公开(公告)号：US20150178337A1

公开(公告)日：2015-06-25

申请号：US14379624

申请日：2012-02-22

申请人： Minna Hellstrom ,  Mikko Lonnfors ,  Eki Monni ,  Istvan Beszteri ,  Mikko Terho

发明人： Minna Hellstrom ,  Mikko Lonnfors ,  Eki Monni ,  Istvan Beszteri ,  Mikko Terho

IPC分类号： G06F17/30 ,  G06N5/00

CPC分类号： G06F17/30324 ,  G06F17/30867 ,  G06F17/30899 ,  G06N5/00 ,  G06Q10/10 ,  G06Q30/02

摘要： The invention relates to predictive browsing. A set of words for use with an experience matrix are formed, wherein the words are descriptive of a context of a system such as a current web page, and wherein said experience matrix comprises sparse vectors associated with words. At least a part of at least one sparse vector of said experience matrix is accessed to form a prediction output, and suggestions of web pages are provided to a user in response to said prediction output.

摘要翻译： 本发明涉及预测浏览。 形成与经验矩阵一起使用的一组单词，其中所述单词描述诸如当前网页的系统的上下文，并且其中所述体验矩阵包括与单词相关联的稀疏向量。 访问所述经验矩阵的至少一个稀疏向量的至少一部分以形成预测输出，并且响应于所述预测输出向用户提供网页的建议。

公开(公告)号：US09811585B2

公开(公告)日：2017-11-07

申请号：US14379574

申请日：2012-02-22

申请人： Minna Hellstrom ,  Mikko Lonnfors ,  Eki Monni ,  Istvan Beszteri ,  Mikko Terho ,  Leo Karkkainen

发明人： Minna Hellstrom ,  Mikko Lonnfors ,  Eki Monni ,  Istvan Beszteri ,  Mikko Terho ,  Leo Karkkainen

IPC分类号： G06F15/18 ,  G06F17/30 ,  G06Q10/10 ,  G06N5/00 ,  G06Q10/04

CPC分类号： G06F17/30663 ,  G06F17/30867 ,  G06N5/003 ,  G06Q10/04 ,  G06Q10/10

摘要： The invention relates to forming a prediction using an experience matrix, a matrix based on sparse vectors such as random index vectors. At least a part of a first experience matrix and at least a part of at least a second experience matrix are caused to be combined (1410) to obtain a combined experience matrix. The experience matrices comprise sparse vectors or essentially similar vectors in nature, and said experience matrices comprise information of at least one system, for example contexts of a system. At least a part of at least one sparse vector of the combined experience matrix is accessed to form a prediction output (1420), and a system is controlled (1430) in response to said prediction output.

公开(公告)号：US10324916B2

公开(公告)日：2019-06-18

申请号：US14379624

申请日：2012-02-22

申请人： Minna Hellstrom ,  Mikko Lonnfors ,  Eki Monni ,  Istvan Beszteri ,  Mikko Terho

发明人： Minna Hellstrom ,  Mikko Lonnfors ,  Eki Monni ,  Istvan Beszteri ,  Mikko Terho

IPC分类号： G06N7/02 ,  G06N7/04 ,  G06N7/06 ,  G06N7/08 ,  G06F16/22 ,  G06F16/957 ,  G06Q30/02 ,  G06Q10/10 ,  G06N5/00 ,  G06F16/9535

摘要： The invention relates to predictive browsing. A set of words for use with an experience matrix are formed, wherein the words are descriptive of a context of a system such as a current web page, and wherein said experience matrix comprises sparse vectors associated with words. At least a part of at least one sparse vector of said experience matrix is accessed to form a prediction output, and suggestions of web pages are provided to a user in response to said prediction output.

公开(公告)号：US09690264B2

公开(公告)日：2017-06-27

申请号：US14376070

申请日：2012-02-22

申请人： Minna Hellstrom ,  Mikko Lonnfors ,  Eki Monni ,  Istvan Beszteri ,  Mikko Terho

发明人： Minna Hellstrom ,  Mikko Lonnfors ,  Eki Monni ,  Istvan Beszteri ,  Mikko Terho

IPC分类号： G06F17/00 ,  G06F17/20 ,  G05B13/02 ,  G06N99/00 ,  G06Q10/10 ,  G06N5/04

CPC分类号： G05B13/026 ,  G06N5/04 ,  G06N99/005 ,  G06Q10/10

摘要： Co-occurrence data representing e.g. preferences and facts observed in a plurality of situations may be stored in a matrix as combinations of high-dimensional sparse vectors. The matrix may be called e.g. as an experience matrix. The data stored in the experience matrix may be subsequently utilized e.g. for predicting a preference of a user in a new situation. A prediction may be determined by a method comprising providing a query comprising one or more query words, accessing the experience matrix containing co-occurrence data stored as vectors of the experience matrix, determining a first auxiliary vector by identifying a vector of the experience matrix associated with a first query word, forming a query vector by using the first auxiliary vector, and determining the prediction by comparing the query vector with the vectors of the experience matrix.

公开(公告)号：US20150081746A1

公开(公告)日：2015-03-19

申请号：US14376082

申请日：2012-08-22

申请人： Minna Hellstrom ,  Mikko Lonnfors ,  Eki Monni ,  Istvan Beszteri ,  Mikko Terho ,  Leo Karkkainen

发明人： Minna Hellstrom ,  Mikko Lonnfors ,  Eki Monni ,  Istvan Beszteri ,  Mikko Terho ,  Leo Karkkainen

IPC分类号： G06F17/30

摘要： Co-occurrence data representing e.g. preferences and facts observed in a plurality of situations may be stored in a matrix as combinations of high-dimensional sparse vectors. The matrix may be called e.g. as an experience matrix. The data stored in the experience matrix may be subsequently utilized e.g. for predicting a preference of a user in a new situation. Co-occurrence data may be stored in the experience matrix may be updated by a method comprising determining a first word based on a state of a system and/or based on a physical quantity detected by a sensor, forming a first word group comprising the first word and a second word, associating the first word and the second word with a common sparse vector, associating the first word words with a first vector of a matrix, associating the second word with a second vector of the matrix, modifying the first vector of the matrix by adding contribution of the common sparse vector to the first vector of the matrix, and modifying the second vector of the matrix by adding contribution of the common sparse vector to the second vector of the matrix.

摘要翻译： 共现数据表示例如。 在多个情况下观察到的偏好和事实可以作为高维稀疏矢量的组合存储在矩阵中。 矩阵可以称为例如。 作为经验矩阵。 存储在体验矩阵中的数据可以随后被使用。 用于在新情况下预测用户的偏好。 共存数据可以存储在经验矩阵中，可以通过包括基于系统的状态确定第一字和/或基于由传感器检测到的物理量来确定第一字的方法来更新，形成包括第一 字和第二字，将所述第一字和所述第二字与公共稀疏矢量相关联，将所述第一字词与矩阵的第一向量相关联，将所述第二词与所述矩阵的第二向量相关联，修改所述第一个向量的第一向量 通过将公共稀疏向量的贡献加到矩阵的第一向量，并且通过将公共稀疏向量的贡献加到矩阵的第二向量来修改矩阵的第二向量来改变矩阵。

公开(公告)号：US10783130B2

公开(公告)日：2020-09-22

申请号：US14376082

申请日：2012-02-22

申请人： Minna Hellstrom ,  Mikko Lonnfors ,  Eki Monni ,  Istvan Beszteri ,  Mikko Terho ,  Leo Karkkainen

发明人： Minna Hellstrom ,  Mikko Lonnfors ,  Eki Monni ,  Istvan Beszteri ,  Mikko Terho ,  Leo Karkkainen

IPC分类号： G06F16/00 ,  G06F16/22 ,  G06Q10/06 ,  G06F16/335 ,  H04M19/04 ,  H04M1/725 ,  G06F40/274 ,  G06F40/284

摘要： Co-occurrence data representing e.g. preferences and facts observed in a plurality of situations may be stored in a matrix as combinations of high-dimensional sparse vectors. The matrix may be called e.g. as an experience matrix. The data stored in the experience matrix may be subsequently utilized e.g. for predicting a preference of a user in a new situation. Co-occurrence data may be stored in the experience matrix may be updated by a method comprising determining a first word based on a state of a system and/or based on a physical quantity detected by a sensor, forming a first word group comprising the first word and a second word, associating the first word and the second word with a common sparse vector, associating the first word words with a first vector of a matrix, associating the second word with a second vector of the matrix, modifying the first vector of the matrix by adding contribution of the common sparse vector to the first vector of the matrix, and modifying the second vector of the matrix by adding contribution of the common sparse vector to the second vector of the matrix.

公开(公告)号：US20150178378A1

公开(公告)日：2015-06-25

申请号：US14379574

申请日：2012-02-22

申请人： Minna Hellstrom ,  Mikko Lonnfors ,  Eki Monni ,  Istvan Beszteri ,  Mikko Terho ,  Leo Karkkainen

发明人： Minna Hellstrom ,  Mikko Lonnfors ,  Eki Monni ,  Istvan Beszteri ,  Mikko Terho ,  Leo Karkkainen

IPC分类号： G06F17/30 ,  G06Q10/04 ,  G06N5/00

CPC分类号： G06F17/30663 ,  G06F17/30867 ,  G06N5/003 ,  G06Q10/04 ,  G06Q10/10

摘要： The invention relates to forming a prediction using an experience matrix, a matrix based on sparse vectors such as random index vectors. At least a part of a first experience matrix and at least a part of at least a second experience matrix are caused to be combined (1410) to obtain a combined experience matrix. The experience matrices comprise sparse vectors or essentially similar vectors in nature, and said experience matrices comprise information of at least one system, for example contexts of a system. At least a part of at least one sparse vector of the combined experience matrix is accessed to form a prediction output (1420), and a system is controlled (1430) in response to said prediction output.

摘要翻译： 本发明涉及使用经验矩阵形成预测，基于诸如随机索引向量的稀疏向量的矩阵。 导致第一经验矩阵和至少第二经验矩阵的至少一部分的至少一部分被组合（1410）以获得组合的经验矩阵。 经验矩阵本质上包括稀疏向量或基本相似的向量，并且所述体验矩阵包括至少一个系统的信息，例如系统的上下文。 访问组合体验矩阵的至少一个稀疏向量的至少一部分以形成预测输出（1420），并响应于所述预测输出控制系统（1430）。

公开(公告)号：US20150100523A1

公开(公告)日：2015-04-09

申请号：US14376070

申请日：2012-02-22

申请人： Minna Hellstrom ,  Mikko Lonnfors ,  Eki Monni ,  Istvan Beszteri ,  Mikko Terho

发明人： Minna Hellstrom ,  Mikko Lonnfors ,  Eki Monni ,  Istvan Beszteri ,  Mikko Terho

IPC分类号： G05B13/02 ,  G06N5/04

CPC分类号： G05B13/026 ,  G06N5/04 ,  G06N99/005 ,  G06Q10/10

摘要： Co-occurrence data representing e.g. preferences and facts observed in a plurality of situations may be stored in a matrix as combinations of high-dimensional sparse vectors. The matrix may be called e.g. as an experience matrix. The data stored in the experience matrix may be subsequently utilized e.g. for predicting a preference of a user in a new situation. A prediction may be determined by a method comprising providing a query comprising one or more query words, accessing the experience matrix containing co-occurrence data stored as vectors of the experience matrix, determining a first auxiliary vector by identifying a vector of the experience matrix associated with a first query word, forming a query vector by using the first auxiliary vector, and determining the prediction by comparing the query vector with the vectors of the experience matrix.

摘要翻译： 共现数据表示例如。 在多个情况下观察到的偏好和事实可以作为高维稀疏矢量的组合存储在矩阵中。 矩阵可以称为例如。 作为经验矩阵。 存储在体验矩阵中的数据可以随后被使用。 用于在新情况下预测用户的偏好。 预测可以通过包括提供包括一个或多个查询词的查询的方法来确定，访问包含存储为经验矩阵的向量的同现数据的体验矩阵，通过识别经验矩阵相关联的向量来确定第一辅助向量 利用第一查询词，通过使用第一辅助向量形成查询向量，并通过将查询向量与经验矩阵的向量进行比较来确定预测。

公开(公告)号：US20080288440A1

公开(公告)日：2008-11-20

申请号：US11749256

申请日：2007-05-16

申请人： Eki Monni

发明人： Eki Monni

IPC分类号： G06F17/30

CPC分类号： H04L12/2809 ,  G06F16/951 ,  H04L2012/2849

摘要： A method including providing a mediator at an access point in a network, the mediator being configured to communication with UPnP devices coupled to the network, detecting a search request from a search engine in the network, using the mediator to search and retrieve content from each UPnP device coupled to the network, the retrieved content being related to the search request, converting the retrieved content into a protocol suitable for the search engine, and passing the retrieved content from the mediator to the search engine.

摘要翻译： 一种方法，包括在网络中的接入点处提供中介器，所述中介器被配置为与耦合到所述网络的UPnP设备进行通信，从所述网络中的搜索引擎检测搜索请求，使用所述中介器来搜索和检索来自每个 UPnP设备耦合到网络，所检索的内容与搜索请求相关，将检索到的内容转换成适合于搜索引擎的协议，并将检索到的内容从中介者传递到搜索引擎。