Machine Learning Carbon Emissions Control via Payroll Automation
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Solution Overview
Problem
It is challenging for entities to accurately determine and manage carbon emissions across various locations due to lack of data access, inefficient data processing, and inability to generate effective actions to reduce emissions.
Innovation Solution
A system utilizing machine learning to analyze data from payroll and utility systems to identify emission factors, generate actions, and automate processes to reduce carbon emissions, integrating payroll processing with utility grid data to optimize energy consumption and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to track and analyze carbon emissions data, then data accuracy can be maintained, but the time and resources required for data processing increase significantly
Solution Approach 1:
The patent replaces manual mechanical data processing with automated machine learning models and algorithms that can analyze carbon emissions data from multiple sources (payroll systems, utility grids, transportation systems) automatically, maintaining accuracy while dramatically reducing processing time and human resource requirements
Solution Approach 2:
The patent introduces machine learning models as intermediary systems that act as mediators between raw data from various entity systems and the carbon emissions analysis, automatically processing and correlating data to produce accurate emissions metrics without requiring manual intervention
2Measurement precision
If comprehensive data from multiple systems is collected to improve carbon emissions analysis, then the accuracy of emission factors increases, but the complexity of data integration and processing increases
Solution Approach 1:
The patent creates a universal machine learning framework that can process and correlate data from multiple different systems (payroll, utility grids, transportation) using the same algorithms and models, reducing integration complexity by applying a unified approach across diverse data sources
Solution Approach 2:
The patent segments the complex data integration task into distinct modules: data collection from various sources, data preprocessing, machine learning model processing, and results generation. This segmentation allows each component to handle specific aspects of data integration independently, reducing overall system complexity
3Productivity
If automated processes are implemented to reduce carbon emissions, then productivity in emission reduction increases, but the initial setup and configuration complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models with historical carbon emissions data and pre-configuring the system architecture before deployment. This preliminary setup enables the automated processes to operate efficiently once deployed, reducing the perceived complexity during actual emission reduction operations
Solution Approach 2:
The patent incorporates feedback mechanisms where the machine learning models continuously learn from actual carbon emissions data and performance outcomes, automatically adjusting parameters and improving efficiency over time. This feedback loop reduces configuration complexity by enabling self-optimization rather than requiring manual reconfiguration
Data Source
AI summary
Carbon emission reduction using machine learning is provided. A system receives data for profiles linked with locations of an entity. The system determines, based at least in part on data from a utility service provider system and the data indicative of energy consumption associated with the locations, a first value of a metric indicative of carbon emissions associated with the locations. The system determines, based on a comparison of the first value of the metric with a threshold, to invoke an automated process via the payroll processing system to reduce the metric. The system generates, generate, using one or more models trained with machine learning, an action to execute via the automated process. The system selects a first profile from that is compatible with the action, and commands the payroll processing system to execute the generated action to cause a reduction in the first value of the metric.