Systems and methods for automatic data analysis, organization, and labelling

An AI pipeline automatically organizes and labels data using machine learning, addressing inefficiencies in user-dependent data management by identifying and grouping relevant keywords, enhancing data utilization and reducing costs.

WO2026106978A1PCT designated stage Publication Date: 2026-05-21SK HYNIX NAND PRODUCT SOLUTIONS CORP
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Patent Information

Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
SK HYNIX NAND PRODUCT SOLUTIONS CORP
Filing Date
2025-11-11
Publication Date
2026-05-21

AI Technical Summary

Technical Problem

Existing data management systems require user intervention for data ranking and reduction, leading to inefficiencies and high costs, and lack a universal definition of valuable data.

Method used

An AI processing pipeline that automatically organizes and labels data using machine learning techniques, including embedding extraction, clustering, and image-to-text techniques, to identify and group relevant keywords without user intervention.

Benefits of technology

Enables efficient data management with minimal user input, allowing for data to be used in various applications such as training models and event detection, while reducing storage and computational costs.

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Abstract

Some embodiments are directed to systems and methods for preparing data. IN one aspect, a computer system obtains input images and groups them into image clusters including a first image cluster that includes a first set of input images. The computer system extracts image keywords from each of the first set of input images and groups the image keywords to identify a plurality of cluster keywords of the first image cluster. The computer device determines a plurality of keyword weights, each associated with a respective one cluster keyword of the plurality of cluster keywords based on cluster locations of the first set of input images in the first image cluster. The computer system labels the first set of input images based on the plurality of cluster keywords and the plurality of keyword weights.
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