Learning Task Patents (Class 706/16)
  • Patent number: 11314212
    Abstract: An embodiment includes duplicating an input dataset being input to a model predictive control (MPC) module for input to a first Hierarchical Temporal Memory (HTM) network. The embodiment also includes generating system behavior data using the MPC module for characteristic data of the input dataset. The embodiment also includes generating first HTM prediction data from the input dataset and the system behavior data using the first HTM network, the first HTM prediction data comprising predictions for respective dimensions of the system. The embodiment also includes generating second HTM prediction data from the first HTM prediction data and system output data using a second HTM network, the second HTM prediction data comprising a distinction between the first HTM prediction and the system output data. Finally, the embodiment includes determining that the distinction of the second HTM prediction data indicates an anomaly and adjusting system input data based on the anomaly.
    Type: Grant
    Filed: January 27, 2020
    Date of Patent: April 26, 2022
    Assignee: KYNDRYL, INC.
    Inventor: Awadesh Tiwari
  • Patent number: 11301755
    Abstract: The disclosure provides a method for predicting a traffic matrix, a computing device, and a storage medium. The method includes: establishing a dataset based on continuous historical traffic matrices; and inputting one or more historical traffic matrices in the dataset into a trained model for predicting traffic matrices, to obtain one or more predicted traffic matrices. The trained model for predicting traffic matrices is obtained by the following actions: establishing a model for predicting traffic matrices based on a correlation-modeling neural network and a temporal-modeling neural network; and training the model for predicting traffic matrices based on a set of training samples, in which the set of training samples includes sample traffic matrices and label traffic matrices corresponding to the sample traffic matrices at prediction moment samples.
    Type: Grant
    Filed: November 3, 2020
    Date of Patent: April 12, 2022
    Assignee: TSINGHUA UNIVERSITY
    Inventors: Dan Li, Kaihui Gao
  • Patent number: 11295231
    Abstract: Systems, methods, and computer-readable media are disclosed for parallel stochastic gradient descent using linear and non-linear activation functions. One method includes: receiving a set of input examples; receiving a global model; and learning a new global model based on the global model and the set of input examples by iteratively performing the following steps: computing a plurality of local models having a plurality of model parameters based on the global model and at least a portion of the set of input examples; computing, for each local model, a corresponding model combiner based on the global model and at least a portion of the set of input examples; and combining the plurality of local models into the new global model based on the current global model and the plurality of corresponding model combiners.
    Type: Grant
    Filed: May 22, 2017
    Date of Patent: April 5, 2022
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Saeed Maleki, Madanlal S. Musuvathi, Todd D. Mytkowicz
  • Patent number: 11295210
    Abstract: Methods and computer systems improve a trained base deep neural network by structurally changing the base deep neural network to create an updated deep neural network, such that the updated deep neural network has no degradation in performance relative to the base deep neural network on the training data. The updated deep neural network is subsequently training. Also, an asynchronous agent for use in a machine learning system comprises a second machine learning system ML2 that is to be trained to perform some machine learning task. The asynchronous agent further comprises a learning coach LC and an optional data selector machine learning system DS. The purpose of the data selection machine learning system DS is to make the second stage machine learning system ML2 more efficient in its learning (by selecting a set of training data that is smaller but sufficient) and/or more effective (by selecting a set of training data that is focused on an important task).
    Type: Grant
    Filed: May 31, 2018
    Date of Patent: April 5, 2022
    Assignee: D5AI LLC
    Inventor: James K. Baker
  • Patent number: 11290764
    Abstract: Aspects of the subject disclosure may include, for example, a device configured for generating a number of content groups by grouping a number of tagged media segments according to their corresponding content designators. The device can be further configured for detect an activity of a user, determining a particular segment length and content designator according to the detected activity, and generating a selected content group according to the particular segment length and content designator. A content group is selected according to the particular segment length and the content designator. Other embodiments are disclosed.
    Type: Grant
    Filed: April 21, 2020
    Date of Patent: March 29, 2022
    Assignee: AT&T Intellectual Property I, L.P.
    Inventors: Andre Fuetsch, Robert Koch, Ari Craine
  • Patent number: 11267132
    Abstract: A robot system includes a robot, a robot controller, a video acquisition device configured to acquire a real video of a work space, and a head-mounted type video display device provided with a visual line tracking section configured to acquire visual line information. A robot controller includes an information storage section configured to store information used for controlling the robot while associating the information with a type of an object, a gaze target identification section configured to identify, in the video, a gaze target viewed by a wearer based on the visual line information, and a display processing section configured to cause the video display device to display the information associated with the object corresponding to the identified gaze target, side by side with the gaze target in the form of one image through which the wearer can visually grasp, select, or set contents of the information.
    Type: Grant
    Filed: January 24, 2020
    Date of Patent: March 8, 2022
    Assignee: FANUC CORPORATION
    Inventor: Takahiro Okamoto
  • Patent number: 11270188
    Abstract: Computer-implemented, machine-learning systems and methods relate to a combination of neural networks. The systems and methods train the respective member networks both (i) to be diverse and yet (ii) according to a common, overall objective. Each member network is trained or retrained jointly with all the other member networks, including member networks that may not have been present in the ensemble when a member is first trained.
    Type: Grant
    Filed: September 26, 2018
    Date of Patent: March 8, 2022
    Assignee: D5AI LLC
    Inventor: James K. Baker
  • Patent number: 11263590
    Abstract: A prediction system and method may include receiving a plurality of discrete applicant data inputs and a supporting document, the applicant data inputs and the supporting document being relevant to a permit application, providing a first predicted probability of approval of the permit application by comparing the discrete applicant data inputs with weighted criteria of previous applicant profiles stored in a first database, analyzing the supporting document to determine a second predicted probability of approval of the permit application by comparing the supporting document with previous applicant supporting documents stored in a second database, performing a sentiment analysis on external publically available information relevant to at least one aspect of the permit application to determine an impact score on the permit application, and determining an overall probability of success based on the first predicted probability, the second predicted probability, and the impact score.
    Type: Grant
    Filed: February 9, 2018
    Date of Patent: March 1, 2022
    Assignee: International Business Machines Corporation
    Inventors: John M. Verones, Michael Bender, Aleem Hooda, Bruno Rositano, Samantha Gauvreau, Tapan Choudhury, Troy Pariag
  • Patent number: 11264036
    Abstract: Provided are a method of generating a trained third neural network to recognize a speaker of a noisy speech signal by combining a trained first neural network which is a skip connection-based neural network for removing noise from the noisy speech signal with a trained second neural network for recognizing the speaker of a speech signal, and a neural network device for operating the neural networks.
    Type: Grant
    Filed: August 19, 2019
    Date of Patent: March 1, 2022
    Assignees: SAMSUNG ELECTRONICS CO., LTD., SEOUL NATIONAL UNIVERSITY R&DB FOUNDATION
    Inventors: Sungchan Kang, Namsoo Kim, Cheheung Kim, Hyungyong Kim
  • Patent number: 11256982
    Abstract: A learning computer system may include a data processing system and a hardware processor and may estimate parameters and states of a stochastic or uncertain system. The system may receive data from a user or other source. Parameters and states of the stochastic or uncertain system are estimated using the received data, numerical perturbations, and previous parameters and states of the stochastic or uncertain system. It is determined whether the generated numerical perturbations satisfy a condition. If the numerical perturbations satisfy the condition, the numerical perturbations are injected into the estimated parameters or states, the received data, the processed data, the masked or filtered data, or the processing units.
    Type: Grant
    Filed: July 20, 2015
    Date of Patent: February 22, 2022
    Assignee: University of Southern California
    Inventors: Kartik Audhkhasi, Bart Kosko, Osonde Osoba
  • Patent number: 11238961
    Abstract: A method, device, and computer program storage product for generating a query to extract clinical features into a set of electronic medical record (EMR) tables based on clinical knowledge. A knowledge tree is constructed according to a set of clinical knowledge data. An EMR graph corresponding to a set of EMR tables is obtained. The EMR graph comprises at set of table nodes and a set of attribute nodes. The set of table nodes and the set of attribute nodes represent a structure of each EMR table in the set of EMR tables and a reference relationship among attributes of set of EMR tables. A plurality of sub-queries is generated based on the knowledge tree and the EMR graph. At least one query is generated by combining the plurality of sub-queries according to the knowledge tree.
    Type: Grant
    Filed: December 10, 2019
    Date of Patent: February 1, 2022
    Assignee: International Business Machines Corporation
    Inventors: Bi Bo Hao, Gang Hu, Jing Li, Wen Sun, Guo Tong Xie, Yi Qin Yu
  • Patent number: 11226893
    Abstract: According to an embodiment of the present disclosure for solving the aforementioned problem, disclosed is a computer program stored in a computer-readable storage medium executable by one or more processors, in which when the computer program is executed by one or more processors of a computer device, the computer program allows the one or more processors to perform the following operations for data processing, and the operations may include: an operation of generating a plurality of transformed data based on each of a plurality of data included in a data set; an operation of generating a test data set based on the plurality of data and the plurality of transformed data; and an operation of testing the performance of the model by calculating the test data set by using the model.
    Type: Grant
    Filed: February 23, 2021
    Date of Patent: January 18, 2022
    Assignee: MakinaRocks Co., Ltd.
    Inventors: Ki Hyun Kim, Jong Seob Jeon, Sangwoo Shim, Sungho Yoon, Hooncheol Shin
  • Patent number: 11210559
    Abstract: An autonomous navigation system for a vehicle includes a controller configured to control the vehicle, sensors configured to detect objects in a path of the vehicle, nonvolatile memory including an artificial neural network configured to classify the objects detected by the sensors, and a processor. The artificial neural network includes a series of neurons in each of an input layer, at least one hidden layer, and an output layer. The memory includes instructions which, when executed by the processor, cause the processor to train the artificial neural network on a first task, identify, utilizing a contrastive excitation backpropagation algorithm, important neurons for the first task, identify, utilizing a learning algorithm, important synapses between the neurons for the first task based on the important neurons identified, and rigidify the important synapses to achieve selective plasticity of the series of neurons in the artificial neural network.
    Type: Grant
    Filed: August 23, 2019
    Date of Patent: December 28, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Soheil Kolouri, Nicholas A. Ketz, Praveen K. Pilly, Charles E. Martin, Michael D. Howard
  • Patent number: 11210477
    Abstract: Embodiments of the present disclosure are directed to a system, methods, and computer-readable media for facilitating stylistic expression transfers in machine translation of source sequence data. Using integrated loss functions for style transfer along with content preservation and/or cross entropy, source sequence data is processed by an autoencoder trained to reduce loss values across the loss functions at each time step encoded for the source sequence data. The target sequence data generated by the autoencoder therefore exhibits reduced loss values for the integrated loss functions at each time step, thereby improving content preservation and providing for stylistic expression transfer.
    Type: Grant
    Filed: May 9, 2019
    Date of Patent: December 28, 2021
    Assignee: Adobe Inc.
    Inventors: Balaji Vasan Srinivasan, Anandhavelu Natarajan, Abhilasha Sancheti
  • Patent number: 11212543
    Abstract: A method for restoring a compressed image according to an embodiment of the present disclosure includes receiving monochrome image data and low resolution color image data generated from an original color image of the monochrome image data, decoding the monochrome image data and generating a low resolution monochrome image, decoding the low resolution color image data generating a low resolution color image; processing the low resolution monochrome image and generating a high resolution monochrome image in accordance with a super resolution imaging neural network; and generating a high resolution color image based on the low resolution color image and the high resolution monochrome image in accordance with a colorization imaging neural network. The imaging neural network of the present disclosure may be a deep neural network generated by machine learning, and images may be input and output in the Internet of Things environment using a 5G network.
    Type: Grant
    Filed: January 9, 2020
    Date of Patent: December 28, 2021
    Assignee: LG ELECTRONICS
    Inventors: Keum Sung Hwang, Seung Hwan Moon, Young Kwon Kim, Hyun Dae Choi
  • Patent number: 11195085
    Abstract: Embodiment of the invention are directed to transmitting signals between neurons of a hardware-implemented, spiking neural network (or SNN). The network includes neuronal connections, each including a synaptic unit connecting a pre-synaptic neuron to a post-synaptic neuron. Spikes received from the pre-synaptic neuron of said each neuronal connection are first modulated, in frequency, based on a synaptic weight stored on said each synaptic unit, to generate post-synaptic spikes, such that a first number of spikes received from the pre-synaptic neuron are translated into a second number of post-synaptic spikes. At least some of the spikes received from the pre-synaptic neuron may, each, be translated into a train of two or more post-synaptic spikes. The post-synaptic spikes generated are subsequently transmitted to the post-synaptic neuron of said each neuronal connection. The novel approach makes it possible to obtain a higher dynamic range in the synapse output.
    Type: Grant
    Filed: March 21, 2019
    Date of Patent: December 7, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Angeliki Pantazi, Stanislaw Andrzej Wozniak, Stefan Abel, Jean Fompeyrine
  • Patent number: 11188796
    Abstract: A processor-implemented data processing method includes: predicting whether there will be an inefficient section, of a neural network set to be implemented, during a processing of data, based on a hardware configuration for processing the data; adjusting a layer parameter corresponding to the inefficient section of the neural network; and processing the data using the neural network with the adjusted layer parameter.
    Type: Grant
    Filed: February 28, 2020
    Date of Patent: November 30, 2021
    Assignee: Samsung Electronics Co., Ltd.
    Inventors: Donghyuk Kwon, Seungwon Lee
  • Patent number: 11157816
    Abstract: The present disclosure relates to systems and methods for using transfer learning in log parsing neural networks. In one implementation, a system for training a neural network to parse unstructured data may include a processor and a non-transitory memory storing instructions that, when executed by the processor, cause the system to: receive unstructured data; apply a classifier to the unstructured data to determine that the unstructured data comprises a new category of unstructured data; in response to the determination, identify an existing category of unstructured data similar to the new category; based on the identified existing category, select a corresponding neural network; reset at least one weight and at least one activation function of the corresponding neural network while retaining structure of the corresponding neural network; train the reset neural network to parse the new category of unstructured data; and output the trained neural network.
    Type: Grant
    Filed: October 17, 2018
    Date of Patent: October 26, 2021
    Assignee: Capital One Services, LLC
    Inventors: Anh Truong, Fardin Abdi Taghi Abad, Mark Watson, Austin Walters, Jeremy Goodsitt, Vincent Pham, Reza Farivar
  • Patent number: 11157798
    Abstract: Embodiments of the present invention provide an artificial neural network system for feature pattern extraction and output labeling. The system comprises a first spiking neural network and a second spiking neural network. The first spiking neural network is configured to autonomously learn complex, temporally overlapping features arising in an input pattern stream. Competitive learning is implemented as spike timing dependent plasticity with lateral inhibition in the first spiking neural network. The second spiking neural network is connected with the first spiking neural network through dynamic synapses, and is trained to interpret and label the output data of the first spiking neural network. Additionally, the labeled output of the second spiking neural network is transmitted to a computing device, such as a central processing unit for post processing.
    Type: Grant
    Filed: February 13, 2017
    Date of Patent: October 26, 2021
    Assignee: BrainChip, Inc.
    Inventors: Peter A J van der Made, Mouna Elkhatib, Nicolas Yvan Oros
  • Patent number: 11120805
    Abstract: Systems, devices, and methods related to a Deep Learning Accelerator and memory are described. For example, a microphone may be configured to execute instructions with matrix operands and configured with: a transducer to convert sound waves to electrical signals; an analog to digital converter to generate audio data according to the electrical signals; random access memory to store instructions executable by the Deep Learning Accelerator and store matrices of an Artificial Neural Network; and a controller to store the audio data in the random access memory as an input to the Artificial Neural Network. The Deep Learning Accelerator can execute the instructions to generate an output of the Artificial Neural Network, which may be provided as the primary output of the microphone to a computer system, such as a voice-based digital assistant.
    Type: Grant
    Filed: June 19, 2020
    Date of Patent: September 14, 2021
    Assignee: Micron Technology, Inc.
    Inventor: Poorna Kale
  • Patent number: 11099406
    Abstract: A system, method and program product for delivering augmented reality content to a user includes a data processing system receiving data indicating the focal point of the contact lens user and delivering augmented reality content to the user via the contact lens at the focal point, the data including data regarding a shape of an eye lens. Alternatively, the data is based on a cognitively predicted activity of the user, the cognitively predicted activity being based on contextual information.
    Type: Grant
    Filed: January 24, 2019
    Date of Patent: August 24, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Sarbajit K. Rakshit, James E. Bostick, John M. Ganci, Jr., Martin G. Keen
  • Patent number: 11100506
    Abstract: A system and method for programmatically revealing misleading confidence values in Fraud Score is presented to protect artificial intelligence models from adversarial neural networks. The method is used to reduce an adversarial learning neural network model effectiveness. With the score manipulation implemented, the adversary models are shown to systematically become less successful in predicting the true behavior of the Fraud detection artificial intelligence model and what it will flag as fraudulent transactions, thus reducing the true fraud dollars penetrated or taken by adversaries.
    Type: Grant
    Filed: May 9, 2017
    Date of Patent: August 24, 2021
    Assignee: FAIR ISAAC CORPORATION
    Inventors: Scott Michael Zoldi, Qing Liu
  • Patent number: 11093594
    Abstract: Embodiments of the present invention provide a method, system and computer program product for cognitive user identification recommendation. In an embodiment of the invention, a method for cognitive user identification recommendation includes monitoring typing patterns of an end user as the end user enters data in different fields of different applications of a computing device having a device type and categorizing each of the fields and applications according to field type and application type. The method further includes generating a data structure mapping the user typing patterns to each type of field and application to model user input behavior of the end user. The method also includes transmitting the data structure to a requesting application for prompting the end user to provide a particular type of password mapped to the modeled user input and consistent with a field type of the password and a type of requesting application.
    Type: Grant
    Filed: November 4, 2018
    Date of Patent: August 17, 2021
    Assignee: International Business Machines Corporation
    Inventors: Hernan Cunico, Paul Alexander Frank, Martin Keen, Richard D. Johnson
  • Patent number: 11079738
    Abstract: According to some embodiments, a system may include a design experience data store containing electronic records associated with prior industrial asset item designs. A deep learning model platform, coupled to the design experience data store, may include a communication port to receive constraint and load information from a designer device. The deep learning platform may further include a computer processor adapted to automatically and generatively create boundaries and geometries, using a deep learning model associated with an additive manufacturing process, for an industrial asset item based on the prior industrial asset item designs and the received constraint and load information. According to some embodiments, the deep learning model computer processor is further to receive design adjustments from the designer device. The received design adjustments might be for example, used to execute an optimization process and/or be fed back to continually re-train the deep learning model.
    Type: Grant
    Filed: August 16, 2017
    Date of Patent: August 3, 2021
    Assignee: General Electric Company
    Inventors: Arun Karthi Subramaniyan, Ananda Barua, Daniel Erno
  • Patent number: 11068787
    Abstract: Systems and methods are disclosed herein for selecting a parameter vector from a set of parameter vectors for a neural network and generating a plurality of copies of the parameter vector. The systems and methods generate a plurality of modified parameter vectors by perturbing each copy of the parameter vector with a different perturbation seed, and determine, for each respective modified parameter vector, a respective measure of novelty. The systems and methods determine an optimal new parameter vector based on each respective measure of novelty for each respective one of the plurality of modified parameter vectors, and determine behavior characteristics of the new parameter vector. The systems and methods store the behavior characteristics of the new parameter vector in an archive.
    Type: Grant
    Filed: December 14, 2018
    Date of Patent: July 20, 2021
    Assignee: Uber Technologies, Inc.
    Inventors: Edoardo Conti, Vashisht Madhavan, Jeffrey Michael Clune, Felipe Petroski Such, Joel Anthony Lehman, Kenneth Owen Stanley
  • Patent number: 11057788
    Abstract: A method and system for detecting abnormal values in an LTE network is provided: dividing measured data into a training and a testing set; defining clusters and parameters in the training set, and finding the cluster to which each point belongs using clustering algorithms; calculating a likelihood of each point based on parameters and clustering results; assigning the likelihood into an abnormal, an intermediate or a normal region according to a set warning and alarming threshold; and applying a calculated model to the testing set, the likelihood of each point is calculated and assigned to a region, thereby finding abnormal values in the testing set. The variation of data points versus time may be better understood by introducing time axes into the model, thereby multiple abnormal values may be discovered from a sequence of multiple points. The method can immediately detect abnormal values and the error rate is low.
    Type: Grant
    Filed: February 6, 2017
    Date of Patent: July 6, 2021
    Assignee: NANJING HOWSO TECHNOLOGY CO., LTD
    Inventors: Donghua Wu, Alexis Huet, Lulu Shi
  • Patent number: 11050793
    Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.
    Type: Grant
    Filed: July 13, 2020
    Date of Patent: June 29, 2021
    Assignee: Abnormal Security Corporation
    Inventors: Sanjay Jeyakumar, Jeshua Alexis Bratman, Dmitry Chechik, Abhijit Bagri, Evan James Reiser, Sanny Xiao Yang Liao, Yu Zhou Lee, Carlos Daniel Gasperi, Kevin Lau, Kai Jing Jiang, Su Li Debbie Tan, Jeremy Kao, Cheng-Lin Yeh
  • Patent number: 11042930
    Abstract: Certain aspects of the present disclosure provide techniques for improving a prediction of whether a non-sufficient funds fee will be incurred by a user utilizing machine learning techniques. For example, a predictive model may be trained using machine learning techniques based on historical data and derived data for a plurality of users. The predictive model may then be used to predict a probability of a particular user incurring an insufficient funds fee. The probability of the particular user may be used to generate an alert and suggestion to be presented to the particular user to avoid incurring the insufficient funds fee.
    Type: Grant
    Filed: March 20, 2018
    Date of Patent: June 22, 2021
    Assignee: INTUIT, INC.
    Inventors: Ido Mintz, Liron Hayman, Elhanan Mishraky
  • Patent number: 11019086
    Abstract: A system includes a network gateway in communication with a plurality of servers, a household behavior model processor which models a household behavior model based at least on expected usage of each of a plurality of network appliances, wherein each one appliance of the plurality of network appliances is associated with one of the plurality of servers, and behavior of users associated with the network gateway, an anomaly detector which determines, on the basis of the household behavior model, if an anomalous control message which has been sent to one of the plurality of network appliances from one of the servers has been received at the network gateway, and a notification server which sends a notification to an application on an administrator's device upon receipt of the anomalous control message at the network gateway. Related systems, apparatus, and methods are also described.
    Type: Grant
    Filed: April 4, 2019
    Date of Patent: May 25, 2021
    Assignee: CISCO TECHNOLOGY, INC.
    Inventors: Steve Epstein, Avi Fruchter, Moshe Kravchik, Yaron Sella, Itay Harush
  • Patent number: 11003988
    Abstract: Methods and apparatus for deep learning-based system design improvement are provided. An example system design engine apparatus includes a deep learning network (DLN) model associated with each component of a target system to be emulated, each DLN model to be trained using known input and known output, wherein the known input and known output simulate input and output of the associated component of the target system, and wherein each DLN model is connected as each associated component to be emulated is connected in the target system to form a digital model of the target system. The example apparatus also includes a model processor to simulate behavior of the target system and/or each component of the target system to be emulated using the digital model to generate a recommendation regarding a configuration of a component of the target system and/or a structure of the component of the target system.
    Type: Grant
    Filed: November 23, 2016
    Date of Patent: May 11, 2021
    Assignee: General Electric Company
    Inventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey
  • Patent number: 11003179
    Abstract: A data collection system in an industrial environment includes a data collector coupled to a plurality of input channels, wherein a collector route determines a subset of the input channels for data collection, the collector route selected based on a data marketplace indicator; a data storage structured to store a plurality of collector routes and collected data that correspond to the input channels, each comprising a different data collection routine for the input channels; a data acquisition circuit structured to interpret a plurality of detection values from the collected data, each corresponding to at least one of the input channels; and a data analysis circuit structured to analyze the collected data from the input channels and evaluate a selected collection routine of the data collector based on the analyzed collected data, wherein the selected collection routine is switched to a second collection routine based on a received data marketplace indicator.
    Type: Grant
    Filed: May 7, 2018
    Date of Patent: May 11, 2021
    Assignee: Strong Force IoT Portfolio 2016, LLC
    Inventors: Charles Howard Cella, Gerald William Duffy, Jr., Jeffrey P. McGuckin, Mehul Desai
  • Patent number: 10990979
    Abstract: Embodiments of an electronic fraud analysis platform system are disclosed which may be used to analyze tax returns for potential fraud. Analysis of tax return data using the tax return analysis platform computing systems and methods discussed herein may provide insight into whether a tax return may be fraudulent based on, for example, an initial screening component configured to filter tax returns which appear fraudulent due to missing or inaccurate information provided with the return; a device activity analysis component configured to identify whether a device used to submit a tax return or to provide further authentication information needed to complete processing of the return may have been used in other fraudulent activities; and a knowledge-based authentication component configured to identify potential fraudsters using dynamically generated questions for which fraudsters typically do not know the answers.
    Type: Grant
    Filed: June 17, 2019
    Date of Patent: April 27, 2021
    Assignee: Experian Information Solutions, Inc.
    Inventors: Neli Coleman, Lloyd Laudorn, Raymond Martin Boileau
  • Patent number: 10963637
    Abstract: A keyword extraction method is provided.
    Type: Grant
    Filed: March 25, 2019
    Date of Patent: March 30, 2021
    Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LTD
    Inventor: Xu Xiang Wang
  • Patent number: 10955852
    Abstract: A method and system for navigating a mobile system and corresponding mobile system is disclosed, in particular for autonomous mobile systems such as robots, for example lawn mowers or even smartphones. The mobile device includes at least one sensor, an electronic control unit, and an output unit. The method includes acquiring sensor data on an environment of the mobile device, calculating a gradient of a difference of a target environmental representation and a current environmental representation, and determining a movement direction to reach a target position corresponding to the target environmental representation based on the estimated gradient. The determined movement direction for navigating the mobile device is output, for example to a steering system of the mobile device or to a display. The method can include generating an environmental representation by performing unsupervised learning from the acquired sensor data.
    Type: Grant
    Filed: February 27, 2018
    Date of Patent: March 23, 2021
    Assignee: HONDA RESEARCH INSTITUTE EUROPE GMBH
    Inventors: Mathias Franzius, Benjamin Metka, Ute Bauer-Wersing
  • Patent number: 10949737
    Abstract: A method for operating an artificial neuron and an apparatus for performing the method are provided. The artificial neuron may calculate a change amount of an activation based on an input signal received via an input synapse, determine whether an event occurs in response to the calculated change amount of the activation, and transmit, to an output synapse, an output signal that corresponds to the event in response to an occurrence of the event.
    Type: Grant
    Filed: May 26, 2017
    Date of Patent: March 16, 2021
    Assignees: SAMSUNG ELECTRONICS CO., LTD., UNIVERSITAET ZUERICH
    Inventors: Jun Haeng Lee, Daniel Neil, Shih-Chii Liu, Tobi Delbruck
  • Patent number: 10943186
    Abstract: A machine learning model training method includes: classifying samples having risk labels in a training sample set as positive samples and classifying samples without risk labels in the training sample set as negative samples; training a risk model with a machine learning method based on the positive samples and the negative samples; obtaining a risk score for each of the negative samples based on the trained risk model; identifying one or more negative samples in the training sample set that have a risk score greater than a preset threshold value; re-classifying the one or more negative samples in the training sample set that have a risk score greater than the preset threshold value as re-classified positive samples to generate an updated training sample set from the training sample set; and re-training the risk model with the machine learning method based on the updated training sample set.
    Type: Grant
    Filed: March 9, 2020
    Date of Patent: March 9, 2021
    Assignee: ADVANCED NEW TECHNOLOGIES CO., LTD.
    Inventor: Long Guo
  • Patent number: 10929747
    Abstract: One embodiment provides a system comprising a memory device for maintaining deterministic neural data relating to a digital neuron and a logic circuit for deterministic neural computation and stochastic neural computation. Deterministic neural computation comprises processing a neuronal state of the neuron based on the deterministic neural data maintained. Stochastic neural computation comprises generating stochastic neural data relating to the neuron and processing the neuronal state of the neuron based on the stochastic neural data generated.
    Type: Grant
    Filed: April 10, 2018
    Date of Patent: February 23, 2021
    Assignee: International Business Machines Corporation
    Inventors: Rodrigo Alvarez-Icaza, John V. Arthur, Andrew S. Cassidy, Bryan L. Jackson, Paul A. Merolla, Dharmendra S. Modha, Jun Sawada
  • Patent number: 10922611
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining update rules for training neural networks. One of the methods includes generating, using a controller neural network, a batch of output sequences, each output sequence in the batch defining a respective update rule; for each output sequence in the batch: training a respective instance of a child neural network using the update rule defined by the output sequence; evaluating a performance of the trained instance of the child neural network on the particular neural network task to determine a performance metric for the trained instance of the child neural network on the particular neural network task; and using the performance metrics for the trained instances of the child neural network to adjust the current values of the controller parameters of the controller neural network.
    Type: Grant
    Filed: October 24, 2019
    Date of Patent: February 16, 2021
    Assignee: Google LLC
    Inventors: Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc V. Le
  • Patent number: 10891540
    Abstract: A method and computer system for managing a neural network. Data is sent into an input layer in a portion of layers of nodes in the neural network. The data moves on an encode path through the portion such that an output layer in the portion outputs encoded data. The encoded data is sent into the output layer on a decode path through the portion back to the input layer to obtain a reconstruction of the data by the input layer. A determination is made as to whether an undesired amount of error has occurred in the output layer based on the data sent into the input layer and the reconstruction of the data. A number of new nodes is added to the output layer when a determination is present that the undesired amount of the error occurred, enabling reducing the error using the number of the new nodes.
    Type: Grant
    Filed: December 18, 2015
    Date of Patent: January 12, 2021
    Assignee: National Technology & Engineering Solutions of Sandia, LLC
    Inventors: Timothy J. Draelos, James Bradley Aimone
  • Patent number: 10878319
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing a compressed recurrent neural network (RNN). One of the systems includes a compressed RNN, the compressed RNN comprising a plurality of recurrent layers, wherein each of the recurrent layers has a respective recurrent weight matrix and a respective inter-layer weight matrix, and wherein at least one of recurrent layers is compressed such that a respective recurrent weight matrix of the compressed layer is defined by a first compressed weight matrix and a projection matrix and a respective inter-layer weight matrix of the compressed layer is defined by a second compressed weight matrix and the projection matrix.
    Type: Grant
    Filed: December 29, 2016
    Date of Patent: December 29, 2020
    Assignee: Google LLC
    Inventors: Ouais Alsharif, Rohit Prakash Prabhavalkar, Ian C. McGraw, Antoine Jean Bruguier
  • Patent number: 10878144
    Abstract: Systems and methods are disclosed for managing the processing and execution of models that may have been developed on a variety of platforms. A multi-model execution module specifying a sequence of models to be executed may be determined. A multi-platform model processing and execution management engine may execute the multi-model execution module internally, or outsource the execution to a distributed model execution orchestration engine. A model data monitoring and analysis engine may monitor the internal and/or distributed execution of the multi-model execution module, and may further transmit notifications to various computing systems.
    Type: Grant
    Filed: August 10, 2017
    Date of Patent: December 29, 2020
    Assignee: Allstate Insurance Company
    Inventors: Robert Andrew Nendorf, Nilesh Malpekar, Mark V. Slusar, Joseph Alan Kleinhenz, Robert Andrew Kreek, Patrick O'Reilly
  • Patent number: 10875176
    Abstract: Systems, methods, and computer program products for programming a controller to control a process. A training module is trained to provide predicted responses of the process to control signals using a generic simulator to model the process. Once the training module has been trained, the controller is programmed to control the process by coupling the controller to the training module and using the training module to generate predicted responses of the process to the reception of control signals from the controller. As part of the programming process, one or more parameters of a control program resident on the controller may be adjusted to reduce errors between the responses predicted by the training module and the desired responses intended to be caused by the control signals.
    Type: Grant
    Filed: April 4, 2018
    Date of Patent: December 29, 2020
    Assignee: KUKA Systems North America LLC
    Inventors: Helmuth Radrich, Aaron Dennis
  • Patent number: 10877924
    Abstract: Embodiments of this application disclose an instruction set processing method based on a chip architecture and apparatus, and a computer-readable storage medium. The method includes compiling a deep learning model based on the architecture of the chip, to obtain a deep learning instruction set corresponding to the chip; compressing the deep learning instruction set, to obtain a compressed instruction set; and storing the compressed instruction set in an instruction set buffer of the chip by writing in a register, the compressed instructions executing a task.
    Type: Grant
    Filed: May 1, 2020
    Date of Patent: December 29, 2020
    Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
    Inventors: Yuwei Wang, Xiaoyu Yu, Lixin Zhang, Bo Zhang
  • Patent number: 10867599
    Abstract: Determining a dialog state of an electronic dialog that includes an automated assistant and at least one user, and performing action(s) based on the determined dialog state. The dialog state can be represented as one or more slots and, for each of the slots, one or more candidate values for the slot and a corresponding score (e.g., a probability) for each of the candidate values. Candidate values for a slot can be determined based on language processing of user utterance(s) and/or system utterance(s) during the dialog. In generating scores for candidate value(s) of a given slot at a given turn of an electronic dialog, various features are determined based on processing of the user utterance and the system utterance using a memory network. The various generated features can be processed using a scoring model to generate scores for candidate value(s) of the given slot at the given turn.
    Type: Grant
    Filed: October 12, 2017
    Date of Patent: December 15, 2020
    Assignee: GOOGLE LLC
    Inventors: Abhinav Rastogi, Larry Paul Heck, Dilek Hakkani-Tur
  • Patent number: 10861106
    Abstract: Computing systems, computer-implemented methods, articles of manufacture for making personalized assessments regarding whether a taxpayer should be presented with a standardized flow of interview screens, questions or topics, or with an itemized deduction flow of interview screens, questions or topics. This assessment is made utilizing a generated user interface and analytic data elements that generate outputs that reflect the taxpayer's data, e.g., in the form of ranges of numerical data that are based on the taxpayer's data. User interface elements representing response options in the form of range data may be selected by the user without entering specific electronic tax return data for the purpose of making standardized v. itemized determinations and to determine which questions or topics can be bypassed.
    Type: Grant
    Filed: January 14, 2016
    Date of Patent: December 8, 2020
    Assignee: INTUIT INC.
    Inventors: Sharon E. Hunt, Yao H. Morin, Alexis Hartford, Brian Lyle Hofmaister, Andrew Roe, Varadarajan Sriram, Sylvia R. Knust, Thai D. Dang, Robert E. Bamford, Carol Ann Howe
  • Patent number: 10846716
    Abstract: Some embodiments relate to techniques for facilitating training of a prediction model for estimating a threshold score for a user. In some embodiments, a first image of at least a first portion of a first vehicle may be provided to a client device, where the first image may be associated with a first damage score. From the client device, a user-provided score for the first image may be received. Based on the user-provided score, a second image of at least a second portion of a second vehicle may be provided to the client device, where the second image may be associated with a second damage score. Training data may be generated based on the first damage score and the second damage score, and the training data may be provided to a prediction model to train the prediction model to estimate a threshold score for a user.
    Type: Grant
    Filed: December 27, 2019
    Date of Patent: November 24, 2020
    Assignee: Capital One Services, LLC
    Inventors: Chih-Hsiang Chow, Elizabeth Furlan, Steven Dang
  • Patent number: 10846588
    Abstract: A system for compressed data storage using a neural network. The system comprises a memory comprising a plurality of memory locations configured to store data; a query neural network configured to process a representation of an input data item to generate a query; an immutable key data store comprising key data for indexing the plurality of memory locations; an addressing system configured to process the key data and the query to generate a weighting associated with the plurality of memory locations; a memory read system configured to generate output memory data from the memory based upon the generated weighting associated with the plurality of memory locations and the data stored at the plurality of memory locations; and a memory write system configured to write received write data to the memory based upon the generated weighting associated with the plurality of memory locations.
    Type: Grant
    Filed: September 27, 2019
    Date of Patent: November 24, 2020
    Assignee: DeepMind Technologies Limited
    Inventors: Jack William Rae, Timothy Paul Lillicrap, Sergey Bartunov
  • Patent number: 10846331
    Abstract: A plurality of entities relating to popular search queries are identified. A set of entities representing musical artists or events is selected from the plurality of entities. Based on a history of online actions of a user, a subset of the selected set of entities that is relevant to the user is determined, and personalized music recommendations are created for the user, where the personalized music recommendations comprise music content associated with the determined subset of entities that each represent a musical artist or event relating to the popular search queries. The personalized music recommendations are provided for presentation to the user.
    Type: Grant
    Filed: September 28, 2017
    Date of Patent: November 24, 2020
    Assignee: GOOGLE LLC
    Inventors: Lawrence Ip, Sean Marney, Shengwei Jiang, Vivek Sharma, Srivaths Ranganathan, Yuh-jiun Wang
  • Patent number: 10831447
    Abstract: Electric charges depending on values of N+ electric signals and values of corresponding positive loads are held in first capture-and-storage circuitry. Electric charges having a size depending on values of (N?N+) electric signals and corresponding absolute values of negative loads are held in second capture-and-storage circuitry. A sum of N+ multiplied values obtained by multiplying each of the positive loads by each of the values of the N+ electric signals is calculated when a voltage held in the first capture-and-storage circuitry reaches a first threshold. A sum of (N?N+) multiplied values obtained by multiplying each of the absolute values by each of the values of the (N?N+) electric signals is calculated when a voltage held in the second capture-and-storage circuitry reaches a second threshold A sum of N multiplied values is obtained by subtracting the sum of (N?N+) multiplied values from the sum of N+ multiplied values.
    Type: Grant
    Filed: August 3, 2017
    Date of Patent: November 10, 2020
    Assignee: Sony Corporation
    Inventors: Takashi Morie, Quan Wang, Hakaru Tamukoh
  • Patent number: RE48438
    Abstract: An accelerator system is implemented on an expansion card comprising a printed circuit board having (a) one or more graphics processing units (GPUs), (b) two or more associated memory banks (logically or physically partitioned), (c) a specialized controller, and (d) a local bus providing signal coupling compatible with the PCI industry standards. The controller handles most of the primitive operations to set up and control GPU computation. Thus, the computer's central processing unit (CPU) can be dedicated to other tasks. In this case a few controls (simulation start and stop signals from the CPU and the simulation completion signal back to CPU), GPU programs and input/output data are exchanged between CPU and the expansion card. Moreover, since on every time step of the simulation the results from the previous time step are used but not changed, the results are preferably transferred back to CPU in parallel with the computation.
    Type: Grant
    Filed: November 9, 2017
    Date of Patent: February 16, 2021
    Assignee: Neurala, Inc.
    Inventors: Anatoli Gorchetchnikov, Heather Marie Ames, Massimiliano Versace, Fabrizio Santini