International business machines corporation (20240135242). FUTUREPROOFING A MACHINE LEARNING MODEL simplified abstract

FUTUREPROOFING A MACHINE LEARNING MODEL

Organization Name

international business machines corporation

Inventor(s)

Kavitha Hassan Yogaraj of Bangalore (IN)

Frederik Frank Flother of Schlieren (CH)

Vladimir Rastunkov of Mundelein IL (US)

FUTUREPROOFING A MACHINE LEARNING MODEL - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240135242 titled 'FUTUREPROOFING A MACHINE LEARNING MODEL

Simplified Explanation

The abstract describes a method, system, and computer program product for futureproofing a machine learning model by generating a futureproofing metric and creating an enhanced machine learning model with historical data and a baseline model as inputs.

• Explanation:

 * Receive historical data for updates and changes to a baseline machine learning model.
 * Generate a futureproofing metric.
 * Create an enhanced machine learning model with a futureproofed version of the baseline model using historical data and the baseline model as inputs.

Potential Applications

The technology can be applied in various industries such as finance, healthcare, marketing, and e-commerce to improve the accuracy and reliability of machine learning models over time.

Problems Solved

1. Ensures machine learning models remain relevant and effective in dynamic environments. 2. Helps in adapting models to changing data patterns and trends.

Benefits

1. Increases the longevity and performance of machine learning models. 2. Enhances the adaptability and robustness of models to changes in data.

Potential Commercial Applications

Optimizing marketing campaigns, improving healthcare diagnostics, enhancing fraud detection systems, and personalizing customer recommendations in e-commerce.

Possible Prior Art

Prior art may include methods for updating machine learning models with new data, techniques for evaluating model performance, and strategies for enhancing model accuracy over time.

How does this technology compare to existing methods for updating machine learning models with new data?

The article does not provide a direct comparison to existing methods for updating machine learning models with new data. It would be beneficial to understand the specific advantages and limitations of this technology compared to traditional approaches.

What are the potential challenges in implementing this technology in real-world applications?

The article does not address the potential challenges in implementing this technology in real-world applications. It would be important to consider factors such as data privacy, computational resources, and integration with existing systems when deploying this solution.

Original Abstract Submitted

provided are a computer-implemented method, a system, and a computer program product for futureproofing a machine learning model, in which historical data for updates and changes to a baseline machine learning model are received. a futureproofing metric is generated. an enhanced machine learning model comprising a futureproofed version of the baseline machine learning model is generated with the historical data and the baseline machine learning model as inputs.