Table-type data analysis system, method, and program using language model

The tabular data analysis system addresses NL2SQL limitations by employing a three-layer language model for efficient and accurate data interpretation and conversion into executable code, supporting complex operations and diverse data formats.

WO2026111490A1PCT designated stage Publication Date: 2026-05-28LG MANAGEMENT DEV INST CO LTD
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Patent Information

Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
LG MANAGEMENT DEV INST CO LTD
Filing Date
2025-11-21
Publication Date
2026-05-28

AI Technical Summary

Technical Problem

Conventional NL2SQL technology struggles with complex mathematical reasoning and performance degradation when handling large or numerous tables, failing to provide efficient and accurate analysis of tabular data due to input length constraints and structural understanding limitations.

Method used

A tabular data analysis system utilizing a three-layer language model architecture, comprising a decoder-only structure for table selection, a text processing model for natural language understanding, and a code generation model for executing analysis requests, enabling flexible and stable data interpretation and conversion into executable code.

Benefits of technology

Enables professional-level analysis of tabular data without programming knowledge, supporting large-scale tables and complex operations, with error correction and flexible handling of various data formats, ensuring accurate and efficient analysis results.

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Abstract

A table-type data analysis system, method, and program using a language model are disclosed. The method comprises the steps of: receiving a query and at least one table-type data, and converting the table-type data into at least one text-type data; inputting the query and the text-type data into a first language model to identify the text-type data corresponding to an analysis request included in the query and extracting the identified text-type data as analysis data; inputting the query and the analysis data into a second language model to generate an execution command for performing the analysis request; inputting the execution command into a third language model to convert the execution command into a code configured to perform the analysis request; and executing the code to output a data analysis result.
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Citation Information

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