International business machines corporation (20240202167). UNSUPERVISED LEARNING FROM PUBLIC TABULAR DATASETS simplified abstract
Contents
- 1 UNSUPERVISED LEARNING FROM PUBLIC TABULAR DATASETS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 UNSUPERVISED LEARNING FROM PUBLIC TABULAR DATASETS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Feature Engineering and Synthetic Data Generation
- 1.13 Original Abstract Submitted
UNSUPERVISED LEARNING FROM PUBLIC TABULAR DATASETS
Organization Name
international business machines corporation
Inventor(s)
Thanh Lam Hoang of Maynooth (IE)
Lam Minh Nguyen of Ossining NY (US)
Dzung Tien Phan of PLEASANTVILLE NY (US)
UNSUPERVISED LEARNING FROM PUBLIC TABULAR DATASETS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240202167 titled 'UNSUPERVISED LEARNING FROM PUBLIC TABULAR DATASETS
Simplified Explanation
The patent application describes a method, computer program product, and system for feature engineering and synthetic data generation. A processor retrieves various data tables with different formats and content, trains a variational auto-encoder (VAE) model on these tables, and then generates synthetic data based on an input data table and the trained VAE model.
Key Features and Innovation
- Utilizes a variational auto-encoder (VAE) model for synthetic data generation.
- Handles heterogeneous data tables with different formats and content.
- Enables feature engineering for data analysis and modeling.
Potential Applications
This technology can be applied in various fields such as:
- Data analytics
- Machine learning
- Artificial intelligence
- Predictive modeling
Problems Solved
- Addresses the challenge of generating synthetic data for training machine learning models.
- Helps in feature engineering for data analysis and modeling.
Benefits
- Improves data analysis and modeling accuracy.
- Facilitates the generation of synthetic data for training purposes.
- Enhances the efficiency of feature engineering processes.
Commercial Applications
- Title: "Enhanced Data Analysis and Modeling Technology"
- This technology can be used in industries such as finance, healthcare, marketing, and cybersecurity for improving data analysis and predictive modeling.
- Market implications include increased accuracy in decision-making processes and enhanced efficiency in data-driven operations.
Prior Art
There may be prior art related to variational auto-encoders, synthetic data generation, and feature engineering in the fields of machine learning and data science.
Frequently Updated Research
Researchers are constantly exploring new techniques and applications for variational auto-encoders, synthetic data generation, and feature engineering in data analysis and modeling.
Questions about Feature Engineering and Synthetic Data Generation
1. How does the use of a variational auto-encoder (VAE) model improve synthetic data generation? 2. What are the potential challenges in implementing feature engineering techniques for heterogeneous data tables?
Original Abstract Submitted
a method, computer program product and system are provided for feature engineering and synthetic data generation. a processor retrieves a plurality of data tables, where the plurality of data tables are heterogeneous in format and content. a processor trains a variational auto-encoder (vae) model on the plurality of data tables. a processor receives an input data table. a processor generates a synthetic data table based on the input data table and the trained vae model.
