Structured Metadata Feature Enhancement for Document Classification

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Solution Overview

Problem

Current document classification systems struggle to efficiently classify structured and semi-structured documents, as they primarily focus on unstructured data or apply uniform analysis to metadata fields, failing to effectively utilize structured metadata for classification.

Innovation Solution

The system analyzes structured metadata to generate enhanced features, combines these features with unstructured content to produce classification data, and automatically classifies documents, thereby efficiently handling structured, semi-structured, and unstructured data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional classification systems apply uniform analysis to all metadata fields and unstructured data, then the classification process is simple to implement, but the classification accuracy for structured and semi-structured documents is poor

Engineering Contradiction:

Improveclassification accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system applies different analysis methods to different data types: structured metadata fields receive feature enhancement and weighted analysis, while unstructured content receives text processing. This localized differentiation of processing quality resolves the contradiction by improving classification accuracy for structured documents without applying unnecessary complexity uniformly across all data types.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system transforms metadata parameters by generating enhanced features from structured fields and assigning weights to different metadata parameters. This parameter transformation allows the system to capture nuanced information from structured data, improving classification accuracy while maintaining manageable system complexity through systematic parameter handling.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If the system uses detailed structured metadata for classification, then classification accuracy improves, but the database structure size increases

Engineering Contradiction:

Improveclassification accuracyVSAvoiddatabase size

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The system extracts only the relevant features from structured metadata that are necessary for classification, rather than storing and processing all possible metadata fields. This selective extraction maintains high classification accuracy by focusing on discriminative features while reducing the overall database size by eliminating redundant or less useful metadata.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

Instead of reducing metadata detail to decrease database size, the system inverts the approach by using feature enhancement and weighting to extract high-value information from compact metadata representations. This allows maintaining detailed classification capability with minimal database overhead by computing enhanced features on-demand rather than storing them.

Inventive Principle:

Principle #13The other way round (Inversion)

3Reliability

If the system processes both structured and unstructured data with enhanced features, then classification performance improves, but the processing time increases

Engineering Contradiction:

Improveclassification performanceVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary processing of structured metadata by generating enhanced features and assigning weights during data ingestion or pre-processing stages. This preliminary action ensures that when classification is needed, the enhanced features are already available, reducing real-time processing time while maintaining high classification performance.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system segments the classification process into separate handling of structured metadata (with feature enhancement) and unstructured content (with text processing). This segmentation allows each data type to be processed using optimized methods, improving overall classification performance while managing processing time through parallel or staged execution of different processing pipelines.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20250124086A1Systems and Methods For Structured Bayesian Classification For Content Management

Publication Date: 2025.04.17 MICRO FOCUS LLC

AI summary

A system includes a processor and a memory. When executed by the processor, the processor is caused to receive a document including at least one of structured data, semi-structured data, and unstructured data, analyze the metadata in the structured format to generate features enhancing the metadata in the structured format, produce classification data for the document based on the features enhancing the metadata in the structured format and the content in the unstructured format, automatically classify the document based on the classification data and store the classification data in a database structure. The classification data is used to effectively search for the document. The structured data includes metadata about the document in a structured format, the semi-structured data includes content of the document in an unstructured format and metadata about the document in the structured format and the unstructured data includes the content of the document in the unstructured format.