Patents Examined by Su-Ting Chuang
  • Patent number: 12056605
    Abstract: A system, electronic device and method for improved neural network training are provided. The electronic device includes: a processor, a memory storing a Generative adversarial network (GAN) to learn from unlabeled data by engaging a generative model in an adversarial game with a discriminator; and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for training the Generative adversarial network using a regularizer to encourage the discriminator to properly use its capacity and hidden representations of the discriminator to have high entropy.
    Type: Grant
    Filed: October 26, 2018
    Date of Patent: August 6, 2024
    Assignee: ROYAL BANK OF CANADA
    Inventors: Yanshuai Cao, Yik Chau Lui, Weiguang Ding, Ruitong Huang
  • Patent number: 11989637
    Abstract: An electronic device, method, and computer readable medium for an invertible wavelet layer for neural networks are provided. The electronic device includes a memory and at least one processor coupled to the memory. The at least one processor is configured to receive an input to a neural network, apply a wavelet transform to the input at a wavelet layer of the neural network, and generate a plurality of subbands of the input as a result of the wavelet transform.
    Type: Grant
    Filed: April 30, 2019
    Date of Patent: May 21, 2024
    Assignee: Samsung Electronics Co., Ltd.
    Inventors: Chenchi Luo, David Liu, Youngjun Yoo
  • Patent number: 11948084
    Abstract: A function creation method is disclosed. The method comprises defining one or more database function inputs, defining cluster processing information, defining a deep learning model, and defining one or more database function outputs. A database function is created based at least in part on the one or more database function inputs, the cluster set-up information, the deep learning model, and the one or more database function outputs. In some embodiments, the database function enables a non-technical user to utilize deep learning models.
    Type: Grant
    Filed: January 31, 2023
    Date of Patent: April 2, 2024
    Assignee: Databricks, Inc.
    Inventors: Sue Ann Hong, Shi Xin, Timothee Hunter, Ali Ghodsi
  • Patent number: 11941516
    Abstract: Systems, methods, and apparatuses related to cooperative learning neural networks are described. Cooperative learning neural networks may include neural networks which utilize sensor data received wirelessly from at least one other wireless communication device to train the neural network. For example, cooperative learning neural networks described herein may be used to develop weights which are associated with objects or conditions at one device and which may be transmitted to a second device, where they may be used to train the second device to react to such objects or conditions. The disclosed features may be used in various contexts, including machine-type communication, machine-to-machine communication, device-to-device communication, and the like. The disclosed techniques may be employed in a wireless (e.g., cellular) communication system, which may operate according to various standardized protocols.
    Type: Grant
    Filed: August 31, 2017
    Date of Patent: March 26, 2024
    Assignee: Micron Technology, Inc.
    Inventors: Fa-Long Luo, Tamara Schmitz, Jeremy Chritz, Jaime Cummins
  • Patent number: 11941518
    Abstract: Systems, methods, and apparatuses related to cooperative learning neural networks are described. Cooperative learning neural networks may include neural networks which utilize sensor data received wirelessly from at least one other wireless communication device to train the neural network. For example, cooperative learning neural networks described herein may be used to develop weights which are associated with objects or conditions at one device and which may be transmitted to a second device, where they may be used to train the second device to react to such objects or conditions. The disclosed features may be used in various contexts, including machine-type communication, machine-to-machine communication, device-to-device communication, and the like. The disclosed techniques may be employed in a wireless (e.g., cellular) communication system, which may operate according to various standardized protocols.
    Type: Grant
    Filed: August 28, 2018
    Date of Patent: March 26, 2024
    Assignee: Micron Technology, Inc.
    Inventors: Fa-Long Luo, Tamara Schmitz, Jeremy Chritz, Jaime Cummins
  • Patent number: 11842265
    Abstract: Disclosed in a processor chip configured to perform neural network processing. The processor chip includes a memory, a first processor configured to perform neural network processing on a data stored in the memory, a second processor and a third processor, and the second processor is configured to transmit a control signal to the first processor and the third processor to cause the first processor and the third processor to perform an operation.
    Type: Grant
    Filed: June 19, 2020
    Date of Patent: December 12, 2023
    Assignee: Samsung Electronics Co., Ltd.
    Inventors: Yongmin Tai, Insang Cho, Wonjae Lee, Chanyoung Hwang
  • Patent number: 11829886
    Abstract: Simulating uncertainty in an artificial neural network is provided. Aleatoric uncertainty is simulated to measure what the artificial neural network does not understand from sensor data received from an object operating in a real-world environment by adding random values to edge weights between nodes in the artificial neural network during backpropagation of output data of the artificial neural network and measuring impact on the output data by the added random values to the edge weights between the nodes. Epistemic uncertainty is simulated to measure what the artificial neural network does not know by dropping out a selected node from each respective layer of the artificial neural network during forward propagation of the sensor data and measuring impact of dropped out nodes on the output data of the artificial neural network. An action corresponding to the object is performed based on the impact of simulating the aleatoric and epistemic uncertainty.
    Type: Grant
    Filed: March 7, 2018
    Date of Patent: November 28, 2023
    Assignee: International Business Machines Corporation
    Inventors: Aaron K Baughman, Stephen C. Hammer, Micah Forster
  • Patent number: 11757728
    Abstract: This invention provides an autonomic method for controlling an algorithm on a multi-terminal computing system, wherein the algorithm is configured to analyse diagnostic data for each terminal and an outcome of the analysis is a first action or a second action, and a device for implementing the method, the method comprising the steps of: receiving a first set of data for the multi-terminal computing system; applying the algorithm to the first set of data to classify each terminal in the multi-terminal computing system as being associated with either a first action or second action; re-classifying a first subset of terminals classified as being associated with the first action as being associated with the second action; and applying the first actions, second actions, and reclassified second actions respectively to each terminal in the multi-terminal computing system.
    Type: Grant
    Filed: December 9, 2016
    Date of Patent: September 12, 2023
    Assignee: BRITISH TELECOMMUNICATIONS PUBLIC LIMITED COMPANY
    Inventors: Kjeld Jensen, Botond Virginas, Stephen Cassidy, Phil Bull, David Rohlfing
  • Patent number: 11734575
    Abstract: A computer-implemented method, computer program product, and computer processing system are provided for Hierarchical Reinforcement Learning (HRL) with a target task. The method includes obtaining, by a processor device, a sequence of tasks based on hierarchical relations between the tasks, the tasks constituting the target task. The method further includes learning, by a processor device, a sequence of constraints corresponding to the sequence of tasks by repeating, for each of the tasks in the sequence, reinforcement learning and supervised learning with a set of good samples and a set of bad samples and by applying an obtained constraint for a current task to a next task.
    Type: Grant
    Filed: July 30, 2018
    Date of Patent: August 22, 2023
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Don Joven Ravoy Agravante, Giovanni De De Magistris, Tu-Hoa Pham, Ryuki Tachibana
  • Patent number: 11734595
    Abstract: An example method for facilitating the generation of a control field for a quantum system is provided. The example method may include receiving quantum system experiment input parameters and generating a set of coefficients defining a plurality of controls. The plurality of controls may be provided as a weighted sum of basis functions that include discrete prolate spheroidal sequences. The example method may further include applying a gradient based optimization, synthesizing the plurality of controls, and configuring a waveform generator with the plurality of controls to enable the waveform generator to generate the control field.
    Type: Grant
    Filed: August 3, 2017
    Date of Patent: August 22, 2023
    Assignee: The Johns Hopkins University
    Inventor: Dennis G. Lucarelli
  • Patent number: 11709895
    Abstract: Systems, apparatuses, and methods are provided for identifying a corresponding string stored in memory based on an incomplete input string. A system can analyze and produce phonetic and distance metrics for a plurality of strings stored in memory by comparing the plurality of strings to an incomplete input string. These similarity metrics can be used as the input to a machine learning model, which can quickly and accurately provide a classification. This classification can be used to identify a string stored in memory that corresponds to the incomplete input string.
    Type: Grant
    Filed: October 29, 2021
    Date of Patent: July 25, 2023
    Assignee: Visa International Service Association
    Inventors: Pranjal Singh, Soumyajyoti Banerjee
  • Patent number: 11704542
    Abstract: A computer-implemented method is provided for machine prediction. The method includes forming, by a hardware processor, a Convolutional Dynamic Boltzmann Machine (C-DyBM) by extending a non-convolutional DyBM with a convolutional operation. The method further includes generating, by the hardware processor using the convolution operation of the C-DyBM, a prediction of a future event at time t from a past patch of time-series of observations. The method also includes performing, by the hardware processor, a physical action responsive to the prediction of the future event at time t.
    Type: Grant
    Filed: January 29, 2019
    Date of Patent: July 18, 2023
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Takayuki Katsuki, Takayuki Osogami, Akira Koseki, Masaki Ono
  • Patent number: 11669744
    Abstract: A method for receiving training data for training a neural network (NN) to perform a machine learning (ML) task and for determining, using the training data, an optimized NN architecture for performing the ML task is described. Determining the optimized NN architecture includes: maintaining population data comprising, for each candidate architecture in a population of candidate architectures, (i) data defining the candidate architecture, and (ii) data specifying how recently a neural network having the candidate architecture has been trained while determining the optimized neural network architecture; and repeatedly performing multiple operations using each of a plurality of worker computing units to generate a new candidate architecture based on a selected candidate architecture having the best measure of fitness, adding the new candidate architecture to the population, and removing from the population the candidate architecture that was trained least recently.
    Type: Grant
    Filed: September 14, 2021
    Date of Patent: June 6, 2023
    Assignee: Google LLC
    Inventors: Yanping Huang, Alok Aggarwal, Quoc V. Le, Esteban Alberto Real
  • Patent number: 11669731
    Abstract: Described is a system for controlling a mobile platform. A neural network that runs on the mobile platform is trained based on a current state of the mobile platform. A Satisfiability Modulo Theories (SMT) solver capable of reasoning over non-linear activation functions is periodically queried to obtain examples of states satisfying specified constraints of the mobile platform. The neural network is then trained on the examples of states. Following training on the examples of states, the neural network selects an action to be performed by the mobile platform in its environment. Finally, the system causes the mobile platform to perform the selected action in its environment.
    Type: Grant
    Filed: November 21, 2019
    Date of Patent: June 6, 2023
    Assignee: HRL LABORATORIES, LLC
    Inventors: Michael A. Warren, Christopher Serrano
  • Patent number: 11663535
    Abstract: An example method includes receiving, by one or more processors, a representation of an utterance spoken at a computing device; identifying, by a first computational agent from a plurality of computational agents and based on the utterance, a multi-element task to be performed, wherein the plurality of computational agents includes one or more first party computational agents and a plurality of third-party computational agents; and performing, by the first computational agent, a first sub-set of elements of the multi-element task, wherein performing the first sub-set of elements comprises selecting a second computational agent from the plurality of computational agents to perform a second sub-set of elements of the multi-element task.
    Type: Grant
    Filed: November 16, 2017
    Date of Patent: May 30, 2023
    Assignee: GOOGLE LLC
    Inventors: Robert Stets, Valerie Nygaard, Bogdan Caprita, Bradley M. Abrams, Jason Brant Douglas
  • Patent number: 11657294
    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. If the desired ploidy of the resultant executable code is one, then the first candidate executable code and the second candidate executable code are combined to produce haploid executable code. If the desired ploidy is two, then the first candidate executable code and the second candidate executable code are combined to produce diploid executable code. 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: May 12, 2021
    Date of Patent: May 23, 2023
    Assignee: Diveplane Corporation
    Inventor: Christopher James Hazard
  • Patent number: 11630987
    Abstract: Technologies for a neural belief reasoner model generative models that specifies belief functions are described. Aspects include receiving, by a device operatively coupled to a processor, a request for a belief function, and processing, by the device, the request for the belief function in the generative model based on trained probability parameters and a minimization function to determine a generalized belief function defined by fuzzy sets. Data corresponding to the generalized belief function is output, e.g., as a belief value and plausibility value.
    Type: Grant
    Filed: April 30, 2018
    Date of Patent: April 18, 2023
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventor: Haifeng Qian
  • Patent number: 11599783
    Abstract: A function creation method is disclosed. The method comprises defining one or more database function inputs, defining cluster processing information, defining a deep learning model, and defining one or more database function outputs. A database function is created based at least in part on the one or more database function inputs, the cluster set-up information, the deep learning model, and the one or more database function outputs. In some embodiments, the database function enables a non-technical user to utilize deep learning models.
    Type: Grant
    Filed: May 31, 2017
    Date of Patent: March 7, 2023
    Assignee: Databricks, Inc.
    Inventors: Sue Ann Hong, Shi Xin, Timothee Hunter, Ali Ghodsi
  • Patent number: 11521090
    Abstract: A model requester node, which is an edge node of a cloud computing network, generates a specification of a machine learning model, distributes the specification to a plurality of other edge nodes, and receives replies to the specification from the plurality of other edge nodes. In response to the replies, the model requester node identifies a set of participating edge nodes based on a learning utility and a cost estimate of each of the plurality of other edge nodes. The model requester node then trains the machine learning model, without exchanging training data among the model requester node and the participating edge nodes, by repeatedly: distributing most recent parameters of the machine learning model to the participating edge nodes; receiving updates to the most recent parameters from the participating edge nodes; and establishing new parameters for the machine learning model by aggregating the updates from the participating edge nodes.
    Type: Grant
    Filed: August 9, 2018
    Date of Patent: December 6, 2022
    Assignee: International Business Machines Corporation
    Inventors: Shiqiang Wang, Theodoros Salonidis
  • Patent number: 11354565
    Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.
    Type: Grant
    Filed: December 22, 2017
    Date of Patent: June 7, 2022
    Assignee: salesforce.com, inc.
    Inventors: Alexander Rosenberg Johansen, Bryan McCann, James Bradbury, Richard Socher