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公开(公告)号：US12118626B2
公开(公告)日：2024-10-15
申请号：US17495573
申请日：2021-10-06
发明人： Manikandan Padmanaban , Ranjini Bangalore Guruprasad , Isaac Waweru Wambugu , Kumar Saurav , Ivan Kayongo , Jagabondhu Hazra
IPC分类号： G06Q50/02 , G06Q10/04 , G06Q30/018
CPC分类号： G06Q50/02 , G06Q10/04 , G06Q30/018 , Y02P90/84
摘要： Methods, systems, and computer program products for generating context-aware process-based model determinations for greenhouse gas emissions from agricultural fields are provided herein. A computer-implemented method includes obtaining data related to multiple conditions pertaining to at least one agricultural field; deriving one or more contextual features for one or more activities associated with the at least one agricultural field, wherein deriving the contextual feature(s) includes processing at least a portion of the obtained data using one or more activity-related models; updating one or more greenhouse gas emission estimates, pertaining to the at least one agricultural field, generated by at least one process-based model by processing at least a portion of the one or more greenhouse gas emission estimates and at least a portion of the derived contextual feature(s) using a spatio-temporal learning model; and performing one or more automated actions based on the one or more updated greenhouse gas emission estimates.
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公开(公告)号：US20230186217A1
公开(公告)日：2023-06-15
申请号：US17549214
申请日：2021-12-13
发明人： Kedar Kulkarni , Reginald Eugene Bryant , Isaac Waweru Wambugu , Ivan Kayongo , Smitkumar Narotambhai Marvaniya , Komminist Weldemariam , Shantanu R. Godbole
CPC分类号： G06Q10/0637 , G06Q10/06313 , G06N20/00
摘要： Methods, systems, and computer program products for dynamically enhancing supply chain strategies based on carbon emission targets are provided herein. A computer-implemented method includes obtaining enterprise-related data and carbon emissions-related data associated with the enterprise; training, using at least a portion of the obtained enterprise-related data and carbon emissions-related data, at least one machine learning-based model configured for enhancing at least one of carbon emissions reduction by the enterprise and value increase for the enterprise; processing carbon emissions data attributed to the enterprise for a given temporal period using the at least one trained machine learning-based model; generating one or more enterprise-related recommendations based at least in part on results of the processing of the carbon emissions data using the at least one trained machine learning-based model; and performing one or more automated actions based at least in part on the one or more enterprise-related recommendations.
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公开(公告)号：US20230401653A1
公开(公告)日：2023-12-14
申请号：US17805926
申请日：2022-06-08
发明人： Jagabondhu Hazra , Manikandan Padmanaban , Isaac Waweru Wambugu , Lloyd A Treinish , Ivan Kayongo , Kumar Saurav , Ranjini Bangalore Guruprasad
CPC分类号： G06Q50/02 , G06Q10/063
摘要： A method, computer system, and a computer program product for improving environmental impact estimations is provided. The present invention may include obtaining data pertaining to an agricultural area. The present invention may include deriving one or more features from the data pertaining to the agricultural area. The present invention may include identifying one or more stubble burning areas within the agricultural areas based on the one or more derived features. The present invention may include determining an environmental impact for each of the one or more stubble burning areas.
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公开(公告)号：US12031964B2
公开(公告)日：2024-07-09
申请号：US17495582
申请日：2021-10-06
发明人： Isaac Waweru Wambugu , Ranjini Bangalore Guruprasad , Manikandan Padmanaban , Kumar Saurav , Ivan Kayongo , Jagabondhu Hazra
IPC分类号： G01N33/00
CPC分类号： G01N33/0036
摘要： Methods, systems, and computer program products for enhancing spatial and temporal resolution of greenhouse gas emission estimates for agricultural fields using cohort analysis techniques are provided herein. A computer-implemented method includes obtaining non-greenhouse gas remote sensing data and contextual information pertaining to agricultural fields; determining cohorts among the agricultural fields by deriving agricultural field-specific features from the obtained data and contextual information; computing agricultural field-level time series of greenhouse gas emission estimates for the cohorts by processing the obtained data and contextual information using a process-based model; calculating bias corrections for the cohorts by processing, using a time series learning model, the time series and background greenhouse gas emission estimates; generating resolution-enhanced greenhouse gas emission estimates for the cohorts based on initial greenhouse gas emission estimates derived from greenhouse gas remote sensing data and the calculated bias corrections; and performing automated actions based on the updated greenhouse gas emission estimates.
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5.发明公开公开(公告)号：US20230153733A1
公开(公告)日：2023-05-18
申请号：US17526183
申请日：2021-11-15
发明人： Kumar Saurav , Ranjini Bangalore Guruprasad , Jagabondhu Hazra , Manikandan Padmanaban , Isaac Waweru Wambugu , Ivan Kayongo
CPC分类号： G06Q10/06375 , G06Q10/08 , G06Q10/067 , G06Q30/018
摘要： Methods, systems, and computer program products for generating GHG emissions estimations associated with logistics contexts using machine learning techniques are provided herein. A computer-implemented method includes obtaining input data related to multiple aspects of at least one logistics context; deriving contextual features from the input data by processing the input data using data profiling techniques; training at least one machine learning model related to energy consumption based on the contextual features; generating at least one energy consumption estimate attributed to at least one logistics implementation by processing data pertaining to the at least one logistics implementation using the at least one trained machine learning model; generating at least one greenhouse gas emissions estimate attributed to the at least one logistics implementation based on the at least one energy consumption estimate; and performing automated actions based on the at least one generated greenhouse gas emissions estimate.
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