17988601. METHOD AND APPARATUS FOR SUPPORTING AUTOMATED RE-LEARNING IN MACHINE TO MACHINE SYSTEM simplified abstract (Hyundai Motor Company)

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

Organization Name

Hyundai Motor Company

Inventor(s)

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 abstract describes a method for automated re-learning in a machine-to-machine (M2M) system using artificial intelligence (AI) models. Here is a simplified explanation of the abstract:

  • The method involves generating a resource that will be used to train an AI model.
  • The AI model undergoes initial learning, where it is trained using the generated resource.
  • Learning data is collected for the purpose of re-learning the AI model.
  • The AI model is then re-learned using the collected learning data.

Potential Applications

This technology has potential applications in various fields, including:

  • Autonomous vehicles: The AI models can be re-learned to improve their decision-making capabilities based on new data collected from real-world driving scenarios.
  • Industrial automation: AI models can be re-learned to adapt to changing manufacturing processes and optimize efficiency.
  • Healthcare: AI models can be re-learned to improve diagnostic accuracy and treatment recommendations based on new medical data.

Problems Solved

This technology addresses the following problems:

  • Stagnation of AI models: AI models may become outdated or less accurate over time as new data becomes available. This method allows for automated re-learning to ensure the models stay up-to-date.
  • Adaptability to changing environments: By re-learning the AI models using new data, they can better adapt to evolving conditions and make more informed decisions.

Benefits

The use of automated re-learning in a machine-to-machine system offers several benefits:

  • Improved accuracy: Re-learning the AI models using new data can enhance their accuracy and performance.
  • Real-time adaptation: The ability to re-learn the models allows them to adapt to changing conditions and make more relevant decisions in real-time.
  • Efficiency: By automating the re-learning process, it reduces the need for manual intervention, saving time and resources.


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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