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1. 发明申请

WO2018140625A1 SCALABLE AND EFFICIENT EPISODIC MEMORY IN COGNITIVE PROCESSING FOR AUTOMATED SYSTEMS 审中-公开
公开(公告)号：WO2018140625A1
公开(公告)日：2018-08-02
申请号：PCT/US2018/015284
申请日：2018-01-25
申请人： HRL LABORATORIES, LLC
发明人： BHATTACHARYYA, Rajan , CHO, Youngkwan , KWON, Hyukseong
IPC分类号： G06F17/30
摘要： Described is a system for an episodic memory used by an automated platform. The system acquires data from an episodic memory that, comprises an event database, an event-sequence graph, and an episode list. Using the event-sequence graph, the system identifies a closest node to a current environment for the automated platform. Based on the closest node and using a hash function or key based on the hash function, the system retrieves from the event database an episode that corresponds to the closest node, the episode including a sequence of even ts, Behavior of the automated platform in the current environment is guided based on the data from the episodic memory.

2. 发明申请

WO2019144025A8 NEURO-ADAPTIVE BODY SENSING FOR USER STATES FRAMEWORK (NABSUS) 审中-公开
公开(公告)号：WO2019144025A8
公开(公告)日：2019-07-25
申请号：PCT/US2019/014322
申请日：2019-01-18
申请人： HRL LABORATORIES, LLC
发明人： BHATTACHARYYA, Rajan , MOHAMMADREZAZADEH, Iman
IPC分类号： G06F3/01 , A61B5/0476 , A61B5/04 , A61B5/00
摘要： Described is a system for personalizing a human-machine interface (HMI) device based on a mental and physical state of a user. During performance of a task in a simulation environment, the system extracts biometric features from data collected from body sensors, and extracts brain entropy features from electroencephalogram (EEG) signals. The brain entropy features are correlated with the biometric features to generate a mental-state model. The mental-state model is deployed in a HMI device during performance of the task in an operational environment for continuous adaptation of the HMI device to its user's mental and physical states.

3. 发明申请

WO2018204877A1 PREDICTION OF MULTI-AGENT ADVERSARIAL MOVEMENTS THROUGH SIGNATURE-FORMATIONS USING RADON-CUMULATIVE DISTRIBUTION TRANSFORM AND CANONICAL CORRELATION ANALYSIS 审中-公开
公开(公告)号：WO2018204877A1
公开(公告)日：2018-11-08
申请号：PCT/US2018/031240
申请日：2018-05-04
申请人： HRL LABORATORIES, LLC
发明人： KOLOURI, Soheil , RAHIMI, Amir, M. , BHATTACHARYYA, Rajan
IPC分类号： G05D1/02 , G06F17/10
摘要： Described is a system for predicting multi-agent movements. A Radon Cumulative Distribution Transform (Radon-CDT) is applied to pairs of signature-formations representing agent movements. Canonical correlation analysis (CCA) components are identified for the pairs of signature-formations. Then, a relationship between the pairs of signature formations is learned using the CCA components. A counter signature-formation for a new dataset is predicted using the learned relationship and a new signature-formation. Control parameters of a device can be adjusted based on the predicted counter signature-formation.

4. 发明申请

WO2023288186A1 VERIFYING OBJECT CLASSIFICATION USING COMPONENT-BASED DESCRIPTORS AND TEMPORAL SIMILARITY MEASURES 审中-公开
公开(公告)号：WO2023288186A1
公开(公告)日：2023-01-19
申请号：PCT/US2022/073573
申请日：2022-07-08
申请人： HRL LABORATORIES, LLC
发明人： RAHIMI, Amir , KWON, Hyukseong , AGARWAL, Amit , BHATTACHARYYA, Rajan
IPC分类号： G06V20/58 , G06V20/17 , G06V10/764 , G06V10/776
摘要： A method and apparatus for verifying object classification includes comparing detected object-components with trained component-based descriptors and similarity measures to generate object classification verification data. The object classification verification data represents object classification confidence or misclassification errors to weight uncertainty for perception-based decision making. A sequence of similarity measures associated with a current frame and prior frames within a reference observation time constraint are compared to a reference temporal similarity measure boundary to generate the object classification verification data.

5. 发明申请

WO2018187252A1 EXPLICIT PREDICTION OF ADVERSARY MOVEMENTS WITH CANONICAL CORRELATION ANALYSIS 审中-公开
公开(公告)号：WO2018187252A1
公开(公告)日：2018-10-11
申请号：PCT/US2018/025770
申请日：2018-04-02
申请人： HRL LABORATORIES, LLC
发明人： RAHIMI, Amir, M. , KOLOURI, Soheil , BHATTACHARYYA, Rajan
IPC分类号： G06Q50/10 , G06F17/10
摘要： Described is a system for prediction of adversary movements. In an aspect, the system includes one or more processors and a memory, the memory being a non- transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform operations of computing relative positions of multiple objects of interest, generating a feature representation by forming a matrix based on the relative positions, predicting movement of the multiple objects of interest by applying clustering to the feature representation and by performing canonical correlation analysis, and controlling a device based on the predicted movement of the multiple objects of interest.

6. 发明申请

WO2016043846A2 A GENERAL FORMAL CONCEPT ANALYSIS (FCA) FRAMEWORK FOR CLASSIFICATION 审中-公开
标题翻译：一般形式概念分析（FCA）框架的分类
公开(公告)号：WO2016043846A2
公开(公告)日：2016-03-24
申请号：PCT/US2015/041744
申请日：2015-07-23
申请人： HRL LABORATORIES LLC , O'BRIEN, Michael, J. , BENVENUTO, James , BHATTACHARYYA, Rajan
发明人： O'BRIEN, Michael, J. , BENVENUTO, James , BHATTACHARYYA, Rajan
IPC分类号： G06N5/02 , G06F17/30
CPC分类号： G06N5/022 , G06N99/005
摘要： Described is a system for data classification using formal concept analysis (FCA). In a training phase, the system generates a FCA classification lattice, having a structure, using a set of training data. The set of training data comprises training presentations and classifications corresponding to the training presentations, in a classification phase, a set of test data having classes that are hierarchical in nature is classified using the structure of the FCA classification lattice.
摘要翻译：描述用于使用形式概念分析（FCA）的数据分类的系统。在训练阶段，系统使用一组训练数据生成FCA分类网格，具有结构。该组训练数据包括对应于训练演示的训练演示和分类，在分类阶段，具有分层性质的分类的一组测试数据使用FCA分类网格的结构进行分类。

7. 发明申请

WO2021178027A1 CONTRAST AND ENTROPY BASED PERCEPTION ADAPTATION USING PROBABILISTIC SIGNAL TEMPORAL LOGIC BASED OPTIMIZATION 审中-公开
公开(公告)号：WO2021178027A1
公开(公告)日：2021-09-10
申请号：PCT/US2020/066972
申请日：2020-12-23
申请人： HRL LABORATORIES, LLC
发明人： KWON, Hyukseong , RAHIMI, Amir, M. , AGARWAL, Amit , BHATTACHARYYA, Rajan
IPC分类号： G06K9/00 , G06K9/03 , G06K9/52 , H04N5/232
摘要： Described is a system for contrast and entropy-based perception adaption to optimize perception. The system is operable for receiving an input image of a scene with a camera system and detecting one or more objects (having perception data) in the input image. The perception data of the one or more objects is converted into probes, which are then converted into axioms using probabilistic signal temporal logic. The axioms are evaluated based on probe bounds. If the axioms are within the probe bounds, then results are provided; however, if the axioms are outside of the probe bounds, the system estimates optimal contrast bounds and entropy bounds as perception parameters. The contrast and entropy in the camera system are then adjusted based on the perception parameters.

8. 发明申请

WO2021061875A1 SYSTEM AND METHOD OF PERCEPTION ERROR EVALUATION AND CORRECTION BY SOLVING OPTIMIZATION PROBLEMS UNDER THE PROBABILISTIC SIGNAL TEMPORAL LOGIC BASED CONSTRAINTS 审中-公开
公开(公告)号：WO2021061875A1
公开(公告)日：2021-04-01
申请号：PCT/US2020/052342
申请日：2020-09-23
申请人： HRL LABORATORIES, LLC
发明人： KWON, Hyukseong , RAHIMI, Amir, M. , AGARWAL, Amit , BHATTACHARYYA, Rajan
IPC分类号： G06K9/00 , G06K9/03 , G06K9/52 , H04N5/232
摘要： Described is a system to evaluate and reduce perception error in object detection and recognition. The system includes a perception module that receives perception data (of an object(s)) from an environment proximate a mobile platform. Perception probes are generated that describe one or more characteristics of the objects. The perception probes are converted into probabilistic signal temporal logic (PSTL)-based constraints that provide axioms having statistical analysis of the perception probes. The axioms are evaluated to classify the perception probes as valid or erroneous. Optimal perception parameters are generated by solving an optimization problem based on the axioms, which allows the system to adjust the perception module based on the optimal perception parameters.

9. 发明申请

WO2018191117A1 COGNITIVE BEHAVIOR PREDICTION SYSTEM FOR AUTONOMOUS SYSTEMS 审中-公开
公开(公告)号：WO2018191117A1
公开(公告)日：2018-10-18
申请号：PCT/US2018/026479
申请日：2018-04-06
申请人： HRL LABORATORIES, LLC
发明人： KIM, Hyun, Tiffany J. , LEBIERE, Christian , VINOKUROV, Jerry , BHATTACHARYYA, Rajan
IPC分类号： G05D1/00 , G05D1/02 , G08G1/01
摘要： Described is a system for predicting the behavior of an autonomous system. The system identifies a taxonomic state of a motion condition of an autonomous vehicle based on a spatiotemporal location of the autonomous vehicle and elements of a driving scenario. Behavior of the autonomous vehicle is predicted based on the taxonomic state of the motion condition. The autonomous vehicle makes and implements a driving operation decision based on the predicted behavior.

10. 发明申请

WO2017014826A1 A PARZEN WINDOW FEATURE SELECTION ALGORITHM FOR FORMAL CONCEPT ANALYSIS (FCA) 审中-公开
标题翻译：用于形式概念分析的PARZEN WINDOW特征选择算法（FCA）
公开(公告)号：WO2017014826A1
公开(公告)日：2017-01-26
申请号：PCT/US2016/031644
申请日：2016-05-10
申请人： HRL LABORATORIES, LLC
发明人： O'BRIEN, Michael, J. , NI, Kang-Yu , BENVENUTO, James , BHATTACHARYYA, Rajan
IPC分类号： G06N5/04 , G06F17/30
CPC分类号： G06N99/005
摘要： Described is a system for feature selection for formal concept analysis (FCA), A set of data points having features is separated into object classes. For each object class, the data points are convolved with a Gaussian function, resulting in a class distribution curve for each known object class. For each class distribution curve, a binary array is generated having ones on intervals of data values on which the class distribution curve is maximum with respect to ail other class distribution curves, and zeroes elsewhere. For each object class, a binary class curve indicating for which interval a performance of the known object class exceeds all other known object classes is generated. The intervals are ranked with respect to a predetermined confidence threshold value. The ranking of the intervals is used to select which features to extract from the set of data points in FCA lattice construction.
摘要翻译：描述了用于形式概念分析（FCA）的特征选择的系统，具有特征的一组数据点被分成对象类。对于每个对象类，数据点与高斯函数进行卷积，得到每个已知对象类的类分布曲线。对于每个类分布曲线，生成二进制数组，其中数据值的间隔相对于其他类分布曲线在类分布曲线最大的数据值，其他地方为零。对于每个对象类，生成指示已知对象类的性能超过所有其他已知对象类的间隔的二进制类曲线。间隔相对于预定置信度阈值进行排序。间隔的排序用于选择从FCA格子构造中的数据点集合中提取哪些特征。

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