Patents Examined by Clint Mullinax
  • Patent number: 12380343
    Abstract: Methods and apparatus for complementary evidence identification in natural language inference. A given question is obtained and a set of N passages is obtained from a database. A probability is determined, for each passage of the set of N passages, of a corresponding passage being a supportive passage for the given question and the set of N passages is ranked based on the determined probabilities. M passages that are ranked 1 to M of the set of N passages are selected. A set of L passages is selected based on a plurality of scores, each score assigned to a set of candidate passages of the set of N passages, each score being based on the determined probabilities, the selected M passages, and a weighted regulation parameter. The set of L passages is provided to a computerized machine learning system to answer the question based on the set of L passages.
    Type: Grant
    Filed: August 10, 2020
    Date of Patent: August 5, 2025
    Assignees: International Business Machines Corporation, Rensselaer Polytechnic Institute
    Inventors: Mo Yu, Li Zhang, Hui Su, Shiyu Chang, Ming Tan, Xiangyang Mou
  • Patent number: 12373740
    Abstract: A network includes a server and a plurality of geographically dispersed computing nodes connected to the server. A detector is coupled to one or more of the computing nodes. A plurality of processors are included. A first processor is in a first computing node. A second processor is in a second computing node. The server is configured to generate a predictive model of an event detected by the detector. The server is configured to distribute predictive modelling computations of the predictive model, in parts across the geographically dispersed computing nodes. The first processor in the first computing node performs at least a first part of the predictive modelling computations. The second processor in the second computing node performs at least a second part of the predictive modelling computations.
    Type: Grant
    Filed: August 12, 2024
    Date of Patent: July 29, 2025
    Inventor: Ryan Honary
  • Patent number: 12333451
    Abstract: Techniques are provided for determining reasons for unsupervised anomaly decisions. One method comprises obtaining values of predefined features associated with a remote user device; applying the predefined feature values to an unsupervised anomaly detection model that generates an unsupervised anomaly decision; applying the predefined feature values to a supervised anomaly detection model that generates a supervised anomaly decision; determining a third anomaly decision using the unsupervised anomaly decision; and determining reasons for the third anomaly decision by analyzing the supervised anomaly decision. The supervised anomaly detection model can be trained using the unsupervised anomaly decision and/or anomalous training data based on known anomalies. The third anomaly decision can be based on the supervised anomaly decision and the unsupervised anomaly decision using ensemble techniques.
    Type: Grant
    Filed: November 27, 2019
    Date of Patent: June 17, 2025
    Assignee: RSA Security USA, LLC
    Inventors: Sashka T. Davis, Alex Zaslavsky
  • Patent number: 12327197
    Abstract: Provided are an artificial intelligence (AI) system using a machine learning algorithm and an application of the AI system. A device for providing content based on a knowledge graph includes: a memory storing instructions; and a processor configured to execute the instructions to: obtain context information related to the device; obtain a first device knowledge graph of a user of the device by inputting the obtained context information to a first AI model for determining a relation between entities related to the user of the device; request, from a server, a server knowledge graph generated by the server; receive the server knowledge graph; obtain a second device knowledge graph of the user by inputting the obtained first device knowledge graph and the received server knowledge graph to a second AI model for extending the first device knowledge graph; and provide content based on the obtained second device knowledge graph.
    Type: Grant
    Filed: October 16, 2019
    Date of Patent: June 10, 2025
    Assignee: SAMSUNG ELECTRONICS CO., LTD.
    Inventors: Jaehun Lee, Yunsu Lee, Taeho Hwang, Jungho Park, Mirae Jeong, Jiyoung Kang
  • Patent number: 12314327
    Abstract: In accordance with an embodiment, described herein are systems and methods for auto-completion of ICS flow using artificial intelligence/machine learning. Next actions prediction is a service that assists users in modeling the flows quickly by predicting and suggesting the next set of actions a user might be thinking of adding. The service also assists the user to follow some of the best practices while creating an integration flow.
    Type: Grant
    Filed: September 10, 2019
    Date of Patent: May 27, 2025
    Assignee: ORACLE INTERNATIONAL CORPORATION
    Inventors: Syed Najeeb Andrabi, Rajan Modi, Venkatesh Mohanram, Muthukumar Palanisamy, Michael Hwang
  • Patent number: 12299551
    Abstract: Aspects of the present disclosure provide techniques for training a machine learning model. Embodiments include receiving a historical support record comprising time-stamped actions, a support initiation time, and an account indication. Embodiments include determining features of the historical support record based at least on differences between times of the time-stamped actions and the support initiation time. Embodiments include determining a label for the features based on the account indication. Embodiments include training an ensemble model, using training data comprising the features and the label, to determine an indication of an account in response to input features, wherein the ensemble model comprises a plurality of tree-based models and a ranking model.
    Type: Grant
    Filed: July 13, 2020
    Date of Patent: May 13, 2025
    Assignee: Intuit Inc.
    Inventors: Shanshan Tuo, Neo Yuchen, Divya Beeram, Valentin Vrzheshch, Tomer Tal, Ngoc Nhung Ho
  • Patent number: 12288164
    Abstract: The present invention relates to a prediction method for stall and surge of an axial compressor based on deep learning. The method comprises the following steps: firstly, preprocessing data with stall and surge of an aeroengine, and partitioning a test data set and a training data set from experimental data. Secondly, constructing an LR branch network module, a WaveNet branch network module and a LR-WaveNet prediction model in sequence. Finally, conducting real-time prediction on the test data: preprocessing test set data in the same manner, and adjusting data dimension according to input requirements of the LR-WaveNet prediction model; giving surge prediction probabilities of all samples by means of the LR-WaveNet prediction model according to time sequence; and giving the probability of surge that data with noise points changes over time by means of the LR-WaveNet prediction model, to test the anti-interference performance of the model.
    Type: Grant
    Filed: September 28, 2020
    Date of Patent: April 29, 2025
    Assignee: DALIAN UNIVERSITY OF TECHNOLOGY
    Inventors: Ximing Sun, Fuxiang Quan, Hongyang Zhao, Yanhua Ma, Pan Qin
  • Patent number: 12271799
    Abstract: Techniques for predictive disease identification using simulations improved via machine learning. A method includes applying at least one machine learning model to features extracted from data including animal characteristics data of an animal, wherein outputs of the at least one machine learning model include a plurality of disease predictor values, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types, wherein each disease type of the plurality of disease types corresponds to a predetermined group of diseases; generating disease contraction statistics based on the outputs of the at least one machine learning model; and determining, based on the disease contraction statistics, at least one disease prediction for the animal.
    Type: Grant
    Filed: July 31, 2023
    Date of Patent: April 8, 2025
    Assignee: Fetch, Inc.
    Inventors: Audrey Ruple, Johannes Paul Wowra, John K. Giannuzzi, Danna Rabin, Christian Debes, Akash Gupta, Karen Leever, Aliya McCullough, Samantha McKinnon
  • Patent number: 12230366
    Abstract: A method for employee biometric tracking is provided. The method comprises providing to a user a plurality of wearable devices capable of being connected to the user, establishing a wireless connection between the plurality of wearable devices and a mobile device, collecting by the plurality of wearable devices a plurality of biometric data from the user, receiving by an application stored on the mobile device the plurality of biometric data, inputting into a predictive engine biometric data selected from the plurality of biometric data, determining by the predictive engine in response to the biometric data whether the user is at, or soon will be at, an alert level, creating an alert signal, and displaying the alert signal to the user.
    Type: Grant
    Filed: October 4, 2022
    Date of Patent: February 18, 2025
    Assignee: BlyncSync Technologies, LLC
    Inventors: Austin Green, Steven Kastelic
  • Patent number: 12223407
    Abstract: In automated machine learning, an approximate best configuration can be selected among multiple candidate machine-learning configurations by progressively sampling training and test datasets for the iterative training and testing of the configurations while progressively pruning the set of candidate configurations based on associated estimated confidence intervals for their respective performance.
    Type: Grant
    Filed: August 23, 2018
    Date of Patent: February 11, 2025
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Chi Wang, Silu Huang, Surajit Chaudhuri, Bolin Ding
  • Patent number: 12210954
    Abstract: A point estimate value for an individual is computed using a Bayesian neural network model (BNN) by training a first BNN model that computes a weight mean value, a weight standard deviation value, a bias mean value, and a bias standard deviation value for each neuron of a plurality of neurons using observations. A plurality of BNN models is instantiated using the first BNN model. Instantiating each BNN model of the plurality of BNN models includes computing, for each neuron, a weight value using the weight mean value, the weight standard deviation value, and a weight random draw and a bias value using the bias mean value, the bias standard deviation value, and a bias random draw. Each instantiated BNN model is executed with the observations to compute a statistical parameter value for each observation vector of the observations. The point estimate value is computed from the statistical parameter value.
    Type: Grant
    Filed: December 6, 2023
    Date of Patent: January 28, 2025
    Assignee: SAS Institute Inc.
    Inventors: Sylvie Tchumtchoua Kabisa, Xilong Chen, Gunce Eryuruk Walton, David Bruce Elsheimer, Ming-Chun Chang
  • Patent number: 12086572
    Abstract: Embodiments herein describe techniques for expressing the layers of a neural network in a software model. In one embodiment, the software model includes a class that describes the various functional blocks (e.g., convolution units, max-pooling units, rectified linear units (ReLU), and scaling functions) used to execute the neural network layers. In turn, other classes in the software model can describe the operation of each of the functional blocks. In addition, the software model can include conditional logic for expressing how the data flows between the functional blocks since different layers in the neural network can process the data differently. A compiler can convert the high-level code in the software model (e.g., C++) into a hardware description language (e.g., register transfer level (RTL)) which is used to configure a hardware system to implement a neural network accelerator.
    Type: Grant
    Filed: October 17, 2017
    Date of Patent: September 10, 2024
    Assignee: XILINX, INC.
    Inventors: Yongjun Wu, Jindrich Zejda, Elliott Delaye, Ashish Sirasao
  • Patent number: 11995519
    Abstract: There is disclosed a method of and a system for training and using a Machine Learning Algorithm (MLA), the MLA using a decision tree model having a decision tree. During training a training object being associated with a categorical feature and is processed at a node of the decision tree. The method comprises calculating a numeric representation of the categorical feature and the value of the splits for the node “in-line” with generating a given iteration of the decision tree.
    Type: Grant
    Filed: June 6, 2018
    Date of Patent: May 28, 2024
    Assignee: Direct Cursus Technology L.L.C
    Inventor: Andrey Vladimirovich Gulin
  • 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: 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: 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: 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: 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: 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: 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