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    • 1. 发明授权
    • Online relevance engine
    • 在线相关引擎
    • US08135739B2
    • 2012-03-13
    • US12344812
    • 2008-12-29
    • Ron KaridiRoy VarshavskyNoga AmitOded ElyadaDaniel SittonLimor LahianiHen FitoussiEran YarivBenny Schlesinger
    • Ron KaridiRoy VarshavskyNoga AmitOded ElyadaDaniel SittonLimor LahianiHen FitoussiEran YarivBenny Schlesinger
    • G06F17/30G06F7/00
    • G06F17/30864
    • Information is automatically located which is relevant to source content that a user is viewing on a user interface without requiring the user to perform an additional search or navigate links of the source content. The source content can be, e.g., a web page or a document from a word processing or email application. The relevant information can include images, videos, web pages, maps or other location-based information, people-based information and special services which aggregate different types of information. Related content is located by analyzing textual content, user behavior and connectivity relative to the source. The related content is scored for similarity to the source. Content which is sufficiently similar but not too similar is selected. Similar related content is grouped to select representative results. The selected content is filtering in multiple stages based on attribute priorities to avoid unnecessary processing of content which is filtered out an early stage.
    • 自动定位与用户正在用户界面上观看的源内容相关的信息,而不需要用户执行附加搜索或浏览源内容的链接。 源内容可以是例如网页或来自文字处理或电子邮件应用的文档。 相关信息可以包括图像,视频,网页,地图或其他基于位置的信息,基于人群的信息和聚合不同类型信息的特殊服务。 通过分析文本内容,用户行为和相对于源的连接来定位相关内容。 相关内容的得分与来源相似。 选择足够相似但不太相似的内容。 类似的相关内容被分组以选择代表性的结果。 所选择的内容是基于属性优先级在多个阶段进行过滤,以避免对早期过滤掉的内容进行不必要的处理。
    • 2. 发明申请
    • ONLINE RELEVANCE ENGINE
    • 在线相关引擎
    • US20100169331A1
    • 2010-07-01
    • US12344812
    • 2008-12-29
    • Ron KaridiRoy VarshavskyNoga AmitOded ElyadaDaniel SittonLimor LahianiHen FitoussiEran YarivBenny Schlesinger
    • Ron KaridiRoy VarshavskyNoga AmitOded ElyadaDaniel SittonLimor LahianiHen FitoussiEran YarivBenny Schlesinger
    • G06F7/06G06F17/30G06F7/00
    • G06F17/30864
    • Information is automatically located which is relevant to source content that a user is viewing on a user interface without requiring the user to perform an additional search or navigate links of the source content. The source content can be, e.g., a web page or a document from a word processing or email application. The relevant information can include images, videos, web pages, maps or other location-based information, people-based information and special services which aggregate different types of information. Related content is located by analyzing textual content, user behavior and connectivity relative to the source. The related content is scored for similarity to the source. Content which is sufficiently similar but not too similar is selected. Similar related content is grouped to select representative results. The selected content is filtering in multiple stages based on attribute priorities to avoid unnecessary processing of content which is filtered out an early stage.
    • 自动定位与用户正在用户界面上观看的源内容相关的信息,而不需要用户执行附加搜索或浏览源内容的链接。 源内容可以是例如网页或来自文字处理或电子邮件应用的文档。 相关信息可以包括图像,视频,网页,地图或其他基于位置的信息,基于人群的信息和聚合不同类型信息的特殊服务。 通过分析文本内容,用户行为和相对于源的连接来定位相关内容。 相关内容的得分与来源相似。 选择足够相似但不太相似的内容。 类似的相关内容被分组以选择代表性的结果。 所选择的内容是基于属性优先级在多个阶段进行过滤,以避免对早期过滤掉的内容进行不必要的处理。
    • 3. 发明申请
    • Dynamically Providing Relevant Browser Content
    • 动态提供相关的浏览器内容
    • US20090313536A1
    • 2009-12-17
    • US12136889
    • 2008-06-11
    • Ron KaridiEran YarivRoy VarshavskyDaniel SittonOded ElyadaNoga AmitOmer Ramote
    • Ron KaridiEran YarivRoy VarshavskyDaniel SittonOded ElyadaNoga AmitOmer Ramote
    • G06F17/00
    • G06F16/972
    • A requested content page is provided with additional relevant content that is dynamically generated. A page originally requested by a browser application is generated and examined to determine key words, address information, and other information for which relevant content may be retrieved. The other information may not be part of the original page content, but it can be the relation between the content page and other pages. The relevant content is determined based on the results of the content page examination. After retrieving the relevant content, the retrieved content is embedded into the requested content page and provided to the requesting user. The retrieved relevant content may be provided with the requested content page in a designated portion within the requested content page, near related content in the page, and/or displayed in response to user input as a pop-up window or in a preview pane. Relevant content can be determined, retrieved and embedded in a content page by a relevant content engine implemented as a server application, client application or browser application plug-in.
    • 请求的内容页面提供动态生成的其他相关内容。 生成并检查由浏览器应用程序最初请求的页面以确定可以检索相关内容的关键字,地址信息和其他信息。 其他信息可能不是原始页面内容的一部分,但它可以是内容页面和其他页面之间的关系。 相关内容是根据内容页面检查的结果确定的。 在检索相关内容之后,检索到的内容被嵌入到所请求的内容页面中并提供给请求用户。 所检索的相关内容可以被提供在所请求的内容页面内的指定部分中,在页面中的相关内容附近,和/或响应于用户输入显示为弹出窗口或在预览窗格中。 相关内容可以由实现为服务器应用程序,客户端应用程序或浏览器应用程序插件的相关内容引擎确定,检索和嵌入到内容页面中。
    • 5. 发明申请
    • EMBEDDED CONTENT BROKERING AND ADVERTISEMENT SELECTION DELEGATION
    • 嵌入式内容分发和广告选择代理
    • US20100257035A1
    • 2010-10-07
    • US12419322
    • 2009-04-07
    • Kfir KarmonRoy VarshavskyRon KaridiHen FitoussiLiza Fireman
    • Kfir KarmonRoy VarshavskyRon KaridiHen FitoussiLiza Fireman
    • G06Q30/00G06F15/16
    • G06Q30/02G06Q30/0208
    • A digital document request can be received at a publisher computing environment from a client computing environment. A document requested by the digital document request can include an embedded content placeholder. A third-party embedded content request can be sent from a content broker computing environment (which may be the same as or different from the publisher computing environment) to an embedded content provider computing environment to request content for the embedded content placeholder. Content corresponding to the third-party embedded content request can be received at the content broker computing environment. In addition, the digital document can be sent from the publisher computing environment to the client computing environment, and the content can be sent from the content broker computing environment to the client computing environment. Advertisement selection can also be delegated to an advertisement selection delegate computing environment.
    • 可以在发布者计算环境中从客户端计算环境接收数字文档请求。 由数字文档请求请求的文档可以包括嵌入式内容占位符。 第三方嵌入式内容请求可以从内容代理计算环境(其可以与发布者计算环境相同或不同)发送到嵌入式内容提供商计算环境,以请求嵌入式内容占位符的内容。 可以在内容代理计算环境下接收对应于第三方嵌入式内容请求的内容。 此外,数字文档可以从发行商计算环境发送到客户端计算环境,并且内容可以从内容代理计算环境发送到客户端计算环境。 广告选择也可以被委派给广告选择委托计算环境。
    • 7. 发明申请
    • KEYWORDS EXTRACTION AND ENRICHMENT VIA CATEGORIZATION SYSTEMS
    • 关键词通过分类系统提取和丰富
    • US20120166441A1
    • 2012-06-28
    • US12978169
    • 2010-12-23
    • Ron KaridiLiat SegalOded ElyadaRotem Bennett
    • Ron KaridiLiat SegalOded ElyadaRotem Bennett
    • G06F17/30
    • G06F17/3071
    • Techniques for determining a set of keywords associated with a document are provided. A document is received that may be classified into a taxonomy that includes a plurality of categories. A categorization ranking is determined for each category for the received document. A set of categories of the taxonomy having highest categorization rankings is determined for the received document. Documents representing the set of categories having highest categorization rankings are combined together into a cumulative representative text that includes a plurality of terms. A cumulative term corpus importance score is determined for each term in the cumulative representative text. The cumulative term corpus importance score for a particular term indicates an importance of the particular term in a context of the cumulative representative text. A set of terms of the cumulative representative text having highest cumulative term corpus importance scores is selected to be keywords for the received document.
    • 提供了用于确定与文档相关联的一组关键词的技术。 收到可被分类为包括多个类别的分类法的文档。 为接收到的文档的每个类别确定分类排名。 对于接收到的文档确定具有最高分类排名的分类的一组类别。 表示具有最高分类排名的类别集合的文档被组合成包括多个项的累积代表性文本。 累积代表性文本中的每个术语确定累积项目语料库重要性分数。 特定术语的累积术语语料库重要性分数表示特定术语在累积代表性文本的上下文中的重要性。 选择具有最高累积项语料库重要性分数的累积代表性文本的一组术语作为接收到的文档的关键字。
    • 8. 发明授权
    • Keywords extraction and enrichment via categorization systems
    • 关键词通过分类系统提取和浓缩
    • US09342590B2
    • 2016-05-17
    • US12978169
    • 2010-12-23
    • Ron KaridiLiat SegalOded ElyadaRotem Bennett
    • Ron KaridiLiat SegalOded ElyadaRotem Bennett
    • G06F17/30
    • G06F17/3071
    • Techniques for determining a set of keywords associated with a document are provided. A document is received that may be classified into a taxonomy that includes a plurality of categories. A categorization ranking is determined for each category for the received document. A set of categories of the taxonomy having highest categorization rankings is determined for the received document. Documents representing the set of categories having highest categorization rankings are combined together into a cumulative representative text that includes a plurality of terms. A cumulative term corpus importance score is determined for each term in the cumulative representative text. The cumulative term corpus importance score for a particular term indicates an importance of the particular term in a context of the cumulative representative text. A set of terms of the cumulative representative text having highest cumulative term corpus importance scores is selected to be keywords for the received document.
    • 提供了用于确定与文档相关联的一组关键词的技术。 收到可被分类为包括多个类别的分类法的文档。 为接收到的文档的每个类别确定分类排名。 对于接收到的文档确定具有最高分类排名的分类的一组类别。 表示具有最高分类排名的类别集合的文档被组合成包括多个项的累积代表性文本。 累积代表性文本中的每个术语确定累积项目语料库重要性分数。 特定术语的累积术语语料库重要性分数表示特定术语在累积代表性文本的上下文中的重要性。 选择具有最高累积项语料库重要性分数的累积代表性文本的一组术语作为接收到的文档的关键字。