18162218. ELECTRONIC APPARATUS AND CONTROL METHOD THEREOF simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
Contents
ELECTRONIC APPARATUS AND CONTROL METHOD THEREOF
Organization Name
Inventor(s)
Kwangyoun Kim of Suwon-si (KR)
ELECTRONIC APPARATUS AND CONTROL METHOD THEREOF - A simplified explanation of the abstract
This abstract first appeared for US patent application 18162218 titled 'ELECTRONIC APPARATUS AND CONTROL METHOD THEREOF
Simplified Explanation
The patent application describes an electronic apparatus and a control method that involve the exchange of artificial intelligence models and learning data between multiple external electronic devices. The method includes the following steps:
- Receiving a first artificial intelligence model and learning data from a first external electronic device, and a second artificial intelligence model and learning data from a second external electronic device.
- Identifying learning data from the first device that corresponds to the learning data received from the second device.
- Training the second artificial intelligence model using the identified learning data from the first device.
- Transmitting the trained second artificial intelligence model back to the second device.
Potential applications of this technology include:
- Collaborative learning: Multiple devices can share their learning data and models to collectively improve their artificial intelligence capabilities.
- Distributed computing: The exchange of models and data allows for distributed computing tasks, where different devices contribute to the training and improvement of AI models.
- Personalized AI: By exchanging models and data, devices can learn from each other and adapt their AI models to better suit individual users' preferences and needs.
Problems solved by this technology include:
- Limited computing resources: By distributing the training process across multiple devices, the computational burden can be shared, allowing for more complex AI models to be trained.
- Data privacy concerns: Instead of sharing raw data, only the trained models are exchanged, reducing the risk of exposing sensitive information.
- Lack of diversity in training data: By combining learning data from different devices, the AI models can benefit from a wider range of experiences and perspectives.
Benefits of this technology include:
- Improved AI performance: By leveraging the collective knowledge and data from multiple devices, the AI models can be trained more effectively, leading to better accuracy and performance.
- Enhanced user experience: Personalized AI models can provide tailored recommendations and services based on individual preferences, resulting in a more satisfying user experience.
- Efficient use of resources: The distributed nature of the training process allows for optimal utilization of computing resources, reducing the overall time and energy required for training AI models.
Original Abstract Submitted
An electronic apparatus and a control method thereof are provided. The control method of the electronic apparatus includes receiving, from a first external electronic apparatus and a second external electronic apparatus, a first artificial intelligence model and a second artificial intelligence model used by the first and second external electronic apparatuses, respectively, and a plurality of learning data stored in the first and second external electronic apparatuses, identifying first learning data, which corresponds to second learning data received from the second external electronic apparatus, among learning data received from the first external electronic apparatus, training the second artificial intelligence model used by the second external electronic apparatus based on the first learning data, and transmitting the trained second artificial intelligence model to the second external electronic apparatus.
