公开(公告)号：US20080244428A1

公开(公告)日：2008-10-02

申请号：US11694160

申请日：2007-03-30

Applicant: Daniel C. Fain

Inventor： Daniel C. Fain

IPC: G06F3/048

CPC classification number: G06F16/338 ,  G06F16/3328 ,  G06F16/9535 ,  G06F16/9566

Abstract: An example embodiment of the present invention provides processes for visually emphasizing the displayed URLs in query results based on implicit relevance feedback. In one process, the process identifies a web page which includes results returned by a search engine. Each result might include a displayed URL and an actual URL. The process determines whether the displayed URL matches any stored URLs which were included in previous results returned by the search engine and clicked through by the user. The process detects a click-through by matching the actual URL in an HTTP request emanating from a browser to an actual URL for a stored URL. The process visually emphasizes the displayed URL when presenting the web page to the user, if the displayed URL does not match any stored URL which has been clicked through and other factors indicate a probability the user will click through the displayed URL.

Abstract translation: 本发明的示例性实施例提供了基于隐式相关性反馈在视觉上强调查询结果中显示的URL的处理。 在一个过程中，该过程识别包括搜索引擎返回的结果的网页。 每个结果可能包括显示的URL和实际的URL。 该过程确定显示的URL是否与由搜索引擎返回并由用户点击的先前结果中包含的任何存储的URL相匹配。 该过程通过将从浏览器发出的HTTP请求中的实际URL与存储的URL的实际URL进行匹配来检测点击。 如果显示的URL与已经点击的任何存储的URL不匹配，并且其他因素指示用户点击显示的URL的概率，则该过程在向用户呈现网页时视觉上强调所显示的URL。

公开(公告)号：US07225184B2

公开(公告)日：2007-05-29

申请号：US10623115

申请日：2003-07-18

Applicant: John Joseph M. Carrasco ,  Daniel C. Fain ,  Gary William Flake

Inventor： John Joseph M. Carrasco ,  Daniel C. Fain ,  Gary William Flake

IPC: G06F17/30

CPC classification number: G06F17/30705 ,  G06F17/30675 ,  Y10S707/99933

Abstract: In one implementation a method for disambiguation of search phrases is provided, which may include identifying interpretation clusters using bidded search terms of content providers and influencing a position of a search result in a search results list based on the interpretation clusters. In one implementation a method is provided for interpretation clustering, which may include identifying terms and associated content providers and calculating a matrix from the identified terms and associated content providers. Similarity scores between content providers based on the matrix are calculated and interpretation clusters are assigned using the similarity scores. Bidded search terms and/or searcher clickthrough terms may be used. In one implementation, a method is provided for increasing the relevance of search results, which may include receiving a search query and identifying the search query as ambiguous.

Abstract translation: 在一个实现中，提供了一种用于消除歧义搜索短语的方法，其可以包括使用内容提供商的出价搜索项识别解释聚类，并且基于解释聚类将搜索结果的位置影响在搜索结果列表中。 在一个实现中，提供了用于解释聚类的方法，其可以包括识别术语和相关联的内容提供者，并根据所识别的术语和相关联的内容提供者计算矩阵。 计算基于矩阵的内容提供者之间的相似度得分，并使用相似性分数来分配解释集。 可以使用投标搜索字词和/或搜索者点击率。 在一个实现中，提供了一种用于增加搜索结果的相关性的方法，其可以包括接收搜索查询并将搜索查询识别为不明确。

公开(公告)号：US20100299343A1

公开(公告)日：2010-11-25

申请号：US12471116

申请日：2009-05-22

Applicant: Sanaz Ahari ,  Xiaoxin Yin ,  Farid Hosseini ,  Sarthak Shah ,  Adam Troy ,  Daniel C. Fain ,  Brian MacDonald ,  Nikhil Dandekar ,  Michael Cameron

Inventor： Sanaz Ahari ,  Xiaoxin Yin ,  Farid Hosseini ,  Sarthak Shah ,  Adam Troy ,  Daniel C. Fain ,  Brian MacDonald ,  Nikhil Dandekar ,  Michael Cameron

IPC: G06F17/30

CPC classification number: G06F17/30864 ,  G06F7/00 ,  G06F17/30 ,  G06F17/30702 ,  G06F17/30867

Abstract: Computer-readable media and computerized methods for automatically organizing search results according to task groups are provided. The methods involve aggregating a gallery of entities (e.g., search queries that share a common categorization) into a query class and assigning a dictionary (e.g., list of terms that are drawn from various sources) to the query class. The task groups are identified from the list of terms within the dictionary. The process of identification includes analyzing patterns of user search behavior to select terms from the list of terms, which reflect popular user search intents, and ranking the selected terms based on predetermined parameters to produce an ordering. Based on the ordering, a set of the selected terms that are highest ranked are declared the task groups. The task groups are employed to arrange the search results on a UI display and to provide a consistent and intuitive format for refining a search.

Abstract translation: 提供了根据任务组自动组织搜索结果的计算机可读介质和计算机化方法。 这些方法包括将实体库（例如，共享公共分类的搜索查询）聚合到查询类中并将查询类别的字典（例如，从各种来源绘制的术语列表）分配。 任务组从字典中的术语列表中识别。 识别过程包括分析用户搜索行为的模式以从反映流行用户搜索意图的术语列表中选择术语，并且基于预定参数对所选项进行排序以产生排序。 根据排序，将排列最高的选定术语集合声明为任务组。 任务组被用于在UI显示器上排列搜索结果，并提供一致和直观的格式来完善搜索。

公开(公告)号：US20080195596A1

公开(公告)日：2008-08-14

申请号：US11673355

申请日：2007-02-09

Applicant: Jacob Sisk ,  Heidi Eldenburg Bramlet ,  Daniel C. Fain ,  Jianchang Mao ,  Charity A. Rieck

Inventor： Jacob Sisk ,  Heidi Eldenburg Bramlet ,  Daniel C. Fain ,  Jianchang Mao ,  Charity A. Rieck

IPC: G06F17/30

CPC classification number: G06F17/30864

Abstract: Various embodiments are directed to a system and method providing associative matching of terms. Candidate terms are selected for building one or more associative matching models from one or more selected candidate sources. Associativity is defined to give editors the ability to label sample associative term pairs from the one or more candidate sources. The editors label sample candidate term pairs as being related. Features are determined that can differentiate associative from non-associative pairs. The selected features are used to build a model. The model is applied to determine whether a received query-candidate pair are associative.

Abstract translation: 各种实施例涉及提供术语的关联匹配的系统和方法。 从一个或多个选择的候选来源中选择候选词来构建一个或多个关联匹配模型。 关联性被定义为使编辑者能够从一个或多个候选来源标记样本关联词对。 编辑者将样品候选词对标记为相关。 确定可以区分缔合和非关联对的特征。 所选功能用于构建模型。 应用该模型来确定接收到的候选对象是否是关联的。

公开(公告)号：US07792818B2

公开(公告)日：2010-09-07

申请号：US11413627

申请日：2006-04-28

Applicant: Daniel C. Fain ,  Paul T. Ryan ,  Peter Savich

Inventor： Daniel C. Fain ,  Paul T. Ryan ,  Peter Savich

IPC: G06F17/30

CPC classification number: G06F17/30707 ,  G06F17/30864 ,  G06F17/30882 ,  Y10S707/99942 ,  Y10S707/99943 ,  Y10S707/99944

Abstract: Described herein are methods for creating categorized documents, categorizing documents in a distributed database and categorizing Resulting Pages. Also described herein is an apparatus for searching a distributed database. The method for creating categorized documents generally comprises: initially assuming all documents are of type 1; filtering out all type 2 documents and placing them in a first category; filtering out all type 3 documents and placing them in a second category; and defining all remaining documents as type 4 documents and placing all type 4 documents in a third category. The apparatus for searching a distributed database generally comprises at least one memory device; a computing apparatus; an indexer; a transactional score generator; and a category assignor; a search server; and a user interface in communication with the search server.

Abstract translation: 这里描述了用于创建分类文档的方法，对分布式数据库中的文档进行分类，并对“结果页”进行分类。 本文还描述了一种用于搜索分布式数据库的装置。 创建分类文档的方法通常包括：最初假设所有文档都是类型1; 过滤所有2类文件并将其放在第一类中; 过滤所有3类文件并将其放在第二类中; 并将所有剩余的文档定义为4类文档，并将所有类型4文档放在第三类中。 用于搜索分布式数据库的装置通常包括至少一个存储装置; 计算装置; 索引器 交易记分发生器; 和类别转让人; 搜索服务器 和与搜索服务器通信的用户界面。

公开(公告)号：US07685084B2

公开(公告)日：2010-03-23

申请号：US11673355

申请日：2007-02-09

Applicant: Jacob Sisk ,  Heidi Eldenburg Bramlet ,  Daniel C. Fain ,  Jianchang Mao ,  Charity A. Rieck

Inventor： Jacob Sisk ,  Heidi Eldenburg Bramlet ,  Daniel C. Fain ,  Jianchang Mao ,  Charity A. Rieck

IPC: G06F17/00

CPC classification number: G06F17/30864

Abstract: Various embodiments are directed to a system and method providing associative matching of terms. Candidate terms are selected for building one or more associative matching models from one or more selected candidate sources. Associativity is defined to give editors the ability to label sample associative term pairs from the one or more candidate sources. The editors label sample candidate term pairs as being related. Features are determined that can differentiate associative from non-associative pairs. The selected features are used to build a model. The model is applied to determine whether a received query-candidate pair are associative.

Abstract translation: 各种实施例涉及提供术语的关联匹配的系统和方法。 从一个或多个选择的候选来源中选择候选词来构建一个或多个关联匹配模型。 关联性被定义为使编辑者能够从一个或多个候选来源标记样本关联词对。 编辑者将样品候选词对标记为相关。 确定可以区分缔合和非关联对的特征。 所选功能用于构建模型。 应用该模型来确定所接收的查询候选对是否是关联的。

公开(公告)号：US07406465B2

公开(公告)日：2008-07-29

申请号：US11013298

申请日：2004-12-14

Applicant: Rosemary Jones ,  Daniel C. Fain

Inventor： Rosemary Jones ,  Daniel C. Fain

IPC: G06F17/30 ,  G06F7/00

CPC classification number: G06Q30/02 ,  G06F17/30867 ,  Y10S707/99933 ,  Y10S707/99935 ,  Y10S707/99936 ,  Y10S707/99945

Abstract: The likely relevance of each term of a search-engine query of two or more terms is determined by their deletion probability scores. If the deletion probability scores are significantly different, the deletion probability score can be used to return targeted ads related to the more relevant term or terms along with the search results. Deletion probability scores are determined by first gathering historical records of search queries of two or more terms in which a subsequent query was submitted by the same user after one or more of the terms had been deleted. The deletion probability score for a particular term of a search query is calculated as the ratio of the number of times that particular term was itself deleted prior to a subsequent search by the same user divided by the number of times there were subsequent search queries by the same user in which any term or terms including that given term was deleted by the same user prior to the subsequent search. Terms are not limited to individual alphabetic words.

Abstract translation: 两个或多个术语的搜索引擎查询的每个术语的可能相关性由它们的删除概率得分确定。 如果删除概率分数显着不同，则可以使用删除概率分数来返回与更相关的术语或术语相关的有针对性的广告以及搜索结果。 通过首先收集两个或更多个术语的搜索查询的历史记录来确定删除概率分数，其中在一个或多个术语被删除之后由同一用户提交后续查询。 搜索查询的特定术语的删除概率得分被计算为在相同用户的后续搜索之前本特定术语被删除的次数除以由下一个搜索查询的次数除以 同一用户，其中包括该给定术语的任何术语或术语在相继的用户之前被删除。 条款不限于单个字母词。

公开(公告)号：US08190601B2

公开(公告)日：2012-05-29

申请号：US12471116

申请日：2009-05-22

Applicant: Sanaz Ahari ,  Xiaoxin Yin ,  Farid Hosseini ,  Sarthak Shah ,  Adam Troy ,  Daniel C. Fain ,  Brian MacDonald ,  Nikhil Dandekar ,  Michael Cameron

Inventor： Sanaz Ahari ,  Xiaoxin Yin ,  Farid Hosseini ,  Sarthak Shah ,  Adam Troy ,  Daniel C. Fain ,  Brian MacDonald ,  Nikhil Dandekar ,  Michael Cameron

IPC: G06F7/00 ,  G06F17/30

CPC classification number: G06F17/30864 ,  G06F7/00 ,  G06F17/30 ,  G06F17/30702 ,  G06F17/30867

Abstract: Computer-readable media and computerized methods for automatically organizing search results according to task groups are provided. The methods involve aggregating a gallery of entities (e.g., search queries that share a common categorization) into a query class and assigning a dictionary (e.g., list of terms that are drawn from various sources) to the query class. The task groups are identified from the list of terms within the dictionary. The process of identification includes analyzing patterns of user search behavior to select terms from the list of terms, which reflect popular user search intents, and ranking the selected terms based on predetermined parameters to produce an ordering. Based on the ordering, a set of the selected terms that are highest ranked are declared the task groups. The task groups are employed to arrange the search results on a UI display and to provide a consistent and intuitive format for refining a search.

Abstract translation: 提供了根据任务组自动组织搜索结果的计算机可读介质和计算机化方法。 这些方法包括将实体库（例如，共享公共分类的搜索查询）聚合到查询类中并将查询类别的字典（例如，从各种来源绘制的术语列表）分配。 任务组从字典中的术语列表中识别。 识别过程包括分析用户搜索行为的模式以从反映流行用户搜索意图的术语列表中选择术语，并且基于预定参数对所选项进行排序以产生排序。 根据排序，将排列最高的选定术语集合声明为任务组。 任务组用于在UI显示器上排列搜索结果，并提供一致和直观的格式来完善搜索。

公开(公告)号：US07685197B2

公开(公告)日：2010-03-23

申请号：US11122780

申请日：2005-05-05

Applicant: Daniel C. Fain ,  Thomas Pierce

Inventor： Daniel C. Fain ,  Thomas Pierce

IPC: G06F17/30

CPC classification number: G06F17/30867 ,  G06Q30/02 ,  G06Q30/0256 ,  G06Q30/0257

Abstract: The present invention provides a method and system for identifying terms in a requested Web document which can be used to identify other web page documents in which the user is likely to have an interest. Terms on previously prepared lists are compared to the content of the Web page document, and where matches are found, mathematical techniques including linear regression can be used to determine the relevance of the textual term to the content of the page. This can be used to suggest other linked Web page documents in which the user is likely to have an interest. Where the prepared lists include a term that has been purchased by advertisers, and there is a match for that term on the Web page document, an ad which is likely to interest the reader can be displayed on the requested Web page document, and can include a link to the advertiser's web site.

Abstract translation: 本发明提供了一种用于识别所请求的Web文档中的术语的方法和系统，其可以用于标识用户可能感兴趣的其他网页文档。 将先前准备的列表的条款与网页文档的内容进行比较，并且在找到匹配的地方，可以使用包括线性回归的数学技术来确定文本术语与页面内容的相关性。 这可以用于建议用户可能感兴趣的其他链接的网页文档。 如果准备好的列表包括广告商购买的术语，并且在网页文档上匹配该术语，则可能在所请求的网页文档上显示可能感兴趣的读者的广告，并且可以包括 指向广告客户网站的链接。

公开(公告)号：US07231395B2

公开(公告)日：2007-06-12

申请号：US10155290

申请日：2002-05-24

Applicant: Daniel C. Fain ,  Paul T. Ryan ,  Peter Savich

Inventor： Daniel C. Fain ,  Paul T. Ryan ,  Peter Savich

IPC: G06F17/00

CPC classification number: G06F17/30707 ,  G06F17/30864 ,  G06F17/30882 ,  Y10S707/99942 ,  Y10S707/99943 ,  Y10S707/99944

Abstract: Described herein are methods for creating categorized documents, categorizing documents in a distributed database and categorizing Resulting Pages. Also described herein is an apparatus for searching a distributed database. The method for creating categorized documents generally comprises: initially assuming all documents are of type 1; filtering out all type 2 documents and placing them in a first category; filtering out all type 3 documents and placing them in a second category; and defining all remaining documents as type 4 documents and placing all type 4 documents in a third category. The apparatus for searching a distributed database generally comprises at least one memory device; a computing apparatus; an indexer; a transactional score generator; and a category assignor; a search server; and a user interface in communication with the search server.

Abstract translation: 这里描述了用于创建分类文档的方法，对分布式数据库中的文档进行分类，并对“结果页”进行分类。 本文还描述了一种用于搜索分布式数据库的装置。 创建分类文档的方法通常包括：最初假设所有文档都是类型1; 过滤所有2类文件并将其放在第一类中; 过滤所有3类文件并将其放在第二类中; 并将所有剩余的文档定义为4类文档，并将所有类型4文档放在第三类中。 用于搜索分布式数据库的装置通常包括至少一个存储装置; 计算装置; 索引器 交易记分发生器; 和类别转让人; 搜索服务器 以及与搜索服务器通信的用户界面。