Patents by Inventor Travis Gaddie
Travis Gaddie 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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Publication number: 20240124951Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: ApplicationFiled: December 28, 2023Publication date: April 18, 2024Applicant: FREEPORT MINERALS CORPORATIONInventors: Dana Geislinger, Travis Gaddie, Margaret Alden Tinsley, Muneeb Alam, Steven Chad Richardson, Akaash Sanyal, Raquel Crossman, Tianfang Ni, Cory A. Demieville, Luke Gerdes, Robyn Freeman, Oleksandr Klesov, Luciano Kiniti Issoe
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Publication number: 20240127135Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: ApplicationFiled: December 28, 2023Publication date: April 18, 2024Applicant: FREEPORT MINERALS CORPORATIONInventors: Dana Geislinger, Travis Gaddie, Margaret Alden Tinsley, Muneeb Alam, Steven Chad Richardson, Akaash Sanyal, Raquel Crossman, Tianfang Ni, Cory A. Demieville, Luke Gerdes, Robyn Freeman, Oleksandr Klesov, Luciano Kiniti Issoe
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Patent number: 11948103Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: GrantFiled: June 22, 2023Date of Patent: April 2, 2024Assignee: FREEPORT MINERALS CORPORATIONInventors: Dana Geislinger, Travis Gaddie, Margaret Alden Tinsley, Muneeb Alam, Steven Chad Richardson, Akaash Sanyal, Raquel Crossman, Tianfang Ni, Cory A. Demieville, Luke Gerdes, Robyn Freeman, Oleksandr Klesov, Luciano Kiniti Issoe
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Patent number: 11893519Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: GrantFiled: April 25, 2023Date of Patent: February 6, 2024Assignee: FREEPORT MINERALS CORPORATIONInventors: Dana Geislinger, Travis Gaddie, Margaret Alden Tinsley, Muneeb Alam, Steven Chad Richardson, Akaash Sanyal, Raquel Crossman, Tianfang Ni, Cory A. Demieville, Luke Gerdes, Robyn Freeman, Oleksandr Klesov, Luciano Kiniti Issoe
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Publication number: 20240037462Abstract: The system may include a secondary irrigation feature that determines a percent of overlap of each of a plurality of submodules in a first lift over each of a plurality of submodules in a second lift and adjusts at least one of leaching operations or a leaching model based on the total tonnage weighted average of metal in the second lift. The method may further comprise determining an acid gap based on a difference between total acid given and total acid consumption; and further adjusting at least one of the leaching operations or the leaching model based on the acid gap. The method may further comprise determining a percentage of compacted material based on the material that is compacted and irrigated divided by the material that is irrigated; and further adjusting at least one of the leaching operations or the leaching model based on the percentage of compacted material.Type: ApplicationFiled: October 11, 2023Publication date: February 1, 2024Applicant: FREEPORT MINERALS CORPORATIONInventors: Dana Geislinger, Travis Gaddie, Margaret Alden Tinsley, Steven Chad Richardson, Raquel Crossman, Cristian Caro, Kevin Cheng, Rosemary D. Blosser, Amelia Briggs
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Publication number: 20230419199Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: ApplicationFiled: June 22, 2023Publication date: December 28, 2023Applicant: FREEPORT MINERALS CORPORATIONInventors: Dana Geislinger, Travis Gaddie, Margaret Alden Tinsley, Muneeb Alam, Steven Chad Richardson, Akaash Sanyal, Raquel Crossman, Tianfang Ni, Cory A. Demieville, Luke Gerdes, Robyn Freeman, Oleksandr Klesov, Luciano Kiniti Issoe
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Publication number: 20230419249Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: ApplicationFiled: June 27, 2022Publication date: December 28, 2023Applicant: FREEPORT MINERALS CORPORATIONInventors: Travis Gaddie, Dana Geislinger, Margaret Alden Tinsley, Luciano Kiniti Issoe, Tianfang Ni, Muneeb Alam, Luke Gerdes, Oleksandr Klesov, Steven Chad Richardson, Akaash Sanyal, Raquel Crossman, Cory A. Demieville, Robyn Freeman
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Publication number: 20230419226Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: ApplicationFiled: June 27, 2022Publication date: December 28, 2023Applicant: FREEPORT MINERALS CORPORATIONInventors: Oleksandr Klesov, Luke Gerdes, Dana Geislinger, Margaret Alden Tinsley, Robyn Freeman, Akaash Sanyal, Muneeb Alam, Raquel Crossman, Travis Gaddie, Steven Chad Richardson, Tianfang Ni, Cory A. Demieville, Luciano Kiniti Issoe
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Publication number: 20230419197Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: ApplicationFiled: April 25, 2023Publication date: December 28, 2023Applicant: FREEPORT MINERALS CORPORATIONInventors: Dana Geislinger, Travis Gaddie, Margaret Alden Tinsley, Muneeb Alam, Steven Chad Richardson, Akaash Sanyal, Raquel Crossman, Tianfang Ni, Cory A. Demieville, Luke Gerdes, Robyn Freeman, Oleksandr Klesov, Luciano Kiniti Issoe
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Publication number: 20230419198Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: ApplicationFiled: April 25, 2023Publication date: December 28, 2023Applicant: FREEPORT MINERALS CORPORATIONInventors: Dana Geislinger, Travis Gaddie, Margaret Alden Tinsley, Muneeb Alam, Steven Chad Richardson, Akaash Sanyal, Raquel Crossman, Tianfang Ni, Cory A. Demieville, Luke Gerdes, Robyn Freeman, Oleksandr Klesov, Luciano Kiniti Issoe
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Publication number: 20230418269Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: ApplicationFiled: June 27, 2022Publication date: December 28, 2023Applicant: FREEPORT MINERALS CORPORATIONInventors: Steven Chad Richardson, Robyn Freeman, Luke Gerdes, Margaret Alden Tinsley, Dana Geislinger, Akaash Sanyal, Travis Gaddie, Muneeb Alam, Raquel Crossman, Tianfang Ni, Cory A. Demieville, Oleksandr Klesov, Luciano Kiniti Issoe
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Publication number: 20230417552Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: ApplicationFiled: June 27, 2022Publication date: December 28, 2023Applicant: FREEPORT MINERALS CORPORATIONInventors: Luciano Kiniti Issoe, Tianfang Ni, Luke Gerdes, Dana Geislinger, Travis Gaddie, Margaret Alden Tinsley, Muneeb Alam, Steven Chad Richardson, Akaash Sanyal, Raquel Crossman, Cory A. Demieville, Robyn Freeman, Oleksandr Klesov
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Publication number: 20230417724Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: ApplicationFiled: June 27, 2022Publication date: December 28, 2023Applicant: FREEPORT MINERALS CORPORATIONInventors: Cory A. Demieville, Dana Geislinger, Travis Gaddie, Margaret Alden Tinsley, Muneeb Alam, Steven Chad Richardson, Akaash Sanyal, Raquel Crossman, Tianfang Ni, Luke Gerdes, Robyn Freeman, Oleksandr Klesov, Luciano Kiniti Issoe
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Publication number: 20230417570Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: ApplicationFiled: June 27, 2022Publication date: December 28, 2023Applicant: FREEPORT MINERALS CORPORATIONInventors: Dana Geislinger, Margaret Alden Tinsley, Akaash Sanyal, Robyn Freeman, Travis Gaddie, Muneeb Alam, Steven Chad Richardson, Raquel Crossman, Tianfang Ni, Cory A. Demieville, Luke Gerdes, Oleksandr Klesov, Luciano Kiniti Issoe
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Publication number: 20230419132Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: ApplicationFiled: June 27, 2022Publication date: December 28, 2023Applicant: FREEPORT MINERALS CORPORATIONInventors: Luciano Kiniti Issoe, Tianfang Ni, Oleksandr Klesov, Luke Gerdes, Raquel Crossman, Muneeb Alam, Dana Geislinger, Travis Gaddie, Margaret Alden Tinsley, Steven Chad Richardson, Akaash Sanyal, Cory A. Demieville, Robyn Freeman
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Patent number: 11823099Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: GrantFiled: April 25, 2023Date of Patent: November 21, 2023Assignee: FREEPORT MINERALS CORPORATIONInventors: Luciano Kiniti Issoe, Tianfang Ni, Oleksandr Klesov, Luke Gerdes, Raquel Crossman, Muneeb Alam, Dana Geislinger, Travis Gaddie, Margaret Alden Tinsley, Steven Chad Richardson, Akaash Sanyal, Cory A. Demieville, Robyn Freeman
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Patent number: 11681959Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: GrantFiled: November 11, 2022Date of Patent: June 20, 2023Assignee: FREEPORT MINERALS CORPORATIONInventors: Dana Geislinger, Travis Gaddie, Margaret Alden Tinsley, Muneeb Alam, Steven Chad Richardson, Akaash Sanyal, Raquel Crossman, Tianfang Ni, Cory A. Demieville, Luke Gerdes, Robyn Freeman, Oleksandr Klesov, Luciano Kiniti Issoe
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Patent number: 11521138Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.Type: GrantFiled: June 27, 2022Date of Patent: December 6, 2022Assignee: FREEPORT MINERALS CORPORATIONInventors: Dana Geislinger, Travis Gaddie, Margaret Alden Tinsley, Muneeb Alam, Steven Chad Richardson, Akaash Sanyal, Raquel Crossman, Tianfang Ni, Cory A. Demieville, Luke Gerdes, Robyn Freeman, Oleksandr Klesov, Luciano Kiniti Issoe