Nec corporation (20240126953). ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM simplified abstract
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 20240126953 titled 'ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM
Simplified Explanation
The abstract describes a system where an updating unit adjusts a spatial estimation model using difference amounts between teaching signals and estimated signals from sensors.
- The system updates a spatial estimation model based on difference amounts between teaching signals and estimated signals.
- The first difference amount is between a first teaching signal and a first estimated signal observed by a sensor.
- The second difference amount is between a second teaching signal and a second estimated signal observed by a different sensor.
- The system uses emission waves and spatial structures to update the estimation model.
Potential Applications
This technology could be applied in:
- Environmental monitoring systems
- Medical imaging devices
- Robotics for navigation and mapping
Problems Solved
This technology addresses:
- Improving accuracy of spatial estimation models
- Enhancing sensor data processing
- Optimizing signal comparison techniques
Benefits
The benefits of this technology include:
- Increased precision in spatial estimation
- Enhanced sensor data interpretation
- Improved performance of systems relying on spatial data
Potential Commercial Applications
Potential commercial applications of this technology include:
- Geospatial mapping software
- Autonomous vehicles
- Industrial automation systems
Possible Prior Art
One possible prior art for this technology could be:
- Systems using sensor fusion for spatial estimation
Unanswered Questions
How does this technology compare to existing sensor fusion systems?
This article does not provide a direct comparison to existing sensor fusion systems.
What specific industries could benefit the most from this technology?
The article does not specify which industries could benefit the most from this technology.
Original Abstract Submitted
in an arithmetic operation system, an updating unit updates a spatial estimation model, based on a first difference amount and a second difference amount. the first difference amount is a difference amount between a first teaching signal and a first estimated signal. the first teaching signal is a spatial distribution signal observed by a first sensor 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. the second difference amount is a difference amount between a second teaching signal and a second estimated signal. the second teaching signal is an observed signal observed by a second sensor different in type from the first sensor. the second estimated signal is a signal for comparing with the second teaching signal, and is a signal of a form similar to that of the second teaching signal.
