Patents by Inventor Faisal El Hussein

Faisal El Hussein 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).

  • Bi-directional contextualized text description
    Patent number: 10963645
    Abstract: Various examples described herein are directed to systems and methods for analyzing text. A computing device may train an autoencoder language model using a plurality of language model training samples. The autoencoder language mode may comprise a first convolutional layer. Also, a first language model training sample of the plurality of language model training samples may comprise a first set of ordered strings comprising a masked string, a first string preceding the masked string in the first set of ordered strings, and a second string after the masked string in the first set of ordered strings. The computing device may generate a first feature vector using an input sample and the autoencoder language model. The computing device may also generate a descriptor of the input sample using a target model, the input sample, and the first feature vector.
    Type: Grant
    Filed: February 7, 2019
    Date of Patent: March 30, 2021
    Assignee: SAP SE
    Inventors: Christian Reisswig, Darko Velkoski, Sohyeong Kim, Hung Tu Dinh, Faisal El Hussein
  • BI-DIRECTIONAL CONTEXTUALIZED TEXT DESCRIPTION
    Publication number: 20200258498
    Abstract: Various examples described herein are directed to systems and methods for analyzing text. A computing device may train an autoencoder language model using a plurality of language model training samples. The autoencoder language mode may comprise a first convolutional layer. Also, a first language model training sample of the plurality of language model training samples may comprise a first set of ordered strings comprising a masked string, a first string preceding the masked string in the first set of ordered strings, and a second string after the masked string in the first set of ordered strings. The computing device may generate a first feature vector using an input sample and the autoencoder language model. The computing device may also generate a descriptor of the input sample using a target model, the input sample, and the first feature vector.
    Type: Application
    Filed: February 7, 2019
    Publication date: August 13, 2020
    Inventors: Christian Reisswig, Darko Velkoski, Sohyeong Kim, Hung Tu Dinh, Faisal El Hussein