Patents by Inventor Veena Basavaraj

Veena Basavaraj 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: 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: 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: 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: 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: 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: 20160162457
    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 9, 2015
    Publication date: June 9, 2016
    Applicant: 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
  • Publication number: 20150012819
    Abstract: A system, apparatus and method are provided for dynamically generating decorated data for presenting a web page, at the time of rendering the web page. A request for the web page is received at a front-end server, and a template for generating the page's markup language is retrieved (e.g., a template generated using the Dust templating language), which has embedded one or more directives for decorating specified data in an indicated manner. Raw (e.g., undecorated) domain data for the page are received from a back-end store. A decoration engine parses the template, identifies the directives and applies them to generate decorated data in context. Directives may include metadata used by the decoration engine and/or a directives library. A render process is then applied to combine the template and the decorated data to produce the page's markup language (e.g., in HTML), which is transmitted to the client that requested the page.
    Type: Application
    Filed: July 29, 2013
    Publication date: January 8, 2015
    Applicant: LinkedIn Corporation
    Inventors: Veena Basavaraj, Eran Leshem, Brian J. Geffon
  • Publication number: 20140325399
    Abstract: The disclosed embodiments relate to a system that displays diagnostic information to facilitate web page development. While a web page is being assembled at a front-end system using data gathered from one or more back-end systems, the system accumulates metadata associated with the assembly process. Next, the system renders the web page using the gathered data. Finally, the system sends the rendered web page along with the metadata to a browser to be displayed to a user, wherein the browser is configured to selectively display the metadata when the web page is displayed.
    Type: Application
    Filed: April 30, 2013
    Publication date: October 30, 2014
    Applicant: LinkedIn Corporation
    Inventors: Georg Puchta, Veena Basavaraj, Johnathan Leppert
  • Patent number: 8875012
    Abstract: The disclosed embodiments relate to a system that displays diagnostic information to facilitate web page development. While a web page is being assembled at a front-end system using data gathered from one or more back-end systems, the system accumulates metadata associated with the assembly process. Next, the system renders the web page using the gathered data. Finally, the system sends the rendered web page along with the metadata to a browser to be displayed to a user, wherein the browser is configured to selectively display the metadata when the web page is displayed.
    Type: Grant
    Filed: April 30, 2013
    Date of Patent: October 28, 2014
    Assignee: LinkedIn Corporation
    Inventors: Georg Puchta, Veena Basavaraj, Johnathan Leppert