Stonebridge Ventures "On-device AI Startup 'Nota'... Wins Grand Prize at 4th Venture Startup Patent Awards"

Stonebridge Ventures announced on the 4th that Nota, an on-device artificial intelligence (AI) startup in which it has invested 10.5 billion KRW over four rounds, won the grand prize at the 4th Venture Startup Patent Awards for its innovative AI model optimization technology.

The awarded patent is a core technology of Nota's on-device AI development platform, NetsPresso®. Last year, the patent was registered in the three major countries of Korea, the United States, and Japan, proving its global technological competitiveness. This is a rare case in the AI industry where a UI patent matched to a product has been recognized, and it is evaluated as an important achievement securing differentiated competitiveness compared to competitors.

The decisive patent for this grand prize is titled "Method for compressing neural network models and an electronic device performing the same." This technology is applied to NetsPresso's Model Compressor module. It features excellent convenience by providing a tool that analyzes models received from users and adjusts compression parameter values suitable for the target device.

This technological excellence sets a new standard for on-device AI optimization and contributes to the commercialization of AI technology. Through this, customers can efficiently utilize AI solutions that deliver optimized performance across various devices.

Chae Myung-su, CEO of Nota, said, “This grand prize award is a meaningful achievement that shows Nota’s AI technology has been recognized in the market,” adding, “We will continue to lead the commercialization and expansion of technology centered on Nota’s innovative AI model optimization technology to create practical changes in various industries.”

Recently, Nota has been securing patent priority for AI optimization technology through successive domestic and international patent registrations. Technologically, Nota’s patent registrations demonstrate innovative approaches and methods in the field of AI model compression and lightweighting. From a business perspective, it is significant in that these technologies enable efficient AI model development and execution.