Patents by Inventor Edgar Nunez

Edgar Nunez 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: 20210165955
    Abstract: Systems and methods are presented for the automatic placement of rules applied to topics in a logical hierarchy when conducting natural language processing. In some embodiments, a method includes: accessing, at a child node in a logical hierarchy, at least one rule associated with the child node; identifying a percolation criterion associated with a parent node to the child node, said percolation criterion indicating that the at least one rule associated with the child node is to be associated also with the parent node; associating the at least one rule with the parent node such that the at least one rule defines a second factor for determining whether the document is to also be classified into the parent node; accessing the document for natural language processing; and determining whether the document is to be classified into the parent node or the child node based on the at least one rule.
    Type: Application
    Filed: March 6, 2020
    Publication date: June 3, 2021
    Inventors: Robert J. Munro, Schuyler D. Erle, Tyler J. Schnoebelen, Jason Brenier, Jessica D. Long, Brendan D. Callahan, Paul A. Tepper, Edgar Nunez
  • 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: 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: 20190311025
    Abstract: Systems and methods are presented for the automatic placement of rules applied to topics in a logical hierarchy when conducting natural language processing. In some embodiments, a method includes: accessing, at a child node in a logical hierarchy, at least one rule associated with the child node; identifying a percolation criterion associated with a parent node to the child node, said percolation criterion indicating that the at least one rule associated with the child node is to be associated also with the parent node; associating the at least one rule with the parent node such that the at least one rule defines a second factor for determining whether the document is to also be classified into the parent node; accessing the document for natural language processing; and determining whether the document is to be classified into the parent node or the child node based on the at least one rule.
    Type: Application
    Filed: November 20, 2018
    Publication date: October 10, 2019
    Applicant: AIPARC HOLDINGS PTE. LTD.
    Inventors: Robert J. Munro, Schuyler D. Erie, Tyler J. Schnoebelen, Jason Brenier, Jessica D. Long, Brendan D. Callahan, Paul A. Tepper, Edgar Nunez
  • 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: 10292001
    Abstract: A method is disclosed for issuing audio alerts within the cabin of a vehicle. The method may include detecting a condition programmatically assigned an audio alert and a virtual source for the audio alert. The method may further include determining, by the computer system based on the virtual source, a gain value for each speaker of a plurality of speakers carried in fix positions onboard the vehicle. Once the gain value for each speaker of the plurality of speak is known, the audio alert may be projected by each speaker of the plurality of speakers according to the gain value determined therefor.
    Type: Grant
    Filed: February 8, 2017
    Date of Patent: May 14, 2019
    Assignee: FORD GLOBAL TECHNOLOGIES, LLC
    Inventors: Alexander Groh, Benjamin Vondersaar, Edgar Nunez, Prabhanjan Kadepurkar, Matthew Dage
  • 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: 20180227695
    Abstract: A method is disclosed for issuing audio alerts within the cabin of a vehicle. The method may include detecting a condition programmatically assigned an audio alert and a virtual source for the audio alert. The method may further include determining, by the computer system based on the virtual source, a gain value for each speaker of a plurality of speakers carried in fix positions onboard the vehicle. Once the gain value for each speaker of the plurality of speak is known, the audio alert may be projected by each speaker of the plurality of speakers according to the gain value determined therefor.
    Type: Application
    Filed: February 8, 2017
    Publication date: August 9, 2018
    Inventors: Alexander Groh, Benjamin Vondersaar, Edgar Nunez, Prabhanjan Kadepurkar, Matthew Dage
  • Publication number: 20170235813
    Abstract: Systems and methods are presented for the automatic placement of rules applied to topics in a logical hierarchy when conducting natural language processing. In some embodiments, a method includes: accessing, at a child node in a logical hierarchy, at least one rule associated with the child node; identifying a percolation criterion associated with a parent node to the child node, said percolation criterion indicating that the at least one rule associated with the child node is to be associated also with the parent node; associating the at least one rule with the parent node such that the at least one rule defines a second factor for determining whether the document is to also be classified into the parent node; accessing the document for natural language processing; and determining whether the document is to be classified into the parent node or the child node based on the at least one rule.
    Type: Application
    Filed: October 14, 2016
    Publication date: August 17, 2017
    Applicant: Idibon, Inc.
    Inventors: Robert J. Munro, Schuyler D. Erle, Tyler J. Schnoebelen, Jason Brenier, Jessica D. Long, Brendan D. Callahan, Paul A. Tepper, Edgar Nunez
  • Patent number: 9495345
    Abstract: Systems and methods are presented for the automatic placement of rules applied to topics in a logical hierarchy when conducting natural language processing. In some embodiments, a method includes: accessing, at a child node in a logical hierarchy, at least one rule associated with the child node; identifying a percolation criterion associated with a parent node to the child node, said percolation criterion indicating that the at least one rule associated with the child node is to be associated also with the parent node; associating the at least one rule with the parent node such that the at least one rule defines a second factor for determining whether the document is to also be classified into the parent node; accessing the document for natural language processing; and determining whether the document is to be classified into the parent node or the child node based on the at least one rule.
    Type: Grant
    Filed: December 9, 2015
    Date of Patent: November 15, 2016
    Assignee: Idibon, Inc.
    Inventors: Robert J. Munro, Schuyler D. Erle, Tyler J. Schnoebelen, Jason Brenier, Jessica D. Long, Brendan D. Callahan, Paul A. Tepper, Edgar Nunez
  • 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: 20160162476
    Abstract: Systems and methods are presented for the automatic placement of rules applied to topics in a logical hierarchy when conducting natural language processing. In some embodiments, a method includes: accessing, at a child node in a logical hierarchy, at least one rule associated with the child node; identifying a percolation criterion associated with a parent node to the child node, said percolation criterion indicating that the at least one rule associated with the child node is to be associated also with the parent node; associating the at least one rule with the parent node such that the at least one rule defines a second factor for determining whether the document is to also be classified into the parent node; accessing the document for natural language processing; and determining whether the document is to be classified into the parent node or the child node based on the at least one rule.
    Type: Application
    Filed: December 9, 2015
    Publication date: June 9, 2016
    Applicant: Idibon, Inc.
    Inventors: Robert J. Munro, Schuyler D. Erle, Tyler J. Schnoebelen, Jason Brenier, Jessica D. Long, Brendan D. Callahan, Paul A. Tepper, Edgar Nunez
  • 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