Patents Examined by Nader Metwalli
  • Patent number: 10726334
    Abstract: The present disclosure is directed to generating and using a machine learning model, such as a neural network, by augmenting another machine learning model with an additional parameter. The additional parameter may be connected to some or all nodes of an internal layer of the neural network. A machine learning model can determine a value associated with the additional parameter using non-behavior or non-event-based information. The machine learning model can be trained using non-behavior or non-event-based information and parameter values of the other machine learning model.
    Type: Grant
    Filed: April 10, 2017
    Date of Patent: July 28, 2020
    Assignee: Amazon Technologies, Inc.
    Inventors: Eiman Mohamed Hamdy Elnahrawy, Vijai Mohan, Eric Nalisnick
  • Patent number: 10726356
    Abstract: Respective statistical distributions of a target variable within a proposed training data set and a proposed test data set for a machine learning model are obtained. A metric indicative of the difference between the two statistical distributions is computed. The difference metric is used to determine whether the proposed test data set is acceptable to evaluate the machine learning model.
    Type: Grant
    Filed: August 1, 2016
    Date of Patent: July 28, 2020
    Assignee: Amazon Technologies, Inc.
    Inventors: Saman Zarandioon, Robert Matthias Steele
  • Patent number: 10706368
    Abstract: The disclosed computer-implemented method for efficiently classifying data objects may include (1) receiving a data object to be classified according to a group of rules, where each rule includes one or more clauses, (2) creating, for each rule, a rule evaluation job that directs a rule evaluation processor to evaluate the data object according to the clauses within the rule, where the rule evaluation processor evaluates the clauses in increasing order of estimated processing time, (3) submitting the rule evaluation jobs created for the rules to rule evaluation queues for processing by the rule evaluation processor, where the rule evaluation jobs are submitted in decreasing order of estimated processing time, (4) receiving an evaluation result for each rule evaluation job, and (5) in response to receiving the evaluation results, classifying the data object according to the evaluation results. Various other methods, systems, and computer-readable media are also disclosed.
    Type: Grant
    Filed: December 30, 2015
    Date of Patent: July 7, 2020
    Assignee: Veritas Technologies LLC
    Inventor: Huw Thomas
  • Patent number: 10699192
    Abstract: A method for optimizing a hyperparameter of an auto-labeling device performing auto-labeling and auto-evaluating of a training image to be used for learning a neural network is provided for computation reduction and achieving high precision. The method includes steps of: an optimizing device, (a) instructing the auto-labeling device to generate an original image with its auto label and a validation image with its true and auto label, to assort the original image with its auto label into an easy-original and a difficult-original images, and to assort the validation image with its own true and auto labels into an easy-validation and a difficult-validation images; and (b) calculating a current reliability of the auto-labeling device, generating a sample hyperparameter set, calculating a sample reliability of the auto-labeling device, and optimizing the preset hyperparameter set. This method can be performed by a reinforcement learning with policy gradient algorithms.
    Type: Grant
    Filed: January 31, 2019
    Date of Patent: June 30, 2020
    Assignee: STRADVISION, INC.
    Inventors: Kye-Hyeon Kim, Yongjoong Kim, Insu Kim, Hak-Kyoung Kim, Woonhyun Nam, SukHoon Boo, Myungchul Sung, Donghun Yeo, Wooju Ryu, Taewoong Jang, Kyungjoong Jeong, Hongmo Je, Hojin Cho
  • Patent number: 10692089
    Abstract: The present disclosure describes techniques for object classification using deep forest networks. One example method includes classifying a user object including features associated with the user based on a deep forest network including identifying one or more user static features, one or more user dynamic features, and one or more user association features from the features included in the user object; providing the user static features to first layers, the user dynamic features to second layers, and the user association features to third layers, the first, second, and third layers being different and each providing classification data to the next layer based at least in part on the input data and the provided user features.
    Type: Grant
    Filed: March 27, 2019
    Date of Patent: June 23, 2020
    Assignee: Alibaba Group Holding Limited
    Inventors: Yalin Zhang, Wenhao Zheng, Longfei Li
  • Patent number: 10570024
    Abstract: In this present disclosure, a computing implemented method is designed for predicting the effluent total nitrogen concentration (TN) in an urban wastewater treatment process (WWTP). The technology of this present disclosure is part of advanced manufacturing technology and belongs to both the field of control engineer and environment engineer. To improve the predicting efficiency, a recurrent self-organizing radial basis function (RBF) neural network (RSORBFNN) can adjust the structure and parameters simultaneously. This RSORBFNN is developed to implement this method, and then the proposed RSORBFNN-based method can predict the effluent TN concentration with acceptable accuracy. Moreover, online information of effluent TN concentration may be predicted by this computing implemented method to enhance the quality monitoring level to alleviate the current situation of wastewater and to strengthen the management of WWTP.
    Type: Grant
    Filed: December 23, 2016
    Date of Patent: February 25, 2020
    Assignee: Beijing University of Technology
    Inventors: Honggui Han, Yanan Guo, Junfei Qiao