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：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：US20080140594A1

公开(公告)日：2008-06-12

申请号：US12021511

申请日：2008-01-29

Applicant: Pascal TANNHOF ,  Ghislain Imbert De Tremiolles

Inventor： Pascal TANNHOF ,  Ghislain Imbert De Tremiolles

IPC: G06F15/18

CPC classification number: G06T3/4046

Abstract: An artificial neural network (ANN) based system that is adapted to process an input pattern to generate an output pattern related thereto having a different number of components than the input pattern. The system (26) is comprised of an ANN (27) and a memory (28), such as a DRAM memory, that are serially connected. The input pattern (23) is applied to a processor (22), where it can be processed or not (the most general case), before it is applied to the ANN and stored therein as a prototype (if learned). A category is associated with each stored prototype. The processor computes the coefficients that allow the determination of the estimated values of the output pattern, these coefficients are the components of a so-called intermediate pattern (24). Assuming the ANN has already learned a number of input patterns, when a new input pattern is presented to the ANN in the recognition phase, the category of the closest prototype is output therefrom and is used as a pointer to the memory. In turn, the memory outputs the corresponding intermediate pattern. The input pattern and the intermediate pattern are applied to the processor to construct the output pattern (25) using the coefficients. Typically, the input pattern is a block of pixels in the field of scaling images.

Abstract translation: 一种基于人造神经网络（ANN）的系统，其适于处理输入模式以产生与其相关的输出模式，该输出模式具有与输入模式不同数量的分量。 系统（26）由串联连接的ANN（27）和存储器（28）（诸如DRAM存储器）组成。 将输入模式（23）应用于处理器（22），在处理器（22）被应用于ANN并作为原型存储（如果被学习）之前）处理器（22），其可被处理（最常见的情况））。 类别与每个存储的原型相关联。 处理器计算允许确定输出图案的估计值的系数，这些系数是所谓的中间图案的分量（24）。 假设ANN已经学习了许多输入模式，当在识别阶段向ANN呈现新的输入模式时，最近的原型的类别从其输出并被用作指向存储器的指针。 反过来，存储器输出相应的中间模式。 将输入图案和中间图案应用于处理器，以使用系数构造输出图案（25）。 通常，输入图案是缩放图像领域的像素块。

公开(公告)号：US07352918B2

公开(公告)日：2008-04-01

申请号：US10321166

申请日：2002-12-17

Applicant: Pascal Tannhof ,  Ghislain Imbert De Tremiolles

Inventor： Pascal Tannhof ,  Ghislain Imbert De Tremiolles

IPC: G06K9/32

CPC classification number: G06T3/4046

Abstract: An artificial neural network (ANN) based system that is adapted to process an input pattern to generate an output pattern related thereto having a different number of components than the input pattern. The system (26) is comprised of an ANN (27) and a memory (28), such as a DRAM memory, that are serially connected. The input pattern (23) is applied to a processor (22), where it can be processed or not (the most general case), before it is applied to the ANN and stored therein as a prototype (if learned). A category is associated with each stored prototype. The processor computes the coefficients that allow the determination of the estimated values of the output pattern, these coefficients are the components of a so-called intermediate pattern (24). Assuming the ANN has already learned a number of input patterns, when a new input pattern is presented to the ANN in the recognition phase, the category of the closest prototype is output therefrom and is used as a pointer to the memory. In turn, the memory outputs the corresponding intermediate pattern. The input pattern and the intermediate pattern are applied to the processor to construct the output pattern (25) using the coefficients. Typically, the input pattern is a block of pixels in the field of scaling images.

Abstract translation: 一种基于人造神经网络（ANN）的系统，其适于处理输入模式以产生与其相关的输出模式，该输出模式具有与输入模式不同数量的分量。 系统（26）由串联连接的ANN（27）和存储器（28）（诸如DRAM存储器）组成。 将输入模式（23）应用于处理器（22），在处理器（22）被应用于ANN并作为原型存储（如果被学习）之前）处理器（22），其可被处理（最常见的情况））。 类别与每个存储的原型相关联。 处理器计算允许确定输出图案的估计值的系数，这些系数是所谓的中间图案的分量（24）。 假设ANN已经学习了许多输入模式，当在识别阶段向ANN呈现新的输入模式时，最近的原型的类别从其输出并被用作指向存储器的指针。 反过来，存储器输出相应的中间模式。 将输入图案和中间图案应用于处理器，以使用系数构造输出图案（25）。 通常，输入图案是缩放图像领域的像素块。

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：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：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。

公开(公告)号：US20100058176A1

公开(公告)日：2010-03-04

申请号：US12088094

申请日：2006-07-25

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 ,  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 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。