与所识别的关键术语相关联的一个或多个注释; 在所述评论关联系统处,通过计算所识别的评论的一个或多个关系得分并将所述关系得分与一个或多个阈值进行比较来确定所识别的注释是否与所述一个或多个主要电子文档充分相关; 并且如果所识别的注释与所述一个或多个主要电子文档充分相关,则将所识别的注释与所述注释关联系统上的所述一个或多个主要电子文档相关联。
    • 2. 发明申请
    • SYSTEM AND METHOD FOR PHRASE IDENTIFICATION
    • 用于鉴别的系统和方法
    • US20110093414A1
    • 2011-04-21
    • US12775547
    • 2010-05-07
    • Liqin XuHyun Chul Lee
    • Liqin XuHyun Chul Lee
    • G06F15/18
    • G06F17/27
    • A phrase identification system and method are provided. The method comprises: identifying one or more phrase candidates in the electronic document; selecting one of the phrase candidates; numerically representing features of the selected phrase candidates to obtain a numeric feature representation associated with that phrase candidate; and inputting the numeric feature representation into a machine learning classifier, the machine learning classifier being configured to determine, based on each numeric feature representation, whether the phrase candidate associated with that numeric feature representation is a phrase.
    • 提供短语识别系统和方法。 该方法包括:识别电子文档中的一个或多个短语候选者; 选择短语候选人之一; 数字地表示所选择的短语候选的特征以获得与该短语候选相关联的数字特征表示; 以及将所述数字特征表示输入到机器学习分类器中,所述机器学习分类器被配置为基于每个数字特征表示来确定与所述数字特征表示相关联的短语候选是短语。
    • 3. 发明授权
    • Systems and methods for creating an interest profile for a user
    • 用于为用户创建兴趣资料的系统和方法
    • US09116979B2
    • 2015-08-25
    • US13309661
    • 2011-12-02
    • Hyun Chul LeeYingbo MiaoLiqin Xu
    • Hyun Chul LeeYingbo MiaoLiqin Xu
    • G06F17/30
    • G06F17/30702G06F17/30029G06F17/30598G06F17/30864
    • Profiling systems and methods of creating and using user interest profiles are described. In some example embodiments, the method includes: creating a topic set which includes topics which are organized in a hierarchical structure which includes a plurality of topic levels including an upper topic level and a lower topic level, each topic in the lower topic level being a subtopic of at least one of the topics in the upper topic level; monitoring interest in a plurality of documents for a user to identify one or more documents-of-interest to the user; and based on the monitored interest for the user, creating an interest profile for the user by determining a measure of topical interest for the user for at least one of the topics at the upper topic level and for a subtopic of that topic, the subtopic being at the lower topic level.
    • 描述创建和使用用户兴趣简档的分析系统和方法。 在一些示例性实施例中,该方法包括:创建主题集合,其包括以包括多个主题级别(包括上级主题级别和较低主题级别)的层次结构组织的主题,较低主题级别中的每个主题为 在上一级主题中至少有一个主题的副主题; 监视对多个文档的兴趣,以供用户识别用户的一个或多个感兴趣的文档; 并且基于对用户的监视兴趣,通过为上一级主题级别的主题中的至少一个和该主题的子主题确定用户的主题兴趣度量,为用户创建兴趣简档,子主题为 在较低的主题层面。
    • 4. 发明授权
    • System and method for text cleaning by classifying sentences using numerically represented features
    • 通过使用数字表示的特征对句子进行分类来进行文本清理的系统和方法
    • US08380492B2
    • 2013-02-19
    • US12775580
    • 2010-05-07
    • Liqin XuHyun Chul Lee
    • Liqin XuHyun Chul Lee
    • G06F17/27G06F17/20G06F17/21
    • G06F17/27
    • A method and system for cleaning an electronic document are provided. The method comprises: identifying at least one sentence in the electronic document; numerically representing features of the sentence to obtain a numeric feature representation associated with the sentence; inputting the numeric feature representation into a machine learning classifier, the machine learning classifier being configured to determine, based on each numeric feature representation, whether the sentence associated with that numeric feature representation is a bad sentence; and removing sentences determined to be bad sentences from the electronic document to create a cleaned document.
    • 提供了一种用于清洁电子文档的方法和系统。 该方法包括:识别电子文档中的至少一个句子; 数字地表示句子的特征以获得与句子相关联的数字特征表示; 将所述数字特征表示输入到机器学习分类器中,所述机器学习分类器被配置为基于每个数字特征表示来确定与所述数字特征表示相关联的句子是否是坏句子; 并且从电子文档中移除确定为不良句子的句子以创建清洁的文档。
    • 5. 发明申请
    • SYSTEM AND METHOD FOR TEXT CLEANING
    • 用于文本清理的系统和方法
    • US20110093258A1
    • 2011-04-21
    • US12775580
    • 2010-05-07
    • Liqin XuHyun Chul Lee
    • Liqin XuHyun Chul Lee
    • G06F17/27G06F15/18
    • G06F17/27
    • A method and system for cleaning an electronic document are provided. The method comprises: identifying at least one sentence in the electronic document; numerically representing features of the sentence to obtain a numeric feature representation associated with the sentence; inputting the numeric feature representation into a machine learning classifier, the machine learning classifier being configured to determine, based on each numeric feature representation, whether the sentence associated with that numeric feature representation is a bad sentence; and removing sentences determined to be bad sentences from the electronic document to create a cleaned document.
    • 提供了一种用于清洁电子文档的方法和系统。 该方法包括:识别电子文档中的至少一个句子; 数字地表示句子的特征以获得与句子相关联的数字特征表示; 将所述数字特征表示输入到机器学习分类器中,所述机器学习分类器被配置为基于每个数字特征表示来确定与所述数字特征表示相关联的句子是否是坏句子; 并且从电子文档中移除确定为不良句子的句子以创建清洁的文档。
    • 6. 发明授权
    • System and method for phrase identification
    • 用于短语识别的系统和方法
    • US08868469B2
    • 2014-10-21
    • US12775547
    • 2010-05-07
    • Liqin XuHyun Chul Lee
    • Liqin XuHyun Chul Lee
    • G06F15/18G06F17/27
    • G06F17/27
    • A phrase identification system and method are provided. The method comprises: identifying one or more phrase candidates in the electronic document; selecting one of the phrase candidates; numerically representing features of the selected phrase candidates to obtain a numeric feature representation associated with that phrase candidate; and inputting the numeric feature representation into a machine learning classifier, the machine learning classifier being configured to determine, based on each numeric feature representation, whether the phrase candidate associated with that numeric feature representation is a phrase.
    • 提供短语识别系统和方法。 该方法包括:识别电子文档中的一个或多个短语候选者; 选择短语候选人之一; 数字地表示所选择的短语候选的特征以获得与该短语候选相关联的数字特征表示; 以及将所述数字特征表示输入到机器学习分类器中,所述机器学习分类器被配置为基于每个数字特征表示来确定与所述数字特征表示相关联的短语候选是短语。
    • 8. 发明申请
    • Hoverboard Attachment
    • US20230030965A1
    • 2023-02-02
    • US17502407
    • 2021-10-15
    • Liqin Xu
    • Liqin Xu
    • B62J11/00F16B2/12
    • This utility model relates to a kind of self-balancing scooter accessory connecting structure and accessory, comprising at least a clamping mechanism used to clamp the driving parts. The said clamping mechanism comprises the 1st gripper and the 2nd gripper. The said 1st gripper and the 2nd gripper may be mounted in a way permitting relative motion. The said 1st gripper comprises the 1st connection and 1st clamp plate. The said 2nd gripper comprises the 2nd connection and 2nd clamp plate; a self-locking structure, comprising the limit stops and length adjustment mechanism. The said 1st connection and 2nd connection are equipped with the said limit stop. The said length adjustment mechanism is mounted between two limit stops. The above self-balancing scooter accessory connecting structure is mounted between the self-balancing scooter saddle and driving parts. The 1st clamp plate and the 2nd clamp plate structures are suitable for fastening the driving parts without use of straps. This approach can eliminate safety hazards on velcro strap's susceptibility to breakdown, and make assembly and disassembly more efficiently.
    • 10. 发明授权
    • Device and method for language model switching and adaptation
    • 语言模型切换和适应的设备和方法
    • US08078467B2
    • 2011-12-13
    • US11683559
    • 2007-03-08
    • Genqing WuLiqin Xu
    • Genqing WuLiqin Xu
    • G10L15/28G10L21/00
    • G06F17/275G10L15/183G10L2015/228
    • This invention provides a device and method for language model switching and adaptation, wherein the device comprises a notification manager which notifies a language model switching section of the current status information or the request for the language model of an destination application when the status of the destination application is changed; a language model switching section which selects one or more language models to be switched from a language model set according to the received current status information or the request; a LMB engine decodes a user input using the one or more selected language models; and a language model adaptation section which receives the decoded result and modifies the one or more selected language models based on the decoded result. Therefore, the user input is more accurate even if the language model switching section performs different switches among different language models and the performance of the language models are improved by the language model adaptation section.
    • 本发明提供了一种用于语言模型切换和适配的装置和方法,其中该装置包括通知管理器,当目的地状态为目的地时,该通知管理器通知语言模型切换部分当前状态信息或目的应用程序的语言模型请求 申请更改; 语言模型切换部,其从根据所接收的当前状态信息或所述请求的语言模型中选择要切换的一个或多个语言模型; LMB引擎使用一个或多个所选择的语言模型解码用户输入; 以及语言模型自适应部分,其接收解码结果,并且基于解码结果修改所选择的一个或多个语言模型。 因此,即使语言模型切换部在不同的语言模型之间执行不同的切换,并且语言模型的表现也被语言模型适配部分改进,用户输入更准确。