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    • 53. 发明授权
    • Accurate text classification through selective use of image data
    • 通过选择性使用图像数据来准确地进行文本分类
    • US08768050B2
    • 2014-07-01
    • US13158484
    • 2011-06-13
    • Anitha KannanPartha Pratim TalukdarNikhil RasiwasiaQifa KeRakesh Agrawal
    • Anitha KannanPartha Pratim TalukdarNikhil RasiwasiaQifa KeRakesh Agrawal
    • G06K9/62
    • G06K9/6268G06K9/6227G06K9/6293
    • Product images are used in conjunction with textual descriptions to improve classifications of product offerings. By combining cues from both text and image descriptions associated with products, implementations enhance both the precision and recall of product description classifications within the context of web-based commerce search. Several implementations are directed to improving those areas where text-only approaches are most unreliable. For example, several implementations use image signals to complement text classifiers and improve overall product classification in situations where brief textual product descriptions use vocabulary that overlaps with multiple diverse categories. Other implementations are directed to using text and images “training sets” to improve automated classifiers including text-only classifiers. Certain implementations are also directed to learning a number of three-way image classifiers focused only on “confusing categories” of the text signals to improve upon those specific areas where text-only classification is weakest.
    • 产品图像与文本描述结合使用,以改进产品分类。 通过结合来自与产品相关的文本和图像描述的提示,实现在基于网络的商业搜索的上下文中增强了产品描述分类的精度和回收。 几个实现旨在改进那些仅文本方法最不可靠的领域。 例如,在简短的文本产品描述使用与多个不同类别重叠的词汇的情况下,多个实现使用图像信号来补充文本分类器并改进整体产品分类。 其他实现涉及使用文本和图像“训练集”来改进自动分类器,包括纯文本分类器。 某些实现也针对学习一些三维图像分类器,仅针对文本信号的“混淆类别”,以改善文本分类最弱的特定领域。
    • 55. 发明申请
    • COMPOSING TEXT AND STRUCTURED DATABASES
    • 组合文本和结构化数据库
    • US20130275441A1
    • 2013-10-17
    • US13561085
    • 2012-07-30
    • Rakesh AgrawalAnitha KannanJohn C. ShaferAriel Fuxman
    • Rakesh AgrawalAnitha KannanJohn C. ShaferAriel Fuxman
    • G06F17/30
    • G06F17/30616
    • A framework is provided for composing texts about objects with structured information about these objects, and thus disclosed are methodologies for linking information from at least two data sources—one comprising a plurality of documents comprising text pertaining to at least one object, and one comprising a plurality of structured records comprising at least one characteristic of the at least one object, each characteristic comprising one property name and an associated property value corresponding to the property name for the at least one object—by determining one or more instance-based traits for each object in both data sources and associating at least one record with at least one document that refers to each object, each trait comprising one or more characteristics that identifiably distinguish each object from all other objects.
    • 提供了一种用于组合关于具有关于这些对象的结构化信息的对象的文本的框架,并且因此公开了用于链接来自至少两个数据源的信息的方法 - 一个包括多个文档的文档,所述文档包括属于至少一个对象的文本, 多个结构化记录,其包括至少一个对象的至少一个特征,每个特征包括一个属性名称和与该至少一个对象的属性名称相对应的关联属性值 - 通过确定每个对象的一个​​或多个基于实例的特征 对象在两个数据源中,并且将至少一个记录与至少一个引用每个对象的文档相关联,每个特征包括一个或多个可识别地将每个对象与所有其他对象区分开的特征。
    • 56. 发明授权
    • Query classification using implicit labels
    • 使用隐式标签的查询分类
    • US08423568B2
    • 2013-04-16
    • US12560427
    • 2009-09-16
    • Ariel D. FuxmanAnitha KannanAndrew Brian GoldbergRakesh Agrawal
    • Ariel D. FuxmanAnitha KannanAndrew Brian GoldbergRakesh Agrawal
    • G06F7/00G06F17/30
    • G06F17/30693
    • Described is a technology for automatically generating labeled training data for training a classifier based upon implicit information associated with the data. For example, whether a query has commercial intent can be classified based upon whether the query was submitted at a commercial website's search portal, as logged in a toolbar log. Positive candidate query-related data is extracted from the toolbar log based upon the associated implicit information. A click log is processed to obtain negative query-related data. The labeled training data is automatically generated by separating at least some of the positive candidate query data from the remaining positive candidate query data based upon the negative query data. The labeled training data may be used to train a classifier, such as to classify an online search query as having a certain type of intent or not.
    • 描述了一种用于根据与数据相关联的隐含信息自动生成用于训练分类器的标记训练数据的技术。 例如,查询是否具有商业意图可以根据在商业网站的搜索门户网站上提交的查询进行分类,如登录在工具栏日志中。 基于相关联的隐含信息,从工具栏日志中提取正候选查询相关数据。 处理点击日志以获取负查询相关数据。 基于负查询数据,将剩余的正候选查询数据中的至少一些正候选查询数据分离出来,自动生成标示训练数据。 标记的训练数据可以用于训练分类器,例如将在线搜索查询分类为具有某种类型的意图。
    • 59. 发明授权
    • Object classification using taxonomies
    • 使用分类法的对象分类
    • US08275726B2
    • 2012-09-25
    • US12414065
    • 2009-03-30
    • Panayiotis TsaparasPanagiotis PapadimitriouAriel D. FuxmanLise C. GetoorRakesh Agrawal
    • Panayiotis TsaparasPanagiotis PapadimitriouAriel D. FuxmanLise C. GetoorRakesh Agrawal
    • G06E1/00G06E3/00G06F15/18G06G7/00
    • G06N99/005
    • As provided herein objects from a source catalog, such as a provider's catalog, can be added to a target catalog, such as an enterprise master catalog, in a scalable manner utilizing catalog taxonomies. A baseline classifier determines probabilities for source objects to target catalog classes. Source objects can be assigned to those classes with probabilities that meet a desired threshold and meet a desired rate. A classification cost for target classes can be determined for respective unassigned source objects, which can comprise determining an assignment cost and separation cost for the source objects for respective desired target classes. The separation and assignment costs can be combined to determine the classification cost, and the unassigned source objects can be assigned to those classes having a desired classification cost.
    • 如本文所提供的,可以使用目录分类法将来自源目录的诸如提供者目录的对象以可扩展的方式添加到目标目录,例如企业主目录。 基准分类器确定源对象到目标目录类的概率。 可以将源对象分配给具有满足期望阈值且满足期望速率的概率的那些类。 可以针对相应的未分配的源对象来确定目标类别的分类成本,其可以包括确定用于各个期望目标类别的源对象的分配成本和分离成本。 分离和分配成本可以组合以确定分类成本,并且未分配的源对象可以被分配给具有期望的分类成本的那些类。
    • 60. 发明申请
    • THIN FILMS FOR PHOTOVOLTAIC CELLS
    • 光伏电池薄膜
    • US20120115312A1
    • 2012-05-10
    • US13321834
    • 2010-05-26
    • Rakesh AgrawalHugh W. HillhouseQijie GuoMahaprasad Kar
    • Rakesh AgrawalHugh W. HillhouseQijie GuoMahaprasad Kar
    • H01L21/20
    • H01L31/0322B82Y30/00C09D11/36H01L21/02485H01L21/02568H01L21/02601H01L21/02614H01L21/02628H01L31/0749H01L31/1864Y02E10/541Y02P70/521
    • In one aspect, a method for forming CIGSSe-based thin films includes depositing at least two layers of particles on a substrate. At least one layer includes a CIGSSe particle having a chemical composition denoted by Cu(InI-xGax)(S1-ySey)2 where 0≦x ≦1 and 0≦y≦1. The particle layers are annealed individually or in combination to form a CIGSSe thin film having a composition profile along the depth of the film In addition, one or more of the particle layers may be also deposited on a pre-existing absorber and annealed to form a film having a composition profile along the depth of the film After depositing thin film precursor layers containing CIGSSe nanoparticles (and/or any other particles) on a suitable substrate in accordance with a desired concentration profile, a subsequent treatment under an Se and/or S containing atmosphere at elevated temperature may be used to convert the precursor layers into a CIGSSe absorber film In a further aspect, a method for forming multinary metal chalcogenide semiconductor layers directly on a substrate from a solution of precursors, includes depositing a plurality of metal chalcogenide particles onto a substrate to form a precursor film A species containing a metal, chalcogen, or combination thereof is dissolved in a solution containing one or more solvents to form a liquid chalcogen medium. The precursor film is contacted with the liquid chalcogen medium at a temperature of at least 50 C to form a multinary metal chalcogenide thin film
    • 一方面,用于形成CIGSSe的薄膜的方法包括在衬底上沉积至少两层颗粒。 至少一层包括具有由Cu(InI-xGax)(S1-ySey)2表示的化学组成的CIGSSe颗粒,其中0和nlE; x和nlE; 1和0& nlE; y≦̸ 1。 颗粒层单独地或组合地退火以形成具有沿着膜的深度的组成分布的CIGSSe薄膜。此外,一个或多个颗粒层也可以沉积在预先存在的吸收体上并退火以形成 具有沿着膜深度的组成轮廓的膜根据期望的浓度分布,在合适的底物上沉积含有CIGSSe纳米颗粒(和/或任何其它颗粒)的薄膜前体层,然后在Se和/或S 可以使用高温下的含氮气氛将前体层转化为CIGSSe吸收膜。在另一方面,从前体溶液直接在基底上形成多金属硫族化物半导体层的方法包括沉积多个金属硫族化物颗粒 在基材上形成前体膜A含有金属,硫族元素或其组合的物质是溶解的 在含有一种或多种溶剂以形成液体硫属培养基的溶液中。 前体膜与液态硫属培养基在至少50℃的温度下接触以形成多元金属硫族化物薄膜