Patents Examined by Benjamin Buss
  • Patent number: 9864953
    Abstract: Techniques for use in connection with performing optimization using an objective function. The techniques include using at least one computer hardware processor to perform: identifying, using an integrated acquisition utility function and a probabilistic model of the objective function, at least a first point at which to evaluate the objective function; evaluating the objective function at least at the identified first point; and updating the probabilistic model of the objective function using results of the evaluating to obtain an updated probabilistic model of the objective function.
    Type: Grant
    Filed: May 30, 2014
    Date of Patent: January 9, 2018
    Assignees: President and Fellows of Harvard College, SOCPRA Sciences et Genie S.E.C., Governing Council of the Univ. of Toronto, The
    Inventors: Ryan P. Adams, Roland Jasper Snoek, Hugo Larochelle
  • Patent number: 9858529
    Abstract: Techniques for use in connection with performing optimization using a plurality of objective functions associated with a respective plurality of tasks. The techniques include using at least one computer hardware processor to perform: identifying, based at least in part on a joint probabilistic model of the plurality of objective functions, a first point at which to evaluate an objective function in the plurality of objective functions; selecting, based at least in part on the joint probabilistic model, a first objective function in the plurality of objective functions to evaluate at the identified first point; evaluating the first objective function at the identified first point; and updating the joint probabilistic model based on results of the evaluation to obtain an updated joint probabilistic model.
    Type: Grant
    Filed: May 30, 2014
    Date of Patent: January 2, 2018
    Assignees: President and Fellows of Harvard College, The Governing Council of the Univ. of Toronto
    Inventors: Ryan P. Adams, Roland Jasper Snoek, Kevin Swersky
  • Patent number: 9852373
    Abstract: A method can include receiving data associated with a geologic environment; based on at least a portion of the data, estimating relationships for multiple properties of the geologic environment; and based at least in part on the relationships, performing simultaneous joint inversion for at least one property of the geologic environment.
    Type: Grant
    Filed: June 1, 2015
    Date of Patent: December 26, 2017
    Assignee: WESTERNGECO L.L.C.
    Inventor: Michele De Stefano
  • Patent number: 9852371
    Abstract: A method and system for analysis of data, including creating a first node, determining a first hyper-cube for the first node, determining whether a sample resides within the first hyper-cube. If the sample does not reside within the first hyper-cube, the method includes determining whether the sample resides within a first hyper-sphere, wherein the first hyper-sphere has a radius equal to a diagonal of the first hyper-cube.
    Type: Grant
    Filed: February 12, 2016
    Date of Patent: December 26, 2017
    Assignee: APPLIED MATERIALS, INC.
    Inventor: Dermot Cantwell
  • Patent number: 9852006
    Abstract: Embodiments of the invention relate to a neural network circuit comprising a memory block for maintaining neuronal data for multiple neurons, a scheduler for maintaining incoming firing events targeting the neurons, and a computational logic unit for updating the neuronal data for the neurons by processing the firing events. The network circuit further comprises at least one permutation logic unit enabling data exchange between the computational logic unit and at least one of the memory block and the scheduler. The network circuit further comprises a controller for controlling the computational logic unit, the memory block, the scheduler, and each permutation logic unit.
    Type: Grant
    Filed: March 28, 2014
    Date of Patent: December 26, 2017
    Assignee: International Business Machines Corporation
    Inventors: Filipp A. Akopyan, Rodrigo Alvarez-Icaza Rivera, John V. Arthur, Andrew S. Cassidy, Bryan L. Jackson, Paul A. Merolla, Dharmendra S. Modha, Jun Sawada
  • Patent number: 9798979
    Abstract: This patent specification relates to apparatus, systems, methods, and related computer program products for providing home security objectives, such as calculating a security score for a home. More particularly, this patent specification relates to a plurality of devices, including intelligent, multi-sensing, network-connected devices, that communicate with each other and/or with a central server or a cloud-computing system to provide any of a variety of useful home security objectives, such as calculating a security score for a home.
    Type: Grant
    Filed: August 12, 2015
    Date of Patent: October 24, 2017
    Assignee: Google Inc.
    Inventors: Anthony M. Fadell, Matthew L. Rogers, Yoky Matsuoka, David Sloo, Maxime Veron, Shigefumi Honjo
  • Patent number: 9792550
    Abstract: Methods and system for providing information selected from a large set of digital content to at least one user. One such method comprises receiving user context information associated with the at least one user and identifying or generating, using at least one processor executing stored program instructions, a first concept in a semantic network, the first concept representing at least a portion of the user context information.
    Type: Grant
    Filed: May 26, 2016
    Date of Patent: October 17, 2017
    Assignee: PRIMAL FUSION INC.
    Inventors: Peter Joseph Sweeney, Ihab Francis Ilyas, Jean-Paul Dupuis, Nadiya Yampolska
  • Patent number: 9792552
    Abstract: At least one aspect of the invention is directed to a power monitoring system including a generator coupled to a fuel tank, a plurality of monitors, and a processor configured to monitor one or more loads drawing power from the generator; monitor one or more parameters that affect the amount of power drawn by the one or more loads; monitor a fuel consumption rate of the generator; generate one or more load profiles for each of the one or more loads; receive a set of the one or more loads for which a predicted time is to be generated; receive values for the one or more parameters; generate a predicted load profile for the set of the one or more loads and the values of the one or more parameters; receive information indicating an amount of remaining fuel; and calculate a predicted available run time.
    Type: Grant
    Filed: June 29, 2012
    Date of Patent: October 17, 2017
    Assignee: SCHNEIDER ELECTRIC USA, INC.
    Inventors: Markus F. Hirschbold, John C. Van Gorp
  • Patent number: 9740981
    Abstract: A tool and technique are employed to distill and prioritize multiple organizational (sub-system) capabilities based on ranked customer requirements or desires. A translational matrix is employed to organize preferences of the sub-system with respect to the customer requirements or desires. Relative importance scores are input into the matrix to reflect prioritized input versus prioritized capabilities. An adjusted relative importance score of the sub-system is automatically calculated and a resultant re-prioritization of sub-system attributes is created for application to new designs, services, or processes.
    Type: Grant
    Filed: October 14, 2014
    Date of Patent: August 22, 2017
    Assignee: International Business Machines Corporation
    Inventors: Karl O. Casserly, Bohdan Demczar, Dale E. Hoffman, William P. Kostenko, John G. Torok
  • Patent number: 9740980
    Abstract: A tool and technique are employed to distill and prioritize multiple organizational (sub-system) capabilities based on ranked customer requirements or desires. A translational matrix is employed to organize preferences of the sub-system with respect to the customer requirements or desires. Relative importance scores are input into the matrix to reflect prioritized input versus prioritized capabilities. An adjusted relative importance score of the sub-system is automatically calculated and a resultant re-prioritization of sub-system attributes is created for application to new designs, services, or processes.
    Type: Grant
    Filed: November 22, 2013
    Date of Patent: August 22, 2017
    Assignee: International Business Machines Corporation
    Inventors: Karl O. Casserly, Bohdan Demczar, Dale E. Hoffman, William P. Kostenko, John G. Torok
  • Patent number: 9727698
    Abstract: A method, computer-readable storage device and apparatus for calculating a health quality measure are disclosed. For example, a method receives characteristics of motion information, wherein the characteristics of motion information is based upon gait information, monitors the characteristics of motion information over a time period to determine a plurality of different modes of motion within the time period, and calculates the health quality measure based upon the plurality of different modes of motion.
    Type: Grant
    Filed: September 12, 2016
    Date of Patent: August 8, 2017
    Assignees: AT&T Intellectual Property I, L.P., President and Fellows of Harvard College
    Inventors: Saeed S. Ghassemzadeh, Lusheng Ji, Robert Raymond Miller, II, Manish Gupta, Vahid Tarokh
  • Patent number: 9727824
    Abstract: Systems, methods and aspects, and embodiments thereof relate to unsupervised or semi-supervised features learning using a quantum processor. To achieve unsupervised or semi-supervised features learning, the quantum processor is programmed to achieve Hierarchal Deep Learning (referred to as HDL) over one or more data sets. Systems and methods search for, parse, and detect maximally repeating patterns in one or more data sets or across data or data sets. Embodiments and aspects regard using sparse coding to detect maximally repeating patterns in or across data. Examples of sparse coding include L0 and L1 sparse coding. Some implementations may involve appending, incorporating or attaching labels to dictionary elements, or constituent elements of one or more dictionaries. There may be a logical association between label and the element labeled such that the process of unsupervised or semi-supervised feature learning spans both the elements and the incorporated, attached or appended label.
    Type: Grant
    Filed: June 26, 2014
    Date of Patent: August 8, 2017
    Assignee: D-Wave Systems Inc.
    Inventors: Geordie Rose, Suzanne Gildert, William G. Macready, Dominic Christoph Walliman
  • Patent number: 9729353
    Abstract: An NFA hardware engine includes a pipeline and a controller. The pipeline includes a plurality of stages, where one of the stages includes a transition table. Both a first automaton and a second automaton are encoded in the same transition table. The controller receives NFA engine commands onto the NFA engine and controls the pipeline in response to the NFA engine commands.
    Type: Grant
    Filed: January 9, 2014
    Date of Patent: August 8, 2017
    Assignee: Netronome Systems, Inc.
    Inventors: Gavin J. Stark, Steven W. Zagorianakos
  • Patent number: 9721213
    Abstract: The information matching apparatus includes: a training data setting unit that sets supervised data in a machine learning device of supervised learning that learns judgment criteria used for a judgment of identicalness, similarity, and relevance between a plurality of records by matching the records configured by sets of values corresponding to items; a check point setting unit that sets a check point configured by one set of two records used for evaluating the set supervised data; and a learning result evaluation unit, for the set check point, acquires a change between a judgment result using judgment criteria derived as a result of learning based on set first supervised data and a judgment result using judgment criteria derived as a result of learning based on set second supervised data set and evaluates the supervised data based on the acquired change.
    Type: Grant
    Filed: February 29, 2016
    Date of Patent: August 1, 2017
    Assignee: FUJITSU LIMITED
    Inventor: Kazuo Mineno
  • Patent number: 9710749
    Abstract: Methods and apparatus are provided for using a breakpoint determination unit to examine an artificial nervous system. One example method generally includes operating at least a portion of the artificial nervous system; using the breakpoint determination unit to detect that a condition exists based at least in part on monitoring one or more components in the artificial nervous system; and at least one of suspending, examining, modifying, or flagging the operation of the at least the portion of the artificial nervous system, based at least in part on the detection.
    Type: Grant
    Filed: May 19, 2014
    Date of Patent: July 18, 2017
    Assignee: QUALCOMM Incorporated
    Inventors: Michael-David Nakayoshi Canoy, William Richard Bell, II, Ramakrishna Kintada, Venkat Rangan
  • Patent number: 9704106
    Abstract: Systems and methods for the annotation of source data using confidence labels in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for determining confidence labels for crowdsourced annotations includes obtaining a set of source data, obtaining a set of training data representative of the set of source data, determining the ground truth for each piece of training data, obtaining a set of training data annotations including a confidence label, measuring annotator accuracy data for at least one piece of training data, and automatically generating a set of confidence labels for the set of unlabeled data based on the measured annotator accuracy data and the set of annotator labels used.
    Type: Grant
    Filed: May 27, 2016
    Date of Patent: July 11, 2017
    Assignee: California Institute of Technology
    Inventors: Peter Welinder, Pietro Perona
  • Patent number: 9704093
    Abstract: Embodiments of the present invention provide a memristor having a first electrode, a second electrode and a memristive layer arranged between the first electrode and the second electrode. Thereby, the memristor is adapted to obtain an asymmetrical current density distribution in the memristive layer.
    Type: Grant
    Filed: July 14, 2015
    Date of Patent: July 11, 2017
    Assignees: Fraunhofer-Gesellschaft zur Foerderung der angewandten Forschung e.V., Technische Universitaet Ilmenau
    Inventors: Frank Klefenz, Peter Husar, Adam Williamson, Lars Schumann, Lars Hiller, Ingo Hoerselmann, Andreas Schober
  • Patent number: 9679247
    Abstract: A method of building a soft linkage between a plurality of graphs includes initializing a correspondence between type-1 and type-2 objects in the plurality of graphs, and reducing a cost function by alternately updating the type-1 correspondence and updating the type-2 correspondence.
    Type: Grant
    Filed: September 19, 2013
    Date of Patent: June 13, 2017
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Danai Koutra, David M. Lubensky, Hanghang Tong
  • Patent number: 9652712
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using recurrent neural networks to analyze health events. One of the methods includes: processing each of a plurality of initial temporal sequences of health events to generate, for each of the initial temporal sequences, a respective network internal state of a recurrent neural network for each time step in the initial temporal sequence; storing, for each of the initial temporal sequences, one or more of the network internal states for the time steps in the temporal sequence in a repository; obtaining a first temporal sequence; processing the first temporal sequence using the recurrent neural network to generate a sequence internal state for the first temporal sequence; and selecting one or more initial temporal sequences that are likely to include health events that are predictive of future health events in the first temporal sequence.
    Type: Grant
    Filed: July 27, 2015
    Date of Patent: May 16, 2017
    Assignee: Google Inc.
    Inventors: Gregory Sean Corrado, Jeffrey Adgate Dean
  • Patent number: 9646634
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a deep neural network. One of the methods for training a deep neural network that includes a low rank hidden input layer and an adjoining hidden layer, the low rank hidden input layer including a first matrix A and a second matrix B with dimensions i×m and m×o, respectively, to identify a keyword includes receiving a feature vector including i values that represent features of an audio signal encoding an utterance, determining, using the low rank hidden input layer, an output vector including o values using the feature vector, determining, using the adjoining hidden layer, another vector using the output vector, determining a confidence score that indicates whether the utterance includes the keyword using the other vector, and adjusting weights for the low rank hidden input layer using the confidence score.
    Type: Grant
    Filed: February 9, 2015
    Date of Patent: May 9, 2017
    Assignee: Google Inc.
    Inventors: Tara N. Sainath, Maria Carolina Parada San Martin