Patents by Inventor Andrew Buhrmann

Andrew Buhrmann 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: 20240176790
    Abstract: This invention provides the ability for an inventor to determine the probability of their invention being granted a patent. The invention disclosure and/or any type of patent document is classified before, during or after being filed by using any of the existing classification systems (e.g., CPC codes) or others such as a new and novel business vernacular classification system (as described as part of this disclosure). This newly created invention persona (classified document) is then compared to the existing patent database to discover granted patents that are similar and may be compared to other public and non-public databases to discover prior art. An analysis is then performed using various algorithms pertaining to the similarity of the classification by examining the branches, proximity, density etc. of the various assigned codes which will uncover the novelty, non-obviousness, and utility of the invention disclosure.
    Type: Application
    Filed: February 7, 2024
    Publication date: May 30, 2024
    Inventors: Andrew Buhrmann, Michael Buhrmann, Ali Shokoufandeh, Devin Miller
  • Publication number: 20240143606
    Abstract: A microprocessor executable method and system for determining the semantic relatedness and meaning between at least two natural language sources is described in a prescribed context. Portions of natural languages are vectorized and mathematically processed to express relatedness as a calculated metric. The metric is associable to the natural language sources to graphically present the level of relatedness between at least two natural language sources. The metric may be re-determined with algorithms designed to compare the natural language sources with a knowledge data bank so the calculated metric can be ascertained with a higher level of certainty.
    Type: Application
    Filed: January 5, 2024
    Publication date: May 2, 2024
    Applicant: VETTD, INC.
    Inventors: Andrew Buhrmann, Michael Buhrmann, Ali Shokoufandeh, Jesse Smith, Yakov Keselman, Kurtis Peter Dane
  • Patent number: 11899674
    Abstract: A microprocessor executable method and system for determining the semantic relatedness and meaning between at least two natural language sources is described in a prescribed context. Portions of natural languages are vectorized and mathematically processed to express relatedness as a calculated metric. The metric is associable to the natural language sources to graphically present the level of relatedness between at least two natural language sources. The metric may be re-determined with algorithms designed to compare the natural language sources with a knowledge data bank so the calculated metric can be ascertained with a higher level of certainty.
    Type: Grant
    Filed: January 13, 2021
    Date of Patent: February 13, 2024
    Assignee: VETTD, INC.
    Inventors: Andrew Buhrmann, Michael Buhrmann, Ali Shokoufandeh, Jesse Smith, Yakov Keselman, Kurtis Peter Dane
  • Publication number: 20230115917
    Abstract: A system provides the ability to predict the likelihood that applicants would accept admission into and matriculate at a given institution based on all or a portion of the natural-language text in their application. An embodiment evaluates an individual application to an institution and analyzes the natural-language text sections of the application to predict whether the applicant would or would not be likely to accept and matriculate at a specific institution.
    Type: Application
    Filed: August 11, 2022
    Publication date: April 13, 2023
    Inventors: Trevor Honsberger, Andrew Buhrmann, Ali Shokoufandeh, Michael Buhrmann
  • Publication number: 20220156271
    Abstract: This invention provides the ability for an inventor to determine the probability of their invention being granted a patent. The invention disclosure and/or any type of patent document is classified before, during or after being filed by using any of the existing classification systems (e.g., CPC codes) or others such as a new and novel business vernacular classification system (as described as part of this disclosure). This newly created invention persona (classified document) is then compared to the existing patent database to discover granted patents that are similar and may be compared to other public and non-public databases to discover prior art. An analysis is then performed using various algorithms pertaining to the similarity of the classification by examining the branches, proximity, density etc. of the various assigned codes which will uncover the novelty, non-obviousness, and utility of the invention disclosure.
    Type: Application
    Filed: January 18, 2022
    Publication date: May 19, 2022
    Inventors: Andrew Buhrmann, Michael Buhrmann, Ali Shokoufandeh, Devin Miller
  • Publication number: 20220027733
    Abstract: A system and method for creating an intelligent agent model for providing an expert opinion on source data includes obtaining at least one first data source for training an intelligent agent model, receiving training input on the at least one first data source from an individual, extracting a set of relevant attributes of the at least one first data source based on the received training input, weighting the set of relevant attributes based on at least one of the received training input or prior received training input, forming at least one neural network that comprises the intelligent agent model based on the set of relevant attributes and the weighting, ranking or scoring at least one section of a second data source using the intelligent agent model, and outputting the ranking or scoring.
    Type: Application
    Filed: August 9, 2021
    Publication date: January 27, 2022
    Inventors: Andrew Buhrmann, Michael Buhrmann, Ali Shokoufandeh, Jesse Smith
  • Publication number: 20210294811
    Abstract: A microprocessor executable method and system for determining the semantic relatedness and meaning between at least two natural language sources is described in a prescribed context. Portions of natural languages are vectorized and mathematically processed to express relatedness as a calculated metric. The metric is associable to the natural language sources to graphically present the level of relatedness between at least two natural language sources. The metric may be re-determined with algorithms designed to compare the natural language sources with a knowledge data bank so the calculated metric can be ascertained with a higher level of certainty.
    Type: Application
    Filed: January 13, 2021
    Publication date: September 23, 2021
    Inventors: Andrew Buhrmann, Michael Buhrmann, Ali Shokoufandeh, Jesse Smith, Yakov Keselman, Kurtis Peter Dane
  • Patent number: 11048879
    Abstract: A microprocessor executable method transforms unstructured natural language texts by way of a preprocessing pipeline into a structured data representation of the entities described in the original text. The structured data representation is conducive to further processing by machine methods. The transformation process is learned by a machine learned model trained to identify relevant text segments and disregard irrelevant text segments The resulting structured data representation is refined to more accurately represent the respective entities.
    Type: Grant
    Filed: June 18, 2018
    Date of Patent: June 29, 2021
    Assignee: Vettd, Inc.
    Inventors: Andrew Buhrmann, Michael Buhrmann, Ali Shokoufandeh, Jesse Smith
  • Patent number: 11003671
    Abstract: A microprocessor executable method and system for determining the semantic relatedness and meaning between at least two natural language sources is described in a prescribed context. Portions of natural languages are vectorized and mathematically processed to express relatedness as a calculated metric. The metric is associable to the natural language sources to graphically present the level of relatedness between at least two natural language sources. The metric may be re-determined with algorithms designed to compare the natural language sources with a knowledge data bank so the calculated metric can be ascertained with a higher level of certainty.
    Type: Grant
    Filed: November 25, 2015
    Date of Patent: May 11, 2021
    Assignee: VETTD, INC.
    Inventors: Andrew Buhrmann, Michael Buhrmann, Ali Shokoufandeh, Jesse Smith, Yakov Keselman, Kurtis Peter Dane
  • Publication number: 20210133213
    Abstract: A method for performing hierarchical classification of data is disclosed. The method is being executed by at least one processing device. The method includes receiving input data for encoding into multiple channels. Further, the method includes extracting one or more features and one or more temporal structures corresponding to the input data. The method further includes identifying one or more feature dependencies in the input data. Further, the method includes combining the extracted one or more features corresponding to the input data, the extracted one or more temporal structures of the input data, and the identified one or more feature dependencies in the input data into a combined feature set. Thereafter, the method includes classifying the combined feature set into one or more output classes, and thereby performing the hierarchical classification of data.
    Type: Application
    Filed: October 30, 2020
    Publication date: May 6, 2021
    Inventors: Andrew Buhrmann, Michael Buhrmann, Dario Salvucci, Ali Shokoufandeh
  • Publication number: 20190294965
    Abstract: A system and method for creating an intelligent agent model for providing an expert opinion on source data includes obtaining at least one first data source for training an intelligent agent model, receiving training input on the at least one first data source from an individual, extracting a set of relevant attributes of the at least one first data source based on the received training input, weighting the set of relevant attributes based on at least one of the received training input or prior received training input, forming at least one neural network that comprises the intelligent agent model based on the set of relevant attributes and the weighting, ranking or scoring at least one section of a second data source using the intelligent agent model, and outputting the ranking or scoring.
    Type: Application
    Filed: March 22, 2019
    Publication date: September 26, 2019
    Inventors: Andrew Buhrmann, Michael Buhrmann, Ali Shokoufandeh, Jesse Smith
  • Publication number: 20180365229
    Abstract: A microprocessor executable method transforms unstructured natural language texts by way of a preprocessing pipeline into a structured data representation of the entities described in the original text. The structured data representation is conducive to further processing by machine methods. The transformation process is learned by a machine learned model trained to identify relevant text segments and disregard irrelevant text segments The resulting structured data representation is refined to more accurately represent the respective entities.
    Type: Application
    Filed: June 18, 2018
    Publication date: December 20, 2018
    Inventors: Andrew Buhrmann, Michael Buhrmann, Ali Shokoufandeh, Jesse Smith
  • Publication number: 20160232160
    Abstract: A microprocessor executable method and system for determining the semantic relatedness and meaning between at least two natural language sources is described in a prescribed context. Portions of natural languages are vectorized and mathematically processed to express relatedness as a calculated metric. The metric is associable to the natural language sources to graphically present the level of relatedness between at least two natural language sources. The metric may be re-determined with algorithms designed to compare the natural language sources with a knowledge data bank so the calculated metric can be ascertained with a higher level of certainty.
    Type: Application
    Filed: November 25, 2015
    Publication date: August 11, 2016
    Inventors: Andrew Buhrmann, Michael Buhrmann, Ali Shokoufandeh, Jesse Smith, Yakov Keselman, Kurtis Peter Dane
  • Publication number: 20140108268
    Abstract: A method of collaborating with a plurality of parties in an online system.
    Type: Application
    Filed: August 6, 2013
    Publication date: April 17, 2014
    Inventors: Devin Miller, Andrew Buhrmann, David Haner, Craig Carns, Trevor Honsberger