NLP Metadata Pipeline for Consistent Object Classification
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Solution Overview
Problem
Managers of electronic networks face challenges in efficiently and consistently generating object metadata from unstructured datasets, as existing methods are labor-intensive, lack consistency, and are resource-intensive.
Innovation Solution
An automated metadata generation system using natural language processing (NLP) that includes an extraction layer for identifying object metadata and term importance scores, an assignment layer for classification, and a generative layer for metadata creation, with a custom NLP pipeline to parse and preprocess input data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated NLP-based metadata generation is implemented, then productivity and consistency are improved, but device complexity increases
Solution Approach 1:
The system divides metadata generation into three distinct layers: extraction layer (identifying object metadata and term importance scores from input data), assignment layer (classifying and verifying metadata), and generative layer (creating new metadata using pretrained generative models). This segmentation allows each layer to specialize in specific tasks, improving overall productivity while managing complexity through modular design.
Solution Approach 2:
The patent introduces an NLP pipeline as an intermediary component that processes input data and coordinates between different layers. The NLP pipeline includes preprocessing modules, attribute extraction models, and classification models that act as mediators, transforming raw data into structured metadata without requiring direct complex interactions between all system components.
2Device complexity
If manual metadata extraction methods are used, then system complexity is reduced, but loss of time and labor intensity increase
Solution Approach 1:
The system enables self-service metadata generation through automated NLP processing. The extraction layer automatically identifies object metadata and term importance scores from input data without human intervention. The generative layer further automates metadata creation using pretrained models, eliminating the need for manual metadata extraction and significantly reducing time loss.
3Manufacturing precision
If traditional metadata generation approaches are used, then resource consumption is reduced, but manufacturing precision and consistency deteriorate
Solution Approach 1:
The system changes key parameters in the metadata generation process by using NLP-based attribute extraction models that analyze text data to identify object metadata and term importance scores. The classification models adjust parameters such as metadata categories and verification criteria, ensuring consistent and precise metadata generation across different inputs while managing processing complexity through algorithmic approaches.
Data Source
AI summary
Systems, computer program products, and methods are described herein for generating, extracting, classifying, and formatting object metadata using natural language processing in an electronic network. The present disclosure is configured to identify at least one input source, comprising at least one input data; parse the at least one input data; output, by an extraction layer, at least one product metadata and a term importance score associated with the input data to a metadata storage, the extraction layer comprising an NLP attribute extraction model; assemble, by an assignment layer, a corpus of text data and key phrases based on the product metadata from the extraction layer, the assignment layer comprising an NLP classification model; classify and verify, by the assignment layer, the product metadata; and output, by a generative layer, at least one generative metadata to the metadata storage, the generative layer comprising a pretrained generative NLP model.