View source for 17988601. METHOD AND APPARATUS FOR SUPPORTING AUTOMATED RE-LEARNING IN MACHINE TO MACHINE SYSTEM simplified abstract (Kia Corporation)

=METHOD AND APPARATUS FOR SUPPORTING AUTOMATED RE-LEARNING IN MACHINE TO MACHINE SYSTEM=


==Organization Name==

[[:Category:Kia Corporation|Kia Corporation]]


[[Category:Kia Corporation]]

==Inventor(s)==

[[:Category:Jae Seung Song of Seoul (KR)|Jae Seung Song of Seoul (KR)]][[Category:Jae Seung Song of Seoul (KR)]]

==METHOD AND APPARATUS FOR SUPPORTING AUTOMATED RE-LEARNING IN MACHINE TO MACHINE SYSTEM - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17988601 titled 'METHOD AND APPARATUS FOR SUPPORTING AUTOMATED RE-LEARNING IN MACHINE TO MACHINE SYSTEM

==Simplified Explanation==

The present disclosure describes a method for automated re-learning in a machine-to-machine (M2M) system. This method involves training an artificial intelligence (AI) model, performing initial learning of the AI model, collecting learning data for re-learning, and performing re-learning of the AI model using the collected data.

* The method involves generating a resource for training an AI model.
* The AI model undergoes initial learning to acquire knowledge and skills.
* Learning data is collected to improve the AI model's performance.
* The collected data is used to re-learn the AI model, enhancing its capabilities.

== Potential Applications ==
* This technology can be applied in various M2M systems, such as autonomous vehicles, industrial automation, and smart home devices.
* It can be used to improve the performance and adaptability of AI models in real-time applications.

== Problems Solved ==
* Traditional AI models often require manual intervention for re-learning, which can be time-consuming and inefficient.
* This method automates the re-learning process, allowing AI models to continuously improve without human intervention.
* It addresses the challenge of keeping AI models up-to-date and adaptable in dynamic environments.

== Benefits ==
* The automated re-learning process saves time and effort by eliminating the need for manual intervention.
* It enables AI models to adapt and improve their performance based on real-time data.
* This technology enhances the overall efficiency and effectiveness of M2M systems by continuously updating and refining AI models.


==Original Abstract Submitted==

The present disclosure may support automated re-learning in a machine-to-machine (M2M) system. A method for operating a device may include: generating a resource for training an artificial intelligence (AI) model; controlling to perform initial learning of the AI model; collecting learning data for re-learning for the AI model; and controlling to perform re-learning of the AI model by using the learning data.



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