Patents Examined by David R. Vincent
  • Patent number: 11574236
    Abstract: Disclosed herein are methods, systems, and processes to automate cluster interpretation in computing environments to develop targeted remediation security actions. To interpret clusters that are generated by a clustering methodology without subjecting clustered data to classifier-based processing, separation quantifiers that indicate a spread in feature values across clusters are determined and used to discover relative feature importances of features that drive the formation of clusters, permitting a security server to identify features that discriminate between clusters.
    Type: Grant
    Filed: December 10, 2018
    Date of Patent: February 7, 2023
    Assignee: Rapid7, Inc.
    Inventors: Vasudha Shivamoggi, Roy Hodgman, Wah-Kwan Lin
  • Patent number: 11573765
    Abstract: A processing unit implements a convolutional neural network (CNN) by fusing at least a portion of a convolution phase of the CNN with at least a portion of a batch normalization phase. The processing unit convolves two input matrices representing inputs and weights of a portion of the CNN to generate an output matrix. The processing unit performs the convolution via a series of multiplication operations, with each multiplication operation generating a corresponding submatrix (or “tile”) of the output matrix at an output register of the processing unit. While an output submatrix is stored at the output register, the processing unit performs a reduction phase and an update phase of the batch normalization phase for the CNN. The processing unit thus fuses at least a portion of the batch normalization phase of the CNN with a portion of the convolution.
    Type: Grant
    Filed: December 13, 2018
    Date of Patent: February 7, 2023
    Assignee: Advanced Micro Devices, Inc.
    Inventors: Milind N. Nemlekar, Prerit Dak
  • Patent number: 11568236
    Abstract: The present technology addresses the problem of quickly and safely improving policies in online reinforcement learning domains. As its solution, an exploration strategy comprising diverse exploration (DE) is employed, which learns and deploys a diverse set of safe policies to explore the environment. DE theory explains why diversity in behavior policies enables effective exploration without sacrificing exploitation. An empirical study shows that an online policy improvement algorithm framework implementing the DE strategy can achieve both fast policy improvement and safe online performance.
    Type: Grant
    Filed: January 24, 2019
    Date of Patent: January 31, 2023
    Assignee: The Research Foundation for The State University of New York
    Inventors: Lei Yu, Andrew Cohen
  • Patent number: 11562291
    Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system predicting parking availability. A computer identifies parking spaces and groups the parking spacing into parking locations. In addition, the computer distinguishes private parking spaces from public parking spaces, and trains a crowd forecast model for each of the parking locations. The computer further receives a destination and preferences, from which the computer creates a geofence based on the destination and preferences. The computer then predicts parking availability based on the crowd forecast models and refines the crowd forecast model.
    Type: Grant
    Filed: May 3, 2019
    Date of Patent: January 24, 2023
    Assignee: International Business Machines Corporation
    Inventors: Florian Pinel, Tova Roth
  • Patent number: 11544551
    Abstract: This disclosure relates to method and system for improving performance of an artificial neural network (ANN). The method may include generating a weight matrix comprising weights of neural nodes in a given layer for each layer of the ANN, determining a marginal contribution value of a given neural node for each neural node in the given layer with respect to other neural nodes in the given layer, executing an elimination decision for each neural node in each layer based on the corresponding marginal contribution value, determining a distributed surplus value of a given remaining neural node in a given layer based on the marginal contribution values of a coalition of remaining neural nodes in the given layer for each remaining neural node in each layer, and updating the weight matrix based on the distributed surplus value of each remaining neural node in each layer.
    Type: Grant
    Filed: November 20, 2018
    Date of Patent: January 3, 2023
    Assignee: Wipro Limited
    Inventors: Prashanth Krishnapura Subbaraya, Raghavendra Hosabettu
  • Patent number: 11537837
    Abstract: Techniques and systems for critical dimension metrology are disclosed. Critical parameters can be constrained with at least one floating parameter and one or more weight coefficients. A neural network is trained to use a model that includes a Jacobian matrix. During training, at least one of the weight coefficients is adjusted, a regression is performed on reference spectra, and a root-mean-square error between the critical parameters and the reference spectra is determined. The training may be repeated until the root-mean-square error is less than a convergence threshold.
    Type: Grant
    Filed: January 30, 2018
    Date of Patent: December 27, 2022
    Assignee: KLA-TENCOR CORPORATION
    Inventors: Yuerui Chen, Xin Li
  • Patent number: 11537878
    Abstract: Provided is a process, including: obtaining a first training dataset of subject-entity records; training a first machine-learning model on the first training dataset; forming virtual subject-entity records by appending members of a set of candidate action sequences to time-series of at least some of the subject-entity records; forming a second training dataset by labeling the virtual subject-entity records with predictions of the first machine-learning model; and training a second machine-learning model on the second training dataset.
    Type: Grant
    Filed: May 9, 2019
    Date of Patent: December 27, 2022
    Assignee: Cerebri AI Inc.
    Inventors: Gabriel M. Silberman, Alain Briançon, Gregory Klose, Michael Wegan, Lee Harper, Andrew Kraemer, Arun Prakash
  • Patent number: 11531878
    Abstract: Systems and methods for modelling time-series data includes testing a testing model with a plurality of hyper-forgetting rates to select a best performing hyper-forgetting rate. A model optimization is tested using the best performing hyper-forgetting rate with the testing model to test combinations of hyper-parameters to select a best performing combination of hyper-parameters. An error of the model is determined using the model optimization. Model parameters are recursively updated according to the least squares regression by determining a pseudo-inverse of a Hessian of the least squares regression at a current time stamp according to a projection of the time-series data at the current time stamp and the pseudo-inverse of the Hessian at a previous time-stamp to determine an optimum model parameter. A next step behavior of the time-series data is predicted using the optimum model parameter. The next step behavior is stored in a database for access by a user.
    Type: Grant
    Filed: February 19, 2019
    Date of Patent: December 20, 2022
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventor: Takayuki Osogami
  • Patent number: 11531501
    Abstract: Disclosed is a system and a method for collaborative decision making in dynamically changing environment. A query corresponding to a problem is received from a user. Further, one or more intermediate steps required to reach a decision is calculated based on metadata associated to the problem. A decision-making flow is established for the one or more intermediate steps required to reach the decision. It may be noted that the decision-making flow corresponds to a sequence for execution of the one or more intermediate steps. Further, a decision space comprising one or more decision options is generated. The decision space is dynamically modified based on one or more uncertain events. A decision knowledge graph depicting modifications in the decision space is generated. Further, the decision space and the decision knowledge graph are updated. Finally, the decision is selected based on the updated decision knowledge graph and the updated decision space.
    Type: Grant
    Filed: December 10, 2021
    Date of Patent: December 20, 2022
    Assignee: Agile Systems, LLC
    Inventors: Satyendra Pal Rana, Ekrem Alper Murat, Ratna Babu Chinnam
  • Patent number: 11526798
    Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system predicting parking availability. A computer identifies parking spaces and groups the parking spacing into parking locations. In addition, the computer distinguishes private parking spaces from public parking spaces, and trains a crowd forecast model for each of the parking locations. The computer further receives a destination and preferences, from which the computer creates a geofence based on the destination and preferences. The computer then predicts parking availability based on the crowd forecast models and refines the crowd forecast model.
    Type: Grant
    Filed: November 14, 2017
    Date of Patent: December 13, 2022
    Assignee: International Business Machines Corporation
    Inventors: Florian Pinel, Tova Roth
  • Patent number: 11526776
    Abstract: A system and method for generating predictions of geopolitical events is provided. Predictions may be generated by retrieving relevant metadata associated with a content item and evaluating one or more signals representative of the same.
    Type: Grant
    Filed: November 27, 2018
    Date of Patent: December 13, 2022
    Assignee: Predata, Inc.
    Inventors: Daniel Joseph Nadler, Andrew Yujin Choi
  • Patent number: 11514358
    Abstract: An artificial intelligence device is disclosed. In an embodiment, the artificial intelligence device includes a sensor configured to acquire an output value according to control of a control system, and an artificial intelligence unit comprising one or more processors configured to obtain one or more updated parameters of a control function of the control system based on the output value using reinforcement learning, and update the control function for providing a control value to the control system with the one or more updated parameters.
    Type: Grant
    Filed: June 27, 2019
    Date of Patent: November 29, 2022
    Assignee: LG ELECTRONICS INC.
    Inventor: Bongsang Kim
  • Patent number: 11514627
    Abstract: The disclosed embodiments concern methods, apparatus, systems and computer program products for determining and displaying pedigrees based on IBD data. Some implementations use a probabilistic relationship model to obtain various likelihoods of various potential relationships based on pairwise IBD data and pairwise age data. Some implementations build large pedigrees by combining smaller pedigrees. Some implementations display pedigree graphs with various features that are informative and easy to understand.
    Type: Grant
    Filed: September 11, 2020
    Date of Patent: November 29, 2022
    Assignee: 23andMe, Inc.
    Inventors: Ethan M. Jewett, Andrew C. Seaman, Kimberly F. McManus, William A. Freyman, Cordell T. Blakkan, Adam Auton, Joanna L. Mountain, Susan M. Furest, Rachel E. Lopatin, Hang Xu, Hilary M. Vance
  • Patent number: 11501204
    Abstract: An information processing apparatus includes a history acquisition section configured to acquire history data including a history indicating that a plurality of selection subjects have selected selection objects; a learning processing section configured to allow a choice model to learn a preference of each selection subject for a feature and an environmental dependence of selection of each selection object in each selection environment using the history data, where the choice model uses a feature value possessed by each selection object, the preference of each selection subject for the feature, and the environmental dependence indicative of ease of selection of each selection object in each of a plurality of selection environments to calculate a selectability with which each of the plurality of selection subjects selects each selection object; and an output section configured to output results of learning by the learning processing section.
    Type: Grant
    Filed: March 26, 2019
    Date of Patent: November 15, 2022
    Assignee: International Business Machines Corporation
    Inventors: Takayuki Katsuki, Takayuki Osogami
  • Patent number: 11500905
    Abstract: A computer processor generates a topic-based dataset based on parsing content received from a plurality of information sources, which includes historical data and scientific data, associated with a location of a natural resource. The processor generates a plurality of clusters, respectively corresponding to like-topic data of the topic-based dataset. The processor determines a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on features associated with each of the plurality of clusters of the like-topic data. The processor combines pairs of clusters, based on a similarity heuristic applied to the one or more pairs of clusters, and the processor determines a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses, associated with the location of a natural resource.
    Type: Grant
    Filed: April 29, 2019
    Date of Patent: November 15, 2022
    Assignee: KYNDRYL, INC.
    Inventors: Aaron K. Baughman, Thomas B. Harrison, Brian M. O'Connell
  • Patent number: 11501202
    Abstract: Querying databases may be performed with references to machine learning models. A database query may be received that references a machine learning model and database. In response to the query, the machine learning model may provide information which may be returned as part of a result of the query or may be used to generate a result of the query. The machine learning model may be generated in response to a request to generate a machine learning model that includes a database query that identifies the data upon which a machine learning technique may be applied to generate the machine learning model.
    Type: Grant
    Filed: April 17, 2018
    Date of Patent: November 15, 2022
    Assignee: Amazon Technologies, Inc.
    Inventor: Stefano Stefani
  • Patent number: 11494633
    Abstract: Examples include techniques to manage training or trained models for deep learning applications. Examples include routing commands to configure a training model to be implemented by a training module or configure a trained model to be implemented by an inference module. The commands routed via out-of-band (OOB) link while training data for the training models or input data for the trained models are routed via inband links.
    Type: Grant
    Filed: December 30, 2017
    Date of Patent: November 8, 2022
    Assignee: Intel Corporation
    Inventors: Francesc Guim Bernat, Suraj Prabhakaran, Kshitij A. Doshi, Da-Ming Chiang
  • Patent number: 11494582
    Abstract: Devices and methods for performing computations of a neural network include determining, for a particular layer of the neural network, a number of input channels and a number of output maps generated for particular pixels of a plurality of pixels. A portion of a processing chip is configured into a plurality of processing units, with the processing chip including a plurality of tensor arrays and a plurality memory cells. A particular processing unit of the plurality of processing units performs computations associated with a particular pixel. Individual processing units of the plurality of processing units each include a number of tensor arrays determined based on the number of input channels and a number of memory cells or pixel arrays corresponding to a number of output maps. The plurality of processing units is assigned to perform computations of the particular layer.
    Type: Grant
    Filed: December 27, 2018
    Date of Patent: November 8, 2022
    Assignee: Western Digital Technologies, Inc.
    Inventor: Luiz M. Franca-Neto
  • Patent number: 11481456
    Abstract: An apparatus and method are provided for machine learning method using a network of agents. The agents are arranged in a network with respective links between pairs of agents, and the links enabling the exchange information. Different agents can apply different reasoning paradigms corresponding to different approaches to machine learning and artificial intelligence. These disparate approaches are seamlessly integrated to aggregate decisions and learning performed using different approaches using an economics model in which a Nash equilibrium is reached through the exchange of information. Each agent selects which other agents to exchange information with by seeking to optimize preference, utility, and objective functions, and these function include how well the agents obtain an assigned goal subject to other desirable features and characteristics (e.g., enforcing diversity).
    Type: Grant
    Filed: February 20, 2018
    Date of Patent: October 25, 2022
    Assignee: KYNDI, INC.
    Inventors: Arun Majumdar, James R. Welsh
  • Patent number: 11475340
    Abstract: A method, performed by an electronic device, of predicting an electronic structure of a material includes: receiving input data of a user related to elements constituting the first material; applying the received input data to a trained model for estimating a density of state of the first material; and outputting a first graph indicating the density of state for each energy level of the first material output from the trained model, wherein the trained model is trained to generate the first graph based on pre-input data about a plurality of second materials composed of at least some of the elements constituting the first material and a plurality of second graphs representing the density of state for each energy level of the plurality of second materials.
    Type: Grant
    Filed: August 13, 2018
    Date of Patent: October 18, 2022
    Assignee: Korea Institute of Science and Technology
    Inventors: Sang Soo Han, Byung Chul Yeo, Dong Hun Kim, Seung Chul Kim