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公开(公告)号：US20080195322A1
公开(公告)日：2008-08-14
申请号：US11673910
申请日：2007-02-12
申请人： Steven J. Altschuler , Lit-Hsin Loo , Lani F. Wu
发明人： Steven J. Altschuler , Lit-Hsin Loo , Lani F. Wu
IPC分类号： G01N33/48
摘要： A multivariate, automated and scalable method for extracting profiles from images to quantify the effects of perturbations on biological samples. Morphological features are determined from images of treated (perturbed) and control (unperturbed) biological samples, and multivariate classification, for example, using a separating decision hyperplane, is used to separate the distribution of measured feature data into control and treated groups. This classification may be used to determine a magnitude of the effect of the particular perturbation under study. A practical application is high-throughput image-based drug screening, wherein the effects of many different compounds, each applied at different doses and for different exposure times, may be profiled to, for example, characterize compound activities and to identify dose-dependent multiphasic drug responses, or to determine and classify the biological effects of new compounds.
摘要翻译： 一种用于从图像中提取轮廓以量化扰动对生物样品的影响的多变量，自动化和可扩展的方法。 形态学特征由处理（扰动）和对照（未扰动）生物样品的图像确定，多变量分类，例如使用分离决策超平面，用于将测量的特征数据分布分为对照组和处理组。 该分类可用于确定正在研究的特定扰动的影响的大小。 实际应用是基于高通量图像的药物筛选，其中各种不同化合物的效果，每种化合物以不同剂量和不同的暴露时间施用，可以被描述为例如表征化合物活性并鉴定剂量依赖性多相 药物反应，或确定和分类新化合物的生物效应。
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公开(公告)号：US5812430A
公开(公告)日：1998-09-22
申请号：US867086
申请日：1997-06-02
申请人： Steven J. Altschuler , William E. Kim , Lani F. Wu
发明人： Steven J. Altschuler , William E. Kim , Lani F. Wu
CPC分类号： G06F8/30
摘要： A method, system and computer product for allowing efficient user interaction with digital time-based signals. User control and filter information (symbolic, procedural or a combination of both) are optimized for greatly improving calculating efficiency. First, filters are symbolically optimized. Then, a single optimized procedure is generated and compiled into an optimized procedure code. The procedure code then processes the input signal according to control information.
摘要翻译： 一种方法，系统和计算机产品，用于允许与数字时基信号的高效用户交互。 用户控制和过滤器信息（符号，程序或两者的组合）进行了优化，大大提高了计算效率。 首先，过滤器被象征性地优化。 然后，生成一个单一的优化过程并将其编译成优化的过程代码。 然后，程序代码根据控制信息处理输入信号。
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3.发明授权Methods and apparatus using task models for targeting marketing information to computer users based on a task being performed 有权基于正在执行的任务，使用任务模型的方法和装置将营销信息定位到计算机用户公开(公告)号：US06330554B1
公开(公告)日：2001-12-11
申请号：US09325167
申请日：1999-06-03
IPC分类号： G06F9445
CPC分类号： G06Q30/02
摘要： Methods and apparatus for analyzing tasks performed by computer users by (i) gathering usage data, (ii) converting logged usage data into a uniform format, (iii) determining or defining task boundaries, and (iv) determining a task analysis model by “clustering” similar tasks together. The task analysis model may be used to (i) help users complete a task (such help, for example, may be in the form of a gratuitous help function), and/or (ii) to target marketing information to users based on user inputs and the task analysis model. The present invention also provides a uniform semantic network for representing different types of objects in a uniform way.
摘要翻译： 用于分析计算机用户执行的任务的方法和装置（i）收集使用数据，（ii）将记录的使用数据转换为统一格式，（iii）确定或定义任务边界，以及（iv）通过“ 聚类“相似的任务在一起。 任务分析模型可以用于（i）帮助用户完成任务（例如，这种帮助可以是无偿帮助功能的形式），和/或（ii）基于用户将营销信息定位到用户 输入和任务分析模型。 本发明还提供了用于以统一的方式表示不同类型的对象的统一语义网络。
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4.发明授权Methods and apparatus for determining or inferring influential rumormongers from resource usage data 失效用于从资源使用数据确定或推断有影响力的运动员的方法和装置
公开(公告)号：US06151585A
公开(公告)日：2000-11-21
申请号：US65970
申请日：1998-04-24
申请人： Steven J. Altschuler , Lani F. Wu , David Ingerman
发明人： Steven J. Altschuler , Lani F. Wu , David Ingerman
CPC分类号： G06Q30/02 , G06Q30/0201 , G06Q99/00
摘要： Resource usage data is used to infer degrees of influence between users. Once such inferences are made, a directed graph representation of the users and the inferred "influence" between the users can be generated. "Influential rumormongers" can then be determined from the directed graph, for example, by using a greedy graph covering algorithm. In this way, marketing information can be targeted to "influential rumormongers" to optimize its dissemination and impact. If actual (explicit) data regarding the influence between users is known, such data may be used to refine or replace at least some edge values.
摘要翻译： 资源使用数据用于推断用户之间的影响程度。 一旦进行了这样的推论，就可以生成用户的有向图表示和用户之间推断的“影响”。 然后可以从有向图确定“有影响力的运动员”，例如通过使用贪心图覆盖算法。 以这种方式，营销信息可以针对“有影响力的运动员”来优化其传播和影响。 如果已知有关用户之间的影响的实际（显式）数据，则可以使用这些数据来细化或替换至少一些边缘值。
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5.发明授权Methods and apparatus for analyzing computer-based tasks to build task models 失效分析基于计算机的任务构建任务模型的方法和装置公开(公告)号：US06778971B1
公开(公告)日：2004-08-17
申请号：US09325169
申请日：1999-06-03
IPC分类号： G06N502
摘要： Methods and apparatus for analyzing tasks performed by computer users by (i) gathering usage data, (ii) converting logged usage data into a uniform format, (iii) determining or defining task boundaries, and (iv) determining a task analysis model by “clustering” similar tasks together. The task analysis model may be used to (i) help users complete a task (such help, for example, may be in the form of a gratuitous help function), and/or (ii) to target marketing information to users based on user inputs and the task analysis model. The present invention also provides a uniform semantic network for representing different types of objects in a uniform way.
摘要翻译： 用于分析计算机用户执行的任务的方法和装置（i）收集使用数据，（ii）将记录的使用数据转换为统一格式，（iii）确定或定义任务边界，以及（iv）通过“ 聚类“相似的任务在一起。 任务分析模型可以用于（i）帮助用户完成任务（例如，这种帮助可以是无偿帮助功能的形式），和/或（ii）基于用户将营销信息定位到用户 输入和任务分析模型。 本发明还提供了用于以统一的方式表示不同类型的对象的统一语义网络。
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公开(公告)号：US06713257B2
公开(公告)日：2004-03-30
申请号：US09781814
申请日：2001-02-12
申请人： Daniel D. Shoemaker , Stewart Scherer , Steven J. Altschuler , Lani F. Wu , Christopher D. Armour
发明人： Daniel D. Shoemaker , Stewart Scherer , Steven J. Altschuler , Lani F. Wu , Christopher D. Armour
IPC分类号： C12Q168
CPC分类号： G06F19/18 , B01J2219/00605 , B01J2219/0061 , B01J2219/00612 , B01J2219/00626 , B01J2219/00659 , B01J2219/00689 , B01J2219/00695 , B01J2219/00707 , B01J2219/00722 , B01J2219/00743 , C12Q1/6809 , C12Q1/6837 , C40B40/06 , G06F19/20
摘要： The invention relates to methods and systems (e.g., computer systems and computer program products) for identifying and characterizing genes using microarrays. In particular, the invention provides for improved, robust methods for detecting genes through the use of microarrays to analyze the expression state of the genome. Genes which are expressed can be mapped to their respective positions in the genome, and the structure of such genes can be determined.
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7.发明授权Methods and apparatus for using task models to help computer users complete tasks 有权使用任务模型帮助计算机用户完成任务的方法和设备公开(公告)号：US06606613B1
公开(公告)日：2003-08-12
申请号：US09325168
申请日：1999-06-03
IPC分类号： G06N302
CPC分类号： G06Q10/10 , Y10S707/99933 , Y10S707/99936
摘要： Methods and apparatus for analyzing tasks performed by computer users by (i) gathering usage data, (ii) converting logged usage data into a uniform format, (iii) determining or defining task boundaries, and (iv) determining a task analysis model by “clustering” similar tasks together. The task analysis model may be used to (i) help users complete a task (such help, for example, may be in the form of a gratuitous help function), and/or (ii) to target marketing information to users based on user inputs and the task analysis model. The present invention also provides a uniform semantic network for representing different types of objects in a uniform way.
摘要翻译： 用于分析计算机用户执行的任务的方法和装置（i）收集使用数据，（ii）将记录的使用数据转换为统一格式，（iii）确定或定义任务边界，以及（iv）通过“ 聚类“相似的任务在一起。 任务分析模型可以用于（i）帮助用户完成任务（例如，这种帮助可以是无偿帮助功能的形式），和/或（ii）基于用户将营销信息定位到用户 输入和任务分析模型。 本发明还提供了用于以统一的方式表示不同类型的对象的统一语义网络。
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8.发明授权Methods and apparatus for using attribute transition probability models for pre-fetching resources 失效用于预取资源的属性转换概率模型的方法和装置
公开(公告)号：US6154767A
公开(公告)日：2000-11-28
申请号：US7898
申请日：1998-01-15
CPC分类号： G06F17/30902
摘要： Building resource (e.g., Internet content) and attribute transition probability models and using such models for pre-fetching resources, editing resource link topology, building resource link topology templates, and collaborative filtering.
摘要翻译： 构建资源（例如互联网内容）和属性转换概率模型，并使用这些模型来预取资源，编辑资源链路拓扑，构建资源链路拓扑模板和协同过滤。
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9.发明授权Sound source separation using convolutional mixing and a priori sound source knowledge 有权使用卷积混合和先验声源知识的声源分离公开(公告)号：US07047189B2
公开(公告)日：2006-05-16
申请号：US10992051
申请日：2004-11-18
IPC分类号： G10L19/12
CPC分类号： G10L25/78 , G10L21/0264 , G10L2021/02082 , G10L2021/02161
摘要： Sound source separation, without permutation, using convolutional mixing independent component analysis based on a priori knowledge of the target sound source is disclosed. The target sound source can be a human speaker. The reconstruction filters used in the sound source separation take into account the a priori knowledge of the target sound source, such as an estimate the spectra of the target sound source. The filters may be generally constructed based on a speech recognition system. Matching the words of the dictionary of the speech recognition system to a reconstructed signal indicates whether proper separation has occurred. More specifically, the filters may be constructed based on a vector quantization codebook of vectors representing typical sound source patterns. Matching the vectors of the codebook to a reconstructed signal indicates whether proper separation has occurred. The vectors may be linear prediction vectors, among others.
摘要翻译： 公开了基于目标声源的先验知识的声源分离，不排列，使用卷积混合独立分量分析。 目标声源可以是人的扬声器。 在声源分离中使用的重建滤波器考虑了目标声源的先验知识，例如估计目标声源的频谱。 滤波器通常可以基于语音识别系统来构造。 将语音识别系统的词典与重构的信号进行匹配，表示是否发生了适当的分离。 更具体地说，滤波器可以基于表示典型声源模式的矢量的矢量量化码本构成。 将码本的向量与重构信号进行匹配，表示是否发生了适当的分离。 矢量可以是线性预测矢量等等。
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10.发明授权Sound source separation using convolutional mixing and a priori sound source knowledge 有权使用卷积混合和先验声源知识的声源分离公开(公告)号：US06879952B2
公开(公告)日：2005-04-12
申请号：US09842416
申请日：2001-04-25
CPC分类号： G10L25/78 , G10L21/0264 , G10L2021/02082 , G10L2021/02161
摘要： Sound source separation, without permutation, using convolutional mixing independent component analysis based on a priori knowledge of the target sound source is disclosed. The target sound source can be a human speaker. The reconstruction filters used in the sound source separation take into account the a priori knowledge of the target sound source, such as an estimate the spectra of the target sound source. The filters may be generally constructed based on a speech recognition system. Matching the words of the dictionary of the speech recognition system to a reconstructed signal indicates whether proper separation has occurred. More specifically, the filters may be constructed based on a vector quantization codebook of vectors representing typical sound source patterns. Matching the vectors of the codebook to a reconstructed signal indicates whether proper separation has occurred. The vectors may be linear prediction vectors, among others.
摘要翻译： 公开了基于目标声源的先验知识的声源分离，不排列，使用卷积混合独立分量分析。 目标声源可以是人的扬声器。 在声源分离中使用的重建滤波器考虑了目标声源的先验知识，例如估计目标声源的频谱。 滤波器通常可以基于语音识别系统来构造。 将语音识别系统的词典与重构的信号进行匹配，表示是否发生了适当的分离。 更具体地说，滤波器可以基于表示典型声源模式的矢量的矢量量化码本构成。 将码本的向量与重构信号进行匹配，表示是否发生了适当的分离。 矢量可以是线性预测矢量等等。
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