Patents by Inventor Tuan Minh Hoang-Trong

Tuan Minh Hoang-Trong has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11954085
    Abstract: A computer implemented method performs data skipping in a hierarchically organized computing system. A group of processor units determines leaf node data sketches for data in leaf nodes in the hierarchically organized computing system. The leaf node data sketches summarize attributes of data in the leaf nodes. The group of processor units aggregates the leaf node data sketches at intermediate nodes in the hierarchically organized computing system to form aggregated data sketches at the intermediate nodes and retains data sketches received at the intermediate nodes from a group of child nodes to form retained data sketches. The retained data sketches are one of leaf node data sketches and the aggregated data sketches. The group of processor units searches the data using the retained data sketches and the data skipping within the hierarchically organized computing system in response to queries made to the intermediate nodes in the hierarchically organized computing system.
    Type: Grant
    Filed: September 22, 2022
    Date of Patent: April 9, 2024
    Assignee: International Business Machines Corporation
    Inventors: Mudhakar Srivatsa, Raghu Kiran Ganti, Joshua M. Rosenkranz, Linsong Chu, Tuan Minh Hoang Trong, Utpal Mangla, Satishkumar Sadagopan, Mathews Thomas
  • Publication number: 20240104075
    Abstract: A computer implemented method performs data skipping in a hierarchically organized computing system. A group of processor units determines leaf node data sketches for data in leaf nodes in the hierarchically organized computing system. The leaf node data sketches summarize attributes of data in the leaf nodes. The group of processor units aggregates the leaf node data sketches at intermediate nodes in the hierarchically organized computing system to form aggregated data sketches at the intermediate nodes and retains data sketches received at the intermediate nodes from a group of child nodes to form retained data sketches. The retained data sketches are one of leaf node data sketches and the aggregated data sketches. The group of processor units searches the data using the retained data sketches and the data skipping within the hierarchically organized computing system in response to queries made to the intermediate nodes in the hierarchically organized computing system.
    Type: Application
    Filed: September 22, 2022
    Publication date: March 28, 2024
    Inventors: MUDHAKAR SRIVATSA, RAGHU KIRAN GANTI, Joshua M. Rosenkranz, Linsong Chu, Tuan Minh HOANG TRONG, Utpal Mangla, SATISHKUMAR SADAGOPAN, Mathews Thomas
  • Publication number: 20230169408
    Abstract: A system, computer program product, and method are provided for distributed data workflow semantics. A pipeline, such as a machine learning (ML) pipeline, is implemented over a data flow graph (DFG) with nodes configured to support rich semantics. The rich semantics include two or more operational semantics, and at least one lineage semantic to selectively combine features that trace lineage to a common input object. The lineage semantic is leveraged to associate training and testing data set pairs in cross validation of the trained ML models produced from parallelizing the selection of ML pipelines.
    Type: Application
    Filed: November 30, 2021
    Publication date: June 1, 2023
    Applicant: International Business Machines Corporation
    Inventors: Carlos Henrique Andrade Costa, RAGHU KIRAN GANTI, MUDHAKAR SRIVATSA, Linsong Chu, Joshua M. Rosenkranz, Tuan Minh HOANG TRONG
  • Patent number: 11532086
    Abstract: Systems and methods that facilitate determining interaction between medications and the brain using a brain measure and a brain model. Hidden nervous system states are difficult to predict, diagnose, and treat with therapeutic medications. A Dual Neural Machine Translation (d-NMT) algorithmic system that utilizes sets of parameters for a relapsing-remitting MS model based on patient medical records and adjusts a method of parameterization to produce a model that can match patients' medical records and medical images. These parameters are can be used by a therapeutic determining model to recommend therapies, doses, and time courses accurately.
    Type: Grant
    Filed: February 20, 2020
    Date of Patent: December 20, 2022
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: James R Kozloski, Viatcheslav Gurev, Tuan Minh Hoang Trong, Adamo Ponzi
  • Publication number: 20210264603
    Abstract: Systems and methods that facilitate determining interaction between medications and the brain using a brain measure and a brain model. Hidden nervous system states are difficult to predict, diagnose, and treat with therapeutic medications. A Dual Neural Machine Translation (d-NMT) algorithmic system that utilizes sets of parameters for a relapsing-remitting MS model based on patient medical records and adjusts a method of parameterization to produce a model that can match patients' medical records and medical images. These parameters are can be used by a therapeutic determining model to recommend therapies, doses, and time courses accurately.
    Type: Application
    Filed: February 20, 2020
    Publication date: August 26, 2021
    Inventors: James R Kozloski, Viatcheslav Gurev, Tuan Minh Hoang Trong, Adamo Ponzi
  • Patent number: 9009095
    Abstract: Systems and methods for determining a probability of changing from one state to another in a stochastic entity, comprising: determining the compact component matrix utilizing characteristic information of the stochastic entity; determining the compact composite component matrix by taking a Kroneker product of the compact component matrix and an identity matrix; determining and placing all current states for the stochastic entity into a state space matrix; determining a Q matrix and/or a transition rate matrix using the compact composite component matrix and basic conditions or variables of the problem domain and/or a compact transition rate matrix; and performing Markov chain Monte Carlo (MCMC) simulation using information from the state space matrix and information from the transition rate matrix to determine the probability of changing from one state to another state in the stochastic entity.
    Type: Grant
    Filed: January 12, 2011
    Date of Patent: April 14, 2015
    Assignee: George Mason University
    Inventors: Mohsin Saleet Fafri, Tuan Minh Hoang-Trong, George Stuart Blair Williams