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1. 发明授权

US09135561B2 Inferring procedural knowledge from data sources 有权
标题翻译：从数据源中推断程序知识
公开(公告)号：US09135561B2
公开(公告)日：2015-09-15
申请号：US13291126
申请日：2011-11-08
申请人： Viswanath Vadlamani , Arungunram Surendran , Prasanth Pulavarthi , Phani Vaddadi , Munirathnam Srikanth , Tarek Najm
发明人： Viswanath Vadlamani , Arungunram Surendran , Prasanth Pulavarthi , Phani Vaddadi , Munirathnam Srikanth , Tarek Najm
IPC分类号： G06F15/18 , G06N5/04 , G06N5/02 , G06N99/00
CPC分类号： G06N5/022 , G06N5/04 , G06N99/005
摘要： A procedural inference system is described herein that infers procedural knowledge from various data sources to help a user complete one or more tasks for which the data sources provide information. The system understands users' queries, identifies a task at hand, provides recommendations on the steps to take and the agents to use based on a knowledge base of tasks and agents, and provides the fabric to determine which different agents can work together to help the user accomplish a task. Tasks can be started on one device and completed on another seamlessly. Users are able to finish complex, multi-step tasks efficiently, without trial and error or data reentry. Thus, the procedural inference system provides a generalized framework that helps users to complete tasks using already available data and does not ask each data provider to invest in infrastructure to build dedicated task information systems.
摘要翻译：本文描述了一种程序性推理系统，其推导来自各种数据源的程序性知识，以帮助用户完成数据源为其提供信息的一个或多个任务。系统了解用户的查询，识别手头的任务，根据任务和代理的知识库提供有关采取的步骤和代理使用的建议，并提供结构以确定哪些不同的代理可以协同工作用户完成任务。任务可以在一个设备上启动，另一个无缝完成。用户能够有效地完成复杂的多步骤任务，无需进行试错和数据重入。因此，程序推理系统提供了一个通用框架，可帮助用户使用已有的数据完成任务，并且不要求每个数据提供商投资于基础架构来构建专用的任务信息系统。

2. 发明申请

US20130117204A1 INFERRING PROCEDURAL KNOWLEDGE FROM DATA SOURCES 有权
标题翻译：从数据来源传播程序知识
公开(公告)号：US20130117204A1
公开(公告)日：2013-05-09
申请号：US13291126
申请日：2011-11-08
申请人： Viswanath Vadlamani , Arungunram Surendran , Prasanth Pulavarthi , Phani Vaddadi , Munirathnam Srikanth , Tarek Najm
发明人： Viswanath Vadlamani , Arungunram Surendran , Prasanth Pulavarthi , Phani Vaddadi , Munirathnam Srikanth , Tarek Najm
IPC分类号： G06F15/18 , G06N5/04
CPC分类号： G06N5/022 , G06N5/04 , G06N99/005
摘要： A procedural inference system is described herein that infers procedural knowledge from various data sources to help a user complete one or more tasks for which the data sources provide information. The system understands users' queries, identifies a task at hand, provides recommendations on the steps to take and the agents to use based on a knowledge base of tasks and agents, and provides the fabric to determine which different agents can work together to help the user accomplish a task. Tasks can be started on one device and completed on another seamlessly. Users are able to finish complex, multi-step tasks efficiently, without trial and error or data reentry. Thus, the procedural inference system provides a generalized framework that helps users to complete tasks using already available data and does not ask each data provider to invest in infrastructure to build dedicated task information systems.
摘要翻译：本文描述了一种程序性推理系统，其推导来自各种数据源的程序性知识，以帮助用户完成数据源为其提供信息的一个或多个任务。系统了解用户的查询，识别手头的任务，根据任务和代理的知识库提供有关采取的步骤和代理使用的建议，并提供结构以确定哪些不同的代理可以协同工作用户完成任务。任务可以在一个设备上启动，另一个无缝完成。用户能够有效地完成复杂的多步骤任务，无需进行试错和数据重入。因此，程序推理系统提供了一个通用框架，可帮助用户使用已有的数据完成任务，并且不要求每个数据提供商投资于基础架构来构建专用的任务信息系统。

3. 发明授权

US07593906B2 Bayesian probability accuracy improvements for web traffic predictions 有权
标题翻译：网络流量预测的贝叶斯概率精度提高
公开(公告)号：US07593906B2
公开(公告)日：2009-09-22
申请号：US11461030
申请日：2006-07-31
申请人： David M. Chickering , Ashis K. Roy , Prasanth Pulavarthi
发明人： David M. Chickering , Ashis K. Roy , Prasanth Pulavarthi
IPC分类号： G06N7/00
CPC分类号： G06N7/005 , G06Q30/0246 , H04L41/08 , H04L41/147 , H04L41/16
摘要： Enhancements to Bayesian prediction models for network location traffic provide increased accuracy in web traffic predictions. The enhancements include implementing user advertising target queries to determine preferred edges of a Bayesian model, employing hierarchical data structures to cleanse training data for a Bayesian model, and/or augmenting existing data with new training data to enhance a previously constructed Bayesian model. Preferred edge enhancements for the Bayesian model utilize target attribute derived preferred edges and/or explicitly specified preferred edges. Training data is cleansed utilizing tag hierarchies that can employ parent-child relationships, ancestor relationships, and/or network location specific parameters. New training data can also be employed to adjust probabilities in a previously constructed Bayesian model. The new training data can be weighted differently than data represented by the previously constructed Bayesian model.
摘要翻译：对网络位置流量的贝叶斯预测模型的增强提高了网络流量预测的准确性。增强包括实现用户广告目标查询以确定贝叶斯模型的优选边缘，采用分层数据结构来清除贝叶斯模型的训练数据，和/或用新的训练数据增强现有数据以增强先前构造的贝叶斯模型。贝叶斯模型的优选边缘增强使用目标属性导出的优选边缘和/或明确指定的优选边缘。使用可以使用父子关系，祖先关系和/或网络位置特定参数的标签层次来清理训练数据。也可以使用新的训练数据来调整先前构造的贝叶斯模型中的概率。新的训练数据可以与先前构造的贝叶斯模型所代表的数据不同。

4. 发明申请

US20080027890A1 BAYESIAN PROBABILITY ACCURACY IMPROVEMENTS FOR WEB TRAFFIC PREDICTIONS 有权
标题翻译：网络交通预测的贝叶斯可靠性准确性改进
公开(公告)号：US20080027890A1
公开(公告)日：2008-01-31
申请号：US11461030
申请日：2006-07-31
申请人： David M. Chickering , Ashis K. Roy , Prasanth Pulavarthi
发明人： David M. Chickering , Ashis K. Roy , Prasanth Pulavarthi
IPC分类号： G06N7/02
CPC分类号： G06N7/005 , G06Q30/0246 , H04L41/08 , H04L41/147 , H04L41/16
摘要： Enhancements to Bayesian prediction models for network location traffic provide increased accuracy in web traffic predictions. The enhancements include implementing user advertising target queries to determine preferred edges of a Bayesian model, employing hierarchical data structures to cleanse training data for a Bayesian model, and/or augmenting existing data with new training data to enhance a previously constructed Bayesian model. Preferred edge enhancements for the Bayesian model utilize target attribute derived preferred edges and/or explicitly specified preferred edges. Training data is cleansed utilizing tag hierarchies that can employ parent-child relationships, ancestor relationships, and/or network location specific parameters. New training data can also be employed to adjust probabilities in a previously constructed Bayesian model. The new training data can be weighted differently than data represented by the previously constructed Bayesian model.
摘要翻译：对网络位置流量的贝叶斯预测模型的增强提高了网络流量预测的准确性。增强包括实现用户广告目标查询以确定贝叶斯模型的优选边缘，采用分层数据结构来清除贝叶斯模型的训练数据，和/或用新的训练数据增强现有数据以增强先前构造的贝叶斯模型。贝叶斯模型的优选边缘增强使用目标属性导出的优选边缘和/或明确指定的优选边缘。使用可以使用父子关系，祖先关系和/或网络位置特定参数的标签层次来清理训练数据。也可以使用新的训练数据来调整先前构造的贝叶斯模型中的概率。新的训练数据可以与先前构造的贝叶斯模型所代表的数据不同。

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