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1. 发明申请

WO2017176423A1 CLASSIFIER GENERATION METHODS AND PREDICTIVE TEST FOR OVARIAN CANCER PATIENT PROGNOSIS UNDER PLATINUM CHEMOTHERAPY 审中-公开
标题翻译：白血病化疗患者卵巢癌患者预后的分类器生成方法及预测性试验
公开(公告)号：WO2017176423A1
公开(公告)日：2017-10-12
申请号：PCT/US2017/021736
申请日：2017-03-10
申请人： BIODESIX, INC.
发明人： STEINGRIMSSON, Arni , RODER, Joanna , GRIGORIEVA, Julia , RODER, Heinrich , MEYER, Krista
IPC分类号： G01N33/48 , G06K9/62 , G06F19/10 , G06F19/24 , H01J49/16
CPC分类号： G06K9/6276 , G01N33/57449 , G01N2800/52 , G06K9/00523 , G06K9/623 , G06K9/6256 , G16B40/00 , G16B99/00 , G16H20/10 , G16H50/70
摘要： A method of generating a classifier includes a step of classifying each member of a development set of samples with a class label in a binary classification scheme with a first classifier; and generating a second classifier using a classifier development process with an input classifier development set being the members of the development set assigned one of the two class labels in the binary classification scheme by the first classifier. The second classifier stratifies the members of the set with an early label into two further sub -groups. We also describe identifying a plurality of different clinical sub-groups within the development set based on the clinical data and for each of the different clinical sub-groups, conducting a classifier generation process for each of the clinical sub-groups thereby generating clinical subgroup classifiers. We further describe an example of a hierarchical arrangement of such classifiers and their use in predicting, in advance of treatment, ovarian cancer patient outcomes on platinum-based chemotherapy.
摘要翻译：一种生成分类器的方法包括以下步骤：利用第一分类器以二元分类方案中的类标签对开发的样本集合的每个成员进行分类; 以及使用分类器开发过程来生成第二分类器，其中输入分类器开发集合是由第一分类器分配给二元分类方案中的两个类别标签之一的开发集合的成员。第二个分类器使用早期标签将该组的成员分层为两个另外的子组。我们还描述了基于临床数据并针对每个不同的临床亚组识别开发组内的多个不同临床亚组，针对每个临床亚组执行分类器生成过程，从而生成临床亚组分类器。我们进一步描述了这种分类器的等级安排的例子，以及它们在治疗之前预测基于铂的化疗的卵巢癌患者结果的用途。

2. 发明申请

WO2017018926A1 SYSTEM AND METHOD FOR HIGH SPEED THREAT INTELLIGENCE MANAGEMENT USING UNSUPERVISED MACHINE LEARNING AND PRIORITIZATION ALGORITHMS 审中-公开
标题翻译：使用不可靠的机器学习和优化算法的高速智能管理系统与方法
公开(公告)号：WO2017018926A1
公开(公告)日：2017-02-02
申请号：PCT/SG2015/050233
申请日：2015-07-24
申请人： CERTIS CISCO SECURITY PTE LTD
发明人： LIM, Keng Leng Albert
IPC分类号： G06F21/56
CPC分类号： G06F21/56 , G06K9/6221 , G06K9/6276 , H04L63/14 , H04L63/1433 , H04L63/20
摘要： This document discloses a system and method for consolidating threat intelligence data for a computer and its related networks. Massive volumes of raw threat intelligence data are collected from a plurality of sources and are partitioned into a common format for cluster analysis whereby the clustering of the data is done using unsupervised machine learning algorithms. The resulting organized threat intelligence data subsequently undergoes a weighted asset based threat severity level correlation process. All the intermediary network vulnerabilities of a particular computer network are utilized as the critical consolidation parameters of this process. The final processed intelligence data gathered through this high speed automated process is then formatted into predefined formats prior to transmission to third parties.
摘要翻译：本文件公开了一种用于整合计算机及其相关网络的威胁情报数据的系统和方法。从多个源收集大量原始威胁情报数据，并将其划分为用于聚类分析的通用格式，从而使用无监督的机器学习算法完成数据的聚类。所产生的有组织的威胁情报数据随后经历基于加权资产的威胁严重性级别相关过程。将特定计算机网络的所有中介网络漏洞用作此过程的关键合并参数。然后，通过这种高速自动化过程收集的最终处理的智能数据在传输给第三方之前被格式化为预定格式。

3. 发明申请

WO2016128806A1 TREE BASED IMAGE STORAGE SYSTEM 审中-公开
标题翻译：基于树的图像存储系统
公开(公告)号：WO2016128806A1
公开(公告)日：2016-08-18
申请号：PCT/IB2015/056001
申请日：2015-08-07
申请人： SONY CORPORATION
发明人： ENGSTROM, Jimmy
IPC分类号： H04N19/48 , H04N19/507 , G06K9/62 , G06F17/30
CPC分类号： G06F17/3028 , G06K9/6219 , G06K9/6276 , H04N19/48 , H04N19/507 , H04N19/90
摘要： The present disclosure generally relates to methods and computer program products for storing images in a computer memory, and in particular to a method and computer program product for storing a plurality of JPEG encoded images in a tree data structure, wherein common parts of images in upper tree nodes are utilized for compressing images in lower parts of the tree.
摘要翻译：本公开一般涉及用于在计算机存储器中存储图像的方法和计算机程序产品，并且具体涉及一种用于在树形数据结构中存储多个JPEG编码图像的方法和计算机程序产品，其中图像上部的公共部分树节点用于压缩树下部的图像。

4. 发明申请

WO2016013885A1 METHOD FOR RETRIEVING IMAGE AND ELECTRONIC DEVICE THEREOF 审中-公开
标题翻译：用于检索图像和电子设备的方法
公开(公告)号：WO2016013885A1
公开(公告)日：2016-01-28
申请号：PCT/KR2015/007673
申请日：2015-07-23
申请人： SAMSUNG ELECTRONICS CO., LTD.
发明人： IYER, Kiran Nanjunda , VEERA, Viswanath , GOPALAKRISHNAN, Viswanath , LOKKOJU, Satish , KUMAR, Raushan
IPC分类号： G06F17/30
CPC分类号： G06T7/143 , G06F17/30268 , G06K9/6276 , G06K2209/27 , G06T7/11 , G06T7/162 , G06T2207/20104
摘要： The present disclosure relates to image retrieval in an electronic device. An operating method of an electronic device includes determining an object included in an image and a position of extent occupied by the object in the image and retrieving at least one image including the object at the position from among a plurality of images.
摘要翻译：本公开涉及电子设备中的图像检索。电子设备的操作方法包括：确定包括在图像中的对象以及图像中所述对象所占据的程度的位置，并从多个图像中检索包括在该位置的对象的至少一个图像。

5. 发明申请

WO2015153691A2 COMPUTER-IMPLEMENTED METHODS, COMPUTER-READABLE MEDIA, AND SYSTEMS FOR TRACKING A PLURALITY OF SPERMATOZOA 审中-公开
标题翻译：计算机实现的方法，计算机可读介质和用于跟踪多种精神分裂症的系统
公开(公告)号：WO2015153691A2
公开(公告)日：2015-10-08
申请号：PCT/US2015/023725
申请日：2015-03-31
申请人： DREXEL UNIVERSITY , THE TRUSTEES OF THE UNIVERSITY OF PENNSYLVANIA
发明人： URBANO, Leonardo , KAM, Moshe , MASSON, Puneet , VERMILYEA, Matthew
IPC分类号： G06F19/26
CPC分类号： G06T7/0012 , G06K9/0014 , G06K9/6276 , G06K2009/3291 , G06T5/30 , G06T7/11 , G06T7/136 , G06T7/155 , G06T7/20 , G06T7/70 , G06T7/90 , G06T2207/10016 , G06T2207/10024 , G06T2207/10056 , G06T2207/30024 , G06T2207/30241 , G06T2210/41
摘要： One aspect of the invention provides a computer-implemented method for tracking a plurality of spermatozoa. The method includes: identifying a coordinate for each of the plurality of spermatozoa in a plurality of video frames; and applying a nearest-neighbor joint probabilistic data association filter (NN-JPDAF) algorithm to associate the coordinates with one or a plurality of sperm tracks. Another aspect of the invention provides a non-transitory computer-readable medium containing program instructions executable by a processor. The computer-readable medium can include program instructions for performing a method as described herein. Another aspect of the invention provides a system including: a processor and a computer-readable medium including program instructions for performing a method as described herein.
摘要翻译：本发明的一个方面提供了用于跟踪多个精子的计算机实现的方法。该方法包括：识别多个视频帧中的多个精子中的每个精子的坐标; 并应用最近邻节点概率数据关联过滤器（NN-JPDAF）算法将坐标与一条或多条精子轨迹相关联。本发明的另一方面提供了包含可由处理器执行的程序指令的非暂时性计算机可读介质。计算机可读介质可以包括用于执行如本文所述的方法的程序指令。本发明的另一方面提供了一种系统，该系统包括：处理器和包括用于执行如本文所述的方法的程序指令的计算机可读介质。

6. 发明申请

WO2013014667A2 SYSTEM AND METHODS FOR COMPUTERIZED MACHINE-LEARNING BASED AUTHENTICATION OF ELECTRONIC DOCUMENTS INCLUDING USE OF LINEAR PROGRAMMING FOR CLASSIFICATION 审中-公开
标题翻译：用于基于计算机学习的电子文档的认证的系统和方法，包括使用线性编程进行分类
公开(公告)号：WO2013014667A2
公开(公告)日：2013-01-31
申请号：PCT/IL2012050265
申请日：2012-07-23
申请人： AU10TIX LTD , DOLEV GUY , MARKIN SERGEY , BAR-NISSIM AVI , UZIEL ASHER
发明人： DOLEV GUY , MARKIN SERGEY , BAR-NISSIM AVI , UZIEL ASHER
IPC分类号： G06K9/62
CPC分类号： G06N99/005 , G06K9/00442 , G06K9/6276
摘要： Electronic document classification comprising providing training documents sorted into classes; linear programming including selecting inputs which maximize an output, given constraints on inputs, the output maximized being a difference between: a. first estimated probability that a document instance will be correctly classified, by a classifier corresponding to given inputs, as belonging to its own class, and b. second estimated probability that document instance will be classified, by the classifier, as not belonging to its own class; and classifying electronic document instances into classes, using a preferred classifier corresponding to the inputs selected by the linear programming. A computerized electronic document forgery detection method provides training documents and uses a processor to select value-ranges of non-trivial parameters, such that selected values-range(s) of parameters are typical to an authentic document of given class, and atypical to a forged document of same class.
摘要翻译：电子文件分类包括提供分类为课堂的培训文件; 线性规划包括选择使输出最大化的输入，给定输入的约束，输出最大化为以下之间的差异：a。通过与给定输入对应的分类器将文档实例正确分类为属于其自己的类的第一估计概率，以及b。文档实例将被分类器分类为不属于其自己的类别的第二估计概率; 以及使用对应于线性节目选择的输入的优选分类器将电子文档实例分类成类。计算机化的电子文件伪造检测方法提供培训文件，并使用处理器来选择非平凡参数的值范围，使得选择的值 - 参数范围对于给定类别的真实文件是典型的，并且对于非典型的伪造同类文件。

7. 发明申请

WO2012051735A1 METHOD AND DEVICE FOR AUTOMATIC PREDICTION OF A VALUE ASSOCIATED WITH A DATA TUPLE 审中-公开
标题翻译：用于自动预测与数据轮相关的数值的方法和装置
公开(公告)号：WO2012051735A1
公开(公告)日：2012-04-26
申请号：PCT/CN2010/001630
申请日：2010-10-18
申请人： TECHNICOLOR (CHINA) TECHNOLOGY CO., LTD. , XU, Feng , LIU, Debing , GU, Xiaodong , CHEN, Zhibo
发明人： XU, Feng , LIU, Debing , GU, Xiaodong , CHEN, Zhibo
IPC分类号： H04N7/32
CPC分类号： G06F17/5018 , G06F17/18 , G06K9/6276 , G06N99/005 , H04N19/14 , H04N19/154 , H04N19/162
摘要： The invention relates to a method and a device for automatic prediction of a current value using a weighted average of a number of current reference values, wherein the current value is associated with a current pair consisting of a first and a second current data tuple. The method comprises using a set of reference pairs, each reference pair consisting of a first and a second reference data tuple and being associated with a reference value, for selecting the current reference values wherein the first reference data tuples, the first current data tuple and a first metric is used for selecting, determining, for each current reference value, an associated weight using the second reference data tuples of the pair associated with the respective selected reference value, the second current data tuple and a second metric, and using the current reference values and the determined weights for determining the weighted average.
摘要翻译：本发明涉及一种使用多个当前参考值的加权平均来自动预测当前值的方法和装置，其中当前值与由第一和第二当前数据元组组成的当前对相关联。该方法包括使用一组参考对，每个参考对由第一和第二参考数据元组组成并与参考值相关联，用于选择当前参考值，其中第一参考数据元组，第一当前数据元组和第一度量用于使用与相应选择的参考值，第二当前数据元组和第二度量相关联的对的第二参考数据元组来选择，确定每个当前参考值相关联的权重，并且使用当前参考值和确定加权平均值的确定权重。

8. 发明申请

WO03012737A2 METHOD AND CIRCUITS TO VIRTUALLY INCREASE THE NUMBER OF PROTOTYPES IN ARTIFICIAL NEURAL NETWORKS 审中-公开
标题翻译：方法和电路虚拟增加人工神经网络中的原型数
公开(公告)号：WO03012737A2
公开(公告)日：2003-02-13
申请号：PCT/EP0208473
申请日：2002-07-11
申请人： IBM , IBM FRANCE
发明人： IMBERT DE TREMIOLLES GHISLAIN , TANNHOF PASCAL
IPC分类号： G06N3/08 , G06K9/62 , G06N3/00 , G06N3/063
CPC分类号： G06K9/6276 , G06N3/063
摘要： There is disclosed an improved artificial neural network (ANN) (120') comprised of a conventional ANN (120), a database block (220) and a compare & update circuit (230). The conventional ANN is formed by a plurality of n neurons (130), each neuron having a prototype memory (140) dedicated to store a prototype and a distance evaluator (150) to evaluate the distance between the input pattern presented to the ANN and the prototype stored therein. The data base block is comprised of three data bases: a first data base (222) containing all the p prototypes arranged in s slices, each slice being capable to store up to n prototypes, a second data base (224) being capable to store the q input patterns to be presented to the ANN (queries) and a third data base (226) being capable to store the q distances resulting of said evaluation performed during the recognition/classification phase. The role of the compare & update circuit is to compare said distance with the distance previously found for the same input pattern (or pre-existing at initialization) and based upon the result of that comparison, to update or not said distance previously stored.
摘要翻译：公开了一种由常规ANN（120），数据库块（220）和比较和更新电路（230）组成的改进的人造神经网络（ANN）（120'）。常规ANN由多个n个神经元（130）形成，每个神经元具有专用于存储原型的原型存储器（140）和距离评估器（150），以评估呈现给ANN的输入模式与原型存储在其中。数据库块由三个数据库组成：第一数据库（222），包含以s个片段排列的所有p个原型，每个片段能够存储多达n个原型，第二数据库（224）能够存储要呈现给ANN（查询）的q个输入模式和第三数据库（226）能够存储在识别/分类阶段期间进行的所述评估的结果。比较和更新电路的作用是将所述距离与先前针对相同输入模式找到的距离进行比较（或者在初始化时预先存在），并且基于该比较的结果来更新或不更新先前存储的所述距离。

9. 发明申请

WO1995008159A1 PATTERN RECOGNITION SYSTEM WITH STATISTICAL CLASSIFICATION 审中-公开
标题翻译：具有统计分类的模式识别系统
公开(公告)号：WO1995008159A1
公开(公告)日：1995-03-23
申请号：PCT/US1994010527
申请日：1994-09-16
申请人： MASSACHUSETTS INSTITUTE OF TECHNOLOGY , MENON, Murali, M. , BOUDREAU, Eric, R.
发明人： MASSACHUSETTS INSTITUTE OF TECHNOLOGY
IPC分类号： G06K09/62
CPC分类号： G06K9/6222 , G06K9/6276
摘要： A pattern recognition system is described. During training, multiple training input patterns from multiple classes of subjects are grouped into clusters within categories by computing correlations between the training patterns and present category definitions. After training, each category is labeled in accordance with the peak class of patterns received within the cluster of the category. If the domination of the peak class over the other classes in the category exceeds a preset threshold, then the peak class defines the category. If the contrast does not exceed the threshold, then the category is defined as unknown. The class statistics for each category are stored in the form of a training class histogram for the category. During testing, frames of test data are received from a subject and are correlated with the category definitions. Each frame is associated with the training class histogram for the closest correlated category. For multiple-frame processing, the histograms are combined into a single observation class histogram which identifies the subject with its peak class within a predefined degree of confidence. In a multiple-channel configuration, the training patterns and testing patterns are divided into multiple features.
摘要翻译：描述了模式识别系统。在训练期间，通过计算训练模式与当前类别定义之间的相关性，将多类科目的多种训练输入模式分组到分类内。经过培训，每个类别都按照类别集群内收到的模式的最高等级标注。如果峰值类别与类别中其他类别的统治超过预设阈值，则峰值类定义该类别。如果对比度不超过阈值，则该类别被定义为未知。每个类别的类统计信息以类别的训练类直方图的形式存储。在测试期间，从主题接收测试数据的帧，并与类别定义相关。每个帧与最近相关类别的训练类直方图相关联。对于多帧处理，将直方图组合成单个观察类直方图，其在预定义的置信度内以其峰值类别识别被摄体。在多通道配置中，训练模式和测试模式分为多个特征。

10. 发明申请

WO2019136908A1 癌症识别方法、装置及存储介质审中-公开
公开(公告)号：WO2019136908A1
公开(公告)日：2019-07-18
申请号：PCT/CN2018/089132
申请日：2018-05-31
申请人：平安科技(深圳)有限公司
发明人：王健宗 , 吴天博 , 刘莉红 , 刘新卉 , 肖京
IPC分类号： G06T7/00
CPC分类号： G06K9/6276 , G06T7/0012 , G06T2207/20021 , G06T2207/20081 , G06T2207/20084 , G06T2207/30096
摘要：本申请公开了一种癌症识别方法、装置及存储介质，该方法包括：接收待癌症识别的病理切片图片；根据待识别的癌症类型与预设类型模型的映射关系，确定该病理切片图片对应的预设类型模型；利用确定的预设类型模型对该病理切片图片进行识别，生成识别结果。本申请通过对病理切片图片中癌变区域图块的识别，判断该图片对应的患者是否有癌症，提高癌症检测的效率。

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