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    • 5. 发明申请
    • USER INTERFACE
    • 用户界面
    • WO2018067478A1
    • 2018-04-12
    • PCT/US2017/054806
    • 2017-10-03
    • MICROSOFT TECHNOLOGY LICENSING, LLC
    • HODAEI, Darius, AmirEDWARDS, Gregor, MarkRAYIT, Baljinder, Pal
    • H04L12/24G06F9/44G06F3/0488
    • G06F17/2247G06F9/451G06F17/211G06F17/218H04L41/22
    • A computer system for use in rendering a user interface comprises: an input configured to receive a series of natural language user interface description elements describing intended user interface attributes; electronic storage configured to hold model data for interpreting the natural language description elements; an interpretation module configured to apply natural language interpretation to the natural language description elements to interpret them according to the model data, thereby identifying the intended user interface attributes; a generation module configured to use results of the natural language interpretation to generate a data structure for rendering a user interface exhibiting the identified attributes; and a rendering module configured to use the data structure to cause a display to render on the display a user interface exhibiting the intended attributes.
    • 用于呈现用户界面的计算机系统包括:输入,其被配置为接收描述预期用户界面属性的一系列自然语言用户界面描述元素; 电子存储器,被配置为保存用于解释自然语言描述元素的模型数据; 解释模块,被配置为将自然语言解释应用于自然语言描述元素以根据模型数据解释它们,从而识别预期用户界面属性; 生成模块,被配置为使用自然语言解释的结果来生成用于呈现展现所识别的属性的用户界面的数据结构; 以及呈现模块,其被配置为使用所述数据结构来使显示器在所述显示器上呈现呈现所述预期属性的用户界面。
    • 6. 发明申请
    • TECHNIQUES FOR DETERMINING THREAT INTELLIGENCE FOR NETWORK INFRASTRUCTURE ANALYSIS
    • 确定威胁智能的网络基础设施分析技术
    • WO2018035163A1
    • 2018-02-22
    • PCT/US2017/047017
    • 2017-08-15
    • RISKIQ, INC.
    • HUNT, AdamEDGEWORTH, JonasKIERNAN, ChrisMANOUSOS, EliasPON, David
    • G06F21/51G06F21/56H04L29/06G06F17/30
    • H04L63/1483G06F21/51G06F21/562H04L41/22H04L43/14H04L63/08H04L63/1425
    • Embodiments of the present disclosure are directed to a network analytic system for tracking and analysis of network infrastructure for network-based digital assets. The network analytic system can detect and track a relationship between assets based on one or more attributes related or shared between any given assets. The network analytic system can analyze network-based digital assets to determine information about a website (e.g., information about electronic documents, such as web pages) that has be used to detect phishing and other abuse of the website. The network analytic system can analyze data about network-based assets to determine whether any are being used or connected to use of unauthorized or malicious activity or known network-based assets. Based on the relationship identified, the network analytic system can associate or link assets together. The network analytic system may provide an interface to view data sets generated by the network analytic system.
    • 本公开的实施例针对用于跟踪和分析用于基于网络的数字资产的网络基础设施的网络分析系统。 网络分析系统可以基于与任何给定资产相关或共享的一个或多个属性来检测和跟踪资产之间的关系。 网络分析系统可以分析基于网络的数字资产以确定关于网站的信息(例如,关于电子文档的信息,例如网页),其已经用于检测网站的网络钓鱼和其他滥用。 网络分析系统可以分析有关基于网络的资产的数据,以确定是否有人使用或连接到使用未授权或恶意活动或已知的基于网络的资产。 根据所确定的关系,网络分析系统可以将资产关联或链接在一起。 网络分析系统可以提供一个界面来查看由网络分析系统生成的数据集。
    • 8. 发明申请
    • STREAMING DATA DECISION-MAKING USING DISTRIBUTIONS WITH NOISE REDUCTION
    • 使用分布减少噪声的流式数据决策
    • WO2017214613A1
    • 2017-12-14
    • PCT/US2017/036971
    • 2017-06-12
    • NIGHTINGALE ANALYTICS, INC.
    • ADLAKHA, SachinO'NEILL, Daniel C.PHAM, Peter T.
    • G06F11/00
    • H04L41/147G06N5/02G06N5/043G06N7/005G06N20/00G06N20/10H04L41/22H04L43/08H04L43/16
    • An example method comprises receiving a first data stream regarding performance of a monitored system at a first time, determining a plurality of distributions from the first data stream using a density function of a plurality of bins for the data, identifying at least one state for each different distribution of the plurality of distributions to identify a plurality of states, classifying each of the plurality of states into classifications, identifying at least one of the plurality of states as being a problematic state using a first log likelihood ratio, for each state recognizing one or more transitions from or to other states of the plurality of states, receiving a second data stream of the monitored system at a second time, identifying a precursor state indicating at least a potential future transition to the problematic state, and generating a warning before the monitored system enters the problematic state.
    • 示例性方法包括:在第一时间接收关于被监视系统的性能的第一数据流;使用用于数据的多个仓的密度函数来确定来自第一数据流的多个分布 ,识别用于所述多个分布的每个不同分布的至少一个状态以识别多个状态,将所述多个状态中的每一个分类为分类,使用第一日志将所述多个状态中的至少一个识别为问题状态 对于识别从所述多个状态中的一个或多个到其他状态或到所述多个状态中的其他状态的一个或多个转变的每个状态,在第二时间接收所监视的系统的第二数据流,识别指示至少潜在地未来转变到所述问题 状态,并在被监控系统进入有问题状态之前生成警告。