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1. 发明申请

US20150032424A1 FAMILIARITY MODELING 有权
标题翻译：家族建模
公开(公告)号：US20150032424A1
公开(公告)日：2015-01-29
申请号：US13951015
申请日：2013-07-25
申请人： Rakesh Gupta , Igor V. Karpov , Antoine Raux , Deepak Ramachandran
发明人： Rakesh Gupta , Igor V. Karpov , Antoine Raux , Deepak Ramachandran
IPC分类号： G06F17/50 , G06N7/00
CPC分类号： G01C21/00 , G01C21/3484 , G01C21/3641 , G06N99/005
摘要： One or more embodiments of techniques or systems for modeling familiarity for a traveler are provided herein. Familiarity evidence can be received, indicative of how familiar a traveler is with an area or road segment, and based on a number of visits the traveler has made to that area. The familiarity evidence can be used to generate one or more familiarity models indicative of a predicted familiarity of locations around the area. Familiarity models can be based on kernels, graph distances, Markov random fields (MRFs), etc. When route directions are generated from an origin location to a destination location, one or more of the directions can be provided based on one or more of the familiarity models. For example, if a familiarity model indicates that a traveler is familiar with a route, driving directions of the route can be adapted to be more succinct.
摘要翻译：本文提供了用于建模旅客的熟悉度的技术或系统的一个或多个实施例。可以接收到熟悉的证据，指示旅行者在区域或路段中的熟悉程度，以及旅客对该地区的访问次数。熟悉证据可用于产生一个或多个熟悉模型，表明该地区周边地区的熟悉程度。熟悉度模型可以基于内核，图形距离，马尔可夫随机场（MRFs）等。当从原点位置到目标位置生成路线方向时，可以基于一个或多个方向来提供一个或多个方向熟悉模式。例如，如果熟悉模式表明旅行者熟悉路线，路线的驾驶方向可以更加简洁。

2. 发明申请

US20130297321A1 LANDMARK-BASED LOCATION BELIEF TRACKING FOR VOICE-CONTROLLED NAVIGATION SYSTEM 有权
标题翻译：用于语音控制导航系统的基于LANDMARK的位置直接跟踪
公开(公告)号：US20130297321A1
公开(公告)日：2013-11-07
申请号：US13801441
申请日：2013-03-13
申请人： Antoine Raux , Rakesh Gupta , Deepak Ramachandran , Yi Ma
发明人： Antoine Raux , Rakesh Gupta , Deepak Ramachandran , Yi Ma
IPC分类号： G01C21/26
CPC分类号： G01C21/26 , G01C21/3608 , G01C21/3644
摘要： An utterance is received from a user specifying a location attribute and a landmark. A set of candidate locations is identified based on the specified location attribute, and a confidence score can be determined for each candidate location. A set of landmarks is identified based on the specified landmark, and confidence scores can be determined for the landmarks. An associated kernel model is generated for each landmark. Each kernel model is centered at the location of the associated landmark on a map, and the amplitude of the kernel model can be based on landmark attributes, landmark confidence scores, characteristics of the user, and the like. The candidate locations are ranked based on the amplitudes of overlapping kernel models at the candidate locations, and can also be ranked based on confidence scores associated with the candidate locations. A candidate location is selected and presented to the user based on the candidate location ranking
摘要翻译：从指定位置属性和地标的用户接收到话语。基于指定的位置属性来识别一组候选位置，并且可以为每个候选位置确定可信度得分。基于指定的地标识别一组地标，并且可以为地标确定置信度得分。为每个地标生成相关的内核模型。每个核心模型集中在地图上相关联的地标的位置，并且内核模型的幅度可以基于地标属性，地标置信度得分，用户特征等。候选位置基于候选位置处的重叠核心模型的幅度进行排序，并且还可以基于与候选位置相关联的置信度得分进行排名。基于候选位置排名，选择候选位置并呈现给用户

3. 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。

4. 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。

5. 发明授权

US09127950B2 Landmark-based location belief tracking for voice-controlled navigation system 有权
标题翻译：用于语音控制导航系统的地标位置信念跟踪
公开(公告)号：US09127950B2
公开(公告)日：2015-09-08
申请号：US13801441
申请日：2013-03-13
申请人： Antoine Raux , Rakesh Gupta , Deepak Ramachandran , Yi Ma
发明人： Antoine Raux , Rakesh Gupta , Deepak Ramachandran , Yi Ma
IPC分类号： G01C21/26 , G01C21/36
CPC分类号： G01C21/26 , G01C21/3608 , G01C21/3644
摘要： An utterance is received from a user specifying a location attribute and a landmark. A set of candidate locations is identified based on the specified location attribute, and a confidence score can be determined for each candidate location. A set of landmarks is identified based on the specified landmark, and confidence scores can be determined for the landmarks. An associated kernel model is generated for each landmark. Each kernel model is centered at the location of the associated landmark on a map, and the amplitude of the kernel model can be based on landmark attributes, landmark confidence scores, characteristics of the user, and the like. The candidate locations are ranked based on the amplitudes of overlapping kernel models at the candidate locations, and can also be ranked based on confidence scores associated with the candidate locations. A candidate location is selected and presented to the user based on the candidate location ranking.
摘要翻译：从指定位置属性和地标的用户接收到话语。基于指定的位置属性来识别一组候选位置，并且可以为每个候选位置确定可信度得分。基于指定的地标识别一组地标，并且可以为地标确定置信度得分。为每个地标生成相关的内核模型。每个核心模型集中在地图上相关联的地标的位置，并且内核模型的幅度可以基于地标属性，地标置信度得分，用户特征等。候选位置基于候选位置处的重叠核心模型的幅度进行排序，并且还可以基于与候选位置相关联的置信度得分进行排名。候选位置被选择并且基于候选位置排名呈现给用户。

6. 发明授权

US09090255B2 Hybrid vehicle fuel efficiency using inverse reinforcement learning 有权
标题翻译：混合动力汽车燃油效率采用反强化学习
公开(公告)号：US09090255B2
公开(公告)日：2015-07-28
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 , B60W10/06 , B60W10/26 , G01C21/34
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要求（Preq）的比例。 α2控制HEV电池的再充电速率。根据α1和α2，决定使用多少发动机功率（Peng）以及使用多少电池电量（Pbatt）。彭和Pbatt被送到动力总成。

7. 发明申请

US20120053945A1 BELIEF TRACKING AND ACTION SELECTION IN SPOKEN DIALOG SYSTEMS 有权
标题翻译：双语对话系统中的直接跟踪和行动选择
公开(公告)号：US20120053945A1
公开(公告)日：2012-03-01
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： G10L15/18
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。

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