20230108419. LEARNING SYSTEM, IMAGE GENERATION SYSTEM, PRODUCTION SYSTEM, LEARNING METHOD, AND NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM simplified abstract (KABUSHIKI KAISHA YASKAWA DENKI)

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LEARNING SYSTEM, IMAGE GENERATION SYSTEM, PRODUCTION SYSTEM, LEARNING METHOD, AND NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM

Organization Name

KABUSHIKI KAISHA YASKAWA DENKI

Inventor(s)

Makoto Mori of Kitakyushu-shi (JP)

Ryo Masumura of Kitakyushu-shi (JP)

LEARNING SYSTEM, IMAGE GENERATION SYSTEM, PRODUCTION SYSTEM, LEARNING METHOD, AND NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM - A simplified explanation of the abstract

This abstract first appeared for US patent application 20230108419 titled 'LEARNING SYSTEM, IMAGE GENERATION SYSTEM, PRODUCTION SYSTEM, LEARNING METHOD, AND NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM

Simplified Explanation

The patent application describes a learning system that combines real and virtual environments to improve the accuracy of image generation. Here are the key points:

  • The learning system consists of three main components: real environment image acquisition circuitry, virtual environment image generation circuitry, and GAN learning circuitry.
  • The real environment image acquisition circuitry captures images of a real environment, including real objects and a real background.
  • The virtual environment image generation circuitry creates images of a virtual environment, which includes virtual objects and a virtual background.
  • The virtual environment image is designed to have different colors from the real environment image, making it easier to distinguish between the two.
  • The GAN learning circuitry uses generative adversarial networks to improve the similarity between the virtual and real environment images.
  • By training the virtual environment image to closely resemble the real environment image, the learning system can generate more accurate and realistic virtual environments.

Potential applications of this technology:

  • Virtual reality and augmented reality: The technology can be used to create more realistic virtual environments for immersive experiences.
  • Training simulations: The system can be used to generate virtual environments that closely resemble real-world scenarios, allowing for more effective training simulations.
  • Gaming: The technology can enhance the visual quality and realism of virtual game environments.

Problems solved by this technology:

  • Accurate image generation: By combining real and virtual environments, the learning system improves the accuracy and realism of image generation.
  • Color distinction: The use of different colors in the virtual environment image helps distinguish it from the real environment image, making it easier to train the system.

Benefits of this technology:

  • Improved realism: The technology enhances the realism of virtual environments, providing a more immersive and engaging experience.
  • Enhanced training effectiveness: The system's ability to generate realistic virtual environments improves the effectiveness of training simulations.
  • More visually appealing games: The technology can be used to create visually stunning and realistic game environments.


Original Abstract Submitted

a learning system includes real environment image acquisition circuitry, virtual environment image generation circuitry, and gan learning circuitry. the real environment image acquisition circuitry is configured to acquire a real environment image indicating a real environment in which real objects and a real background are provided. the virtual environment image generation circuitry is configured to generate a virtual environment image indicating a virtual environment in which virtual objects and a virtual background are provided. the virtual environment image includes at least one of the virtual background and the virtual objects which have a different color or different colors different from colors of the real background and the real objects. the gan learning circuitry is configured to perform gan (generative adversarial networks) learning via which the virtual environment image is got more similar to the real environment image based on the real environment image and the virtual environment image.

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