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1. 发明申请

US20060184491A1 Responding to situations using knowledge representation and inference 有权
标题翻译：响应使用知识表示和推理的情况
公开(公告)号：US20060184491A1
公开(公告)日：2006-08-17
申请号：US11046343
申请日：2005-01-28
申请人： Rakesh Gupta , Vasco Pedro
发明人： Rakesh Gupta , Vasco Pedro
IPC分类号： G06N5/02
CPC分类号： G06N5/04
摘要： A system, apparatus and application for providing robots with the ability to intelligently respond to perceived situations are described. A knowledge database is assembled automatically, based on distributed knowledge capture. The knowledge base embodies the “common sense,” that is, the consensus, of the subjects who contribute the knowledge. Systems are provided to automatically preprocess, or “clean” the information to make it more useful. The knowledge thus refined is utilized to construct a multidimensional semantic network, or MSN. The MSN provides a compact and efficient semantic representation suitable for extraction of knowledge for inference purposes and serves as the basis for task and response selection. When the robot perceives a situation that warrants a response, an appropriate subset of the MSN is extracted into a Bayes network. The resultant network is refined, and used to derive a set of response probabilities, which the robot uses to formulate a response.
摘要翻译：描述了一种用于向机器人提供智能响应感知情况的能力的系统，设备和应用。基于分布式知识捕获，自动组合知识数据库。知识库体现了贡献知识的科目的“常识”，即共识。提供系统以自动预处理或“清理”信息，使其更有用。这样精炼的知识被用来构建一个多维语义网络或MSN。 MSN提供了一种适用于推理目的的知识提取的紧凑有效的语义表示，并且作为任务和响应选择的基础。当机器人感知到需要响应的情况时，将MSN的适当子集提取到贝叶斯网络中。所得到的网络被改进，并且用于导出机器人用于制定响应的一组响应概率。

2. 发明授权

US08676583B2 Belief tracking and action selection in spoken dialog systems 有权
标题翻译：在口语对话系统中的信念跟踪和动作选择
公开(公告)号：US08676583B2
公开(公告)日：2014-03-18
申请号：US13221155
申请日：2011-08-30
申请人： Rakesh Gupta , Deepak Ramachandran , Antoine Raux , Neville Mehta , Stefan Krawczyk , Matthew Hoffman
发明人： Rakesh Gupta , Deepak Ramachandran , Antoine Raux , Neville Mehta , Stefan Krawczyk , Matthew Hoffman
IPC分类号： G10L15/22
CPC分类号： G10L15/22
摘要： An action is performed in a spoken dialog system in response to a user's spoken utterance. A policy which maps belief states of user intent to actions is retrieved or created. A belief state is determined based on the spoken utterance, and an action is selected based on the determined belief state and the policy. The action is performed, and in one embodiment, involves requesting clarification of the spoken utterance from the user. Creating a policy may involve simulating user inputs and spoken dialog system interactions, and modifying policy parameters iteratively until a policy threshold is satisfied. In one embodiment, a belief state is determined by converting the spoken utterance into text, assigning the text to one or more dialog slots associated with nodes in a probabilistic ontology tree (POT), and determining a joint probability based on probability distribution tables in the POT and on the dialog slot assignments.
摘要翻译：响应于用户的说话话语，在口语对话系统中执行动作。检索或创建将用户意图的信念状态映射到动作的策略。信仰状态是根据口语说出来确定的，并且基于确定的信念状态和策略选择动作。该动作被执行，并且在一个实施例中，涉及请求澄清来自用户的说话话语。创建策略可以包括模拟用户输入和对话系统交互，并且迭代地修改策略参数，直到满足策略阈值。在一个实施例中，通过将口语发音转换成文本来确定置信状态，将文本分配给与概率本体树（POT）中的节点相关联的一个或多个对话时隙，以及基于概率分布表中的概率分布表确定联合概率 POT和对话框插槽分配。

3. 发明申请

US20140018985A1 Hybrid Vehicle Fuel Efficiency Using Inverse Reinforcement Learning 有权
标题翻译：混合动力车辆燃料效率采用逆向加固学习
公开(公告)号：US20140018985A1
公开(公告)日：2014-01-16
申请号：US13838922
申请日：2013-03-15
申请人： Rakesh Gupta , Deepak Ramachandran , Adam C. Vogel , Antoine Raux
发明人： Rakesh Gupta , Deepak Ramachandran , Adam C. Vogel , Antoine Raux
IPC分类号： B60W20/00 , B60W10/26 , B60W10/06
CPC分类号： B60W20/104 , B60W10/06 , B60W10/26 , B60W20/12 , B60W2530/14 , B60W2550/143 , B60W2550/402 , B60W2600/00 , G01C21/3469 , Y10S903/93
摘要： A powertrain of a hybrid electric vehicle (HEV) is controlled. A first value α1 and a second value α2 are determined, α1 represents a proportion of an instantaneous power requirement (Preq) supplied by an engine of the HEV. α2 controls a recharging rate of a battery of the HEV. A determination is performed, based on α1 and α2, regarding how much engine power to use (Peng) and how much battery power to use (Pbatt). Peng and Pbatt are sent to the powertrain.
摘要翻译：混合动力汽车（HEV）的动力总成被控制。确定第一值α1和第二值α2，α1表示由HEV的发动机提供的瞬时功率需求（Preq）的比例。 alpha2控制HEV电池的充电速率。基于alpha1和alpha2，确定使用多少引擎功率（Peng）以及使用多少电池电量（Pbatt）。彭和Pbatt被送到动力总成。

4. 发明授权

US08623964B2 Room temperature crosslinkable ion conductive polymer system 有权
标题翻译：室温可交联离子导电聚合物体系
公开(公告)号：US08623964B2
公开(公告)日：2014-01-07
申请号：US13235881
申请日：2011-09-19
申请人： Zhiquang Song , Suruliappa G. Jeganathan , Jacqueline Lau , Rakesh Gupta
发明人： Zhiquang Song , Suruliappa G. Jeganathan , Jacqueline Lau , Rakesh Gupta
IPC分类号： C08C19/22 , C08F8/30 , C08F8/32
CPC分类号： C08G65/333 , C08G73/0206 , C08G73/024 , C08J5/2256 , H01M8/1027 , H01M8/103 , H01M8/1072 , H01M10/0525 , H01M10/0565 , Y02P70/56
摘要： A room temperature crosslinkable polymer system comprising an anhydride containing polymer and an oxyalkylene amine and a polymer electrolyte derived therefrom are prepared and employed as ion conducting materials for batteries such as lithium ion battery, solar cells and electrochromic devices is disclosed.
摘要翻译：制备包含含酸酐的聚合物和氧化烯胺和由其衍生的聚合物电解质的室温可交联聚合物体系，并且用作锂离子电池，太阳能电池和电致变色装置等电池的离子导电材料。

5. 发明授权

US08196105B2 Test framework for automating multi-step and multi-machine electronic calendaring application test cases 有权
标题翻译：用于自动化多步和多机电子日历应用测试用例的测试框架
公开(公告)号：US08196105B2
公开(公告)日：2012-06-05
申请号：US11824473
申请日：2007-06-29
申请人： Ashish Kumar Singhal , Sai Shankar , Pankaj Lohumi , Rakesh Gupta
发明人： Ashish Kumar Singhal , Sai Shankar , Pankaj Lohumi , Rakesh Gupta
IPC分类号： G06F9/44
CPC分类号： G06F11/3684
摘要： A test framework for automating multi-user, multi-step and/or multi-machine test cases for electronic calendaring application objects is provided. Test cases may be automated where coordination between a number of machines is required to test electronic calendaring application user scenarios. In addition, automation of multifold interaction between two electronic calendaring application users is provided.
摘要翻译：提供了一种用于自动化用于电子日历应用对象的多用户，多步和/或多机测试用例的测试框架。测试用例可以是自动化的，需要在多台机器之间协调测试电子日历应用程序用户场景。此外，还提供了两台电子日历应用用户之间的多重交互自动化。

6. 发明授权

US08077919B2 Toro: tracking and observing robot 有权
标题翻译：托罗：跟踪和观察机器人
公开(公告)号：US08077919B2
公开(公告)日：2011-12-13
申请号：US12249849
申请日：2008-10-10
申请人： Rakesh Gupta , Deepak Ramachandran
发明人： Rakesh Gupta , Deepak Ramachandran
IPC分类号： G06K9/00 , G06F11/00
CPC分类号： G06K9/00295 , G06T7/143 , G06T7/277 , G06T2207/10016
摘要： The present invention provides a method for tracking entities, such as people, in an environment over long time periods. A region-based model is generated to model beliefs about entity locations. Each region corresponds to a discrete area representing a location where an entity is likely to be found. Each region includes one or more positions which more precisely specify the location of an entity within the region so that the region defines a probability distribution of the entity residing at different positions within the region. A region-based particle filtering method is applied to entities within the regions so that the probability distribution of each region is updated to indicate the likelihood of the entity residing in a particular region as the entity moves.
摘要翻译：本发明提供了一种用于在长时间的环境中跟踪诸如人之类的实体的方法。生成基于区域的模型来模拟关于实体位置的信念。每个区域对应于表示实体可能被找到的位置的离散区域。每个区域包括一个或多个位置，其更精确地指定区域内的实体的位置，使得该区域定义驻留在区域内的不同位置的实体的概率分布。基于区域的粒子滤波方法被应用于区域内的实体，使得每个区域的概率分布被更新，以指示实体在实体移动时驻留在特定区域中的可能性。

7. 发明授权

US07725418B2 Responding to situations using multidimensional semantic net and Bayes inference 有权
标题翻译：响应使用多维语义网和贝叶斯推理的情况
公开(公告)号：US07725418B2
公开(公告)日：2010-05-25
申请号：US11046343
申请日：2005-01-28
申请人： Rakesh Gupta , Vasco Calais Pedro
发明人： Rakesh Gupta , Vasco Calais Pedro
IPC分类号： G06N5/02
CPC分类号： G06N5/04
摘要： A system, apparatus and application for providing robots with the ability to intelligently respond to perceived situations are described. A knowledge database is assembled automatically, based on distributed knowledge capture. The knowledge base embodies the “common sense,” that is, the consensus, of the subjects who contribute the knowledge. Systems are provided to automatically preprocess, or “clean” the information to make it more useful. The knowledge thus refined is utilized to construct a multidimensional semantic network, or MSN. The MSN provides a compact and efficient semantic representation suitable for extraction of knowledge for inference purposes and serves as the basis for task and response selection. When the robot perceives a situation that warrants a response, an appropriate subset of the MSN is extracted into a Bayes network. The resultant network is refined, and used to derive a set of response probabilities, which the robot uses to formulate a response.
摘要翻译：描述了一种用于向机器人提供智能响应感知情况的能力的系统，设备和应用。基于分布式知识捕获，自动组合知识数据库。知识库体现了贡献知识的科目的“常识”，即共识。提供系统以自动预处理或“清理”信息，使其更有用。这样精炼的知识被用来构建一个多维语义网络或MSN。 MSN提供了一种适用于推理目的的知识提取的紧凑有效的语义表示，并且作为任务和响应选择的基础。当机器人感知到需要响应的情况时，将MSN的适当子集提取到贝叶斯网络中。所得到的网络被改进，并且用于导出机器人用于制定响应的一组响应概率。

8. 发明授权

US07603330B2 Meta learning for question classification 有权
标题翻译：元分析问题分类
公开(公告)号：US07603330B2
公开(公告)日：2009-10-13
申请号：US11410443
申请日：2006-04-24
申请人： Rakesh Gupta , Samarth Swarup
发明人： Rakesh Gupta , Samarth Swarup
IPC分类号： G06N3/02 , G06N3/08
CPC分类号： G06N99/005 , G06N3/0454
摘要： A system and a method are disclosed for automatic question classification and answering. A multipart artificial neural network (ANN) comprising a main ANN and an auxiliary ANN classifies a received question according to one of a plurality of defined categories. Unlabeled data is received from a source, such as a plurality of human volunteers. The unlabeled data comprises additional questions that might be asked of an autonomous machine such as a humanoid robot, and is used to train the auxiliary ANN in an unsupervised mode. The unsupervised training can comprise multiple auxiliary tasks that generate labeled data from the unlabeled data, thereby learning an underlying structure. Once the auxiliary ANN has trained, the weights are frozen and transferred to the main ANN. The main ANN can then be trained using labeled questions. The original question to be answered is applied to the trained main ANN, which assigns one of the defined categories. The assigned category is used to map the original question to a database that most likely contains the appropriate answer. An object and/or a property within the original question can be identified and used to formulate a query, using, for example, system query language (SQL), to search for the answer within the chosen database. The invention makes efficient use of available information, and improves training time and error rate relative to use of single part ANNs.
摘要翻译：公开了一种用于自动问题分类和回答的系统和方法。包括主ANN和辅助ANN的多部分人造神经网络（ANN）根据多个定义的类别之一对接收到的问题进行分类。从诸如多个人类志愿者的来源接收未标记的数据。未标记的数据包括可能会询问诸如人形机器人的自主机器的另外的问题，并且用于以无监督的方式训练辅助ANN。无监督训练可以包括从未标记的数据生成标记数据的多个辅助任务，从而学习底层结构。辅助ANN训练后，重量被冻结并转移到主ANN。然后可以使用标记的问题来训练主要的ANN。要回答的原始问题适用于经过训练的主ANN，该ANN分配一个定义的类别。分配的类别用于将原始问题映射到最有可能包含适当答案的数据库。原始问题中的对象和/或属性可以被识别并用于使用例如系统查询语言（SQL）来制定查询来搜索所选择的数据库中的答案。本发明有效利用可用信息，并且相对于使用单一部件ANN而提高训练时间和错误率。

9. 发明申请

US20090171956A1 TEXT CATEGORIZATION WITH KNOWLEDGE TRANSFER FROM HETEROGENEOUS DATASETS 有权
标题翻译：来自异质数据库的知识转移的文本分类
公开(公告)号：US20090171956A1
公开(公告)日：2009-07-02
申请号：US12249809
申请日：2008-10-10
申请人： Rakesh Gupta , Lev Ratinov
发明人： Rakesh Gupta , Lev Ratinov
IPC分类号： G06F17/30
CPC分类号： G06F17/30705
摘要： The present invention provides a method for incorporating features from heterogeneous auxiliary datasets into input text data for use in classification, a plurality of heterogeneous auxiliary datasets, such as labeled datasets and unlabeled datasets, are accessed after receiving input text data. A plurality of features are extracted from each of the plurality of heterogeneous auxiliary datasets. The plurality of features are combined with the input text data to generate a set of features which may potentially be used to classify the input text data. Classification features are then extracted from the set of features and used to classify the input text data. In one embodiment, the classification features are extracted by calculating a mutual information value associated with each feature in the set of features and identifying features having a mutual information value exceeding a threshold value.
摘要翻译：本发明提供了一种将异构辅助数据集中的特征结合到用于分类的输入文本数据中的方法，在接收输入文本数据之后访问多个异构辅助数据集，例如标记数据集和未标记的数据集。从多个异构辅助数据集中的每一个提取多个特征。多个特征与输入文本数据组合以产生可能用于对输入文本数据进行分类的一组特征。然后从特征集中提取分类特征，并用于对输入文本数据进行分类。在一个实施例中，通过计算与特征集合中的每个特征相关联的互信息值并且识别具有超过阈值的互信息值的特征来提取分类特征。

10. 发明授权

US07551801B2 Classification of composite actions involving interaction with objects 有权
标题翻译：涉及与物体相互作用的复合动作的分类
公开(公告)号：US07551801B2
公开(公告)日：2009-06-23
申请号：US11735829
申请日：2007-04-16
申请人： Rakesh Gupta , Trupti Devdas Nayak
发明人： Rakesh Gupta , Trupti Devdas Nayak
IPC分类号： G06K9/36
CPC分类号： G06K9/00355
摘要： The present invention provides a method for using stored data associated with simple actions to identify and classify an observed composite action which comprises a combination of simple actions. Image data comprising the composite action is received and used to generate a feature vector, such as a combination of a horizontal translation, a vertical translation, a scale value and a skin detection value, that is associated with the composite action. Baseline reference vectors representing combinations of simple actions represented by stored reference data are then generated. The generated feature vector is then preprocessed to modify the feature vector towards a baseline reference vector describing a combination of simple actions. Each of the baseline reference vectors is then compared to the modified feature vector to determine the baseline reference vector and corresponding combination of simple actions that most accurately describes the composite action.
摘要翻译：本发明提供了一种使用与简单动作相关联的存储数据来识别和分类观察到的复合动作的方法，其包括简单动作的组合。接收包括复合动作的图像数据并用于生成与复合动作相关联的特征向量，例如水平平移，垂直平移，比例值和皮肤检测值的组合。然后生成表示由存储的参考数据表示的简单动作的组合的基准参考向量。然后对所生成的特征向量进行预处理，以向描述简单动作的组合的基线参考向量修改特征向量。然后将每个基线参考向量与修改的特征向量进行比较，以确定基准参考向量以及最准确地描述复合动作的简单动作的对应组合。

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