Patents by Inventor Abdul Al-Haimi

Abdul Al-Haimi 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: 11263185
    Abstract: Aspects discussed herein relate to employing deep learning to automate mapping and transformation of a source data set to a target data schema. A system may utilize deep learning algorithms to determine a mapping from the source schema to the target schema through identifying the source schema and creating a correspondence between source fields and target fields, and a corresponding data transformation. Artificial neural networks, configured as schema-level and instance-level classifiers, may generate a set of predictions based on the fields of the source data set and fields of the target data schema. These predictions may be combined with other predictions based on other criteria (such as similarity between the fields) to generate a complete prediction of a schema mapping. Similarly, deep learning techniques may be employed to determine an appropriate data transformation to transform source data content to an appropriate format for corresponding fields of the target schema.
    Type: Grant
    Filed: March 18, 2019
    Date of Patent: March 1, 2022
    Assignee: PerkinElmer Informatics, Inc.
    Inventors: Abdul Al-Haimi, Chad Millen
  • Publication number: 20210172800
    Abstract: Aspects of the disclosure relate to techniques for analyzing unknown sample compositions using a prediction model based on optical emission spectra.
    Type: Application
    Filed: December 10, 2019
    Publication date: June 10, 2021
    Inventors: Abdul Al-Haimi, James McQuay, Hamid Badiei
  • Publication number: 20190286620
    Abstract: Aspects discussed herein relate to employing deep learning to automate mapping and transformation of a source data set to a target data schema. A system may utilize deep learning algorithms to determine a mapping from the source schema to the target schema through identifying the source schema and creating a correspondence between source fields and target fields, and a corresponding data transformation. Artificial neural networks, configured as schema-level and instance-level classifiers, may generate a set of predictions based on the fields of the source data set and fields of the target data schema. These predictions may be combined with other predictions based on other criteria (such as similarity between the fields) to generate a complete prediction of a schema mapping. Similarly, deep learning techniques may be employed to determine an appropriate data transformation to transform source data content to an appropriate format for corresponding fields of the target schema.
    Type: Application
    Filed: March 18, 2019
    Publication date: September 19, 2019
    Inventors: Abdul Al-Haimi, Chad Millen