Patents by Inventor Mehdi Sadeghzadeh

Mehdi Sadeghzadeh 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: 12327200
    Abstract: Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data and line production data, determine one or more production associations between the cell data and the line production data; evaluate the one or more production associations to identify one or more critical production associations; retrieve the cell data and the line production data associated with the one or more critical production associations; and train a predictive model with the retrieved cell data and the retrieved line production data to predict the production level of the manufacturing assembly line.
    Type: Grant
    Filed: August 11, 2022
    Date of Patent: June 10, 2025
    Assignee: ATS Corporation
    Inventors: Nicholas Willison, Mehdi Sadeghzadeh, Masoud Kheradmandi, Bo Yuan Chang, Stephen Bacso, Yang Wang, Nick Foisy, Stanley Kleinikkink
  • Patent number: 11790255
    Abstract: Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data, extract feature data from the cell data, determine a plurality of faults, determine a priority level for each fault by applying the extracted feature data to a predictive model, determine at least one high priority fault, and generate at least one operator alert based on the at least one high priority fault.
    Type: Grant
    Filed: March 29, 2021
    Date of Patent: October 17, 2023
    Inventors: Nicholas Willison, Mehdi Sadeghzadeh, Masoud Kheradmandi, Bo Yuan Chang, Stephen Bacso, Yang Wang, Nick Foisy, Stanley Kleinikkink
  • Publication number: 20230095827
    Abstract: Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data and line production data, determine one or more production associations between the cell data and the line production data; evaluate the one or more production associations to identify one or more critical production associations; retrieve the cell data and the line production data associated with the one or more critical production associations; and train a predictive model with the retrieved cell data and the retrieved line production data to predict the production level of the manufacturing assembly line.
    Type: Application
    Filed: August 11, 2022
    Publication date: March 30, 2023
    Inventors: Nicholas Willison, Mehdi Sadeghzadeh, Masoud Kheradmandi, Bo Yuan Chang, Stephen Bacso, Yang Wang, Nick Foisy, Stanley Kleinikkink
  • Publication number: 20220401037
    Abstract: In an example, a method includes collecting biodata of a subject. The method includes generating or updating a personalized ML model of the subject from the biodata of the subject. The method includes detecting anomalies in the biodata based on the personalized ML model. The method includes filtering the detected anomalies to determine whether the detected anomalies indicate that the subject has a clinical condition or is at risk of having the clinical condition.
    Type: Application
    Filed: June 16, 2021
    Publication date: December 22, 2022
    Inventors: Mehdi Sadeghzadeh, David Jonq Wang, Nivedita Khobragade, Zongde Qiu, I-Ting Chen, Christopher Charles Reynolds, Alexander Katsis, James R. Mault
  • Patent number: 11514344
    Abstract: Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data, extract feature data from the cell data, determine a plurality of cell configurations, determine an efficiency score by applying the feature data to a predictive model generated for predicting a production level of the manufacturing assembly line, determine at least one target cell configuration from the cell configurations based on the efficiency score, and apply the at least one target cell configuration to at least one cell by implementing each target cell configuration to a corresponding cell.
    Type: Grant
    Filed: March 29, 2021
    Date of Patent: November 29, 2022
    Inventors: Nicholas Willison, Mehdi Sadeghzadeh, Masoud Kheradmandi, Bo Yuan Chang, Stephen Bacso, Yang Wang, Nick Foisy, Stanley Kleinikkink
  • Patent number: 11449778
    Abstract: Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data and line production data, determine one or more production associations between the cell data and the line production data; evaluate the one or more production associations to identify one or more critical production associations; retrieve the cell data and the line production data associated with the one or more critical production associations; and train a predictive model with the retrieved cell data and the retrieved line production data to predict the production level of the manufacturing assembly line.
    Type: Grant
    Filed: March 29, 2021
    Date of Patent: September 20, 2022
    Assignee: ATS Automation Tooling Systems Inc.
    Inventors: Nicholas Willison, Mehdi Sadeghzadeh, Masoud Kheradmandi, Bo Yuan Chang, Stephen Bacso, Yang Wang, Nick Foisy, Stanley Kleinikkink
  • Publication number: 20210304036
    Abstract: Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data and line production data, determine one or more production associations between the cell data and the line production data; evaluate the one or more production associations to identify one or more critical production associations; retrieve the cell data and the line production data associated with the one or more critical production associations; and train a predictive model with the retrieved cell data and the retrieved line production data to predict the production level of the manufacturing assembly line.
    Type: Application
    Filed: March 29, 2021
    Publication date: September 30, 2021
    Inventors: Nicholas Willison, Mehdi Sadeghzadeh, Masoud Kheradmandi, Bo Yuan Chang, Stephen Bacso, Yang Wang, Nick Foisy, Stanley Kleinikkink
  • Publication number: 20210302944
    Abstract: Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data, extract feature data from the cell data, determine a plurality of cell configurations, determine an efficiency score by applying the feature data to a predictive model generated for predicting a production level of the manufacturing assembly line, determine at least one target cell configuration from the cell configurations based on the efficiency score, and apply the at least one target cell configuration to at least one cell by implementing each target cell configuration to a corresponding cell.
    Type: Application
    Filed: March 29, 2021
    Publication date: September 30, 2021
    Inventors: Nicholas Willison, Mehdi Sadeghzadeh, Masoud Kheradmandi, Bo Yuan Chang, Stephen Bacso, Yang Wang, Nick Foisy, Stanley Kleinikkink
  • Publication number: 20210302278
    Abstract: Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data, extract feature data from the cell data, determine a plurality of faults, determine a priority level for each fault by applying the extracted feature data to a predictive model, determine at least one high priority fault, and generate at least one operator alert based on the at least one high priority fault.
    Type: Application
    Filed: March 29, 2021
    Publication date: September 30, 2021
    Inventors: Nicholas Willison, Mehdi Sadeghzadeh, Masoud Kheradmandi, Bo Yuan Chang, Stephen Bacso, Yang Wang, Nick Foisy, Stanley Kleinikkink