会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 2. 发明申请
    • METHODS, COMPUTER-ACCESSIBLE MEDIUM AND SYSTEMS FOR FACILITATING DATA ANALYSIS AND REASONING ABOUT TOKEN/SINGULAR CAUSALITY
    • 方法,计算机可访问的媒体和系统,用于促进数据分析和关于TOKEN / SINGULAR CAUSALITY
    • WO2011103526A2
    • 2011-08-25
    • PCT/US2011/025574
    • 2011-02-20
    • NEW YORK UNIVERSITYKLEINBERG, SamanthaMISHRA, Bhubaneswar
    • KLEINBERG, SamanthaMISHRA, Bhubaneswar
    • G06F19/00G06F17/10
    • G06N5/048G06N5/045
    • Exemplary embodiments of exemplary methods, procedures, computer- accessible medium and systems according to the present disclosure can be provided which can be used for determining token causality. For example, data which comprises token-level time course data and type-level causal relationships can be obtained. In addition, a determination can be made as to whether the type-level causal relationships are instantiated in the token-level time course data, and using a computing arrangement. Further, exemplary significance scores for the causal relationships can be determined based on the determination procedure. It is also possible to determine probabilities associated with the type-level causal relationships using the token-level time course data and a probabilistic temporal model and/or type- level time course data when at least one of the type-level causal relationships have indeterminate truth values. The exemplary determination of the probabilities can be performed using a prior causal information inference procedure.
    • 可以提供可用于确定令牌因果关系的根据本公开的示例性方法,过程,计算机可访问介质和系统的示例性实施例。 例如,可以获得包括令牌级时程数据和类型因果关系的数据。 此外,可以确定在令牌级时间过程数据中是否实例化了类型级因果关系,并且使用计算机构。 此外,可以基于确定过程来确定因果关系的示例性重要性得分。 当类型级别因果关系中的至少一个具有不确定性时,也可以使用令牌级时间过程数据和概率时间模型和/或类型级时间过程数据来确定与类型级因果关系相关联的概率 真值。 概率的示例性确定可以使用先验因果信息推理过程来执行。
    • 3. 发明申请
    • METHODS, COMPUTER-ACCESSIBLE MEDIUM, AND SYSTEMS FOR GENERATING A GENOME WIDE HAPLOTYPE SEQUENCE
    • 方法,计算机可用介质和用于生成基因组宽泛的HAPLOTYPE序列的系统
    • WO2008112754A2
    • 2008-09-18
    • PCT/US2008/056648
    • 2008-03-12
    • NEW YORK UNIVERSITYMISHRA, BhubaneswarANANTHARAMAN, ThomasLIM, Sang
    • MISHRA, BhubaneswarANANTHARAMAN, ThomasLIM, Sang
    • G06G7/48
    • G06F19/18G06F19/24
    • Methods, computer-accessible medium, and systems for generating a genome wide probe map and/or a genome wide haplotype sequence are provided. In particular, a genome wide prob map can be generated by obtaining a plurality of detectable oligonucleotide probes hybridized to at least one double stranded nucleic acid molecule cleaved with at least one restriction enzyme, and detecting the location of the detectable oligonucleotide probes. For example, genome wide haplotype sequence can be generated by analyzing at least one genome wide restriction map in conjunction with at least one genome wide probe map to determine distances between restriction sites of the at least one genome wide restriction map and locations of detectable oligonucleotide probes of the at least one genome wide probe map and defining a consensus map indicating restriction sites based on each of the at least one genome wide restriction map and locations of detectable oligonucleotide probes based on each of the at least one genome wide probe map.
    • 提供了用于产生全基因组探针图和/或全基因组单倍型序列的方法,计算机可用介质和系统。 特别地,可以通过获得与用至少一种限制酶切割的至少一个双链核酸分子杂交的多个可检测寡核苷酸探针,并检测可检测寡核苷酸探针的位置来产生全基因组范围概率图。 例如,可以通过分析至少一个基因组范围的限制性图谱以及至少一个全基因组范围的探针图来确定全基因组范围的单倍型序列,以确定至少一个全基因组范围的限制性图谱的限制性位点与可检测的寡核苷酸探针的位置之间的距离 并且基于所述至少一个全基因组范围的限制性图中的每一个和基于所述至少一个全基因组范围的探针图中的每一个的可检测寡核苷酸探针的位置定义指示限制性位点的共有图。 p>
    • 5. 发明申请
    • METHOD, COMPUTER-ACCESSIBLE MEDIUM AND SYSTEM FOR BASE-CALLING AND ALIGNMENT
    • 方法,计算机可访问的媒体和基站和对齐的系统
    • WO2010129301A2
    • 2010-11-11
    • PCT/US2010/032613
    • 2010-04-27
    • NEW YORK UNIVERSITYMISHRA, BhubaneswarNARZISI, Giuseppe
    • MISHRA, BhubaneswarNARZISI, Giuseppe
    • G06F19/00C12Q1/68
    • G06F19/22
    • Exemplary methods, procedures, computer-accessible medium, and systems for base-calling, aligning and polymorphism detection and analysis using raw output from a sequencing platform can be provided. A set of raw outputs can be used to detect polymorphisms in an individual by obtaining a plurality of sequence read data from one or more technologies (e.g., using sequencing-by-synthesis, sequencing-by-ligation, sequencing-by-hybridization, Sanger sequencing, etc.). For example, provided herein are exemplary methods, procedures, computer-accessible medium and systems, which can include and/or be configured for obtaining raw output from a sequencing platform configured to be used for reading fragment(s) of genomes, obtaining reference sequences for the genomes obtained independently from the raw output, and generating a base-call interpretation and/or alignment using the raw output and the reference sequences. For example, a score function can be determined based on information associated with the sequencing platform that can be used to analyze polymorphisms based on the base-call interpretation and/or alignment.
    • 可以提供示例性的方法,程序,计算机可访问介质以及使用来自测序平台的原始输出的基本调用,对准和多态性检测和分析的系统。 一组原始输出可以用于通过从一种或多种技术获得多个序列读取数据来检测个体中的多态性(例如,使用按合成顺序,通过连接测序,序列化杂交,Sanger 测序等)。 例如,本文提供的是示例性方法,程序,计算机可访问介质和系统,其可以包括和/或配置用于从被配置为用于读取基因组片段的测序平台获得原始输出,获得参考序列 对于从原始输出独立获得的基因组,以及使用原始输出和参考序列生成基本呼叫解释和/或对齐。 例如,可以基于与可以用于基于基本呼叫解释和/或对齐来分析多态性的测序平台相关联的信息来确定分数函数。
    • 9. 发明申请
    • RANK, CLUSTER, CHARACTERIZE AND CUSTOMIZE USERS, DIGITAL CONTENTS AND ADVERTISEMENT CAMPAIGNS BASED ON IMPLICIT CHARACTERISTIC DETERMINATION
    • 排名,聚类,特征和定制用户,数字内容和基于隐含特征确定的广告投放
    • WO2017048784A1
    • 2017-03-23
    • PCT/US2016/051641
    • 2016-09-14
    • GENESIS MEDIA LLCNEW YORK UNIVERSITY
    • DATTA, SouptikFEUER, JoshuaMISHRA, Bhubaneswar
    • G06F15/16
    • G06Q30/0255G06Q30/02G06Q30/0251G06Q30/0271G06Q30/0276G06Q30/0277G06Q50/01
    • A statistical algorithm-driven digital system is provided for automated optimization of a large number of key performance indicators (KPI) involved in social digital interactions among users, contents and advertisement, further augmented by data-driven verification and recommendation. Users include humans from diverse socio-cultural-economic groups, whose identity may be pseudonymous, whose explicit features may remain private, though statistically imputable. Contents include webpages, downloads, videos, music, or content accessed by users. Advertisements include product placement, branding, appeal, surveys, or other third-party contents, not explicitly sought by user. Application executing on server digital device responds to requests received from client devices for delivering requested digital content, customizes selected piece of digital content delivered to client application (in response to request) based on ordinal rankings for users, contents and advertisements, computed by tensor based algorithm. Rankings computed are predictive of user's future social interactions, as determined by past statistical data, summarized in sparse high-dimensional tensors.
    • 提供统计算法驱动的数字系统,用于自动优化涉及用户,内容和广告之间的社会数字交互的大量关键绩效指标(KPI),并进一步通过数据驱动的验证和推荐来增强。 用户包括来自不同社会文化经济群体的人,其身份可能是假名的,其明确的特征可能保持私有化,尽管统计学上无法估量。 内容包括用户访问的网页,下载,视频,音乐或内容。 广告包括用户未明确寻求的产品展示位置,品牌,呼吁,调查或其他第三方内容。 在服务器数字设备上执行的应用响应从客户端设备接收的用于递送所请求的数字内容的请求,根据用户,内容和广告的顺序排列,定制被递送给客户端应用的所选择的数字内容(响应请求),由基于张量的 算法。 计算的排名是对用户未来社会交往的预测,由过去的统计数据确定,在稀疏高维张量中总结。