公开(公告)号：US10986113B2

公开(公告)日：2021-04-20

申请号：US16199128

申请日：2018-11-23

Applicant: HRL Laboratories, LLC

Inventor： Vincent De Sapio ,  Hyun (Tiffany) J. Kim ,  Kyungnam Kim ,  Nigel D. Stepp ,  Kang-Yu Ni ,  Jose Cruz-Albrecht ,  Braden Mailloux

IPC: G06F11/00 ,  G06F12/14 ,  G06F12/16 ,  G08B23/00 ,  H04L29/06 ,  H04W12/12 ,  H04L12/24 ,  G06N3/063 ,  G06F21/55 ,  G06F21/50 ,  H04W12/122 ,  H04W12/128

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.

公开(公告)号：US10748063B2

公开(公告)日：2020-08-18

申请号：US16294815

申请日：2019-03-06

Applicant: HRL Laboratories, LLC

Inventor： Aruna Jammalamadaka ,  Nigel D. Stepp

IPC: G06N3/08 ,  G06N3/063 ,  G06N7/00 ,  G08G9/02

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.

公开(公告)号：US20190318241A1

公开(公告)日：2019-10-17

申请号：US16294815

申请日：2019-03-06

Applicant: HRL Laboratories, LLC

Inventor： Aruna Jammalamadaka ,  Nigel D. Stepp

IPC: G06N3/08 ,  G06N7/00 ,  G06N3/063

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.

公开(公告)号：US20170316311A1

公开(公告)日：2017-11-02

申请号：US15079899

申请日：2016-03-24

Applicant: HRL Laboratories, LLC

Inventor： Praveen K. Pilly ,  Nigel D. Stepp ,  Narayan Srinivasa

IPC: G06N3/08 ,  G06N5/04

CPC classification number: G06N3/08 ,  G06K9/4628 ,  G06K9/6249 ,  G06K9/6272 ,  G06K9/64 ,  G06N3/0454 ,  G06N3/0481 ,  G06N5/04

Abstract: Described is a sparse inference module that can be incorporated into a deep learning system. For example, the deep learning system includes a plurality of hierarchical feature channel layers, each feature channel layer having a set of filters. A plurality of sparse inference modules can be included such that a sparse inference module resides electronically within each feature channel layer. Each sparse inference module is configured to receive data and match the data against a plurality of pattern templates to generate a degree of match value for each of the pattern templates, with the degree of match values being sparsified such that only those degree of match values that exceed a predetermined threshold, or a fixed number of the top degree of match values, are provided to subsequent feature channels in the plurality of hierarchical feature channels, while other, losing degree of match values are quenched to zero.

公开(公告)号：US11238470B2

公开(公告)日：2022-02-01

申请号：US16724130

申请日：2019-12-20

Applicant: HRL Laboratories, LLC

Inventor： Nigel D. Stepp ,  David J. Huber ,  Tsai-Ching Lu

IPC: G06Q30/02 ,  G06Q10/04 ,  G06F17/16

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.

公开(公告)号：US11199839B2

公开(公告)日：2021-12-14

申请号：US16519814

申请日：2019-07-23

Applicant: HRL Laboratories, LLC

Inventor： Qin Jiang ,  Youngkwan Cho ,  Nigel D. Stepp ,  Steven W. Skorheim ,  Vincent De Sapio ,  Praveen K. Pilly ,  Ruggero Scorcioni

IPC: G05D1/00 ,  G05B13/00 ,  G08G1/16 ,  G06K5/00 ,  G06K7/00 ,  G06N20/00 ,  G05D1/02 ,  G05B13/02

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.

公开(公告)号：US11113597B2

公开(公告)日：2021-09-07

申请号：US16561735

申请日：2019-09-05

Applicant: HRL LABORATORIES, LLC

Inventor： Charles E. Martin ,  Nicholas A. Ketz ,  Praveen K. Pilly ,  Soheil Kolouri ,  Michael D. Howard ,  Nigel D. Stepp

IPC: G06N3/04 ,  G06N3/08 ,  G06K9/62

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.

公开(公告)号：US20200286108A1

公开(公告)日：2020-09-10

申请号：US16724130

申请日：2019-12-20

Applicant: HRL Laboratories, LLC

Inventor： Nigel D. Stepp ,  David J. Huber ,  Tsai-Ching Lu

IPC: G06Q30/02 ,  G06Q10/04 ,  G06F17/16

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.

公开(公告)号：US20200184324A1

公开(公告)日：2020-06-11

申请号：US16792791

申请日：2020-02-17

Applicant: HRL Laboratories, LLC

Inventor： Nigel D. Stepp ,  Aruna Jammalamadaka

IPC: G06N3/063 ,  G06N3/04 ,  G06N3/08

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.

公开(公告)号：US20190318235A1

公开(公告)日：2019-10-17

申请号：US16294886

申请日：2019-03-06

Applicant: HRL Laboratories, LLC

Inventor： Nigel D. Stepp ,  Aruna Jammalamadaka

IPC: G06N3/063 ,  G06N3/04 ,  G06N3/08

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.