Patents by Inventor Soheil Kolouri

Soheil Kolouri has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11176477
    Abstract: Described is a system for unsupervised domain adaptation in an autonomous learning agent. The system adapts a learned model with a set of unlabeled data from a target domain, resulting in an adapted model. The learned model was previously trained to perform a task using a set of labeled data from a source domain. The set of labeled data has a first input data distribution, and the set of unlabeled target data has a second input data distribution that is distinct from the first input data distribution. The adapted model is implemented in the autonomous learning agent, causing the autonomous learning agent to perform the task in the target domain.
    Type: Grant
    Filed: December 18, 2019
    Date of Patent: November 16, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Alexander J. Gabourie, Mohammad Rostami, Soheil Kolouri, Kyungnam Kim
  • Patent number: 11169258
    Abstract: Systems and methods according to one or more embodiments are provided for registration of synthetic aperture range profile data to aid in SAR-based navigation. In one example, a SAR-based navigation system includes a memory comprising a plurality of executable instructions. The SAR-based navigation system further includes a processor adapted to receive range profile data associated with observed views of a scene, compare the range profile data to a template range profile data of the scene, and estimate registration parameters associated with the range profile data relative to the template range profile data to determine a deviation from the template range profile data.
    Type: Grant
    Filed: May 9, 2019
    Date of Patent: November 9, 2021
    Assignee: The Boeing Company
    Inventors: Shankar R. Rao, Kang-Yu Ni, Soheil Kolouri
  • Patent number: 11113597
    Abstract: A method for retraining an artificial neural network trained on data from an old task includes training the artificial neural network on data from a new task different than the old task, calculating a drift, utilizing Sliced Wasserstein Distance, in activation distributions of a series of hidden layer nodes during the training of the artificial neural network with the new task, calculating a number of additional nodes to add to at least one hidden layer based on the drift in the activation distributions, resetting connection weights between input layer nodes, hidden layer nodes, and output layer nodes to values before the training of the artificial neural network on the data from the new task, adding the additional nodes to the at least one hidden layer, and training the artificial neural network on data from the new task.
    Type: Grant
    Filed: September 5, 2019
    Date of Patent: September 7, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Charles E. Martin, Nicholas A. Ketz, Praveen K. Pilly, Soheil Kolouri, Michael D. Howard, Nigel D. Stepp
  • Patent number: 11086299
    Abstract: Described is a system for controlling autonomous platform. Based on an input image, the system generates a motor control command decision for the autonomous platform. A probability of the input image belonging to a set of training images is determined, and a reliability measure for the motor control command decision is generated using the determined probability. An exploratory action is performed when the reliability measure is above a predetermined threshold. Otherwise, an exploitation action corresponding with the motor control command decision is performed when the reliability measure is below a predetermined threshold.
    Type: Grant
    Filed: January 30, 2019
    Date of Patent: August 10, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Soheil Kolouri, Heiko Hoffmann
  • Publication number: 20210231795
    Abstract: A synthetic aperture radar (SAR) system is disclosed. The SAR comprises a memory, a convolutional neural network (CNN), a machine-readable medium on the memory, and a machine-readable medium on the memory. The machine-readable medium storing instructions that, when executed by the CNN, cause the SAR system to perform operations. The operation comprises: receiving range profile data associated with observed views of a scene; concatenating the range profile data with a template range profile data of the scene; and estimating registration parameters associated with the range profile data relative to the template range profile data to determine a deviation from the template range profile data.
    Type: Application
    Filed: January 24, 2020
    Publication date: July 29, 2021
    Inventors: Soheil Kolouri, Shankar R. Rao
  • Patent number: 11069069
    Abstract: Described is a system for implicitly predicting movement of an object. 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 providing an image of a first trajectory to a predictive autoencoder, and using the predictive autoencoder, generating a predicted tactical response that comprises a second trajectory based on images of previous tactical responses that were used to train the predictive autoencoder, and controlling a device based on the predicted tactical response.
    Type: Grant
    Filed: April 9, 2018
    Date of Patent: July 20, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Amir M. Rahimi, Soheil Kolouri, Rajan Bhattacharyya
  • Publication number: 20210192363
    Abstract: Described is a system for continual adaptation of a machine learning model implemented in an autonomous platform. The system adapts knowledge previously learned by the machine learning model for performance in a new domain. The system receives a consecutive sequence of new domains comprising new task data. The new task data and past learned tasks are forced to share a data distribution in an embedding space, resulting in a shared generative data distribution. The shared generative data distribution is used to generate a set of pseudo-data points for the past learned tasks. Each new domain is learned using both the set of pseudo-data points and the new task data. The machine learning model is updated using both the set of pseudo-data points and the new task data.
    Type: Application
    Filed: October 8, 2020
    Publication date: June 24, 2021
    Inventors: Soheil Kolouri, Mohammad Rostami, Praveen K. Pilly
  • Publication number: 20210182618
    Abstract: Described is a system for learning object labels for control of an autonomous platform. Pseudo-task optimization is performed to identify an optimal pseudo-task for each source model of one or more source models. An initial target network is trained using the optimal pseudo-task. Source image components are extracted from source models, and an attribute dictionary of attributes is generated from the source image components. Using zero-shot attribution distillation, the unlabeled target data is aligned with the source models similar to the unlabeled target data. The unlabeled target data are mapped onto attributes in the attribute dictionary. A new target network is generated from the mapping, and the new target network is used to assign an object label to an object in the unlabeled target data. The autonomous platform is controlled based on the object label.
    Type: Application
    Filed: October 26, 2020
    Publication date: June 17, 2021
    Inventors: Heiko Hoffmann, Soheil Kolouri
  • Patent number: 11037030
    Abstract: A method for computing classifications of raw tomographic data includes: supplying the raw tomographic data to a sinogram-convolutional neural network including blocks, at least one of the blocks being configured to perform a convolution of the raw tomographic data in Radon space with a convolutional kernel by: slicing the raw tomographic data into a plurality of one-dimensional tomographic data slices along an angle dimension of the raw tomographic data; slicing the convolutional kernel into a plurality of one-dimensional kernel slices along the angle dimension of the convolutional kernel; for each angle, computing a one-dimensional convolution between: a corresponding one of the one-dimensional tomographic data slices at the angle; and a corresponding one of the one-dimensional kernel slices at the angle; and collecting the one-dimensional convolutions at the angles; computing a plurality of features from the convolution; and computing the classifications of the raw tomographic data based on the features.
    Type: Grant
    Filed: October 4, 2019
    Date of Patent: June 15, 2021
    Assignee: HRL Laboratories, LLC
    Inventor: Soheil Kolouri
  • Patent number: 11023789
    Abstract: Described is a system for classifying objects and scenes in images. The system identifies salient regions of an image based on activation patterns of a convolutional neural network (CNN). Multi-scale features for the salient regions are generated by probing the activation patterns of the CNN at different layers. Using an unsupervised clustering technique, the multi-scale features are clustered to identify key attributes captured by the CNN. The system maps from a histogram of the key attributes onto probabilities for a set of object categories. Using the probabilities, an object or scene in the image is classified as belonging to an object category, and a vehicle component is controlled based on the object category causing the vehicle component to perform an automated action.
    Type: Grant
    Filed: March 26, 2018
    Date of Patent: June 1, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Soheil Kolouri, Charles E. Martin, Heiko Hoffmann
  • Publication number: 20210109210
    Abstract: Described is a stripmap SAR system on a vehicle comprising an antenna that is fixed and directed outward from the side of the vehicle, a SAR sensor, a storage, and a computing device. The computing device comprises a memory, one or more processing units, and a machine-readable medium on the memory. The machine-readable medium stores instructions that, when executed by the one or more processing units, cause the stripmap SAR system to perform various operations. The operations comprise: receiving stripmap range profile data associated with observed views of a scene; transforming the received stripmap range profile data into partial circular range profile data; comparing the partial circular range profile data to a template range profile data of the scene; and estimating registration parameters associated with the partial circular range profile data relative to the template range profile data to determine a deviation from the template range profile data.
    Type: Application
    Filed: October 14, 2019
    Publication date: April 15, 2021
    Inventors: Adour V. Kabakian, Soheil Kolouri, Brian N. Limketkai, Shankar R. Rao
  • Publication number: 20210089762
    Abstract: Described is a system for learning actions for image-based action recognition in an autonomous vehicle. The system separates a set of labeled action image data from a source domain into components. The components are mapped onto a set of action patterns, thereby creating a dictionary of action patterns. For each action in the set of labeled action data, a mapping is learned from the action pattern representing the action onto a class label for the action. The system then maps a set of new unlabeled target action image data onto a shared embedding feature space in which action patterns can be discriminated. For each target action in the set of new unlabeled target action image data, a class label for the target action is identified. Based on the identified class label, the autonomous vehicle is caused to perform a vehicle maneuver corresponding to the identified class label.
    Type: Application
    Filed: July 16, 2020
    Publication date: March 25, 2021
    Inventors: Amir M. Rahimi, Hyukseong Kwon, Heiko Hoffmann, Soheil Kolouri
  • Patent number: 10908616
    Abstract: Described is a system for object recognition. The system generates a training image set of object images from multiple image classes. Using a training image set and annotated semantic attributes, a model is trained that maps visual features from known images to the annotated semantic attributes using joint sparse representations with respect to dictionaries of visual features and semantic attributes. The trained model is used for mapping visual features of an unseen input image to its semantic attributes. The unseen input image is classified as belonging to an image class, and a device is controlled based on the classification of the unseen input image.
    Type: Grant
    Filed: July 12, 2018
    Date of Patent: February 2, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Soheil Kolouri, Mohammad Rostami, Kyungnam Kim, Yuri Owechko
  • Publication number: 20210019632
    Abstract: Described is a system for continual learning using experience replay. In operation, the system receives a plurality of tasks sequentially, from which a current task is fed to an encoder. The current task has data points associated with the current task. The encoder then maps the data points into an embedding space, which reflects the data points as discriminative features. A decoder then generates pseudo-data points from the discriminative features, which are provided back to the encoder. The discriminative features are updated in the embedding space based on the pseudo-data points. The encoder then learns (updates) a classification of a new task by matching the new task with the discriminative features in the embedding space.
    Type: Application
    Filed: May 15, 2020
    Publication date: January 21, 2021
    Inventors: Mohammad Rostami, Soheil Kolouri, Praveen K. Pilly
  • Publication number: 20200410098
    Abstract: Described is a system for detecting backdoor attacks in deep convolutional neural networks (CNNs). The system compiles specifications of a pretrained CNN into an executable model, resulting in a compiled model. A set of Universal Litmus Patterns (ULPs) are fed through the compiled model, resulting in a set of model outputs. The set of model outputs are classified and used to determine presence of a backdoor attack in the pretrained CNN. The system performs a response based on the presence of the backdoor attack.
    Type: Application
    Filed: April 21, 2020
    Publication date: December 31, 2020
    Inventors: Soheil Kolouri, Heiko Hoffmann
  • Patent number: 10878276
    Abstract: Described is a system for detecting change of context in a video stream on an autonomous platform. The system extracts salient patches from image frames in the video stream. Each salient patch is translated to a concept vector. A recurrent neural network is enervated with the concept vector, resulting in activations of the recurrent neural network. The activations are classified, and the classified activations are mapped onto context classes. A change in context class is detected in the image frames, and the system causes the autonomous platform to perform an automatic operation to adapt to the change of context class.
    Type: Grant
    Filed: May 17, 2019
    Date of Patent: December 29, 2020
    Assignee: HRL Laboratories, LLC
    Inventors: Charles E. Martin, Nigel D. Stepp, Soheil Kolouri, Heiko Hoffmann
  • Publication number: 20200355822
    Abstract: Systems and methods according to one or more embodiments are provided for registration of synthetic aperture range profile data to aid in SAR-based navigation. In one example, a SAR-based navigation system includes a memory comprising a plurality of executable instructions. The SAR-based navigation system further includes a processor adapted to receive range profile data associated with observed views of a scene, compare the range profile data to a template range profile data of the scene, and estimate registration parameters associated with the range profile data relative to the template range profile data to determine a deviation from the template range profile data.
    Type: Application
    Filed: May 9, 2019
    Publication date: November 12, 2020
    Inventors: Shankar R. Rao, Kang-Yu Ni, Soheil Kolouri
  • Patent number: 10803356
    Abstract: Described is a system for understanding machine-learning decisions. In an unsupervised learning phase, the system extracts, from input data, concepts represented by a machine-learning (ML) model in an unsupervised manner by clustering patterns of activity of latent variables of the concepts, where the latent variables are hidden variables of the ML model. The extracted concepts are organized into a concept network by learning functional semantics among the extracted concepts. In an operational phase, a subnetwork of the concept network is generated. Nodes of the subnetwork are displayed as a set of visual images that are annotated by weights and labels, and the ML model per the weights and labels.
    Type: Grant
    Filed: April 5, 2018
    Date of Patent: October 13, 2020
    Assignee: HRL Laboratories, LLC
    Inventors: Charles E. Martin, Soheil Kolouri, Heiko Hoffmann
  • Patent number: 10755424
    Abstract: 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.
    Type: Grant
    Filed: May 4, 2018
    Date of Patent: August 25, 2020
    Assignee: HRL Laboratories, LLC
    Inventors: Soheil Kolouri, Amir M. Rahimi, Rajan Bhattacharyya
  • Patent number: 10755149
    Abstract: Described is a system that can recognize novel objects that the system has never before seen. The system uses a training image set to learn a model that maps visual features from known images to semantic attributes. The learned model is used to map visual features of an unseen input image to semantic attributes. The unseen input image is classified as belonging to an image class with a class label. A device is controlled based on the class label.
    Type: Grant
    Filed: April 10, 2018
    Date of Patent: August 25, 2020
    Assignee: HRL Laboratories, LLC
    Inventors: Soheil Kolouri, Shankar R. Rao, Kyungnam Kim