Patents by Inventor Nikita Iserson

Nikita Iserson 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).

  • Efficient factor analysis on large datasets using categorical variables
    Patent number: 11914622
    Abstract: Methods and apparatus are disclosed for efficient factor analysis of a large population of data records, using factors that are categorical variables. Computation is balanced between extracting key factors by training a machine learning classifier on a reduced sample of data records, for computational efficiency, and scoring the categorical values of the key factors on the entire population, for accuracy of results. A joint factor is constructed by combining all proposed root factors, and the sample is generated by stratified sampling on the joint factor. The key factors are selected from candidate factors which can be combinations of the root factors. Original variables of a dataset, whether categorical or not, can be binned to obtain new categorical factors. Variations and user interfaces are also disclosed.
    Type: Grant
    Filed: May 27, 2020
    Date of Patent: February 27, 2024
    Assignee: SAP SE
    Inventors: Nikita Iserson, Sawinder Kaur, Yogeshwaran Kandasamy, Balaji Elumalai, Ashish Tripathy, Karl-Peter Nos
  • EFFICIENT FACTOR ANALYSIS ON LARGE DATASETS USING CATEGORICAL VARIABLES
    Publication number: 20210319045
    Abstract: Methods and apparatus are disclosed for efficient factor analysis of a large population of data records, using factors that are categorical variables. Computation is balanced between extracting key factors by training a machine learning classifier on a reduced sample of data records, for computational efficiency, and scoring the categorical values of the key factors on the entire population, for accuracy of results. A joint factor is constructed by combining all proposed root factors, and the sample is generated by stratified sampling on the joint factor. The key factors are selected from candidate factors which can be combinations of the root factors. Original variables of a dataset, whether categorical or not, can be binned to obtain new categorical factors. Variations and user interfaces are also disclosed.
    Type: Application
    Filed: May 27, 2020
    Publication date: October 14, 2021
    Applicant: SAP SE
    Inventors: Nikita Iserson, Sawinder Kaur, Yogeshwaran Kandasamy, Balaji Elumalai, Ashish Tripathy, Karl-Peter Nos