Patents Examined by Hal Schnee
  • Patent number: 11216739
    Abstract: A method, system and computer-usable medium are disclosed for automated analysis of ground truth using confidence model to prioritize correction options. In certain embodiments, the ground truth data is analyzed to identify review-candidates. A confidence level may be assigned to each of the identified review-candidates and the review-candidates are prioritized, at least in part, using the assigned confidence levels. The review-candidates are electronically presented in prioritized order to solicit verification or correction feedback for updating the ground truth data.
    Type: Grant
    Filed: July 25, 2018
    Date of Patent: January 4, 2022
    Assignee: International Business Machines Corporation
    Inventors: Andrew R. Freed, Kyle G. Christianson, Christopher Phipps
  • Patent number: 11200296
    Abstract: Limited duration supply for heuristic algorithms is disclosed. A supply manager receives, from a first subsystem, a first request for a first supply. The supply manager determines that the first supply is not executing. The supply manager initiates the first supply, the first supply to return supply data upon request. The supply manager provides to the first subsystem a supply reference that refers to the first supply that allows the first subsystem to request the supply data directly from the first supply. The supply manager subsequently determines that no subsystem requires the first supply and disables the first supply.
    Type: Grant
    Filed: October 20, 2017
    Date of Patent: December 14, 2021
    Assignee: Red Hat, Inc.
    Inventors: Lukas Petrovicky, Geoffrey De Smet
  • Patent number: 11200492
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a document classification neural network. One of the methods includes training an autoencoder neural network to autoencode input documents, wherein the autoencoder neural network comprises the one or more LSTM neural network layers and an autoencoder output layer, and wherein training the autoencoder neural network comprises determining pre-trained values of the parameters of the one or more LSTM neural network layers from initial values of the parameters of the one or more LSTM neural network layers; and training the document classification neural network on a plurality of training documents to determine trained values of the parameters of the one or more LSTM neural network layers from the pre-trained values of the parameters of the one or more LSTM neural network layers.
    Type: Grant
    Filed: January 6, 2020
    Date of Patent: December 14, 2021
    Assignee: Google LLC
    Inventors: Andrew M. Dai, Quoc V. Le
  • Patent number: 11195082
    Abstract: Embodiments relate to a first processing node that processes an input data having a temporal sequence of spatial patterns by retaining a higher-level context of the temporal sequence. The first processing node performs temporal processing based at least on feedback inputs received from a second processing node. The first processing node determines whether learned temporal sequences are included in the input data based on sequence inputs transmitted within the same level of a hierarchy of processing nodes and the feedback inputs received from an upper level of the hierarchy of processing nodes.
    Type: Grant
    Filed: December 3, 2019
    Date of Patent: December 7, 2021
    Assignee: Numenta, Inc.
    Inventors: Jeffrey C. Hawkins, Subutai Ahmad
  • Patent number: 11195089
    Abstract: Described herein is a crossbar array that includes a cross-point synaptic device at each of a plurality of crosspoints. The cross-point synaptic device includes a weight storage element comprising a set of nanocrystal dots. Further, the cross-point synaptic device includes at least three terminals for interacting with the weight storage element, wherein a weight is stored in the weight storage element by sending a first electric pulse via a gate terminal from the at least three terminals, the first electric pulse causes the nanocrystal dots to store a corresponding charge, and the weight is erased from the weight storage element by sending a second electric pulse via the gate terminal, the second electric pulse having an opposite polarity of the first electric pulse.
    Type: Grant
    Filed: June 28, 2018
    Date of Patent: December 7, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Kevin K. Chan, Martin M. Frank, Jin Ping Han
  • Patent number: 11195110
    Abstract: A score explanation method for explaining a score includes at least steps of: a1) providing a first score associated with a first vector containing the first values of the parameters; b) generating a first set of lists, each list including a second number of indicators; c) generating, from a list, of at least a third vector wherein each parameter has a third value; the third value being equal to the corresponding first value when the list does not include an indicator of the corresponding parameter, and different from the corresponding first value otherwise; d) calculating the score of at least one third vector; e) evaluating, from the scores calculated for each of the third vectors, an indicator of significance of each parameter; f) elaborating, from the evaluated indicators of significance, an explanation of the first score.
    Type: Grant
    Filed: March 25, 2016
    Date of Patent: December 7, 2021
    Assignee: THALES
    Inventors: Helia Pouyllau, Christophe Labreuche, Benedicte Goujon
  • Patent number: 11188035
    Abstract: A computer-implemented method for reducing computation cost associated with a machine learning task performed by a computer system by implementing continuous control of attention for a deep learning network includes initializing a control-value function, an observation-value function and a sequence of states associated with a current episode. If a current epoch associated with the current episode is odd, an observation-action is selected, the observation-action is executed to observe a partial image, and the observation-value function is updated based on the partial image and the control-value function. If the current epoch is even, a control-action is selected, the control-action is executed to obtain a reward corresponding to the control-action, and the control-value function is updated based on the reward and the observation-value function.
    Type: Grant
    Filed: July 19, 2018
    Date of Patent: November 30, 2021
    Assignee: International Business Machines Corporation
    Inventors: Shohei Ohsawa, Takayuki Osogami
  • Patent number: 11188825
    Abstract: A computer-implemented method of mixed-precision deep learning with multi-memristive synapses may be provided. The method comprises representing, each synapse of an artificial neural network by a combination of a plurality of memristive devices, wherein each of the plurality of memristive devices of each of the synapses contributes to an overall synaptic weight with a related device significance, accumulating a weight gradient ?W for each synapse in a high-precision variable, and performing a weight update to one of the synapses using an arbitration scheme for selecting a respective memristive device, according to which a threshold value related to the high-precision variable for performing the weight update is set according to the device significance of the respective memristive device selected by the arbitration schema.
    Type: Grant
    Filed: January 9, 2019
    Date of Patent: November 30, 2021
    Assignee: International Business Machines Corporation
    Inventors: Irem Boybat Kara, Manuel Le Gallo-Bourdeau, Nandakumar Sasidharan Rajalekshmi, Abu Sebastian, Evangelos Stavros Eleftheriou
  • Patent number: 11188810
    Abstract: Systems and methods disclosed herein relate to autonomous agents. A first autonomous agent receives, from a first sensor, a first set of event data indicating events relating to a subject. The first autonomous agent provides the first set of event data to a data aggregator. The first autonomous agent receives, from the data aggregator, correlated event data including events sensed by the first autonomous agent and a second autonomous agent. The first autonomous agent applies machine learning model to the correlated event data to predict a first pattern of activity and determines, based on the first pattern of activity, that a first action is to be performed, causing the first actuator module to perform the first action.
    Type: Grant
    Filed: June 26, 2018
    Date of Patent: November 30, 2021
    Assignee: AT&T Intellectual Property I, L.P.
    Inventors: Chuxin Chen, George Dome, John Oetting
  • Patent number: 11170309
    Abstract: A machine learning model inference routing system in a machine learning service is described herein. The machine learning model inference routing system includes load balancer(s), network traffic router(s), an endpoint registry, and a feedback processing system that collectively allow the machine learning model inference routing system to adjust the routing of inferences based on machine learning model accuracy, demand, and/or the like. In addition, the arrangement of components in the machine learning model inference routing system enables the machine learning service to perform shadow testing, support ensemble machine learning models, and/or improve existing machine learning models using feedback data.
    Type: Grant
    Filed: November 22, 2017
    Date of Patent: November 9, 2021
    Assignee: Amazon Technologies, Inc.
    Inventors: Stefano Stefani, Leo Parker Dirac, Taylor Goodhart
  • Patent number: 11164076
    Abstract: A trained computer model includes a direct network and an indirect network. The indirect network generates expected weights or an expected weight distribution for the nodes and layers of the direct network. These expected characteristics may be used to regularize training of the direct network weights and encourage the direct network weights towards those expected, or predicted by the indirect network. Alternatively, the expected weight distribution may be used to probabilistically predict the output of the direct network according to the likelihood of different weights or weight sets provided by the expected weight distribution. The output may be generated by sampling weight sets from the distribution and evaluating the sampled weight sets.
    Type: Grant
    Filed: October 20, 2017
    Date of Patent: November 2, 2021
    Assignee: Uber Technologies, Inc.
    Inventors: Zoubin Ghahramani, Douglas Bemis, Theofanis Karaletsos
  • Patent number: 11151464
    Abstract: An approach is provided in which an information handing system determines a hidden cycle of hidden evidence based on one of multiple signals in a frequency-based representation of source evidence. The information handling system extrapolates the hidden evidence to create a forecast data set and, in turn, utilizes the forecast data set to process a request.
    Type: Grant
    Filed: January 3, 2018
    Date of Patent: October 19, 2021
    Assignee: International Business Machines Corporation
    Inventors: Aaron K. Baughman, Mauro Marzorati, Ashok K. Panda, Ashish K. Tanuku
  • Patent number: 11151450
    Abstract: Systems and methods that use a neural network architecture for extracting interpretable relationships among predictive input variables. This leads to neural network models that are interpretable and explainable. More importantly, these systems and methods lead to discovering new interpretable variables that are functions of predictive input variables, which in turn can be extracted as new features and utilized in other types of interpretable models, like scorecards (fraud score, etc.), but with higher predictive power than conventional systems and methods.
    Type: Grant
    Filed: May 21, 2018
    Date of Patent: October 19, 2021
    Assignee: FAIR ISAAC CORPORATION
    Inventors: Scott Michael Zoldi, Shafi Rahman
  • Patent number: 11138510
    Abstract: A user expertise classifying method, system, and computer program product, include analyzing an input by a user based on at least one of vocabulary, orthography, and grammar of the user input, processing user background data obtained from a database, and calculating an expertise score of the user based on the analyzed user input and the processed background data.
    Type: Grant
    Filed: September 13, 2019
    Date of Patent: October 5, 2021
    Assignee: Airbnb, Inc.
    Inventors: Ana Paula Appel, Victor Boa Juliani, Andre Gama Leal, Claudio Santos Pinhanez, Marcela Megumi Terakado
  • Patent number: 11126897
    Abstract: Techniques are provided for unification of classifier models across device platforms of varying form factors and/or sensor calibrations. A methodology implementing the techniques according to an embodiment includes extracting classification features from data provided by sensors associated with a first device platform. The method also includes applying a feature mapping function to the extracted features. The feature mapping function is configured to transform the features such that the are suitable for use by a classifier model that is trained on data provided by sensors associated with a second device platform. The method further includes executing the classifier model on the transformed features to generate classifications, for example recognized activities associated with use of the first device. The feature mapping function is based on application of a statistical distribution distance minimization between a sampling of data provided by sensors of the first device and sensors of the second device.
    Type: Grant
    Filed: December 30, 2016
    Date of Patent: September 21, 2021
    Assignee: Intel Corporation
    Inventors: Xiaodong Cai, Ke Han, Lu Wang, Lili Ma
  • Patent number: 11126927
    Abstract: Techniques for auto-scaling hosted machine learning models for production inference are described. A machine learning model can be deployed in a hosted environment such that the infrastructure supporting the machine learning model scales dynamically with demand so that performance is not impacted. The model can be auto-scaled using reactive techniques or predictive techniques.
    Type: Grant
    Filed: November 24, 2017
    Date of Patent: September 21, 2021
    Assignee: Amazon Technologies, Inc.
    Inventors: Stefano Stefani, Steven Andrew Loeppky, Thomas Albert Faulhaber, Jr., Craig Wiley, Edo Liberty
  • Patent number: 11120337
    Abstract: A method and system for augmenting a training dataset for a generative adversarial network (GAN). The training dataset includes labelled data samples and unlabelled data samples. The method includes: receiving generated samples generated using a first neural network of the GAN and the unlabelled samples of training dataset; determining a decision value for a sample from a decision function, wherein the sample is a generated sample of the generated samples or an unlabelled sample of the unlabelled samples of the training dataset; comparing the decision value to a threshold; in response to determining that the decision value exceeds the threshold: predicting a label for a sample; assigning the label to the sample; and augmenting the training dataset to include the sample with the assigned label as a labelled sample.
    Type: Grant
    Filed: October 20, 2017
    Date of Patent: September 14, 2021
    Assignee: HUAWEI TECHNOLOGIES CO., LTD.
    Inventors: Dalei Wu, Md Akmal Haidar, Mehdi Rezagholizadeh, Alan Do-Omri
  • Patent number: 11120338
    Abstract: Methods for genetic generation of tools for use in a convolutional neural network are provided. Randomly generated starting points and sets of positive and negative tasks are distributed to multiple processors. Each processor iterates an instruction queue over its received tasks based on existing analysis tools, generating a test score for each iteration. A set of instructions is saved as a new tool if its generated test score determines a successful test. A convolutional neural network is executed over complex test cases based on a tool set that includes the new tools. Output results of the convolutional neural network are analyzed and a new tool set is created by removing tools that are not utilized in generating the output results. Systems and machine-readable media are also provided.
    Type: Grant
    Filed: November 20, 2017
    Date of Patent: September 14, 2021
    Assignee: Colossio, Inc.
    Inventor: Joseph A. Jaroch
  • Patent number: 11106975
    Abstract: The amount of time required to train a neural network may be decreased by modifying the neural network to allow for greater parallelization of computations. The computations for cells of the neural network may be modified so that the matrix-vector multiplications of the cell do not depend on a previous cell and thus allowing the matrix-vector computations to be performed outside of the cells. Because the matrix-vector multiplications can be performed outside of the cells, they can be performed in parallel to decrease the computation time required for processing a sequence of training vectors with the neural network. The trained neural network may be applied to a wide variety of applications, such as performing speech recognition, determining a sentiment of text, determining a subject matter of text, answering a question in text, or translating text to another language.
    Type: Grant
    Filed: October 20, 2017
    Date of Patent: August 31, 2021
    Assignee: ASAPP, INC.
    Inventor: Tao Lei
  • Patent number: 11106996
    Abstract: A method for machine learning based database management is provided. The method may include training a machine learning model to detect an anomaly that is present and/or developing in a database system. The anomaly in the database system may be detected by at least processing, with a trained machine learning model, one or more performance metrics for the database system. In response to detecting the presence of the anomaly at the database system, one or more remedial actions may be determined for correcting and/or preventing the anomaly at the database system. The one or more remedial actions may further be sent to a database management system associated with the database system. Related systems and articles of manufacture are also provided.
    Type: Grant
    Filed: August 23, 2017
    Date of Patent: August 31, 2021
    Assignee: SAP SE
    Inventors: Helmut Fieres, Jean-Pierre Djamdji, Klaus Dickgiesser, Olena Kushakovska, Venkatesh R