Patents by Inventor Nigel D. Stepp

Nigel D. Stepp 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).

  • Publication number: 20220398360
    Abstract: A method for remaining useful life prediction includes generating parameter data related to a performance of an electro-mechanical element. The method includes generating simulated behavior data of the electro-mechanical element by executing a digital-twin simulation model based on estimated operating conditions, and generating deviation data that characterizes how the parameter data deviates from the simulated behavior data. The deviation data includes a deterministic component and a stochastic component. The method includes generating extrapolated deviation data by extrapolating the deterministic component and the stochastic component of the deviation data forward in time, calculating a remaining useful life of the electro-mechanical element in response to the extrapolated deviation data, and reporting the remaining useful life to a person associated with the vehicle.
    Type: Application
    Filed: March 2, 2022
    Publication date: December 15, 2022
    Applicant: The Boeing Company
    Inventors: Nigel D. Stepp, Alexander N. Waagen, Tsai-Ching Lu
  • Patent number: 11521053
    Abstract: Described is a system for specifying control of a device based on a Bayesian network model. The system includes a Bayesian neuromorphic compiler having a network composition module having probabilistic computation units (PCUs) arranged in a hierarchical composition containing multi-level dependencies. The Bayesian neuromorphic compiler receives a Bayesian network model as input and produces a spiking neural network topology and configuration that implements the Bayesian network model. The network composition module learns conditional probabilities of the Bayesian network model. The system computes a conditional probability and controls a device based on the computed conditional probability.
    Type: Grant
    Filed: February 17, 2020
    Date of Patent: December 6, 2022
    Assignee: HRL LABORATORIES, LLC
    Inventors: Nigel D. Stepp, Aruna Jammalamadaka
  • Patent number: 11449735
    Abstract: Described is a system for computing conditional probabilities of random variables for Bayesian inference. The system implements a spiking neural network of neurons to compute the conditional probability of two random variables X and Y. The spiking neural network includes an increment path for a synaptic weight that is proportional to a product of the synaptic weight and a probability of X, a decrement path for the synaptic weight that is proportional to a probability of X, Y, and delay and spike timing dependent plasticity (STDP) parameters such that the synaptic weight increases and decreases with the same magnitude for a single firing event.
    Type: Grant
    Filed: September 20, 2019
    Date of Patent: September 20, 2022
    Assignee: HRL LABORATORIES, LLC
    Inventors: Hao-Yuan Chang, Aruna Jammalamadaka, Nigel D. Stepp
  • Patent number: 11347221
    Abstract: A method of training an artificial neural network having a series of layers and at least one weight matrix encoding connection weights between neurons in successive layers. The method includes receiving, at an input layer of the series of layers, at least one input, generating, at an output layer of the series of layers, at least one output based on the at least one input, generating a reward based on a comparison of between the at least one output and a desired output, and modifying the connection weights based on the reward. Modifying the connection weights includes maintaining a sum of synaptic input weights to each neuron to be substantially constant and maintaining a sum of synaptic output weights from each neuron to be substantially constant.
    Type: Grant
    Filed: October 23, 2019
    Date of Patent: May 31, 2022
    Assignee: HRL Laboratories, LLC
    Inventors: Steven W. Skorheim, Nigel D. Stepp, Ruggero Scorcioni
  • Patent number: 11288572
    Abstract: Described is a system for performing probabilistic computations on mobile platform sensor data. The system translates a Bayesian model representing input mobile platform sensor data to a spiking neuronal network unit that implements the Bayesian model. Using the spiking neuronal network unit, conditional probabilities are computed for the input mobile platform sensor data, where the input mobile platform sensor data is a time series of mobile platform error codes encoded as neuronal spikes. The neuronal spikes are decoded and represent a mobile platform failure mode. The system causes the mobile platform to initiate a mitigation action based on the mobile platform failure mode.
    Type: Grant
    Filed: March 6, 2019
    Date of Patent: March 29, 2022
    Assignee: HRL Laboratories, LLC
    Inventors: Nigel D. Stepp, Aruna Jammalamadaka
  • Patent number: 11238470
    Abstract: A method for collecting and processing user input. In some embodiments the method includes presenting a first user with a prompt for eliciting a first response, the first response including a numerical portion including one or more numbers, and an explanatory portion; receiving, from the first user, the first response; receiving from each of a plurality of other users, a respective response of a plurality of other responses; and displaying, to the first user, an ordered list of other responses. Within the ordered list, a second response, of the plurality of other responses, may be earlier than a third response, of the plurality of other responses, the second response being, according to a measure of distance, more distant, than the third response, from the first response.
    Type: Grant
    Filed: December 20, 2019
    Date of Patent: February 1, 2022
    Assignee: HRL Laboratories, LLC
    Inventors: Nigel D. Stepp, David J. Huber, Tsai-Ching Lu
  • Patent number: 11199839
    Abstract: Described is a system for online vehicle recognition in an autonomous driving environment. Using a learning network comprising an unsupervised learning component and a supervised learning component, images of moving vehicles extracted from videos captured in the autonomous driving environment are learned and classified. Vehicle feature data is extracted from input moving vehicle images. The extracted vehicle feature data is clustered into different vehicle classes using the unsupervised learning component. Vehicle class labels for the different vehicle classes are generated using the supervised learning component. Based on a vehicle class label for a moving vehicle in the autonomous driving environment, the system selects an action to be performed by the autonomous vehicle, and causes the selected action to be performed by the autonomous vehicle in the autonomous driving environment.
    Type: Grant
    Filed: July 23, 2019
    Date of Patent: December 14, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Qin Jiang, Youngkwan Cho, Nigel D. Stepp, Steven W. Skorheim, Vincent De Sapio, Praveen K. Pilly, Ruggero Scorcioni
  • Patent number: 11150327
    Abstract: A system configured to identify a target in a synthetic aperture radar signal includes: a feature extractor configured to extract a plurality of features from the synthetic aperture radar signal; an input spiking neural network configured to encode the features as a first plurality of spiking signals; a multi-layer recurrent neural network configured to compute a second plurality of spiking signals based on the first plurality of spiking signals; a readout neural layer configured to compute a signal identifier based on the second plurality of spiking signals; and an output configured to output the signal identifier, the signal identifier identifying the target.
    Type: Grant
    Filed: July 24, 2018
    Date of Patent: October 19, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Qin Jiang, Youngkwan Cho, Nigel D. Stepp, Steven W. Skorheim, Vincent De Sapio, Jose Cruz-Albrecht, Praveen K. Pilly
  • 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: 10986113
    Abstract: Described is a low power system for mobile devices that provides continuous, behavior-based security validation of mobile device applications using neuromorphic hardware. A mobile device comprises a neuromorphic hardware component that runs on the mobile device for continuously monitoring time series related to individual mobile device application behaviors, detecting and classifying pattern anomalies associated with a known malware threat in the time series related to individual mobile device application behaviors, and generating an alert related to the known malware threat. The mobile device identifies pattern anomalies in dependency relationships of mobile device inter-application and intra-applications communications, detects pattern anomalies associated with new malware threats, and isolates a mobile device application having a risk of malware above a predetermined threshold relative to a risk management policy.
    Type: Grant
    Filed: November 23, 2018
    Date of Patent: April 20, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Vincent De Sapio, Hyun (Tiffany) J. Kim, Kyungnam Kim, Nigel D. Stepp, Kang-Yu Ni, Jose Cruz-Albrecht, Braden Mailloux
  • Patent number: 10976429
    Abstract: A system configured to identify a target in a synthetic aperture radar signal includes: a feature extractor configured to extract a plurality of features from the synthetic aperture radar signal; a spiking neural network configured to encode the features as a plurality of spiking signals; a readout neural layer configured to compute a signal identifier based on the spiking signals; and an output configured to output the signal identifier, the signal identifier identifying the target.
    Type: Grant
    Filed: October 16, 2017
    Date of Patent: April 13, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Qin Jiang, Nigel D. Stepp, Praveen K. Pilly, Jose Cruz-Albrecht
  • Patent number: 10902115
    Abstract: Described is neuromorphic system for authorized user detection. The system includes a client device comprising a plurality of sensor types providing streaming sensor data and one or more processors. The one or more processors include an input processing component and an output processing component. A neuromorphic electronic component is embedded in or on the client device for continuously monitoring the streaming sensor data and generating out-spikes based on the streaming sensor data. Further, the output processing component classifies the streaming sensor data based on the out-spikes to detect an anomalous signal and classify the anomalous signal.
    Type: Grant
    Filed: April 10, 2019
    Date of Patent: January 26, 2021
    Assignees: HRL Laboratories, LLC, The Boeing Company
    Inventors: Richard J. Patrick, Nigel D. Stepp, Vincent De Sapio, Jose Cruz-Albrecht, John Richard Haley, Jr., Thomas M. Trostel
  • 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
  • Patent number: 10787278
    Abstract: A method and apparatus for maintaining a vehicle, such as an aircraft. A plurality of maintenance messages generated during operation of the vehicle are stored to form a plurality of stored maintenance messages. The stored maintenance messages are filtered to remove from the stored maintenance messages those maintenance messages that are correlated to minimum equipment list actions to form filtered stored maintenance messages. A predicted maintenance message is generated from the filtered stored maintenance messages by applying a machine learning algorithm to the filtered stored maintenance messages. The predicted maintenance message may be used to perform a maintenance operation on the vehicle.
    Type: Grant
    Filed: September 29, 2017
    Date of Patent: September 29, 2020
    Assignee: The Boeing Company
    Inventors: David J. Huber, Nigel D. Stepp, Tsai-Ching Lu
  • Publication number: 20200286108
    Abstract: A method for collecting and processing user input. In some embodiments the method includes presenting a first user with a prompt for eliciting a first response, the first response including a numerical portion including one or more numbers, and an explanatory portion; receiving, from the first user, the first response; receiving from each of a plurality of other users, a respective response of a plurality of other responses; and displaying, to the first user, an ordered list of other responses. Within the ordered list, a second response, of the plurality of other responses, may be earlier than a third response, of the plurality of other responses, the second response being, according to a measure of distance, more distant, than the third response, from the first response.
    Type: Application
    Filed: December 20, 2019
    Publication date: September 10, 2020
    Inventors: Nigel D. Stepp, David J. Huber, Tsai-Ching Lu
  • Patent number: 10748063
    Abstract: Described is a system for estimating conditional probabilities for operation of a mobile device. Input data streams from first and second mobile device sensors are input into a neuronal network, where the first and second input data streams are converted into variable spiking rates of first and second neurons. The system learns a conditional probability between the first and second input data streams. A synaptic weight of interest between the first and second neurons converges to a fixed-point value, where the fixed-point value corresponds to the conditional probability. Based on the conditional probability and a new input data stream, a probability of an event is estimated. Based on the probability of the event, the system causes the mobile device to perform a mobile device operation.
    Type: Grant
    Filed: March 6, 2019
    Date of Patent: August 18, 2020
    Assignee: HRL Laboratories, LLC
    Inventors: Aruna Jammalamadaka, Nigel D. Stepp
  • Publication number: 20200257943
    Abstract: A method for generating human-machine hybrid predictions of answers to forecasting problems includes: parsing text of an individual forecasting problem to identify keywords; generating machine models based on the keywords; scraping data sources based on the keywords to collect scraped data relevant to the individual forecasting problem; providing the scraped data to the machine models; receiving machine predictions of answers to the individual forecasting problem from the machine models based on the scraped data; providing, by the computer system via a user interface, the scraped data to human participants; receiving, by the computer system via the user interface, human predictions of answers to the individual forecasting problem from the human participants; aggregating the machine predictions with the human predictions to generate aggregated predictions; and generating and outputting a hybrid prediction based on the aggregated predictions.
    Type: Application
    Filed: December 9, 2019
    Publication date: August 13, 2020
    Inventors: David J. Huber, Tsai-Ching Lu, Nigel D. Stepp, Aruna Jammalamadaka, Hyun J. Kim, Samuel D. Johnson
  • Publication number: 20200184324
    Abstract: Described is a system for specifying control of a device based on a Bayesian network model. The system includes a Bayesian neuromorphic compiler having a network composition module having probabilistic computation units (PCUs) arranged in a hierarchical composition containing multi-level dependencies. The Bayesian neuromorphic compiler receives a Bayesian network model as input and produces a spiking neural network topology and configuration that implements the Bayesian network model. The network composition module learns conditional probabilities of the Bayesian network model. The system computes a conditional probability and controls a device based on the computed conditional probability.
    Type: Application
    Filed: February 17, 2020
    Publication date: June 11, 2020
    Inventors: Nigel D. Stepp, Aruna Jammalamadaka
  • Publication number: 20200133273
    Abstract: A method of training an artificial neural network having a series of layers and at least one weight matrix encoding connection weights between neurons in successive layers. The method includes receiving, at an input layer of the series of layers, at least one input, generating, at an output layer of the series of layers, at least one output based on the at least one input, generating a reward based on a comparison of between the at least one output and a desired output, and modifying the connection weights based on the reward. Modifying the connection weights includes maintaining a sum of synaptic input weights to each neuron to be substantially constant and maintaining a sum of synaptic output weights from each neuron to be substantially constant.
    Type: Application
    Filed: October 23, 2019
    Publication date: April 30, 2020
    Inventors: Steven W. Skorheim, Nigel D. Stepp, Ruggero Scorcioni
  • Publication number: 20200125930
    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: Application
    Filed: September 5, 2019
    Publication date: April 23, 2020
    Inventors: Charles E. Martin, Nicholas A. Ketz, Praveen K. Pilly, Soheil Kolouri, Michael D. Howard, Nigel D. Stepp