公开(公告)号：US20080033996A1

公开(公告)日：2008-02-07

申请号：US11499181

申请日：2006-08-03

申请人： Anandsudhakar Kesari

发明人： Anandsudhakar Kesari

IPC分类号： G06F17/00 ,  G06F17/28

CPC分类号： G06F17/212 ,  G06F16/951 ,  G06F16/972 ,  G06F17/2247

摘要： To approximate a visual layout of a web page without rendering the page, an object tree representing elements within the page is recursively traversed to determine bounds for the width of the elements, resulting in lower bounds induced for non-leaf nodes by elements within these nodes and upper bounds induced by ancestors and siblings of nodes. For each element, the minimum required width (lower bound), the desired width were there no constraints, and the maximum available width (upper bound) based on constraints of parents are computed, and an approximate width is derived therefrom. A positioning process positions each element within its corresponding parent container by advancing a cursor according to the elements' approximate width and appropriate constraints. The element that contains the most meaningful content is determined based on the amount of weighted content of elements and their position within the page.

摘要翻译： 为了近似网页的可视化布局而不渲染页面，递归地遍历表示页面内的元素的对象树，以确定元素的宽度的界限，从而导致这些节点内的元素对非叶节点的下界 和祖先和节点兄弟姐妹所诱导的上界。 对于每个元素，最小所需宽度（下限），所需宽度没有约束，并且基于父节点的约束计算最大可用宽度（上限），并从其导出近似宽度。 定位过程通过根据元素的大致宽度和适当的约束推进光标来将每个元素定位在其相应的父容器内。 包含最有意义内容的元素基于元素的加权内容的数量及其在页面内的位置来确定。

公开(公告)号：US20110191168A1

公开(公告)日：2011-08-04

申请号：US12700538

申请日：2010-02-04

申请人： Stefan Schroedl ,  Anandsudhakar Kesari ,  Leonardo Neumeyer ,  Anish Nair ,  Sharath Rao

发明人： Stefan Schroedl ,  Anandsudhakar Kesari ,  Leonardo Neumeyer ,  Anish Nair ,  Sharath Rao

IPC分类号： G06Q30/00 ,  G06Q10/00

CPC分类号： G06Q30/0246 ,  G06Q30/0256 ,  G06Q30/0275

摘要： A method for executing multiple, cascading ad auctions, including receiving a request to deliver ads in response to a search query for display on a search results page having a plurality of ad sections; executing at least first and second sequential auctions for the plurality of ad sections by: ranking the ads in the first auction and identifying which of the ads placeable in the North section to deliver according to a first utility function that considers a relevance of the search results page with the ads placed; ranking a remainder of the ads not identified for delivery to the North section in the second auction and identifying which of the remainder of the ranked ads to deliver to a second section based on a second utility function; and delivering the ads identified by the first utility function for display in the North section and the ads identified by the second utility function for display in the second section.

摘要翻译： 一种用于执行多个级联广告拍卖的方法，包括接收响应于搜索查询递送广告以在具有多个广告部分的搜索结果页面上显示的请求; 对所述多个广告部分执行至少第一和第二顺序拍卖，其中：对所述第一拍卖中的广告进行排序，并且根据考虑所述搜索结果的相关性的第一效用函数来识别所述北部可放置在哪个广告中的哪个广告 页面放置广告; 在第二次拍卖中排列未被识别用于递送到北部的广告的剩余部分，并且基于第二效用函数来确定要传送到第二部分的剩余排名广告中的哪一个; 并且递送由第一效用函数识别的广告以在北部显示，以及由第二效用函数标识的广告在第二部分中显示。

公开(公告)号：US20090063538A1

公开(公告)日：2009-03-05

申请号：US11847989

申请日：2007-08-30

申请人： Krishna Prasad CHITRAPURA ,  Anandsudhakar Kesari ,  Alok Kirpal ,  Mahesh Tiyyagura

发明人： Krishna Prasad CHITRAPURA ,  Anandsudhakar Kesari ,  Alok Kirpal ,  Mahesh Tiyyagura

IPC分类号： G06F17/30

CPC分类号： G06F16/9566

摘要： Techniques are described for normalizing dynamic URLs using a hierarchical organization of a web site. Given web pages associated with a web site, an information extraction method is used to generate data structures that represent the content or structure of each of the web pages. These data structures are appended to the corresponding dynamic URLs. The modified URLs with the data structures are tokenized with the resulting tokens clustered to create a hierarchical organization. Nodes of the hierarchical organization may be merged based upon occurrence or patterns of content and structure. The merged hierarchical organization may then be pruned to remove irrelevant information and to reduce the memory footprint of the hierarchical organization. When a new dynamic URL is received, the new dynamic URL is matched to the hierarchical organization. Important parameters are taken into account and irrelevant information may be removed. Based upon the matching to the hierarchical organization, a normalized URL is returned.

摘要翻译： 描述了使用网站的分级组织规范化动态URL的技术。 给定与网站相关联的网页，使用信息提取方法来生成表示每个网页的内容或结构的数据结构。 这些数据结构附加到相应的动态URL。 具有数据结构的修改的URL被标记化，并且生成的令牌被聚集以创建分层组织。 可以基于内容和结构的发生或模式来合并分层组织的节点。 然后可以修剪合并的层次结构，以消除不相关的信息并减少分层组织的内存占用。 当接收到新的动态URL时，新的动态URL与分层组织相匹配。 考虑重要参数，可能会删除不相关的信息。 基于与分级组织的匹配，返回归一化的URL。

公开(公告)号：US20110231241A1

公开(公告)日：2011-09-22

申请号：US12727068

申请日：2010-03-18

申请人： Anandsudhakar Kesari ,  Stefan Schroedl ,  Leonardo Neumeyer

发明人： Anandsudhakar Kesari ,  Stefan Schroedl ,  Leonardo Neumeyer

IPC分类号： G06Q30/00 ,  G06F17/30

CPC分类号： G06Q30/0243 ,  G06F16/335 ,  G06F16/9535 ,  G06Q30/02

摘要： Embodiments are directed towards employing long and short term historical user click propensity behaviors to adapt or filter a number of advertisements displayed and their location on a search results' page. A network device tracks a user's short and long term historical click behaviors. For a given search query for the user, a variety of candidate advertisements are selected. A normalized click-through rate (COEC) is estimated for each advertisement. The COEC and the user's short and long term click behavior, represented by User Click Propensity (UCP), is used to generate a User effective Cost Per Thousand (UeCPM) value. Candidate advertisements are filtered based on a minimum threshold value for UeCPMs. Page placement for the remaining advertisements is determined based on a user expected revenue for an advertisement determined from the UCP. Advertisements having a user expected revenue above another threshold are placed in a north page location.

摘要翻译： 实施例旨在采用长期和短期的历史用户点击倾向行为来适应或过滤所显示的广告数量及其在搜索结果页面上的位置。 网络设备跟踪用户的短期和长期历史点击行为。 对于用户的给定搜索查询，选择各种候选广告。 对于每个广告估计正常点击率（COEC）。 用户点击倾向（UCP）代表的COEC和用户的短期和长期点击行为用于生成用户有效成本（UeCPM）值。 根据UeCPM的最小阈值对候选广告进行过滤。 基于从UCP确定的广告的用户预期收入来确定剩余广告的页面位置。 具有用户预期收入高于另一阈值的广告被放置在北页位置。

公开(公告)号：US20110191315A1

公开(公告)日：2011-08-04

申请号：US12700438

申请日：2010-02-04

申请人： Leonardo Neumeyer ,  Anish Nair ,  Stefan Schroedl ,  Anandsudhakar Kesari

发明人： Leonardo Neumeyer ,  Anish Nair ,  Stefan Schroedl ,  Anandsudhakar Kesari

IPC分类号： G06F17/30 ,  G06Q30/00

CPC分类号： G06Q30/0243

摘要： A method for reducing ad impact on users in a search results page includes receiving a request to deliver ads in response to a search query for display on a search results page; receiving relevance scores for a plurality of ranked web results that are to be served to the search results page; ranking a plurality of ads identified as relevant to the search query according to a position-normalized, click-through-rate metric and bid values, wherein a predetermined number of the top-ranked ads are placeable in a plurality of North ad slots; incrementally and additively placing the placeable ads sequentially according to rank (k) in their respective North ad slots until a utility score generated by a utility function for a current iteration of ads fails to exceed a threshold value; and delivering to the search results page the ads placed in the North ad slots.

摘要翻译： 用于减少对搜索结果页面中的用户的广告影响的方法包括接收响应于搜索查询来递送广告以在搜索结果页面上显示的请求; 接收将被提供给搜索结果页面的多个排名网络结果的相关性得分; 根据位置归一化，点击率指标和出价值对与搜索查询相关的多个广告进行排序，其中预定数量的顶级广告可放置在多个北广告时隙中; 按照其各自的北广告插槽中的等级（k）逐个地并且相加地放置可放置广告，直到由当前广告的迭代的效用函数产生的效用得分不能超过阈值; 并向搜索结果页面投放放置在北方广告位置的广告。

公开(公告)号：US09536015B1

公开(公告)日：2017-01-03

申请号：US13604874

申请日：2012-09-06

申请人： Matthew E. Kulick ,  Anandsudhakar Kesari ,  Sumitro Samaddar ,  Jinan Lou ,  Maureen Heymans

发明人： Matthew E. Kulick ,  Anandsudhakar Kesari ,  Sumitro Samaddar ,  Jinan Lou ,  Maureen Heymans

IPC分类号： G06F17/30

CPC分类号： G06Q50/01 ,  G06Q30/0201

摘要： Techniques for using social networking information may include making a determination that content authored by a first party references a second party more than a specified number of times. An adjustment in affinity may be made between the first party and the second party in the social network in response to the determination. Making the adjustment in affinity may include modifying a social graph of at least the first party that is referenced by the social network in order to reflect the adjustment in affinity. Modifying the social graph includes creating a closer connection between the first party and the second party than had existed prior to the determination. In response to the adjustment in the affinity, promotion is adjusted, to the first party, of content in the social network that has a connection to the second party.

摘要翻译： 用于使用社交网络信息的技术可以包括确定由第一方创作的内容引用第二方超过指定次数。 响应于确定，可以在社交网络中的第一方和第二方之间进行亲和度的调整。 进行亲和度调整可以包括修改由社交网络引用的至少第一方的社交图，以反映亲和度的调整。 修改社交图包括建立第一方与第二方之间的紧密联系，而不是在确定之前存在。 为了回应亲和力的调整，对与第二方有联系的社交网络中的内容进行第一方调整。

公开(公告)号：US20090138362A1

公开(公告)日：2009-05-28

申请号：US12363756

申请日：2009-01-31

申请人： Stefan Schroedl ,  Anandsudhakar Kesari

发明人： Stefan Schroedl ,  Anandsudhakar Kesari

IPC分类号： G06Q30/00 ,  G06Q40/00 ,  G06F15/18 ,  G06F19/00 ,  G06N5/02

CPC分类号： G06Q30/02 ,  G06Q30/0256 ,  G06Q30/0257 ,  G06Q30/0263 ,  G06Q30/0275 ,  G06Q30/0277 ,  G06Q40/04

摘要： The present invention is directed towards systems and methods for ranking and providing advertisements in a position auction. The method of the present invention comprises receiving a search query and selecting at least one keyword based upon the search query. A list containing at least one keyword based upon the search query is returned and a list comprising at least one bid corresponding to the returned list of keywords is retrieved. The search query and list comprising at least one bid are used to train an offline simulator. The offline simulator creates a model that predicts optimal scoring factors. A priority score corresponding to each bid is computed using the optimal scoring factors and used to rank the list of bids. Advertisements are then provided corresponding to a plurality of the highest ranking bids.

摘要翻译： 本发明涉及用于在位置拍卖中排列和提供广告的系统和方法。 本发明的方法包括接收搜索查询，并且基于搜索查询来选择至少一个关键字。 返回基于搜索查询包含至少一个关键字的列表，并且检索包括与返回的关键字列表相对应的至少一个出价的列表。 包括至少一个出价的搜索查询和列表用于训练离线模拟器。 离线模拟器创建一个预测最佳评分因子的模型。 使用最优评分因子计算与每个出价相对应的优先级分数，并用于对出价列表进行排名。 然后根据多个最高排名的出价提供广告。