Patents by Inventor Sarah K. Luger

Sarah K. Luger 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: 20210232762
    Abstract: 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: Application
    Filed: February 3, 2021
    Publication date: July 29, 2021
    Applicant: Al IP INVESTMENTS LTD
    Inventors: 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: 20210232763
    Abstract: Methods and systems are disclosed for creating and linking a series of interfaces configured to display information and receive confirmation of classifications made by a natural language modeling engine to improve organization of a collection of documents into an hierarchical structure. In some embodiments, the interfaces may display to an annotator a plurality of labels of potential classifications for a document as identified by a natural language modeling engine, collect annotated responses from the annotator, aggregate the annotated responses across other annotators, analyze the accuracy of the natural language modeling engine based on the aggregated annotated responses, and predict accuracies of the natural language modeling engine's classifications of the documents.
    Type: Application
    Filed: February 22, 2021
    Publication date: July 29, 2021
    Applicant: AI IP INVESTMENTS LTD
    Inventors: Robert J. Munro, Christopher Walker, Sarah K. Luger, Jason Brenier, Paul A. Tepper, Ross Mechanic, Andrew Gilchrist-Scott, Gary C. King, Brendan D. Callahan, Tyler J. Schnoebelen, Edgar Nunez, Haley Most
  • Publication number: 20210150130
    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.
    Type: Application
    Filed: February 20, 2020
    Publication date: May 20, 2021
    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 Casban, Ujjwal Sarin, Aneesh Nair, Veena Basavaraj, Tripti Saxena, Edgar Nunez, Martha G. Hinrichs, Haley Most, Tyler Schnoebelen
  • Publication number: 20200184146
    Abstract: Methods, apparatuses and computer readable medium are presented for generating a natural language model. A method for generating a natural language model comprises: receiving more than one annotation of a document; calculating a level of agreement among the received annotations; determining that a criterion among a first criterion, a second criterion, and a third criterion is satisfied based at least in part on the level of agreement; determining an aggregated annotation representing an aggregation of information in the received annotations and training a natural language model using the aggregated annotation, when the first criterion is satisfied; generating at least one human readable prompt configured to receive additional annotations of the document, when the second criterion is satisfied; and discarding the received annotations from use in training the natural language model, when the third criterion is satisfied.
    Type: Application
    Filed: January 22, 2020
    Publication date: June 11, 2020
    Inventors: Robert J. Munro, Christopher Walker, Sarah K. Luger, Brendan D. Callahan, Gary C. King, Paul A. Tepper, Jana N. Thompson, Tyler J. Schnoebelen, Jason Brenier, Jessica D. Long
  • Publication number: 20200034737
    Abstract: 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: Application
    Filed: February 28, 2019
    Publication date: January 30, 2020
    Applicant: 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: 20190361966
    Abstract: Methods and systems are disclosed for creating and linking a series of interfaces configured to display information and receive confirmation of classifications made by a natural language modeling engine to improve organization of a collection of documents into an hierarchical structure. In some embodiments, the interfaces may display to an annotator a plurality of labels of potential classifications for a document as identified by a natural language modeling engine, collect annotated responses from the annotator, aggregate the annotated responses across other annotators, analyze the accuracy of the natural language modeling engine based on the aggregated annotated responses, and predict accuracies of the natural language modeling engine's classifications of the documents.
    Type: Application
    Filed: December 14, 2018
    Publication date: November 28, 2019
    Applicant: AlPARC HOLDINGS PTE. LTD.
    Inventors: Robert J. Munro, Christopher Walker, Sarah K. Luger, Jason Brenier, Paul A, Tepper, Ross Mechanic, Andrew Gilchrist-Scott, Gary C. King, Brendan D. Callahan, Tyler J. Schnoebelen, Edgar Nunez, Haley Most
  • Publication number: 20190311024
    Abstract: Methods, apparatuses and computer readable medium are presented for generating a natural language model. A method for generating a natural language model comprises: receiving more than one annotation of a document; calculating a level of agreement among the received annotations; determining that a criterion among a first criterion, a second criterion, and a third criterion is satisfied based at least in part on the level of agreement; determining an aggregated annotation representing an aggregation of information in the received annotations and training a natural language model using the aggregated annotation, when the first criterion is satisfied; generating at least one human readable prompt configured to receive additional annotations of the document, when the second criterion is satisfied; and discarding the received annotations from use in training the natural language model, when the third criterion is satisfied.
    Type: Application
    Filed: November 9, 2018
    Publication date: October 10, 2019
    Applicant: AIPARC HOLDINGS PTE. LTD.
    Inventors: Robert J. Munro, Christopher Walker, Sarah K. Luger, Brendan D. Callahan, Gary C. King, Paul A. Tepper, Jana N. Thompson, Tyler J. Schnoebelen, Jason Brenier, Jessica D. Long
  • Publication number: 20190303428
    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.
    Type: Application
    Filed: November 5, 2018
    Publication date: October 3, 2019
    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 Schnoebelen
  • Patent number: 10127214
    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.
    Type: Grant
    Filed: December 9, 2015
    Date of Patent: November 13, 2018
    Assignee: 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: 20160162456
    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.
    Type: Application
    Filed: December 9, 2015
    Publication date: June 9, 2016
    Applicant: 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
  • Publication number: 20160162458
    Abstract: Methods and systems are disclosed for creating and linking a series of interfaces configured to display information and receive confirmation of classifications made by a natural language modeling engine to improve organization of a collection of documents into an hierarchical structure. In some embodiments, the interfaces may display to an annotator a plurality of labels of potential classifications for a document as identified by a natural language modeling engine, collect annotated responses from the annotator, aggregate the annotated responses across other annotators, analyze the accuracy of the natural language modeling engine based on the aggregated annotated responses, and predict accuracies of the natural language modeling engine's classifications of the documents.
    Type: Application
    Filed: December 9, 2015
    Publication date: June 9, 2016
    Applicant: Idibon, Inc.
    Inventors: Robert J. Munro, Christopher Walker, Sarah K. Luger, Jason Brenier, Paul A. Tepper, Ross Mechanic, Andrew Gilchrist-Scott, Gary C. King, Brendan D. Callahan, Tyler J. Schnoebelen, Edgar Nunez, Haley Most
  • Publication number: 20160162464
    Abstract: Methods, apparatuses and computer readable medium are presented for generating a natural language model. A method for generating a natural language model comprises: receiving more than one annotation of a document; calculating a level of agreement among the received annotations; determining that a criterion among a first criterion, a second criterion, and a third criterion is satisfied based at least in part on the level of agreement; determining an aggregated annotation representing an aggregation of information in the received annotations and training a natural language model using the aggregated annotation, when the first criterion is satisfied; generating at least one human readable prompt configured to receive additional annotations of the document, when the second criterion is satisfied; and discarding the received annotations from use in training the natural language model, when the third criterion is satisfied.
    Type: Application
    Filed: December 9, 2015
    Publication date: June 9, 2016
    Applicant: Idibon, Inc.
    Inventors: Robert J. Munro, Christopher Walker, Sarah K. Luger, Brendan D. Callahan, Gary C. King, Paul A. Tepper, Jana N. Thompson, Tyler J. Schnoebelen, Jason Brenier, Jessica D. Long