Patents by Inventor Tripti Saxena
Tripti Saxena 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).
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Patent number: 11675977Abstract: Systems, methods, and apparatuses are presented for a novel natural language tokenizer and tagger. In some embodiments, a method for tokenizing text for natural language processing comprises: generating from a pool of documents, a set of statistical models comprising one or more entries each indicating a likelihood of appearance of a character/letter sequence in the pool of documents; receiving a set of rules comprising rules that identify character/letter sequences as valid tokens; transforming one or more entries in the statistical models into new rules that are added to the set of rules when the entries indicate a high likelihood; receiving a document to be processed; dividing the document to be processed into tokens based on the set of statistical models and the set of rules, wherein the statistical models are applied where the rules fail to unambiguously tokenize the document; and outputting the divided tokens for natural language processing.Type: GrantFiled: March 27, 2020Date of Patent: June 13, 2023Assignee: Daash Intelligence, Inc.Inventors: Robert J. Munro, Rob Voigt, Schuyler D. Erle, Brendan D. Callahan, Gary C. King, Jessica D. Long, Jason Brenier, Tripti Saxena, Stefan Krawczyk
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Patent number: 11593458Abstract: Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.Type: GrantFiled: May 21, 2020Date of Patent: February 28, 2023Assignee: Accenture Global Solutions LimitedInventors: Phillip Henry Rogers, Andrew E. Fano, Joshua Neland, Allan Enemark, Tripti Saxena, Jana A. Thompson, David William Vinson
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Patent number: 11288444Abstract: 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: GrantFiled: December 11, 2020Date of Patent: March 29, 2022Assignee: 100.co, LLCInventors: 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
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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
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Publication number: 20210232760Abstract: 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: ApplicationFiled: December 11, 2020Publication date: July 29, 2021Inventors: 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
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Publication number: 20210157984Abstract: Systems, methods, and apparatuses are presented for a novel natural language tokenizer and tagger. In some embodiments, a method for tokenizing text for natural language processing comprises: generating from a pool of documents, a set of statistical models comprising one or more entries each indicating a likelihood of appearance of a character/letter sequence in the pool of documents; receiving a set of rules comprising rules that identify character/letter sequences as valid tokens; transforming one or more entries in the statistical models into new rules that are added to the set of rules when the entries indicate a high likelihood; receiving a document to be processed; dividing the document to be processed into tokens based on the set of statistical models and the set of rules, wherein the statistical models are applied where the rules fail to unambiguously tokenize the document; and outputting the divided tokens for natural language processing.Type: ApplicationFiled: March 27, 2020Publication date: May 27, 2021Inventors: Robert J. Munro, Rob Voigt, Schuyler D. Erle, Brendan D. Callahan, Gary C. King, Jessica D. Long, Jason Brenier, Tripti Saxena, Stefan Krawczyk
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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
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Publication number: 20200285903Abstract: Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.Type: ApplicationFiled: May 21, 2020Publication date: September 10, 2020Inventors: Phillip Henry Rogers, Andrew E. Fano, Joshua Neland, Allan Enemark, Tripti Saxena, Jana A. Thompson, David William Vinson
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Publication number: 20200234002Abstract: 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: ApplicationFiled: November 21, 2018Publication date: July 23, 2020Inventors: 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
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Patent number: 10691976Abstract: Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.Type: GrantFiled: November 16, 2017Date of Patent: June 23, 2020Assignee: Accenture Global Solutions LimitedInventors: Phillip Henry Rogers, Andrew E. Fano, Joshua Neland, Allan Enemark, Tripti Saxena, Jana A. Thompson, David William Vinson
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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
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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
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Publication number: 20190205377Abstract: Systems, methods, and apparatuses are presented for a novel natural language tokenizer and tagger. In some embodiments, a method for tokenizing text for natural language processing comprises: generating from a pool of documents, a set of statistical models comprising one or more entries each indicating a likelihood of appearance of a character/letter sequence in the pool of documents; receiving a set of rules comprising rules that identify character/letter sequences as valid tokens; transforming one or more entries in the statistical models into new rules that are added to the set of rules when the entries indicate a high likelihood; receiving a document to be processed; dividing the document to be processed into tokens based on the set of statistical models and the set of rules, wherein the statistical models are applied where the rules fail to unambiguously tokenize the document; and outputting the divided tokens for natural language processing.Type: ApplicationFiled: August 6, 2018Publication date: July 4, 2019Applicant: Idibon, Inc.Inventors: Robert J. Munro, Rob Voigt, Schuyler D. Erle, Brendan D. Callahan, Gary C. King, Jessica D. Long, Jason Brenier, Tripti Saxena, Stefan Krawczyk
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Publication number: 20190147297Abstract: Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.Type: ApplicationFiled: November 16, 2017Publication date: May 16, 2019Inventors: Phillip Henry Rogers, Andrew E. Fano, Joshua Neland, Allan Enemark, Tripti Saxena, Jana A. Thompson, David William Vinson
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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
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Patent number: 9965458Abstract: Systems, methods, and apparatuses are presented for a novel natural language tokenizer and tagger. In some embodiments, a method for tokenizing text for natural language processing comprises: generating from a pool of documents, a set of statistical models comprising one or more entries each indicating a likelihood of appearance of a character/letter sequence in the pool of documents; receiving a set of rules comprising rules that identify character/letter sequences as valid tokens; transforming one or more entries in the statistical models into new rules that are added to the set of rules when the entries indicate a high likelihood; receiving a document to be processed; dividing the document to be processed into tokens based on the set of statistical models and the set of rules, wherein the statistical models are applied where the rules fail to unambiguously tokenize the document; and outputting the divided tokens for natural language processing.Type: GrantFiled: December 9, 2015Date of Patent: May 8, 2018Assignee: Sansa AI Inc.Inventors: Robert J. Munro, Rob Voigt, Schuyler D. Erle, Brendan D. Callahan, Gary C. King, Jessica D. Long, Jason Brenier, Tripti Saxena, Stefan Krawczyk
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Publication number: 20180095946Abstract: Systems, methods, and apparatuses are presented for a novel natural language tokenizer and tagger. In some embodiments, a method for tokenizing text for natural language processing comprises: generating from a pool of documents, a set of statistical models comprising one or more entries each indicating a likelihood of appearance of a character/letter sequence in the pool of documents; receiving a set of rules comprising rules that identify character/letter sequences as valid tokens; transforming one or more entries in the statistical models into new rules that are added to the set of rules when the entries indicate a high likelihood; receiving a document to be processed; dividing the document to be processed into tokens based on the set of statistical models and the set of rules, wherein the statistical models are applied where the rules fail to unambiguously tokenize the document; and outputting the divided tokens for natural language processing.Type: ApplicationFiled: May 16, 2017Publication date: April 5, 2018Applicant: Idibon, Inc.Inventors: Robert Munro, Rob Voigt, Schuyler D. Erle, Brendan D. Callahan, Gary C. King, Jessica D. Long, Jason Brenier, Tripti Saxena, Stefan Krawczyk
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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
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Publication number: 20160162466Abstract: Systems, methods, and apparatuses are presented for a novel natural language tokenizer and tagger. In some embodiments, a method for tokenizing text for natural language processing comprises: generating from a pool of documents, a set of statistical models comprising one or more entries each indicating a likelihood of appearance of a character/letter sequence in the pool of documents; receiving a set of rules comprising rules that identify character/letter sequences as valid tokens; transforming one or more entries in the statistical models into new rules that are added to the set of rules when the entries indicate a high likelihood; receiving a document to be processed; dividing the document to be processed into tokens based on the set of statistical models and the set of rules, wherein the statistical models are applied where the rules fail to unambiguously tokenize the document; and outputting the divided tokens for natural language processing.Type: ApplicationFiled: December 9, 2015Publication date: June 9, 2016Applicant: Idibon, Inc.Inventors: Robert J. Munro, Rob Voigt, Schuyler D. Erle, Brendan D. Callahan, Gary C. King, Jessica D. Long, Jason Brenier, Tripti Saxena, Stefan Krawczyk
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Publication number: 20160162457Abstract: 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: ApplicationFiled: December 9, 2015Publication date: June 9, 2016Applicant: Idibon, Inc.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