Patents Assigned to Minitab, LLC
  • Publication number: 20250147495
    Abstract: An aspect includes inspecting, using a sensor array, a batch of items in a stage of a process for defects that meet a predefined defect criteria, obtaining from the sensor array, a number of items in the batch with the defects, the batch of items is a sample from a population of items. The aspect includes computing a statistical significance level of a difference between a proportion of defects in the stage of the process and a predefined proportion threshold by calculating a p-value of a statistical test about the proportion of the defects through computing a solution to an equation derived from inverting an Agresti-Coull confidence interval for the proportion of defects.
    Type: Application
    Filed: November 2, 2023
    Publication date: May 8, 2025
    Applicant: Minitab, LLC
    Inventors: Senin J. Banga, Cheryl L. Pammer
  • Publication number: 20250131290
    Abstract: Computing an input relevance measure of process inputs by obtaining from a sensor array sensor measurements that relate process inputs to a process output, computing from the sensor measurements, a number of data partitions, building for each data partition of the number of data partitions a corresponding stochastic gradient boosting model, and computing for each process input, a number of partial dependency plots with each partial dependency plot being based on the corresponding stochastic gradient boosting model. The input relevance measure is then computed for each process input based on the number of partial dependency plots of the process input to estimate a degree of change in the process output obtained by varying the process input.
    Type: Application
    Filed: October 18, 2023
    Publication date: April 24, 2025
    Applicant: Minitab, LLC
    Inventor: Mykhaylo M. Golovnya
  • Publication number: 20250068151
    Abstract: Selecting an optimal model by acquiring, via a predictive analytics engine of a learning machine, an input dataset, and receiving a number of possible regression models for selection, the input dataset includes a plurality of labeled cases. Each candidate regression which is generated through a forward selection procedure is fitted to the input dataset to describe a relationship between one or more explanatory variable values and response variable values of the input dataset. A predictive power of the possible regression model is measured by computing a usual square coefficient of multiple determination, and either a point estimate of the square cross-validated correlation or a two-sided confidence interval of the square cross-validated correlation associated with the given regression sample. Based on the predictive power, the possible regression model that meets a predictive power threshold is selected as an optimal regression model.
    Type: Application
    Filed: August 21, 2023
    Publication date: February 27, 2025
    Applicant: Minitab, LLC
    Inventors: Senin J. Banga, Cheryl L. Pammer
  • Publication number: 20240193446
    Abstract: A computing device reads, a dataset representative of a plurality of distribution agnostic manufacturing measurements, the dataset includes a plurality of observations. Each observation includes a response variable value and a plurality of explanatory variable values. The instructions further cause the computer system to fit a linear regression solver to the dataset to express the response variable value as a function of the explanatory variables; compute a sample coefficient of multiple determination based on the function; compute a kurtosis of a sample of fitted values; and output, a one- or two-sided robust confidence interval for a corresponding population coefficient of multiple determination that is insensitive to normality.
    Type: Application
    Filed: December 9, 2022
    Publication date: June 13, 2024
    Applicant: Minitab, LLC
    Inventors: Senin J. Banga, Cheryl L. Pammer
  • Publication number: 20240078167
    Abstract: A method of validating a regulated application by generating, on a local data processing system, an automated agent to oversee a validation process of the regulated application on the local data processing system regardless of a local or web-based nature of the regulated application.
    Type: Application
    Filed: September 2, 2022
    Publication date: March 7, 2024
    Applicant: Minitab, LLC
    Inventors: Dawn Elaine Keller, Martin Dean Johnson, Jeremy C. Zerbe, Duane Long, Michael J. Yeaney
  • Publication number: 20220292315
    Abstract: Models are k-fold cross-validated to determine how results of an analysis will generalize to an independent data set. By obtaining an inverse transformation of a set of residuals representative of a traditional repetitive train-then-test approach, models can be k-fold cross-validated in an accelerated manner to reduce computational cost and eliminate or substantially eliminate restrictions on the number of folds to include in the cross-validation.
    Type: Application
    Filed: March 11, 2021
    Publication date: September 15, 2022
    Applicant: Minitab, LLC
    Inventors: Senin J. Banga, Robert E. Kelly