Patents Examined by Brent Johnston Hoover
  • Patent number: 11222278
    Abstract: A method of estimating one or more conditional probabilities may be provided. A method may include determining one or more states based on user input, and determining a similarity measurement between at least one state pair of one or more state pairs. The method may further include determining a likelihood of probability for the at least one state pair of the one or more state pairs. Moreover, the method may include estimating a conditional probability for the at least one state pair of the one or more state pairs based on the determined likelihood of probability and the determined one or more states.
    Type: Grant
    Filed: September 8, 2016
    Date of Patent: January 11, 2022
    Assignee: FUJITSU LIMITED
    Inventors: Ramya Malur Srinivasan, Ajay Chander
  • Patent number: 11216744
    Abstract: In one embodiment, a machine learning server in a computer network determines a plurality of computing features shared across a given set of computing products, and collects, from each computing product of the given set, problem-solution data for each computing feature of the plurality of computing features. Problem-solution data is indicative of problems related to a respective computing feature, attempted solution actions for the problems, and outcomes of the attempted solutions on the problem. The machine learning server updates a machine learning model of suggested solutions for computing-feature-specific problems based on the collected problem-solution data, and provides, based on the machine learning model, a particular suggested solution for a particular computing-feature-specific problem to a particular computing product.
    Type: Grant
    Filed: April 28, 2017
    Date of Patent: January 4, 2022
    Assignee: Cisco Technology, Inc.
    Inventors: Kabiraj Sethi, Ajith Chandran, Amitesh Shukla
  • Patent number: 11200493
    Abstract: A computer-implemented method comprising: training a pre-trained neural network that comprises: an input layer; a plurality of hidden layers, wherein each of the plurality of hidden layers has one or more nodes, wherein each of said one or more nodes has an associated weight trained based on data from a source domain; and an output layer. Training the pre-trained neural network comprises: introducing at least one additional layer to the plurality of hidden layers, wherein said additional layer has one or more nodes having associated weights; keeping weights of the nodes in the plurality of hidden layers of the pre-trained neural network unchanged; inputting data from a target domain to the input layer; and adjusting weights of the one or more nodes in the at least one additional layer based on features obtained at the output layer.
    Type: Grant
    Filed: February 14, 2020
    Date of Patent: December 14, 2021
    Assignee: Advanced New Technologies Co., Ltd.
    Inventor: Jianshu Li
  • Patent number: 11194834
    Abstract: Aspects of the present disclosure relate to data visualization, and more specifically, to technology that automatically visualizes various analytics and predictions generated for mass participation endurance events, or other events of interest.
    Type: Grant
    Filed: April 3, 2017
    Date of Patent: December 7, 2021
    Assignee: Northwestern University
    Inventors: Karen R. Smilowitz, George T. Chiampas, Taylor G. Hanken, Rachel G. Lin, Ryan W. Rose, Bruno P. Velazquez, Samuel H. Young
  • Patent number: 11176485
    Abstract: In one embodiment, a method includes a system building a first machine-learning module and one or more secondary machine-learning modules for operating with an application. The first and second modules may be configured to utilize, in operation, particular types of processing hardware, respectively. The system may receive from a client device a request to download the application, and in response send to the device the application with the first module. The system may then receive another request to download a selected one of the secondary modules, which may be selected based on a determination by the application running on the device that the associated type of processing hardware is available. In response, the system may send the selected module to the device. The application may be configured to selectively use the first module or the selected module to perform an operation.
    Type: Grant
    Filed: September 6, 2017
    Date of Patent: November 16, 2021
    Assignee: Facebook Inc.
    Inventors: Yangqing Jia, Hsiu-Tung Alex Yu, Joel Curtis McCall, Frank James Eisenhart, III, Andrew M. Rogers
  • Patent number: 11164110
    Abstract: A testing framework associated with a decision metaphor model tool reads table profile files to generate requests for a test of a decision metaphor. The testing framework sends the requests for the test to a decision engine and receives responses for the requests for comparison against expected values and possible errors. The testing framework also outputs an output file that includes a result of the test, where the output file is formatted in a computer-displayable and user-readable graphical format.
    Type: Grant
    Filed: November 25, 2019
    Date of Patent: November 2, 2021
    Assignee: FAIR ISAAC CORPORATION
    Inventor: Pradeep Niranjan Ballal
  • Patent number: 11164072
    Abstract: Devices and methods for systolically processing data according to a neural network. A first processing unit performs computations of a first node of a first layer to generate a first output and attaches a first tag to the first output identifying the first processing unit. A second processing unit performs computations of a second node of the first layer to generate a second output and attaches a second tag to the second output identifying the second processing unit. A third processing unit performs computations of a third node of a second layer including receiving the first and second outputs, using a first convolutional engine to perform a first convolution on the first output using a first weight identified by the first tag, and using a second convolutional engine of to perform a second convolution on the second output using a second weight identified by the second tag.
    Type: Grant
    Filed: May 16, 2018
    Date of Patent: November 2, 2021
    Assignee: Western Digital Technologies, Inc.
    Inventor: Luiz M. Franca-Neto
  • Patent number: 11164081
    Abstract: The present disclosure relates to systems and methods for generating and using a singular ensemble model.
    Type: Grant
    Filed: October 17, 2018
    Date of Patent: November 2, 2021
    Assignee: Capital One Services, LLC
    Inventors: Vincent Pham, Mark Watson, Jeremy Goodsitt, Reza Farivar, Austin Walters, Kenneth Taylor, Fardin Abdi Taghi Abad, Anh Truong
  • Patent number: 11164074
    Abstract: At least a subset of first processing units of a first arrangement of a first systolic processing chip is assigned to a first layer of a neural network and at least a subset of second processing units of a second arrangement of the first systolic processing chip is assigned to a second layer of the neural network. At least a subset of third processing units of a third arrangement of a second systolic processing chip is assigned to a third layer of the neural network. Input data is processed using the subset of the first processing units to generate first activation output values. The first activation output values are systollically pulsed to the subset of the second processing units and processed to generate second activation output values. The second activation output values are processed using the subset of the third processing units of the second systolic processing chip.
    Type: Grant
    Filed: May 16, 2018
    Date of Patent: November 2, 2021
    Assignee: Western Digital Technologies, Inc.
    Inventor: Luiz M. Franca-Neto
  • Patent number: 11151451
    Abstract: A data processing method in a data processing device is provided. First to-be-processed data input into a neural network are obtained. Iterative training is performed on the neural network for a first preset number of times by using first target data in the first to-be-processed data, to obtain a seed model of the neural network. First newly added data generated after an elapse of time corresponding to the first time window is obtained, and the first newly added data and the first to-be-processed data are combined into second to-be-processed data. Iterative training is performed on the seed model for a second preset number of times by using second target data in the second to-be-processed data, to obtain a first incremental model of the neural network. A first preset area overlaps between the second time window and the first time window. The first incremental model online is published.
    Type: Grant
    Filed: July 3, 2018
    Date of Patent: October 19, 2021
    Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
    Inventors: Yi Li, Xing Jin, Shubin Zhang, Zhimao Guo, Wei Xue
  • Patent number: 11151202
    Abstract: A system and a computer program product are provided for evaluating question-answer pairs in an answer key by generating a predicted answer to a test question based on the answer key modification history for comparison matching against a generated answer that is generated in response to the test question, and then comparing the predicted answer and generated answer to determine an accuracy score match indication therebetween so as to present an indication that the answer key may have a problem if there is a match between the predicted answer and generated answer.
    Type: Grant
    Filed: October 31, 2017
    Date of Patent: October 19, 2021
    Assignee: International Business Machines Corporation
    Inventors: Edward G. Katz, John A. Riendeau, Sean T. Thatcher, Alexander C. Tonetti
  • Patent number: 11151440
    Abstract: Aspects provide human detector devices based on neuronal response, wherein the devices are configured to obtain electroencephalogram signals from an entity during a presentation of first sensory information to the entity, and compares the obtained electroencephalogram signals to each of a plurality of trained electroencephalogram signal profile portions that are labeled as the first sensory information that represent electroencephalogram signals most commonly generated by different persons as a function of presentation to the persons of sensory information corresponding to the first sensory information. Thus, the configured processor determines whether the entity is a human as a function of a strength of match of the obtained electroencephalogram signals to ones of the trained electroencephalogram signal profile portions labeled as first sensory information that have highest most-common weightings.
    Type: Grant
    Filed: November 8, 2017
    Date of Patent: October 19, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Cesar Augusto Rodriguez Bravo, Erik Rueger
  • Patent number: 11144602
    Abstract: A system and a computer program product are provided for evaluating question-answer pairs in an answer key by generating a predicted answer to a test question based on the answer key modification history for comparison matching against a generated answer that is generated in response to the test question, and then comparing the predicted answer and generated answer to determine an accuracy score match indication therebetween so as to present an indication that the answer key may have a problem if there is a match between the predicted answer and generated answer.
    Type: Grant
    Filed: August 31, 2017
    Date of Patent: October 12, 2021
    Assignee: International Business Machines Corporation
    Inventors: Edward G. Katz, John A. Riendeau, Sean T. Thatcher, Alexander C. Tonetti
  • Patent number: 11144817
    Abstract: A device for determining a CNN model for a database according to the present disclosure includes: a selecting unit configured to select at least two CNN models from multiple CNN models whose classification capacity is known; a fitting unit configured to fit, based on the classification capacity and first parameters of the at least two CNN models, a curve taking classification capacity and a first parameter as variables; a predicting unit configured to predict, based on the curve, a first parameter of other CNN models; and a determining unit configured to determine a CNN model applicable to the database from the multiple CNN models. With the device and the method according to the present disclosure, there is no need to train all the CNN models, thereby greatly reducing the amount of computation and simplifying the process of designing the CNN model.
    Type: Grant
    Filed: October 31, 2017
    Date of Patent: October 12, 2021
    Assignee: FUJITSU LIMITED
    Inventors: Li Sun, Song Wang, Wei Fan, Jun Sun
  • Patent number: 11138514
    Abstract: An apparatus and method are provided for review-based machine learning. Included are a non-transitory memory storing instructions and one or more processors in communication with the non-transitory memory. The one or more processors execute the instructions to receive first data, generate a plurality of first features based on the first data, and identify a first set of labels for the first data. A first model is trained using the first features and the first set of labels. The first model is reviewed to generate a second model, by receiving a second set of labels for the first data, and reusing the first features with the second set of labels in connection with training the second model.
    Type: Grant
    Filed: March 23, 2017
    Date of Patent: October 5, 2021
    Assignee: Futurewei Technologies, Inc.
    Inventors: Luhui Hu, Hui Zang, Ziang Hu
  • Patent number: 11133098
    Abstract: A system and method for the preparation of food is provided. The system utilizes a nutritional information module which allows nutritional information to be aggregated for an entire menu along with portion information and the ability to adjust serving weight by preferred caloric value for any individual recipe. The system also utilizes a scheduler module which compiles task information for any individual recipe items per paragraph. The schedule module may have any plurality of different task times including a passive task time and/or an active task time and may time stamp the time it takes to prepare any specific recipe. The schedule may compile cook information and store information in a memory bank for analysis of the individual cook's cooking style and cook time. Moreover, the system may also provide a feedback module which uses personal time co-efficients to predict how long it should take for any particular menu choice preparation.
    Type: Grant
    Filed: January 24, 2017
    Date of Patent: September 28, 2021
    Inventor: Chet Harrison
  • Patent number: 11120362
    Abstract: Aspects of the present disclosure relate to identifying a product in a document. A computing machine accesses a product mention in a scientific or research-related text, the product mention including one or more attribute values for a plurality of attributes, each attribute being associated with either a single attribute value or no attribute value. The computing machine determines that the attribute values of the product mention correspond to two or more candidate product matches in a product directory. The computing machine identifies, based at least in part on stored data related to the scientific or research-related text, a product match from among the candidate product matches, the product match corresponding to the product mention in the scientific or research-related text. The computing machine provides an output of the product match for storage in conjunction with the product mention in the scientific or research-related text.
    Type: Grant
    Filed: June 28, 2017
    Date of Patent: September 14, 2021
    Assignee: ResearchGate GmbH
    Inventors: Viacheslav Zholudev, Darren Alvares, Niall Kelly, Tilo Mathes, Axel Tölke, Vincenz Priesnitz, Thoralf Klein
  • Patent number: 11120336
    Abstract: According to an exemplary embodiment of the present disclosure, disclosed is a computer program stored in a computer readable storage medium. When the computer program is executed in one or more processors, the computer program performs the following method for anomaly detection of data using a network function, and the method includes: generating an anomaly detection model including a plurality of anomaly detection sub models including a trained network function using a plurality of training data sub sets included in the training data set; calculating input data using at least one of the plurality of generated anomaly detection sub models; and determining whether there is an anomaly in the input data based on output data for input data of at least one of the plurality of generated anomaly detection sub models and the input data.
    Type: Grant
    Filed: September 10, 2020
    Date of Patent: September 14, 2021
    Assignee: MAKINAROCKS CO., LTD.
    Inventors: Andre S. Yoon, Yongsub Lim, Sangwoo Shim
  • Patent number: 11120348
    Abstract: The present invention relates generally to identifying relationships between items. Certain embodiments of the present invention are configurable to identify the probability that a certain event will occur by identifying relationships between items. Certain embodiments of the present invention provide an improved supervised machine learning system.
    Type: Grant
    Filed: March 16, 2020
    Date of Patent: September 14, 2021
    Assignee: University of Massachusetts
    Inventor: Hong Yu
  • Patent number: 11113596
    Abstract: Data is input to one of a plurality of neural networks. Each of the plurality of neural networks is to be of a different size. A propagation time is determined for the inputted data. The propagation time relates to a time for the inputted data to propagate through one of the plurality of neural networks. One of the plurality of neural networks is selected based on the propagation time.
    Type: Grant
    Filed: May 22, 2015
    Date of Patent: September 7, 2021
    Inventors: David Pye, Christopher Waple