Dell products l.p. (20240135163). METHOD AND SYSTEM FOR PERFORMING NOISE REMOVAL AND KNOWLEDGE EXTRACTION TO IMPROVE PREDICTION MODEL PERFORMANCE simplified abstract
Contents
- 1 METHOD AND SYSTEM FOR PERFORMING NOISE REMOVAL AND KNOWLEDGE EXTRACTION TO IMPROVE PREDICTION MODEL PERFORMANCE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND SYSTEM FOR PERFORMING NOISE REMOVAL AND KNOWLEDGE EXTRACTION TO IMPROVE PREDICTION MODEL PERFORMANCE - 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 Original Abstract Submitted
METHOD AND SYSTEM FOR PERFORMING NOISE REMOVAL AND KNOWLEDGE EXTRACTION TO IMPROVE PREDICTION MODEL PERFORMANCE
Organization Name
Inventor(s)
Kuruba Ajay Kumar of Anantapur (IN)
METHOD AND SYSTEM FOR PERFORMING NOISE REMOVAL AND KNOWLEDGE EXTRACTION TO IMPROVE PREDICTION MODEL PERFORMANCE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135163 titled 'METHOD AND SYSTEM FOR PERFORMING NOISE REMOVAL AND KNOWLEDGE EXTRACTION TO IMPROVE PREDICTION MODEL PERFORMANCE
Simplified Explanation
The patent application describes a method for knowledge extraction and noise removal for prediction models using live tabular data.
- Obtaining live tabular data by a prediction system.
- Performing data preprocessing on the live tabular data to generate processed live tabular data.
- Generating a knowledge vector based on the processed live tabular data using a dimensionality reduction model and a tabular attention model.
- Generating a prediction using a prediction model and the knowledge vector.
- Providing the prediction to a client for prediction processing.
Potential Applications
This technology can be applied in various fields such as finance, healthcare, marketing, and more for accurate prediction and decision-making processes.
Problems Solved
1. Noise removal from live tabular data. 2. Efficient knowledge extraction for prediction models.
Benefits
1. Improved accuracy of predictions. 2. Enhanced decision-making processes. 3. Reduction of errors in predictions.
Potential Commercial Applications
Optimizing marketing strategies, improving healthcare diagnostics, enhancing financial forecasting, and more.
Possible Prior Art
There may be prior art related to data preprocessing techniques, dimensionality reduction models, and prediction models in various industries.
What are the limitations of this method in handling large volumes of data?
The method described in the patent application may face challenges when dealing with large volumes of data due to potential scalability issues and increased computational requirements.
How does this method compare to existing noise removal techniques in terms of efficiency and accuracy?
The efficiency and accuracy of this method in noise removal may vary compared to existing techniques depending on the specific use case and the complexity of the data involved. Further comparative studies would be needed to determine the superiority of this method over existing techniques.
Original Abstract Submitted
techniques described herein relate to a method for performing knowledge extraction and noise removal for prediction models. the method includes obtaining, by a prediction system, live tabular data; in response to obtaining live tabular data: performing data preprocessing on the live tabular data to generate processed live tabular data; generating a knowledge vector based on the processed live tabular data using a dimensionality reduction model and a tabular attention model; generating a prediction using a prediction model and the knowledge vector; and providing the prediction to a client; wherein the client performs prediction processing using the prediction.
