公开(公告)号：US20250117696A1

公开(公告)日：2025-04-10

申请号：US18524989

申请日：2023-11-30

Applicant: Korea Electronics Technology Institute

Inventor： JaeWon Moon ,  Seung Woo Kum ,  Seungtaek Oh

IPC: G06N20/00

Abstract: A method of generating a predictive model is proposed. The method may include receiving single time-series data or multiple types of time-series data collected in a specific domain, drawing time-series data corresponding to the same domain and time interval, among the received single time-series data or multiple types of time-series data. The method may also include pre-processing the drawn time-series data by applying a pre-processing pipeline built by applying at least one pre-processing module to the drawn time-series data, and generating a pattern classification model for classifying patterns of the pre-processed time-series data based on the clustering of the pre-processed time-series data. The method may further include generating a predictive model for predicting feature information of the drawn time-series data based on a cluster that is generated as the results of the clustering of the pre-processed time-series data, and storing the pattern classification model and the predictive model.

公开(公告)号：US20250156167A1

公开(公告)日：2025-05-15

申请号：US18915125

申请日：2024-10-14

Applicant: Korea Electronics Technology Institute

Inventor： Seung Woo Kum ,  JaeWon Moon ,  JeongCheol Yeom ,  Seungtaek Oh

IPC: G06F8/61

Abstract: Proposed is a computing device for operating an artificial intelligence (AI) service. The computing device may include a local intelligence storage, in which a deep learning model for AI applications based on a container is stored. The computing device may also include a local intelligence/model management unit configured to scan the local intelligence storage to identify information on the deep learning model installed in the computing device and provide the identified information on the deep learning model through a network interface. The local intelligence/model management unit may manage the deep learning model to be stored on the local intelligence storage that is independent from the container.