Patents by Inventor Joseph Peper

Joseph Peper 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: 10824818
    Abstract: Systems and methods for synthesizing training data for multi-intent utterance segmentation include identifying a first corpus of utterances comprising a plurality of distinct single-intent in-domain utterances; identifying a second corpus of utterances comprising a plurality of distinct single-intent out-of-domain utterances; identifying a third corpus comprising a plurality of distinct conjunction terms; forming a multi-intent training corpus comprising synthetic multi-intent utterances, wherein forming each distinct multi-intent utterance includes: selecting a first distinct in-domain utterance from the first corpus of utterances; probabilistically selecting one of a first out-of-domain utterance from the second corpus and a second in-domain utterance from the first corpus; probabilistically selecting or not selecting a distinct conjunction term from the third corpus; and forming a synthetic multi-intent utterance including appending the first in-domain utterance with one of the first out-of-domain utteranc
    Type: Grant
    Filed: April 21, 2020
    Date of Patent: November 3, 2020
    Assignee: Clinc, Inc.
    Inventors: Joseph Peper, Parker Hill, Kevin Leach, Sean Stapleton, Jonathan K. Kummerfeld, Johann Hauswald, Michael Laurenzano, Lingjia Tang, Jason Mars
  • Publication number: 20200257857
    Abstract: Systems and methods for synthesizing training data for multi-intent utterance segmentation include identifying a first corpus of utterances comprising a plurality of distinct single-intent in-domain utterances; identifying a second corpus of utterances comprising a plurality of distinct single-intent out-of-domain utterances; identifying a third corpus comprising a plurality of distinct conjunction terms; forming a multi-intent training corpus comprising synthetic multi-intent utterances, wherein forming each distinct multi-intent utterance includes: selecting a first distinct in-domain utterance from the first corpus of utterances; probabilistically selecting one of a first out-of-domain utterance from the second corpus and a second in-domain utterance from the first corpus; probabilistically selecting or not selecting a distinct conjunction term from the third corpus; and forming a synthetic multi-intent utterance including appending the first in-domain utterance with one of the first out-of-domain utteranc
    Type: Application
    Filed: April 21, 2020
    Publication date: August 13, 2020
    Inventors: Joseph Peper, Parker Hill, Kevin Leach, Sean Stapleton, Jonathan K. Kummerfeld, Johann Hauswald, Michael Laurenzano, Lingjia Tang, Jason Mars
  • Publication number: 20200257856
    Abstract: Systems and methods for synthesizing training data for multi-intent utterance segmentation include identifying a first corpus of utterances comprising a plurality of distinct single-intent in-domain utterances; identifying a second corpus of utterances comprising a plurality of distinct single-intent out-of-domain utterances; identifying a third corpus comprising a plurality of distinct conjunction terms; forming a multi-intent training corpus comprising synthetic multi-intent utterances, wherein forming each distinct multi-intent utterance includes: selecting a first distinct in-domain utterance from the first corpus of utterances; probabilistically selecting one of a first out-of-domain utterance from the second corpus and a second in-domain utterance from the first corpus; probabilistically selecting or not selecting a distinct conjunction term from the third corpus; and forming a synthetic multi-intent utterance including appending the first in-domain utterance with one of the first out-of-domain utteranc
    Type: Application
    Filed: February 6, 2020
    Publication date: August 13, 2020
    Inventors: Joseph Peper, Parker Hill, Kevin Leach, Sean Stapleton, Jonathan K. Kummerfeld, Johann Hauswald, Michael A. Laurenzano, Lingjia Tang, Jason Mars