18168982. SEMANTIC VECTORIZATION FOR FEATURE ENGINEERING simplified abstract (SAP SE)
Contents
SEMANTIC VECTORIZATION FOR FEATURE ENGINEERING
Organization Name
Inventor(s)
Mohamed Bouadi of La Garenne-Colombes (FR)
Salima Benbernou of Colombes (FR)
Mourad Ouziri of Gennevilliers (FR)
SEMANTIC VECTORIZATION FOR FEATURE ENGINEERING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18168982 titled 'SEMANTIC VECTORIZATION FOR FEATURE ENGINEERING
Simplified Explanation: The patent application describes a method for determining features, creating feature vectors based on a logical entity taxonomy, combining these vectors into a composite vector, determining an operator based on the composite vector, and deriving a new feature using the operator.
Key Features and Innovation:
- Determination of features based on a taxonomy of logical entities
- Creation of feature vectors for each feature
- Combination of feature vectors into a composite vector
- Determination of an operator based on the composite vector
- Derivation of a new feature using the operator
Potential Applications: This technology could be applied in various fields such as data analysis, machine learning, and pattern recognition.
Problems Solved: This technology helps in efficiently analyzing and processing data by deriving new features based on existing ones.
Benefits:
- Improved data analysis capabilities
- Enhanced pattern recognition
- Efficient feature derivation process
Commercial Applications: Potential commercial applications include data mining software, predictive analytics tools, and automated decision-making systems.
Questions about the Technology: 1. How does this technology improve data analysis processes? 2. What are the potential implications of using this technology in machine learning algorithms?
Frequently Updated Research: Stay updated on advancements in feature extraction methods and data analysis techniques to enhance the efficiency of this technology.
Original Abstract Submitted
Systems and methods include determination of a plurality of features, determination, for each of the plurality of features, of a feature vector based on a taxonomy of logical entities, combination of the determined feature vectors into a composite feature vector, determination of an operator based on the composite feature vector, and determination of a new feature based on the operator.
