公开(公告)号：US08818862B2

公开(公告)日：2014-08-26

申请号：US13341881

申请日：2011-12-30

申请人： Peter Sweeney

发明人： Peter Sweeney

IPC分类号： G06Q30/00 ,  G06Q30/02

CPC分类号： G06Q30/0273 ,  G06F17/2785 ,  G06Q30/0251

摘要： Disclosed is a system and method for enhancing value for advertisers by helping them select and initiate the purchase of an advertisement associated with an advertising (ad) words or phrases that have strong semantic relationships to a given context, but which are not necessarily the most popular ad words or phrases with the highest costs. Advertisements associated with ad words or phrases that have strong semantic relationships to a given context, and yet are still cost effective in that their calculated value exceeds the costs of purchasing the ad keywords, are bid for and bought. In an embodiment, the system and method may be adapted to automatically purchase advertisements associated with ad words or phrases when the ad words or phrases fall within a desired price range based on their calculated value. By automatically purchasing such advertisements, the return on investment (ROI) for an advertiser may be improved.

摘要翻译： 公开了一种通过帮助他们选择并发起与给定上下文具有强语义关系的广告（广告）单词或短语相关联的广告的购买来增强广告商的价值的系统和方法，但是不一定是最受欢迎的 费用最高的广告词或短语。 与给定上下文具有强烈语义关系的广告词或短语相关联的广告，但仍然具有成本效益，因为其计算价值超过购买广告关键字的费用，即出价和购买。 在一个实施例中，当广告词或短语基于其计算的值而落在期望的价格范围内时，系统和方法可以适于自动地购买与广告词或短语相关联的广告。 通过自动购买这样的广告，可以提高广告商的投资回报率（ROI）。

公开(公告)号：US20120323899A1

公开(公告)日：2012-12-20

申请号：US13527914

申请日：2012-06-20

申请人： Ihab Francis Ilyas ,  Mohamed A. Soliman ,  Peter Joseph Sweeney

发明人： Ihab Francis Ilyas ,  Mohamed A. Soliman ,  Peter Joseph Sweeney

IPC分类号： G06F17/30

CPC分类号： G06F17/30867 ,  G06F17/3053 ,  G06F17/30731

摘要： Techniques for specifying user preferences in a semantic network. The techniques include receiving, using at least one processor, a plurality of first-order user preferences for at least one concept in a semantic network, wherein the plurality of first-order user preferences are indicative of a user's preferences among children of attributes of the at least one concept in the semantic network; receiving, using the at least one processor, at least one second-order user preference for the at least one concept in the semantic network, wherein the at least one second-order user preference is indicative of the user's preferences among attributes of the at least one concept; and performing at least one semantic processing act by using the semantic network, the plurality of first-order user preferences, and the at least one second-order user preference.

摘要翻译： 用于在语义网络中指定用户偏好的技术。 所述技术包括：在语义网络中接收使用至少一个处理器中的至少一个概念的多个一阶用户偏好，其中所述多个一阶用户偏好指示所述用户在所述第 语义网络中至少有一个概念; 使用所述至少一个处理器接收所述语义网络中的所述至少一个概念的至少一个二阶用户偏好，其中所述至少一个二阶用户偏好指示所述至少一个以上的属性中的所述用户的偏好 一个概念; 以及通过使用所述语义网络，所述多个一阶用户偏好以及所述至少一个二阶用户偏好来执行至少一个语义处理动作。

公开(公告)号：US20120166372A1

公开(公告)日：2012-06-28

申请号：US13345640

申请日：2012-01-06

申请人： Ihab Francis Ilyas ,  Wu Zhou ,  Wayne Oldford

发明人： Ihab Francis Ilyas ,  Wu Zhou ,  Wayne Oldford

IPC分类号： G06F15/18

CPC分类号： G06N5/02 ,  G06F17/30914 ,  G06N7/005

摘要： Techniques for analyzing and synthesizing complex knowledge representations (KRs) may utilize an atomic knowledge representation model including both an elemental data structure and knowledge processing rules stored as machine-readable data and/or programming instructions. One or more of the knowledge processing rules may be applied to analyze an input complex KR to deconstruct its complex concepts and/or concept relationships to elemental concepts and/or concept relationships to be included in the elemental data structure. One or more of the knowledge processing rules may be applied to synthesize an output complex KR from the stored elemental data structure in accordance with context information. Methods of populating an elemental data structure and methods of synthesizing a complex KR from the elemental data structure may rely on statistical inference techniques.

摘要翻译： 用于分析和合成复杂知识表示（KR）的技术可以利用原子知识表示模型，其包括存储为机器可读数据和/或编程指令的元数据结构和知识处理规则。 可以应用知识处理规则中的一个或多个来分析输入复合体KR以将其复杂概念和/或概念关系解构为要包括在基本数据结构中的元素概念和/或概念关系。 知识处理规则中的一个或多个可以应用于根据上下文信息从存储的基本数据结构合成输出复合体KR。 填充元素数据结构的方法和从元素数据结构合成复合体KR的方法可以依赖统计推理技术。

公开(公告)号：US07606781B2

公开(公告)日：2009-10-20

申请号：US11550457

申请日：2006-10-18

申请人： Peter Sweeney ,  Zhengrong Yao

发明人： Peter Sweeney ,  Zhengrong Yao

IPC分类号： G06F17/00 ,  G06N5/02

CPC分类号： G06N5/02 ,  G06F17/30914

摘要： Automated facet analysis of input information selected from a domain of information in accordance with a source data structure is described. Facet analysis may proceed by discovering at least one of facets, facet attributes, and facet attribute hierarchies of the input information using pattern augmentation and statistical analyses to identify patterns of facet attribute relationships in the input information.

摘要翻译： 描述根据源数据结构从信息域中选择的输入信息的自动小面分析。 通过使用模式增加和统计分析发现输入信息的方面，小平面属性和小平面属性层次中的至少一个来进行方面分析，以识别输入信息中的方面属性关系的模式。

公开(公告)号：US07596574B2

公开(公告)日：2009-09-29

申请号：US11469258

申请日：2006-08-31

申请人： Peter Sweeney

发明人： Peter Sweeney

IPC分类号： G06F17/00

CPC分类号： G06N5/02 ,  G06F17/30914 ,  Y10S707/99942 ,  Y10S707/99943 ,  Y10S707/99944 ,  Y10S707/99945

摘要： A complex-adaptive system is described for providing a faceted classification of a domain of information. A dimensional concept taxonomy classifying a domain may be constructed from a faceted data set comprising facets, facet attributes, and facet attribute hierarchies. An enhanced method of faceted classification provides the taxonomy which assigns facet attributes to objects of the domain to be classified in accordance with concepts (defined using the facet attributes) that ascribe meaning to the objects. Further the taxonomy expresses dimensional concept relationships between concept definitions in accordance with the faceted data set. The complex-adaptive system selects dimensional concept taxonomy information to facilitate varying the faceted data set and subsequent iterations of the taxonomy. The complex-adaptive system may involve a machine-based approach using statistical analysis of source data structures or user interactions with the taxonomy to provide the feedback. Computer system, method and software aspects are provided.

摘要翻译： 描述了一种复杂自适应系统，用于提供信息领域的分面分类。 可以从包括facet，facet属性和facet属性层次的多面数据集构建分类域的维概念分类法。 增强的分面分类方法提供了分类法，根据归结于对象的含义的概念（使用方面属性定义），将分面属性分配给要分类的领域的对象。 此外，分类法根据分面数据集来表示概念定义之间的维度概念关系。 复杂自适应系统选择维概念分类信息，以便于改变分面数据集和随后的分类学迭代。 复杂自适应系统可能涉及基于机器的方法，使用源数据结构的统计分析或与分类法的用户交互来提供反馈。 提供计算机系统，方法和软件方面。