Patents Examined by Kamran Afshar
  • Patent number: 10692005
    Abstract: Feature selection methods and processes that facilitate reduction of model components available for iterative modeling. It has been discovered that methods of eliminating model components that do not meaningfully contribute to a solution can be preliminarily discovered and discarded, thereby dramatically decreasing computational requirements in iterative programming techniques. This development unlocks the ability of iterative modeling to be used to solve complex problems that, in the past, would have required computation time on orders of magnitude too great to be useful.
    Type: Grant
    Filed: October 3, 2017
    Date of Patent: June 23, 2020
    Assignee: Liquid Biosciences, Inc.
    Inventor: Patrick Lilley
  • Patent number: 10691997
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting neural networks to generate additional outputs. One of the systems includes a neural network and a sequence processing subsystem, wherein the sequence processing subsystem is configured to perform operations comprising, for each of the system inputs in a sequence of system inputs: receiving the system input; generating an initial neural network input from the system input; causing the neural network to process the initial neural network input to generate an initial neural network output for the system input; and determining, from a first portion of the initial neural network output for the system input, whether or not to cause the neural network to generate one or more additional neural network outputs for the system input.
    Type: Grant
    Filed: December 21, 2015
    Date of Patent: June 23, 2020
    Assignee: DeepMind Technologies Limited
    Inventors: Alexander Benjamin Graves, Ivo Danihelka, Gregory Duncan Wayne
  • Patent number: 10692004
    Abstract: Detection systems, methods and computer program products comprising a non-transitory tangible storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method for anomaly detection, a detected anomaly being indicative of an undesirable event. A detection system comprises a computer and an anomaly detection engine executable by the computer, the anomaly detection engine configured to perform a method comprising receiving data comprising a plurality m of multidimensional data points (MDDPs), each data point having n features, constructing a dictionary D based on the received data, embedding dictionary D into a lower dimension embedded space and classifying, based in the lower dimension embedded space, a MDDP as an anomaly or as normal.
    Type: Grant
    Filed: June 30, 2019
    Date of Patent: June 23, 2020
    Assignee: ThetaRay Ltd.
    Inventor: David Segev
  • Patent number: 10692002
    Abstract: A method for learning a pedestrian detector to be used for robust surveillance or military purposes based on image analysis is provided for a solution to a lack of labeled images and for a reduction of annotation costs. The method can be also performed by using generative adversarial networks (GANs). The method includes steps of: a learning device generating an image patch by cropping each of regions on a training image, and instructing an adversarial style transformer to generate a transformed image patch by converting each of pedestrians into transformed pedestrians capable of impeding a detection; and generating a transformed training image by replacing each of the regions with the transformed image patch, instructing the pedestrian detector to detecting the transformed pedestrians, and learning parameters of the pedestrian detector to minimize losses. This learning, as a self-evolving system, is robust to adversarial patterns by generating training data including hard examples.
    Type: Grant
    Filed: January 28, 2019
    Date of Patent: June 23, 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: 10685281
    Abstract: Systems and methods for providing a predictive framework are provided. The predictive framework comprises plural neural layers of adaptable, executable neurons. Neurons accept one or more input signals and produce an output signal that may be used by an upper-level neural layer. Input signals are received by an encoding neural layer, where there is a 1:1 correspondence between an input signal and an encoding neuron. Input signals for a set of data are received at the encoding layer and processed successively by the plurality of neural layers. An objective function utilizes the output signals of the topmost neural layer to generate predictive results for the data set according to an objective. In one embodiment, the objective is to determine the likelihood of user interaction with regard to a specific item of content in a set of search results, or the likelihood of user interaction with regard to any item of content in a set of search results.
    Type: Grant
    Filed: August 2, 2016
    Date of Patent: June 16, 2020
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Ying Shan, Thomas Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, JC Mao
  • Patent number: 10685260
    Abstract: Systems and methods are disclosed that enable distributed execution of prediction models by disparate, remote systems. Prediction model code is transmitted to the disparate, distributed systems for execution by the disparate, remote systems. Default model input data may be independently modified by a given system, and the modified input data may be used when the given system executes the model. Model predictions and associated model parameters are received from the disparate, distributed systems. The accuracy of the received model predictions from the disparate, distributed systems are analyzed. Based on the analyzed accuracy of the received model predictions, a determination is made as to which model predictions satisfy at least a first criterion. Computer-based resources are allocated using the determination as to which model predictions satisfy at least the first criterion.
    Type: Grant
    Filed: June 6, 2019
    Date of Patent: June 16, 2020
    Assignee: Finiti Research Limited
    Inventors: Jesse David Adelaar, Werner Janjic, Christoph Giess
  • Patent number: 10685004
    Abstract: Disclosed embodiments are related to feature hashing techniques. A processing device of a database system may identify a set of machine learning features; generate a first hash map of said set of machine learning features and a second different hash map of said set of machine learning features. The processing device may generate a memory compact model for an online machine learning system using the first and second hash maps, and store the memory compact model in the memory device.
    Type: Grant
    Filed: July 11, 2016
    Date of Patent: June 16, 2020
    Assignee: SALESFORCE.COM, INC.
    Inventors: Pingping Xiu, Scott Douglas White, Parijat Mazumdar
  • Patent number: 10685065
    Abstract: Method, system, and programs for recommending content to a user. First information related to one or more previous users is first obtained. A model that maps from users to topics of interest is then established based on the first information related to the one or more previous users. Second information related to the current user is also obtained. One or more topics of interest are identified for the current user based on the model. Content is recommended to the current user in accordance with the one or more topics of interest for the current user. Eventually, an updated model is generated by integrating information associated with the current user with the model established based on the first information related to the one or more previous users. The information associated with the current user includes the second information related to the current user.
    Type: Grant
    Filed: March 17, 2012
    Date of Patent: June 16, 2020
    Assignee: HAIZHI WANGJU NETWORK TECHNOLOGY (BEIJING) CO., LTD.
    Inventors: Zhaohui Zheng, Xin Li, Rongqing Lu, Shuanghong Yang
  • Patent number: 10682288
    Abstract: A computer-implemented method of treating a patient's and automated enteral feeding, comprising: monitoring a plurality of reflux-related parameters and at least one reflux event while the patient is automatically enterally fed by an enteral feeding controller according to a baseline feeding profile including a target nutritional goal, training a classifier component of a model for predicting likelihood of a future reflux event according to an input of scheduled and/or predicted plurality of reflux-related parameters, the classifier trained according to computed correlations between the plurality of reflux-related parameters and the at least one reflux event, feeding scheduled and/or predicted reflux-related parameters into the trained classifier component of the model for outputting risk of likelihood of a future reflux event, and computing, by the model, an adjustment to the baseline feeding profile for reducing likelihood of the future reflux event and for meeting the target nutritional goal.
    Type: Grant
    Filed: March 4, 2019
    Date of Patent: June 16, 2020
    Assignee: ART Medical Ltd.
    Inventors: Liron Elia, Gavriel J. Iddan
  • Patent number: 10679100
    Abstract: Systems and methods of intelligent formation and acquisition of machine learning training data for implementing an artificially intelligent dialogue system includes constructing a corpora of machine learning test corpus that comprise a plurality of historical queries and commands sampled from production logs of a deployed dialogue system; configuring training data sourcing parameters to source a corpora of raw machine learning training data from remote sources of machine learning training data; calculating efficacy metrics of the corpora of raw machine learning training data, wherein calculating the efficacy metrics includes calculating one or more of a coverage metric value and a diversity metric value of the corpora of raw machine learning training data; using the corpora of raw machine learning training data to train the at least one machine learning classifier if the calculated coverage metric value of the corpora of machine learning training data satisfies a minimum coverage metric threshold.
    Type: Grant
    Filed: April 10, 2019
    Date of Patent: June 9, 2020
    Assignee: Clinc, Inc.
    Inventors: Yiping Kang, Yunqi Zhang, Jonathan K. Kummerfeld, Parker Hill, Johann Hauswald, Michael A. Laurenzano, Lingjia Tang, Jason Mars
  • Patent number: 10679124
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. One of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.
    Type: Grant
    Filed: December 2, 2016
    Date of Patent: June 9, 2020
    Assignee: Google LLC
    Inventors: Gregory S. Corrado, Kai Chen, Jeffrey A. Dean, Gary R. Holt, Julian P. Grady, Sharat Chikkerur, David W. Sculley, II
  • Patent number: 10679121
    Abstract: A neuromorphic device includes a synapse. The synapse includes a first electrode, a second electrode spaced apart from the first electrode, an oxygen-containing layer disposed between the first electrode and the second electrode, the oxygen-containing layer including oxygen ions, and a stack structure disposed between the oxygen-containing layer and the second electrode, the stack structure including a plurality of reactive metal layers alternately arranged with a plurality of oxygen diffusion-retarding layers. The plurality of reactive metal layers are capable of reacting with oxygen ions of the oxygen-containing layer. The plurality of oxygen diffusion-retarding layers interfere with a movement of the oxygen ions from the oxygen-containing layer to the plurality of reactive metal layers.
    Type: Grant
    Filed: December 22, 2016
    Date of Patent: June 9, 2020
    Assignee: SK hynix Inc.
    Inventors: Sang-Su Park, Hyung-Dong Lee
  • Patent number: 10679130
    Abstract: A method includes receiving a set of parameters for a given project and generating, using information from a knowledge database, a plurality of combinations of group members based at least in part on the set of parameters. The method also includes evaluating a set of metrics for each of the combinations of group members, the set of metrics comprising at least one novelty metric and at least one collective intelligence metric. The method further includes generating one or more strategy matrices for each of at least a subset of the combinations of group members using information from the knowledge database, evaluating the combinations of group members in the subset using the strategy matrices to determine respective predicted success values, and selecting a given one of the combinations of group members for the given project based at least in part on the sets of metrics and predicted success values.
    Type: Grant
    Filed: January 5, 2017
    Date of Patent: June 9, 2020
    Assignee: International Business Machines Corporation
    Inventors: Florian Pinel, Krishna C. Ratakonda, Lav R. Varshney, Dashun Wang
  • Patent number: 10679126
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a system configured to select actions to be performed by an agent that interacts with an environment. The system comprises a manager neural network subsystem and a worker neural network subsystem. The manager subsystem is configured to, at each of the multiple time steps, generate a final goal vector for the time step. The worker subsystem is configured to, at each of multiple time steps, use the final goal vector generated by the manager subsystem to generate a respective action score for each action in a predetermined set of actions.
    Type: Grant
    Filed: July 15, 2019
    Date of Patent: June 9, 2020
    Assignee: DeepMind Technologies Limited
    Inventors: Simon Osindero, Koray Kavukcuoglu, Alexander Vezhnevets
  • Patent number: 10679125
    Abstract: Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing counterfactual regret minimization (CFR) for strategy searching in strategic interaction between two or more parties. One of the methods includes: storing multiple regret samples in a first data store, wherein the multiple regret samples are obtained in two or more iterations of a CFR algorithm in strategy searching in strategic interaction between two or more parties; storing multiple strategy samples in a second data store; updating parameters of a first neural network for predicting a regret value of a possible action in a state of a party based on the multiple regret samples in the first data store; and updating parameters of a second neural network for predicting a strategy value of a possible action in a state of the party based on the multiple strategy samples in the second data store.
    Type: Grant
    Filed: June 21, 2019
    Date of Patent: June 9, 2020
    Assignee: Alibaba Group Holding Limited
    Inventors: Hui Li, Kailiang Hu, Le Song
  • Patent number: 10679137
    Abstract: Systems and methods disclosed herein compactly store representations of segment-specific interaction data from a real time data stream of data interactions by multiple entities to facilitate segment-specific analytics for particular time periods. Segment rules defining characteristics of entities within a segment are received. A first probabilistic data structure is created representing unique entity IDs included in instances of interaction data in the real time data stream during a first time period. A second probabilistic data structure representing unique entity IDs included in instances of interaction data in the real time data stream during a second time period different from the first time period. The first probabilistic data structure represents only entity IDs of entities within the segment and the second probabilistic data structure represents only entity IDs of entities within the segment. The first and second probabilistic data structures are indexed and stored.
    Type: Grant
    Filed: January 4, 2016
    Date of Patent: June 9, 2020
    Assignee: Adobe Inc.
    Inventors: Natalee Villa, Scott Pead, James Nyland, Craig Mathis
  • Patent number: 10679150
    Abstract: A system and method for improving a machine learning-based dialogue system includes: sourcing a corpus of raw machine learning training data from sources of training data based on a plurality of seed training samples, wherein the corpus of raw machine learning training data comprises a plurality of distinct instances of training data; generating a vector representation for each distinct instance of training data; identifying statistical characteristics of the corpus of raw machine learning training data based on a mapping of the vector representation for each distinct instance of training data; identifying anomalous instances of the plurality of distinct instances of training data of the corpus of raw machine learning training data based on the identified statistical characteristics of the corpus; and curating the corpus of raw machine learning training data based on each of the instances of training data identified as anomalous instances.
    Type: Grant
    Filed: November 20, 2019
    Date of Patent: June 9, 2020
    Assignee: Clinc, Inc.
    Inventors: Stefan Larson, Anish Mahendran, Andrew Lee, Jonathan K. Kummerfeld, Parker Hill, Michael A. Laurenzano, Johann Hauswald, Lingjia Tang, Jason Mars
  • Patent number: 10679141
    Abstract: An identity and access management IAM system is augmented to provide for supervised, iterative machine learning (ML), preferably with a user-generated training set for classification. The training set may include various types of data, including characteristics or attributes of the account types, the users, or the like. A goal of the initial ML training, which may include one or multiple passes, is to enable the machine to identify specific characteristics or attributes that provide a good classification result, with the resulting classifications then applied within the IAM system. In particular, the output of the ML system may be used by the IAM system for enforcing rights associated with the identified accounts, managing accounts, and so forth.
    Type: Grant
    Filed: September 29, 2015
    Date of Patent: June 9, 2020
    Assignee: International Business Machines Corporation
    Inventors: Brian Robert Matthiesen, Gee Ngoo Chia, John Leslie Harter, David Walsh Palmieri
  • Patent number: 10672407
    Abstract: Systems and methods are disclosed for customizing, distributing and processing audio fingerprint data. An example method includes receiving, at a first device, an activation signal and a first audio fingerprint via first wireless communications between the first device and a communications network, the receiving occurring while the first device is not recording audio via a microphone of the first device; based on the activation signal, recording audio using the microphone during a first time period; generating a second audio fingerprint representative of the recorded audio; determining whether the second audio fingerprint matches the first audio fingerprint; and sending an indication of whether the second audio fingerprint matches the first audio fingerprint to an audience measurement entity via second wireless communications between the first device and the communications network.
    Type: Grant
    Filed: July 21, 2016
    Date of Patent: June 2, 2020
    Assignee: The Nielsen Company (US), LLC
    Inventors: Wendell Lynch, Alan Neuhauser, Taymoor Arshi, Anand Jain
  • Patent number: 10674313
    Abstract: A device may perform an iteration of an optimization procedure. The device may apply a smoothing technique to a value relating to the optimization procedure after performing the iteration of the optimization procedure. The device may selectively terminate the optimization procedure based on applying the smoothing technique to the value relating to the optimization procedure. The device may provide information identifying a result of the optimization procedure based on selectively terminating the optimization procedure.
    Type: Grant
    Filed: March 17, 2016
    Date of Patent: June 2, 2020
    Assignee: VIAVI Solutions UK Limited
    Inventors: Gareth James Smith, Stefan Ulrich Thiel, Christopher Michael Murphy