Digital Standard Generation Using ML-Based Concept Classification
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Solution Overview
Problem
Existing methods for converting paper standards to digital formats are inefficient and inaccurate, leading to inconsistent and difficult-to-navigate electronic standards due to manual rule-based classification, which often misses information and lacks a standardized format for display and search functionality.
Innovation Solution
Utilizing a machine-learning model to extract and classify conceptual units from underlying standards into classification groups based on a schema, enabling accurate conversion to a digital standard with intuitive search and filter functions through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual rule-based classification is used to convert paper standards to digital formats, then the conversion process is simple to implement, but the accuracy and consistency of the digital standards deteriorate
Solution Approach 1:
The patent replaces manual rule-based classification (mechanical system) with an automated machine learning model (intelligent system). The NLP model automatically extracts and classifies conceptual units from paper standards into digital formats, eliminating manual intervention while improving accuracy and consistency of the conversion process.
Solution Approach 2:
The patent introduces an NLP-based machine learning model as an intermediary between paper standards and digital formats. This intermediary automatically processes the conversion by extracting conceptual units and classifying them according to a schema, resolving the contradiction between simple implementation and high accuracy.
2Device complexity
If manual rule-based classification is used to convert standards, then the implementation process is straightforward, but information loss and inconsistency increase
Solution Approach 1:
The patent replaces complex manual classification processes with an automated NLP model that systematically extracts and classifies all conceptual units from paper standards. This substitution reduces process complexity while preventing information loss through comprehensive automated processing.
Solution Approach 2:
The machine learning model performs self-service by automatically extracting, classifying, and structuring information from paper standards without human intervention. This self-service capability ensures complete information capture and consistent classification, eliminating the information loss and inconsistency associated with manual processes.
3Manufacturing precision
If automated machine-learning models are used to extract and classify conceptual units, then conversion accuracy and consistency improve, but the complexity of the system increases
Solution Approach 1:
The patent uses an NLP-based machine learning model as an intermediary layer between paper standards and digital formats. This intermediary handles the complex tasks of extraction and classification automatically, achieving high conversion accuracy while managing system complexity through modular architecture and standardized schemas.
4Productivity
If digital standards are generated without standardized format and search functionality, then the generation process is simpler, but usability and information retrieval efficiency deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-defining a standardized schema and classification framework before the conversion process. This preliminary structure enables automatic classification of conceptual units during generation, ensuring both high productivity and excellent usability through consistent formatting and built-in search functionality.
Solution Approach 2:
The patent creates digital standards with multi-functional capabilities including standardized formatting, automatic classification, search functionality, and filter capabilities. This universal design approach maintains generation efficiency while dramatically improving usability and information retrieval efficiency through integrated features.
Data Source
AI summary
One embodiment provides a method for generating a digital standard, the method including: receiving an underlying standard; extracting conceptual units from the underlying standard; classifying at least a portion of the extracted conceptual units into one of a plurality of classification groups, wherein the classifying includes classifying conceptual units from the underlying standard based upon sections of a schema corresponding to a digital standard; storing the classified extracted conceptual units into a data repository, wherein the storing is performed as defined by the schema; displaying, within a user interface, a digital standard in a format based upon the schema, wherein the displaying includes accessing conceptual units from the data repository corresponding to the digital standard and displaying the conceptual units in a format in accordance with the schema; and providing, within the user interface, search and filter functions allowing for finding information related to the digital standard.