18375663. ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM simplified abstract (NEC Corporation)
Contents
- 1 ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM - 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
ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM
Organization Name
Inventor(s)
Tsubasa Nakamura of Tokyo (JP)
ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18375663 titled 'ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM
Simplified Explanation
The abstract describes a patent application for an arithmetic operation system that involves calculating a difference amount between a teaching signal and an estimated signal. The teaching signal is a spatial distribution signal observed in a target space, while the estimated signal is formed based on estimated density associated with sample points acquired from a spatial estimation model.
- Evaluation unit calculates difference amount between teaching signal and estimated signal
- Teaching signal is spatial distribution signal observed in target space
- Estimated signal is formed based on estimated density associated with sample points
- Updating unit updates spatial estimation model based on difference amount
Potential Applications
This technology could be applied in various fields such as:
- Signal processing
- Image recognition
- Machine learning
Problems Solved
This technology helps in:
- Improving accuracy of spatial estimation
- Enhancing signal processing capabilities
- Optimizing data analysis
Benefits
The benefits of this technology include:
- Increased efficiency in calculations
- Enhanced accuracy in spatial distribution analysis
- Improved performance in signal processing tasks
Potential Commercial Applications
With its capabilities in signal processing and spatial estimation, this technology could be valuable in industries such as:
- Healthcare for medical imaging analysis
- Robotics for object recognition and localization
- Environmental monitoring for data analysis
Possible Prior Art
One possible prior art could be the use of spatial estimation models in signal processing systems to improve accuracy and efficiency.
What are the specific spatial estimation models used in this technology?
The abstract does not provide details on the specific spatial estimation models utilized in this technology.
How does the updating unit determine the updates to the spatial estimation model based on the difference amount?
The abstract does not elaborate on the specific methodology employed by the updating unit to update the spatial estimation model.
Original Abstract Submitted
In an arithmetic operation system, an evaluation unit calculates a difference amount between a teaching signal and an estimated signal. The teaching signal is a spatial distribution signal observed with respect to a spatial structure on a path of an emission wave in a target space (i.e., a teaching space) by using the emission wave. In addition, the estimated signal is a signal for comparing with the teaching signal, and is an estimated spatial distribution signal. The estimated signal is formed based on estimated density associated to each sample point acquired from a spatial estimation model, by a sampling unit inputting information about a position of each of a plurality of sample points on the path to the spatial estimation model. An updating unit updates the spatial estimation model, based on the difference amount.