会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 1. 发明授权
    • Management system of graphic data
    • 图形数据管理系统
    • US4803477A
    • 1989-02-07
    • US942633
    • 1986-12-17
    • Takafumi MiyatakeHitoshi MatsushimaShigeko OhtaniJunichi Higashino
    • Takafumi MiyatakeHitoshi MatsushimaShigeko OhtaniJunichi Higashino
    • G06F17/30G06T1/00G06T9/20G06T11/20G06T11/80G09G1/14
    • G06T9/20G06T11/203
    • A graphic data management system including a segment table in which several kinds of graphic data placed in a multi-dimensional space are stored correspondingly to graphic element numbers, and an index table in which, for each of the cell numbers provided when the multi-dimensional coordinate system is partitioned into predetermined cells, and all the graphic element numbers at least a part of which included in a cell are stored. The graphic data management system carries out the search, addition and deletion referring to a segment table and the index table. The index table includes a cell pointer table in which pointers indicative of the addresses where the graphic element numbers are stored are listed for each cell number, a cell table in which the graphic element number and concatenation pointers which indicate the storing addresses of the subsequent graphic element numbers are listed as pairs, and a space management pointer indicative of the address of a free space area in the cell table.
    • 一种图形数据管理系统,包括其中放置在多维空间中的多种图形数据对应于图形元素编号的段表,以及索引表,其中,对于当多维空间中提供的每个单元格数 坐标系被划分为预定的单元,并且存储包括在单元中的至少一部分的所有图形单元编号。 图形数据管理系统执行搜索,添加和删除,参考段表和索引表。 索引表包括单元指针表,其中针对每个单元号列出了指示存储图形单元编号的地址的指针,其中表示后续图形的存储地址的图形单元编号和级联指针的单元表 元素号被列为对,以及空间管理指针,其指示小区表中的自由空间区域的地址。
    • 2. 发明授权
    • Multi-layer network and learning method therefor
    • 多层网络及其学习方法
    • US5212767A
    • 1993-05-18
    • US625166
    • 1990-12-10
    • Junichi HigashinoHitoshi Matsushima
    • Junichi HigashinoHitoshi Matsushima
    • G06F15/18G06N3/04G06N3/063G06N3/08G06N3/10G06N99/00
    • G06N3/10G06N3/063
    • A multi-layer neural network comprising an input layer, a hidden layer and an output layer and a learning method for such a network are disclosed. A processor belonging to the hidden layer stores both the factors of multiplication or weights of link for a successive layer nearer to the input layer and the factors of multiplication or weights of link for a preceding layer nearer to the output layer. Namely, the weight for a certain connection is doubly stored in processors which are at opposite ends of that connection. Upon forward calculation, the access to the weights for the successive layer among the weights stored in the processors of the hidden layer can be made by the processors independently from each other. Similarly, upon backward calculation, the access to weights for the preceding layer can be made by the processors independently from each other.
    • 公开了一种包括输入层,隐藏层和输出层的多层神经网络和用于这种网络的学习方法。 属于隐藏层的处理器存储更接近于输入层的连续层的相乘因子或链接权重以及更靠近输出层的先前层的乘法因子或链路权重。 也就是说,某个连接的权重被双重存储在处于与该连接相对端的处理器中。 在正向计算时,可以由处理器彼此独立地对存储在隐藏层的处理器中的权重之间对连续层的权重的访问进行。 类似地,在反向计算时,可以由处理器彼此独立地进行对前一层的权重的访问。
    • 3. 发明授权
    • Document storage and retrieval system for storing and retrieving
document image and full text data
    • 用于存储和检索文档图像和全文数据的文档存储和检索系统
    • US5628003A
    • 1997-05-06
    • US111511
    • 1993-08-24
    • Hiromichi FujisawaAtsushi HatakeyamaYasuaki NakanoJunichi HigashinoToshihiro Hananoi
    • Hiromichi FujisawaAtsushi HatakeyamaYasuaki NakanoJunichi HigashinoToshihiro Hananoi
    • G06F17/30
    • G06F17/30985G06F17/30253G06F17/30265G06F17/3061Y10S707/99933Y10S707/99945Y10S707/99948
    • A document storage and retrieval system is provided with means for storing a document body in the form of image, means for storing text information in the form of a character code string for retrieval, means for executing a retrieval with reference to the text information, and means for displaying a document image relating thereto on a retrieval terminal according to the retrieval result. Such a form of the system is available for retrieving the full contents of a document and also for displaying the document body printed in a format easy to read straight in the form of image. Accordingly, users are capable of retrieving documents with arbitrary words and also capable of reading even such a document as is complicated to include mathematical expressions and charts through a terminal in the form of image, the same as on paper. Further, the invention provides a system wherein the text information for retrieval is extracted automatically from the document image through character recognition. Since a precision of the character recognition has not been satisfactory hitherto, a visual retrieval and correction have been carried out without fail by operators. However, there is no necessity for the operators to attend therefor according to the invention. Thus, the text information for retrieval can be generated at the cost of practical time and money even in case of volumes of documents.
    • 文件存储和检索系统提供有用于以图像形式存储文件主体的装置,用于存储用于检索的字符代码串形式的文本信息的装置,参考文本信息执行检索的装置,以及 用于根据检索结果在检索终端上显示与其相关的文档图像的装置。 系统的这种形式可用于检索文档的全部内容,并且还用于以图像的形式直接显示以易于阅读的格式打印的文档主体。 因此,用户能够以任意的单词检索文档,并且还能够读取甚至这样一个文件的复杂的文档,包括通过图像形式的终端的数学表达和图表,与纸上相同。 此外,本发明提供一种系统,其中通过字符识别从文档图像中自动提取用于检索的文本信息。 由于字符识别的精确度迄今尚未令人满意,因此操作者已经进行了视觉检索和校正。 然而,根据本发明,操作者不需要参加。 因此,即使在文件量的情况下,也可以以实际的时间和金钱为代价来生成用于检索的文本信息。
    • 4. 发明授权
    • Learning method and apparatus for neural networks and simulator with
neural network
    • 神经网络的神经网络和模拟器的学习方法和装置
    • US5390284A
    • 1995-02-14
    • US903243
    • 1992-06-23
    • Hisao OgataHiroshi SakouMasahiro AbeJunichi Higashino
    • Hisao OgataHiroshi SakouMasahiro AbeJunichi Higashino
    • G06F15/18G06F17/10G06G7/60G06N3/08G06N3/10G06N99/00
    • G06N3/08G06N3/10
    • A neural network (100) has an input layer, a hidden layer, and an output layer. The neural network stores weight values which operate on data input at the input layer to generate output data at the output layer. An error computing unit (87) receives the output data and compares it with desired output data from a learning data storage unit (105) to calculate error values representing the difference. An error gradient computing unit (81) calculates an error gradient, i.e. rate and direction of error change. A ratio computing unit (82) computes a ratio or percentage of a prior conjugate vector and combines the ratio with the error gradient. A conjugate vector computing unit (83) generates a present line search conjugate vector from the error gradient value and a previously calculated line search gradient vector. A line search computing unit (95) includes a weight computing unit (88) which calculates a weight correction value. The weight correction value is compared (18) with a preselected maximum or upper limit correction value (.kappa.). The line search computing unit (95) limits adjustment of the weight values stored in the neural network in accordance with the maximum weight correction value.
    • 神经网络(100)具有输入层,隐藏层和输出层。 神经网络存储对在输入层输入的数据进行操作的权重值,以在输出层产生输出数据。 错误计算单元(87)接收输出数据并将其与来自学习数据存储单元(105)的期望输出数据进行比较,以计算表示该差异的误差值。 误差梯度计算单元(81)计算误差梯度,即误差变化的速率和方向。 比率计算单元(82)计算先前共轭向量的比率或百分比,并将该比率与误差梯度组合。 共轭向量计算单元(83)从误差梯度值和先前计算的线搜索梯度矢量生成当前行搜索共轭向量。 线搜索计算单元(95)包括计算权重校正值的权重计算单元(88)。 将权重校正值与预选的最大或上限校正值(kappa)进行比较(18)。 行搜索计算单元(95)根据最大权重校正值限制存储在神经网络中的权重值的调整。
    • 6. 发明授权
    • Method and apparatus for structured document difference string extraction
    • 用于结构化文档差分字符串提取的方法和装置
    • US06526410B1
    • 2003-02-25
    • US09604261
    • 2000-06-27
    • Yuki AoyamaJunichi Higashino
    • Yuki AoyamaJunichi Higashino
    • G06F1730
    • G06F17/30923G06F17/2211G06F17/2247G06F17/24Y10S707/99931Y10S707/99935Y10S707/99943Y10S707/99945
    • A document difference extraction method and apparatus which is used for extracting the difference between structured documents properly meeting the sense of a document editor taking the logical meaning and structure of the structured documents into consideration. Structured documents are edited and stored in a memory unit by a document editing program. With reference to a comparison criterion set for the logical structure of each structured document before and after edition, the logical structure of the structural documents before and after edition read from the memory unit is analyzed by a structured document parsing program, and the difference between the structured documents is extracted by a structured document difference extraction program in such a manner as to satisfy the comparison criterion in accordance with the result of parsing. The comparison criterion assumes the form of a table containing a plurality of tags representing logical structures and types of tags for the comparison criterion. The tag types for comparison criterion include tags having contents which are compared only when the particular tags are coincident with each other, tags having contents which are ignored at the time of comparison, a set of tags having the same logical meaning, and a set of tags having contents which are not compared with each other.
    • 一种文献差异提取方法和装置,用于提取正确满足文档编辑者感觉的结构化文档之间的差异,考虑到结构化文档的逻辑含义和结构。 结构化文档通过文档编辑程序编辑并存储在存储器单元中。 参考在编辑之前和之后为每个结构化文档的逻辑结构设置的比较标准,通过结构化文档解析程序分析从存储器单元读取的版本之前和之后的结构文档的逻辑结构, 结构化文档由结构化文档差异提取程序提取,以满足根据解析结果的比较标准。 比较标准采用表格的形式,该表格包含表示用于比较标准的逻辑结构和标签类型的多个标签。 用于比较标准的标签类型包括具有仅当特定标签彼此重合时才被比较的内容的标签,具有在比较时被忽略的内容的标签,具有相同逻辑意义的标签集合,以及一组 具有不相互比较的内容的标签。