Patents by Inventor Wojciech Sobala

Wojciech Sobala 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: 12106191
    Abstract: Embodiments of present disclosure relate to a method for predicting a performance of a machine learning module (ML-Module). The method may comprise detecting a change in the performance of the ML-Module over a period of time on the basis of labeled input datasets for the ML-Module and detecting a change in a predicted performance of the ML-Module over the period of time computed using the drift module. A value of a first key figure is determined, the value of the first key figure indicating a correlation between the change in the performance of the ML-Module and the change in the predicted performance of the ML-Module. A signal is provided, the signal indicating the value of the first key figure.
    Type: Grant
    Filed: February 25, 2021
    Date of Patent: October 1, 2024
    Assignee: International Business Machines Corporation
    Inventors: Rafal Bigaj, Wojciech Sobala, Lukasz G. Cmielowski, Maksymilian Erazmus
  • Publication number: 20240220270
    Abstract: Processing within a computing environment is facilitated by determining a correlation quantity indicative of similarity between respective nodes of processing pipelines of the computing environment. Consolidating of respective nodes of the process pipelines is initiated where the correlation quantity has a predefined relationship with a correlation threshold for consolidating nodes of the process pipelines within the computing environment.
    Type: Application
    Filed: January 3, 2023
    Publication date: July 4, 2024
    Inventors: Lukasz G. CMIELOWSKI, Szymon KUCHARCZYK, Daniel Jakub RYSZKA, Wojciech SOBALA
  • Patent number: 11922279
    Abstract: An approach for selecting a transformed prediction model for estimating the uncertainty of prediction associated with machine learning is disclosed. The approach determines a function corresponding to a prediction interval based on one or more datasets and calculates one or more sets of prediction interval parameters associated with the function based on training a set of regression models with the one or more datasets. The approach creates one or more transformed predictions based on the one or more sets of parameters and based on a logical distance and selects a transformed prediction model based on a mean absolute correlation from the one or more transformed predictions. Furthermore, the approach outputs the selected transformed prediction model.
    Type: Grant
    Filed: June 12, 2020
    Date of Patent: March 5, 2024
    Assignee: International Business Machines Corporation
    Inventors: Wojciech Sobala, Rafal Bigaj, Lukasz G. Cmielowski, Maksymilian Erazmus
  • Patent number: 11853392
    Abstract: A method, computer system, and a computer program product for providing reduced training data for training a machine learning model (ML-model) is disclosed. The present invention may include generating the reduced training data dependent on a first two batches of data records of original training data. The present invention may include generating an updated version of the reduced training data dependent on the reduced training data and a further batch of the original training data, wherein a size of the updated version of the reduced training data is equal or less than a size of the limited storage capacity and less than a combined size of the reduced training data and the further batch together. The reading of a further batch of the data records of the original training data and the generating of an updated version of the reduced training data may be repeated.
    Type: Grant
    Filed: November 30, 2021
    Date of Patent: December 26, 2023
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Lukasz G Cmielowski, Amadeusz Masny, Daniel Jakub Ryszka, Wojciech Sobala
  • Patent number: 11816542
    Abstract: Identifying a change of an indicator value for a system of interdependent entities includes determining the indicator value, logging input data for the system of interdependent entities, logging scoring payload data and related results of a machine-learning model used as part of the system of interdependent entities, wherein the scoring payload data are derived from the input data, clustering the input data into a number of clusters resulting in defined clusters, determining metric values of the machine-learning model by mapping each of the defined clusters onto the scoring payload data resulting in a vector of metric values, re-determining the indicator value for each defined cluster, resulting in a vector of re-calculated indicator values, and determining correlation matrix values for a matrix between the vector of re-determined indicator values and the vector of the metric values for each of the defined clusters.
    Type: Grant
    Filed: January 3, 2020
    Date of Patent: November 14, 2023
    Assignee: International Business Machines Corporation
    Inventors: Lukasz G. Cmielowski, Rafal Bigaj, Wojciech Sobala, Maksymilian Erazmus
  • Patent number: 11790256
    Abstract: A computer-implemented method, system and computer program product for analyzing test result failures using artificial intelligence models. A first machine learning model is trained to differentiate between a bug failure and a test failure within the test failures based on the failure attributes and historical failures. The failure type for each failed test in test failure groups is then determined using the first machine learning model. The failed tests in the test failure groups are then clustered into a set of clusters according to the failure attributes and the determined failure type for each failed test. A root cause failure for each cluster is identified based on the set of clusters and the failure attributes. The root cause of an unclassified failure is predicted using a second machine learning model trained to predict a root cause of the unclassified failure based on identifying the root cause failure for each cluster.
    Type: Grant
    Filed: July 22, 2022
    Date of Patent: October 17, 2023
    Assignee: International Business Machines Corporation
    Inventors: Lukasz G Cmielowski, Maksymilian Erazmus, Rafal Bigaj, Wojciech Sobala
  • Patent number: 11748638
    Abstract: A dataset is received that is for processing by a machine learning model. A scoring payload for the dataset and that regards the machine learning model is also received. A set of features of the machine learning model is determined by analyzing the scoring payload. The scoring payload is structured in accordance with the set of features such that the structured scoring payload is ready for analysis for a monitor of the machine learning model.
    Type: Grant
    Filed: July 22, 2020
    Date of Patent: September 5, 2023
    Assignee: International Business Machines Corporation
    Inventors: Rafal Bigaj, Lukasz G. Cmielowski, Wojciech Sobala, Maksymilian Erazmus
  • Patent number: 11704220
    Abstract: An overall performance metric of a computer system may be determined for each bin of the set of analysis bins. In case one or more bins of the set of analysis bins do not have at least a predefined minimum number of records, a new set of analysis bins may be redefined by joining analysis bins of the set of analysis bins. For each bin of the redefined set of bins a machine learning (ML) performance metric of the ML model may be computed. The ML performance metric may be estimated for the set of analysis bins using the ML performance metrics of the redefined bins. The computer system may be configured based on a correlation over the set of analysis bins between the computed overall performance metric and the ML performance metric.
    Type: Grant
    Filed: April 11, 2022
    Date of Patent: July 18, 2023
    Assignee: International Business Machines Corporation
    Inventors: Rafal Bigaj, Lukasz G. Cmielowski, Wojciech Sobala, Maksymilian Erazmus
  • Patent number: 11699071
    Abstract: A first machine learning model processes a set of inputs to generate a first set of results. Based on that first set of results, a quality control range is calculated. A second machine learning model calculates a mean accuracy of a second set of results, based on the set of inputs. A determination of whether the mean accuracy of the second set of results is within the quality control range is made, and a user is notified of that determination.
    Type: Grant
    Filed: November 20, 2019
    Date of Patent: July 11, 2023
    Assignee: International Business Machines Corporation
    Inventors: Lukasz G. Cmielowski, Wojciech Sobala, Rafal Bigaj, Maksymilian Erazmus
  • Patent number: 11699082
    Abstract: A method, system, and computer program product for correlation detection between artificial intelligence (AI) transactions. The method stores a set of transaction records associated with an AI decision engine. Each transaction record has a set of record characteristics. The method assigns the set of transaction records to a set of batches on the set of record characteristics. A set of batch characteristics are determined for a batch of the set of batches. The method determines one or more correlations among the set of batch characteristics. The one or more correlations are compared with one or more threshold batches. The method determines, from the one or more correlations and the comparing, an impact of one or more recommendations of the AI decision engine. The one or more recommendations are defined by the set of transaction records.
    Type: Grant
    Filed: November 21, 2019
    Date of Patent: July 11, 2023
    Assignee: International Business Machines Corporation
    Inventors: Lukasz G. Cmielowski, Wojciech Sobala, Maksymilian Erazmus, Rafal Bigaj
  • Publication number: 20230169148
    Abstract: A method, computer system, and a computer program product for providing reduced training data for training a machine learning model (ML-model) is disclosed. The present invention may include generating the reduced training data dependent on a first two batches of data records of original training data. The present invention may include generating an updated version of the reduced training data dependent on the reduced training data and a further batch of the original training data, wherein a size of the updated version of the reduced training data is equal or less than a size of the limited storage capacity and less than a combined size of the reduced training data and the further batch together. The reading of a further batch of the data records of the original training data and the generating of an updated version of the reduced training data may be repeated.
    Type: Application
    Filed: November 30, 2021
    Publication date: June 1, 2023
    Inventors: Lukasz G. Cmielowski, Amadeusz Masny, Daniel Jakub Ryszka, Wojciech Sobala
  • Publication number: 20230068816
    Abstract: The present disclosure relates to a method for generating a machine learning (ML) model. The method comprises: receiving a selection of a desired value of the metric for predicting a value of the first label attribute based on a current training dataset. Previously obtained sets of training settings may be used for determining a set of training settings that corresponds to the desired value of the metric. A ML engine may be controlled to generate using the current training dataset a machine learning model according to the determined set of training settings. The generated machine learning model may be deployed for performing predictions of values of the first label attribute.
    Type: Application
    Filed: August 26, 2021
    Publication date: March 2, 2023
    Inventors: Lukasz G. Cmielowski, Daniel Jakub Ryszka, Wojciech Sobala, Jan Soltysik
  • Publication number: 20230061222
    Abstract: Disclosed herein is a method of training an artificial intelligence model that comprises an iterative training loop. Said iterative training loop comprises: receiving a current set of training data; dividing said current set of training data into a predetermined number of training data subsets; sequentially training said artificial intelligence model with each of said predetermined number of training data subsets using a training portion and calculating a performance metric using a validation portion; and comparing performance metrics from a previous iteration of said iterative training loop to said calculated performance metric to determine if an improving performance metric condition is met. Said method further comprises halting said iterative training loop unless said improving performance metric condition is not met at least once within a predetermined number of previous iterations.
    Type: Application
    Filed: August 30, 2021
    Publication date: March 2, 2023
    Inventors: Lukasz G. Cmielowski, Daniel Jakub Ryszka, Wojciech Sobala, Gregory Bramble
  • Patent number: 11593388
    Abstract: A method and a computer program product are used generating an index of a scoring payload dataset. Correlation coefficients for correlations between input data values and output data values of the machine learning model provided by the scoring payload datasets as well as performance data values of the processes provided by process datasets are calculated. Features of which feature values are used as input data values are ranked according to their importance using the correlation coefficients. For the features of a set of highest-ranking features feature value sets with feature values of the respective features are selected from the scoring payload datasets and a database index of the selected feature value sets is generated.
    Type: Grant
    Filed: March 19, 2021
    Date of Patent: February 28, 2023
    Assignee: International Business Machines Corporation
    Inventors: Rafal Bigaj, Lukasz G. Cmielowski, Wojciech Sobala, Maksymilian Erazmus
  • Patent number: 11556860
    Abstract: An ML root model format having a root model definition, is converted into an ML target model format not having the root model definition. A learning system is assigned to the root model definition that is convertible to the machine learning target model format. A new version of the root model definition is ported from the ML root model to the format of the ML target model to generate a new version of the machine learning target model after a learning iteration of the learning system. Quality limits are determined using an X control chart method based on a cross-validation of fold results generated during a validation of the new version of the machine learning root model. A quality metric value of the new version of the ML target model is evaluated against the quality threshold values of the new version of the ML root model.
    Type: Grant
    Filed: April 15, 2019
    Date of Patent: January 17, 2023
    Assignee: International Business Machines Corporation
    Inventors: Lukasz G. Cmielowski, Rafal Bigaj, Blazej Rafal Rutkowski, Wojciech Sobala
  • Patent number: 11551152
    Abstract: Identifying input feature significance for a machine learning model is provided. A set of scoring payload data corresponding to a set of input features of the machine learning model is sliced into a predefined number of batches. Using the sliced set of scoring payload data, a correlation coefficient matrix for each respective input feature of the machine learning model is generated based on input of each particular input feature into the machine learning model and a corresponding output from the machine learning model for each particular input feature. A correlation coefficient value is extracted from the correlation coefficient matrix for each particular input feature and the corresponding output from the machine learning model. A set of significant input features having a corresponding correlation coefficient value greater than a predefined correlation coefficient threshold level is identified. A set of action steps is performed regarding the set of significant input features.
    Type: Grant
    Filed: September 3, 2020
    Date of Patent: January 10, 2023
    Assignee: International Business Machines Corporation
    Inventors: Lukasz G. Cmielowski, Maksymilian Erazmus, Rafal Bigaj, Wojciech Sobala
  • Publication number: 20220405525
    Abstract: A method, system, and computer program product for classifying input data of a machine learning model. The method includes obtaining a dataset. The method also includes determining pairwise correlations of the set of features using their values in the dataset. The method also includes selecting one or more pairs of features that are highly correlated. The method also includes creating a density map that contains a set of points. The method also includes determining a low-density area on the density map having a low-density of points from the density analysis. The method also includes identifying records of the dataset that belong to the determined low-density areas. The method also includes labeling the identified records as low-density and labeling the remaining records of the dataset as high-density. The method also includes training a classifier to classify an input record having the set of features as a low-density or high-density record.
    Type: Application
    Filed: June 22, 2021
    Publication date: December 22, 2022
    Inventors: Wojciech Sobala, Lukasz G. Cmielowski, Maksymilian Erazmus, Rafal Bigaj
  • Patent number: 11507670
    Abstract: The present disclosure relates to a computer implemented method for testing an artificial intelligence module (AI-module). The method comprises generating a substitute module using first input datasets and first output datasets, wherein the first output datasets are generated on the basis of the first input datasets using the AI-module. Adversarial input datasets are generated on the basis of the first input datasets using the substitute module. The adversarial input datasets are used for assessing a resilience of the AI-module against adversarial attacking by using the first output datasets and second output datasets, wherein the second output datasets are generated on the basis of the adversarial input datasets using the AI-module.
    Type: Grant
    Filed: March 4, 2020
    Date of Patent: November 22, 2022
    Assignee: International Business Machines Corporation
    Inventors: Lukasz G. Cmielowski, Rafal Bigaj, Wojciech Sobala, Maksymilian Erazmus
  • Patent number: 11501239
    Abstract: Embodiments are disclosed for a method for machine learning model training outliers. The method includes determining multiple metric values for corresponding transactions generated by a machine learning model. The method also includes deleting multiple preliminary outliers from the transactions based on a derived cut-off value. Further, the method includes identifying an absolute goal for improving a metric of the machine learning model. Additionally, the method includes identifying multiple training outliers from the remaining transactions. The remaining transactions include the transactions remaining after deleting the preliminary outliers. Also, a metric value of the remaining transactions meets the absolute goal.
    Type: Grant
    Filed: March 18, 2020
    Date of Patent: November 15, 2022
    Assignee: International Business Machines Corporation
    Inventors: Lukasz G. Cmielowski, Wojciech Sobala, Maksymilian Erazmus, Rafal Bigaj
  • Publication number: 20220358381
    Abstract: A computer-implemented method, system and computer program product for analyzing test result failures using artificial intelligence models. A first machine learning model is trained to differentiate between a bug failure and a test failure within the test failures based on the failure attributes and historical failures. The failure type for each failed test in test failure groups is then determined using the first machine learning model. The failed tests in the test failure groups are then clustered into a set of clusters according to the failure attributes and the determined failure type for each failed test. A root cause failure for each cluster is identified based on the set of clusters and the failure attributes. The root cause of an unclassified failure is predicted using a second machine learning model trained to predict a root cause of the unclassified failure based on identifying the root cause failure for each cluster.
    Type: Application
    Filed: July 22, 2022
    Publication date: November 10, 2022
    Inventors: Lukasz G. Cmielowski, Maksymilian Erazmus, Rafal Bigaj, Wojciech Sobala