International business machines corporation (20240103457). MACHINE LEARNING-BASED DECISION FRAMEWORK FOR PHYSICAL SYSTEMS simplified abstract
Contents
- 1 MACHINE LEARNING-BASED DECISION FRAMEWORK FOR PHYSICAL SYSTEMS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING-BASED DECISION FRAMEWORK FOR PHYSICAL SYSTEMS - 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
MACHINE LEARNING-BASED DECISION FRAMEWORK FOR PHYSICAL SYSTEMS
Organization Name
international business machines corporation
Inventor(s)
Dzung Tien Phan of Pleasantville NY (US)
Lam Minh Nguyen of Ossining NY (US)
MACHINE LEARNING-BASED DECISION FRAMEWORK FOR PHYSICAL SYSTEMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240103457 titled 'MACHINE LEARNING-BASED DECISION FRAMEWORK FOR PHYSICAL SYSTEMS
Simplified Explanation
The abstract describes a decision-improvement framework that involves obtaining regression functions to predict outputs of processes in a physical system, generating constraints and objective functions for a model based on the regression functions and system representation, identifying parameter values for controlling the system, generating a score based on predicted improvement, and configuring the system based on the score meeting a threshold.
- Obtaining regression functions to predict outputs of processes in a physical system
- Generating constraints and objective functions for a model based on regression functions and system representation
- Identifying parameter values for controlling the physical system
- Generating a score based on predicted improvement of the physical system
- Configuring the physical system based on the generated score meeting a threshold
Potential Applications
This technology could be applied in various industries such as manufacturing, energy management, healthcare, and transportation to optimize processes and improve system performance.
Problems Solved
This technology helps in making data-driven decisions, optimizing processes, and improving overall system performance based on historical data and predictive modeling.
Benefits
The benefits of this technology include increased efficiency, cost savings, improved decision-making, and enhanced system performance.
Potential Commercial Applications
Optimizing manufacturing processes for increased productivity and cost savings.
Possible Prior Art
One possible prior art could be traditional optimization techniques used in various industries to improve system performance.
Unanswered Questions
How does this technology handle real-time data inputs for decision-making?
The abstract does not provide information on how the system handles real-time data inputs for making decisions.
What are the potential limitations of this decision-improvement framework?
The abstract does not mention any potential limitations or challenges that may arise when implementing this framework.
Original Abstract Submitted
methods, systems, and computer program products for a decision-improvement framework are provided herein. a computer-implemented method includes obtaining regression functions that predict an output of processes of a physical system based on inputs received at each process; automatically generating one or more constraints and one or more objective functions for a model for the physical system based on the regression functions and a representation of the physical system, where the representation specifies relationships between at least a portion of the processes; identifying a set of parameter values for controlling the physical system based on the model; generating a score, for the set of parameter values, based on a predicted improvement of the physical system relative to historical performance of the physical system; and in response to the generated score satisfying a threshold, causing the physical system to be configured in accordance with the set of parameter values.
