The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Nov. 13, 2018
Filed:
Dec. 09, 2015
Idibon, Inc., San Francisco, CA (US);
Robert J. Munro, San Francisco, CA (US);
Schuyler D. Erle, San Francisco, CA (US);
Christopher Walker, San Francisco, CA (US);
Sarah K. Luger, San Francisco, CA (US);
Jason Brenier, Oakland, CA (US);
Gary C. King, Los Altos, CA (US);
Paul A. Tepper, San Francisco, CA (US);
Ross Mechanic, San Francisco, CA (US);
Andrew Gilchrist-Scott, Berkeley, CA (US);
Jessica D. Long, San Francisco, CA (US);
James B. Robinson, San Francisco, CA (US);
Brendan D. Callahan, Philadelphia, PA (US);
Michelle Casbon, San Antonio, TX (US);
Ujjwal Sarin, San Francisco, CA (US);
Aneesh Nair, Fremont, CA (US);
Veena Basavaraj, San Francisco, CA (US);
Tripti Saxena, Cupertino, CA (US);
Edgar Nunez, Union City, CA (US);
Martha G. Hinrichs, San Francisco, CA (US);
Haley Most, San Francisco, CA (US);
Tyler J. Schnoebelen, San Francisco, CA (US);
Sansa Al Inc., San Francisco, CA (US);
Abstract
Methods are presented for generating a natural language model. The method may comprise: ingesting training data representative of documents to be analyzed by the natural language model, generating a hierarchical data structure comprising at least two topical nodes within which the training data is to be subdivided into by the natural language model, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document indicating which node among the at least two topical nodes said document is to be classified into, receiving the annotation based on the annotation prompt; and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.