Patents Examined by Alexey Shmatov
  • Patent number: 11928556
    Abstract: Methods and systems for a reinforcement learning system. A spatial and temporal representation of an observed state of an environment is encoded. A previous state is estimated from a given state and a size of a reward is adjusted based on a difference between the estimated previous state and the previous state.
    Type: Grant
    Filed: December 29, 2018
    Date of Patent: March 12, 2024
    Assignee: International Business Machines Corporation
    Inventors: Guy Hadash, Boaz Carmeli, George Kour
  • Patent number: 11928599
    Abstract: A method and device for model compression of a neural network. The method comprises: recording input and output parameters of each layer of network in a network structure; dividing the network structure into several small networks according to the input and output parameters; setting a pruning flag bit of a first convolutional layer in each small network to be zero to obtain a pruned small network; training each pruned small network to obtain a network weight and a weight mask; recording a pruned channel index number of each convolutional layer of a pruned small network with the weight mask of zero; and carrying out decomposition calculation on each pruned small network according to the pruned channel index number. According to the method, a calculation amount and the size of a model is reduced, and during network deployment, the model can be loaded with one click, thus reducing usage difficulty.
    Type: Grant
    Filed: July 23, 2020
    Date of Patent: March 12, 2024
    Assignee: Inspur Suzhou Intelligent Technology Co., Ltd.
    Inventor: Shaoyan Guo
  • Patent number: 11922290
    Abstract: Provided is a system for analyzing a multivariate time series that includes at least one processor programmed or configured to receive a time series of historical data points, determine a historical time period, determine a contemporary time period, determine a first time series of data points associated with a historical transaction metric from the historical time period, determine a second time series of data points associated with a historical target transaction metric from the historical time period, determine a third time series of data points associated with a contemporary transaction metric from the contemporary time period, and generate a machine learning model, wherein the machine learning model is configured to provide an output that comprises a predicted time series of data points associated with a contemporary target transaction metric. Methods and computer program products are also provided.
    Type: Grant
    Filed: May 24, 2022
    Date of Patent: March 5, 2024
    Assignee: Visa International Service Association
    Inventors: Zhongfang Zhuang, Michael Yeh, Wei Zhang, Mengting Gu, Yan Zheng, Liang Wang
  • Patent number: 11907833
    Abstract: A method includes receiving input data including a plurality of feature vectors and labeling each feature vector based on a temporal proximity of the feature vector to occurrence of a fault. Feature vectors that are within a threshold temporal proximity to the occurrence of the fault are labeled with a first label value and other feature vectors are labeled with a second label value. The method includes determining, for each feature vector of a subset, a probability that the label associated with the feature vector is correct. The subset includes feature vectors having labels that indicate the first label value. The method includes reassigning labels of one or more feature vectors of the subset having a probability that fails to satisfy a probability threshold and, after reassigning the labels, training an aircraft fault prediction classifier using supervised training data including the plurality of feature vectors and the labels.
    Type: Grant
    Filed: November 27, 2018
    Date of Patent: February 20, 2024
    Assignee: THE BOEING COMPANY
    Inventors: Rashmi Sundareswara, Franz David Betz, Tsai-Ching Lu
  • Patent number: 11907858
    Abstract: One or more computing devices, systems, and/or methods for entity disambiguation are provided. For example, a document may be analyzed to identify a first mention and a second mention. One or more techniques may be used to select and link a candidate entity, from a first set of candidate entities, to the first mention and select and link a candidate entity, from a second set of candidate entities, to the second mention.
    Type: Grant
    Filed: February 6, 2017
    Date of Patent: February 20, 2024
    Assignee: YAHOO ASSETS LLC
    Inventors: Aasish Pappu, Roi Blanco, Yashar Mehdad, Amanda Stent, Kapil Thadani
  • Patent number: 11900238
    Abstract: Some embodiments provide a method for reducing complexity of a machine-trained (MT) network that receives input data and computes output data for each input data. The MT network includes multiple computation nodes that (i) generate output values and (ii) use output values of other computation nodes as input values. During training of the MT network, the method introduces probabilistic noise to the output values of a set of the computation nodes. the method determines a subset of the computation nodes for which the introduction of the probabilistic noise to the output value does not affect the computed output data for the network. The method removes the subset of computation nodes from the trained MT network.
    Type: Grant
    Filed: February 3, 2020
    Date of Patent: February 13, 2024
    Assignee: PERCEIVE CORPORATION
    Inventors: Steven L. Teig, Eric A. Sather
  • Patent number: 11886984
    Abstract: In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to expose embedded cast operations in at least one of a load instruction or a store instruction; determine a target precision level for the cast operations; and load the cast operations at the target precision level. Other embodiments are also disclosed and claimed.
    Type: Grant
    Filed: August 10, 2021
    Date of Patent: January 30, 2024
    Assignee: INTEL CORPORATION
    Inventors: Uzi Sarel, Ehud Cohen, Tomer Schwartz, Amitai Armon, Yahav Shadmiy, Amit Bleiweiss, Gal Leibovich, Jeremie Dreyfuss, Lev Faivishevsky, Tomer Bar-On, Yaniv Fais, Jacob Subag
  • Patent number: 11887013
    Abstract: In certain embodiments, resolved exceptions information regarding resolved exceptions may be obtained. The resolved exceptions information may indicate the resolved exceptions and, for each resolved exception of the resolved exceptions, a set of attributes of a transaction for which the resolved exception was triggered. The resolved exceptions information may be provided as input to a prediction model to obtain multiple decision trees via the prediction model. Each decision tree of the multiple decision trees may comprise nodes and conditional branches, each node of the nodes of the decision tree indicating a probability of a dividend-related classification for a transaction that corresponds to the node. A decision tree may be obtained from the multiple decision trees.
    Type: Grant
    Filed: August 28, 2018
    Date of Patent: January 30, 2024
    Assignee: THE BANK OF NEW YORK MELLON
    Inventors: Vikas Kohli, Chetan Agarwal, Durgesh Chouksey, Abhay Jayant Joshi
  • Patent number: 11886957
    Abstract: A method may include receiving a communication from a device at an artificial intelligence controller including state information for a software application component running on the device, the state information including information corresponding to at least one potential state change available to the software application component, and metrics associated with at least one end condition, interpreting the state information using the artificial intelligence controller, and selecting an artificial intelligence algorithm from a plurality of artificial intelligence algorithms for use by the software application component based on the interpreted state information; and transmitting, to the device, an artificial intelligence algorithm communication, the artificial intelligence algorithm communication indicating the selected artificial intelligence algorithm for use in the software application component on the device.
    Type: Grant
    Filed: October 26, 2016
    Date of Patent: January 30, 2024
    Assignee: Apple Inc.
    Inventors: Ross R. Dexter, Michael R. Brennan, Bruno M. Sommer, Norman N. Wang
  • Patent number: 11880692
    Abstract: Provided is an apparatus configured to determine a common neural network based on a comparison between a first neural network included in a first application program and a second neural network included in a second application program, utilize the common neural network when the first application program or the second application program is executed.
    Type: Grant
    Filed: November 18, 2020
    Date of Patent: January 23, 2024
    Assignee: SAMSUNG ELECTRONICS CO., LTD.
    Inventors: Hyunjoo Jung, Jaedeok Kim, Chiyoun Park
  • Patent number: 11880776
    Abstract: A graph neural network (GNN)-based prediction system for total organic carbon (TOC) in shale solves the problem that the existing shale TOC prediction method cannot fully analyze the complex nonlinear relationship between all logging curves and TOC. The prediction system adopts a method including: acquiring and preprocessing a plurality of logging curves of a target well location in a target shale bed to acquire a plurality of standardized logging curves, windowing the plurality of standardized logging curves, and inputting the windowed logging curves and weight matrix into a trained GNN-based TOC prediction network to acquire TOC of the target well location. The prediction system inputs the plurality of logging curves as correlative multi-dimensional dynamic graph data for analysis and can acquire the complex nonlinear relationship between the logging curves and TOC, thus improving the prediction accuracy of TOC.
    Type: Grant
    Filed: November 23, 2022
    Date of Patent: January 23, 2024
    Assignee: INSTITUTE OF GEOLOGY AND GEOPHYSICS, CHINESE ACADEMY OF SCIENCES
    Inventors: Xiaocai Shan, Wang Zhang, Yongjian Zhou
  • Patent number: 11868878
    Abstract: Disclosed herein are techniques for implementing a large fully-connected layer in an artificial neural network. The large fully-connected layer is grouped into multiple fully-connected subnetworks. Each fully-connected subnetwork is configured to classify an object into an unknown class or a class in a subset of target classes. If the object is classified as the unknown class by a fully-connected subnetwork, a next fully-connected subnetwork may be used to further classify the object. In some embodiments, the fully-connected layer is grouped based on a ranking of target classes.
    Type: Grant
    Filed: March 23, 2018
    Date of Patent: January 9, 2024
    Assignee: Amazon Technologies, Inc.
    Inventors: Randy Huang, Ron Diamant
  • Patent number: 11855849
    Abstract: At a rule processing unit of an evolving, self-organized machine learning-based resource management service, a rule of a first rule set is applied to a value of a first collected metric, resulting in the initiation of a first corrective action. A set of metadata indicating the metric value and the corrective action is transmitted to a repository, and is used as part of an input data set for a machine learning model trained to generate rule modification recommendations. In response to determining that the corrective actions did not meet a success criterion, an escalation message is transmitted to another rule processing unit.
    Type: Grant
    Filed: September 26, 2017
    Date of Patent: December 26, 2023
    Assignee: Amazon Technologies, Inc.
    Inventors: Melissa Elaine Davis, Renaud Bordelet, Charles Alexander Carman, David Elfi, Anton Vladilenovich Goldberg, Kyle Bradley Peterson, Christopher Allen Suver
  • Patent number: 11836211
    Abstract: Mechanisms for generating different lines of questioning based on evaluation of a previous line of questioning are provided. A set of evidential data specifying a plurality of concept entities and input specifying a scenario to be evaluated are received. The scenario specifies a hypothetical link between at least two of the concept entities. A first set of questions corresponding to the at least two information concept entities are evaluated based on the set of evidential data. Based on results of evaluating the first set of questions, a second set of questions is automatically generated to further expand upon and investigate the results of evaluating the first set of questions. The second set of questions are processed and an indication of the scenario and a corresponding measure of support for or against the scenario being a valid scenario is output.
    Type: Grant
    Filed: November 21, 2014
    Date of Patent: December 5, 2023
    Assignee: International Business Machines Corporation
    Inventors: Darryl M. Adderly, Corville O. Allen, Robert K. Tucker
  • Patent number: 11836594
    Abstract: Embodiments of the invention include computer-implemented methods, computer systems, and computer program products for predicting sensory perception. A non-limiting example of the computer-implemented method includes receiving at a processor a library including a plurality of indexed sensory descriptors. A sensory target descriptor is also received at the processor. The processor is configured to calculate a coefficient matrix based in part on the semantic distance between an indexed sensory descriptor and a sensory target descriptor. The processor is further configured to generate a perceptual descriptor prediction for the sensory target.
    Type: Grant
    Filed: May 15, 2019
    Date of Patent: December 5, 2023
    Assignee: International Business Machines Corporation
    Inventors: Pablo Meyer Rojas, Elkin Dario Gutierrez, Guillermo Cecchi
  • Patent number: 11836637
    Abstract: A construction method of human-object-space interaction model based on knowledge graph belongs to the technical field of knowledge graph construction and smart communities and includes steps of: obtaining information from a large number of active and passive sensing devices and thereby building a knowledge base; and fusing perceptual information in the knowledge base, forming entity-relation-entity structured data, and building a general knowledge graph conceptual model with entity-relation attributes. The construction method can overcome technical problems such as difficulty in multi-source information extraction, inability to fuse heterogeneous data, and inability of interaction of human-object-space caused by poor universality of perception technology and complex entity relations in a smart community environment, and provide a method support for monitoring and early warning of dangerous events in the smart community and community environment situational awareness.
    Type: Grant
    Filed: October 17, 2022
    Date of Patent: December 5, 2023
    Assignee: TIANJIN UNIVERSITY
    Inventors: Xiulong Liu, Juncheng Ma, Xuesong Gao, Wenyu Qu
  • Patent number: 11823013
    Abstract: Embodiments of the present invention provide a computer-implemented method for performing unsupervised feature representation learning for text data. The method generates reference text data having a set of random text sequences, in which each text sequence of set of random text sequences is of a random length and comprises a number of random words, and in which each random length is sampled from a minimum length to a maximum length. The random words of each text sequence in the set are drawn from a distribution. The method generates a feature matrix for raw text data based at least in part on a set of computed distances between the set of random text sequences and the raw text data. The method provides the feature matrix as an input to one or more machine learning models.
    Type: Grant
    Filed: August 29, 2017
    Date of Patent: November 21, 2023
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Michael J. Witbrock, Lingfei Wu
  • Patent number: 11823053
    Abstract: The disclosure discloses a method of neural network model computation-oriented intermediate representation and apparatus thereof. The method includes the following steps: S1, parsing an input model file so as to acquire topological structure information of a neural network; S2, constructing a logical computation graph; S21, inferring physical layout information of each operator in the logical computation graph; S22, inferring meta attributes of each operator in the logical computation graph; S23, inferring description information of input and output logical tensors of each operator in the logical computation graph; S3, constructing a physical computation graph; S31, generating a physical computation graph, etc.
    Type: Grant
    Filed: April 6, 2022
    Date of Patent: November 21, 2023
    Assignee: ZHEJIANG LAB
    Inventors: Hongsheng Wang, Wei Hua, Weiqiang Jia, Hujun Bao
  • Patent number: 11816574
    Abstract: An input weight pattern of a machine learning model may be received. The input weight pattern may be pruned to produce an output weight pattern based on a predetermined pruning algorithm. The pruning algorithm may include partitioning the input weight pattern into a plurality of sub-patterns, each row of the input weight pattern including sub-rows of a first number of sub-patterns, and each column of the input weight pattern including sub-columns of a second number of sub-patterns; and pruning sub-columns and sub-rows from the plurality of sub-patterns to achieve predetermined column and row sparsities respectively, with a constraint that at least one sub-row in each row of the input weight pattern is not pruned. The output weight pattern may further be compressed to produce a compact weight pattern. The compact weight pattern has lower memory and computational overheads as compared to the input weight pattern for the machine learning model.
    Type: Grant
    Filed: October 25, 2019
    Date of Patent: November 14, 2023
    Assignee: Alibaba Group Holding Limited
    Inventors: Ao Ren, Yuhao Wang, Tao Zhang, Yuan Xie
  • Patent number: 11816572
    Abstract: A machine learning hardware accelerator architecture and associated techniques are disclosed. The architecture features multiple memory banks of very wide SRAM that may be concurrently accessed by a large number of parallel operational units. Each operational unit supports an instruction set specific to machine learning, including optimizations for performing tensor operations and convolutions. Optimized addressing, an optimized shift reader and variations on a multicast network that permutes and copies data and associates with an operational unit that support those operations are also disclosed.
    Type: Grant
    Filed: October 14, 2021
    Date of Patent: November 14, 2023
    Assignee: Intel Corporation
    Inventors: Jeremy Bruestle, Choong Ng