-
1.发明授权Precomputation of context-sensitive policies for automated inquiry and action under uncertainty 有权对不确定性进行自动查询和行动的情境敏感政策的预先计算公开(公告)号：US07428521B2
公开(公告)日：2008-09-23
申请号：US11172016
申请日：2005-06-29
申请人： Eric J. Horvitz , Paul B. Koch , Raman K. Sarin
发明人： Eric J. Horvitz , Paul B. Koch , Raman K. Sarin
摘要： Learning, inference, and decision making with probabilistic user models, including considerations of preferences about outcomes under uncertainty, may be infeasible on portable devices. The subject invention provides systems and methods for pre-computing and storing policies based on offline preference assessment, learning, and reasoning about ideal actions and interactions, given a consideration of uncertainties, preferences, and/or future states of the world. Actions include ideal real-time inquiries about a state, using pre-computed value-of-information analyses. In one specific example, such pre-computation can be applied to automatically generate and distribute call-handling policies for cell phones. The methods can employ learning of Bayesian network user models for predicting whether users will attend meetings on their calendar and the cost of being interrupted by incoming calls should a meeting be attended.
摘要翻译： 使用概率用户模型进行学习，推理和决策，包括对不确定性下的结果的偏好的考虑，在便携式设备上可能是不可行的。 本发明提供了在考虑到世界的不确定性，偏好和/或未来状态的情况下，基于离线偏好评估，学习和关于理想动作和交互的推理来预先计算和存储策略的系统和方法。 行动包括使用预先计算的信息价值分析的理想实时查询状态。 在一个具体示例中，可以应用这种预计算来自动生成和分发用于蜂窝电话的呼叫处理策略。 这些方法可以利用贝叶斯网络用户模型的学习来预测用户是否参加会议，以及如果出席会议，将会有来电打扰的成本。
-
2.发明授权Precomputation of context-sensitive policies for automated inquiry and action under uncertainty 有权对不确定性进行自动查询和行动的情境敏感政策的预先计算公开(公告)号：US07613670B2
公开(公告)日：2009-11-03
申请号：US11969053
申请日：2008-01-03
申请人： Eric J. Horvitz , Paul B. Koch , Raman K. Sarin
发明人： Eric J. Horvitz , Paul B. Koch , Raman K. Sarin
摘要： Learning, inference, and decision making with probabilistic user models, including considerations of preferences about outcomes under uncertainty, may be infeasible on portable devices. The subject invention provides systems and methods for pre-computing and storing policies based on offline preference assessment, learning, and reasoning about ideal actions and interactions, given a consideration of uncertainties, preferences, and/or future states of the world. Actions include ideal real-time inquiries about a state, using pre-computed value-of-information analyses. In one specific example, such pre-computation can be applied to automatically generate and distribute call-handling policies for cell phones. The methods can employ learning of Bayesian network user models for predicting whether users will attend meetings on their calendar and the cost of being interrupted by incoming calls should a meeting be attended.
摘要翻译： 使用概率用户模型进行学习，推理和决策，包括对不确定性下的结果的偏好的考虑，在便携式设备上可能是不可行的。 本发明提供了在考虑到世界的不确定性，偏好和/或未来状态的情况下，基于离线偏好评估，学习和关于理想动作和交互的推理来预先计算和存储策略的系统和方法。 行动包括使用预先计算的信息价值分析的理想实时查询状态。 在一个具体示例中，可以应用这种预计算来自动生成和分发用于蜂窝电话的呼叫处理策略。 这些方法可以利用贝叶斯网络用户模型的学习来预测用户是否参加会议，以及如果出席会议，将会有来电打扰的成本。
-
3.发明申请PRECOMPUTATION OF CONTEXT-SENSITIVE POLICIES FOR AUTOMATED INQUIRY AND ACTION UNDER UNCERTAINTY 有权用于自动查询和不确定行为的上下文敏感政策的预处理公开(公告)号：US20080162394A1
公开(公告)日：2008-07-03
申请号：US11969053
申请日：2008-01-03
申请人： Eric J. Horvitz , Paul B. Koch , Raman K. Sarin
发明人： Eric J. Horvitz , Paul B. Koch , Raman K. Sarin
IPC分类号： G06N5/02
摘要： Learning, inference, and decision making with probabilistic user models, including considerations of preferences about outcomes under uncertainty, may be infeasible on portable devices. The subject invention provides systems and methods for pre-computing and storing policies based on offline preference assessment, learning, and reasoning about ideal actions and interactions, given a consideration of uncertainties, preferences, and/or future states of the world. Actions include ideal real-time inquiries about a state, using pre-computed value-of-information analyses. In one specific example, such pre-computation can be applied to automatically generate and distribute call-handling policies for cell phones. The methods can employ learning of Bayesian network user models for predicting whether users will attend meetings on their calendar and the cost of being interrupted by incoming calls should a meeting be attended.
摘要翻译： 使用概率用户模型进行学习，推理和决策，包括对不确定性下的结果的偏好的考虑，在便携式设备上可能是不可行的。 本发明提供了在考虑到世界的不确定性，偏好和/或未来状态的情况下，基于离线偏好评估，学习和关于理想动作和交互的推理来预先计算和存储策略的系统和方法。 行动包括使用预先计算的信息价值分析的理想实时查询状态。 在一个具体示例中，可以应用这种预计算来自动生成和分发用于蜂窝电话的呼叫处理策略。 这些方法可以利用贝叶斯网络用户模型的学习来预测用户是否参加会议，以及如果出席会议，将会有来电打扰的成本。
-
公开(公告)号：US20080004789A1
公开(公告)日：2008-01-03
申请号：US11428175
申请日：2006-06-30
CPC分类号： G08G1/0104 , G01C21/3492 , G08G1/096811
摘要： Sensing, learning, inference, and route analysis methods are described that center on the development and use of models that predict road speeds. In use, the system includes a receiver component that receives a traffic system representation, the traffic system representation includes velocities for a plurality of road segments over different contexts. A predictive component analyzes the traffic system representation and automatically assigns velocities to road segments within the traffic system representation, thereby providing more realistic velocities for different contexts where only statistics and/or posted speed limits were available before. The predictive component makes predictions about velocities for road segments at a current time or at specified times in the future by considering available velocity information as well as such information as the properties of roads, geometric relationships among roads of different types, proximal terrain and businesses, and other resources near road segments, and/or contextual information.
摘要翻译： 描述了感知，学习，推理和路线分析方法，其中以开发和使用预测道路速度的模型为基础。 在使用中，系统包括接收业务系统表示的接收机组件，业务系统表示包括在不同上下文中的多个道路段的速度。 预测组件分析交通系统表示，并自动将流量分配给交通系统表示中的路段，从而为仅在之前仅提供统计信息和/或发布速度限制的不同上下文提供更逼真的速度。 预测性组件通过考虑可用的速度信息以及诸如道路性质，不同类型的道路，近地域和企业之间的几何关系等信息，对当前时间或未来特定时间的道路段的速度进行预测， 和路段附近的其他资源，和/或上下文信息。
-
公开(公告)号：US07706964B2
公开(公告)日：2010-04-27
申请号：US11428175
申请日：2006-06-30
IPC分类号： G08G1/09
CPC分类号： G08G1/0104 , G01C21/3492 , G08G1/096811
摘要： Sensing, learning, inference, and route analysis methods are described that center on the development and use of models that predict road speeds. In use, the system includes a receiver component that receives a traffic system representation, the traffic system representation includes velocities for a plurality of road segments over different contexts. A predictive component analyzes the traffic system representation and automatically assigns velocities to road segments within the traffic system representation, thereby providing more realistic velocities for different contexts where only statistics and/or posted speed limits were available before. The predictive component makes predictions about velocities for road segments at a current time or at specified times in the future by considering available velocity information as well as such information as the properties of roads, geometric relationships among roads of different types, proximal terrain and businesses, and other resources near road segments, and/or contextual information.
摘要翻译： 描述了感知，学习，推理和路线分析方法，其中以开发和使用预测道路速度的模型为基础。 在使用中，系统包括接收业务系统表示的接收机组件，业务系统表示包括在不同上下文中的多个道路段的速度。 预测组件分析交通系统表示，并自动将流量分配给交通系统表示中的路段，从而为仅在之前仅提供统计信息和/或发布速度限制的不同上下文提供更逼真的速度。 预测性组件通过考虑可用的速度信息以及诸如道路性质，不同类型的道路，近地域和企业之间的几何关系等信息，对当前时间或未来特定时间的道路段的速度进行预测， 和路段附近的其他资源，和/或上下文信息。
-
公开(公告)号：US07493369B2
公开(公告)日：2009-02-17
申请号：US10881429
申请日：2004-06-30
IPC分类号： G06F15/16
CPC分类号： G06Q10/109
摘要： The present invention relates to a system and methodology to facilitate collaboration and communications between entities such as between parties to a communication, automated applications and components, and/or combinations thereof. The systems and methods of the present invention include a service that supports collaboration and communication by learning predictive models that provide forecasts of one or more aspects of a user's presence and availability. Presence forecasts include a user's current location or future locations at different levels of location precision and of the availability to users of different devices or applications. Availability assessments include inferences about the cost of interrupting a user in different ways and a user's current or future access to one or more communication channels that may be supported by one or more devices with appropriate capabilities. The predictive models are constructed via statistical learning methods from data collected by considering user activity and proximity from multiple devices, in addition to analysis of the content of users' calendars, the time of day, and day of week, for example. Beyond ambient data collection, users can provide input via batch input tools or via intermittent probes of their situation and context. Various applications are provided that employ the presence and availability information supplied by the models in order to facilitate collaboration and communications between entities.
摘要翻译： 本发明涉及促进实体之间的协作和通信的系统和方法，例如通信的各方，自动应用和组件，和/或其组合。 本发明的系统和方法包括通过学习提供用户的存在和可用性的一个或多个方面的预测的预测模型来支持协作和通信的服务。 在线状态预测包括不同位置精度的用户当前位置或未来位置以及不同设备或应用的用户的可用性。 可用性评估包括关于以不同方式中断用户的成本的推论，以及用户当前或未来访问一个或多个可能由具有适当能力的一个或多个设备支持的通信信道的推论。 除了分析用户日历的内容，一天中的一天和一周中的一天之外，还通过考虑用户活动和多个设备的邻近度所收集的数据，通过统计学习方法构建预测模型。 除了环境数据收集之外，用户还可以通过批量输入工具或通过对其情境和环境的间歇探测来提供输入。 提供了各种应用，其使用由模型提供的存在和可用性信息，以便于实体之间的协作和通信。
-
7.发明授权Decision-theoretic methods for identifying relevant substructures of a hierarchical file structure to enhance the efficiency of document access, browsing, and storage 有权用于识别分层文件结构的相关子结构以提高文档访问，浏览和存储的效率的决策理论方法公开(公告)号：US07346622B2
公开(公告)日：2008-03-18
申请号：US11394774
申请日：2006-03-31
申请人： Eric J. Horvitz , Paul B. Koch
发明人： Eric J. Horvitz , Paul B. Koch
IPC分类号： G06F17/30
CPC分类号： G06F17/30067 , Y10S707/99945
摘要： A system and methodology is provided for improving directory operations within a system providing an electronic hierarchical directory of items. The system includes a component which analyzes probabilities and utilities associated with determining potential target directories for storing and accessing data, and a component for building a subset of the potential target directories that are predicted to be the target directory. The probabilities and/or utilities are functions of expected navigation costs associated with traversing from a displayed directory to at least one of the potential target directories. Methods in accordance with the present invention can be coupled with displays of substructures that format the substructures into a coherent hierarchical view.
摘要翻译： 提供了一种用于改进提供物品的电子分级目录的系统内的目录操作的系统和方法。 该系统包括分析与确定用于存储和访问数据的潜在目标目录相关联的概率和实用程序的组件，以及用于构建预测为目标目录的潜在目标目录的子集的组件。 概率和/或实用程序是与从显示的目录到潜在目标目录中的至少一个相关联的预期导航成本的功能。 根据本发明的方法可以与将子结构格式化成相干层次视图的子结构的显示器相结合。
-
8.发明申请METHODS AND ARCHITECTURES FOR CONTEXT-SENSITIVE REMINDERS AND SERVICE FACILITATION 审中-公开上下文敏感提示的方法和架构和服务辅助公开(公告)号：US20080004926A1
公开(公告)日：2008-01-03
申请号：US11428228
申请日：2006-06-30
IPC分类号： G06F17/50
CPC分类号： G06Q10/047 , G06Q10/06315 , G06Q10/06316 , G06Q10/06375 , G06Q30/02
摘要： Methods and architectures for context-sensitive reminding and service facilitating are disclosed. The architectures monitor user context and activity, senses or infers relevant reminders, goals, such as those that come from a growing need of the user that should be fulfilled, and computes best reminders, and recommend plans on fulfilling need(s) in an optimum way. Statistical models of a user's knowledge and recall in different settings may be employed. Facilities, services, and merchants can be identified along a route that the user can take, and cost-benefit analysis is performed for determining which merchant(s) to select to fulfill the need(s). Routes may be created as opportunistic modifications of trips underway. Merchants can respond back with offers of sale to the user for all available needed items, and the user can respond with acceptance or denial of the offers. Merchants can also respond in a bidding fashion in order to gain user's patronage.
摘要翻译： 披露了上下文敏感提醒和服务促进的方法和体系结构。 架构监视用户环境和活动，感知或推测相关提醒，目标，例如来自用户日益增长的需求应该实现的目标，并计算最佳提醒，并推荐在满足需求的计划 办法。 可以采用不同设置中用户的知识和召回的统计模型。 设备，服务和商家可以沿着用户可以采用的路线被识别，并且执行成本效益分析以确定哪些商家选择以满足需要。 路线可能是正在进行的旅行的机会性修改。 商家可以回复用户的所有可用物品的销售优惠，用户可以接受或拒绝提供回复。 招商人员也可以以招标方式进行回应，以获得用户的惠顾。
-
9.发明授权公开(公告)号：US09305263B2
公开(公告)日：2016-04-05
申请号：US12827307
申请日：2010-06-30
申请人： Eric Horvitz , Paul B. Koch , Severin Hacker
发明人： Eric Horvitz , Paul B. Koch , Severin Hacker
CPC分类号： G06N5/043 , G06Q10/06 , G06Q10/101
摘要： Methods are described for ideally joining human and machine computing resources to solve tasks, based on the construction of predictive models from case libraries of data about the abilities of people and machines and their collaboration. Predictive models include methods for folding together human contributions, such as voting, with machine computation, such as automated visual analyses, as well as the routing of tasks to people based on prior performance and interests. An optimal distribution of tasks to selected participants of the plurality of participants is determined according to a model that considers the demonstrated competencies of people based on a value of information analysis that considers the value of human computation and the ideal people for providing a contribution.
摘要翻译： 描述了用于理想地连接人机和计算机资源以解决任务的方法，基于来自案例库的关于人和机器的能力及其协作的数据库的预测模型的构建。 预测模型包括将人类贡献（例如投票）与机器计算（如自动化视觉分析）以及基于先前绩效和兴趣将任务路由到人员的方法。 根据考虑到人类计算能力的信息分析价值和提供贡献的理想人员的信息分析价值，考虑了人们表现出的能力的模型来确定对多个参与者的选定参与者的最佳分配。
-
10.发明申请公开(公告)号：US20120005131A1
公开(公告)日：2012-01-05
申请号：US12827307
申请日：2010-06-30
申请人： Eric Horvitz , Paul B. Koch , Severin Hacker
发明人： Eric Horvitz , Paul B. Koch , Severin Hacker
IPC分类号： G06N5/02
CPC分类号： G06N5/043 , G06Q10/06 , G06Q10/101
摘要： Methods are described for ideally joining human and machine computing resources to solve tasks, based on the construction of predictive models from case libraries of data about the abilities of people and machines and their collaboration. Predictive models include methods for folding together human contributions, such as voting, with machine computation, such as automated visual analyses, as well as the routing of tasks to people based on prior performance and interests. An optimal distribution of tasks to selected participants of the plurality of participants is determined according to a model that considers the demonstrated competencies of people based on a value of information analysis that considers the value of human computation and the ideal people for providing a contribution.
摘要翻译： 描述了用于理想地连接人机和计算机资源以解决任务的方法，基于来自案例库的关于人和机器的能力及其协作的数据库的预测模型的构建。 预测模型包括将人类贡献（例如投票）与机器计算（如自动化视觉分析）以及基于先前绩效和兴趣将任务路由到人员的方法。 根据考虑到人类计算能力的信息分析价值和提供贡献的理想人员的信息分析价值，考虑了人们表现出的能力的模型来确定对多个参与者的选定参与者的最佳分配。
-
-
-
-
-
-
-
-
-
搜索结果
11 条记录 (耗时 0.027 秒)
