XBRL Tag Suggestion via Machine Learning Confidence Validation
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Solution Overview
Problem
Selecting the correct XBRL tags from thousands of available tags is a challenging task for those preparing financial statements or other business reports using XBRL.
Innovation Solution
The implementation of a method using a trained machine learning model to suggest and validate XBRL tags, where the model analyzes XBRL documents to generate suggested tags with confidence values, and compares these suggestions with assigned tags to determine tag confidence values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual XBRL tag selection is used from thousands of available tags, then flexibility and control are maintained, but the task becomes extremely challenging and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical process of selecting XBRL tags from thousands of options with an automated machine learning system. The NLP model automatically analyzes document content and predicts appropriate XBRL tags, substituting human manual selection with computational automation. This resolves the contradiction by dramatically improving productivity while reducing time investment.
Solution Approach 2:
The system enables self-service by allowing the XBRL tagging process to occur automatically without requiring expert human intervention. The machine learning model independently analyzes documents and assigns tags based on learned patterns from training data, making the process self-sufficient and eliminating the time-consuming manual search through thousands of tags.
2Productivity
If automated tag suggestion systems are implemented, then productivity increases, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary NLP processing layer between the document input and XBRL tag output. This intermediary system includes tokenization, embedding, and prediction components that mediate the transformation from raw text to structured tags. While this adds complexity, it enables automated tagging functionality that dramatically improves productivity compared to manual processes.
Solution Approach 2:
The system segments the complex task of XBRL tag selection into manageable components: document preprocessing, feature extraction, tag prediction, and validation. By dividing the overall process into discrete sequential steps, the system manages complexity while maintaining high productivity through automated processing of each segment.
3Measurement precision
If machine learning models are used to predict XBRL tags, then accuracy improves to over 95%, but the need for training data and model validation increases complexity
Solution Approach 1:
The patent applies preliminary action by training the machine learning model on extensive XBRL datasets before deployment. The model undergoes pre-training and validation phases using labeled examples, establishing accurate prediction capabilities in advance. This preliminary preparation achieves high accuracy (over 95%) while the training complexity is a one-time investment rather than ongoing operational complexity.
Solution Approach 2:
The system implements feedback mechanisms through model validation and performance monitoring. Predicted tags are evaluated against ground truth data during training, and the model receives feedback on prediction accuracy to refine its parameters. This feedback loop ensures high measurement precision while managing training complexity through systematic evaluation and iteration.
Data Source
AI summary
Disclosed are systems and methods for XBRL tag suggestion and validation. In some embodiments, the method includes the steps of: receiving an XBRL document associated with one or more assigned XBRL tags; analyzing the XBRL document using a trained machine learning model to generate one or more suggested XBRL tags and determine one or more corresponding confidence values; comparing the one or more assigned XBRL tags with the one or more suggested XBRL tags to generate comparison results; and determining a tag confidence value associated with each assigned XBRL tag of the one or more assigned XBRL tags based on the comparison results.