Patents by Inventor Ben J. Schaper

Ben J. Schaper 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).

  • Semantic text comparison using artificial intelligence identified source document topics
    Patent number: 11669686
    Abstract: A computer assigns a similarity value to a comparison document. The computer receives, reference document contextual word embeddings in first set of topic clusters, each with a representative embedding. The computer receives comparison document contextual word embeddings. The computer determines, using a trained neural network model classifier, for each comparison document contextual word embedding, topic correspondence values relative to the representative embeddings of said first set of clusters. The computer generates a second set of clusters by assigning comparison document embeddings to best matching one of the first clusters, according to the topic correspondence values. The computer determines a second set of representative embeddings and uses a comparison method, to determine a cluster similarity value for second set clusters compared to first set representative embeddings. The computer determines document similarity values based, at least in part, on at least one of cluster similarity values.
    Type: Grant
    Filed: May 20, 2021
    Date of Patent: June 6, 2023
    Assignee: International Business Machines Corporation
    Inventors: Richard Obinna Osuala, Christopher M. Lohse, Ben J. Schaper, Marcell Streile, Charles E. Beller
  • SEMANTIC TEXT COMPARISON USING ARTIFICIAL INTELLIGENCE IDENTIFIED SOURCE DOCUMENT TOPICS
    Publication number: 20220374598
    Abstract: A computer assigns a similarity value to a comparison document. The computer receives, reference document contextual word embeddings in first set of topic clusters, each with a representative embedding. The computer receives comparison document contextual word embeddings. The computer determines, using a trained neural network model classifier, for each comparison document contextual word embedding, topic correspondence values relative to the representative embeddings of said first set of clusters. The computer generates a second set of clusters by assigning comparison document embeddings to best matching one of the first clusters, according to the topic correspondence values. The computer determines a second set of representative embeddings and uses a comparison method, to determine a cluster similarity value for second set clusters compared to first set representative embeddings. The computer determines document similarity values based, at least in part, on at least one of cluster similarity values.
    Type: Application
    Filed: May 20, 2021
    Publication date: November 24, 2022
    Inventors: Richard Obinna Osuala, Christopher M. Lohse, Ben J. Schaper, Marcell Streile, Charles E. Beller