17930860. GENERATING AND UTILIZING PERFORATIONS TO IMPROVE DECISIONMAKING simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
Contents
- 1 GENERATING AND UTILIZING PERFORATIONS TO IMPROVE DECISIONMAKING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GENERATING AND UTILIZING PERFORATIONS TO IMPROVE DECISIONMAKING - 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 Original Abstract Submitted
GENERATING AND UTILIZING PERFORATIONS TO IMPROVE DECISIONMAKING
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
Magesh Rajamani of Chennai (IN)
Gandhi Sivakuma of Bentleigh (AU)
RAMANAKUMAR Natarajan of Boca Raton FL (US)
GENERATING AND UTILIZING PERFORATIONS TO IMPROVE DECISIONMAKING - A simplified explanation of the abstract
This abstract first appeared for US patent application 17930860 titled 'GENERATING AND UTILIZING PERFORATIONS TO IMPROVE DECISIONMAKING
Simplified Explanation
The embodiment described in the abstract manages machine learning models to generate and utilize perforations within the models to improve their ability to learn from exception decisions.
- Automatically detects exception decisions in a base model.
- Determines data considered in making the exception decision.
- Identifies and stores known features from the gathered data in a database.
- Identifies and stores remaining additional features considered in the database.
- Generates and stores perforations corresponding to the remaining additional features considered.
- Validates feature boundaries within the generated perforations from a set of data sources in response to subsequent decisions involving shared additional features.
- Calculates scores for subsequent decisions using both the base model and corresponding perforation.
- Outputs decision recommendations for the subsequent decisions.
Potential Applications
This technology could be applied in various fields such as finance, healthcare, and e-commerce for improving decision-making processes based on machine learning models.
Problems Solved
1. Enhances the ability of machine learning models to learn from exception decisions. 2. Improves the accuracy and reliability of decision-making processes. 3. Helps in identifying and utilizing additional features for better decision outcomes.
Benefits
1. Increased efficiency in decision-making. 2. Enhanced model performance and adaptability. 3. Better utilization of data for improving outcomes.
Potential Commercial Applications
Improving Decision-Making Processes in Various Industries with Enhanced Machine Learning Models
Original Abstract Submitted
An embodiment for managing machine learning models to generate and utilize perforations within machine learning models to improve their ability to consider and learn from exception decisions. The embodiment may detect an exception decision in a base model. The embodiment may automatically determine data considered in making the exception decision and identify and store in a database known features from the gathered data. The embodiment may automatically identify and store in the database remaining additional features considered, and generate and store perforations corresponding to the remaining additional features considered. The embodiment may, in response to detecting subsequent decisions involving shared additional features contained in the generated perforations, automatically validate feature boundaries within the generated perforations from a set of data sources. The embodiment may automatically calculate scores for the subsequent decisions using both the base model and corresponding perforation and output decision recommendations for the subsequent decisions.
