Patents Examined by Li B. Zhen
  • Patent number: 11195094
    Abstract: A method of updating a neural network may be provided. A method may include selecting a number of neurons for a layer for a neural network such that the number of neurons in the layer is less than at least one of a number of neurons in a first layer of the neural network and a number of neurons in a second, adjacent layer of the neural network. The method may further include and at least one of inserting the layer between the first layer and the second layer of the neural network and replacing one of the first layer and the second layer with the layer to reduce a number of connections in the neural network.
    Type: Grant
    Filed: January 17, 2017
    Date of Patent: December 7, 2021
    Assignee: FUJITSU LIMITED
    Inventor: Michael Lee
  • Patent number: 11187446
    Abstract: Embodiments for fault diagnosis and analysis of refrigeration condenser systems by a processor. An energy usage anomaly is detected in a condenser by comparing an energy usage profile of the condenser against a knowledge domain of energy usage standards and energy usage standards anomalies.
    Type: Grant
    Filed: April 19, 2017
    Date of Patent: November 30, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Niall Brady, Paulito P. Palmes
  • Patent number: 11188828
    Abstract: A semantic embedding model using geometrical set-centric approach to capture both ABox and TBox representational models is disclosed. The model transforms a semantic-rich knowledge graph into a set of overlapping, disjoint, and/or subsumed n-dimensional spheres that captures and represents semantics embedded in the knowledge graph.
    Type: Grant
    Filed: January 31, 2017
    Date of Patent: November 30, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Gonzalo Ignacio Diaz Caceres, Achille Belly Fokoue-Nkoutche, Mohammad Sadoghi Hamedani, Oktie Hassanzadeh, Mariano Rodriguez Muro
  • Patent number: 11173599
    Abstract: Some implementations of this specification are directed generally to deep machine learning methods and apparatus related to predicting motion(s) (if any) that will occur to object(s) in an environment of a robot in response to particular movement of the robot in the environment. Some implementations are directed to training a deep neural network model to predict at least one transformation (if any), of an image of a robot's environment, that will occur as a result of implementing at least a portion of a particular movement of the robot in the environment. The trained deep neural network model may predict the transformation based on input that includes the image and a group of robot movement parameters that define the portion of the particular movement.
    Type: Grant
    Filed: May 16, 2017
    Date of Patent: November 16, 2021
    Assignee: GOOGLE LLC
    Inventors: Sergey Levine, Chelsea Finn, Ian Goodfellow
  • Patent number: 11170313
    Abstract: One factor in limiting the speed of conventional implementations of mixture models is that the algorithm involves many decisions where different operations are fetched and performed depending on the outcome of the decisions. These decisions cause flushing of the pipeline, and thus prevent the realization of a highly parallel pipeline in a processor. Without parallelism, the throughput of the pipeline in the processor, i.e., the ability to process many samples of the digital input at a time, is limited. To alleviate this issue, implementation of the mixture model is reformulated, among other things, by embedding decisions into the process flow as multiplicative factors. The resulting implementation alleviates the need to use if-else statements for the decisions and reduces the number of times the pipeline has to be flushed. The implementation enables a pipeline with a higher degree of parallelism and thereby increases throughput and speed of the implementation.
    Type: Grant
    Filed: December 18, 2014
    Date of Patent: November 9, 2021
    Assignee: Analog Devices International Unlimited Company
    Inventor: Raka Singh
  • Patent number: 11164085
    Abstract: A computer-implemented method for training a neural network system. The method includes receiving at least a first data vector at a first layer of the neural network system; applying a function to the first data vector to generate at least a second data vector, wherein the function is based on a layer parameter of the first layer that includes at least a weight matrix of the first layer; comparing at least the first data vector and the second data vector to obtain a loss value that represents a difference between the first data vector and the second data vector; updating the layer parameter based on the loss value; and adjusting the layer parameter based on a comparison of the updated layer parameter with a threshold value of the first layer.
    Type: Grant
    Filed: April 25, 2019
    Date of Patent: November 2, 2021
    Assignee: BOOZ ALLEN HAMILTON INC.
    Inventor: Arash Rahnama Moghaddam
  • Patent number: 11164082
    Abstract: The present disclosure provides methods for applying artificial neural networks to flow cytometry data generated from biological samples to diagnose and characterize cancer in a subject. The disclosure also provides methods of training, testing, and validating artificial neural networks.
    Type: Grant
    Filed: June 6, 2019
    Date of Patent: November 2, 2021
    Assignee: ANIXA DIAGNOSTICS CORPORATION
    Inventors: Amit Kumar, John Roop, Anthony J. Campisi, George Dominguez
  • 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: 11151443
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting neural networks with an external memory. One of the systems includes a sparse memory access subsystem that is configured to perform operations comprising generating a sparse set of reading weights that includes a respective reading weight for each of the plurality of locations in the external memory using the read key, reading data from the plurality of locations in the external memory in accordance with the sparse set of reading weights, generating a set of writing weights that includes a respective writing weight for each of the plurality of locations in the external memory, and writing the write vector to the plurality of locations in the external memory in accordance with the writing weights.
    Type: Grant
    Filed: February 3, 2017
    Date of Patent: October 19, 2021
    Assignee: DeepMind Technologies Limited
    Inventors: Ivo Danihelka, Gregory Duncan Wayne, Fu-min Wang, Edward Thomas Grefenstette, Jack William Rae, Alexander Benjamin Graves, Timothy Paul Lillicrap, Timothy James Alexander Harley, Jonathan James Hunt
  • Patent number: 11151617
    Abstract: A recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.
    Type: Grant
    Filed: April 15, 2015
    Date of Patent: October 19, 2021
    Assignee: Nara Logics, Inc.
    Inventors: Nathan R. Wilson, Emily A. Hueske, Thomas C. Copeman
  • Patent number: 11151452
    Abstract: A system is configured to receive first training data, train a first neural network (NN) based on the first training data, receive second training data, train a second NN based on the second training data, receive a first plain English phrase, provide the first plain English phrase to the first NN, generate, via the first NN, one or more first legal clauses based on the first plain English phrase, receive a second plain English phrase, provide the second plain English phrase to the first NN, generate, via the first NN, one or more second legal clauses based on the second plain English phrase, provide the one or more first legal clauses and the one or more second legal clauses to the second NN, and generate, via the second NN, a legal document based on the one or more first legal clauses and the one or more second legal clauses.
    Type: Grant
    Filed: February 15, 2019
    Date of Patent: October 19, 2021
    Assignee: CAPITAL ONE SERVICES, LLC
    Inventors: Austin Walters, Jeremy Edward Goodsitt, Fardin Abdi Taghi Abad, Reza Farivar, Vincent Pham, Mark Watson, Kenneth Taylor, Anh Truong
  • Patent number: 11144718
    Abstract: In configuring a processing system with an application made up of machine learning components, where the application has been trained on a set of training data, the application is executed on the processing system using another set of training data. Outputs of the application produced from the other set of training data identified that concur with ground truth data are identified. The components are adapted to produce outputs of the application that concur with the ground truth data using the identified outputs of the application.
    Type: Grant
    Filed: February 28, 2017
    Date of Patent: October 12, 2021
    Assignee: International Business Machines Corporation
    Inventors: Youngja Park, Siddharth A. Patwardhan
  • Patent number: 11144839
    Abstract: A device may receive issue resolution information, associated with a cognitive model, including an item of issue resolution information that describes an issue and a resolution to the issue. The device may assign the item of issue resolution information to a domain hierarchy, where the assigning is associated with a first user. The device may generate a question and an answer corresponding to the item of issue resolution information, where the generating of the question and the answer is associated with a second user. The device may approve the question and the answer, where the approving is associated with a third user. The device may generate a question/answer (QA) pair for the question and the answer. The device may create a data corpus including the QA pair, and provide the data corpus to cause the cognitive model to be trained based on the data corpus.
    Type: Grant
    Filed: January 20, 2017
    Date of Patent: October 12, 2021
    Assignee: Accenture Global Solutions Limited
    Inventors: Nitin Madhukar Sawant, Rajendra T. Prasad, Bhavin Mehta, Jayant Swamy, Gopali Raval Contractor, Manish Vijaywargiya
  • Patent number: 11144834
    Abstract: A predictive analytics system and method in the setting of multi-class classification are disclosed, for identifying systematic changes in an evaluation dataset processed by a fraud-detection model by examining the time series histories of an ensemble of entities such as accounts. The ensemble of entities is examined and processed both individually and in aggregate, via a set of features determined previously using a distinct training dataset. The specific set of features in question may be calculated from the entity's time series history, and may or may not be used by the model to perform the classification. Certain properties of the detected changes are measured and used to improve the efficacy of the predictive model.
    Type: Grant
    Filed: October 9, 2015
    Date of Patent: October 12, 2021
    Assignee: FAIR ISAAC CORPORATION
    Inventors: Scott Michael Zoldi, Jim Coggeshall, Yuting Jia
  • Patent number: 11138501
    Abstract: A method for hardware-implemented training of a feedforward artificial neural network is provided. The method comprises: generating a first output signal by processing an input signal with the network, wherein a cost quantity assumes a first cost value; measuring the first cost value; defining a group of at least one synaptic weight of the network for variation; varying each weight of the group by a predefined weight difference; after the variation, generating a second output signal from the input signal to measure a second cost value; comparing the first and second cost values; and determining, based on the comparison, a desired weight change for each weight of the group such that the cost function does not increase if the respective desired weight changes are added to the weights of the group. The desired weight change is based on the weight difference times ?1, 0, or +1.
    Type: Grant
    Filed: February 22, 2018
    Date of Patent: October 5, 2021
    Assignee: International Business Machines Corporation
    Inventors: Stefan Abel, Veeresh Vidyadhar Deshpande, Jean Fompeyrine, Abu Sebastian
  • Patent number: 11138515
    Abstract: A data analysis apparatus executes: acquiring a group of learning input data; setting a plurality of first hash functions; substituting each learning input data in each of the plurality of first hash functions to thereby calculate a plurality of first hash values; selecting, for the group of learning input data and for each of the plurality of first hash functions, a specific first hash value that satisfies a predetermined statistical condition from among the plurality of first hash values; setting a second hash function; substituting, for the group of learning input data, each specific first hash value in the second hash function to thereby calculate a plurality of second hash values; and generating a learning feature vector that indicates features of the group of learning input data by aggregating the plurality of second hash values corresponding to respective specific first hash values and obtained as a result of the calculation.
    Type: Grant
    Filed: September 30, 2015
    Date of Patent: October 5, 2021
    Assignee: Hitachi, Ltd.
    Inventor: Takuma Shibahara
  • Patent number: 11138523
    Abstract: A method, system and computer-usable medium are disclosed for reducing labeled data imbalances when training an active learning system. The ratio of instances having positive labels or negative labels in a collection of labeled instances associated with an input category used for learning is determined. A first instance for annotation is selected from a collection of unlabeled instances if a first threshold for negative instances, and a first threshold confidence level of being a positive instance of the input category, have been met. A second instance for annotation is selected if a second threshold for positive instances, and a second threshold confidence level of being a negative instance of the input category, have been met. The first and second instances are respectively annotated with a positive and negative label and added to the collection of labeled instances, which are then used for training.
    Type: Grant
    Filed: July 27, 2016
    Date of Patent: October 5, 2021
    Assignee: International Business Machines Corporation
    Inventors: Md Faisal M. Chowdhury, Sarthak Dash, Alfio M. Gliozzo
  • Patent number: 11132619
    Abstract: Some embodiments perform, in a multi-layer neural network in a computing device, a convolution operation on input feature maps with multiple convolutional filters. The convolutional filters have multiple filter precisions. In other embodiments, electronic design automation (EDA) systems, methods, and computer-readable media are presented for adding such a multi-layer neural network into an integrated circuit (IC) design.
    Type: Grant
    Filed: February 24, 2017
    Date of Patent: September 28, 2021
    Assignee: Cadence Design Systems, Inc.
    Inventors: Raúl Alejandro Casas, Samer Lutfi Hijazi, Piyush Kaul, Rishi Kumar, Xuehong Mao, Christopher Rowen
  • Patent number: 11120299
    Abstract: An artificial intelligence (“AI”) engine having multiple independent processes on one or more computing platforms is disclosed, where the one or more computing platforms are located on premises of an organization such that i) the one or more computing platforms are configurable for one or more users in the organization having at least administrative rights on the one or more computing platforms in order to configure hardware components thereof to execute and load the multiple independent processes of the AI engine; ii) the one or more users of the organization are able to physically access the one or more computing platforms; and iii) the hardware components of the one or more computing platforms are connected to each other through a Local Area Network (LAN), and the LAN is configurable such that the one or more users in the organization have a right to control an operation of the LAN.
    Type: Grant
    Filed: June 14, 2018
    Date of Patent: September 14, 2021
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Matthew Haigh, Chetan Desh, Jett Jones, Shane Arney
  • Patent number: 11119464
    Abstract: A machine learning device of a controller observes, as state variables that express a current state of an environment, feeding amount data indicating a feeding amount per unit cycle of a tool and vibration amount data indicating a vibration amount of a cutting part of the tool when the cutting part of the tool passes through the workpiece. In addition, the machine learning device acquires determination data indicating a propriety determination result of the vibration amount of the cutting part of the tool when the cutting part of the tool passes through the workpiece. Then, the machine learning device learns the feeding amount per unit cycle of the tool when the cutting part of the tool passes through the workpiece in association with the vibration amount data, using the state variables and the determination data.
    Type: Grant
    Filed: May 18, 2018
    Date of Patent: September 14, 2021
    Assignee: Fanuc Corporation
    Inventor: Yuanming Xu