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1. 发明申请

US20090012922A1 METHOD AND APPARATUS FOR REWARD-BASED LEARNING OF IMPROVED SYSTEMS MANAGEMENT POLICIES 失效
标题翻译：改进的系统管理政策的基于学习的方法和装置
公开(公告)号：US20090012922A1
公开(公告)日：2009-01-08
申请号：US12165144
申请日：2008-06-30
申请人： GERALD James TESAURO , RAJARSHI DAS , NICHOLAS K. JONG , JEFFREY O. KEPHART
发明人： GERALD James TESAURO , RAJARSHI DAS , NICHOLAS K. JONG , JEFFREY O. KEPHART
IPC分类号： G06F15/18
CPC分类号： G06Q10/06
摘要： In one embodiment, the present invention is a method for reward-based learning of improved systems management policies. One embodiment of the inventive method involves supplying a first policy and a reward mechanism. The first policy maps states of at least one component of a data processing system to selected management actions, while the reward mechanism generates numerical measures of value responsive to particular actions (e.g., management actions) performed in particular states of the component(s). The first policy and the reward mechanism are applied to the component(s), and results achieved through this application (e.g., observations of corresponding states, actions and rewards) are processed in accordance with reward-based learning to derive a second policy having improved performance relative to the first policy in at least one state of the component(s).
摘要翻译：在一个实施例中，本发明是改进的系统管理策略的基于奖励学习的方法。本发明方法的一个实施例涉及提供第一策略和奖励机制。第一策略将数据处理系统的至少一个组件的状态映射到所选择的管理动作，而奖励机制响应于在组件的特定状态中执行的特定动作（例如，管理动作）生成值的数值测量。第一个政策和奖励机制适用于组件，通过此应用程序实现的结果（例如，对应的状态，行动和奖励的观察）根据奖励学习进行处理，以得到改进的第二个策略在组件的至少一个状态下相对于第一策略的性能。

2. 发明授权

US10296581B2 Multi-word autocorrection 有权
公开(公告)号：US10296581B2
公开(公告)日：2019-05-21
申请号：US13604439
申请日：2012-09-05
申请人： Christopher P. Willmore , Nicholas K. Jong , Stephen W. Swales
发明人： Christopher P. Willmore , Nicholas K. Jong , Stephen W. Swales
IPC分类号： G06F17/27
摘要： Methods and systems of multi-word automatic correction (“autocorrect”) are provided. Autocorrect generally can select a corrected word based on a typed word and a dictionary of correctly-spelled words. Multi-word autocorrect can add to this functionality by revisiting the selection of an initial corrected word if a subsequently-typed word indicates that it would be more appropriate to instead select an additional corrected word. In some cases, an autocorrect system can make a multi-word correction based on a multi-word phrase in a dictionary, such as replacing “new york” with “New York” as described above. In other cases, an autocorrect system can make a multi-word correction to correct a mistakenly-typed delimiter character. In other cases, an autocorrect system can use grammar rules to obtain additional context information with each subsequently-typed word and make multi-word corrections on that basis.

3. 发明授权

US08001063B2 Method and apparatus for reward-based learning of improved policies for management of a plurality of application environments supported by a data processing system 失效
标题翻译：用于基于奖励学习的改进策略的方法和装置，用于管理由数据处理系统支持的多个应用环境
公开(公告)号：US08001063B2
公开(公告)日：2011-08-16
申请号：US12165144
申请日：2008-06-30
申请人： Gerald James Tesauro , Rajarshi Das , Nicholas K. Jong , Jeffrey O. Kephart
发明人： Gerald James Tesauro , Rajarshi Das , Nicholas K. Jong , Jeffrey O. Kephart
IPC分类号： G06F15/18
CPC分类号： G06Q10/06
摘要： In one embodiment, the present invention is a method for reward-based learning of improved systems management policies. One embodiment of the inventive method involves obtaining a decision-making entity and a reward mechanism. The decision-making entity manages a plurality of application environments supported by a data processing system, where each application environment operates on data input to the data processing system. The reward mechanism generates numerical measures of value responsive to actions performed in states of the application environments. The decision-making entity and the reward mechanism are applied to the application environments, and results achieved through this application are processed in accordance with reward-based learning to derive a policy. The reward mechanism and the policy are then applied to the application environments, and the results of this application are processed in accordance with reward-based learning to derive a new policy.
摘要翻译：在一个实施例中，本发明是改进的系统管理策略的基于奖励学习的方法。本发明方法的一个实施例涉及获得决策实体和奖励机制。决策实体管理由数据处理系统支持的多个应用环境，其中每个应用环境对输入到数据处理系统的数据进行操作。奖励机制响应于在应用环境的状态中执行的动作产生数值的值测量。决策实体和奖励机制适用于应用环境，通过本申请实现的结果根据奖励学习进行处理，得出政策。然后将奖励机制和政策应用于应用环境，并根据奖励学习处理本应用的结果，以得出新的策略。

4. 发明申请

US20130332822A1 MULTI-WORD AUTOCORRECTION 审中-公开
标题翻译：多字自动注册
公开(公告)号：US20130332822A1
公开(公告)日：2013-12-12
申请号：US13604439
申请日：2012-09-05
申请人： Christopher P. WILLMORE , Nicholas K. JONG , Stephen W. SWALES
发明人： Christopher P. WILLMORE , Nicholas K. JONG , Stephen W. SWALES
IPC分类号： G06F17/24
CPC分类号： G06F17/273
摘要： Methods and systems of multi-word automatic correction (“autocorrect”) are provided. Autocorrect generally can select a corrected word based on a typed word and a dictionary of correctly-spelled words. Multi-word autocorrect can add to this functionality by revisiting the selection of an initial corrected word if a subsequently-typed word indicates that it would be more appropriate to instead select an additional corrected word. In some cases, an autocorrect system can make a multi-word correction based on a multi-word phrase in a dictionary, such as replacing “new york” with “New York” as described above. In other cases, an autocorrect system can make a multi-word correction to correct a mistakenly-typed delimiter character. In other cases, an autocorrect system can use grammar rules to obtain additional context information with each subsequently-typed word and make multi-word corrections on that basis.
摘要翻译：提供了多字自动校正（“自动校正”）的方法和系统。自动校正通常可以根据键入的单词和正确拼写的单词的字典来选择一个更正的单词。多字自动更正可以通过重新访问初始校正字的选择来添加此功能，如果后续类型的字表示更适合选择附加的校正字。在一些情况下，自动更正系统可以基于字典中的多字短语进行多字校正，例如如上所述用“纽约”替换“纽约”。在其他情况下，自动更正系统可以进行多字更正以纠正错误类型的分隔符。在其他情况下，自动更正系统可以使用语法规则来获取每个后续类型的单词的附加上下文信息，并在此基础上进行多字更正。

5. 发明授权

US08994660B2 Text correction processing 有权
标题翻译：文字校正处理
公开(公告)号：US08994660B2
公开(公告)日：2015-03-31
申请号：US13220202
申请日：2011-08-29
申请人： Alice E. Neels , Nicholas K. Jong
发明人： Alice E. Neels , Nicholas K. Jong
IPC分类号： G09G5/00 , G06F3/041 , G06F3/0488 , G06F3/023
CPC分类号： G06F3/0237 , G06F3/04886
摘要： Text correction processing is disclosed. An initial score is assigned to each of a plurality of candidate sequences of one or more characters, based at least in part on a keyboard geometry-based value associated with the received user input with respect to the candidate key. Further processing is performed with respect to a subset of the candidate sequences having the highest initial score(s) to determine for each candidate sequence in the subset a refined score. A candidate sequence is selected for inclusion in a result set based at least in part on a determination that a refined score of the selected candidate is higher than an initial score of one or more candidate sequences that are not included in the subset and with respect to which the further processing has not been performed.
摘要翻译：公开了文本校正处理。至少部分地基于与所接收的用户输入相对于所述候选键相关联的基于键盘几何的值，将初始分数分配给一个或多个字符的多个候选序列中的每一个。对具有最高初始分数的候选序列的子集执行进一步处理，以确定子集中每个候选序列的精确分数。至少部分地基于所选择的候选的精确分数高于不包括在子集中的一个或多个候选序列的初始分数的确定来选择候选序列以包括在结果集中，并且相对于哪些未进行进一步的处理。

6. 发明申请

US20130050089A1 TEXT CORRECTION PROCESSING 有权
公开(公告)号：US20130050089A1
公开(公告)日：2013-02-28
申请号：US13220202
申请日：2011-08-29
申请人： Alice E. Neels , Nicholas K. Jong
发明人： Alice E. Neels , Nicholas K. Jong
IPC分类号： G06F3/02
CPC分类号： G06F3/0237 , G06F3/04886
摘要： Text correction processing is disclosed. An initial score is assigned to each of a plurality of candidate sequences of one or more characters, based at least in part on a keyboard geometry-based value associated with the received user input with respect to the candidate key. Further processing is performed with respect to a subset of the candidate sequences having the highest initial score(s) to determine for each candidate sequence in the subset a refined score. A candidate sequence is selected for inclusion in a result set based at least in part on a determination that a refined score of the selected candidate is higher than an initial score of one or more candidate sequences that are not included in the subset and with respect to which the further processing has not been performed.

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