Patents by Inventor Johann A. Larusson
Johann A. Larusson 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: 11361235Abstract: Systems and methods for automatic generating of a Bayes net content graph are disclosed herein. The system can include a memory including a mapping matrix. The system can include at least one server. The at least one server can generate a user matrix having n columns and p rows. In some aspects, each of the n columns is associated with a student and each of the p rows is associated with a content item. The at least one server can: store the user matrix in the memory; retrieve the mapping matrix from the memory; iteratively identify prerequisite relationships between the skills identified in the user matrix; generate edges between the skills in the user matrix based on the iteratively identified prerequisite relationships; and orient the edges between the skill.Type: GrantFiled: January 23, 2018Date of Patent: June 14, 2022Assignee: PEARSON EDUCATION, INC.Inventors: Jose Gonzalez-Brenes, John Behrens, Johann Larusson, Yetian Chen
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Patent number: 11068043Abstract: Systems and methods for virtual reality interaction evaluation are disclosed herein. The system can include a memory including: an interaction sub-database containing information relating to user interactions with at least one virtual asset in a virtual environment, and a content library database containing a plurality of virtual assets and information relating to those virtual assets. The system can include at least one server that can determine user engagement with at least one of the plurality of virtual assets, receive data indicative of an interaction with at least one of the plurality of virtual assets, and determine an interaction type of the interaction associated with the received data. The server can perform a speech capture and analysis process, perform a manipulation process, generate an evaluation of the user interactions with the at least one of the plurality of virtual assets, and deliver the generated evaluation.Type: GrantFiled: June 29, 2018Date of Patent: July 20, 2021Assignee: PEARSON EDUCATION, INC.Inventors: David Strong, Scott Hellman, Johann Larusson, Jake Noble, Timothy J. Stewart, Alex Nickel, Luis Oros, Quinn Lathrop, Daniel Tonks, Peter Foltz
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Patent number: 10872118Abstract: Systems and methods for automated objective identification are disclosed herein. The system can include a user device and a memory. The memory can include a filed database including field data associated with previously identified objectives. The memory can include a scoring database containing a machine-learning scoring algorithm. The system can include at least one server that can receive an identifier of a content portion, extract field information identifying at least one attribute of the identified content portion from the content portion, input extracted field information into the machine-learning scoring algorithm; receive identification of objectives forming a set of objectives from the machine-learning scoring algorithm; sort the objectives forming the set of objectives according to scores generated for each of the objectives in the set of objectives; and output the sorted objectives to the user device.Type: GrantFiled: December 22, 2017Date of Patent: December 22, 2020Assignee: PEARSON EDUCATION, INC.Inventors: Johann A. Larusson, Timothy J. Stewart, David W. Strong, Kristina Evans, Quinn N. Lathrop, Luis M. Oros
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Patent number: 10839304Abstract: Systems and methods for automatic generating of a Bayes net content graph are disclosed herein. The system can include a memory including a mapping matrix. The system can include at least one server. The at least one server can generate a user matrix having n columns and p rows. In some aspects, each of the n columns is associated with a student and each of the p rows is associated with a content item. The at least one server can: store the user matrix in the memory; retrieve the mapping matrix from the memory; iteratively identify prerequisite relationships between the skills identified in the user matrix; generate edges between the skills in the user matrix based on the iteratively identified prerequisite relationships; and orient the edges between the skill.Type: GrantFiled: January 23, 2018Date of Patent: November 17, 2020Assignee: PEARSON EDUCATION, INC.Inventors: James David Corbin, Johann Larusson, David Strong, Todd Fredrich, Timothy Stewart
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Patent number: 10713225Abstract: Systems and methods for simplifying data structuring in a database by applying a first content structure to interrelate a plurality of data packets and refining the interrelations based on data packet attributes are disclosed herein. The system can include a memory including: a structure database that can include a content structure including a plurality of content categories; and a content library database. The content library database can include a plurality of data packets. The system can include a server that can receive a plurality of data packets, organize the content according to the content structure, and update the organization of at least some of the data packets based on a determined attribute of those data packets.Type: GrantFiled: October 30, 2015Date of Patent: July 14, 2020Assignee: PEARSON EDUCATION, INC.Inventors: Jose P. Gonzalez-Brenes, Ilya Goldin, Johann A. Larusson, John Behrens, Thomas McTavish, Yun Jin Rho, Jacob M. Anderson, Gennadiy A. Kukartsev
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Publication number: 20190272770Abstract: Systems and methods for automated content delivery and evaluation are disclosed herein. The system can include a memory. The memory can include a content library database including a plurality of problems and data for stepwise evaluation of each of the plurality of problems. The system can include at least one server. The at least one server can automatically decompose a content item into a plurality of potential steps and associate attributes with the potential steps. The at least one server can receive a response from a user for the content item, identify steps in the received response, and select a next action based the identified steps of the received response.Type: ApplicationFiled: March 1, 2019Publication date: September 5, 2019Inventors: Victoria Kortan, Kateryna Lapina, David Strong, Eric Kattwinkel, Luis Oros, Quinn Lathrop, Matthew Sweeten, Thomas McTavish, David King, Johann Larusson, Timothy Stewart, Nina Shamsi, James David Corbin, Alex Nickel, Jacob Noble
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Publication number: 20190272775Abstract: Systems and methods for automated content delivery and evaluation are disclosed herein. The system can include a memory. The memory can include a content library database including a plurality of problems and data for stepwise evaluation of each of the plurality of problems. The system can include at least one server. The at least one server can automatically decompose a content item into a plurality of potential steps and associate attributes with the potential steps. The at least one server can receive a response from a user for the content item, identify steps in the received response, and select a next action based the identified steps of the received response.Type: ApplicationFiled: March 1, 2019Publication date: September 5, 2019Inventors: Jacob Noble, Victoria Kortan, Kateryna Lapina, David Strong, Eric Kattwinkel, Luis Oros, Quinn Lathrop, Matthew Sweeten, Thomas McTavish, David King, Johann Larusson, Timothy Stewart, Nina Shamsi, James David Corbin, Alex Nickel, Ron Itelman
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Publication number: 20190197194Abstract: Systems and methods for automated objective identification are disclosed herein. The system can include a user device and a memory. The memory can include a filed database including field data associated with previously identified objectives. The memory can include a scoring database containing a machine-learning scoring algorithm. The system can include at least one server that can receive an identifier of a content portion, extract field information identifying at least one attribute of the identified content portion from the content portion, input extracted field information into the machine-learning scoring algorithm; receive identification of objectives forming a set of objectives from the machine-learning scoring algorithm; sort the objectives forming the set of objectives according to scores generated for each of the objectives in the set of objectives; and output the sorted objectives to the user device.Type: ApplicationFiled: December 22, 2017Publication date: June 27, 2019Inventors: Johann A. Larusson, Timothy J. Stewart, David W. Strong, Kristina Evans, Quinn N. Lathrop, Luis M. Oros
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Publication number: 20190026357Abstract: Systems and methods for virtual reality interaction evaluation are disclosed herein. The system can include a memory including: an interaction sub-database containing information relating to user interactions with at least one virtual asset in a virtual environment, and a content library database containing a plurality of virtual assets and information relating to those virtual assets. The system can include at least one server that can determine user engagement with at least one of the plurality of virtual assets, receive data indicative of an interaction with at least one of the plurality of virtual assets, and determine an interaction type of the interaction associated with the received data. The server can perform a speech capture and analysis process, perform a manipulation process, generate an evaluation of the user interactions with the at least one of the plurality of virtual assets, and deliver the generated evaluation.Type: ApplicationFiled: June 29, 2018Publication date: January 24, 2019Inventors: David Strong, Scott Hellman, Johann Larusson, Jake Noble, Timothy J. Stewart, Alex Nickel, Luis Oros, Quinn Lathrop, Daniel Tonks, Peter Foltz
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Publication number: 20190027141Abstract: Systems and methods for virtual reality interaction evaluation are disclosed herein. The system can include a memory including: an interaction sub-database containing information relating to user interactions with at least one virtual asset in a virtual environment, and a content library database containing a plurality of virtual assets and information relating to those virtual assets. The system can include at least one server that can determine user engagement with at least one of the plurality of virtual assets, receive data indicative of an interaction with at least one of the plurality of virtual assets, and determine an interaction type of the interaction associated with the received data. The server can perform a speech capture and analysis process, perform a manipulation process, generate an evaluation of the user interactions with the at least one of the plurality of virtual assets, and deliver the generated evaluation.Type: ApplicationFiled: June 29, 2018Publication date: January 24, 2019Inventors: David Strong, Scott Hellman, Johann Larusson, Jake Noble, Timothy J. Stewart, Alex Nickel, Luis Oros, Quinn Lathrop, Daniel Tonks, Peter Foltz
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Publication number: 20190025906Abstract: Systems and methods for virtual reality interaction evaluation are disclosed herein. The system can include a memory including: an interaction sub-database containing information relating to user interactions with at least one virtual asset in a virtual environment, and a content library database containing a plurality of virtual assets and information relating to those virtual assets. The system can include at least one server that can determine user engagement with at least one of the plurality of virtual assets, receive data indicative of an interaction with at least one of the plurality of virtual assets, and determine an interaction type of the interaction associated with the received data. The server can perform a speech capture and analysis process, perform a manipulation process, generate an evaluation of the user interactions with the at least one of the plurality of virtual assets, and deliver the generated evaluation.Type: ApplicationFiled: June 29, 2018Publication date: January 24, 2019Inventors: David Strong, Scott Hellman, Johann Larusson, Jake Noble, Timothy J. Stewart, Alex Nickel, Luis Oros, Quinn Lathrop, Daniel Tonks, Peter Foltz
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Publication number: 20180211554Abstract: Systems and methods for automatic generating of a Bayes net content graph are disclosed herein. The system can include a memory including a mapping matrix. The system can include at least one server. The at least one server can generate a user matrix having n columns and p rows. In some aspects, each of the n columns is associated with a student and each of the p rows is associated with a content item. The at least one server can: store the user matrix in the memory; retrieve the mapping matrix from the memory; iteratively identify prerequisite relationships between the skills identified in the user matrix; generate edges between the skills in the user matrix based on the iteratively identified prerequisite relationships; and orient the edges between the skill.Type: ApplicationFiled: January 23, 2018Publication date: July 26, 2018Inventors: James David Corbin, II, Johann Larusson, David Strong, Todd Fredrich, Timothy Stewart
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Publication number: 20180211177Abstract: Systems and methods for automatic generating of a Bayes net content graph are disclosed herein. The system can include a memory including a mapping matrix. The system can include at least one server. The at least one server can generate a user matrix having n columns and p rows. In some aspects, each of the n columns is associated with a student and each of the p rows is associated with a content item. The at least one server can: store the user matrix in the memory; retrieve the mapping matrix from the memory; iteratively identify prerequisite relationships between the skills identified in the user matrix; generate edges between the skills in the user matrix based on the iteratively identified prerequisite relationships; and orient the edges between the skill.Type: ApplicationFiled: January 23, 2018Publication date: July 26, 2018Inventors: James David Corbin, II, Johann Larusson, David Strong, Todd Fredrich, Timothy Stewart
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Publication number: 20180210948Abstract: Systems and methods for automatic generating of a Bayes net content graph are disclosed herein. The system can include a memory including a mapping matrix. The system can include at least one server. The at least one server can generate a user matrix having n columns and p rows. In some aspects, each of the n columns is associated with a student and each of the p rows is associated with a content item. The at least one server can: store the user matrix in the memory; retrieve the mapping matrix from the memory; iteratively identify prerequisite relationships between the skills identified in the user matrix; generate edges between the skills in the user matrix based on the iteratively identified prerequisite relationships; and orient the edges between the skill.Type: ApplicationFiled: January 23, 2018Publication date: July 26, 2018Inventors: Jose Gonzalez-Brenes, John Behrens, Johann Larusson, Yetian Chen
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Patent number: 10027740Abstract: Systems and methods for increasing data transmission rates through a content distribution network by generating a customized aggregation comprising data packets selected to maximize a data acceptance rate are disclosed herein. The system can include a memory including a content library database having a plurality of data packets and a user profile database. The system can further include a server that can: receive aggregation information identifying a set of delivery data packets and a set of assessment data packets; receive data packet data from the content library database; identify a recipient cohort; determine a recipient cohort lapsed time; generate an estimate of data packet transfer time for the aggregation; generate an updated aggregation based on the recipient cohort lapsed time and the estimate of data packet transfer time; and provide the updated aggregation to the user devices.Type: GrantFiled: October 29, 2015Date of Patent: July 17, 2018Assignee: PEARSON EDUCATION, INC.Inventors: José Pablo González Brenes, Ilya Goldin, Johann A. Larusson, John Behrens, Thomas Mctavish
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Publication number: 20170308556Abstract: Systems and methods for simplifying data structuring in a database by applying a first content structure to interrelate a plurality of data packets and refining the interrelations based on data packet attributes are disclosed herein. The system can include a memory including: a structure database that can include a content structure including a plurality of content categories; and a content library database. The content library database can include a plurality of data packets. The system can include a server that can receive a plurality of data packets, organize the content according to the content structure, and update the organization of at least some of the data packets based on a determined attribute of those data packets.Type: ApplicationFiled: October 30, 2015Publication date: October 26, 2017Inventors: Jose GONZALES-BRENES, Jr., Ilya GOLDIN, Johann A. LARUSSON, John BEHRENS, Thomas MCTAVISH, Yun Jin RHO, Jacob M. ANDERSON, Gennadiy A. KUKARTSEV
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Publication number: 20170193837Abstract: A method/apparatus/system for generating a recommendation based on user interactions with nodes and associated tasks within a prerequisite graph. The recommendation is generated by identifying the user's current position within the prerequisite graph and identifying potential next nodes to which the user could move. Based on the user's past interactions with nodes and/or tasks within the prerequisite graph, the user's likelihood of successfully completing the potential next node is calculated, and a recommendation is made based on this calculated likelihood of the user successfully completing the potential next node.Type: ApplicationFiled: March 23, 2017Publication date: July 6, 2017Inventors: Patrick M. Supanc, Pedro Martello, James Mills, Johann A. Larusson, Robyn L. Lewis
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Patent number: 9672470Abstract: A method/apparatus/system for generating a recommendation based on user interactions with nodes and associated tasks within a prerequisite graph. The recommendation is generated by identifying the user's current position within the prerequisite graph and identifying potential next nodes to which the user could move. Based on the user's past interactions with nodes and/or tasks within the prerequisite graph, the user's likelihood of successfully completing the potential next node is calculated, and a recommendation is made based on this calculated likelihood of the user successfully completing the potential next node.Type: GrantFiled: November 13, 2015Date of Patent: June 6, 2017Assignee: PEARSON EDUCATION, INC.Inventors: Patrick M. Supanc, Pedro Martello, James Mills, Johann A. Larusson, Robyn L. Lewis
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Publication number: 20160127244Abstract: Systems and methods for increasing data transmission rates through a content distribution network by generating a customized aggregation comprising data packets selected to maximize a data acceptance rate are disclosed herein. The system can include a memory including a content library database having a plurality of data packets and a user profile database. The system can further include a server that can: receive aggregation information identifying a set of delivery data packets and a set of assessment data packets; receive data packet data from the content library database; identify a recipient cohort; determine a recipient cohort lapsed time; generate an estimate of data packet transfer time for the aggregation; generate an updated aggregation based on the recipient cohort lapsed time and the estimate of data packet transfer time; and provide the updated aggregation to the user devices.Type: ApplicationFiled: October 29, 2015Publication date: May 5, 2016Inventors: José Pablo González Brenes, Ilya Goldin, Johann A. Larusson, John Behrens, Thomas Mctavish
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Publication number: 20160071019Abstract: A method/apparatus/system for generating a recommendation based on user interactions with nodes and associated tasks within a prerequisite graph. The recommendation is generated by identifying the user's current position within the prerequisite graph and identifying potential next nodes to which the user could move. Based on the user's past interactions with nodes and/or tasks within the prerequisite graph, the user's likelihood of successfully completing the potential next node is calculated, and a recommendation is made based on this calculated likelihood of the user successfully completing the potential next node.Type: ApplicationFiled: November 13, 2015Publication date: March 10, 2016Inventors: Patrick M. SUPANC, Pedro MARTELLO, James MILLS, Johann A. LARUSSON, Robyn L. LEWIS