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    • 72. 发明申请
    • Method, System and Device for Obtaining a Trust Type of a Non-3GPP Access System
    • 用于获取非3GPP接入系统的信任类型的方法,系统和设备
    • US20110138447A1
    • 2011-06-09
    • US12991423
    • 2009-05-05
    • Hui Xu
    • Hui Xu
    • G06F15/16
    • H04L63/126H04L63/08H04L63/0892H04W12/08H04W92/10
    • The invention provides a method for obtaining a trust type of a non-3GPP access system comprising the following steps: a user equipment UE establishing a underlying link with a non-3GPP access system selected by the UE; the UE initiating an access authentication request and sending the identification information of the UE and the information of the non-3GPP access system to an Authentication, Authorization, Accounting server through the non-3GPP access system; the UE receiving a returned access authentication response and the trust type of the non-3GPP access system, and the trust type of the non-3GPP access system being determined by the AAA server based on the identification information of the UE, the information of the non-3GPP access system and the operator's strategy. The invention can realize that the trust type of the non-3GPP access system is determined and is informed to the UE by the AAA server during the access authentication performed by the UE, so that the UE can obtain the trust type of the non-3GPP access system.
    • 本发明提供了一种用于获得非3GPP接入系统的信任类型的方法,包括以下步骤:用户设备UE与由UE选择的非3GPP接入系统建立基础链路; UE通过非3GPP接入系统发起接入认证请求,并将UE的识别信息和非3GPP接入系统的信息发送给认证授权计费服务器; UE接收到返回的接入认证响应和非3GPP接入系统的信任类型,以及由AAA服务器基于UE的识别信息确定的非3GPP接入系统的信任类型, 非3GPP接入系统和运营商的策略。 本发明可以认识到,在由UE执行的接入认证期间,确定了非3GPP接入系统的信任类型,并由AAA服务器通知给UE,使得UE可以获得非3GPP接入系统的信任类型 访问系统
    • 75. 发明申请
    • CJK NAME DETECTION
    • CJK名称检测
    • US20100306139A1
    • 2010-12-02
    • US12746465
    • 2007-12-06
    • Jun WuHui XuYifei Zhang
    • Jun WuHui XuYifei Zhang
    • G06N5/02G06F15/18
    • G06F17/278
    • Aspects directed to name detection are provided. A method includes generating a raw name detection model using a collection of family names and an annotated corpus including a collection of n-grams, each n-gram having a corresponding probability of occurring. The method includes applying the raw name detection model to a collection of semi-structured data to form annotated semi?structured data identifying n-grams identifying names and n?grams not identifying names and applying the raw name detection model to a large unannotated corpus to form a large annotated corpus data identifying n-grams of the large unannotated corpus identifying names and n-grams not identifying names. The method includes generating a name detection model, including deriving a name model using the annotated semi-structured data identifying names and the large annotated corpus data identifying names, deriving a not-name model using the semi?structured data not identifying names, and deriving a language model using the large annotated corpus.
    • 提供了针对名称检测的方面。 一种方法包括使用族名集合和注释语料库生成原始名称检测模型,所述注释语料库包括n克的集合,每个n-gram具有相应的发生概率。 该方法包括将原始名称检测模型应用于半结构化数据的集合,以形成标识n-gram标识姓名的标识的半结构化数据,以及不识别名称的n?克,并将原始名称检测模型应用于大型未注释语料库 形成一个大的注释语料库数据,用于识别大型未注释语料库识别名称的n-gram和不识别姓名的n-gram。 该方法包括生成名称检测模型,包括使用标识名称的注释半结构化数据和识别名称的大型注释语料库数据导出名称模型,使用不识别名称的半结构化数据导出非名称模型,以及导出 一种使用大型注释语料库的语言模型。