17972257. METHOD AND SYSTEM FOR PERFORMING NOISE REMOVAL AND KNOWLEDGE EXTRACTION TO IMPROVE PREDICTION MODEL PERFORMANCE simplified abstract (Dell Products L.P.)

METHOD AND SYSTEM FOR PERFORMING NOISE REMOVAL AND KNOWLEDGE EXTRACTION TO IMPROVE PREDICTION MODEL PERFORMANCE

Organization Name

Dell Products L.P.

Inventor(s)

Saurabh Jha of Austin TX (US)

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 17972257 titled 'METHOD AND SYSTEM FOR PERFORMING NOISE REMOVAL AND KNOWLEDGE EXTRACTION TO IMPROVE PREDICTION MODEL PERFORMANCE

Simplified Explanation

The method described in the patent application involves extracting knowledge and removing noise from live tabular data for prediction models. Here is a simplified explanation of the abstract:

• Obtaining live tabular data by a prediction system
• Preprocessing the live tabular data to generate processed live tabular data
• Generating a knowledge vector based on the processed data using dimensionality reduction and tabular attention models
• Generating a prediction using a prediction model and the knowledge vector
• Providing the prediction to a client for prediction processing

Potential Applications

The technology described in this patent application could be applied in various fields such as finance, healthcare, marketing, and more for making accurate predictions based on live tabular data.

Problems Solved

This technology solves the problem of extracting valuable knowledge from noisy tabular data, which can improve the accuracy and reliability of prediction models.

Benefits

The benefits of this technology include enhanced prediction accuracy, improved decision-making based on real-time data, and increased efficiency in processing live tabular data.

Potential Commercial Applications

The technology could be utilized in industries such as stock trading, medical diagnosis, customer behavior analysis, and more for making informed decisions and predictions based on live data.

Possible Prior Art

One possible prior art for this technology could be existing methods for data preprocessing and dimensionality reduction in machine learning models.

How does this method compare to traditional prediction models that do not involve knowledge extraction and noise removal techniques?

This article does not provide a direct comparison between this method and traditional prediction models without knowledge extraction and noise removal techniques. It would be interesting to see a study or analysis on the performance differences between the two approaches.

What are the potential limitations or challenges of implementing this method in real-world applications?

The article does not address any potential limitations or challenges that may arise when implementing this method in real-world applications. It would be beneficial to understand any constraints or obstacles that users may face when utilizing this technology.

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.