Patents Examined by Stanley K Hill
  • Patent number: 11195118
    Abstract: A mechanism is provided in a data processing system comprising a processor and a memory. The memory comprises instructions which are executed by the processor to cause the processor to be specifically configured to implement a recognizer module for detecting user input in a cognitive environment. The recognizer module receives sensor signals from at least one wearable device being worn by a user. The recognizer module analyzes the sensor signals using a machine learning model to determine at least one user input indicator describing user input activity of the user. The recognizer module communicates the at least one user input indicator to a cognitive system executing within the cognitive environment. The cognitive system performs at least one cognitive action based on the at least one user input indicator.
    Type: Grant
    Filed: November 20, 2017
    Date of Patent: December 7, 2021
    Assignee: International Business Machines Corporation
    Inventor: Victor C. Dibia
  • Patent number: 11188817
    Abstract: Methods and system for converting a plurality of weights of a filter of a Deep Neural Network (DNN) in a first number format to a second number format, the second number format having less precision than the first number format, to enable the DNN to be implemented in hardware logic.
    Type: Grant
    Filed: August 24, 2020
    Date of Patent: November 30, 2021
    Assignee: Imagination Technologies Limited
    Inventors: Cagatay Dikici, Paul Brasnett, Muhammad Asad, Stephen Morphet
  • Patent number: 11188813
    Abstract: The invention is directed to a hybrid architecture that comprises a stacked autoencoder and a deep echo state layer for temporal pattern discovery in high-dimensional sequence data. The stacked autoencoder plays a preprocessing role that exploits spatial structure in data and creates a compact representation. The compact representation is then fed to the echo state layer in order to generate a short-term memory of the inputs. The output of the network may be trained to generate any target output.
    Type: Grant
    Filed: March 1, 2018
    Date of Patent: November 30, 2021
    Inventors: Alireza Goudarzi, Darko Stefanovic, Steven Wayde Graves, Daniel Kalb
  • Patent number: 11188789
    Abstract: One embodiment provides a method comprising receiving a training set comprising a plurality of data points, where a neural network is trained as a classifier based on the training set. The method further comprises, for each data point of the training set, classifying the data point with one of a plurality of classification labels using the trained neural network, and recording neuronal activations of a portion of the trained neural network in response to the data point. The method further comprises, for each classification label that a portion of the training set has been classified with, clustering a portion of all recorded neuronal activations that are in response to the portion of the training set, and detecting one or more poisonous data points in the portion of the training set based on the clustering.
    Type: Grant
    Filed: August 7, 2018
    Date of Patent: November 30, 2021
    Assignee: International Business Machines Corporation
    Inventors: Bryant Chen, Wilka Carvalho, Heiko H. Ludwig, Ian Michael Molloy, Taesung Lee, Jialong Zhang, Benjamin J. Edwards
  • Patent number: 11187619
    Abstract: Method and apparatus for detecting vibrational and/or acoustic transfers in a mechanical system A method and apparatus for detecting vibro-acoustic transfers in a mechanical system are provided. The method comprises: while operating the mechanical system, acquiring, at each of multiple input points, an input signal indicative of a mechanical load acting on the input point, and acquiring, at a response point, a response signal indicative of a mechanical response; training a neural network device using the input signals acquired at the input points and using the response signal acquired at the response point; and, for each of the input points: providing only the input signal acquired at the respective input point to the trained neural network device and obtaining, from the neural network device, a contribution signal indicative of a predicted contribution of the respective input signal to the response signal.
    Type: Grant
    Filed: May 30, 2018
    Date of Patent: November 30, 2021
    Assignee: SIEMENS INDUSTRY SOFTWARE NV
    Inventors: Fabio Marques dos Santos, Peter Mas
  • Patent number: 11188839
    Abstract: A method includes identifying a graph of a social network, the graph including nodes and edges, each edge connects two nodes, some of the plurality of nodes represent members of the social network, some edges of the plurality of edges represent a relationship between two associated nodes, creating a first taste profile for a first member node, the taste profile identifying a first entity of interest to the first member, identifying a second member node based on an absence of a second taste profile for the second member node, the second member node is connected to the first member node, determining that the second member node is connected to the first member node, creating a second taste profile for the second member node using the first taste profile, and providing a recommendation to a member associated with the second member node based on the created second taste profile.
    Type: Grant
    Filed: August 4, 2016
    Date of Patent: November 30, 2021
    Assignee: eBay Inc.
    Inventors: Thomas Pinckney, Christopher Dixon, Matthew Ryan Gattis
  • Patent number: 11182671
    Abstract: A system configured for learning new trained concepts used to retrieve content relevant to the concepts learned. The system may comprise one or more hardware processors configured by machine-readable instructions to obtain one or more digital media items. The one or more hardware processors may be further configured to obtain an indication conveying a concept to be learned from the one or more digital media items. The one or more hardware processors may be further configured to receive feedback associated with individual ones of the one or more digital media items. The one or more hardware processors may be configured to obtain individual neural network representations for the individual ones of the one or more digital media items. The one or more hardware processors may be configured to determine a trained concept based on the feedback and the neural network representations of the one or more digital media items.
    Type: Grant
    Filed: April 17, 2019
    Date of Patent: November 23, 2021
    Assignee: CLARIFAL, INC.
    Inventor: Matthew D. Zeiler
  • Patent number: 11176491
    Abstract: Embodiments for intelligent learning for explaining anomalies to a user by a processor. One or more anomalous records may be identified in a knowledge base. A list of ranked candidate explanations may be generated for the one or more anomalous records. An active learning dialog may be initiated with one or more users to increase accuracy of the knowledge base, a domain knowledge, and each of the ranked candidate explanations.
    Type: Grant
    Filed: October 11, 2018
    Date of Patent: November 16, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Joao H. Bettencourt-Silva, Vanessa Lopez Garcia, Valentina Rho, Theodora Brisimi
  • Patent number: 11176441
    Abstract: Mechanisms are provided to implement a medical coding engine to perform medical coding using a neural network architecture that leverages hierarchical semantics between medical concepts. The medical coding engine configures a medical coding neural network to comprise an first layer of nodes comprising preferred terminology (PT) nodes, a second layer comprising lowest level terminology (LLT) nodes, and a third layer comprising weighted values for each connection between each PT node and each LLT node forming a PT node/LLT node connection. Responsive to receiving an adverse event from a cognitive system, a PT node is identified in the first layer associated with a citation from the adverse event. One or more nodes are identified from the second layer based on the identification PT node and a weight associated with the PT node/LLT node connection. A medical code associated with each the one or more LLT nodes is then output.
    Type: Grant
    Filed: May 1, 2018
    Date of Patent: November 16, 2021
    Assignee: International Business Machines Corporation
    Inventors: Nitish Aggarwal, Sheng Hua Bao, Pathirage Perera
  • Patent number: 11176442
    Abstract: A synchrophasor measurement-based disturbance identification method is described considering different penetration levels of renewable energy. A differential Teager-Kaiser energy operator (dTKEO)-based algorithm is first utilized to improve multiple-disturbances detection accuracy. Then, feature extractions via the integrated additive angular margin (AAM) loss and the long short-term memory (LSTM) network is described. This enables one to deal with intra-class similarity and inter-class variance of disturbances when high penetration renewable energy occurs. With the extracted features, a multi-stage weighted summing (MSWS) loss-based criterion is described for adaptive data window determination and fast disturbance pre-classification. Finally, the re-identification model based on feature similarity is established to identify unknown disturbances, a challenge for existing machine learning algorithms.
    Type: Grant
    Filed: February 11, 2021
    Date of Patent: November 16, 2021
    Assignee: North China Electric Power University
    Inventors: Hao Liu, Tianshu Bi, Zikang Li, Ke Jia
  • Patent number: 11158398
    Abstract: Histopathological scoring can be based on the areas of certain types of cells or the expression of genotypic or phenotypic characteristics of those cells, as identified by a biological assay. Automating a scoring process with an image analysis algorithm includes correctly delineating the areas of interest, a process known as segmentation. The present systems and methods accomplish this segmentation using a generative adversarial network trained to generate masks covering each area of interest. The invention can perform both segmentation and classification by using a separate image band for each class. A scoring algorithm may utilize the classifications of, for example, a tumor area and an area of immune cell staining by interpreting the separate image bands of each area. Classification problems with more bands would use images with the equivalent number of bands. There is no limit to the number of bands an image can encode for each pixel.
    Type: Grant
    Filed: February 5, 2021
    Date of Patent: October 26, 2021
    Assignee: Origin Labs, Inc.
    Inventors: Darick M. Tong, Nishant Borude, Nivedita Suresh, Evan Szu, Clifford Szu
  • Patent number: 11157793
    Abstract: The method for query training can include: determining a graphical representation, determining an inference network based on the graphical representation, determining a query distribution, sampling one or more train queries from the query distribution, and optionally determining a trained inference network by training the untrained inference network using the train query. The method can optionally include determining an inference query and determining an inference query result for the inference query using the trained inference network.
    Type: Grant
    Filed: October 22, 2020
    Date of Patent: October 26, 2021
    Assignee: Vicarious FPC, Inc.
    Inventors: Miguel Lazaro-Gredilla, Wolfgang Lehrach, Nishad Gothoskar, Guangyao Zhou, Antoine Dedieu, Dileep George
  • Patent number: 11144823
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for hierarchical weight-sparse convolution processing are described. An exemplary method comprises: obtaining an input tensor and a filter at a convolution layer of a neural network; segmenting the filter into a plurality of sub-filters; generating a hierarchical bit representation of the filter representing a plurality of non-zero weights in the filter, wherein the hierarchical bit representation comprises a first layer, the first layer comprising a plurality of bits respectively corresponding to the plurality of sub-filters in the filter, each of the plurality of bits indicating whether the corresponding sub-filter includes at least one non-zero weight; and performing multiply-and-accumulate (MAC) operations based on the hierarchical bit representation of the filter and the input tensor.
    Type: Grant
    Filed: April 5, 2021
    Date of Patent: October 12, 2021
    Assignee: MOFFETT TECHNOLOGIES CO., LIMITED
    Inventors: Zhibin Xiao, Enxu Yan, Wei Wang, Yong Lu
  • Patent number: 11144827
    Abstract: A supervised learning processing (SLP) system and non-transitory, computer program product provides cooperative operation of a network of supervised learning processors to concurrently distribute supervised learning processor training, generate predictions, and provide prediction driven responses to input objects, such as NL statements. The SLP system includes SLP stages that are distributed across multiple SLP subsystems. Concurrently training SLP's provides accurate predictions of input objects and responses thereto, the SLP system and non-transitory, computer program product enhance the network by providing high quality value predictions and responses and avoiding potential training and operational delays. The SLP system can enhance the network of SLP subsystems by providing flexibility to incorporate multiple SLP models into the network and train at least a proper subset of the SLP models while concurrently using the SLP system and non-transitory, computer program product in commercial operation.
    Type: Grant
    Filed: June 5, 2018
    Date of Patent: October 12, 2021
    Assignee: OJO Labs, Inc.
    Inventors: Joshua Howard Levy, Jacy Myles Legault, David Robert Rubin, John Kenneth Berkowitz, David Ross Pratt
  • Patent number: 11144825
    Abstract: A method for creating an interpretable model for healthcare predictions includes training, by a deep learning processor, a neural network to predict health information by providing training data, including multiple combinations of measured or observed health metrics and corresponding medical results, to the neural network. The method also includes determining, by the deep learning processor and using the neural network, prediction data including predicted results for the measured or observed health metrics for each of the multiple combinations of the measured or observed health metrics based on the training data. The method also includes training, by the deep learning processor or a learning processor, an interpretable machine learning model to make similar predictions as the neural network by providing mimic data, including combinations of the measured or observed health metrics and corresponding predicted results of the prediction data, to the interpretable machine learning model.
    Type: Grant
    Filed: December 1, 2017
    Date of Patent: October 12, 2021
    Assignee: UNIVERSITY OF SOUTHERN CALIFORNIA
    Inventors: Yan Liu, Zhengping Che, Sanjay Purushotham
  • Patent number: 11126946
    Abstract: The present disclosure relates to system(s) and method(s) for continuous business optimization of an organization based on a cognitive decision making process. In one embodiment, the method comprises generating an opportunity instance package associated with a business opportunity from a set of business opportunities associated with an organization based on analysis of a stream of raw data. Further, the method comprises generating a strategy using the opportunity instance package and one or more of a predictive technique, prescriptive technique and optimization technique. Furthermore, the method comprises generating a set of instruction associated with one or more actors associated with the organization based on the strategy, thereby enabling continuous business optimization of the organization based on a cognitive decision-making process.
    Type: Grant
    Filed: October 19, 2017
    Date of Patent: September 21, 2021
    Assignee: DIWO, LLC
    Inventors: Satyendra Pal Rana, Chandra Puttanna Keerthy, Krishna Prakash Kallakuri
  • Patent number: 11100414
    Abstract: One or more multi-layer systems are used to perform inference. A multi-layer system may correspond to a node that receives a set of sensory input data for hierarchical processing, and may be grouped to perform processing for sensory input data. Inference systems at lower layers of a multi-layer system pass representation of objects to inference systems at higher layers. Each inference system can perform inference and form their own versions of representations of objects, regardless of the level and layer of the inference systems. The set of candidate objects for each inference system is updated to those consistent with feature-location representations for the sensors as well as object representations at lower layers. The set of candidate objects is also updated to those consistent with candidate objects from other inference systems, such as inference systems at other layers of the hierarchy or inference systems included in other multi-layer systems.
    Type: Grant
    Filed: February 5, 2019
    Date of Patent: August 24, 2021
    Assignee: Numenta, Inc.
    Inventors: Jeffrey C. Hawkins, Subutai Ahmad
  • Patent number: 11093860
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a plurality of model representations of predictive models, each model representation associated with a respective user and expresses a respective predictive model, and selecting a model implementation for each of the model representations based on one or more system usage properties associated with the user associated with the corresponding model representation.
    Type: Grant
    Filed: December 3, 2018
    Date of Patent: August 17, 2021
    Assignee: Google LLC
    Inventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
  • Patent number: 11093828
    Abstract: A machine learning device is connected to a fiber laser device. The machine learning device observes, as a state variable representing a driving state of the fiber laser device, a state quantity including time-series data on output light detection results obtained by detecting a light output of laser light emitted from the fiber laser device and time-series data on reflected light detection results obtained by detecting reflected light of the laser light, and acquires determination data representing a failure occurrence situation in the fiber laser device as determined from a difference between the output light detection results and a light output instruction of the fiber laser device. The machine learning device learns a boundary condition for failure occurrence caused by the reflected light by using the state variable and the determination data.
    Type: Grant
    Filed: January 4, 2019
    Date of Patent: August 17, 2021
    Assignee: Fanuc Corporation
    Inventors: Hiroshi Takigawa, Hisatada Machida
  • Patent number: 11080159
    Abstract: A monitor-mine-manage cycle is described, for example, for managing a data center, a manufacturing process, an engineering process or other processes. In various example, the following steps are performed as a continuous automated loop: receiving raw events from an observed system; monitoring the raw events and transforming them into complex events; mining the complex events and reasoning on results; making a set of proposed actions based on the mining; and managing the observed system by applying one or more of the proposed actions to the system. In various examples, the continuous automated loop proceeds while raw events are continuously received from the observed system and monitored. In some examples an application programming interface is described comprising programming statements which allow a user to implement a monitor-mine-manage loop.
    Type: Grant
    Filed: September 20, 2018
    Date of Patent: August 3, 2021
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Panos Periorellis, Eldar Akchurin, Joris Claessens, Ivo Jose Garcia dos Santos, Oliver Nano