International business machines corporation (20240161000). ARTIFICIAL INTELLIGENCE-BASED SUSTAINABILITY CONTROL simplified abstract
Contents
- 1 ARTIFICIAL INTELLIGENCE-BASED SUSTAINABILITY CONTROL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ARTIFICIAL INTELLIGENCE-BASED SUSTAINABILITY CONTROL - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
ARTIFICIAL INTELLIGENCE-BASED SUSTAINABILITY CONTROL
Organization Name
international business machines corporation
Inventor(s)
Gary Zasman of Boulder CO (US)
Mansura Habiba of Jhonstown (IE)
ARTIFICIAL INTELLIGENCE-BASED SUSTAINABILITY CONTROL - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161000 titled 'ARTIFICIAL INTELLIGENCE-BASED SUSTAINABILITY CONTROL
Simplified Explanation
The patent application describes a system for autonomous sustainability control by using normalized data from various sources to train an artificial intelligence model to learn dynamic key performance indicators related to the performance of a functional objective in relation to sustainability. The model then identifies anomalies and generates corrective actions to mitigate risks associated with these anomalies, enabling autonomous sustainability control.
- The system uses normalized data from heterogeneous sources to train an AI model.
- The AI model learns dynamic key performance indicators related to sustainability.
- Anomalies are identified by the model, and corrective actions are generated to address associated risks.
- The corrective actions facilitate autonomous sustainability control related to achieving the functional objective.
Potential Applications
The technology can be applied in various industries such as manufacturing, energy management, agriculture, and transportation to optimize sustainability practices and improve overall performance.
Problems Solved
This technology addresses the challenge of integrating sustainability considerations into operational processes effectively and autonomously, reducing the risk of environmental impact and resource inefficiency.
Benefits
The system enables real-time monitoring and adjustment of sustainability performance, leading to improved efficiency, reduced environmental footprint, and enhanced compliance with sustainability regulations.
Potential Commercial Applications
"Autonomous Sustainability Control System for Manufacturing Operations"
Possible Prior Art
One possible prior art could be a system that uses AI for anomaly detection in manufacturing processes but does not specifically focus on sustainability control.
Unanswered Questions
How does the system ensure data privacy and security when collecting and analyzing data from multiple sources?
The patent application does not provide details on the data privacy and security measures implemented in the system to protect sensitive information.
What is the scalability of the system in handling a large volume of data from diverse sources?
The scalability of the system in processing and analyzing a significant amount of data from heterogeneous sources is not explicitly discussed in the patent application.
Original Abstract Submitted
autonomous sustainability control is provided related to performing a functional objective. normalized data is obtained from heterogeneous data obtained from a plurality of data sources, where the heterogeneous data is related, at least in part, to the functional objective. the normalized data is used to train an artificial intelligence model to learn dynamic key performance indicators relating, at least in part, performance of the functional objective to sustainability. the artificial intelligence model is used to learn a set of dynamic key performance indicators to relate current performance of the functional objective to sustainability. the learned set of dynamic key performance indicators is used to identify an anomaly, and one or more corrective actions are generated to remediate a risk associated with the anomaly. the one or more actions facilitate the autonomous sustainability control related to performing the functional objective.
