Patents Examined by Alexey Shmatov
  • Patent number: 10482658
    Abstract: Systems, devices and methods for controlling remote devices by modification of visual data prior to presentation to a person in order to make the person's response effectively the same as if the person were responding to data transmitted, processed and acted on instantaneously are disclosed. The systems, devices and methods advantageously minimize or eliminate the risks caused by a human response to data that has been delayed in transmission, processing and presentation. In an embodiment, a person controlling a remote device using an augmented reality interface is able to control the device based on predicted positions of an object at the time action is taken, thereby advantageously compensating for delays in receiving data, acting on such data and transmitting instructions or a response to the remote device.
    Type: Grant
    Filed: March 31, 2015
    Date of Patent: November 19, 2019
    Inventors: Gary Stephen Shuster, Charles Marion Curry
  • Patent number: 10474959
    Abstract: A computing device computes a weight matrix to compute a predicted value. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.
    Type: Grant
    Filed: June 19, 2019
    Date of Patent: November 12, 2019
    Assignee: SAS Institute Inc.
    Inventors: Xin Jiang Hunt, Saba Emrani, Jorge Manuel Gomes da Silva, Ilknur Kaynar Kabul
  • Patent number: 10467536
    Abstract: Systems and methods of the present invention provide for one or more server computers communicatively coupled to a network and configured to: aggregate a plurality of knowledge base data comprising a plurality of tokens; identify a plurality of available domain names based on a difference between the plurality of tokens within the knowledge base data; eliminate from the plurality of available domain names, at least one grammatically incorrect domain name; rank the plurality of available domain names according to a machine learning algorithm; and transmit the plurality of available domain names to a client computer communicatively coupled to the network.
    Type: Grant
    Filed: December 12, 2014
    Date of Patent: November 5, 2019
    Assignee: Go Daddy Operating Company, LLC
    Inventors: Wei-Cheng Lai, Yang Zhao, Moninder Jheeta, Tapan Kamdar
  • Patent number: 10460599
    Abstract: An approach is provided for determining one or more real-time traffic data series associated with one or more road segments over one or more time intervals. The approach involves causing, at least in part, a determination of one or more differences of the real-time data series with one or more historical models. The approach also involves determining whether the one or more real-time difference exceeds a threshold difference. The approach involves causing, at least in part, a selection of one or more other historical models for the one or more road segments over the one or more time intervals based, at least in part, on a determination that the real-time difference exceeds the threshold difference.
    Type: Grant
    Filed: April 8, 2015
    Date of Patent: October 29, 2019
    Assignee: HERE Global B.V.
    Inventors: Colin Watts-Fitzgerald, Michael Weinberger, Shu Xie
  • Patent number: 10454785
    Abstract: In one embodiment, possible voting nodes in a network are identified. The possible voting nodes each execute a classifier that is configured to select a label from among a plurality of labels based on a set of input features. A set of one or more eligible voting nodes is selected from among the possible voting nodes based on a network policy. Voting requests are then provided to the one or more eligible voting nodes that cause the one or more eligible voting nodes to select labels from among the plurality of labels. Votes are received from the eligible voting nodes that include the selected labels and are used to determine a voting result.
    Type: Grant
    Filed: May 8, 2014
    Date of Patent: October 22, 2019
    Assignee: Cisco Technology, Inc.
    Inventors: Javier Cruz Mota, Jean-Philippe Vasseur, Andrea Di Pietro
  • Patent number: 10438143
    Abstract: Disclosed is systems, methods, and computer program products that provide for a technique for reducing computing resources, storage space needs, and network bandwidth associated with collaborative decision making. More particularly, this disclosure relates to a system for performing automatic predictive decision making using predictive fit models derived from previous user responses and the user characteristics of each responding user, and using the results to reduce the amount of computing and operational resources needed to operate a collaborative decision engine.
    Type: Grant
    Filed: September 28, 2015
    Date of Patent: October 8, 2019
    Assignee: Bank of America Corporation
    Inventors: Srikanth Vemula, Sunil Reddy Gaddam, Sasidhar Purushothaman
  • Patent number: 10417558
    Abstract: A computer-implemented method is disclosed. The method may include receiving, at a neuron of an artificial neural network, a sequence of synapse messages. Each synapse message may include a timestamp of the time the synapse message was sent. The method may include determining, based on the timestamp of each synapse message, whether the neuron processed the sequence of synapse messages out of order with respect to the timestamps. The method may include, in response to the neuron processing the sequence of synapse messages out of order, reverse-computing at least one computation performed by the neuron in response to processing the sequence of synapse messages out of order. The method may include performing the at least one computation based on receipt of the sequence in a correct order as determined by the timestamp of each synapse message in the sequence of synapse messages.
    Type: Grant
    Filed: September 28, 2018
    Date of Patent: September 17, 2019
    Assignee: Deep Insight Solutions, Inc.
    Inventors: Christopher D. Carothers, David W. Bauer, Jr., Justin Lapre
  • Patent number: 10410138
    Abstract: There is provided a method for generating features for use in an automated machine learning process, comprising: receiving a first training dataset comprising unclassified raw data instances each including a set of objects of arbitrary types; applying a function to each data instance to calculate a set of first results; generating a set of classification features each including the function for application to a newly received data instance to calculate a second result, and a condition defined by a respective member of the set of first results applied to the second result; applying each classification feature to each instance of an unclassified second training dataset to generate a set of extracted features; selecting a subset of pivotal classification features from the set of classification features according to a correlation requirement between classification variable(s) and each respective member of the set of extracted features; and documenting the subset of pivotal features.
    Type: Grant
    Filed: May 26, 2016
    Date of Patent: September 10, 2019
    Assignee: SparkBeyond Ltd.
    Inventors: Meir Maor, Ron Karidi, Sagie Davidovich, Amir Ronen
  • Patent number: 10402741
    Abstract: A computing device computes a weight matrix to predict a value for a characteristic in a scoring dataset. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.
    Type: Grant
    Filed: December 6, 2017
    Date of Patent: September 3, 2019
    Assignee: SAS INSTITUTE INC.
    Inventors: Xin Jiang Hunt, Saba Emrani, Jorge Manuel Gomes da Silva, Ilknur Kaynar Kabul
  • Patent number: 10402453
    Abstract: Aspects discussed herein present a solution for utilizing large-scale knowledge graphs for inference at scale and generating explanations for the conclusions. In some embodiments, aspects discussed herein learn inference paths from a knowledge graph and determine a confidence score for each inference path. Aspects discussed herein may apply the inference paths to the knowledge graph to improve database lookup, keyword searches, inferences, etc. Aspects discussed herein may generate a natural language explanation for each conclusion or result from one or more inference paths that led to that conclusion or result. Aspects discussed herein may present the best conclusions or results to the user based on selection strategies. The presented results or conclusions may include generated natural language explanations rather than links to documents with word occurrences highlighted.
    Type: Grant
    Filed: November 24, 2014
    Date of Patent: September 3, 2019
    Assignee: Nuance Communications, Inc.
    Inventors: Peter Zei-Chan Yeh, Adwait Ratnaparkhi, Benjamin Birch Douglas, William Lawrence Jarrold
  • Patent number: 10402726
    Abstract: A method includes receiving an input data set, each entry including multiple features. The method includes receiving a user input identifying a target feature of the multiple features and a target value of the target feature. The method includes determining, one or more correlated features of the multiple features. The method includes providing the input data set to multiple neural networks (including multiple VAEs) to train the multiple neural networks. The method includes generating a simulated data set based on the input data set, each entry including at least the target feature and the one or more correlated features. Values of the one or more correlated features are randomized or pseudorandomized and the target feature is fixed at the target value. The method includes providing the simulated data set to the multiple neural networks to generate output data and displaying a GUI based on the output data.
    Type: Grant
    Filed: May 3, 2018
    Date of Patent: September 3, 2019
    Assignee: SparkCognition, Inc.
    Inventors: Keith D. Moore, Marissa Wiseman, Daniel P. Meador, James R. Eskew
  • Patent number: 10387794
    Abstract: Machine learning with model filtering and model mixing for edge devices in a heterogeneous environment is disclosed. In an example embodiment, an edge device includes a communication module, a data collection device, a memory, a machine learning module, and a model mixing module. The edge device analyzes collected data with a model for a first task, outputs a result, and updates the model to create a local model. The edge device communicates with other edge devices in a heterogeneous group, transmits a request for local models to the heterogeneous group, and receives local models from the heterogeneous group. The edge device filters the local models by structure metadata, including second local models, which relate to a second task. The edge device performs a mix operation of the second local models to generate a mixed model which relates to the second task, and transmits the mixed model to the heterogeneous group.
    Type: Grant
    Filed: January 22, 2015
    Date of Patent: August 20, 2019
    Assignee: PREFERRED NETWORKS, INC.
    Inventors: Daisuke Okanohara, Justin B. Clayton, Toru Nishikawa, Shohei Hido, Nobuyuki Kubota, Nobuyuki Ota, Seiya Tokui
  • Patent number: 10366342
    Abstract: Data is received that include values that correspond to a plurality of variables. A score is then generated based on the received data and using a boosted ensemble of segmented scorecard models. The boosted ensemble of segmented scorecard models includes two or more segmented scorecard models. Subsequently, data including the score can be provided (e.g., displayed, transmitted, loaded, stored, etc.). Related apparatus, systems, techniques and articles are also described.
    Type: Grant
    Filed: March 10, 2014
    Date of Patent: July 30, 2019
    Assignee: FAIR ISAAC CORPORATION
    Inventors: Xing Zhao, Peter Hamilton, Andrew K. Story
  • Patent number: 10354182
    Abstract: A computer-implemented technique is described herein for identifying one or more content items that are relevant to an input linguistic item (e.g., an input query) using a deep-structured neural network, trained based on a corpus of click-through data. The input linguistic item has a collection of input tokens. The deep-structured neural network includes a first part that produces word embeddings associated with the respective input tokens, a second part that generates state vectors that capture context information associated with the input tokens, and a third part which distinguishes important parts of the input linguistic item from less important parts. The second part of the deep-structured neural network can be implemented as a recurrent neural network, such as a bi-directional neural network. The third part of the deep-structured neural network can generate a concept vector by forming a weighted sum of the state vectors.
    Type: Grant
    Filed: October 29, 2015
    Date of Patent: July 16, 2019
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Keng-hao Chang, Ruofei Zhang, Shuangfei Zhai
  • Patent number: 10354187
    Abstract: A method for confidentiality classification of files includes vectorizing a file to reduce the file to a single structured representation; and analyzing the single structured representation with a machine learning engine that generates a confidentiality classification for the file based on previous training. A system for confidentiality classification of files includes a file vectorization engine to vectorize a file to reduce the file to a single structured representation; and a machine learning engine to receive the single structured representation of the file and generate a confidentiality classification for the file based on previous training.
    Type: Grant
    Filed: January 17, 2013
    Date of Patent: July 16, 2019
    Assignee: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
    Inventors: Kas Kasravi, James C. Cooper
  • Patent number: 10346743
    Abstract: A tool computes fitness values for a first generation of a first sub-population of a plurality of sub-populations. A population of candidate solutions for an optimization problem was previously divided into the plurality of sub-populations. The population of candidate solutions was created for an iterative computing process in accordance with an evolutionary algorithm to identify a most fit candidate solution for the optimization problem. The tool determines a speculative ranking of the first generation of the first sub-population prior to the fitness values being computed for all candidate solutions in the first generation of the first sub-population. The tool generates a next generation of the first sub-population based, at least in part, on the speculative ranking prior to completion of computation of the fitness values for the first generation of the first sub-population.
    Type: Grant
    Filed: January 31, 2017
    Date of Patent: July 9, 2019
    Assignee: International Business Machines Corporation
    Inventor: Jason F. Cantin
  • Patent number: 10311361
    Abstract: A technology for propagating themes is provided. In one example, a method may include identifying media having a content feature contained in a presentation of the media. The method may include extracting the content feature of the media as a continuous variable and discretizing the continuous variable into a bucket representing a discrete value. A theme label may be applied to the media and may be propagated to other media with continuous variables discretized into the subset of buckets.
    Type: Grant
    Filed: June 25, 2014
    Date of Patent: June 4, 2019
    Assignee: Amazon Technologies, Inc.
    Inventors: Brandon Scott Durham, Haowei Lu, Christopher Lon McGilliard, Darren Levi Malek, Joshua Fredrick Lutes, Toby Ray Latin-Stoermer
  • Patent number: 10304008
    Abstract: Systems and methods are disclosed for operating a machine, by receiving training data from one or more sensors; training a machine learning module with the training data by: partitioning a data matrix into smaller submatrices to process in parallel and optimized for each processing node; for each submatrix, performing a greedy search for rank-one solutions; using alternating direction method of multipliers (ADMM) to ensure consistency over different data blocks; and controlling one or more actuators using live data and the learned module during operation.
    Type: Grant
    Filed: March 7, 2016
    Date of Patent: May 28, 2019
    Assignee: NEC Corporation
    Inventors: Renqiang Min, Dongjin Song
  • Patent number: 10296841
    Abstract: Systems and methods for content selection with first and second recommendation engines are disclosed herein. The system can include a memory include a content library database and a model database. The system can include a user device having a first network interface and a first I/O subsystem. The system can include one or more servers that can include a packet selection system and a presentation system. These one or more servers can: receive response data from the user device; provide received response data to a first recommendation engine; alert a second recommendation engine when a selected next node is a placeholder node; retrieve at least one statistical model relevant to selection of next node content; and select next node content based on an output of the at least one statistical model.
    Type: Grant
    Filed: August 25, 2016
    Date of Patent: May 21, 2019
    Assignee: PEARSON EDUCATION, INC.
    Inventors: Brian Moriarty, Mark Potter
  • Patent number: 10282672
    Abstract: A processing device determines a plurality of visual concepts for visual data based on at least one of visual entities in the visual data or feature-level attributes in the visual data, wherein the visual entities are based on the feature-level attributes, and wherein each of the plurality of visual concepts comprises a subject visual entity related to an object visual entity by a predicate. The processing device further determines one or more visual semantics for the visual data based on the plurality of visual concepts, wherein the one or more visual semantics define relationships between the plurality of visual concepts.
    Type: Grant
    Filed: June 26, 2014
    Date of Patent: May 7, 2019
    Assignee: Amazon Technologies, Inc.
    Inventors: Pragyana K. Mishra, Danny Guan