View source for Fujifilm corporation (20240303974). LEARNING DATA GENERATION APPARATUS AND METHOD, AND LEARNING MODEL GENERATION APPARATUS AND METHOD simplified abstract

=LEARNING DATA GENERATION APPARATUS AND METHOD, AND LEARNING MODEL GENERATION APPARATUS AND METHOD=


==Organization Name==

[[:Category:fujifilm corporation|fujifilm corporation]]


[[Category:fujifilm corporation]]

==Inventor(s)==

[[:Category:Masaaki Oosake of Kanagawa (JP)|Masaaki Oosake of Kanagawa (JP)]][[Category:Masaaki Oosake of Kanagawa (JP)]]

==LEARNING DATA GENERATION APPARATUS AND METHOD, AND LEARNING MODEL GENERATION APPARATUS AND METHOD - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240303974 titled 'LEARNING DATA GENERATION APPARATUS AND METHOD, AND LEARNING MODEL GENERATION APPARATUS AND METHOD

**Simplified Explanation:**
The patent application describes a method and apparatus for generating learning data and models efficiently by combining image data with regions of interest.

**Key Features and Innovation:**
* Acquires first and second image data with regions of interest.
* Combines images based on predetermined conditions to generate third image data.
* Trains a learning model using the third image data.

**Potential Applications:**
This technology can be used in various fields such as computer vision, machine learning, and artificial intelligence for tasks like object recognition, image classification, and pattern detection.

**Problems Solved:**
This technology addresses the need for efficient learning data generation and model training by combining relevant image data to improve accuracy and performance.

**Benefits:**
* Enhanced learning efficiency.
* Improved model accuracy.
* Streamlined data processing.

**Commercial Applications:**
Potential commercial applications include automated image analysis systems, surveillance systems, medical imaging technologies, and autonomous vehicles.

**Prior Art:**
Researchers can explore prior art related to image data processing, machine learning algorithms, and computer vision techniques to understand the evolution of similar technologies.

**Frequently Updated Research:**
Stay updated on advancements in computer vision, deep learning, and image processing techniques to enhance the capabilities of this technology.

**Questions about the Technology:**
1. How does this technology improve the efficiency of learning data generation?
2. What are the potential real-world applications of this technology?

1. **A relevant generic question not answered by the article, with a detailed answer:**
How does this technology compare to traditional methods of image data processing and model training?
This technology offers a more efficient approach by combining relevant image data to generate learning data, leading to improved model accuracy and performance compared to traditional methods.

2. **Another relevant generic question, with a detailed answer:**
What impact does this technology have on the field of artificial intelligence?
This technology plays a crucial role in advancing AI capabilities by enhancing learning data generation and model training processes, leading to more accurate and reliable AI systems.


==Original Abstract Submitted==

there are provided a learning data generation apparatus and method and a learning model generation apparatus and method that can attain efficient learning. the learning data generation apparatus acquires first image data and second image data each having a region of interest, and when a positional relationship between the region of interest of the first image data and the region of interest of the second image data satisfies a predetermined condition, combines an image of a region, of the first image data, that includes the region of interest and an image of a region, of the second image data, that includes the region of interest to generate third image data. the learning model generation apparatus acquires the third image data generated by the learning data generation apparatus and trains a learning model by using the third image data to generate the learning model.



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