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    • 1. 发明申请
    • Traffic forecasting employing modeling and analysis of probabilistic interdependencies and contextual data
    • 流量预测采用建模和分析概率相互依赖关系和语境数据
    • US20060106530A1
    • 2006-05-18
    • US11171791
    • 2005-06-30
    • Eric HorvitzJohnson ApacibleRaman Sarin
    • Eric HorvitzJohnson ApacibleRaman Sarin
    • G06F19/00
    • G08G1/0104
    • Systems and methods are described for constructing predictive models, based on statistical machine learning, that can make forecasts about traffic flows and congestions, based on an abstraction of a traffic system into a set of random variables, including variables that represent the amount of time until there will be congestion at key troublespots and the time until congestions will resolve. Observational data includes traffic flows and dynamics, and other contextual data such as the time of day and day of week, holidays, school status, the timing and nature of major gatherings such as sporting events, weather reports, traffic incident reports, and construction and closure reports. The forecasting methods are used in alerting, the display graphical information about predictions about congestion on desktop on mobile devices, and in offline and real-time automated route recommendations and planning.
    • 描述了基于统计机器学习构建预测模型的系统和方法,其基于将交通系统抽象成一组随机变量,其中包括代表时间量的变量,直到 关键问题将会堵塞,直到拥堵才能解决。 观测数据包括交通流量和动态,以及诸如体育赛事,天气报告,交通事故报告和建筑等主要聚会的时间和性质等其他情景数据,如时间和星期几,假期,学校状况,以及 关闭报告。 预测方法用于警报,显示关于移动设备上台式机拥塞预测的图形信息,以及离线和实时自动路由建议和规划中的预测方法。
    • 5. 发明申请
    • SYSTEMS AND METHODS FOR PERSONAL UBIQUITOUS INFORMATION RETRIEVAL AND REUSE
    • 个人信息检索和重用的系统和方法
    • US20070112742A1
    • 2007-05-17
    • US11619949
    • 2007-01-04
    • Susan DumaisEric HorvitzEdward CutrellJonathan CadizGavin JanckeRaman SarinDaniel RobbinsAnoop GuptaGeorge RobertsonMeredith RingelJeremy Goecks
    • Susan DumaisEric HorvitzEdward CutrellJonathan CadizGavin JanckeRaman SarinDaniel RobbinsAnoop GuptaGeorge RobertsonMeredith RingelJeremy Goecks
    • G06F17/30
    • G06F16/9535G06F16/31Y10S707/99933Y10S707/99935Y10S707/99943Y10S715/963Y10S715/968
    • The present invention relates to systems and methods providing content-access-based information retrieval. Information items from a plurality of disparate information sources that have been previously accessed or considered are automatically indexed in a data store, whereby a multifaceted user interface is provided to efficiently retrieve the items in a cognitively relevant manner. Various display output arrangements are possible for the retrieved information items including timeline visualizations and multidimensional grid visualizations. Input options include explicit, implicit, and standing queries for retrieving data along with explicit and implicit tagging of items for ease of recall and retrieval. In one aspect, an automated system is provided that facilitates concurrent searching across a plurality of information sources. A usage analyzer determines user accessed items and a content analyzer stores subsets of data corresponding to the items, wherein at least two of the items are associated with disparate information sources, respectively. An automated indexing component indexes the data subsets according to past data access patterns as determined by the usage analyzer. A search component responds to a search query, initiates a search across the indexed data, and outputs links to locations of a subset and/or provides sparse representations of the subset.
    • 本发明涉及提供基于内容访问的信息检索的系统和方法。 来自先前访问或考虑的多个不同信息源的信息项目被自动索引到数据存储器中,由此提供多方面用户界面以有效地以认知相关的方式检索项目。 对于所检索的信息项,包括时间线可视化和多维网格可视化,各种显示输出布置是可能的。 输入选项包括用于检索数据的显式,隐式和常规查询,以及容易召回和检索的项目的显式和隐式标记。 在一个方面,提供了一种便于在多个信息源上并行搜索的自动化系统。 使用分析器确定用户访问的项目,并且内容分析器存储对应于项目的数据子集,其中至少两个项目分别与不同的信息源相关联。 自动索引组件根据使用分析器确定的过去的数据访问模式来索引数据子集。 搜索组件响应搜索查询,在索引的数据上发起搜索,并且输出到子集的位置的链接和/或提供子集的稀疏表示。
    • 6. 发明申请
    • Precomputation of context-sensitive policies for automated inquiry and action under uncertainty
    • 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
    • US20070022075A1
    • 2007-01-25
    • US11172016
    • 2005-06-29
    • Eric HorvitzPaul KochRaman Sarin
    • Eric HorvitzPaul KochRaman Sarin
    • G06N7/02G06F9/44G06N7/06
    • G06N7/005G06F8/35G06F8/36
    • 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.
    • 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。
    • 7. 发明申请
    • Conserving Power Using Predictive Modelling and Signaling
    • 使用预测建模和信令节约能源
    • US20100100716A1
    • 2010-04-22
    • US12255877
    • 2008-10-22
    • James ScottPaul NewsonRaman SarinEric Horvitz
    • James ScottPaul NewsonRaman SarinEric Horvitz
    • G06F15/177G06F1/00
    • G06F1/266G06F1/3209H04L12/12Y02D50/40
    • Methods and systems for conserving power using predictive models and signaling are described. Parameters of a power management policy are set based on predictions based on user activity and/or signals received from a remote computer which define a user preference. In an embodiment, the power management policy involves putting the computer into a sleep state and periodically waking it up. On waking, the computer determines whether to remain awake or to return to the sleep state dependent upon the output of a predictive model or signals that encode whether a remote user has requested that computer remain awake. Before returning to the sleep state, a wake-up timer is set and this timer triggers the computer to subsequently wake-up. The length of time that the timer is set to may depend on factors such as the request from the remote user, context sensors and usage data.
    • 描述了使用预测模型和信令来节省功率的方法和系统。 基于基于用户活动的预测和/或从定义用户偏好的远程计算机接收的信号来设置功率管理策略的参数。 在一个实施例中,电源管理策略涉及将计算机置于睡眠状态并周期性地唤醒它。 在唤醒时,计算机根据预测模型的输出或编码远程用户是否请求该计算机保持唤醒的信号确定是否保持唤醒或返回睡眠状态。 在返回睡眠状态之前,设置一个唤醒定时器,该定时器触发计算机随后唤醒。 定时器设置的时间长度可能取决于诸如来自远程用户的请求,上下文传感器和使用数据等因素。