Patents Examined by Kakali Chaki
  • Patent number: 11030484
    Abstract: A system for determining data requirements to generate machine-learning models. The system may include one or more processors and one or more storage devices storing instructions. When executed, the instructions may configure the one or more processors to perform operations including: receiving a sample dataset, generating a plurality of data categories based on the sample dataset; generating a plurality of primary models of different model types using data from the corresponding one of the data categories as training data; generating a sequence of secondary models by training the corresponding one of the primary models with progressively less training data; identifying minimum viable models in the sequences of secondary models; determining a number of samples required for the minimum viable models; and generating entries in the database associating: model types; corresponding data categories; and corresponding numbers of samples in the training data used for the minimum viable models.
    Type: Grant
    Filed: March 22, 2019
    Date of Patent: June 8, 2021
    Assignee: Capital One Services, LLC
    Inventors: Austin Walters, Jeremy Goodsitt, Vincent Pham
  • Patent number: 11030530
    Abstract: A system and method provide a sequence learning model. The method for training the sequence learning model comprises retrieving input sequence data. The input sequence data includes one or more input time sequences. The method also encodes the input sequence data into output symbol data using a sequence learning model. The output symbol data includes one or more symbolic representations. The method decodes, based on a neural network, the output symbol data to decoded sequence data, where the decoded sequence data includes one or more decoded time sequences that are to match the one or more input time sequences in the input sequence data. The method further compares the decoded sequence data with the input sequence data and updates the sequence learning model based on the comparison.
    Type: Grant
    Filed: December 22, 2017
    Date of Patent: June 8, 2021
    Assignee: Onu Technology Inc.
    Inventors: Volkmar Frinken, Guha Jayachandran, Shriphani Palakodety, Veni Singh
  • Patent number: 11030529
    Abstract: Evolution and coevolution of neural networks via multitask learning is described. The foundation is (1) the original soft ordering, which uses a fixed architecture for the modules and a fixed routing (i.e. network topology) that is shared among all tasks. This architecture is then extended in two ways with CoDeepNEAT: (2) by coevolving the module architectures (CM), and (3) by coevolving both the module architectures and a single shared routing for all tasks using (CMSR). An alternative evolutionary process (4) keeps the module architecture fixed, but evolves a separate routing for each task during training (CTR). Finally, approaches (2) and (4) are combined into (5), where both modules and task routing are coevolved (CMTR).
    Type: Grant
    Filed: December 13, 2018
    Date of Patent: June 8, 2021
    Assignee: Cognizant Technology Solutions U.S. Corporation
    Inventors: Jason Zhi Liang, Elliot Meyerson, Risto Miikkulainen
  • Patent number: 11030523
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes generating, using a controller neural network, a batch of output sequences, each output sequence in the batch defining a respective architecture of a child neural network that is configured to perform a particular neural network task; for each output sequence in the batch: training a respective instance of the child neural network having the architecture defined by the output sequence; evaluating a performance of the trained instance of the child neural network on the particular neural network task to determine a performance metric for the trained instance of the child neural network on the particular neural network task; and using the performance metrics for the trained instances of the child neural network to adjust the current values of the controller parameters of the controller neural network.
    Type: Grant
    Filed: April 29, 2019
    Date of Patent: June 8, 2021
    Assignee: Google LLC
    Inventors: Barret Zoph, Quoc V. Le
  • Patent number: 11023806
    Abstract: A learning apparatus includes at least one memory and at least one circuit. The circuit (a) obtains a first neural network that has learned by using source learning data and obtains target learning data, the target learning data including a plurality of first data items each of which is given a first label and a plurality of second data items each of which is given a second label, (b) obtains a plurality of first output vectors by inputting the plurality of first data items to a second neural network and obtains a plurality of second output vectors by inputting the plurality of second data items to the second neural network, and (c) generates a first relation vector corresponding to the first label by using the plurality of first output vectors and generates a second relation vector corresponding to the second label by using the plurality of second output vectors.
    Type: Grant
    Filed: July 3, 2017
    Date of Patent: June 1, 2021
    Assignee: PANASONIC INTELLECTUAL PROPERTY MANAGEMENT CO., LTD.
    Inventors: Yoshihide Sawada, Toru Nakada, Yoshikuni Sato
  • Patent number: 11017323
    Abstract: A method for improving a profile analysis of an interpretive framework stored in a memory may include producing and displaying visual stimuli on a computerized device to test visual and visual motor responses of an individual subject in response to the displayed visual stimuli. The method may also include classifying and categorizing digitally measured visual and visual motor responses of the individual subject to the displayed visual stimuli. The method may further include continually modifying parameters of the profile analysis of the interpretive framework corresponding to at least one condition based at least in part on an item analysis corresponding to a pattern of performance determined during the classifying and categorizing of the digitally measured visual and visual motor responses of the individual subject.
    Type: Grant
    Filed: January 20, 2016
    Date of Patent: May 25, 2021
    Inventors: Karen Sue Silberman, Roxanne Elizabeth Helm-Stevens, Dana Louise Khudaverdyan, John Randy Fall, David Sevak Khudaverdyan
  • Patent number: 11017293
    Abstract: A programming method for an artificial neuron network having synapses, each including a single resistive random-access memory having first and second electrodes on either side of an active zone, the method including determining a number N of conductance intervals, where N?3; for each memory: choosing a conductance interval from amongst the N intervals; a step i) for application of a voltage pulse of a first type between the first and second electrodes, and for reading the conductance value of the memory; if the conductance value does not belong to the previously chosen conductance interval, a sub-step ii) for application of a voltage pulse of a second type between the first and second electrodes, and for reading the conductance value; if the conductance value does not belong to the chosen conductance interval, a step according to which step i) is reiterated, with steps i) and ii) being repeated until the conductance value belongs to the interval.
    Type: Grant
    Filed: August 15, 2016
    Date of Patent: May 25, 2021
    Assignee: COMMISSARIAT À L'ÉNERGIE ATOMIQUE ET AUX ÉNERGIES ALTERNATIVES
    Inventors: Elisa Vianello, Olivier Bichler
  • Patent number: 11010672
    Abstract: Techniques are provided for evolutionary computer-based optimization and artificial intelligence systems, and include receiving first and second candidate executable code (with ploidy of at least two and one, respectively) each selected at least in part based on a fitness score. The first candidate executable code and the second candidate executable code are combined to produce resultant executable code of the desired ploidy. A fitness score is determined for the resultant executable code, and a determination is made whether the resultant executable code will be used as a future candidate executable code based at least in part on the third fitness score. If an exit condition is met, then the resultant executable code is used as evolved executable code.
    Type: Grant
    Filed: September 1, 2017
    Date of Patent: May 18, 2021
    Assignee: Google LLC
    Inventor: Christopher James Hazard
  • Patent number: 11003994
    Abstract: A system and method for evolving a deep neural network structure that solves a provided problem includes: a memory storing a candidate supermodule genome database having a pool of candidate supermodules having values for hyperparameters for identifying a plurality of neural network modules in the candidate supermodule and further storing fixed multitask neural networks; a training module that assembles and trains N enhanced fixed multitask neural networks and trains each enhanced fixed multitask neural network using training data; an evaluation module that evaluates a performance of each enhanced fixed multitask neural network using validation data; a competition module that discards supermodules in accordance with assigned fitness values and saves others in an elitist pool; an evolution module that evolves the supermodules in the elitist pool; and a solution harvesting module providing for deployment of a selected one of the enhanced fixed multitask neural networks, instantiated with supermodules selected fr
    Type: Grant
    Filed: December 7, 2018
    Date of Patent: May 11, 2021
    Assignee: Cognizant Technology Solutions U.S. Corporation
    Inventors: Jason Zhi Liang, Elliot Meyerson, Risto Miikkulainen
  • Patent number: 10996937
    Abstract: A device may receive an instruction to automatically install a program using a click area prediction model. The click area prediction model may be associated with predicting a click area of a user interface that, when selected, causes a program installation procedure to proceed. The device may identify an installation user interface associated with installing the program. The device may determine a group of regions included in the installation user interface. The device may identify sets of features associated with the group of regions. The device may determine, based on the sets of features and the click area prediction model, a group of scores associated with the group of regions. The device may identify a particular region as a predicted click area based on the group of scores. The device may select the predicted click area to attempt to cause the program installation procedure to proceed.
    Type: Grant
    Filed: October 24, 2016
    Date of Patent: May 4, 2021
    Assignee: Juniper Networks, Inc.
    Inventors: Jacob Asher Langton, Daniel J. Quinlan, Kyle Adams
  • Patent number: 10997496
    Abstract: A method, computer program product, and system perform computations using a sparse convolutional neural network accelerator. Compressed-sparse data is received for input to a processing element, wherein the compressed-sparse data encodes non-zero elements and corresponding multi-dimensional positions. The non-zero elements are processed in parallel by the processing element to produce a plurality of result values. The corresponding multi-dimensional positions are processed in parallel by the processing element to produce destination addresses for each result value in the plurality of result values. Each result value is transmitted to a destination accumulator associated with the destination address for the result value.
    Type: Grant
    Filed: March 14, 2017
    Date of Patent: May 4, 2021
    Assignee: NVIDIA Corporation
    Inventors: William J. Dally, Angshuman Parashar, Joel Springer Emer, Stephen William Keckler, Larry Robert Dennison
  • Patent number: 10990872
    Abstract: A multiplexed neural core circuit according to one embodiment comprises, for an integer multiplexing factor T that is greater than zero, T sets of electronic neurons, T sets of electronic axons, where each set of the T sets of electronic axons corresponds to one of the T sets of electronic neurons, and a synaptic interconnection network comprising a plurality of electronic synapses that each interconnect a single electronic axon to a single electronic neuron, where the interconnection network interconnects each set of the T sets of electronic axons to its corresponding set of electronic neurons.
    Type: Grant
    Filed: March 31, 2016
    Date of Patent: April 27, 2021
    Assignee: International Business Machines Corporation
    Inventors: Filipp A. Akopyan, Rodrigo Alvarez-Icaza, John V. Arthur, Andrew S. Cassidy, Steven K. Esser, Bryan L. Jackson, Paul A. Merolla, Dharmendra S. Modha, Jun Sawada
  • Patent number: 10989786
    Abstract: Described herein is a framework for outdoor localization. In accordance with one aspect of the framework, a set of hotspot labels are received from one or more user devices connected to an outdoor wireless local area network. Manifold learning may be performed based on the set of hotspot labels to construct one or more manifolds. Using the one or more constructed manifolds, the framework may then estimate a location of a particular user device associated with a query record received from during an online location query.
    Type: Grant
    Filed: December 28, 2016
    Date of Patent: April 27, 2021
    Assignee: SAP SE
    Inventors: Jin Wang, Jun Luo, Sinno Jialin Pan
  • Patent number: 10992609
    Abstract: Technology is directed to text message based concierge services (“the technology”). A user interacts with a concierge service (CS) via text messages to obtain a specific concierge service. For example, the user can send a text message to the CS, e.g., to a contact number provided by the CS, requesting for a recommendation of a restaurant, and the CS can respond by sending the recommendation as a text message. The CS determines a context of the request and generates recommendations that are personalized to the user and is relevant to the context. The CS can use various techniques, e.g., artificial intelligence, machine learning, natural language processing, to determine a context of the request and generate the recommendations accordingly. The CS can also receive additional information from a person associated with the CS, such as a concierge, to further customize or personalize the recommendations to the user.
    Type: Grant
    Filed: March 31, 2015
    Date of Patent: April 27, 2021
    Assignee: CloLa, Inc.
    Inventor: Harold Hildebrand
  • Patent number: 10981013
    Abstract: A computer-implemented method for determining the volume of activation of neural tissue. In one embodiment, the method uses one or more parametric equations that define a volume of activation, wherein the parameters for the one or more parametric equations are given as a function of an input vector that includes stimulation parameters. After receiving input data that includes values for the stimulation parameters and defining the input vector using the input data, the input vector is applied to the function to obtain the parameters for the one or more parametric equations. The parametric equation is solved to obtain a calculated volume of activation.
    Type: Grant
    Filed: June 4, 2019
    Date of Patent: April 20, 2021
    Assignee: The Cleveland Clinic Foundation
    Inventors: J. Luis Lujan, Ashutosh Chaturvedi, Cameron McIntyre
  • Patent number: 10984331
    Abstract: Analyzing a set of policies. A goal comprising a particular outcome is received. An analysis object comprising a data structure maintaining information needed to perform an analysis of the goal is defined. The analysis object is configured to limit a number of calculations needed to achieve the goal. Each member of a set of expressions found in the set of policies has an output. The output is the same for each expression. One of the set of expressions is solved. The solved output is cached in the analysis object such that the solved output is associated with each member of the set of expressions. The analysis object is processed to create a set of values that achieves the goal. Processing includes referencing the cache to retrieve the solved output each time a member of the set of expressions is to be solved during processing of the analysis object.
    Type: Grant
    Filed: January 27, 2014
    Date of Patent: April 20, 2021
    Assignee: The Boeing Company
    Inventors: Paul L. Allen, David J. Finton, Charles Theodore Kitzmiller
  • Patent number: 10984309
    Abstract: A system continuously estimating the state of a dynamical system and classifying signals comprising a computer processor and a computer readable medium having computer executable instructions for providing: a module estimating of the state of a dynamical system assumed to be generated by a Dynamic Movement Primitive; a module classifying signals through inspecting dynamical system state estimates; and a coupling between the two modules such that classifications reset the dynamical system state estimate.
    Type: Grant
    Filed: February 16, 2017
    Date of Patent: April 20, 2021
    Assignee: Applied Brain Research Inc.
    Inventor: Trevor Bekolay
  • Patent number: 10956820
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. The method includes: training an action selection policy neural network, and during the training of the action selection neural network, training one or more auxiliary control neural networks and a reward prediction neural network. Each of the auxiliary control neural networks is configured to receive a respective intermediate output generated by the action selection policy neural network and generate a policy output for a corresponding auxiliary control task. The reward prediction neural network is configured to receive one or more intermediate outputs generated by the action selection policy neural network and generate a corresponding predicted reward.
    Type: Grant
    Filed: May 3, 2019
    Date of Patent: March 23, 2021
    Assignee: DeepMind Technologies Limited
    Inventors: Volodymyr Mnih, Wojciech Czarnecki, Maxwell Elliot Jaderberg, Tom Schaul, David Silver, Koray Kavukcuoglu
  • Patent number: 10949735
    Abstract: A resistive memory cell is connected in circuitry which has a first input terminal for applying neuron input signals including a read portion and a write portion. The circuitry includes a read circuit producing a read signal dependent on resistance of the memory cell, and an output terminal providing a neuron output signal, dependent on the read signal in a first state of the memory cell. The circuitry also includes a storage circuit storing a measurement signal dependent on the read signal, and a switch set operable to supply the read signal to the storage circuit during application of the read portion of each neuron input signal to the memory cell, and, after application of the read portion, to apply the measurement signal in the apparatus to enable resetting of the memory cell to a second state.
    Type: Grant
    Filed: June 9, 2019
    Date of Patent: March 16, 2021
    Assignee: Samsung Electronics Co., Ltd.
    Inventors: Evangelos S. Eleftheriou, Angeliki Pantazi, Abu Sebastian, Tomas Tuma
  • Patent number: 10929763
    Abstract: A heterogeneous log pattern editing recommendation system and computer-implemented method are provided. The system has a processor configured to identify, from heterogeneous logs, patterns including variable fields and constant fields. The processor is also configured to extract a category feature, a cardinality feature, and a before-after n-gram feature by tokenizing the variable fields in the identified patterns. The processor is additionally configured to generate target similarity scores between target fields to be potentially edited and other fields from among the variable fields in the heterogeneous logs using pattern editing operations based on the extracted category feature, the extracted cardinality feature, and the extracted before-after n-gram feature. The processor is further configured to recommend, to a user, log pattern edits for at least one of the target fields based on the target similarity scores between the target fields in the heterogeneous logs.
    Type: Grant
    Filed: August 23, 2017
    Date of Patent: February 23, 2021
    Inventors: Jianwu Xu, Biplob Debnath, Bo Zong, Hui Zhang, Guofei Jiang, Hancheng Ge