Patents by Inventor Eitan VESELY

Eitan VESELY 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).

  • SYSTEM AND METHOD FOR VALIDATING UNSUPERVISED MACHINE LEARNING MODELS
    Publication number: 20220300857
    Abstract: A system and method for validating unsupervised machine learning models. The method includes: analyzing, via unsupervised machine learning, a plurality of sensory inputs associated with a machine, wherein the unsupervised machine learning outputs at least one normal behavior pattern of the machine; generating, based on the at least one normal behavior pattern, at least one artificial anomaly, wherein each artificial anomaly deviates from the at least one normal behavior pattern; injecting the at least one artificial anomaly into the plurality of sensory inputs to create an artificial dataset; and analyzing the artificial dataset to determine whether a candidate model is a valid representation of operation of the machine, wherein analyzing the artificial dataset further comprises running the candidate model using the artificial dataset as an input.
    Type: Application
    Filed: June 10, 2022
    Publication date: September 22, 2022
    Applicant: Aktiebolaget SKF
    Inventors: David LAVID BEN LULU, Eitan VESELY
  • System and method for validating unsupervised machine learning models
    Patent number: 11403551
    Abstract: A system and method for validating unsupervised machine learning models. The method includes: analyzing, via unsupervised machine learning, a plurality of sensory inputs associated with a machine, wherein the unsupervised machine learning outputs at least one normal behavior pattern of the machine; generating, based on the at least one normal behavior pattern, at least one artificial anomaly, wherein each artificial anomaly deviates from the at least one normal behavior pattern; injecting the at least one artificial anomaly into the plurality of sensory inputs to create an artificial dataset; and analyzing the artificial dataset to determine whether a candidate model is a valid representation of operation of the machine, wherein analyzing the artificial dataset further comprises running the candidate model using the artificial dataset as an input.
    Type: Grant
    Filed: June 13, 2018
    Date of Patent: August 2, 2022
    Assignee: Presenso, Ltd.
    Inventors: David Lavid Ben Lulu, Eitan Vesely
  • SYSTEM AND METHOD FOR UNSUPERVISED PREDICTION OF MACHINE FAILURES
    Publication number: 20210397501
    Abstract: A system and method for unsupervised prediction of machine failures. The method includes monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.
    Type: Application
    Filed: September 2, 2021
    Publication date: December 23, 2021
    Applicant: Aktiebolaget SKF
    Inventors: David LAVID BEN LULU, Eitan VESELY
  • System and method for unsupervised prediction of machine failures
    Patent number: 11138056
    Abstract: A system and method for unsupervised prediction of machine failures. The method includes monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.
    Type: Grant
    Filed: June 12, 2018
    Date of Patent: October 5, 2021
    Assignee: Aktiebolaget SKF
    Inventors: David Lavid Ben Lulu, Eitan Vesely
  • SYSTEM AND METHOD FOR UNSUPERVISED PREDICTION OF MACHINE FAILURES
    Publication number: 20180293125
    Abstract: A system and method for unsupervised prediction of machine failures. The method includes monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.
    Type: Application
    Filed: June 12, 2018
    Publication date: October 11, 2018
    Applicant: Presenso, Ltd.
    Inventors: David LAVID BEN LULU, Eitan VESELY
  • SYSTEM AND METHOD FOR VALIDATING UNSUPERVISED MACHINE LEARNING MODELS
    Publication number: 20180293516
    Abstract: A system and method for validating unsupervised machine learning models. The method includes: analyzing, via unsupervised machine learning, a plurality of sensory inputs associated with a machine, wherein the unsupervised machine learning outputs at least one normal behavior pattern of the machine; generating, based on the at least one normal behavior pattern, at least one artificial anomaly, wherein each artificial anomaly deviates from the at least one normal behavior pattern; injecting the at least one artificial anomaly into the plurality of sensory inputs to create an artificial dataset; and analyzing the artificial dataset to determine whether a candidate model is a valid representation of operation of the machine, wherein analyzing the artificial dataset further comprises running the candidate model using the artificial dataset as an input.
    Type: Application
    Filed: June 13, 2018
    Publication date: October 11, 2018
    Applicant: Presenso, Ltd.
    Inventors: David LAVID BEN LULU, Eitan VESELY