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公开(公告)号：US20230419249A1
公开(公告)日：2023-12-28
申请号：US17850895
申请日：2022-06-27
发明人： 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
IPC分类号： G06Q10/08
CPC分类号： G06Q10/087
摘要： 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.
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12.发明公开公开(公告)号：US20230419199A1
公开(公告)日：2023-12-28
申请号：US18339998
申请日：2023-06-22
发明人： 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
IPC分类号： G06Q10/04 , G06Q50/02 , C22B15/00 , G06Q10/0631 , C22B3/06
CPC分类号： G06Q10/04 , G06Q50/02 , C22B15/0095 , G06Q10/0631 , C22B15/0067 , C22B3/06
摘要： 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.
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公开(公告)号：US20230419197A1
公开(公告)日：2023-12-28
申请号：US18306534
申请日：2023-04-25
发明人： 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
IPC分类号： G06Q10/04 , G06Q50/02 , G06Q10/0631 , C22B15/00 , C22B3/06
CPC分类号： G06Q10/04 , G06Q50/02 , G06Q10/0631 , C22B15/0095 , C22B3/06 , C22B15/0067
摘要： 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.
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公开(公告)号：US11681959B1
公开(公告)日：2023-06-20
申请号：US17985446
申请日：2022-11-11
发明人： 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
IPC分类号： G06Q10/04 , G06Q50/02 , G06Q10/0631 , C22B15/00 , C22B3/06
CPC分类号： G06Q10/04 , C22B3/06 , C22B15/0067 , C22B15/0095 , G06Q10/0631 , G06Q50/02
摘要： 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.
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公开(公告)号：US12106247B2
公开(公告)日：2024-10-01
申请号：US17850864
申请日：2022-06-27
发明人： 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
IPC分类号： G01N33/24 , C22B15/00 , G06Q10/06 , G06Q10/0637 , G06Q50/02
CPC分类号： G06Q10/06375 , C22B15/0065 , G01N33/24 , G06Q50/02
摘要： 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.
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公开(公告)号：US12067505B2
公开(公告)日：2024-08-20
申请号：US18306640
申请日：2023-04-25
发明人： 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
IPC分类号： G06Q10/04 , C22B3/06 , C22B15/00 , G06Q10/0631 , G06Q50/02
CPC分类号： G06Q10/04 , C22B3/06 , C22B15/0067 , C22B15/0095 , G06Q10/0631 , G06Q50/02
摘要： 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.
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公开(公告)号：US11948103B2
公开(公告)日：2024-04-02
申请号：US18339998
申请日：2023-06-22
发明人： 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
IPC分类号： G06Q10/04 , C22B3/06 , C22B15/00 , G06Q10/0631 , G06Q50/02
CPC分类号： G06Q10/04 , C22B3/06 , C22B15/0067 , C22B15/0095 , G06Q10/0631 , G06Q50/02
摘要： 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.
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公开(公告)号：US11893519B2
公开(公告)日：2024-02-06
申请号：US18306534
申请日：2023-04-25
发明人： 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
IPC分类号： G06Q10/04 , G06Q50/02 , C22B15/00 , C22B3/06 , G06Q10/0631
CPC分类号： G06Q10/04 , C22B3/06 , C22B15/0067 , C22B15/0095 , G06Q10/0631 , G06Q50/02
摘要： 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.
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公开(公告)号：US20230419226A1
公开(公告)日：2023-12-28
申请号：US17850864
申请日：2022-06-27
发明人： 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
CPC分类号： G06Q10/06375 , G06Q50/02 , G01N33/24
摘要： 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.
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公开(公告)号：US20230419198A1
公开(公告)日：2023-12-28
申请号：US18306640
申请日：2023-04-25
发明人： 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
IPC分类号： G06Q10/04 , G06Q50/02 , G06Q10/0631 , C22B15/00 , C22B3/06
CPC分类号： G06Q10/04 , G06Q50/02 , G06Q10/0631 , C22B15/0095 , C22B3/06 , C22B15/0067
摘要： 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.
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