Automated XBRL Taxonomy Classification via Attribute Extraction
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Solution Overview
Problem
The increasing number of definition elements in XBRL taxonomies leads to a significant workload burden for manual classification, making it inefficient to manage and store these elements effectively.
Innovation Solution
A storage control method that accepts files containing text data and corresponding attribute information, automatically classifies these elements into groups based on attributes, and generates files for each group to be stored in appropriate storage destinations, reducing manual classification burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual classification of definition elements is performed, then classification accuracy can be maintained, but the work burden becomes immense
Solution Approach 1:
The system enables automatic self-classification of definition elements by extracting attributes from XBRL taxonomy files and automatically grouping elements based on these attributes, eliminating the need for manual classification operations
Solution Approach 2:
The patent replaces manual mechanical classification operations with an automated information processing system that uses computational methods to extract, process, and classify definition elements based on their attributes
2Adaptability or versatility
If the number of definition elements increases, then XBRL taxonomy becomes more comprehensive, but classification work burden increases
Solution Approach 1:
The system continuously processes XBRL taxonomy files and automatically classifies definition elements as they are added, maintaining continuous classification operation rather than requiring periodic manual intervention
Solution Approach 2:
The patent changes the approach from manual classification to automated attribute-based classification, using computational parameters and algorithms to process and categorize definition elements efficiently
Data Source
AI summary
A storage control method comprising steps of; accepting a file that includes a plurality of pieces of text data and a piece of information indicating a correspondence between each of the plurality of pieces of text data and attributes of the piece of text data; classifying the plurality of pieces of text data into a plurality of groups in accordance with the plurality of attributes that are each associated with any one of the plurality of pieces of text data, based on the piece of information indicating the correspondence included in the accepted file; generating, for each of the plurality of groups, a file that stores one or a plurality of pieces of text data included in the group; and storing the generated file in a storage destination corresponding to the attributes of the one or plurality of pieces of text data included in the file.