Patents by Inventor Xuan-Hong Dang
Xuan-Hong Dang 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).
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Patent number: 11966340Abstract: 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: GrantFiled: March 15, 2022Date of Patent: April 23, 2024Assignee: International Business Machines CorporationInventors: 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
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Patent number: 11915123Abstract: Embodiments relate to a system, program product, and method for employing deep learning techniques to fuse data across modalities. A multi-modal data set is received, including a first data set having a first modality and a second data set having a second modality, with the second modality being different from the first modality. The first and second data sets are processed, including encoding the first data set into one or more first vectors, and encoding the second data set into one or more second vectors. The processed multi-modal data set is analyzed, and the encoded features from the first and second modalities are iteratively and asynchronously fused. The fused modalities include combined vectors from the first and second data sets representing correlated temporal behavior. The fused vectors are then returned as output data.Type: GrantFiled: November 14, 2019Date of Patent: February 27, 2024Assignee: International Business Machines CorporationInventors: Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos, Nancy Anne Greco
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Publication number: 20230297876Abstract: Selecting a time-series forecasting pipeline by receiving target variable time-series data and exogenous variable time-series data, generating a regular forecasting pipeline comprising a model according to the target variable time-series data, generating an exogenous forecasting pipeline comprising a model according to the target variable time-series data and the exogenous variable time-series data, evaluating the regular forecasting pipeline and the exogenous forecasting pipeline, selecting a pipeline according to the evaluation, and providing the selected pipeline.Type: ApplicationFiled: March 17, 2022Publication date: September 21, 2023Inventors: Xuan-Hong Dang, SYED YOUSAF SHAH, Dhavalkumar C. Patel, Wesley M. Gifford, Petros ZERFOS
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Publication number: 20230297881Abstract: Providing time-series forecasting by receiving target variable data and exogenous variable data, training a plurality of time-series models according to the target variable data and the exogenous variable data, determining a historical error for each of the plurality of time series models, and providing a time-series forecasting model having a lowest historical error.Type: ApplicationFiled: March 21, 2022Publication date: September 21, 2023Inventors: SYED YOUSAF SHAH, Petros ZERFOS, Xuan-Hong Dang
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Publication number: 20230259117Abstract: A first set of data associated with assets can be received. An ontology graph can be constructed based on the first set of data. A second set of data associated with the assets can be received, the second set of data having a first frequency of sampling. Based on the second set of data, nodes of the ontology graph representing the assets can be characterized. A third set of data associated with the assets can be received, the third set of data having a second frequency of sampling. The third set of data can include real time data associated with the assets. Based on the third set of data and information associated with the assets represented by the ontology graph, a deep learning neural network can be trained to predict a future state of at least one asset of the assets and discover dynamic mutual impact of the assets.Type: ApplicationFiled: February 11, 2022Publication date: August 17, 2023Inventors: Irene Lizeth Manotas Gutierrez, Xuan-Hong Dang
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Patent number: 11681914Abstract: Techniques regarding multivariate time series data analysis are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a time series analysis component that generates a machine learning model that discovers a dependency between multivariate time series data using an attention mechanism controlled by an uncertainty measure.Type: GrantFiled: May 8, 2020Date of Patent: June 20, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Xuan-Hong Dang, Yunqi Guo, Syed Yousaf Shah, Petros Zerfos
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Patent number: 11620582Abstract: Techniques regarding one or more automated machine learning processes that analyze time series data are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a time series analysis component that selects a machine learning pipeline for meta transfer learning on time series data by sequentially allocating subsets of training data from the time series data amongst a plurality of machine learning pipeline candidates.Type: GrantFiled: July 29, 2020Date of Patent: April 4, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Bei Chen, Long Vu, Syed Yousaf Shah, Xuan-Hong Dang, Peter Daniel Kirchner, Si Er Han, Ji Hui Yang, Jun Wang, Jing James Xu, Dakuo Wang, Dhavalkumar C. Patel, Gregory Bramble, Horst Cornelius Samulowitz, Saket Sathe, Chuang Gan
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Patent number: 11494720Abstract: Techniques are provided for the automated risk assessment of a document. In one embodiment, the techniques involve mapping, via a risk assessment engine, one or more sentences in a first document to one or more risk categories, identifying, via a classification engine, risk-associated language of the one or more sentences based on the one or more risk categories, mapping, via a risk assessment engine, the risk-associated language of the one or more sentences to one or more risk criterion of a risk criterion document, and generating, via a risk assessment engine, a first risk assessment based on the one or more risk criterion of the risk criterion document.Type: GrantFiled: June 30, 2020Date of Patent: November 8, 2022Assignee: International Business Machines CorporationInventors: Raji Lakshmi Akella, Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos, Milton Orlando Laverde Echeverria, Ashley Potter
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Publication number: 20220327058Abstract: 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: ApplicationFiled: March 15, 2022Publication date: October 13, 2022Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: 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
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Publication number: 20220261598Abstract: To rank time series forecasting in machine learning pipelines, time series data may be incrementally allocated from a time series data set for testing by candidate machine learning pipelines based on seasonality or a degree of temporal dependence of the time series data. Intermediate evaluation scores may be provided by each of the candidate machine learning pipelines following each time series data allocation. One or more machine learning pipelines may be automatically selected from a ranked list of the one or more candidate machine learning pipelines based on a projected learning curve generated from the intermediate evaluation scores.Type: ApplicationFiled: October 26, 2021Publication date: August 18, 2022Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Bei CHEN, Long VU, Dhavalkumar C. PATEL, Syed Yousaf SHAH, Gregory BRAMBLE, Peter Daniel KIRCHNER, Horst Cornelius SAMULOWITZ, Xuan-Hong DANG, Petros ZERFOS
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Publication number: 20220036246Abstract: Techniques regarding one or more automated machine learning processes that analyze time series data are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a time series analysis component that selects a machine learning pipeline for meta transfer learning on time series data by sequentially allocating subsets of training data from the time series data amongst a plurality of machine learning pipeline candidates.Type: ApplicationFiled: July 29, 2020Publication date: February 3, 2022Inventors: Bei Chen, Long VU, Syed Yousaf Shah, Xuan-Hong Dang, Peter Daniel Kirchner, Si Er Han, Ji Hui Yang, Jun Wang, Jing James Xu, Dakuo Wang, Dhavalkumar C. Patel, Gregory Bramble, Horst Cornelius Samulowitz, Saket Sathe, Chuang Gan
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Publication number: 20210406788Abstract: Techniques are provided for the automated risk assessment of a document. In one embodiment, the techniques involve mapping, via a risk assessment engine, one or more sentences in a first document to one or more risk categories, identifying, via a classification engine, risk-associated language of the one or more sentences based on the one or more risk categories, mapping, via a risk assessment engine, the risk-associated language of the one or more sentences to one or more risk criterion of a risk criterion document, and generating, via a risk assessment engine, a first risk assessment based on the one or more risk criterion of the risk criterion document.Type: ApplicationFiled: June 30, 2020Publication date: December 30, 2021Inventors: Raji Lakshmi AKELLA, Xuan-Hong DANG, Syed Yousaf SHAH, Petros ZERFOS, Milton Orlando LAVERDE ECHEVERRIA, Ashley POTTER
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Publication number: 20210350225Abstract: Techniques regarding multivariate time series data analysis are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a time series analysis component that generates a machine learning model that discovers a dependency between multivariate time series data using an attention mechanism controlled by an uncertainty measure.Type: ApplicationFiled: May 8, 2020Publication date: November 11, 2021Inventors: Xuan-Hong Dang, Yunqi Guo, Syed Yousaf Shah, Petros Zerfos
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Publication number: 20210150315Abstract: Embodiments relate to a system, program product, and method for employing deep learning techniques to fused data across modalities. A multi-modal data set is received, including a first data set having a first modality and a second data set having a second modality, with the second modality being different from the first modality. The first and second data sets are processed, including encoding the first data set into one or more first vectors, and encoding the second data set into one or more second vectors. The processed multi-modal data set is analyzed, and the encoded features from the first and second modalities are iteratively and asynchronously fused. The fused modalities include combined vectors from the first and second data sets representing correlated temporal behavior. The fused vectors are then returned as output data.Type: ApplicationFiled: November 14, 2019Publication date: May 20, 2021Applicant: International Business Machines CorporationInventors: Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos, Nancy Anne Greco
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Patent number: 11012463Abstract: For a plurality of hosts, observe first time-varying characteristics including network throughput, central processing unit (CPU) usage, and/or memory usage; second time-varying characteristics including software configuration; and time-invariant characteristics including hardware configuration, at a plurality of timestamps. Construct a restricted HMM configured to predict actual host states, wherein the first time-varying characteristics include observed variables. The current observed variables depend on current values of the hidden variables and prior timestamp distribution of the observed variables. The former in turn depend on prior timestamp values of the hidden variables, the time-invariant characteristics of the hosts. and current timestamp values of the second time-varying characteristics.Type: GrantFiled: November 7, 2018Date of Patent: May 18, 2021Assignee: International Business Machines CorporationInventors: Long Vu, Xuan-Hong Dang
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Publication number: 20200145448Abstract: For a plurality of hosts, observe first time-varying characteristics including network throughput, central processing unit (CPU) usage, and/or memory usage; second time-varying characteristics including software configuration; and time-invariant characteristics including hardware configuration, at a plurality of timestamps. Construct a restricted HMM configured to predict actual host states, wherein the first time-varying characteristics include observed variables. The current observed variables depend on current values of the hidden variables and prior timestamp distribution of the observed variables. The former in turn depend on prior timestamp values of the hidden variables, the time-invariant characteristics of the hosts. and current timestamp values of the second time-varying characteristics.Type: ApplicationFiled: November 7, 2018Publication date: May 7, 2020Inventors: Long Vu, XUAN-HONG DANG
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Publication number: 20190354836Abstract: Techniques for determining temporal dependencies and inter-time series dependencies in multi-variate time series data are provided. For example, embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor that can execute the computer executable components stored in the memory. The computer executable components can include: a computing component that encodes recurrent neural networks (RNNs) with time series data and determines decoded RNNs based on temporal context vectors, to determine temporal dependencies in time series data; a combining component that combines the decoded RNNs and determines an inter-time series dependence context vector and an RNN dependence decoder; and an analysis component that determines inter-time series dependencies in the time series data and forecast values for the time series data based on the inter-time series dependence context vector and the RNN dependence decoder.Type: ApplicationFiled: May 17, 2018Publication date: November 21, 2019Inventors: Syed Yousaf Shah, Xuan-Hong Dang, Petros Zerfos