Patents by Inventor Aruna Jammalamadaka

Aruna Jammalamadaka 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: 11551156
    Abstract: A method for computing a human-machine hybrid ensemble prediction includes: receiving an individual forecasting question (IFP); classifying the IFP into one of a plurality of canonical question topics; identifying machine models associated with the canonical question topic; for each of the machine models: receiving, from one of a plurality of human participants: a first task input including a selection of sets of training data; a second task input including selections of portions of the selected sets of training data; and a third task input including model parameters to configure the machine model; training the machine model in accordance with the first, second, and third task inputs; and computing a machine model forecast based on the trained machine model; computing an aggregated forecast from machine model forecasts computed by the machine models; and sending an alert in response to determining that the aggregated forecast satisfies a threshold condition.
    Type: Grant
    Filed: December 13, 2019
    Date of Patent: January 10, 2023
    Assignee: HRL LABORATORIES, LLC.
    Inventors: Aruna Jammalamadaka, David J. Huber, Samuel D. Johnson, 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: 11475334
    Abstract: Described is a system for large-scale event prediction and a corresponding response. The system, using an agent-based model, predicts how many users (agent accounts) on a social media platform will become activists related to a large-scale event. This process is accomplished using both Before and During models. Before the large-scale event, the system operates to generate agent attributes and a posting network based on posts on the social media platform. During the large-scale event and based on the agent attributes and posting network, the system determines if a social media user (agent account) will become an activist of the large-scale event and a corresponding magnitude of the large-scale event. Depending on the magnitude, the system can implement a responsive measure and control a device based on the prediction of the activists.
    Type: Grant
    Filed: December 19, 2017
    Date of Patent: October 18, 2022
    Assignee: HRL LABORATORIES, LLC
    Inventors: Krishna Bathina, Aruna Jammalamadaka, Jiejun Xu, Tsai-Ching Lu
  • 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
  • Publication number: 20220261603
    Abstract: Described is a system for learning and predicting key phrases. The system learns based on a dataset of historical forecasting questions, their associated time-series data for a quantity of interest, and associated keyword sets. The system learns the optimal policy of actions to take given the associated keyword sets and the optimal set of keywords which are predictive of the quantity of interest. Given a new forecasting question, the system extracts an initial keyword set from a new forecasting question, which are perturbed to generate an optimal predictive key-phrase set. Key-phrase time-series data are extracted for the optimal predictive key-phrase set, which are used to generate a forecast of future values for a value of interest. The forecast can be used for a variety of purposes, such as advertising online.
    Type: Application
    Filed: April 27, 2022
    Publication date: August 18, 2022
    Inventors: Victor Ardulov, Aruna Jammalamadaka, Tsai-Ching Lu
  • Patent number: 11361200
    Abstract: Described is a system for learning and predicting key phrases. The system learns based on a dataset of historical forecasting questions, their associated time-series data for a quantity of interest, and associated keyword sets. The system learns the optimal policy of actions to take given the associated keyword sets and the optimal set of keywords which are predictive of the quantity of interest. Given a new forecasting question, the system extracts an initial keyword set from a new forecasting question, which are perturbed to generate an optimal predictive key-phrase set. Key-phrase time-series data are extracted for the optimal predictive key-phrase set, which are used to generate a forecast of future values for a value of interest. The forecast can be used for a variety of purposes, such as advertising online.
    Type: Grant
    Filed: December 11, 2019
    Date of Patent: June 14, 2022
    Assignee: HRL Laboratories, LLC
    Inventors: Victor Ardulov, Aruna Jammalamadaka, Tsai-Ching Lu
  • Publication number: 20220177154
    Abstract: A testing system, a testing method, and a training method for the testing system are disclosed. According to an example, a computing system of the testing system processes a set of data streams of test data for a test subject in combination with a previously trained nominal model by, for each parameter of the test subject: selecting a parameter-specific control band defined by the nominal model for the parameter; comparing a time-based series of measurements of the test data for the parameter to the parameter-specific control band for the parameter, and selectively generating a test result for the parameter responsive to whether a condition is satisfied with respect to any of the time-based series of measurements exceeding the parameter-specific control band for the parameter.
    Type: Application
    Filed: November 2, 2021
    Publication date: June 9, 2022
    Inventors: Nigel Stepp, Aruna Jammalamadaka, Tsai-Ching Lu, Greg Blaire, Joseph Wilson, Heath W. Haga, Mark Daniel McCleary, Brandon M. Courter, Kangyu Ni
  • 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: 11148672
    Abstract: Described is a system for analyzing time series data. A sequence of symbols is generated from a set of time series input data related to a moving vehicle using automatic segmentation. A grammar is extracted from the sequence of symbols, and the grammar is a subset of a probabilistic context-free grammar (PCFG). Using the grammar, time series input data can be analyzed, and a prediction of the vehicle's movement can be made. Vehicle operations for an autonomous vehicle are determined using the prediction.
    Type: Grant
    Filed: March 28, 2018
    Date of Patent: October 19, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Kenji Yamada, Rajan Bhattacharyya, Aruna Jammalamadaka, Dmitriy V. Korchev, Chong Ding
  • Patent number: 10860022
    Abstract: The present application generally relates to a method and apparatus for generating an action policy for controlling an autonomous vehicle. In particular, the method is operative to receive an input indicative of a training event, segmenting the driving episode into a plurality of time steps, generate a parse tree in response to each time step, and generate a most probable parse tree from a combination of the generated parse trees.
    Type: Grant
    Filed: April 11, 2018
    Date of Patent: December 8, 2020
    Assignee: GM GLOBAL TECHNOLOGY OPERATIONS LLC
    Inventors: Dmitriy V. Korchev, Rajan Bhattacharyya, Aruna Jammalamadaka
  • Publication number: 20200311615
    Abstract: A method for computing a human-machine hybrid ensemble prediction includes: receiving an individual forecasting question (IFP); classifying the IFP into one of a plurality of canonical question topics; identifying machine models associated with the canonical question topic; for each of the machine models: receiving, from one of a plurality of human participants: a first task input including a selection of sets of training data; a second task input including selections of portions of the selected sets of training data; and a third task input including model parameters to configure the machine model; training the machine model in accordance with the first, second, and third task inputs; and computing a machine model forecast based on the trained machine model; computing an aggregated forecast from machine model forecasts computed by the machine models; and sending an alert in response to determining that the aggregated forecast satisfies a threshold condition.
    Type: Application
    Filed: December 13, 2019
    Publication date: October 1, 2020
    Inventors: Aruna Jammalamadaka, David J. Huber, Samuel D. Johnson, 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: 20200258120
    Abstract: Described is a system for learning and predicting key phrases. The system learns based on a dataset of historical forecasting questions, their associated time-series data for a quantity of interest, and associated keyword sets. The system learns the optimal policy of actions to take given the associated keyword sets and the optimal set of keywords which are predictive of the quantity of interest. Given a new forecasting question, the system extracts an initial keyword set from a new forecasting question, which are perturbed to generate an optimal predictive key-phrase set. Key-phrase time-series data are extracted for the optimal predictive key-phrase set, which are used to generate a forecast of future values for a value of interest. The forecast can be used for a variety of purposes, such as advertising online.
    Type: Application
    Filed: December 11, 2019
    Publication date: August 13, 2020
    Inventors: Victor Ardulov, Aruna Jammalamadaka, Tsai-Ching Lu
  • Publication number: 20200219020
    Abstract: Described is a system for structuring rationales for collaborative forecasting between users of a crowdsourcing platform. For a given forecasting question, the system produces a forecasting rationale model from a combination of variables related to users and topics in a discussion of the users' forecasting rationale for making an initial forecast of an event. A relationship between the variables is determined, and based on the relationship between the variables, a prediction of each user's performance in making the initial forecast. Based on the predictions, top performing users and their forecasting rationales are selected, and the forecasting rationales of the top performing users are shared with other users of the crowdsourcing platform, allowing the other users to revise their initial forecasts in response to the shared forecasting rationales, resulting in revised forecasts. A forecast of the event that combines the revised forecasts is then output.
    Type: Application
    Filed: October 2, 2019
    Publication date: July 9, 2020
    Inventors: Robert Giaquinto, Tsai-Ching Lu, Aruna Jammalamadaka, Ryan M. Uhlenbrock
  • 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
  • Patent number: 10678245
    Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: receiving sensor data sensed from an environment associated with the vehicle; processing, by a processor, the sensor data to determine observation data, the observation data including differential features associated with an agent in the environment; determining, by the processor, a context associated with the agent based on the observation; selecting, by the processor, a first probability model associated with the context; processing, by the processor, the observation data with the selected first probability model to determine a set of predictions; processing, by the processor, the set of predictions with a second probability model to determine a final prediction of interaction behavior associated with the agent; and selectively controlling, by the processor, the vehicle based on the final prediction of interaction behavior associated with the agent.
    Type: Grant
    Filed: July 27, 2018
    Date of Patent: June 9, 2020
    Assignee: GM GLOBAL TECHNOLOGY OPERATIONS LLC
    Inventors: Aruna Jammalamadaka, Rajan Bhattacharyya, Michael J Daily
  • Publication number: 20200033855
    Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: receiving sensor data sensed from an environment associated with the vehicle; processing, by a processor, the sensor data to determine observation data, the observation data including differential features associated with an agent in the environment; determining, by the processor, a context associated with the agent based on the observation; selecting, by the processor, a first probability model associated with the context; processing, by the processor, the observation data with the selected first probability model to determine a set of predictions; processing, by the processor, the set of predictions with a second probability model to determine a final prediction of interaction behavior associated with the agent; and selectively controlling, by the processor, the vehicle based on the final prediction of interaction behavior associated with the agent.
    Type: Application
    Filed: July 27, 2018
    Publication date: January 30, 2020
    Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC
    Inventors: ARUNA JAMMALAMADAKA, RAJAN BHATTACHARYYA, MICHAEL J. DAILY
  • Publication number: 20200026981
    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: Application
    Filed: September 20, 2019
    Publication date: January 23, 2020
    Inventors: Hao-Yuan Chang, Aruna Jammalamadaka, Nigel D. Stepp
  • Publication number: 20190317496
    Abstract: The present application generally relates to a method and apparatus for generating an action policy for controlling an autonomous vehicle. In particular, the method is operative to receive an input indicative of a training event, segmenting the driving episode into a plurality of time steps, generate a parse tree in response to each time step, and generate a most probable parse tree from a combination of the generated parse trees.
    Type: Application
    Filed: April 11, 2018
    Publication date: October 17, 2019
    Inventors: Dmitriy V. Korchev, Rajan Bhattacharyya, Aruna Jammalamadaka