Patents by Inventor Paul A. Tepper

Paul A. Tepper 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: 20210232761
    Abstract: Systems and methods are presented for providing improved machine performance in natural language processing. In some example embodiments, an API module is presented that is configured to drive processing of a system architecture for natural language processing. Aspects of the present disclosure allow for a natural language model to classify documents while other documents are being retrieved in real time. The natural language model and the documents are configured to be stored in a stateless format, which also allows for additional functions to be performed on the documents while the natural language model is used to continue classifying other documents.
    Type: Application
    Filed: December 22, 2020
    Publication date: July 29, 2021
    Inventors: Schuyler D. Erle, Robert J. Munro, Brendan D. Callahan, Jason Brenier, Paul A. Tepper, Jessica D. Long, James B. Robinson, Aneesh Nair, Michelle Casbon, Stefan Krawczyk
  • 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: 20210232760
    Abstract: Methods, apparatuses and computer readable medium are presented for generating a natural language model. A method for generating a natural language model comprises: selecting from a pool of documents, a first set of documents to be annotated; receiving annotations of the first set of documents elicited by first human readable prompts; training a natural language model using the annotated first set of documents; determining documents in the pool having uncertain natural language processing results according to the trained natural language model and/or the received annotations; selecting from the pool of documents, a second set of documents to be annotated comprising documents having uncertain natural language processing results; receiving annotations of the second set of documents elicited by second human readable prompts; and retraining a natural language model using the annotated second set of documents.
    Type: Application
    Filed: December 11, 2020
    Publication date: July 29, 2021
    Inventors: Robert J. Munro, Schuyler D. Erle, Jason Brenier, Paul A. Tepper, Tripti Saxena, Gary C. King, Jessica D. Long, Brendan D. Callahan, Tyler J. Schnoebelen, Stefan Krawczyk, Veena Basavaraj
  • 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: 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
  • Patent number: 10956480
    Abstract: A method, computer program product, and computing system for automatically generating a dialogue graph is executed on a computing device and includes receiving a plurality of conversation data. A plurality of utterance pairs from the plurality of conversation data may be clustered into a plurality of utterance pair clusters. A dialogue graph may be generated with a plurality of nodes representative of the plurality of utterance pair clusters.
    Type: Grant
    Filed: June 29, 2018
    Date of Patent: March 23, 2021
    Assignee: Nuance Communications, Inc.
    Inventors: Jean-Francois Beaumont, Nastaran Jafarpour Khameneh, Peter Stubley, Paul A. Tepper, Abhishek Rohatgi, Flaviu Gelu Negrean, Marco Antonio Padron Chavez
  • Publication number: 20210081611
    Abstract: Methods, apparatuses, and systems are presented for generating natural language models using a novel system architecture for feature extraction. A method for extracting features for natural language processing comprises: accessing one or more tokens generated from a document to be processed; receiving one or more feature types defined by user; receiving selection of one or more feature types from a plurality of system-defined and user-defined feature types, wherein each feature type comprises one or more rules for generating features; receiving one or more parameters for the selected feature types, wherein the one or more rules for generating features are defined at least in part by the parameters; generating features associated with the document to be processed based on the selected feature types and the received parameters; and outputting the generated features in a format common among all feature types.
    Type: Application
    Filed: April 29, 2020
    Publication date: March 18, 2021
    Applicant: Singapore Biotech PTE. LTD.
    Inventors: Robert J. Munro, Schuyler D. Erle, Tyler J. Schnoebelen, Brendan D. Callahan, Jessica D. Long, Gary C. King, Paul A. Tepper, Jason A. Brenier, Stefan Krawczyk
  • Publication number: 20200234002
    Abstract: Methods, apparatuses and computer readable medium are presented for generating a natural language model. A method for generating a natural language model comprises: selecting from a pool of documents, a first set of documents to be annotated; receiving annotations of the first set of documents elicited by first human readable prompts; training a natural language model using the annotated first set of documents; determining documents in the pool having uncertain natural language processing results according to the trained natural language model and/or the received annotations; selecting from the pool of documents, a second set of documents to be annotated comprising documents having uncertain natural language processing results; receiving annotations of the second set of documents elicited by second human readable prompts; and retraining a natural language model using the annotated second set of documents.
    Type: Application
    Filed: November 21, 2018
    Publication date: July 23, 2020
    Inventors: Robert J. Munro, Schuyler D. Erle, Jason Brenier, Paul A. Tepper, Tripti Saxena, Gary C. King, Jessica D. Long, Brendan D. Callahan, Tyler J. Schnoebelen, Stefan Krawczyk, Veena Basavaraj
  • 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: 20200004878
    Abstract: A method, computer program product, and computing system for automatically generating a dialogue graph is executed on a computing device and includes receiving a plurality of conversation data. A plurality of utterance pairs from the plurality of conversation data may be clustered into a plurality of utterance pair clusters. A dialogue graph may be generated with a plurality of nodes representative of the plurality of utterance pair clusters.
    Type: Application
    Filed: June 29, 2018
    Publication date: January 2, 2020
    Inventors: JEAN-FRANCOIS BEAUMONT, Nastaran Jafarpour Khameneh, Peter Stubley, Paul A. Tepper, Abhishek Rohatgi, Flaviu Gelu Negrean, Marco Antonio Padron Chavez
  • Publication number: 20190377788
    Abstract: Methods, apparatuses, and systems are presented for generating natural language models using a novel system architecture for feature extraction. A method for extracting features for natural language processing comprises: accessing one or more tokens generated from a document to be processed; receiving one or more feature types defined by user; receiving selection of one or more feature types from a plurality of system-defined and user-defined feature types, wherein each feature type comprises one or more rules for generating features; receiving one or more parameters for the selected feature types, wherein the one or more rules for generating features are defined at least in part by the parameters; generating features associated with the document to be processed based on the selected feature types and the received parameters; and outputting the generated features in a format common among all feature types.
    Type: Application
    Filed: January 2, 2019
    Publication date: December 12, 2019
    Applicant: AIPARC HOLDINGS PTE. LTD.
    Inventors: Robert J. Munro, Schuyler D. Erle, Tyler J. Schnoebelen, Brendan D. Callahan, Jessica D. Long, Gary C. King, Paul A. Tepper, Jason A. Brenier, Stefan Krawczyk
  • 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: 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
  • Publication number: 20190243886
    Abstract: Systems and methods are presented for providing improved machine performance in natural language processing. In some example embodiments, an API module is presented that is configured to drive processing of a system architecture for natural language processing. Aspects of the present disclosure allow for a natural language model to classify documents while other documents are being retrieved in real time. The natural language model and the documents are configured to be stored in a stateless format, which also allows for additional functions to be performed on the documents while the natural language model is used to continue classifying other documents.
    Type: Application
    Filed: September 7, 2018
    Publication date: August 8, 2019
    Applicant: Idibon, Inc.
    Inventors: Schuyler D. Erle, Robert J. Munro, Brendan D. Callahan, Jason Brenier, Paul A. Tepper, Jessica D. Long, James B. Robinson, Aneesh Nair, Michelle Casbon, Stefan Krawczyk
  • 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: 20180157636
    Abstract: Methods, apparatuses, and systems are presented for generating natural language models using a novel system architecture for feature extraction. A method for extracting features for natural language processing comprises: accessing one or more tokens generated from a document to be processed; receiving one or more feature types defined by user; receiving selection of one or more feature types from a plurality of system-defined and user-defined feature types, wherein each feature type comprises one or more rules for generating features; receiving one or more parameters for the selected feature types, wherein the one or more rules for generating features are defined at least in part by the parameters; generating features associated with the document to be processed based on the selected feature types and the received parameters; and outputting the generated features in a format common among all feature types.
    Type: Application
    Filed: November 15, 2017
    Publication date: June 7, 2018
    Applicant: Idibon, Inc.
    Inventors: Robert J. Munro, Schuyler D. Erle, Tyler j. Schnoebelen, Brendan D. Callahan, Jessica D. Long, Gary C. King, Paul A. Tepper, Jason A. Brenier, Stefan Krawczyk