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    • 1. 发明授权
    • Combining predictive models of forgetting, relevance, and cost of interruption to guide automated reminding
    • 结合遗忘,相关性和中断成本的预测模型来指导自动提醒
    • US08195584B2
    • 2012-06-05
    • US13172405
    • 2011-06-29
    • Semiha Ece KamarEric J. Horvitz
    • Semiha Ece KamarEric J. Horvitz
    • G06N5/00
    • G06Q10/109
    • The claimed matter provides systems and/or techniques that develop or use predictive models of human forgetting to effectuate automated reminding. The system includes the use of predictive models that infer the probability that aspects of items will be forgotten, models that evaluate the relevance of recalling aspects of items in different settings, based on contextual information related to user attributes associated with the items, and models of the context-sensitive cost of interrupting users with reminders. The system can combine the probability of users forgetting aspects of an item with an assessed cost of forgetting those aspects to ascertain expected costs for not being reminded about events, compare expected costs for not being reminded with expected costs for interrupting users, and based on comparisons between expected costs for being reminded and expected costs for interrupting users regarding events, generate and deliver reminder notifications to users about items.
    • 所要求保护的内容提供开发或使用人类遗忘预测模型以实现自动提醒的系统和/或技术。 该系统包括使用预测模型来推断项目的各个方面将被遗忘的概率,基于与项目相关联的用户属性相关的上下文信息来评估在不同设置中的项目的召回方面的相关性的模型,以及模型 中断用户提醒的上下文敏感成本。 该系统可以将用户遗忘项目方面的概率与忘记这些方面的评估成本相结合,以确定不被提醒的事件的预期成本,将预期成本与提醒用户的预期成本进行比较,并根据比较 在提醒的预期成本和中断用户关于事件的预期成本之间,生成并向用户发送关于项目的提醒通知。
    • 2. 发明申请
    • COMBINING PREDICTIVE MODELS OF FORGETTING, RELEVANCE, AND COST OF INTERRUPTION TO GUIDE AUTOMATED REMINDING
    • 组合中断引导自动提醒的预测模型,相关性和成本
    • US20110258153A1
    • 2011-10-20
    • US13172405
    • 2011-06-29
    • Semiha Ece KamarEric Horvitz
    • Semiha Ece KamarEric Horvitz
    • G06N5/02G06F15/18
    • G06Q10/109
    • The claimed matter provides systems and/or techniques that develop or use predictive models of human forgetting to effectuate automated reminding. The system includes the use of predictive models that infer the probability that aspects of items will be forgotten, models that evaluate the relevance of recalling aspects of items in different settings, based on contextual information related to user attributes associated with the items, and models of the context-sensitive cost of interrupting users with reminders. The system can combine the probability of users forgetting aspects of an item with an assessed cost of forgetting those aspects to ascertain expected costs for not being reminded about events, compare expected costs for not being reminded with expected costs for interrupting users, and based on comparisons between expected costs for being reminded and expected costs for interrupting users regarding events, generate and deliver reminder notifications to users about items.
    • 所要求保护的内容提供开发或使用人类遗忘预测模型以实现自动提醒的系统和/或技术。 该系统包括使用预测模型来推断项目的各个方面将被遗忘的概率,基于与项目相关联的用户属性相关的上下文信息来评估在不同设置中的项目的召回方面的相关性的模型,以及模型 中断用户提醒的上下文敏感成本。 该系统可以将用户遗忘项目方面的概率与忘记这些方面的评估成本相结合,以确定不被提醒的事件的预期成本,将预期成本与提醒用户的预期成本进行比较,并根据比较进行比较 在提醒的预期成本和中断用户关于事件的预期成本之间,生成并向用户发送关于项目的提醒通知。
    • 3. 发明申请
    • MULTI-STEP IMPRESSION CAMPAIGNS
    • 多阶段印象裁判
    • US20130006754A1
    • 2013-01-03
    • US13174329
    • 2011-06-30
    • Eric HorvitzLili ChengRoger BargaXuedong HuangZachary ApterSemiha Ece Kamar
    • Eric HorvitzLili ChengRoger BargaXuedong HuangZachary ApterSemiha Ece Kamar
    • G06Q30/00
    • G06Q30/00G06Q30/0251
    • Various embodiments are described for computerized advertising systems and methods. The system may include an ad server that includes an impression campaign engine configured to associate a target user profile with a plurality of computing devices. The ad server is also configured to receive a multi-step impression plan including a plurality of triggers from an advertiser. Each trigger is associated with a different advertisement to be served to at least one of the plurality of devices. The system also includes an ad serving engine configured to serve a first advertisement to a first device in response to making an inference from sensors or detecting a first trigger, and a second advertisement to a second device in response to a second inference or detecting a second trigger, according to the impression plan. A predictive model developed from machine learning may be used to develop a learning-based multi-step impression plan.
    • 针对计算机广告系统和方法描述了各种实施例。 系统可以包括广告服务器,其包括被配置为将目标用户简档与多个计算设备相关联的印象活动引擎。 广告服务器还被配置为接收包括来自广告主的多个触发器的多步展示计划。 每个触发器与要被提供给多个设备中的至少一个的不同广告相关联。 该系统还包括一个广告服务引擎,该广告服务引擎被配置为响应于从传感器进行推断或检测第一触发而向第一设备发送第一广告,并响应于第二推断或检测第二个推断,向第二设备发送第二广告 触发,按照印象计划。 可以使用从机器学习开发的预测模型来开发基于学习的多步印象计划。
    • 4. 发明申请
    • GENERATION OF IMPRESSION PLANS FOR PRESENTING AND SEQUENCING ADVERTISEMENT AND SALES OPPORTUNITIES ALONG POTENTIAL ROUTES
    • 陈述和排序广告的印制计划的生成和潜在的路线上的机会
    • US20100332315A1
    • 2010-12-30
    • US12492861
    • 2009-06-26
    • Semiha Ece KamarEric HorvitzChristopher A. MeekStephen Lombardi
    • Semiha Ece KamarEric HorvitzChristopher A. MeekStephen Lombardi
    • G06Q30/00G06F17/10
    • G06Q30/0247G06Q30/02G06Q30/0254G06Q30/0261
    • A mobile device may present advertisements to users. However, advertisements may be ineffective or dangerous if presented when the attention of the user is unavailable (e.g., while operating a vehicle at a busy intersection.) It may also be desirable to select a sequence of advertisements that interrelate, or that relate the route of the user to an advertised product or service. Therefore, potential routes may be identified (e.g., based on user history or nearby locations of interest), and for potential routes, advertisement opportunities may be identified where the user may have an at least partial attention availability (e.g., traffic signals and fuel stops.) Advertisements may be selected for presentation at the advertisement opportunities of respective potential routes. Additionally, advertisement opportunities may be offered to advertisers in an auction model, and advertisers may specify conditions of advertisements (e.g., competitive placement exclusive of competitors' advertisements, or combinatorial placement of several advertisements.)
    • 移动设备可以向用户呈现广告。 然而,如果在用户的注意不可用时(例如,在繁忙的十字路口操作车辆时),则在广告可能无效或危险的情况下可能是无效的或者危险的。还可能期望选择一系列的广告,这些广告相互关联或涉及路线 用户到广告的产品或服务。 因此,可以识别潜在路线(例如,基于用户历史或附近的兴趣位置),并且对于潜在的路线,可以识别广告机会,其中用户可能具有至少部分注意力可用性(例如,交通信号和燃料停止 可以选择广告来呈现各自潜在路线的广告机会。 此外,可以在拍卖模式中向广告商提供广告机会,并且广告商可以指定广告的条件(例如,排除竞争对手的广告的竞争性布置,或组合放置多个广告)。
    • 5. 发明申请
    • COMBINING PREDICTIVE MODELS OF FORGETTING, RELEVANCE, AND COST OF INTERRUPTION TO GUIDE AUTOMATED REMINDING
    • 组合中断引导自动提醒的预测模型,相关性和成本
    • US20090327169A1
    • 2009-12-31
    • US12163045
    • 2008-06-27
    • Semiha Ece KamarEric Horvitz
    • Semiha Ece KamarEric Horvitz
    • G06F17/00G06N5/02
    • G06Q10/109
    • The claimed matter provides systems and/or techniques that develop or use predictive models of human forgetting to effectuate automated reminding. The system includes the use of predictive models that infer the probability that aspects of items will be forgotten, models that evaluate the relevance of recalling aspects of items in different settings, based on contextual information related to user attributes associated with the items, and models of the context-sensitive cost of interrupting users with reminders. The system can combine the probability of users forgetting aspects of an item with an assessed cost of forgetting those aspects to ascertain expected costs for not being reminded about events, compare expected costs for not being reminded with expected costs for interrupting users, and based on comparisons between expected costs for being reminded and expected costs for interrupting users regarding events, generate and deliver reminder notifications to users about items.
    • 所要求保护的内容提供开发或使用人类遗忘预测模型以实现自动提醒的系统和/或技术。 该系统包括使用预测模型来推断项目的各个方面将被遗忘的概率,基于与项目相关联的用户属性相关的上下文信息来评估在不同设置中的项目的召回方面的相关性的模型,以及模型 中断用户提醒的上下文敏感成本。 该系统可以将用户遗忘项目方面的概率与忘记这些方面的评估成本相结合,以确定不被提醒的事件的预期成本,将预期成本与提醒用户的预期成本进行比较,并根据比较进行比较 在提醒的预期成本和中断用户关于事件的预期成本之间,生成并向用户发送关于项目的提醒通知。
    • 6. 发明授权
    • Combining predictive models of forgetting, relevance, and cost of interruption to guide automated reminding
    • 结合遗忘,相关性和中断成本的预测模型来指导自动提醒
    • US07996338B2
    • 2011-08-09
    • US12163045
    • 2008-06-27
    • Semiha Ece KamarEric J. Horvitz
    • Semiha Ece KamarEric J. Horvitz
    • G06N5/00
    • G06Q10/109
    • The claimed matter provides systems and/or techniques that develop or use predictive models of human forgetting to effectuate automated reminding. The system includes the use of predictive models that infer the probability that aspects of items will be forgotten, models that evaluate the relevance of recalling aspects of items in different settings, based on contextual information related to user attributes associated with the items, and models of the context-sensitive cost of interrupting users with reminders. The system can combine the probability of users forgetting aspects of an item with an assessed cost of forgetting those aspects to ascertain expected costs for not being reminded about events, compare expected costs for not being reminded with expected costs for interrupting users, and based on comparisons between expected costs for being reminded and expected costs for interrupting users regarding events, generate and deliver reminder notifications to users about items.
    • 所要求保护的内容提供开发或使用人类遗忘预测模型以实现自动提醒的系统和/或技术。 该系统包括使用预测模型来推断项目的各个方面将被遗忘的概率,基于与项目相关联的用户属性相关的上下文信息来评估在不同设置中的项目的召回方面的相关性的模型,以及模型 中断用户提醒的上下文敏感成本。 该系统可以将用户遗忘项目方面的概率与忘记这些方面的评估成本相结合,以确定不被提醒的事件的预期成本,将预期成本与提醒用户的预期成本进行比较,并根据比较 在提醒的预期成本和中断用户关于事件的预期成本之间,生成并向用户发送关于项目的提醒通知。