Dell products l.p. (20240232608). METHOD AND SYSTEM FOR PERFORMING NOISE REMOVAL AND KNOWLEDGE EXTRACTION TO IMPROVE PREDICTION MODEL PERFORMANCE simplified abstract
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 20240232608 titled 'METHOD AND SYSTEM FOR PERFORMING NOISE REMOVAL AND KNOWLEDGE EXTRACTION TO IMPROVE PREDICTION MODEL PERFORMANCE
The abstract describes a method for knowledge extraction and noise removal for prediction models using live tabular data.
- Data preprocessing is performed on live tabular data to generate processed data.
- A knowledge vector is generated based on the processed data using dimensionality reduction and tabular attention models.
- A prediction is made using a prediction model and the knowledge vector.
- The prediction is provided to a client for further processing.
Potential Applications: - This technology can be applied in various industries such as finance, healthcare, and e-commerce for predictive analytics. - It can be used in fraud detection systems, recommendation engines, and risk assessment models.
Problems Solved: - Helps in improving the accuracy and efficiency of prediction models by extracting relevant knowledge from live data. - Reduces noise and improves the quality of predictions by preprocessing the data effectively.
Benefits: - Enhances the performance of prediction models by incorporating knowledge extraction techniques. - Increases the reliability of predictions by removing noise from the data.
Commercial Applications: - This technology can be utilized by companies offering predictive analytics solutions to improve the accuracy of their models. - It can be integrated into existing systems to enhance decision-making processes based on predictive insights.
Questions about the technology: 1. How does the dimensionality reduction model contribute to generating the knowledge vector? 2. What are the key differences between tabular attention models and traditional prediction models?
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.
