公开(公告)号：US20120331379A1

公开(公告)日：2012-12-27

申请号：US13605057

申请日：2012-09-06

Applicant: Fernando Incertis Carro ,  Rita Asuncion Jarillo Sanchez ,  Ghislain Imbert De Tremiolles

Inventor： Fernando Incertis Carro ,  Rita Asuncion Jarillo Sanchez ,  Ghislain Imbert De Tremiolles

IPC: G06F17/21

CPC classification number: G06F17/218 ,  G06F3/04812 ,  G06F3/04842 ,  G06F9/543 ,  G06F17/2235 ,  G06F17/2247 ,  G06F17/24 ,  G06F17/241

Abstract: A method and system for processing electronic documents. A temporary computer object is created. An address of a first electronic document is obtained. A first tag, a second tag, and the address of the first electronic document are copied into a header of the created temporary computer object. Selected text from the first electronic document is obtained. The first and second tag respectively mark the beginning and the end of the header. The address of the first electronic document is disposed between the first and second tags. The selected text and a third tag are copied into the created temporary computer object. The third tag marks the end of the created temporary computer object. The selected text is disposed between the header of the created temporary computer object and the third tag. The created temporary computer object is stored in a second electronic document.

Abstract translation: 一种处理电子文件的方法和系统。 创建一个临时计算机对象。 获得第一电子文档的地址。 将第一标签，第二标签和第一电子文档的地址复制到所创建的临时计算机对象的标题中。 获得了第一个电子文档中的选定文本。 第一个和第二个标签分别标记标题的开头和结尾。 第一电子文档的地址设置在第一和第二标签之间。 所选文本和第三个标签被复制到创建的临时计算机对象中。 第三个标记标记创建的临时计算机对象的结束。 所选择的文本被放置在创建的临时计算机对象的标题和第三标记之间。 创建的临时计算机对象被存储在第二电子文档中。

公开(公告)号：US08027942B2

公开(公告)日：2011-09-27

申请号：US09951786

申请日：2001-09-12

Applicant: Ghislain Imbert de Tremiolles ,  Pascal Tannhof

Inventor： Ghislain Imbert de Tremiolles ,  Pascal Tannhof

IPC: G06F15/18 ,  G06E1/00 ,  G06E3/00 ,  G06G7/00

CPC classification number: G06K9/6215 ,  G06K9/6276 ,  G06N3/063

Abstract: The method and circuits of the present invention aim to associate a complex component operator (CC_op) to each component of an input pattern presented to an input space mapping algorithm based artificial neural network (ANN) during the distance evaluation process. A complex operator consists in the description of a function and a set of parameters attached thereto. The function is a mathematical entity (either a logic operator e.g. match(Ai,Bi), abs(Ai−Bi), . . . or an arithmetic operator, e.g. >,

Abstract translation: 本发明的方法和电路旨在将复杂分量算子（CC_op）与在距离评估过程中呈现给基于输入空间映射算法的人造神经网络（ANN）的输入模式的每个分量相关联。 复杂的运算符在于对附加到其上的函数和一组参数的描述。 该函数是数学实体（逻辑运算符，例如匹配（Ai，Bi），abs（Ai-Bi），...或算术运算符，例如>，<，...。）或一组软件指令 有条件。 在第一实施例中，ANN被提供有存储所有CC_ops的ANN的所有神经元共用的全局存储器。 在另一个实施例中，CC_ops的集合存储在神经元的原型存储器中，使得全局存储器不再是物理上必需的。 根据本发明，存储的原型的组件现在可以指定不同性质的对象。 另外，这两种实现都显着减少神经元所需的组件数量，从而在ANN集成在硅芯片中时节省空间。

公开(公告)号：US07254565B2

公开(公告)日：2007-08-07

申请号：US10137969

申请日：2002-05-03

Applicant: Ghislain Imbert De Tremiolles ,  Pascal Tannhof

Inventor： Ghislain Imbert De Tremiolles ,  Pascal Tannhof

IPC: G06E1/00 ,  G06E3/00 ,  G06F15/18 ,  G06G7/00 ,  G06N3/00 ,  G06N3/08 ,  G06N3/04

CPC classification number: G06K9/6276 ,  G06N3/063

Abstract: An improved Artificial Neural Network (ANN) is disclosed that comprises a conventional ANN, a database block, and a compare and update circuit. The conventional ANN is formed by a plurality of neurons, each neuron having a prototype memory dedicated to store a prototype and a distance evaluator to evaluate the distance between the input pattern presented to the ANN and the prototype stored therein. The database block has: all the prototypes arranged in slices, each slice being capable to store up to a maximum number of prototypes; the input patterns or queries to be presented to the ANN; and the distances resulting of the evaluation performed during the recognition/classification phase. The compare and update circuit compares the distance with the distance previously found for the same input pattern updates or not the distance previously stored.

Abstract translation: 公开了一种改进的人造神经网络（ANN），其包括常规ANN，数据库块以及比较和更新电路。 常规ANN由多个神经元形成，每个神经元具有专用于存储原型的原型存储器和距离评估器，以评估呈现给ANN的输入模式与存储在其中的原型之间的距离。 数据库块具有：所有原型以切片排列，每个切片能够存储最多数量的原型; 要呈现给ANN的输入模式或查询; 以及在识别/分类阶段期间进行评估的距离。 比较和更新电路将距离与先前发现的相同输入模式更新的距离进行比较，或将之前存储的距离进行比较。

公开(公告)号：US06347309B1

公开(公告)日：2002-02-12

申请号：US09223478

申请日：1998-12-30

Applicant: Ghislain Imbert De Tremiolles ,  Pascal Tannhof

Inventor： Ghislain Imbert De Tremiolles ,  Pascal Tannhof

IPC: G06N306

CPC classification number: G06K9/6271 ,  G06N3/063

Abstract: The improved neural network of the present invention results from the combination of a dedicated logic block with a conventional neural network based upon a mapping of the input space usually employed to classify an input data by computing the distance between said input data and prototypes memorized therein. The improved neural network is able to classify an input data, for instance, represented by a vector A even when some of its components are noisy or unknown during either the learning or the recognition phase. To that end, influence fields of various and different shapes are created for each neuron of the conventional neural network. The logic block transforms at least some of the n components (A1, . . . , An) of the input vector A into the m components (V1, . . . , Vm) of a network input vector V according to a linear or non-linear transform function F. In turn, vector V is applied as the input data to said conventional neural network. The transform function F is such that certain components of vector V are not modified, e.g. Vk=Aj, while other components are transformed as mentioned above, e.g. Vi=Fi(A1, . . . , An). In addition, one (or more) component of vector V can be used to compensate an offset that is present in the distance evaluation of vector V. Because, the logic block is placed in front of the said conventional neural network any modification thereof is avoided.

Abstract translation: 本发明的改进的神经网络是基于通常用于通过计算所述输入数据与其中存储的原型之间的距离来对输入数据进行分类的输入空间的映射，将专用逻辑块与传统神经网络的组合。 改进的神经网络能够对例如由向量A表示的输入数据进行分类，即使在学习或识别阶段期间，其一些组件是噪声或未知的。 为此，为传统神经网络的每个神经元创建各种不同形状的影响场。 逻辑块根据线性或非线性将输入矢量A的n个分量（A1，...，An）中的至少一些变换成网络输入矢量V的m个分量（V1，...，Vm） 然后将矢量V作为输入数据施加到所述常规神经网络。 变换函数F使得向量V的某些分量不被修改，例如， Vk = Aj，而其它组分如上所述被转化，例如。 Vi = Fi（A1，...，An）。 另外，矢量V的一个（或多个）分量可以用于补偿矢量V的距离评估中存在的偏移。因为逻辑块被放置在所述传统神经网络的前面，所以避免了其任何修改 。

公开(公告)号：US06782373B2

公开(公告)日：2004-08-24

申请号：US09682035

申请日：2001-07-12

Applicant: Ghislain Imbert de Tremiolles ,  Pascal Tannhof

Inventor： Ghislain Imbert de Tremiolles ,  Pascal Tannhof

IPC: G06N302

CPC classification number: G06K9/6215 ,  G06K9/6276 ,  G06N3/063

Abstract: The method and circuits of the present invention aim to associate a norm to each component of an input pattern presented to an input space mapping algorithm based artificial neural network (ANN) during the distance evaluation process. The set of norms, referred to as the “component” norms is memorized in specific memorization means in the ANN. In a first embodiment, the ANN is provided with a global memory, common for all the neurons of the ANN, that memorizes all the component norms. For each component of the input pattern, all the neurons perform the elementary (or partial) distance calculation with the corresponding prototype components stored therein during the distance evaluation process using the associated component norm. The distance elementary calculations are then combined using a “distance” norm to determine the final distance between the input pattern and the prototypes stored in the neurons. In another embodiment, the set of component norms is memorized in the neurons themselves in the prototype memorization means, so that the global memory is no longer physically necessary. This implementation allows to significantly optimize the consumed silicon area when the ANN is integrated in a silicon chip.

Abstract translation: 本发明的方法和电路旨在将距离评估过程中给出的输入模式的每个分量与基于输入空间映射算法的人造神经网络（ANN）相关联。 被称为“组件”规范的一套规范被记录在ANN中的具体记忆手段中。 在第一实施例中，ANN被提供有存储ANN的所有神经元的全局存储器，其存储所有的分量规范。 对于输入模式的每个分量，所有神经元使用相关的分量范数在距离评估过程中，使用存储在其中的对应的原型分量执行基本（或部分）距离计算。 然后使用“距离”范数组合距离基本计算，以确定输入模式和存储在神经元中的原型之间的最终距离。 在另一个实施例中，组件规范的集合被存储在原型存储装置中的神经元本身中，使得全局存储器不再是物理上必需的。 当ANN集成在硅芯片中时，该实现允许显着优化消耗的硅面积。

公开(公告)号：US06748405B2

公开(公告)日：2004-06-08

申请号：US09754639

申请日：2001-01-04

Applicant: Ghislain Imbert de Tremiolles ,  Didier Louis ,  Pascal Tannhof

Inventor： Ghislain Imbert de Tremiolles ,  Didier Louis ,  Pascal Tannhof

IPC: G06F700

CPC classification number: G06F7/544 ,  G06F7/22 ,  G06F9/30021

Abstract: In the search of the minimum value among a set of p Numbers coded on q bits, each Number is split into K sub-values coded on n bits (q>=K×n). Parameter K thus assigns a rank to each sub-value so that K slices of bits are formed wherein each slice is composed of sub-values of the same rank. Each sub-value is then encoded on m bits (m>n) using a “thermometric” coding technique. A parallel search is then performed on the first slice of encoded sub-values (MSBs) to determine the minimum sub-value of that slice. All the Numbers associated to sub-values that are greater than the minimum sub-value that has been evaluated are deselected. The evaluation process is continued the same way until the last slice (LSBs) has been processed. At the end of the evaluation process, the Number which remains selected has the minimum value. The response time (i.e. the number of processing steps) now only depends upon the number K of sub-values in which the Numbers have been split up. The method applies to search the maximum as well.

Abstract translation: 在搜索在q位上编码的p个编码集合中的最小值时，每个数字被分割为以n位编码的K个子值（q> = Kxn）。 因此，参数K为每个子值分配等级，使得形成K个比特片，其中每个切片由相同等级的子值组成。 然后使用“温度测量”编码技术，以m位（m> n）对每个子值进行编码。 然后对编码子值（MSB）的第一切片执行并行搜索以确定该切片的最小子值。 所有与子值相关联的数字大于已评估的最小子值的数字将被取消选择。 评估过程以相同的方式继续，直到最后一个切片（LSB）被处理。 在评估过程结束时，保持选中的数字具有最小值。 响应时间（即处理步骤的数目）现在只取决于数字已被分割的子值的数量K. 该方法也适用于搜索最大值。

公开(公告)号：US08332747B2

公开(公告)日：2012-12-11

申请号：US12088094

申请日：2006-07-25

Applicant: Fernando Incertis Carro ,  Rita Asuncion Jarillo Sanchez, legal representative ,  Ghislain Imbert De Tremiolles

Inventor： Fernando Incertis Carro ,  Ghislain Imbert De Tremiolles

IPC: G06N3/00

CPC classification number: G06F17/218 ,  G06F3/04812 ,  G06F3/04842 ,  G06F9/543 ,  G06F17/2235 ,  G06F17/2247 ,  G06F17/24 ,  G06F17/241

Abstract: A method and systems for copying textual objects from source documents into an object document, and for tagging, linking and processing said copied textual portions, including the disclosure of a new type of hyperlinking mechanism, for enabling to identify and trace the sources and the authorship of said copied textual portions or of all textual sub-portions or fragments of text that could be generated from said copied textual portions by editing the object document. The invention can be implemented by means of software implementing the disclosed system and method running on word-processors and web browsers.

Abstract translation: 一种用于将文本对象从源文档复制到对象文档中的方法和系统，以及用于标记，链接和处理所述复制的文本部分，包括公开新型的超链接机制，以便能够识别和追踪源和作者身份 所述复制的文本部分或所有文本子部分或文本片段可以通过编辑对象文档从所述复制的文本部分生成。 本发明可以通过软件来实现，该软件实现了在文字处理器和web浏览器上运行的所公开的系统和方法。

公开(公告)号：US07133854B2

公开(公告)日：2006-11-07

申请号：US10014166

申请日：2001-12-11

Applicant: Ghislain Imbert De Tremiolles ,  Pascal Tannhof

Inventor： Ghislain Imbert De Tremiolles ,  Pascal Tannhof

IPC: G10L19/12 ,  G06E1/00 ,  G06E3/00 ,  G06F15/18 ,  G06G7/00 ,  G06F15/16

CPC classification number: G06T9/00

Abstract: Let us consider a plurality of input patterns having an essential characteristic in common but which differ on at least one parameter (this parameter modifies the input pattern in some extent but not this essential characteristic for a specific application). During the learning phase, each input pattern is normalized in a normalizer, before it is presented to a classifier. If not recognized, it is learned, i.e. the normalized pattern is stored in the classifier as a prototype with its category associated thereto. From a predetermined reference value of that parameter, the normalizer computes an element related to said parameter which allows to set the normalized pattern from the input pattern and vice versa to retrieve the input pattern from the normalized pattern. As a result, all these input patterns are represented by the same normalized pattern. The above method and circuits allow to reduce the number of required prototypes in the classifier, improving thereby its response quality.

公开(公告)号：US06983265B2

公开(公告)日：2006-01-03

申请号：US10316250

申请日：2002-12-10

Applicant: Pascal Tannhof ,  Ghislain Imbert de Tremiolles

Inventor： Pascal Tannhof ,  Ghislain Imbert de Tremiolles

IPC: G06N3/06

CPC classification number: G06K9/6276 ,  G06N3/04

Abstract: A method is described to improve the data transfer rate between a personal computer or a host computer and a neural network implemented in hardware by merging a plurality of input patterns into a single input pattern configured to globally represent the set of input patterns. A base consolidated vector (U′*n) representing the input pattern is defined to describe all the vectors (Un, . . . , Un+6) representing the input patterns derived thereof (U′n, . . . , U′n+6) by combining components having fixed and ‘don't care’ values. The base consolidated vector is provided only once with all the components of the vectors. An artificial neural network (ANN) is then configured as a combination of sub-networks operating in parallel. In order to compute the distances with an adequate number of components, the prototypes are to include also components having a definite value and ‘don't care’ conditions. During the learning phase, the consolidated vectors are stored as prototypes. During the recognition phase, when a new base consolidated vector is provided to ANN, each sub-network analyses a portion thereof After computing all the distances, they are sorted one sub-network at a time to obtain the distances associated to each vector.

Abstract translation: 描述了一种方法，以通过将多个输入模式合并为被配置为全局地表示该组输入模式的单个输入模式来改善个人计算机或主机计算机与硬件中实现的神经网络之间的数据传输速率。 定义表示输入模式的基本合并向量（U'* N n N）来描述所有向量（U N，N，N，N，N） 代表其导出的输入模式（U'，N“，...，U”n + 6）的组合，通过组合具有固定的“不” 护理价值观。 基本合并向量仅与向量的所有分量一起提供。 然后将人造神经网络（ANN）配置为并行操作的子网络的组合。 为了用足够数量的组件计算距离，原型还包括具有确定值和“无关紧要”条件的组件。 在学习阶段，合并的向量存储为原型。 在识别阶段，当向ANN提供新的基本合并向量时，每个子网络分析其一部分。在计算所有距离之后，它们一次对一个子网进行排序，以获得与每个向量相关联的距离。

公开(公告)号：US09292366B2

公开(公告)日：2016-03-22

申请号：US13605057

申请日：2012-09-06

Applicant: Fernando Incertis Carro ,  Rita Asuncion Jarillo Sanchez ,  Ghislain Imbert De Tremiolles

Inventor： Fernando Incertis Carro ,  Ghislain Imbert De Tremiolles

IPC: G06F17/00 ,  G06F9/54 ,  G06F17/21 ,  G06F17/22 ,  G06F17/24

CPC classification number: G06F17/218 ,  G06F3/04812 ,  G06F3/04842 ,  G06F9/543 ,  G06F17/2235 ,  G06F17/2247 ,  G06F17/24 ,  G06F17/241

Abstract: A method and system for processing electronic documents. A temporary computer object is created. An address of a first electronic document is obtained. A first tag, a second tag, and the address of the first electronic document are copied into a header of the created temporary computer object. Selected text from the first electronic document is obtained.The first and second tag respectively mark the beginning and the end of the header. The address of the first electronic document is disposed between the first and second tags. The selected text and a third tag are copied into the created temporary computer object. The third tag marks the end of the created temporary computer object. The selected text is disposed between the header of the created temporary computer object and the third tag. The created temporary computer object is stored in a second electronic document.

Abstract translation: 一种处理电子文件的方法和系统。 创建一个临时计算机对象。 获得第一电子文档的地址。 将第一标签，第二标签和第一电子文档的地址复制到所创建的临时计算机对象的标题中。 获得了第一个电子文档中的选定文本。 第一个和第二个标签分别标记标题的开头和结尾。 第一电子文档的地址设置在第一和第二标签之间。 所选文本和第三个标签被复制到创建的临时计算机对象中。 第三个标记标记创建的临时计算机对象的结束。 所选择的文本被放置在创建的临时计算机对象的标题和第三标记之间。 创建的临时计算机对象被存储在第二电子文档中。