Patents by Inventor Creighton K. Heakulani

Creighton K. Heakulani 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).

  • Method and apparatus for processing text with variations in vocabulary usage
    Patent number: 9251250
    Abstract: Text is processed to construct a model of the text. The text has a shared vocabulary. The text is partitioned into sets and subsets of texts. The usage of the shared vocabulary in two or more sets is different, and the topics of two or more subsets are different. A probabilistic model is defined for the text. The probabilistic model considers each word in the text to be a token having a position and a word value, and the usage of the shared vocabulary, topics, subtopics, and word values for each token in the text are represented using distributions of random variables in the probabilistic model, wherein the random variables are discrete. Parameters are estimated for the model corresponding to the vocabulary usages, the word values, the topics, and the subtopics associated with the words.
    Type: Grant
    Filed: March 28, 2012
    Date of Patent: February 2, 2016
    Assignee: Mitsubishi Electric Research Laboratories, Inc.
    Inventors: John R. Hershey, Jonathan Le Roux, Creighton K Heakulani
  • Method and Apparatus for Processing Text with Variations in Vocabulary Usage
    Publication number: 20130262083
    Abstract: Text is processed to construct a model of the text. The text has a shared vocabulary. The text is partitioned into sets and subsets of texts. The usage of the shared vocabulary in two or more sets is different, and the topics of two or more subsets are different. A probabilistic model is defined for the text. The probabilistic model considers each word in the text to be a token having a position and a word value, and the usage of the shared vocabulary, topics, subtopics, and word values for each token in the text are represented using distributions of random variables in the probabilistic model, wherein the random variables are discrete. Parameters are estimated for the model corresponding to the vocabulary usages, the word values, the topics, and the subtopics associated with the words.
    Type: Application
    Filed: March 28, 2012
    Publication date: October 3, 2013
    Inventors: John R. Hershey, Jonathan Le Roux, Creighton K. Heakulani