Patents Examined by Tri T Nguyen
  • Patent number: 11915152
    Abstract: A machine learning (ML) system includes a student ML system, a learning coach ML system, and a reference system that generates training data for the student ML system. The learning coach ML system learns to make an enhancement to the student ML system or to its learning process, such as updated hyperparameter or a network structural change, based on training of the student ML system with the training data generated by the reference system. The system may also comprise a learning experimentation system that communicates with the reference system to conduct experiments on the learning of the student learning system. Also, the learning experimentation system can determine a cost function for the learning coach ML system.
    Type: Grant
    Filed: March 5, 2018
    Date of Patent: February 27, 2024
    Assignee: D5AI LLC
    Inventor: James K. Baker
  • Patent number: 11900235
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using recurrent neural networks. One of the systems includes a main recurrent neural network comprising one or more recurrent neural network layers and a respective hyper recurrent neural network corresponding to each of the one or more recurrent neural network layers, wherein each hyper recurrent neural network is configured to, at each of a plurality of time steps: process the layer input at the time step to the corresponding recurrent neural network layer, the current layer hidden state of the corresponding recurrent neural network layer, and a current hypernetwork hidden state of the hyper recurrent neural network to generate an updated hypernetwork hidden state.
    Type: Grant
    Filed: September 9, 2021
    Date of Patent: February 13, 2024
    Assignee: Google LLC
    Inventors: Andrew M. Dai, Quoc V. Le, David Ha
  • Patent number: 11861488
    Abstract: A neuron circuit, comprising first and second NDR devices biased each with opposite polarities, said first and second NDR devices being coupled to first and second grounded capacitors.
    Type: Grant
    Filed: May 10, 2018
    Date of Patent: January 2, 2024
    Assignee: HRL LABORATORIES, LLC
    Inventor: Wei Yi
  • Patent number: 11829890
    Abstract: Example implementations described herein are directed to a novel Automated Machine Learning (AutoML) framework that is generated on an AutoML library so as to facilitate functionality to incorporate multiple machine learning model libraries within the same framework through a solution configuration file. The example implementations further involve a solution generator that identifies solution candidates and parameters for machine learning models to be applied to a dataset specified by the solution configuration file.
    Type: Grant
    Filed: June 25, 2020
    Date of Patent: November 28, 2023
    Assignee: HITACHI VANTARA, LLC
    Inventors: Yongqiang Zhang, Wei Lin, William Schmarzo
  • Patent number: 11769072
    Abstract: The structure of an untagged document can be derived using a predictive model that is trained in a supervised learning framework based on a corpus of tagged training documents. Analyzing the training documents results in a plurality of document part feature vectors, each of which correlates a category defining a document part (for example, “title” or “body paragraph”) with one or more feature-value pairs (for example, “font=Arial” or “alignment=centered”). Any suitable machine learning algorithm can be used to train the predictive model based on the document part feature vectors extracted from the training documents. Once the predictive model has been trained, it can receive feature-value pairs corresponding to a portion of an untagged document and make predictions with respect to the how that document part should be categorized. The predictive model can therefore generate tag metadata that defines a structure of the untagged document in an automated fashion.
    Type: Grant
    Filed: August 8, 2016
    Date of Patent: September 26, 2023
    Assignee: Adobe Inc.
    Inventor: Michael Kraley
  • Patent number: 11755885
    Abstract: A system, method and computer program product for disambiguating one or more entity mentions in one or more documents. The method facilitates the simultaneous linking entity mentions in a document based on convolution neural networks and recurrent neural networks that model both the local and global features for entity linking. The framework uses the capacity of convolution neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. The RNN functions to accumulate information about the previous entity mentions and/or target entities, and provide them as the global constraints for the linking process of a current entity mention.
    Type: Grant
    Filed: April 6, 2020
    Date of Patent: September 12, 2023
    Assignee: International Business Machines Corporation
    Inventors: Nicolas R. Fauceglia, Alfio M. Gliozzo, Oktie Hassanzadeh, Thien H. Nguyen, Mariano Rodriguez Muro, Mohammad Sadoghi Hamedani
  • Patent number: 11742091
    Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and active updates of outcomes. Embodiments of computer network architecture automatically update forecasts of outcomes of patient episodes and annual costs for each patient of interest after hospital discharge. Embodiments may generate such updated forecasts either occasionally on demand, or periodically, or as triggered by events such as an update of available data for such forecasts. Embodiments may include a combination of third-party databases to generate the updated forecasts for pending patient clinical episodes, and to drive the forecasting models for the same, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.
    Type: Grant
    Filed: October 22, 2022
    Date of Patent: August 29, 2023
    Assignee: Clarify Health Solutions, Inc.
    Inventors: Todd Gottula, Jean P. Drouin, Yale Wang, Samuel H. Bauknight, Adam F. Rogow, Jeffrey D. Larson, Justin Warner, Erik Talvola
  • Patent number: 11715021
    Abstract: A variable embedding method, for solving a large-scale problem using dedicated hardware by dividing variables of a problem graph into partial problems and by repeating an optimization process of the partial problems when an interaction of the variables of an optimization problem is expressed in the problem graph, includes: determining whether a duplicate allocation of the variables of the optimization problem to the vertices of the hardware graph is required when embedding at least a part of all the variables into the vertices of the hardware graph; and selecting one of the variables requiring no duplicate allocation and embedding selected variable in one of the vertices of the hardware graph without using another one of the variables requiring the duplicate allocation as one of the variables of the partial problem.
    Type: Grant
    Filed: June 18, 2019
    Date of Patent: August 1, 2023
    Assignees: DENSO CORPORATION, TOHOKU UNIVERSITY
    Inventors: Shuntaro Okada, Masayoshi Terabe, Masayuki Ohzeki
  • Patent number: 11704589
    Abstract: Disclosed are various embodiments for automatically identifying whether applications are static or dynamic. In one embodiment, code of an application is analyzed to determine instances of requesting data via a network in the application. Characteristics of the instances of requesting data via the network are provided to a machine learning model. The application is automatically classified as either dynamic or static according to the machine learning model.
    Type: Grant
    Filed: March 20, 2017
    Date of Patent: July 18, 2023
    Assignee: AMAZON TECHNOLOGIES, INC.
    Inventors: Saurabh Sohoney, Vineet Shashikant Chaoji, Pranav Garg
  • Patent number: 11704571
    Abstract: A method for pruning weights of an artificial neural network based on a learned threshold includes determining a pruning threshold for pruning a first set of pre-trained weights of multiple pre-trained weights based on a function of a classification loss and a regularization loss. Weights are pruned from the first set of pre-trained weights when a first value of the weight is less than the pruning threshold. A second set of pre-trained weights of the multiple pre-trained weights is fine-tuned or adjusted in response to a second value of each pre-trained weight in the second set of pre-trained weights being greater than the pruning threshold.
    Type: Grant
    Filed: October 9, 2020
    Date of Patent: July 18, 2023
    Assignee: QUALCOMM Incorporated
    Inventors: Kambiz Azarian Yazdi, Tijmen Pieter Frederik Blankevoort, Jin Won Lee, Yash Sanjay Bhalgat
  • Patent number: 11669915
    Abstract: Systems, methods, and non-transitory computer-readable media can identify a set of accounts, each account of the set of accounts having a number of followers. The set of accounts are grouped into a plurality of groups based on number of followers, wherein each group is associated with a value score. A machine learning model is trained using a set of training data comprising account recommendation conversion information, wherein the account recommendation conversion information comprises a plurality of successful account recommendations, and each successful account recommendation is assigned a weight based on the value scores associated with the plurality of groups. One or more accounts of the set of accounts are selected to present as account recommendations based on the machine learning model.
    Type: Grant
    Filed: September 27, 2017
    Date of Patent: June 6, 2023
    Assignee: Meta Platforms, Inc.
    Inventors: Alan Si, Jialu Zhu, Sourav Chatterji, Brian Dolhansky
  • Patent number: 11657285
    Abstract: Methods, systems and media for random semi-structured row-wise pruning of filters of a convolutional neural network are described. Rows of weights are pruned from kernels of filters of a convolutional layer of a convolutional neural network according to a pseudo-randomly-generated row pruning mask. The convolutional neural network is trained to perform a particular task using the pruned filters that include the rows of weights that have not been pruned from the kernels of filters. The process may be repeated multiple times, with the best-performing row pruning mask being selected for use in pruning row weights from kernel filters when the trained convolutional neural network is deployed to processing system and used for an inference. Computation time may be decreased further with the use of multiple parallel hardware computation units of a processing system performing pipelined row-wise convolution.
    Type: Grant
    Filed: July 30, 2020
    Date of Patent: May 23, 2023
    Assignee: XFUSION DIGITAL TECHNOLOGIES CO., LTD.
    Inventors: Vanessa Courville, Mehdi Ahmadi, Mahdi Zolnouri
  • Patent number: 11636374
    Abstract: The disclosure is in the technical field of circuit-model quantum computation. Generally, it concerns methods to use quantum computers to perform computations on classical spin models, where the classical spin models involve a number of spins that is exponential in the number of qubits that comprise the quantum computer. Examples of such computations include optimization and calculation of thermal properties, but extend to a wide variety of calculations that can be performed using the configuration of a spin model with an exponential number of spins. Spin models encompass optimization problems, physics simulations, and neural networks (there is a correspondence between a single spin and a single neuron). This disclosure has applications in these three areas as well as any other area in which a spin model can be used.
    Type: Grant
    Filed: November 30, 2021
    Date of Patent: April 25, 2023
    Assignee: QC Ware Corp.
    Inventors: Peter L. McMahon, Robert Michael Parrish
  • Patent number: 11621085
    Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and active updates of outcomes. Embodiments of computer network architecture automatically update forecasts of outcomes of patient episodes and annual costs for each patient of interest after hospital discharge. Embodiments may generate such updated forecasts either occasionally on demand, or periodically, or as triggered by events such as an update of available data for such forecasts. Embodiments may include a combination of third-party databases to generate the updated forecasts for pending patient clinical episodes, and to drive the forecasting models for the same, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.
    Type: Grant
    Filed: April 18, 2019
    Date of Patent: April 4, 2023
    Assignee: CLARIFY HEALTH SOLUTIONS, INC.
    Inventors: Todd Gottula, Jean P. Drouin, Yale Wang, Samuel H. Bauknight, Adam F. Rogow, Jeffrey D. Larson, Justin Warner, Erik Talvola
  • Patent number: 11615337
    Abstract: A method, apparatus and product includes obtaining a logical representation of a quantum circuit that is implementable by a plurality of alternative physical representations of the quantum circuit, each of which implementing the logical representation with a different error correction scheme and defining error correction schemes for the quantum circuit. The defining error correction schemes includes implementing a search algorithm on the alternative physical representations, wherein the search algorithm is configured to search for a physical representation of the quantum circuit with an assignment of a plurality of physical qubits to a plurality of logical qubits that is defined in view of a quality score. A quality metric used to compute the quality score is monotonically correlated to error rates of logical output qubits of the quantum circuit when implementing each alternative physical representation. The assignment is utilized to define the error correction schemes for the quantum circuit.
    Type: Grant
    Filed: April 18, 2022
    Date of Patent: March 28, 2023
    Assignee: CLASSIQ TECHNOLOGIES LTD.
    Inventors: Amir Naveh, Shmuel Ur, Eyal Cornfeld, Ofek Kirzner, Yehuda Naveh, Lior Gazit
  • Patent number: 11599812
    Abstract: A condition determination system includes: an operation condition data obtaining unit that obtains operation condition data indicating an operation condition of a facility; and a determination unit that determines, based on the operation condition data, a level of a phenomenon that occurs due to the operation condition of the facility.
    Type: Grant
    Filed: March 25, 2016
    Date of Patent: March 7, 2023
    Assignee: MITSUBISHI HEAVY INDUSTRIES, LTD.
    Inventors: Eisuke Noda, Satoshi Hanada, Yusuke Yamada, Mizuki Kasamatsu, Takae Yamashita
  • Patent number: 11580374
    Abstract: An artificial neuron including: a membrane capacitor; an input of an external synaptic excitation in current, the membrane capacitor integrating the input current; a negative-feedback impulse circuit, supplied by a power supply at a negative voltage between ?200 mV and 0 mV and at a positive voltage between 0 mV and +200 mV, including: a bridge based on pMOS and nMOS transistors in series and linked by a midpoint to the membrane capacitor, the midpoint defining the output of the artificial neuron, at least one delay capacitor between the gate and the source of one of the transistors of the bridge, at least two CMOS inverters between the membrane capacitor and the gates of the transistors of the bridge.
    Type: Grant
    Filed: April 7, 2017
    Date of Patent: February 14, 2023
    Assignees: UNIVERSITE DE LILLE, CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE
    Inventors: Alain Cappy, Francois Danneville, Virginie Hoel, Christophe Loyez
  • Patent number: 11494657
    Abstract: Some embodiments of the invention provide a novel method for training a quantized machine-trained network. Some embodiments provide a method of scaling a feature map of a pre-trained floating-point neural network in order to match the range of output values provided by quantized activations in a quantized neural network. A quantization function is modified, in some embodiments, to be differentiable to fix the mismatch between the loss function computed in forward propagation and the loss gradient used in backward propagation. Variational information bottleneck, in some embodiments, is incorporated to train the network to be insensitive to multiplicative noise applied to each channel. In some embodiments, channels that finish training with large noise, for example, exceeding 100%, are pruned.
    Type: Grant
    Filed: October 8, 2019
    Date of Patent: November 8, 2022
    Assignee: PERCEIVE CORPORATION
    Inventors: Eric A. Sather, Steven L. Teig
  • Patent number: 11461689
    Abstract: Techniques are disclosed for systems and methods for learning the behavior of and/or for performing automated testing of a system under test (SUT). The learning/testing is accomplished solely via the graphical user interface (GUI) of the SUT and requires no a priori metadata/knowledge about the GUI objects. The learning engine operates by performing actions on the GUI and by observing the results of these actions. If the actions result in a change in the screen/page of the GUI then a screenshot is taken for further processing. Objects are detected from the screenshot, new actions that need to be performed on the objects are guessed, those actions are performed, the results are observed and the process repeats. Text labels on the screens are also read and are used for generating contextualized inputs for the screens. The learning process continues until any predetermined learning/testing criteria are satisfied.
    Type: Grant
    Filed: January 6, 2017
    Date of Patent: October 4, 2022
    Inventor: Sigurdur Runar Petursson
  • Patent number: 11449760
    Abstract: Methods and apparatus for quantum assisted optimization. In one aspect, a method includes obtaining a set of initial input states, applying one or more of (i) dynamical thermal fluctuations and (ii) cluster update algorithms to the set of input states and subsequent input states when the states evolve within the classical information processors, applying dynamical quantum fluctuations to the set of input states and subsequent states when the states evolve within the quantum systems and repeating the application steps until a desirable output state is obtained.
    Type: Grant
    Filed: December 30, 2016
    Date of Patent: September 20, 2022
    Assignee: Google LLC
    Inventors: Vasil S. Denchev, Masoud Mohseni, Hartmut Neven