Patents by Inventor Long Vu

Long Vu 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).

  • Publication number: 20250068626
    Abstract: The present disclosure relates to manufacturing training data by leveraging an automated pipeline that manufactures visualization training datasets to train a machine learning model to convert a natural language utterance into meaning representation language logical form that includes one or more visualization actions. Aspects are directed towards accessing an original training dataset, a visualization query dataset, an incremental visualization dataset, a manipulation visualization dataset, or any combination thereof. One or more visualization training datasets are generated by: (i) modifying examples in the original training dataset, the visualization query dataset, or both to include visualization actions, (ii) generating examples, using the incremental visualization dataset, the manipulation visualization dataset, or both, that include visualization actions, or (iii) both (i) and (ii).
    Type: Application
    Filed: March 1, 2024
    Publication date: February 27, 2025
    Applicant: Oracle International Corporation
    Inventors: Gioacchino Tangari, Steve Wai-Chun Siu, Dalu Guo, Cong Duy Vu Hoang, Berk Sarioz, Chang Xu, Stephen Andrew McRitchie, Mark Edward Johnson, Christopher Mark Broadbent, Thanh Long Duong, Srinivasa Phani Kumar Gadde, Vishal Vishnoi, Chandan Basavaraju, Kenneth Khiaw Hong Eng
  • Publication number: 20250068627
    Abstract: Techniques are disclosed herein for transforming natural language conversations into a visual output. In one aspect, a computer-implement method includes generating an input string by concatenating a natural language utterance with a schema representation comprising a set of entities for visualization actions, generating, by a first encoder of a machine learning model, one or more embeddings of the input string, encoding, by a second encoder of the machine learning model, relations between elements in the schema representation and words in the natural language utterance based on the one or more embeddings, generating, by a grammar-based decoder of the machine learning model and based on the encoded relations and the one or more embeddings, an intermediate logical form that represents at least the query, the one or more visualization actions, or the combination thereof, and generating, based on the intermediate logical form, a command for a computing system.
    Type: Application
    Filed: March 26, 2024
    Publication date: February 27, 2025
    Applicant: Oracle International Corporation
    Inventors: Cong Duy Vu Hoang, Gioacchino Tangari, Stephen Andrew McRitchie, Nitika Mathur, Aashna Devang Kanuga, Steve Wai-Chun Siu, Dalu Guo, Chang Xu, Mark Edward Johnson, Christopher Mark Broadbent, Thanh Long Duong, Srinivasa Phani Kumar Gadde, Vishal Vishnoi, Chandan Basavaraju, Kenneth Khiaw Hong Eng
  • Publication number: 20250068902
    Abstract: Methods and systems for tuning a model include generating pipelines. The pipelines have elements that include at least an agent, a foundation model, and a tuning type. Hyperparameters of elements of the pipelines are set in accordance with an input task. Elements of the pipelines are tuned in accordance with the input task. The input task is performed using a highest-performance pipeline.
    Type: Application
    Filed: August 22, 2023
    Publication date: February 27, 2025
    Inventors: Long VU, Dharmashankar Subramanian, Radu Marinescu
  • Publication number: 20250045608
    Abstract: A method for Markov Decision Process (“MDP”) decomposition includes receiving data elements for a problem that include finite state data for a set of state variables and a finite set of actions. A portion of the state data corresponding to state variables represents states. The method incudes creating two or more sub-MDPs. Each sub-MDP includes a portion of the set of state variables, the set of actions and a same reward function. The method includes executing each sub-MDP. Results include a policy and an expected reward from the reward function. The policy of the sub-MDP maps states of the sub-MDP to actions. The method includes aggregating, based on the expected rewards of the results, the actions of the policies of the sub-MDPs to create a resultant policy with resultant actions and generating, using state entries for the set of state variables, results to the problem based on the resultant policy.
    Type: Application
    Filed: August 3, 2023
    Publication date: February 6, 2025
    Inventors: Alexander Zadorojniy, Long Vu, Dharmashankar Subramanian
  • Patent number: 12210830
    Abstract: In some aspects, a computing device may receive, at a data processing system, a set of utterances for training or inferencing with a named entity recognizer to assign a label to each token piece from the set of utterances. The computing device may determine a length of each utterance in the set and when the length of the utterance exceeds a pre-determined threshold of token pieces: dividing the utterance into a plurality of overlapping chunks of token pieces; assigning a label together with a confidence score for each token piece in a chunk; determining a final label and an associated confidence score for each chunk of token pieces by merging two confidence scores; determining a final annotated label for the utterance based at least on the merging the two confidence scores; and storing the final annotated label in a memory.
    Type: Grant
    Filed: May 20, 2022
    Date of Patent: January 28, 2025
    Assignee: Oracle International Corporation
    Inventors: Thanh Tien Vu, Tuyen Quang Pham, Mark Edward Johnson, Thanh Long Duong, Ying Xu, Poorya Zaremoodi, Omid Mohamad Nezami, Budhaditya Saha, Cong Duy Vu Hoang
  • Patent number: 12210842
    Abstract: Techniques for using logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system. The chatbot system can input the utterance into a machine-learning model including a set of binary classifiers. Each binary classifier of the set of binary classifiers can be associated with a modified logit function. The method can also include the machine-learning model using the modified logit function to generate a set of distance-based logit values for the utterance. The method can also include the machine-learning model applying an enhanced activation function to the set of distance-based logit values to generate a predicted output. The method can also include the chatbot system classifying, based on the predicted output, the utterance as being associated with the particular class.
    Type: Grant
    Filed: December 19, 2023
    Date of Patent: January 28, 2025
    Assignee: Oracle International Corporation
    Inventors: Ying Xu, Poorya Zaremoodi, Thanh Tien Vu, Cong Duy Vu Hoang, Vladislav Blinov, Yu-Heng Hong, Yakupitiyage Don Thanuja Samodhye Dharmasiri, Vishal Vishnoi, Elias Luqman Jalaluddin, Manish Parekh, Thanh Long Duong, Mark Edward Johnson
  • Publication number: 20240428130
    Abstract: According to a present invention embodiment, a system identifies a plurality of configurations for machine learning models. Each configuration indicates a machine learning model and a corresponding technique to determine parameters for the machine learning model. The plurality of configurations are evaluated by training the machine learning model of the plurality of configurations according to the parameters determined by the corresponding technique. Performance of the machine learning models of the plurality of configurations is monitored, and resources used for evaluating at least one configuration are adjusted based on the performance of the machine learning model for the at least one configuration relative to the performance of the machine learning models of others of the plurality of configurations. Embodiments of the present invention further include a method and computer program product for training machine learning models in substantially the same manner described above.
    Type: Application
    Filed: June 26, 2023
    Publication date: December 26, 2024
    Inventors: Long VU, Peter Daniel Kirchner, Radu Marinescu, Dharmashankar Subramanian, Nhan Huu Pham
  • Publication number: 20240428124
    Abstract: Embodiments of the invention are directed to a computer system including a memory communicatively coupled to a processor system. The processor system is operable to perform processor system operations that include using a first machine learning (ML) algorithm to convert to-be-classified-data (TBC-data) from a TBC-data format to a second data format; and extract features from the TBC-data in the second data format. A second ML algorithm is used to perform a task that includes determining, based at least in part on the features of the TBC-data in the second data format, that the TBC-data having the second data format is an outlier.
    Type: Application
    Filed: June 21, 2023
    Publication date: December 26, 2024
    Inventors: Long Vu, Peter Daniel Kirchner, Horst Cornelius Samulowitz, Charu C. Aggarwal
  • Publication number: 20240403726
    Abstract: Disclosed embodiments may include a system for identifying Markov Decision Process (MDP) solutions. The system may receive input data including one or more first states and one or more first actions. The system may identify, via a machine learning model (MLM), a subset of the input data. The system may formulate, via the MLM, a search space based on the subset of the input data, the search space including one or more second states and one or more second actions. The system may conduct, via the MLM, hyperparameter tuning of the search space. The system may generate, via the MLM, an MDP instance based on the hyperparameter tuning. The system may determine, via the MLM, whether the generated MDP instance includes a first MDP solution.
    Type: Application
    Filed: June 1, 2023
    Publication date: December 5, 2024
    Inventors: Long Vu, Alexander Zadorojniy, Dharmashankar Subramanian
  • Patent number: 12066813
    Abstract: A relationship between an input, a set-point of a plurality of processes and an output of a corresponding process is learned using machine learning. A regression function is derived for each process based upon historical data. An autoencoder is trained for each process based upon the historical data to form a regularizer and the regression functions and regularizers are merged together into a unified optimization problem. System level optimization is performed using the regression functions and regularizers and a set of optimal set-points of a global optimal solution for operating the processes is determined. An industrial system is operated based on the set of optimal set-points.
    Type: Grant
    Filed: March 16, 2022
    Date of Patent: August 20, 2024
    Assignee: International Business Machines Corporation
    Inventors: Dzung Tien Phan, Long Vu, Dharmashankar Subramanian
  • Patent number: 11966340
    Abstract: To automate time series forecasting machine learning pipeline generation, a data allocation size of time series data may be determined based on one or more characteristics of a time series data set. The time series data may be allocated for use by candidate machine learning pipelines based on the data allocation size. Features for the time series data may be determined and cached by the candidate machine learning pipelines. Predictions of each of the candidate machine learning pipelines using at least the one or more features may be evaluated. A ranked list of machine learning pipelines may be automatically generated from the candidate machine learning pipelines for time series forecasting based upon evaluating predictions of each of the one or more candidate machine learning pipelines.
    Type: Grant
    Filed: March 15, 2022
    Date of Patent: April 23, 2024
    Assignee: International Business Machines Corporation
    Inventors: Long Vu, Bei Chen, Xuan-Hong Dang, Peter Daniel Kirchner, Syed Yousaf Shah, Dhavalkumar C. Patel, Si Er Han, Ji Hui Yang, Jun Wang, Jing James Xu, Dakuo Wang, Gregory Bramble, Horst Cornelius Samulowitz, Saket K. Sathe, Wesley M. Gifford, Petros Zerfos
  • Patent number: 11868230
    Abstract: Computer hardware and/or software that performs the following operations: (i) assessing a performance of a plurality of unsupervised machine learning pipelines against a plurality of data sets; (ii) associating the performance with meta-features corresponding to respective pipeline/data set combinations; (iii) training a supervised meta-learning model using the associated performance and meta-features as training data; and (iv) utilizing the trained model to identify one or more pipelines for processing an input data set.
    Type: Grant
    Filed: March 11, 2022
    Date of Patent: January 9, 2024
    Assignee: International Business Machines Corporation
    Inventors: Saket K. Sathe, Long Vu, Peter Daniel Kirchner, Horst Cornelius Samulowitz
  • Patent number: 11829799
    Abstract: A method, a structure, and a computer system for predicting pipeline training requirements. The exemplary embodiments may include receiving one or more worker node features from one or more worker nodes, extracting one or more pipeline features from one or more pipelines to be trained, and extracting one or more dataset features from one or more datasets used to train the one or more pipelines. The exemplary embodiments may further include predicting an amount of one or more resources required for each of the one or more worker nodes to train the one or more pipelines using the one or more datasets based on one or more models that correlate the one or more worker node features, one or more pipeline features, and one or more dataset features with the one or more resources. Lastly, the exemplary embodiments may include identifying a worker node requiring a least amount of the one or more resources of the one or more worker nodes for training the one or more pipelines.
    Type: Grant
    Filed: October 13, 2020
    Date of Patent: November 28, 2023
    Assignee: International Business Machines Corporation
    Inventors: Saket Sathe, Gregory Bramble, Long Vu, Theodoros Salonidis
  • Publication number: 20230342627
    Abstract: Predefined pipelines may be created with predefined meta-features. Time series data may be segmented using lookback window parameters. Meta-features may be determined for windowed data. Those of the predefined pipelines having a maximum amount of matching predefined meta-features may be determined. Those of the lookback window parameters that result in the windowed data having the meta-features most similar to the meta-features of one or more of the plurality of predefined pipelines may be identified.
    Type: Application
    Filed: April 22, 2022
    Publication date: October 26, 2023
    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Long VU, Saket K SATHE, Peter Daniel KIRCHNER, Gregory BRAMBLE
  • Publication number: 20230316150
    Abstract: A method includes training, by one or more processing devices, a plurality of machine learning predictive models, thereby generating a plurality of trained machine learning predictive models. The method further includes generating, by the one or more processing devices, a solved machine learning optimization model, based at least in part on the plurality of trained machine learning predictive models. The method further includes outputting, by the one or more processing devices, one or more control input and predicted outputs based at least in part on the solved machine learning optimization model.
    Type: Application
    Filed: March 30, 2022
    Publication date: October 5, 2023
    Inventors: Dzung Tien Phan, Long Vu, Lam Minh Nguyen, Dharmashankar Subramanian
  • Publication number: 20230297073
    Abstract: A relationship between an input, a set-point of a plurality of processes and an output of a corresponding process is learned using machine learning. A regression function is derived for each process based upon historical data. An autoencoder is trained for each process based upon the historical data to form a regularizer and the regression functions and regularizers are merged together into a unified optimization problem. System level optimization is performed using the regression functions and regularizers and a set of optimal set-points of a global optimal solution for operating the processes is determined. An industrial system is operated based on the set of optimal set-points.
    Type: Application
    Filed: March 16, 2022
    Publication date: September 21, 2023
    Inventors: Dzung Tien Phan, Long VU, Dharmashankar Subramanian
  • Publication number: 20230289277
    Abstract: Computer hardware and/or software that performs the following operations: (i) assessing a performance of a plurality of unsupervised machine learning pipelines against a plurality of data sets; (ii) associating the performance with meta-features corresponding to respective pipeline/data set combinations; (iii) training a supervised meta-learning model using the associated performance and meta-features as training data; and (iv) utilizing the trained model to identify one or more pipelines for processing an input data set.
    Type: Application
    Filed: March 11, 2022
    Publication date: September 14, 2023
    Inventors: Saket K. Sathe, Long VU, Peter Daniel Kirchner, Horst Cornelius Samulowitz
  • Publication number: 20230237385
    Abstract: A computer-implemented method for configuring a plurality of machine learning pipelines into a machine learning pipeline ensemble is disclosed. The computer-implemented method includes determining, by a reinforcement learning agent coupled to a machine learning pipeline, performance information of the machine learning pipeline. The computer-implemented method further includes receiving, by the reinforcement learning agent, configuration parameter values of uncoupled machine learning pipelines of the plurality of machine learning pipelines. The computer-implemented method further includes adjusting, by the reinforcement learning agent, configuration parameter values of the machine learning pipeline based on the performance information of the machine learning pipeline and the configuration parameter values of the uncoupled machine learning pipelines.
    Type: Application
    Filed: January 25, 2022
    Publication date: July 27, 2023
    Inventors: Lan Ngoc Hoang, Long Vu
  • Patent number: 11688111
    Abstract: Systems, computer-implemented methods, and computer program products to facilitate visualization of a model selection process are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interaction backend handler component that obtains one or more assessment metrics of a model pipeline candidate. The computer executable components can further comprise a visualization render component that renders a progress visualization of the model pipeline candidate based on the one or more assessment metrics.
    Type: Grant
    Filed: July 29, 2020
    Date of Patent: June 27, 2023
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Dakuo Wang, Bei Chen, Ji Hui Yang, Abel Valente, Arunima Chaudhary, Chuang Gan, John Dillon Eversman, Voranouth Supadulya, Daniel Karl I. Weidele, Jun Wang, Jing James Xu, Dhavalkumar C. Patel, Long Vu, Syed Yousaf Shah, Si Er Han
  • Publication number: 20230177387
    Abstract: A method, system, and computer program product for a metalearner for automated machine learning are provided. The method receives a labeled data set. A set of data subsets is generated from the labeled data set. A set of unsupervised machine learning pipelines is generated. A training set is generated from the set of data subsets and the set of unsupervised machine learning pipelines. The method trains a metalearner for unsupervised tasks based on the training set.
    Type: Application
    Filed: December 8, 2021
    Publication date: June 8, 2023
    Inventors: Saket Sathe, Long Vu, Peter Daniel Kirchner, Charu C. Aggarwal