Nec corporation (20240160920). METHOD OF LEARNING NEURAL NETWORK, RECORDING MEDIUM, AND REMAINING LIFE PREDICTION SYSTEM simplified abstract
Contents
- 1 METHOD OF LEARNING NEURAL NETWORK, RECORDING MEDIUM, AND REMAINING LIFE PREDICTION SYSTEM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD OF LEARNING NEURAL NETWORK, RECORDING MEDIUM, AND REMAINING LIFE PREDICTION SYSTEM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
METHOD OF LEARNING NEURAL NETWORK, RECORDING MEDIUM, AND REMAINING LIFE PREDICTION SYSTEM
Organization Name
Inventor(s)
Masanao Natsumeda of Tokyo (JP)
METHOD OF LEARNING NEURAL NETWORK, RECORDING MEDIUM, AND REMAINING LIFE PREDICTION SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240160920 titled 'METHOD OF LEARNING NEURAL NETWORK, RECORDING MEDIUM, AND REMAINING LIFE PREDICTION SYSTEM
Simplified Explanation
The abstract of the patent application describes a method of learning a neural network.
- The method involves providing a simplified explanation of the neural network.
- The method includes training the neural network using specific techniques.
- The method may involve adjusting the parameters of the neural network based on feedback.
- The method aims to improve the performance and accuracy of the neural network.
Potential Applications
This technology could be applied in various fields such as:
- Image recognition
- Natural language processing
- Autonomous vehicles
- Medical diagnosis
Problems Solved
This technology helps in addressing the following issues:
- Improving the accuracy of neural networks
- Enhancing the performance of machine learning models
- Streamlining the training process for neural networks
Benefits
The benefits of this technology include:
- Increased efficiency in learning neural networks
- Enhanced capabilities of machine learning algorithms
- Improved accuracy in predictive modeling
Potential Commercial Applications
The potential commercial applications of this technology include:
- Developing advanced AI systems
- Creating innovative products in various industries
- Offering consulting services for machine learning implementation
Possible Prior Art
One possible prior art for this technology could be the use of gradient descent algorithms in training neural networks.
Unanswered Questions
What are the specific techniques used in training the neural network in this method?
The specific techniques used in training the neural network are not detailed in the abstract. Further information on the training process would provide a clearer understanding of the innovation.
How does this method compare to existing methods of learning neural networks?
The abstract does not mention how this method compares to existing methods. A comparison with traditional techniques would help in evaluating the novelty and effectiveness of this approach.
Original Abstract Submitted
a method of learning a neural network includes:
