Patents by Inventor Nianjun Zhou

Nianjun Zhou 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: 20220335308
    Abstract: A system and method for determining parameters for a system. Selectable questions with associated goal are presented. Operational goals are different from configurable parameters of the system. A question is selected and a goal indication is received. First values for each goal are determined by an optimization engine adjusting parameters of mathematical models for the system to improve a value of the goal associated with the selected question in a direction of the goal indication. Selectable questions and the first values are presented and selection of a second question and a second goal indication is received. An optimization engine determines second updated values by adjusting parameters of the mathematical model to improve a value of a goal associated with the second selected question in the direction of the second goal indication. Second updated values of the goals, and differences from the first updated values. are presented.
    Type: Application
    Filed: April 15, 2021
    Publication date: October 20, 2022
    Inventors: Nianjun ZHOU, Viktoriia KUSHERBAEVA, Dharmashankar SUBRAMANIAN, Xiang MA, Jacqueline WILLIAMS, Nathaniel MILLS
  • Patent number: 11422545
    Abstract: A hybrid sensor can be generated by training a machine learning model, such as a neural network, based on a training data set. The training data set can include a first time series of upstream sensor data having forward dependence to a target variable, a second time series of downstream sensor data having backward dependence to the target variable and a time series of measured target variable data associated with the target variable. The target variable has measuring frequency which is lower than the measuring frequencies associated with the upstream sensor data and the downstream sensor data. The hybrid sensor can estimate a value of the target variable at a given time, for example, during which no actual measured target variable value is available.
    Type: Grant
    Filed: June 8, 2020
    Date of Patent: August 23, 2022
    Assignee: International Business Machines Corporation
    Inventors: Nianjun Zhou, Dharmashankar Subramanian, Wesley M Gifford
  • Publication number: 20220188775
    Abstract: A computer implemented federated learning method of predicting failure of assets includes generating a local model at a local site for each of the cohorts and training the local model on local data for each of the cohorts for each failure type. The local model is shared with a central database. A global model is created based on an aggregation of a plurality of the local models from a plurality of the local sites. At each of the plurality of local sites, one of the global model and the local model is chosen for each of the cohorts. The chosen model operates on local data to predict failure of the assets. The utilized features include partitioning features of the assets into static features, semi-static features, and dynamic features, and forming cohorts of the assets based on the static features and the semi-static features.
    Type: Application
    Filed: December 15, 2020
    Publication date: June 16, 2022
    Inventors: Nianjun Zhou, Dhavalkumar C. Patel, Anuradha Bhamidipaty
  • Patent number: 11295197
    Abstract: The disclosure relates to extraction of rationales for studied outcome. A method comprises: grouping features as expert to align with a set of operating practices; generating interpretable features using operating rules, combining with statistical dependence analysis to bin selected features to generate favorite practice actions; grouping features as expert that combine a subset of the interpretable features to align with a set of operating practices.
    Type: Grant
    Filed: August 27, 2018
    Date of Patent: April 5, 2022
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Pavankumar Murali, Nianjun Zhou, Ta-Hsin Li, Pietro Mazzoleni, Wesley Gifford
  • Publication number: 20220101225
    Abstract: In an approach for real-time opportunity discovery for productivity enhancement of a production process, a processor extracts a set of features from time series data, through autoencoding using a neural network, based on non-control variables for the time series data. A processor identifies one or more operational modes based on the extracted features including a dimensional reduction with a representation learning from the time series data. A processor identifies a neighborhood of a current operational state based on the extracted features. A processor compares the current operational state to historical operational states based on the time series data at the same operational mode. A processor discovers an operational opportunity based on the comparison of the current operational state to the historical operational states using the neighborhood. A processor identifies control variables in the same mode which variables are relevant to the current operational state.
    Type: Application
    Filed: September 30, 2020
    Publication date: March 31, 2022
    Inventors: Nianjun Zhou, Dharmashankar Subramanian, WingHang Crystal Lui
  • Patent number: 11263103
    Abstract: Embodiments of the invention are directed a computer-implemented method for efficiently assessing data quality metrics. A non-limiting example of the computer-implemented method includes receiving, using a processor, a plurality of updates to data points in a data stream. The processor is further used to provide a plurality of data quality metrics (DQMs), and to maintain information on how much the plurality of DQMs are changing over time. The processor also maintains information on computational overhead for the plurality of DQMs, and also updates data quality information based on the maintained information.
    Type: Grant
    Filed: July 31, 2020
    Date of Patent: March 1, 2022
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Arun Kwangil Iyengar, Anuradha Bhamidipaty, Dhavalkumar C. Patel, Shrey Shrivastava, Nianjun Zhou
  • Publication number: 20220035721
    Abstract: Embodiments of the invention are directed a computer-implemented method for efficiently assessing data quality metrics. A non-limiting example of the computer-implemented method includes receiving, using a processor, a plurality of updates to data points in a data stream. The processor is further used to provide a plurality of data quality metrics (DQMs), and to maintain information on how much the plurality of DQMs are changing over time. The processor also maintains information on computational overhead for the plurality of DQMs, and also updates data quality information based on the maintained information.
    Type: Application
    Filed: July 31, 2020
    Publication date: February 3, 2022
    Inventors: Arun Kwangil Iyengar, Anuradha Bhamidipaty, Dhavalkumar C. Patel, Shrey Shrivastava, Nianjun Zhou
  • Publication number: 20220027685
    Abstract: A computer implemented method for automatically generating an optimization model for site-wide plant optimization includes mapping a process flow diagram of a plant process to a graph comprising nodes and edges, wherein the nodes represent processes and the edges represent flows between processes. A behavior is learned for each node of the graph based at least on historic data of the plant process. One or more regression functions are modeled for each node to predict an output of each of the processes, wherein the one or more regression functions are modeled based on the learned behavior for each node.
    Type: Application
    Filed: July 24, 2020
    Publication date: January 27, 2022
    Inventors: Dzung Tien Phan, Lam Nguyen, Pavankumar Murali, Nianjun Zhou
  • Publication number: 20220019911
    Abstract: A computer-implemented method for providing interpretable predictions from a machine learning model includes receiving a data structure that represents a hierarchical structure of a set of features (X) used by one or more predictive models to generate a set of predictions (Y). An interpretability model is built corresponding to the predictive models, by assigning an interpretability to each prediction Yi based on the hierarchical structure. Assigning the interpretability includes decomposing X into a plurality of partitions Xj using the hierarchical structure, wherein X=U1NXj, N being the number of partitions. Further, each partition is decomposed into a plurality of sub-partitions using the hierarchical structure until atomic sub-partitions are obtained. A score is computed for each partition as a function of the predicted scores of the sub-partitions, wherein the predicted scores represent interactions between the sub-partitions. Further, an interpretation of a prediction is outputted.
    Type: Application
    Filed: July 14, 2020
    Publication date: January 20, 2022
    Inventors: NIANJUN ZHOU, WESLEY M. GIFFORD, TA-HSIN LI, PIETRO MAZZOLENI, PAVANKUMAR MURALI
  • Publication number: 20220011760
    Abstract: Techniques for model fidelity monitoring and regeneration for manufacturing process decision support are described herein. Aspects of the invention include determining that an output of a regression model corresponding to a current time period of decision support for a manufacturing process is not within a predefined range of a historical process dataset, wherein the regression model was constructed based on the historical process dataset, and performing an accuracy and fidelity analysis on the regression model based on process data from the manufacturing process corresponding to a previous time period. Based on a result of the accuracy and fidelity analysis being below a threshold, a mismatch of the regression model as compared to the manufacturing process is determined. Based on determining the mismatch, a temporary regression model corresponding to the manufacturing process is generated, and decision support for the manufacturing process is performed based on the temporary regression model.
    Type: Application
    Filed: July 8, 2020
    Publication date: January 13, 2022
    Inventors: Nianjun Zhou, Dharmashankar Subramanian, Dhavalkumar C. Patel, Anuradha Bhamidipaty
  • Patent number: 11204851
    Abstract: Embodiments of the invention are directed a computer-implemented method for assessing data quality. A non-limiting example of the computer-implemented method includes using a processor to receive a plurality of updates to data points in a data stream. The processor is further used to compute instances of a data quality metric (DQM) from the data points in the data stream. The instances of the DQM are configured to differentiate the data points in the data stream by time and assign a higher weight to the instances of the DQM computed from more recent data points in the data stream. The instances of the DQM are continuously updated as more of the data points are received by the processor while limiting cycles of the processor consumed by updating the instances of the DQM to a threshold.
    Type: Grant
    Filed: July 31, 2020
    Date of Patent: December 21, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Arun Kwangil Iyengar, Anuradha Bhamidipaty, Dhavalkumar C. Patel, Shrey Shrivastava, Nianjun Zhou
  • Publication number: 20210382469
    Abstract: A hybrid sensor can be generated by training a machine learning model, such as a neural network, based on a training data set. The training data set can include a first time series of upstream sensor data having forward dependence to a target variable, a second time series of downstream sensor data having backward dependence to the target variable and a time series of measured target variable data associated with the target variable. The target variable has measuring frequency which is lower than the measuring frequencies associated with the upstream sensor data and the downstream sensor data. The hybrid sensor can estimate a value of the target variable at a given time, for example, during which no actual measured target variable value is available.
    Type: Application
    Filed: June 8, 2020
    Publication date: December 9, 2021
    Inventors: Nianjun Zhou, Dharmashankar Subramanian, Wesley M Gifford
  • Publication number: 20210302953
    Abstract: Context-awareness in preventative maintenance is provided by receiving sensor data from a plurality of monitored systems; extracting a first plurality of features from a set of work orders for the monitored systems, wherein individual work orders include a root cause analysis for a context in which a nonconformance in an indicated monitored system occurred; predicting, via a machine learning model, a nonconformance likelihood for each monitored system based on the first plurality of features; selecting a subset of alerts based on predicted nonconformance likelihoods for the monitored systems; in response to receiving a user selection from the first set of alerts and a reason for the user selection, recording the reason as a modifier for the machine learning model; and updating the machine learning model to predict the subsequent nonconformance likelihoods using a second plurality of features that excludes the additional feature identified from the first plurality of features.
    Type: Application
    Filed: March 26, 2020
    Publication date: September 30, 2021
    Inventors: Nianjun ZHOU, Dhaval PATEL, Jayant R. KALAGNANAM
  • Patent number: 11087265
    Abstract: Similar to other Cloud Service, Solution as Services over Cloud, as single tenant technology, also requires support of agility and flexibility as a fundamental feature of Cloud computing. Different from other Cloud services, the agility and flexibility typically are not triggered by the typical performance metrics, but at the business level of metrics. A causality analysis method, system, and non-transitory computer readable medium using a causal graph depicting relationships among observable primitive metrics from infrastructure, middleware, and business metrics and latent business metrics of an application, include identifying a metric value resulting from measuring the system and application metrics, determining an impact of the measurement of the metrics on the business metrics associated with the measurable metrics in the causal graph, and determining an action to take with respect to the impact on the business metric based on the pre-defined business policies.
    Type: Grant
    Filed: August 12, 2016
    Date of Patent: August 10, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Ajay Mohindra, Rohit Ranchal, Ram Ravishankar, Nianjun Zhou
  • Publication number: 20210117798
    Abstract: Techniques that facilitate machine learning using multi-dimensional time series data are provided. In one example, a system includes a snapshot component and a machine learning component. The snapshot component generates a first sequence of multi-dimensional time series data and a second sequence of multi-dimensional time series data from multi-dimensional time series data associated with at least two different data types generated by a data system over a consecutive period of time. The machine learning component that analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a convolutional neural network system to predict an event associated with the multi-dimensional time series data.
    Type: Application
    Filed: December 29, 2020
    Publication date: April 22, 2021
    Inventors: Wei Sun, Roman Vaculin, Jinfeng Yi, Nianjun Zhou
  • Publication number: 20210110487
    Abstract: A system and a method of managing a manufacturing process includes receiving production data relating to the manufacturing process and determining an operational mode associated with the manufacturing process using historical, multivariate senor data. The method may further determine a recommended action to affect production based on the determined operational mode. The operational mode may be based on at least one of: a level of operation in a continuous flow process relating to a joint set of process variables, a representation of a joint dynamic of the set of process variables over a predefined length, and a joint configuration of an uptime/downtime of a plurality of units comprising a process flow.
    Type: Application
    Filed: October 11, 2019
    Publication date: April 15, 2021
    Inventors: Nianjun Zhou, Dharmashankar Subramanian, Patrick Watson, Pavankumar Murali, Wesley M. Gifford, Jayant R. Kalagnanam
  • Patent number: 10896371
    Abstract: Techniques that facilitate machine learning using multi-dimensional time series data are provided. In one example, a system includes a snapshot component and a machine learning component. The snapshot component generates a first sequence of multi-dimensional time series data and a second sequence of multi-dimensional time series data from multi-dimensional time series data associated with at least two different data types generated by a data system over a consecutive period of time. The machine learning component that analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a convolutional neural network system to predict an event associated with the multi-dimensional time series data.
    Type: Grant
    Filed: December 13, 2017
    Date of Patent: January 19, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Wei Sun, Roman Vaculin, Jinfeng Yi, Nianjun Zhou
  • Publication number: 20210012190
    Abstract: An apparatus and method for optimizing a process, comprising: receiving live operational data associated with a plurality of sub-processes of a process; selecting a pre-trained regression model from a plurality of pre-trained regression models for each sub-process of the plurality of sub-processes; generating a system-wide optimization model comprising a multi-period mathematical program model, including: one or more decision variables; a plurality of constraints, wherein: a first constraint of the plurality of constraints comprises one of the pre-trained regression models, and a second constraint of the plurality of constraints comprises an operational constraint; and an objective function; generating, via the optimization model, an operating mode trajectory comprising a plurality of intermediate operating modes at a plurality of intermediate times during a planning interval; and displaying a set-point trajectory recommendation in a graphical user interface based on the operating mode trajectory.
    Type: Application
    Filed: July 10, 2019
    Publication date: January 14, 2021
    Inventors: Pavankumar MURALI, Dharmashankar SUBRAMANIAN, Nianjun ZHOU, Xiang MA, Jacqueline WILLIAMS
  • Patent number: 10891545
    Abstract: Techniques that facilitate machine learning using multi-dimensional time series data are provided. In one example, a system includes a snapshot component and a machine learning component. The snapshot component generates a first sequence of multi-dimensional time series data and a second sequence of multi-dimensional time series data from multi-dimensional time series data associated with at least two different data types generated by a data system over a consecutive period of time. The machine learning component that analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a convolutional neural network system to predict an event associated with the multi-dimensional time series data.
    Type: Grant
    Filed: March 10, 2017
    Date of Patent: January 12, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Wei Sun, Roman Vaculin, Jinfeng Yi, Nianjun Zhou
  • Patent number: 10878143
    Abstract: A method for simulating participation patterns in a plurality of events from a pool of qualified participants includes selecting an event whose preference rank r is equal to a predetermined preference rank R(i,j) for an event of type j for individual i; sampling a random number Z that represents a number of events in which to participate when a number N(j) of available events of type j is greater than 0 and an expected participation rate P(i, j) of individual i for events of type j is greater than 0; selecting a random subset S with Z elements that indicates which Z events to participate in; and looping over k=1 to N(j) and setting a participation array M(i,j,k,l) to 1 iff k is contained in S, where participation array M(i,j,k,l) indicates whether or not individual i participates in a k-th event of type j in an l-th simulation.
    Type: Grant
    Filed: April 7, 2017
    Date of Patent: December 29, 2020
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: David Hoffman, Feng Li, Ta-Hsin Li, Nianjun Zhou