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    • 8. 发明授权
    • System and method for providing persistent advertising with third party content in a distributed internet access environment
    • 用于在分布式互联网访问环境中提供第三方内容的持续广告的系统和方法
    • US08090799B2
    • 2012-01-03
    • US11671336
    • 2007-02-05
    • James D. Keeler
    • James D. Keeler
    • G06F15/16G06Q30/00
    • G06Q30/02G06Q30/0241
    • A method, system, and computer program product that enables persistent display of advertising and other service provider content during access via a gateway access system to third party content on a separate network. A connection is enabled between a personal computing device (PCD) and the gateway access system. The gateway access system facilitates retrieval of a first content from the network to be displayed within a first window on the PCD. The gateway access system provides program code to the PCD that when executed enables persistent display of a second window on the PCD that does not overlap with the first window. A second content is transmitted for display within the second window on the PCD, where the second content is displayed within the persistent window on the PCD along with the display of the first content.
    • 一种方法,系统和计算机程序产品,其能够在通过网关访问系统访问时在单独网络上的第三方内容持续显示广告和其他服务提供商内容。 在个人计算设备(PCD)和网关接入系统之间启用连接。 网关接入系统便于从网络检索第一内容以在PCD的第一窗口内显示。 网关接入系统向PCD提供程序代码,当执行时,可以持续显示PCD上与第一个窗口不重叠的第二个窗口。 发送第二内容以在PCD的第二窗口内显示,其中在PCD的持久窗口中显示第二内容以及第一内容的显示。
    • 9. 发明授权
    • Predictive network with learned preprocessing parameters
    • 具有学习预处理参数的预测网络
    • US6144952A
    • 2000-11-07
    • US330326
    • 1999-06-11
    • James D. KeelerEric J. HartmanSteven A. O'HaraJill L. KempfDevendra B. Godbole
    • James D. KeelerEric J. HartmanSteven A. O'HaraJill L. KempfDevendra B. Godbole
    • G06N3/04G06F15/18
    • G06N3/0472G06N3/049
    • A predictive network is disclosed for operating in a runtime mode and in a training mode. The network includes a preprocessor (34') for preprocessing input data in accordance with parameters stored in a storage device (14') for output as preprocessed data to a delay device (36'). The delay device (36') provides a predetermined amount of delay as defined by predetermined delay settings in a storage device (18). The delayed data is input to a system model (26') which is operable in a training mode or a runtime mode. In the training mode, training data is stored in a data file (10) and retrieved therefrom for preprocessing and delay and then input to the system model (26'). Model parameters are learned and then stored in the storage device (22). During the training mode, the preprocess parameters are defined and stored in a storage device (14) in a particular sequence and delay settings are determined in the storage device (18). During the runtime mode, runtime data is derived from a distributed control system (24) and then preprocessed in accordance with predetermined process parameters and delayed in accordance with the predetermined delay settings. The preprocessed data is then input to the system model (26') to provide a predicted output, which is a control output to the distributed control system (24).
    • 公开了用于在运行时模式和训练模式下操作的预测网络。 网络包括用于根据存储在存储设备(14')中的参数来预处理输入数据的预处理器(34'),作为预处理数据输出到延迟设备(36')。 延迟装置(36')提供由存储装置(18)中的预定延迟设置所限定的预定量的延迟。 延迟数据被输入到可在训练模式或运行时模式下操作的系统模型(26')。 在训练模式中,将训练数据存储在数据文件(10)中并从中检索用于预处理和延迟,然后输入到系统模型(26')。 学习模型参数,然后存储在存储设备(22)中。 在训练模式期间,将预处理参数以特定顺序定义并存储在存储装置(14)中,并且在存储装置(18)中确定延迟设置。 在运行时模式期间,从分布式控制系统(24)导出运行时数据,然后根据预定的过程参数进行预处理,并根据预定的延迟设置进行延迟。 然后将预处理数据输入到系统模型(26'),以提供预测输出,该预测输出是对分布式控制系统(24)的控制输出。
    • 10. 发明授权
    • Method and apparatus for operating neural network with missing and/or
incomplete data
    • 用于使用缺失和/或不完整的数据操作神经网络的方法和装置
    • US5613041A
    • 1997-03-18
    • US531100
    • 1995-09-20
    • James D. KeelerEric J. HartmanRalph B. Ferguson
    • James D. KeelerEric J. HartmanRalph B. Ferguson
    • G06F15/18G06F17/17G06N3/04G06N3/08
    • G06N3/0472G06F17/17G06N3/049
    • A neural network system is provided that models the system in a system model (12) with the output thereof providing a predicted output. This predicted output is modified or controlled by an output control (14). Input data is processed in a data preprocess step (10) to reconcile the data for input to the system model (12). Additionally, the error resulted from the reconciliation is input to an uncertainty model to predict the uncertainty in the predicted output. This is input to a decision processor (20) which is utilized to control the output control (14). The output control (14) is controlled to either vary the predicted output or to inhibit the predicted output whenever the output of the uncertainty model (18) exceeds a predetermined decision threshold, input by a decision threshold block (22). Additionally, a validity model (16) is also provided which represents the reliability or validity of the output as a function of the number of data points in a given data region during training of the system model (12). This predicts the confidence in the predicted output which is also input to the decision processor (20). The decision processor (20) therefore bases its decision on the predicted confidence and the predicted uncertainty. Additionally, the uncertainty output by the data preprocess block (10) can be utilized to train the system model (12).
    • 提供了一种神经网络系统,其在系统模型(12)中对系统进行建模,其输出提供预测输出。 该预测输出由输出控制(14)修改或控制。 在数据预处理步骤(10)中处理输入数据,以便调整用于输入到系统模型(12)的数据。 另外,由和解产生的误差被输入到不确定性模型中,以预测预测输出的不确定性。 这被输入到用于控制输出控制(14)的决策处理器(20)。 控制输出控制器(14),以便在不确定性模型(18)的输出超过由判定阈值块(22)输入的预定判定阈值时改变预测输出或禁止预测输出。 此外,还提供了有效性模型(16),其表示在系统模型(12)的训练期间作为给定数据区域中的数据点的数量的函数的输出的可靠性或有效性。 这预测了也输入到决策处理器(20)的预测输出的置信度。 因此,决策处理器(20)将其决定基于预测的置信度和预测的不确定性。 此外,可以利用数据预处理块(10)输出的不确定性来训练系统模型(12)。