Nec corporation (20240320550). LEARNING SYSTEM AND LEARNING METHOD simplified abstract
Contents
- 1 LEARNING SYSTEM AND LEARNING METHOD
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 LEARNING SYSTEM AND LEARNING METHOD - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about the Technology
- 1.13 Original Abstract Submitted
LEARNING SYSTEM AND LEARNING METHOD
Organization Name
Inventor(s)
Tomoyuki Yoshiyama of Tokyo (JP)
LEARNING SYSTEM AND LEARNING METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240320550 titled 'LEARNING SYSTEM AND LEARNING METHOD
Simplified Explanation
The patent application describes a system where parameters of multiple operations are learned based on common input data and the weighted sum of output data is calculated. These parameters are then sent from client-side to server-side for recalculation and redistribution.
Key Features and Innovation
- Learning parameters of predetermined multiple operations based on common input data.
- Calculating the weighted sum of output data using the learned parameters.
- Sending parameters from client-side to server-side for recalculation.
- Redistributing recalculated parameters to each client.
Potential Applications
This technology can be applied in various fields such as machine learning, data analysis, and optimization algorithms.
Problems Solved
This technology addresses the need for efficient parameter learning and calculation in complex operations involving multiple inputs and outputs.
Benefits
- Improved accuracy in parameter learning.
- Enhanced efficiency in calculating weighted sums.
- Streamlined communication between client-side and server-side systems.
Commercial Applications
- This technology can be utilized in industries such as finance, healthcare, and e-commerce for optimizing data processing and analysis.
Prior Art
Further research can be conducted in the fields of machine learning, distributed systems, and optimization algorithms to explore similar technologies.
Frequently Updated Research
Stay updated on advancements in machine learning algorithms, distributed computing systems, and parameter optimization techniques for potential improvements in this technology.
Questions about the Technology
How does this technology improve parameter learning efficiency?
This technology enhances parameter learning efficiency by recalculating parameters based on common input data.
What are the potential applications of this technology beyond data analysis?
This technology can be applied in various fields such as optimization algorithms, machine learning models, and distributed computing systems.
Original Abstract Submitted
the learning means learns parameters of predetermined multiple operations that are related in that common input data is given and that weighted sum of output data is calculated, and parameters related to calculation of the weighted sum. the client-side parameter sending means sends parameters of the predetermined multiple operations, among the parameters of the predetermined multiple operations and the parameters related to the calculation of the weighted sum, to the server . the parameter calculation means recalculates the parameters of the predetermined multiple operations, based on the parameters of the predetermined multiple operations received from each client. the server-side parameter sending means sends the parameters of the predetermined multiple operations to each client
