18575363. LEARNING SYSTEM AND LEARNING METHOD simplified abstract (NEC Corporation)
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 18575363 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. These parameters are then sent to a server by the client-side parameter sending means, recalculated, and sent back to the clients by the server-side parameter sending means.
Key Features and Innovation
- Learning parameters of predetermined multiple operations based on common input data.
- Calculation of weighted sum of output data.
- Client-side parameter sending means for sending parameters to the server.
- Recalculation of parameters based on received data.
- Server-side parameter sending means for sending recalculated parameters back to clients.
Potential Applications
This technology could 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 systems with multiple operations.
Benefits
- Improved accuracy in parameter learning.
- Enhanced efficiency in calculating weighted sums.
- Streamlined communication between clients and servers.
Commercial Applications
- This technology could be utilized in industries such as finance, healthcare, and e-commerce for data analysis and optimization purposes.
Prior Art
Researchers can explore prior art related to parameter learning, weighted sum calculation, and client-server communication systems.
Frequently Updated Research
Stay updated on advancements in machine learning algorithms, data analysis techniques, and optimization strategies related to 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 received data, leading to more accurate results.
What are the potential commercial applications of this technology?
The technology can be applied in various industries for data analysis, optimization, and communication between clients and servers.
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
