Patents Examined by Omar F. Fernandez Rivas
  • Patent number: 11443231
    Abstract: A processing device can establish a vector-trained, deep learning model to produce software dependency recommendations. The processing device can build a list of software dependencies and corresponding metatags for each of the software dependencies, and generate a probability distribution from the list. The processing device can sample the probability distribution to produce a latent vector space that includes representative vectors for the software dependencies. The processing device can train a hybrid deep learning model to produce software dependency recommendations using the latent vector space as well as collaborative data for the software dependencies.
    Type: Grant
    Filed: October 19, 2018
    Date of Patent: September 13, 2022
    Assignee: RED HAT, INC.
    Inventors: SriKrishna Paparaju, Avishkar Gupta
  • Patent number: 11442416
    Abstract: A plant control supporting apparatus includes a segment selector configured to select, from among a plurality of segments defined in a plant, a segment for which learning for acquiring an optimal value of at least one parameter representing an operation state is executed, a reward function definer configured to define a reward function used for the learning, a parameter extractor configured to extract at least one parameter that is a target for the learning in the selected segment on the basis of input and output information of a device used in the plant and segment information representing a configuration of a device included in the selected segment, and a learner configured to perform the learning for acquiring the optimal value for each segment on the basis of the reward function and the at least one parameter.
    Type: Grant
    Filed: July 10, 2018
    Date of Patent: September 13, 2022
    Assignee: Yokogawa Electric Corporation
    Inventors: Hiroaki Kanokogi, Go Takami
  • Patent number: 11436479
    Abstract: A system and method are shown for transferring weight information to analog non-volatile memory elements wherein the programming pulse duration is directly proportional to the difference in weights. Furthermore, the system and method avoid weight transfers when the weights are already well-matched.
    Type: Grant
    Filed: August 9, 2018
    Date of Patent: September 6, 2022
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Pritish Narayanan, Geoffrey W Burr
  • Patent number: 11429865
    Abstract: A system and method design and optimize neural networks. The system and method include a data store that stores a plurality of gene vectors that represent diverse and distinct neural networks and an evaluation queue stored with the plurality of gene vectors. Secondary nodes construct, train, and evaluate the neural network and automatically render a plurality of fitness values asynchronously. A primary node executes a gene amplification on a select plurality of gene vectors, a crossing-over of the amplified gene vectors, and a mutation of the crossing-over gene vectors automatically and asynchronously, which are then transmitted to the evaluation queue. The process continuously repeats itself by processing the gene vectors inserted into the evaluation queue until a fitness level is reached, a network's accuracy level plateaus, a processing time period expires, or when some stopping condition or performance metric is met or exceeded.
    Type: Grant
    Filed: February 1, 2019
    Date of Patent: August 30, 2022
    Assignee: UT-BATTELLE, LLC
    Inventors: Robert M. Patton, Steven R. Young, Derek C. Rose, Thomas P. Karnowski, Seung-Hwan Lim, Thomas E. Potok, J. Travis Johnston
  • Patent number: 11429848
    Abstract: In disclosed approaches of neural network processing, a host computer system copies an input data matrix from host memory to a shared memory for performing neural network operations of a first layer of a neural network by a neural network accelerator. The host instructs the neural network accelerator to perform neural network operations of each layer of the neural network beginning with the input data matrix. The neural network accelerator performs neural network operations of each layer in response to the instruction from the host. The host waits until the neural network accelerator signals completion of performing neural network operations of layer i before instructing the neural network accelerator to commence performing neural network operations of layer i+1, for i?1. The host instructs the neural network accelerator to use a results data matrix in the shared memory from layer i as an input data matrix for layer i+1 for i?1.
    Type: Grant
    Filed: October 17, 2017
    Date of Patent: August 30, 2022
    Assignee: XILINX, INC.
    Inventors: Aaron Ng, Elliott Delaye, Jindrich Zejda, Ashish Sirasao
  • Patent number: 11416764
    Abstract: Automatically generating and/or automatically transmitting a status of a user. The status is transmitted for presentation to one or more additional users via corresponding computing device(s) of the additional user(s). Some implementations are directed to determining both: a status of a user, and a predicted duration of that status; and generating a status notification that includes the status and that indicates the predicted duration. Some implementations are additionally or alternatively directed to utilizing at least one trust criterion in determining whether to provide a status notification of a user to an additional user and/or in determining what status notification to provide to the additional user. Some implementations are additionally or alternatively directed to training and/or use of machine learning model(s) in determining a status of a user and/or a predicted duration of that status.
    Type: Grant
    Filed: January 23, 2017
    Date of Patent: August 16, 2022
    Assignee: GOOGLE LLC
    Inventors: Sebastian Millius, Sandro Feuz
  • Patent number: 11410023
    Abstract: A computer-implemented method is provided for modified Lexicographic Reinforcement Learning. The computer implemented method includes obtaining, by a hardware processor, a sequence of tasks. Each of the tasks corresponds to, and has a one-to-one correspondence with, a respective award from among set of rewards. The method further includes performing, by the hardware processor for each of the tasks, reinforcement learning and deep learning for both of (i) one or more policies and (ii) one or more value functions, with a plurality of sets of samples. A plurality of solutions in a form of the one or more policies and the one or more value functions are parametrized by a single neural network with a selector which selects an input of the single neural network from among the plurality of sets of samples.
    Type: Grant
    Filed: March 1, 2019
    Date of Patent: August 9, 2022
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Don Joven R. Agravante, Asim Munawar, Ryuki Tachibana
  • Patent number: 11403554
    Abstract: An artificial intelligence testing apparatus may include processing circuitry configured to execute instructions that, when executed, cause the apparatus to create initial sample points based on a simulation received at the apparatus, and employ cyclic evaluation of the simulation until a stopping criteria is met. Employing the cyclic evaluation includes running the simulation at design points for a set of queries associated with a current iteration of the cyclic evaluation, training a set of meta-models of parameter space associated with the simulation for the current iteration, computing a set of metrics for the current iteration, and employing a selected sampling approach to select a new set of design points for a next iteration of the cyclic evaluation.
    Type: Grant
    Filed: January 29, 2019
    Date of Patent: August 2, 2022
    Assignee: The Johns Hopkins University
    Inventors: Matthew B. Wagner, David R. Witman, Peter A. Sevich, Patrick J. Trainor, Liam R. Cusack, William A. Patterson
  • Patent number: 11403532
    Abstract: A method for finding a solution to a problem is provided. The method includes storing candidate individuals in a candidate pool and evolving the candidate individuals by performing steps including (i) testing each of the candidate individuals to obtain test results, (ii) assigning a performance measure to the tested candidate individuals, (iii) discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and (iv) adding, to the candidate pool, a new candidate individual procreated from candidate individuals remaining in the candidate pool.
    Type: Grant
    Filed: March 2, 2018
    Date of Patent: August 2, 2022
    Assignee: Cognizant Technology Solutions U.S. Corporation
    Inventors: Risto Miikkulainen, Hormoz Shahrzad, Nigel Duffy, Philip M. Long
  • Patent number: 11397894
    Abstract: A method for pruning a neural network includes initializing a plurality of threshold values respectively corresponding to a plurality of layers included in the neural network; selecting one of the plurality of layers; adjusting the threshold value of the selected layer; and adjusting a plurality of weights respectively corresponding to a plurality of synapses included in the neural network.
    Type: Grant
    Filed: June 28, 2018
    Date of Patent: July 26, 2022
    Assignees: SK hynix Inc., Seoul National University R&DB Foundation
    Inventors: Seunghwan Cho, Sungjoo Yoo, Youngjae Jin
  • Patent number: 11392413
    Abstract: A location history manager may be configured to determine a location history associated with a user, and a resource usage manager may be configured to determine a computing resource usage history associated with the user. A location monitor may be configured to determine a location of the user. A resource predictor may be configured to generate a prediction of a computing resource, based on the location history, the computing resource usage history, and the location. A resource provider may be configured to provide the computing resource, based on the prediction.
    Type: Grant
    Filed: September 5, 2018
    Date of Patent: July 19, 2022
    Assignee: Google LLC
    Inventors: Andrew Bowers, Kevin Tom, Amy Han
  • Patent number: 11373086
    Abstract: Systems, methods, and computer readable media related to determining one or more responses to provide that are responsive to an electronic communication that is generated through interaction with a client computing device. For example, determining one or more responses to provide for presentation to a user as suggestions for inclusion in a reply to an electronic communication sent to the user. Some implementations are related to training and/or using separate input and response neural network models for determining responses for electronic communications. The input neural network model and the response neural network model can be separate, but trained and/or used cooperatively.
    Type: Grant
    Filed: March 31, 2017
    Date of Patent: June 28, 2022
    Assignee: GOOGLE LLC
    Inventors: Brian Strope, Yun-hsuan Sung, Matthew Henderson, Rami Al-Rfou′, Raymond Kurzweil
  • Patent number: 11366998
    Abstract: Systems and techniques for neuromorphic accelerator multitasking are described herein. A neuron address translation unit (NATU) may receive a spike message. Here, the spike message includes a physical neuron identifier (PNID) of a neuron causing the spike. The NATU may then translate the PNID into a network identifier (NID) and a local neuron identifier (LNID). The NATU locates synapse data based on the NID and communicates the synapse data and the LNID to an axon processor.
    Type: Grant
    Filed: March 27, 2018
    Date of Patent: June 21, 2022
    Assignee: Intel Corporation
    Inventors: Seth Pugsley, Berkin Akin
  • Patent number: 11354594
    Abstract: Methods and systems for determining an optimized setting for one or more process parameters of a machine learning training process are described. One of the methods includes processing a current network input using a recurrent neural network in accordance with first values of the network parameters to obtain a current network output, obtaining a measure of the performance of the machine learning training process with an updated setting defined by the current network output, and generating a new network input that includes (i) the updated setting defined by the current network output and (ii) the measure of the performance of the training process with the updated setting defined by the current network output.
    Type: Grant
    Filed: October 14, 2019
    Date of Patent: June 7, 2022
    Assignee: DeepMind Technologies Limited
    Inventors: Yutian Chen, Joao Ferdinando Gomes de Freitas
  • Patent number: 11347191
    Abstract: A weld production knowledge system for processing welding data collected from one of a plurality of welding systems, the weld production knowledge system comprising a communication interface communicatively coupled with a plurality of welding systems situated at one or more physical locations. The communication interface may be configured to receive, from one of said plurality of welding systems, welding data associated with a weld. The weld production knowledge system may comprise an analytics computing platform operatively coupled with the communication interface and a weld data store. The weld data store employs a dataset comprising (1) welding process data associated with said one or more physical locations, and/or (2) weld quality data associated with said one or more physical locations. The analytics computing platform may employ a weld production knowledge machine learning algorithm to analyze the welding data vis-à-vis the weld data store to identify a defect in said weld.
    Type: Grant
    Filed: July 28, 2016
    Date of Patent: May 31, 2022
    Assignee: Illinois Tool Works Inc.
    Inventor: Christopher Hsu
  • Patent number: 11328214
    Abstract: A method and associated systems provide real-time response to a request received from a real-time system like a self-driving vehicle or a device that communicates interactively with its environment. The response is selected from a set of candidate feasible responses by a group of computerized agents that each sort the feasible responses in order of that agent's specific preferences, based on that agent's particular priorities or expertise. The agents then reconcile their differences through an iterative procedure. During each iteration, each agent decides whether to retain its current preferences or to adopt the preferences of another agent. This decision is made by determining which preferences are most similar to that agent's own initial preferences, and by which preferences would be most useful in helping to achieve that agent's particular goals. When the agents reach consensus, the group's most-preferred response is returned quickly enough to provide real-time, interactive response.
    Type: Grant
    Filed: September 28, 2017
    Date of Patent: May 10, 2022
    Assignee: Kyndryl, Inc.
    Inventors: Sougata Mukherjea, Amit A. Nanavati, Ramasuri Narayanam, Gyana Ranjan Parija
  • Patent number: 11315041
    Abstract: Methods, systems, and apparatus, including computer-readable media, for machine learning in a multi-tenant data sharing platform. In some implementations, a server system provides a multi-tenant data sharing platform configured to selectively use stored data collected for different tenant organizations according to policy data for the respective tenant organizations. A request from one organization is received to perform a machine learning task involving a data set of a different tenant organization. The server system uses stored policy data to determine an applicable data policy, and based on the determination, the server system performs the machine learning task and provides the results of the machine learning task.
    Type: Grant
    Filed: September 22, 2020
    Date of Patent: April 26, 2022
    Assignee: VigNet Incorporated
    Inventors: Praduman Jain, Dave Klein, Josh Schilling, Addisu Alemu
  • Patent number: 11308148
    Abstract: Techniques are shown for generating image frames from a media presentation. In one embodiment a computer implemented method is provided. The method includes identifying, by a processing device, image frames from a media presentation comprising a plurality of image frames. Candidate thumbnails are selected from the identified image frames. A probability is determined that a selected candidate thumbnail with a success ranking higher than other selected thumbnails is an optimum candidate thumbnail for representing the media presentation in view of a relationship between the success ranking of the selected candidate thumbnail and the success rankings of the other selected candidate thumbnails. Thereupon, data for displaying the selected candidate thumbnail to a user as a representative of the media presentation is provided by the processing device.
    Type: Grant
    Filed: February 12, 2016
    Date of Patent: April 19, 2022
    Assignee: Google LLC
    Inventors: Justin Lewis, Henry Benjamin, Stanley Charles Ross Wolf
  • Patent number: 11308391
    Abstract: In one embodiment, a system to accelerate batch-normalized convolutional neural network (CNN) models is disclosed. The system extracts a plurality of first groups of layers from a first CNN model, each group of the first groups having a first convolutional layer and a first batch-norm layer. For each group of the plurality of first groups, the system calculates a first scale vector and a first shift vector based on the first batch-norm layer, and generates a second convolutional layer representing the corresponding group of the plurality of first groups based on the first convolutional layer and the first scale and the first shift vectors. The system generates an accelerated CNN model based on the second convolutional layer corresponding to the plurality of the first groups, such that the accelerated CNN model is utilized subsequently to classify an object perceived by an autonomous driving vehicle (ADV).
    Type: Grant
    Filed: March 6, 2017
    Date of Patent: April 19, 2022
    Assignee: BAIDU USA LLC
    Inventors: Zhenhua Yu, Xiao Bo, Jun Zhou, Weide Zhang, Tony Han
  • Patent number: 11295204
    Abstract: Architectures for multicore neuromorphic systems are provided. In various embodiments, a neural network description is read. The neural network description describes a plurality of logical cores. A plurality of precedence relationships are determined among the plurality of logical cores. Based on the plurality of precedence relationships, a schedule is generated that assigns the plurality of logical cores to a plurality of physical cores at a plurality of time slices. Based on the schedule, the plurality of logical cores of the neural network description are executed on the plurality of physical cores.
    Type: Grant
    Filed: January 6, 2017
    Date of Patent: April 5, 2022
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventor: Dharmendra S. Modha