Patents Examined by Alexey Shmatov
  • Patent number: 10970621
    Abstract: A color predictor is provided to predict the color of a food item given its formula comprising the ingredients and its quantities. The color predictor may utilize machine learning algorithms and a set of recipe data to train the color predictor. The color predictor can also be used by a color recommender to recommend changes in the given formula to achieve a target color.
    Type: Grant
    Filed: October 8, 2019
    Date of Patent: April 6, 2021
    Assignee: NOTCO DELEWARE, LLC
    Inventors: Karim Pichara, Pablo Zamora, Matías Muchnick, Yoni Lerner, Osher Lerner
  • Patent number: 10963817
    Abstract: Certain aspects involve training tree-based machine-learning models for computing predicted responses and generating explanatory data for the models. For example, independent variables having relationships with a response variable are identified. Each independent variable corresponds to an action or observation for an entity. The response variable has outcome values associated with the entity. Splitting rules are used to generate the tree-based model, which includes decision trees for determining relationships between independent variables and a predicted response associated with the response variable. The tree-based model is iteratively adjusted to enforce monotonicity with respect to representative response values of the terminal nodes. For instance, one or more decision trees are adjusted such that one or more representative response values are modified and a monotonic relationship exists between each independent variable and the response variable.
    Type: Grant
    Filed: October 30, 2017
    Date of Patent: March 30, 2021
    Assignee: EQUIFAX INC.
    Inventors: Lewis Jordan, Matthew Turner, Finto Antony
  • Patent number: 10963797
    Abstract: A system for remote monitoring of a machine is provided. The system includes a data store to store machine data associated with an operation of the machine. The system includes an analyzer comprising a plurality of analytics engines to analyze the machine data. The analyzer selects one or more analytics engines based at least on one of machine data and a type of the machine. The analyzer is further configured to analyze machine data using the selected one or more analytics engines and to determine a plurality of exceptions. The system includes a rules engine to process at least two of the plurality of exceptions and determine a smart exception, wherein the smart exception is a hierarchical combination of the at least two of the plurality of exceptions. The system includes an interface to display a notification to a user in the event of a smart exception.
    Type: Grant
    Filed: February 9, 2017
    Date of Patent: March 30, 2021
    Assignee: Caterpillar Inc.
    Inventors: Bhavin J. Vyas, Ankitkumar P. Dhorajiya, Vishnu G. Selvaraj, William D. Hankins
  • Patent number: 10963783
    Abstract: Technologies for optimization of machine learning training include a computing device to train a machine learning network with a training algorithm that is configured with configuration parameters. The computing device may perform many training instances in parallel. The computing device captures a time series of partial accuracy values from the training. Each partial accuracy value is indicative of machine learning network accuracy at an associated training iteration. The computing device inputs the configuration parameters to a feed-forward neural network to generate a representation and inputs the representation to a recurrent neural network. The computing device trains the feed-forward neural network and the recurrent neural network against the partial accuracy values. The computing device optimizes the feed-forward neural network and the recurrent neural network to determine optimized configuration parameters.
    Type: Grant
    Filed: February 19, 2017
    Date of Patent: March 30, 2021
    Assignee: INTEL CORPORATION
    Inventors: Lev Faivishevsky, Amitai Armon
  • Patent number: 10963779
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing operations using data from a data source. In one aspect, a method includes a neural network system including a controller neural network configured to: receive a controller input for a time step and process the controller input and a representation of a system input to generate: an operation score distribution that assigns a respective operation score to an operation and a data score distribution that assigns a respective data score in the data source. The neural network system can also include an operation subsystem configured to: perform operations to generate operation outputs, wherein at least one of the operations is performed on data in the data source, and combine the operation outputs in accordance with the operation score distribution and the data score distribution to generate a time step output for the time step.
    Type: Grant
    Filed: November 11, 2016
    Date of Patent: March 30, 2021
    Assignee: Google LLC
    Inventors: Quoc V. Le, Ilya Sutskever, Arvind Neelakantan
  • Patent number: 10957306
    Abstract: Techniques for generating a personality trait model are described. According to an example, a system is provided that can generate text data and linguistic data, and apply psycholinguistic data to the text data and the linguistic data, resulting in updated text data and updated linguistic data. The system is further operable to combine the updated text data with the updated linguistic data to generate a personality trait model. In various embodiments, the personality trait model can be trained and updated as additional data is received from various inputs.
    Type: Grant
    Filed: November 16, 2016
    Date of Patent: March 23, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Yue Chen, Lin Luo, Qin Shi, Zhong Su, Changhua Sun, Enliang Xu, Shiwan Zhao
  • Patent number: 10949736
    Abstract: Systems, apparatus and methods are described including operations for a flexible neural network accelerator.
    Type: Grant
    Filed: November 3, 2016
    Date of Patent: March 16, 2021
    Assignee: Intel Corporation
    Inventors: Michael E Deisher, Ohad Falik
  • Patent number: 10936821
    Abstract: An approach is provided for an information handling system that includes a processor and a memory to improve the quality of question-answer sets used as inputs to a question-answering (QA) system. In the approach, a question-answer pair is analyzed using natural language processing (NLP) components. Some of the NLP components may be taken from the QA system whose input is being analyzed The question-answer pair includes a question and an answer to the question. Based on the analysis, one or more shortcomings of the question-answer pair are identified. The shortcomings relate to an ability of the target QA system to analyze the question. A human-readable feedback is provided to a user. The feedback recommends one or more possible actions to address the identified shortcomings.
    Type: Grant
    Filed: July 5, 2016
    Date of Patent: March 2, 2021
    Assignee: International Business Machines Corporation
    Inventors: Jacqueline T. Barbetta, David C. Fallside, Drew A. Logsdon, Peter J. Parente
  • Patent number: 10936949
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model. In one aspect, a method includes receiving training data for training the machine learning model on a plurality of tasks, where each task includes multiple batches of training data. A task is selected in accordance with a current task selection policy. A batch of training data is selected from the selected task. The machine learning model is trained on the selected batch of training data to determine updated values of the model parameters. A learning progress measure that represents a progress of the training of the machine learning model as a result of training the machine learning model on the selected batch of training data is determined. The current task selection policy is updated using the learning progress measure.
    Type: Grant
    Filed: July 10, 2019
    Date of Patent: March 2, 2021
    Assignee: DeepMind Technologies Limited
    Inventors: Marc Gendron-Bellemare, Jacob Lee Menick, Alexander Benjamin Graves, Koray Kavukcuoglu, Remi Munos
  • Patent number: 10929761
    Abstract: Systems and methods for automatically detecting annotation discrepancies in annotated training data samples and repairing the annotated training data samples for a machine learning-based automated dialogue system include evaluating a corpus of a plurality of distinct training data samples; identifying one or more of a slot span defect and a slot label defect of a target annotated slot span of a target training data sample of the corpus based on the evaluation; and automatically correcting one or more annotations of the target annotated slot span based on the identified one or more of the slot span defect and the slot label defect.
    Type: Grant
    Filed: June 8, 2020
    Date of Patent: February 23, 2021
    Assignee: Clinic, Inc.
    Inventors: Stefan Larson, Anish Mahendran, Parker Hill, Jonathan K. Kummerfeld, Michael A. Laurenzano, Lingjia Tang, Jason Mars
  • Patent number: 10922604
    Abstract: In one respect, there is provided a system for training a neural network adapted for classifying one or more instruction sequences. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one processor provides operations including: training, based at least on training data, a machine learning model to detect one or more predetermined interdependencies amongst a plurality of tokens in the training data; and providing the trained machine learning model to enable classification of one or more instruction sequences. Related methods and articles of manufacture, including computer program products, are also provided.
    Type: Grant
    Filed: November 7, 2016
    Date of Patent: February 16, 2021
    Assignee: Cylance Inc.
    Inventors: Xuan Zhao, Matthew Wolff, John Brock, Brian Wallace, Andy Wortman, Jian Luan, Mahdi Azarafrooz, Andrew Davis, Michael Wojnowicz, Derek Soeder, David Beveridge, Eric Petersen, Ming Jin, Ryan Permeh
  • Patent number: 10915808
    Abstract: A computer implemented method for training a neural network to capture a structural feature specific to a set of chemical compounds is disclosed. In the method, the computer system reads an expression describing a structure of the chemical compound for each chemical compound in the set and enumerates one or more combinations of a position and a type of a structural element appearing in the expression for each chemical compound in the set. The computer system also generates training data based on the one or more enumerated combinations for each chemical compound in the set. The training data includes one or more values with a length, each of which indicates whether or not a corresponding type of the structural element appears at a corresponding position for each combination. Furthermore, the computer system trains the neural network based on the training data for the set of the chemical compounds.
    Type: Grant
    Filed: July 5, 2016
    Date of Patent: February 9, 2021
    Assignee: International Business Machines Corporation
    Inventors: Satoshi Hara, Gakuto Kurata, Shigeru Nakagawa, Seiji Takeda
  • Patent number: 10908591
    Abstract: A machine learning device acquires from a numerical controller information relating to machining when the machining is performed, and further acquires an actual delay time due to servo control and due to machine movement which are caused in the machining when the machining is performed. Then, the device performs supervised learning using the acquired machining-related information as input data, and using the acquired actual delay time due to servo control and due to machine movement as supervised data, and constructs a learning model, thereby predicting the machine delay time caused in a machine with high precision.
    Type: Grant
    Filed: October 19, 2017
    Date of Patent: February 2, 2021
    Assignee: FANUC CORPORATION
    Inventor: Hisateru Ishiwari
  • Patent number: 10909460
    Abstract: An apparatus includes a processor to: provide a set of feature routines to a set of processor cores to detect features of a data set distributed thereamong; generate metadata indicative of the detected features; generate context data indicative of contextual aspects of the data set; provide the metadata and context data to each processor core, and distribute a set of suggestion models thereamong to enable derivation of a suggested subset of data preparation operations to be suggested to be performed on the data set; transmit indications of the suggested subset to a viewing device, and receive therefrom indications of a selected subset of data preparation operations selected to be performed; compare the selected and suggested subsets; and in response to differences therebetween, re-train at least one suggestion model of the set of suggestion models based at least on the combination of the metadata, context data and selected subset.
    Type: Grant
    Filed: December 24, 2019
    Date of Patent: February 2, 2021
    Assignee: SAS INSTITUTE INC.
    Inventors: Nancy Anne Rausch, Roger Jay Barney, John P. Trawinski
  • Patent number: 10909630
    Abstract: Embodiments of the present invention are generally directed towards providing systems and methods for providing risk recommendation, mitigation and prediction. In particular, embodiments of the present invention are configured to allow for input of data related to known hazards to be interpreted and tracked or estimation of risk present in a variety of scenarios. Further embodiments of the present invention are configured to allow for predictive modeling and analysis of risk based on data as well as predictive behavior and other modeled information.
    Type: Grant
    Filed: March 14, 2017
    Date of Patent: February 2, 2021
    Inventor: David Baxter
  • Patent number: 10911318
    Abstract: System and method embodiments are provided for adaptive anomaly detection based predictor for network data. In an embodiment, a computer-implemented method in a network component for predicting values of future network time series data includes receiving, with one or more receivers, network time series data; determining, with one or more processors, whether an anomaly is detected in the network time series data; generating, with the one or more processors, a prediction associated with the network data according to a primary predictor when no anomaly is detected in the network time series data; generating, with the one or more processors, the prediction associated with the network data according to an alternative predictor when an anomaly in the network time series data is detected; and sending, with one or more transmitters, the prediction to a network controller, wherein the network controller uses the prediction to adjust network parameters.
    Type: Grant
    Filed: March 22, 2016
    Date of Patent: February 2, 2021
    Assignee: Futurewei Technologies, Inc.
    Inventors: Nandu Gopalakrishnan, Yirui Hu
  • Patent number: 10896372
    Abstract: A tool computes fitness values for a first generation of a first sub-population of a plurality of sub-populations. A population of candidate solutions for an optimization problem was previously divided into the plurality of sub-populations. The population of candidate solutions was created for an iterative computing process in accordance with an evolutionary algorithm to identify a most fit candidate solution for the optimization problem. The tool determines a speculative ranking of the first generation of the first sub-population prior to the fitness values being computed for all candidate solutions in the first generation of the first sub-population. The tool generates a next generation of the first sub-population based, at least in part, on the speculative ranking prior to completion of computation of the fitness values for the first generation of the first sub-population.
    Type: Grant
    Filed: June 3, 2019
    Date of Patent: January 19, 2021
    Assignee: International Business Machines Corporation
    Inventor: Jason F. Cantin
  • Patent number: 10891536
    Abstract: An artificial neuron includes a signal mixer that combines input signals to provide a first stochastic bit-stream as output and a stochastic activation function circuit configured to receive the first stochastic bit-stream from the signal mixer and to generate therefrom a second stochastic bit-stream. The first stochastic bit-stream is representative of a first output value. In the stochastic activation function circuit, n independent stochastic bit-streams, each representative of the first output value, are summed to provide a selection signal that is provided to a multiplexer to select between n+1 coefficient bit-streams and provide the second stochastic bit-stream. The activation function has a characteristic determined by the proportion of ones in each of the n+1 coefficients bit-streams. One or more artificial neurons may be used in an Artificial Neural Network, such as a Time Delay Reservoir network.
    Type: Grant
    Filed: May 25, 2017
    Date of Patent: January 12, 2021
    Assignee: The United States of America as represented by the Secretary of the Air Force
    Inventor: Corey E. Merkel
  • Patent number: 10891551
    Abstract: Systems and methods for projecting one or more trends in electronic data and generating enhanced data. A system includes a data forecasting system is in electronic communication with one or more electronic data sources via an electronic network. The data forecasting system is configured to: monitor the electronic data source(s) for data that meet one or more predetermined criteria; obtain at least a portion of the monitored data from electronic data source(s) based on the predetermined criteria; create a data set from the obtained data; derive one or more data values associated with the data set over a predetermined period according to a forward-looking term methodology; and utilize the data set and the derived value(s) over the predetermined period to derive at least one data forecast metric associated with the data set.
    Type: Grant
    Filed: October 24, 2019
    Date of Patent: January 12, 2021
    Assignee: ICE Benchmark Administration Limited
    Inventors: Emma Nicolette Vick, Andrew John Hill, Gary David Hooper, Paul Anderson Rhodes, Timothy Joseph Bowler, Charles Abboud, Stelios Etienne Tselikas, Thomas Evans
  • Patent number: 10891539
    Abstract: A system and method may be used to evaluate content on one or more social media networks. A deep learning model may be stored. A communication may be received, that has been or is to be communicated on a social network. The deep learning model may be applied to the communication to obtain an automated evaluation of the communication. User input may be received, and may include a user evaluation of the communication. The user evaluation may be applied to train the deep learning model. The steps of receiving the communication, applying the deep learning model to obtain the automated evaluation, receiving the user evaluation, and applying the user evaluation to train the model, may be iterated to enhance the accuracy of the automated evaluations.
    Type: Grant
    Filed: October 30, 2018
    Date of Patent: January 12, 2021
    Assignee: STA Group, Inc.
    Inventors: Vasant Kearney, Samuel Haaf, John Dorsey, Aaron Schoenberger