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1. 发明授权

US09449280B2 System and method for mining large, diverse, distributed, and heterogeneous datasets 有权
标题翻译：挖掘大型，多样化，分布式和异构数据集的系统和方法
公开(公告)号：US09449280B2
公开(公告)日：2016-09-20
申请号：US14316439
申请日：2014-06-26
申请人： Stuart Harvey Rubin
发明人： Stuart Harvey Rubin
IPC分类号： G06N7/02 , G06N5/02 , G06N5/04
CPC分类号： G06N7/02 , G06N5/025 , G06N5/048
摘要： A method for directed mining of a heterogeneous dataset with a computer comprising: populating a rule base with known rules, wherein each rule has a context and a situation; populating a case base with known cases, wherein each case has a context and a situation, and wherein the case base is partitioned from the rule base; ascribing a natural language semantics to predicates of the known cases and rules; randomly transforming the known rules and the known cases to form new rules by extracting a maximum number of common predicates; segmenting the rules and the cases on the basis of shared predicates without making distinction between context and situation predicates; abducing new knowledge from the dataset by fuzzily matching the context of a new rule to a situation the new rule does not cover; and issuing a query to a user to supply missing predicates of the fuzzy match.
摘要翻译：一种用计算机定向挖掘异构数据集的方法，包括：用已知规则填充规则库，其中每个规则具有上下文和情况; 使用已知情况填充案例库，其中每种情况都具有上下文和情况，并且其中所述案例库与所述规则库进行分区; 将自然语言语义归结为已知案例和规则的谓词; 通过提取最大数量的公共谓词，随机变换已知规则和已知案例以形成新规则; 基于共享谓词分割规则和案例，而不区分语境和情境谓词; 通过将新规则的上下文模糊匹配到新规则不覆盖的情况，从数据集中吸取新知识; 并向用户发出查询以提供模糊匹配的缺失谓词。

2. 发明授权

US07623410B1 Hard real-time adaptive waveform synthesis and evolution 失效
标题翻译：硬实时自适应波形合成与演化
公开(公告)号：US07623410B1
公开(公告)日：2009-11-24
申请号：US11691080
申请日：2007-03-26
申请人： Stuart Harvey Rubin
发明人： Stuart Harvey Rubin
IPC分类号： G03B42/06
CPC分类号： G03B42/06
摘要： A method for hard real-time adaptive wave modulation, comprising: populating a case base with waveform pairs, each waveform pair comprising a primary waveform, and a secondary waveform, wherein the primary waveform induces the secondary waveform; synthesizing a known target waveform s(t) by selecting waveform pairs from the case base such that the expression ∥s(t)−g∥2 is minimized, where g is the secondary waveform; and mutating the primary waveforms for each waveform pair based on a normal distribution until ∥s(t)−g′∥2
摘要翻译：一种用于硬实时自适应波调制的方法，包括：用波形对填充壳体基座，每个波形对包括主波形和次波形，其中主波形引起次波形; 通过从情况基中选择波形对来合成已知目标波形s（t），使得表达式‖s（t）-g‖2最小化，其中g是次级波形; 并且基于正态分布对每个波形对的主波形进行突变，直到∥s（t）-g'‖2<‖s（t）-g‖2其中g'是由突变主波形引起的新的次级波形。

3. 发明授权

US08768869B1 BRIAN: a basic regimen for intelligent analysis using networks 有权
标题翻译： BRIAN：使用网络进行智能分析的基本方案
公开(公告)号：US08768869B1
公开(公告)日：2014-07-01
申请号：US13451348
申请日：2012-04-19
申请人： Stuart Harvey Rubin
发明人： Stuart Harvey Rubin
IPC分类号： G06F17/00
CPC分类号： G06N5/04
摘要： A method for machine-learning by analogy, comprising: providing a network of component computer systems, each component having sequential layers, each layer having parallel segments, each segment comprises a processor and a memory store, each memory store is configured to store a domain-specific case base, which is defined by a situation-action pair; stochastically transforming cases in each case base through automatic deterministic generalization and analogy when the corresponding processor is in a dream mode to create transformed cases; providing a user-entered situation; modifying the user-entered situation by expanding its contextual mnemonics; searching each case base for cases and transformed cases that include contextual subsets of the modified, user-entered situation; for a given case base, mapping the modified, user-entered situation to a matched action within the given case base; creating a new case comprising the user-entered situation and the matched action.
摘要翻译：一种通过类比机器学习的方法，包括：提供组件计算机系统的网络，每个组件具有顺序层，每个层具有并行段，每个段包括处理器和存储器存储，每个存储器存储器被配置为存储域特定病例基，由情绪动作对定义; 当相应的处理器处于梦想模式以创建转换的案例时，通过自动确定性泛化和类比随机变换每个案例中的案例; 提供用户输入的情况; 通过扩展其语境助记符来修改用户输入的情况; 搜索每个案例库的案例和转换案例，其中包括修改的，用户输入的情况的上下文子集; 对于给定的案例库，将修改后的用户输入的情况映射到给定案例库中的匹配操作; 创建包括用户输入的情况和匹配动作的新情况。

4. 发明授权

US07840506B1 System and method for geodesic data mining 有权
标题翻译：测地数据挖掘的系统和方法
公开(公告)号：US07840506B1
公开(公告)日：2010-11-23
申请号：US11971393
申请日：2008-01-09
申请人： Stuart Harvey Rubin
发明人： Stuart Harvey Rubin
IPC分类号： G06F15/18
CPC分类号： G06F17/30539
摘要： In various embodiments, geodesic mining systems and methods are disclosed. For example, a method for forming and adapting a geodesic adaptive network may include embedding a set of rules into a two-dimensional adaptive network having N-rows and M-columns with rule independent variables embedded in a first column of the two-dimensional adaptive network and rule dependent variables embedded in the last column of the two-dimensional adaptive network, where N and M are positive integers greater than two, and repetitively selecting a pair of rows of the two-dimensional adaptive network having common dependent attributes using a random process, then adapting the two-dimensional adaptive network based on the selected pair of rows.
摘要翻译：在各种实施例中，公开了测地采矿系统和方法。例如，用于形成和适应测地自适应网络的方法可以包括将一组规则嵌入到具有N行和M列的二维自适应网络中，其中规则独立变量嵌入在二维自适应的第一列中嵌入在二维自适应网络的最后一列中的网络和规则相关变量，其中N和M是大于2的正整数，并且使用随机的重复地选择具有共同依赖属性的二维自适应网络的一对行过程，然后基于所选择的一对行来适配二维自适应网络。

5. 发明授权

US08117147B1 Type 4 KASER (knowledge amplification by structured expert randomization) providing case-based inductive and analogical reasoning 有权
标题翻译：提供基于案例的归纳和类比推理的4型KASER（通过结构化专家随机化的知识扩增）
公开(公告)号：US08117147B1
公开(公告)日：2012-02-14
申请号：US12430224
申请日：2009-04-27
申请人： Stuart Harvey Rubin
发明人： Stuart Harvey Rubin
IPC分类号： G06F17/00 , G06N5/02
CPC分类号： G06N5/04
摘要： A method for reaching conclusions from stratified knowledge statements (SKSs) comprising: storing a list of SKSs in at least one memory store on a computer, wherein the list of SKSs is composed of cases, generalizations, and analogs, wherein cases are the most valid, and analogs are the least valid of the SKSs; creating a new generalization based on one of the SKSs; redefining a user-validated generalization as a new case; moving the validated new generalization to the logical head of the case list; expunging the new generalization from the list if the new generalization contradicts an existing case creating with the computer a new analog based on three of the SKSs; redefining a user-validated analog as a new case; moving the new case to the logical head of the case list; and expunging the new analog from the list if the new analog contradicts an existing case, or generalization.
摘要翻译：一种用于从分层知识语句（SKS）得出结论的方法，包括：将SKS列表存储在计算机上的至少一个存储器存储器中，其中所述SKS列表由案例，概括和类比组成，其中情况是最有效的，而类似物是SKS中最不有效的; 基于SKS之一创建新的泛化; 将用户验证的泛化重新定义为新案例; 将验证的新概括转移到案例列表的逻辑头; 如果新的泛化与现有案例相冲突，则将新的泛化从列表中删除，与计算机一起创建基于三个SKS的新模拟; 将用户验证的模拟重新定义为新案例; 将新案件移交到案件清单的逻辑头; 如果新的模拟与现有案例或泛化相矛盾，则从列表中删除新的模拟数据。

6. 发明授权

US08073804B1 System and method for type 2 KASER (Knowledge Amplification by Structured Expert Randomization) 有权
标题翻译： 2型KASER的系统和方法（结构化专家随机化的知识扩增）
公开(公告)号：US08073804B1
公开(公告)日：2011-12-06
申请号：US12390633
申请日：2009-02-23
申请人： Stuart Harvey Rubin
发明人： Stuart Harvey Rubin
IPC分类号： G06F17/00 , G06N5/02
CPC分类号： G06N5/04
摘要： A KASER (Knowledge Amplification by Structured Expert Randomization) engine reaches conclusions in a semantic format or which take the form of a series of rules. The conclusions are parsed into an array structure having a hierarchical order of validity. A set of inserted rules is received as an initial rules array and are configured so that an antecedent comprises a non-empty, sorted set and a consequent comprises a non-empty sequence. A hierarchy of validity for the rules is determined and the rules are sorted according to the hierarchy. At least one rule set which optimizes the selection of rules is determined, with the rule set meeting predetermined validity requirements, and the rules are ordered in an order of validity, such as an order based on a maximal length antecedent set as a highest rank.
摘要翻译： KASER（通过结构化专家随机化的知识扩增）引擎以语义格式得出结论，或采取一系列规则的形式。将结论解析成具有层次有序的顺序的阵列结构。接收一组插入的规则作为初始规则阵列，并且被配置为使得先行包括非空的排序集合，并且结果包括非空序列。确定规则的有效性层次，并根据层次结构对规则进行排序。确定优化规则选择的至少一个规则集，其中规则集满足预定的有效性要求，并且按照有效性的顺序排序规则，例如基于设置为最高等级的最大长度前提的顺序。

7. 发明授权

US07899674B1 GUI for the semantic normalization of natural language 有权
标题翻译： GUI自然语言的语义规范化
公开(公告)号：US07899674B1
公开(公告)日：2011-03-01
申请号：US11668831
申请日：2007-01-30
申请人： Stuart Harvey Rubin
发明人： Stuart Harvey Rubin
IPC分类号： G10L21/00
CPC分类号： G10L15/26
摘要： A graphical user interface for a semantic normalizer of natural language comprising: a link to a preference menu, in which a user may set the semantic normalizer to operate in a predictive or learning mode; an input textbox disposed to display user-typed text in a first font color and user-spoken text in a second font color; a semantic echo textbox disposed to display semantically normalized text in a third font color, wherein the first, second, and third font colors are different from each other; graphical buttons that are only enabled when the semantic normalizer is in learning mode, wherein the graphical buttons may be selected by the user after the user has been prompted by the semantic normalizer to verify the accuracy of the semantically normalized text, the graphical buttons comprising a “Yes” button, a “No” button, and an “Enter Correction” button.
摘要翻译：一种用于自然语言的语义规范化器的图形用户界面，包括：到偏好菜单的链接，其中用户可以将语义规范化器设置为以预测或学习模式操作; 输入文本框，其被设置为以第一字体颜色显示用户类型的文本和以第二字体颜色的用户口头文本; 设置为以第三字体颜色显示语义规范化文本的语义回声文本框，其中所述第一，第二和第三字体颜色彼此不同; 图形按钮仅在语义规范化器处于学习模式时启用，其中图形按钮可以在用户被语义规范化器提示以验证语义规范化文本的准确性之后由用户选择，图形按钮包括 “是”按钮，“否”按钮和“输入校正”按钮。

8. 发明授权

US07840518B1 System and method for object recognition utilizing fusion of multi-system probabalistic output 有权
标题翻译：利用多系统概率输出融合的对象识别系统和方法
公开(公告)号：US07840518B1
公开(公告)日：2010-11-23
申请号：US11849428
申请日：2007-09-04
申请人： Stuart Harvey Rubin
发明人： Stuart Harvey Rubin
IPC分类号： G06N5/00
CPC分类号： G06N5/025
摘要： A method for object recognition includes generating a set of rules, using multiple systems to recognize a target object, applying the set of rules to a set of responses to determine an output, and displaying the output to a user. Each rule contains predicates and a consequent, each predicate comprising a rule token identifier and a rule probability of recognition. The rule token identifiers are generated from multiple systems. Each rule token identifier represents a system recognized object. Each rule is derived by associating a range of rule probabilities of recognition for one or more rule token identifiers to a known object. The range of rule probabilities of recognition is determined by at least one system and by combining multiple rule probabilities of recognition. Each system produces a response having a response token identifier and a response probability of recognition. Responses are combined to form the sets of responses.
摘要翻译：用于对象识别的方法包括生成一组规则，使用多个系统来识别目标对象，将该组规则应用于一组响应以确定输出，以及向用户显示输出。每个规则包含谓词和结果，每个谓词包括规则令牌标识符和规则识别概率。规则令牌标识符是从多个系统生成的。每个规则令牌标识符表示系统识别的对象。通过将一个或多个规则令牌标识符的识别的规则概率的范围与已知对象相关联来导出每个规则。识别的规则概率的范围由至少一个系统确定并且通过组合多个识别的规则概率。每个系统产生具有响应标记标识符和识别响应概率的响应。回应结合起来形成一套回应。

9. 发明申请

US20150178636A1 System and Method for Mining Large, Diverse, Distributed, and Heterogeneous Datasets 有权
标题翻译：用于挖掘大量，多样化，分布式和异构数据集的系统和方法
公开(公告)号：US20150178636A1
公开(公告)日：2015-06-25
申请号：US14316439
申请日：2014-06-26
申请人： Stuart Harvey Rubin
发明人： Stuart Harvey Rubin
IPC分类号： G06N7/02 , G06N5/02
CPC分类号： G06N7/02 , G06N5/025 , G06N5/048
摘要： A method for directed mining of a heterogeneous dataset with a computer comprising: populating a rule base with known rules, wherein each rule has a context and a situation; populating a case base with known cases, wherein each case has a context and a situation, and wherein the case base is partitioned from the rule base; ascribing a natural language semantics to predicates of the known cases and rules; randomly transforming the known rules and the known cases to form new rules by extracting a maximum number of common predicates; segmenting the rules and the cases on the basis of shared predicates without making distinction between context and situation predicates; abducing new knowledge from the dataset by fuzzily matching the context of a new rule to a situation the new rule does not cover; and issuing a query to a user to supply missing predicates of the fuzzy match.
摘要翻译：一种用计算机定向挖掘异构数据集的方法，包括：用已知规则填充规则库，其中每个规则具有上下文和情况; 使用已知情况填充案例库，其中每种情况都具有上下文和情况，并且其中所述案例库与所述规则库进行分区; 将自然语言语义归结为已知案例和规则的谓词; 通过提取最大数量的公共谓词，随机变换已知规则和已知案例以形成新规则; 基于共享谓词分割规则和案例，而不区分语境和情境谓词; 通过将新规则的上下文模糊匹配到新规则不覆盖的情况，从数据集中吸取新知识; 并向用户发出查询以提供模糊匹配的缺失谓词。

10. 发明授权

US08250022B1 Type 5 knowledge amplification by structured expert randomization (KASER) 有权
标题翻译：结构化专家随机化（KASER）的5型知识扩增
公开(公告)号：US08250022B1
公开(公告)日：2012-08-21
申请号：US12652215
申请日：2010-01-05
申请人： Stuart Harvey Rubin
发明人： Stuart Harvey Rubin
IPC分类号： G06F17/00 , G06N5/04
CPC分类号： G06N99/005
摘要： A new production generation method comprising: a.) storing a list of productions on a memory store on a computer, wherein each production comprises a context and a consequent, each context and consequent comprising at least one feature; b.) searching the production list for productions with contexts that match a user-provided context; and c.) if no context-matching production is found, i.) randomly selecting a sub-set of features from the user-provided context, ii.) selecting from the production list by uniform chance a first production with a context that matches the selected feature sub-set, iii.) substituting the feature or feature sub-set with the consequent of the first production to create a first feature set, iv.) replacing features in the first feature set as specified by predefined rules to create a new feature set, and v.) displaying a new production consisting of the user-provided context and the new feature set.
摘要翻译：一种新的生产生成方法，包括：a）将计算机上的存储列表存储在计算机的存储器存储器中，其中每个制作包括上下文和后续的每个上下文，并且包括至少一个特征; b。）使用与用户提供的上下文匹配的上下文来搜索生产列表中的生产; 和c）如果没有找到上下文匹配生产，i。）从用户提供的上下文中随机选择特征的子集，ii）通过统一的机会从生产列表中选择具有匹配的上下文的第一次生产所选择的特征子集，iii。）用第一次生产的结果代替特征或特征子集以创建第一特征集合，iv）替换由预定义规则指定的第一特征集中的特征，以创建新功能集和v。）显示由用户提供的上下文和新功能集组成的新制作。

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