Patents by Inventor Martha G. Hinrichs
Martha G. Hinrichs 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).
-
Publication number: 20210232762Abstract: Systems are presented for generating a natural language model. The system may comprise a database module, an application program interface (API) module, a background processing module, and an applications module, each stored on the at least one memory and executable by the at least one processor. The system may be configured to generate the natural language model by: ingesting training data, generating a hierarchical data structure, 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, 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.Type: ApplicationFiled: February 3, 2021Publication date: July 29, 2021Applicant: Al IP INVESTMENTS LTDInventors: Robert J. Munro, Schuyler D. Erie, Christopher Walker, Sarah K. Luger, Jason Brenier, Gary C. King, Paul A. Tepper, Ross Mechanic, Andrew Gilchrist-Scott, Jessica D. Long, James B. Robinson, Brendan D. Callahan, Michelle Casbon, Ujjwal Sarin, Aneesh Nair, Veena Basavaraj, Tripti Saxena, Edgar Nunez, Martha G. Hinrichs, Haley Most, Tyler J. Schnoebelen
-
Publication number: 20210150130Abstract: 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.Type: ApplicationFiled: February 20, 2020Publication date: May 20, 2021Inventors: Robert J. Munro, Schuyler D. Erle, Christopher Walker, Sarah K. Luger, Jason Brenier, Gary C. King, Paul A. Tepper, Ross Mechanic, Andrew Gilchrist-Scott, Jessica D. Long, James B. Robinson, Brendan D. Callahan, Michelle Casban, Ujjwal Sarin, Aneesh Nair, Veena Basavaraj, Tripti Saxena, Edgar Nunez, Martha G. Hinrichs, Haley Most, Tyler Schnoebelen
-
Publication number: 20200034737Abstract: Systems are presented for generating a natural language model. The system may comprise a database module, an application program interface (API) module, a background processing module, and an applications module, each stored on the at least one memory and executable by the at least one processor. The system may be configured to generate the natural language model by: ingesting training data, generating a hierarchical data structure, 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, 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.Type: ApplicationFiled: February 28, 2019Publication date: January 30, 2020Applicant: AIPARC HOLDINGS PTE. LTD. `Inventors: Robert J. Munro, Schuyler D. Erle, Christopher Walker, Sarah K. Luger, Jason Brenier, Gary C. King, Paul A. Tepper, Ross Mechanic, Andrew Gilchrist-Scott, Jessica D. Long, James B. Robinson, Brendan D. Callahan, Michelle Casbon, Ujjwal Sarin, Aneesh Nair, Veena Basavaraj, Tripti Saxena, Edgar Nunez, Martha G. Hinrichs, Haley Most, Tyler J. Schnoebelen
-
Publication number: 20190303428Abstract: 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.Type: ApplicationFiled: November 5, 2018Publication date: October 3, 2019Inventors: Robert J. Munro, Schuyler D. Erle, Christopher Walker, Sarah K. Luger, Jason Brenier, Gary C. King, Paul A. Tepper, Ross Mechanic, Andrew Gilchrist-Scott, Jessica D. Long, James B. Robinson, Brendan D. Callahan, Michelle Casbon, Ujjwal Sarin, Aneesh Nair, Veena Basavaraj, Tripti Saxena, Edgar Nunez, Martha G. Hinrichs, Haley Most, Tyler Schnoebelen
-
Patent number: 10127214Abstract: 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.Type: GrantFiled: December 9, 2015Date of Patent: November 13, 2018Assignee: Sansa Al Inc.Inventors: Robert J. Munro, Schuyler D. Erle, Christopher Walker, Sarah K. Luger, Jason Brenier, Gary C. King, Paul A. Tepper, Ross Mechanic, Andrew Gilchrist-Scott, Jessica D. Long, James B. Robinson, Brendan D. Callahan, Michelle Casbon, Ujjwal Sarin, Aneesh Nair, Veena Basavaraj, Tripti Saxena, Edgar Nunez, Martha G. Hinrichs, Haley Most, Tyler J. Schnoebelen
-
Publication number: 20160162456Abstract: 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.Type: ApplicationFiled: December 9, 2015Publication date: June 9, 2016Applicant: Idibon, Inc.Inventors: Robert J. Munro, Schuyler D. Erle, Christopher Walker, Sarah K. Luger, Jason Brenier, Gary C. King, Paul A. Tepper, Ross Mechanic, Andrew Gilchrist-Scott, Jessica D. Long, James B. Robinson, Brendan D. Callahan, Michelle Casbon, Ujjwal Sarin, Aneesh Nair, Veena Basavaraj, Tripti Saxena, Edgar Nunez, Martha G. Hinrichs, Haley Most, Tyler J. Schnoebelen