View source for 18305712. METHOD AND ELECTRONIC DEVICE FOR AUTOMATED MACHINE LEARNING MODEL RETRAINING simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)

=METHOD AND ELECTRONIC DEVICE FOR AUTOMATED MACHINE LEARNING MODEL RETRAINING=


==Organization Name==

[[:Category:SAMSUNG ELECTRONICS CO., LTD.|SAMSUNG ELECTRONICS CO., LTD.]]


[[Category:SAMSUNG ELECTRONICS CO., LTD.]]

==Inventor(s)==

[[:Category:Sukhdeep Singh of Bangalore (IN)|Sukhdeep Singh of Bangalore (IN)]][[Category:Sukhdeep Singh of Bangalore (IN)]]

[[:Category:Vivek Sapru of Bangalore (IN)|Vivek Sapru of Bangalore (IN)]][[Category:Vivek Sapru of Bangalore (IN)]]

[[:Category:Joseph Thaliath of Bangalore (IN)|Joseph Thaliath of Bangalore (IN)]][[Category:Joseph Thaliath of Bangalore (IN)]]

[[:Category:Ganesh Kumar Thangavel of Bangalore (IN)|Ganesh Kumar Thangavel of Bangalore (IN)]][[Category:Ganesh Kumar Thangavel of Bangalore (IN)]]

[[:Category:Ashish Jain of Bangalore (IN)|Ashish Jain of Bangalore (IN)]][[Category:Ashish Jain of Bangalore (IN)]]

[[:Category:Seungil Yoon of SUWON-SI (KR)|Seungil Yoon of SUWON-SI (KR)]][[Category:Seungil Yoon of SUWON-SI (KR)]]

[[:Category:Hoejoo Lee of Seoul (KR)|Hoejoo Lee of Seoul (KR)]][[Category:Hoejoo Lee of Seoul (KR)]]

[[:Category:Hunje Yeon of Seoul (KR)|Hunje Yeon of Seoul (KR)]][[Category:Hunje Yeon of Seoul (KR)]]

==METHOD AND ELECTRONIC DEVICE FOR AUTOMATED MACHINE LEARNING MODEL RETRAINING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18305712 titled 'METHOD AND ELECTRONIC DEVICE FOR AUTOMATED MACHINE LEARNING MODEL RETRAINING

==Simplified Explanation==

The patent application describes a system and method for automated machine learning (ML) model retraining by an electronic device. The system and method involve running a first ML model and a second ML model, predicting the accuracy degradation of the first ML model using the second ML model, determining if the predicted accuracy degradation meets a pre-defined threshold, and retraining the first ML model when the predicted accuracy degradation meets the threshold.

* The system and method involve using two ML models, one as the primary model and the other as a predictor of accuracy degradation.
* The second ML model predicts the accuracy degradation of the first ML model.
* The system determines if the predicted accuracy degradation meets a pre-defined threshold.
* If the predicted accuracy degradation meets the threshold, the first ML model is retrained.

Potential applications of this technology:

* Automated retraining of ML models can be used in various industries such as finance, healthcare, and manufacturing.
* It can be applied in fraud detection systems to continuously improve accuracy and adapt to new fraud patterns.
* In healthcare, it can be used to retrain ML models for disease diagnosis and treatment recommendations based on new data.

Problems solved by this technology:

* ML models can experience accuracy degradation over time due to changes in data patterns or new data.
* Manual retraining of ML models can be time-consuming and resource-intensive.
* This technology automates the process of detecting accuracy degradation and retraining ML models, reducing the need for manual intervention.

Benefits of this technology:

* Continuous improvement of ML model accuracy by automatically retraining when accuracy degradation is predicted.
* Reduction in manual effort and resources required for ML model retraining.
* Increased efficiency and effectiveness of ML models in various applications.


==Original Abstract Submitted==

A system and/or method for automated ML model retraining by an electronic device. The system and/or method may include one or more of: running a first ML model and a second ML model, predicting an accuracy degradation of the first ML model using the second ML model, determining whether the predicted accuracy degradation meets a pre-defined threshold, and/or retraining the first ML model when the predicted accuracy degradation meets the pre-defined threshold.



[[Category:G06N20/00]]