公开(公告)号：US20160063573A1

公开(公告)日：2016-03-03

申请号：US14565197

申请日：2014-12-09

申请人： TubeMogul, Inc.

发明人： Kevin Thakkar ,  John M. Trenkle ,  John Hughes ,  Adam Rose ,  Jason Lopatecki

IPC分类号： G06Q30/02

CPC分类号： G06Q30/0275 ,  G06Q30/0242 ,  G06Q30/0249

摘要： Systems and methods are disclosed for optimizing an online advertising campaign both before the campaign begins, and dynamically during the campaign. Optimizations are performed comparatively between a plurality of MPs (Media Properties) based on their relative cost-per-engagement. Comparisons are performed by first stack ranking MP inventory including any of sites, feeds, and verticals, based on cost per engagement. Once ranked, scores are assigned to the targeted inventory and a mean score is determined. Then, the inventory is rated as high, normal, or low impact based on their scores compared with the mean and a standard deviation for all scores. Higher impact sites with scores at least a standard deviation above the mean are initially favored, and the MP targeting strategy is dynamically adjusted during the campaign based on periodically re-evaluating the MP rankings, frequencies of engagement, and campaign progress relative to fulfillment in an allotted run time.

摘要翻译： 公开了系统和方法，用于在活动开始之前优化在线广告活动，并在活动期间动态地进行。 基于它们的相对每参与成本，在多个MP（媒体属性）之间进行相对优化。 基于每次参与成本，通过第一堆排名MP库存（包括任何站点，Feed和垂直）进行比较。 一旦排名，分数被分配到目标库存，并确定平均分数。 然后，根据与平均值和所有得分的标准差相比，库存被评为高，正常或低的影响。 最初有利于得分至少达到平均值以上的标准偏差的较高影响力站点，并且在活动期间根据定期重新评估MP排名，参与频率和相对于在 分配运行时间。

公开(公告)号：US20170094331A1

公开(公告)日：2017-03-30

申请号：US15348780

申请日：2016-11-10

申请人： TubeMogul, Inc.

发明人： Jason Lopatecki ,  Adam Rose ,  John Hughes ,  Brett Wilson

IPC分类号： H04N21/25 ,  H04N21/658 ,  G06F17/30 ,  H04N21/2668 ,  H04N21/278 ,  H04N21/4788 ,  H04N21/442 ,  H04N21/2662

CPC分类号： H04N21/252 ,  G06F15/16 ,  G06F17/3082 ,  G06F17/3084 ,  G06F17/30849 ,  H04L67/02 ,  H04N21/2662 ,  H04N21/2668 ,  H04N21/278 ,  H04N21/44222 ,  H04N21/4788 ,  H04N21/6582

摘要： Presentation of a video clip is made to persons having a high probability of viewing the clip. A database containing viewers of previously offered video clips is analyzed to determine similarities of preferences among viewers. When a new video clip has been watched by one or more viewers in the database, those viewers who have watched the new clip with positive results are compared with others in the database who have not yet seen it. Prospective viewers with similar preferences are identified as high likelihood candidates to watch the new clip when presented. Bids to offer the clip are based on the degree of likelihood. For one embodiment, a data collection agent (DCA) is loaded to a player and/or to a web page to collect viewing and behavior information to determine viewer preferences. Viewer behavior may be monitored passively by different disclosed methods.

公开(公告)号：US20140278938A1

公开(公告)日：2014-09-18

申请号：US14144016

申请日：2013-12-30

申请人： TUBEMOGUL, INC.

发明人： John Hughes ,  Adam Rose ,  John M. Trenkle

IPC分类号： G06Q30/02

CPC分类号： G06Q30/0246 ,  G06Q30/0273

摘要： Systems and methods are disclosed for characterizing websites and viewers, for predicting GRPs (Gross Rating Points) for online advertising media campaigns, and for pricing media campaigns according to GRPs delivered as opposed to impressions delivered. To predict GRPs for a campaign, systems and methods are disclosed for first characterizing polarized websites and then characterizing polarized viewers. To accomplish this, a truth set of viewers with known characteristics is first established and then compared with historic and current media viewing activity to determine a degree of polarity for different Media Properties (MPs)—typically websites offering ads—with respect to gender and age bias. A broader base of polarized viewers is then characterized for age and gender bias, and their propensity to visit a polarized MP is rated. Based on observed and calculated parameters, a GRP total is then predicted and priced to a client/advertiser for an online ad campaign.

公开(公告)号：US20140289017A1

公开(公告)日：2014-09-25

申请号：US14295811

申请日：2014-06-04

申请人： TUBEMOGUL, INC.

发明人： John M. Trenkle ,  John Hughes ,  Adam Rose ,  Kevin Thakkar ,  Jason Lopatecki

IPC分类号： G06Q30/02

CPC分类号： G06Q30/0275 ,  G06Q10/06375 ,  G06Q10/067 ,  G06Q30/0204 ,  G06Q30/0242

摘要： Systems and methods are disclosed for employing supervised machine learning methods with activities and attributes of viewers with truth as input, to produce models that are utilized in determining probabilities that an unknown viewer belongs to one or more demographic segment categories. Using these models for processing viewer behavior, over a period of time a database of known categorized viewers is established, each categorized viewer having a probability of belonging to one or more segment categories. These probabilities are then used in bidding for online advertisements in response to impression opportunities offered in online media auctions. The probabilities are also used in predicting on-target impressions and GRPs (Gross Rating Points) in advance of online advertising media campaigns, and pricing those campaigns to advertiser/clients. Strategies are also disclosed for fulfilling a campaign when an available inventory of known categorized viewers is not adequate to fulfill a campaign in a required runtime.

摘要翻译： 公开了采用监督机器学习方法的系统和方法，其具有以真实为输入的观众的活动和属性，以产生用于确定未知观看者属于一个或多个人口统计分类类别的概率的模型。 使用这些模型来处理查看器行为，在一段时间内建立已知分类的观众的数据库，每个分类的观众具有属于一个或多个分段类别的概率。 然后，这些概率可用于招标在线广告，以回应在线媒体拍卖中提供的印象。 这些概率也可用于在网络广告媒体广告系列之前预测目标印象和GRP（总评分），并将这些广告系列定价给广告客户/客户。 当已知分类的观众的可用广告资源不足以在所需的运行时间内完成广告系列时，还会披露策略来实现广告系列。

公开(公告)号：US20140278912A1

公开(公告)日：2014-09-18

申请号：US14167183

申请日：2014-01-29

申请人： TubeMogul, Inc.

发明人： John Hughes ,  Adam Rose ,  John M. Trenkle ,  Kevin Thakkar ,  Jason Lopatecki

IPC分类号： G06Q30/02

CPC分类号： G06Q30/0242

摘要： Systems and methods are disclosed for characterizing websites and viewers, for predicting GRPs (Gross Rating Points) for online advertising media campaigns, and for pricing media campaigns according to GRPs delivered as opposed to impressions delivered. To predict GRPs for a campaign, systems and methods are disclosed for first characterizing polarized websites and then characterizing polarized viewers. To accomplish this, a truth set of viewers with known characteristics is first established and then compared with historic and current media viewing activity to determine a degree of polarity for different Media Properties (MPs)—typically websites offering ads—with respect to gender and age bias. A broader base of polarized viewers is then characterized for age and gender bias, and their propensity to visit a polarized MP is rated. Based on observed and calculated parameters, a GRP total is then predicted and priced to a client/advertiser for an online ad campaign.

摘要翻译： 披露了用于表征网站和观众的系统和方法，用于预测在线广告媒体活动的GRP（总评分），以及根据交付的展示交付而不是展示的广告资源定价媒体活动。 为了预测广告系列的GRP，首先公开了系统和方法来首先描绘极化网站，然后描绘极化观众。 要做到这一点，首先建立一个具有已知特征的真实观众，然后与历史和当前的媒体观看活动进行比较，以确定不同媒体属性（MP）的极性程度 - 特别是提供广告的网站 - 关于性别和年龄 偏压。 然后，更广泛的极化观众基础的特征是年龄和性别偏见，并且评估他们访问极化MP的倾向。 根据观察和计算的参数，然后预测GRP总额并将其定价到在线广告系列的客户/广告客户。

公开(公告)号：US20170026479A1

公开(公告)日：2017-01-26

申请号：US15285356

申请日：2016-10-04

申请人： TubeMogul, Inc.

发明人： Jason Lopatecki ,  Adam Rose ,  John Hughes ,  Brett Wilson

IPC分类号： H04L29/08 ,  H04N21/442 ,  H04N21/258 ,  H04N21/25

CPC分类号： H04L67/22 ,  G06F15/16 ,  H04N21/252 ,  H04N21/25891 ,  H04N21/44222 ,  H04N21/6582

摘要： Various user behaviors are passively monitored and recorded when a user/viewer interacts with a network video player, e.g. a web video player, while watching an online video clip. For one embodiment, a data collection agent (DCA) is loaded to the player and/or to a web page that displays the video clip. The DCA passively collects detailed viewing and behavior information without requiring any specific input or actions on the part of the user. Indications of user preferences are inferred by user actions leading up to viewing the video, while viewing the video, and just after and still related to viewing the video. The DCA periodically sends this information to a central server where it is stored in a central database and where it is used to determine preference similarities among different users. Recorded user preference information may also be used to rate a video itself.

摘要翻译： 当用户/观看者与网络视频播放器进行交互时，各种用户行为被动地监视和记录。 一个网络视频播放器，同时观看在线视频剪辑。 对于一个实施例，数据采集代理（DCA）被加载到播放器和/或显示视频剪辑的网页。 DCA被动地收集详细的观察和行为信息，而不需要用户的任何特定的输入或动作。 用户偏好的指示通过用户行为导致观看视频，观看视频以及与观看视频相关仍然相关的用户动作来推断。 DCA将该信息周期性地发送到中央服务器，其中它被存储在中央数据库中，并且其用于确定不同用户之间的偏好相似性。 记录的用户偏好信息也可以用于对视频本身进行评估。

公开(公告)号：US09485316B2

公开(公告)日：2016-11-01

申请号：US13975196

申请日：2013-08-23

申请人： TubeMogul, Inc.

发明人： Jason Lopatecki ,  Adam Rose ,  John Hughes ,  Brett Wilson

IPC分类号： H04H60/33 ,  H04H60/45 ,  H04H60/56 ,  H04H60/32 ,  H04L29/08 ,  G06F15/16 ,  H04N21/25 ,  H04N21/442 ,  H04N21/658

CPC分类号： H04L67/22 ,  G06F15/16 ,  H04N21/252 ,  H04N21/25891 ,  H04N21/44222 ,  H04N21/6582

摘要： Various user behaviors are passively monitored and recorded when a user/viewer interacts with a network video player, e.g. a web video player, while watching an online video clip. For one embodiment, a data collection agent (DCA) is loaded to the player and/or to a web page that displays the video clip. The DCA passively collects detailed viewing and behavior information without requiring any specific input or actions on the part of the user. Indications of user preferences are inferred by user actions leading up to viewing the video, while viewing the video, and just after and still related to viewing the video. The DCA periodically sends this information to a central server where it is stored in a central database and where it is used to determine preference similarities among different users. Recorded user preference information may also be used to rate a video itself.

公开(公告)号：US20140278937A1

公开(公告)日：2014-09-18

申请号：US14143984

申请日：2013-12-30

申请人： TUBEMOGUL, INC.

发明人： John Hughes ,  Adam Rose ,  John M. Trenkle

IPC分类号： G06Q30/02

CPC分类号： G06Q30/0246 ,  G06Q30/0273

摘要： Systems and methods are disclosed for characterizing websites and viewers, for predicting GRPs (Gross Rating Points) for online advertising media campaigns, and for pricing media campaigns according to GRPs delivered as opposed to impressions delivered. To predict GRPs for a campaign, systems and methods are disclosed for first characterizing polarized websites and then characterizing polarized viewers. To accomplish this, a truth set of viewers with known characteristics is first established and then compared with historic and current media viewing activity to determine a degree of polarity for different Media Properties (MPs)—typically websites offering ads—with respect to gender and age bias. A broader base of polarized viewers is then characterized for age and gender bias, and their propensity to visit a polarized MP is rated. Based on observed and calculated parameters, a GRP total is then predicted and priced to a client/advertiser for an online ad campaign.