Batch Document Hierarchy Classification with AI Verification
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Solution Overview
Problem
Traditional document management systems rely heavily on manual processes for identifying and verifying document relationships, leading to time-consuming efforts and errors, especially when dealing with large sets of unstructured data.
Innovation Solution
A system and method that utilize artificial intelligence models and user-defined rules to automate the classification of parent-child and peer-to-peer document relationships by extracting metadata attributes, with manual verification by specialists to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used to identify and verify document relationships, then accuracy can be maintained through human judgment, but the process becomes time-consuming and error-prone when dealing with large volumes of documents
Solution Approach 1:
The verification process is segmented into multiple levels: first-level automated verification using AI/ML models for initial classification, and second-level manual verification by specialists for complex or uncertain cases. This segmentation allows the system to handle large volumes of documents efficiently while maintaining accuracy through targeted human review only when necessary.
Solution Approach 2:
An automated AI/ML-based verification system acts as an intermediary between manual document processing and final relationship determination. This intermediary performs initial verification of parent-child relationships, extracting metadata and comparing it against expected patterns, thereby reducing the time required for manual verification while maintaining reliability through the intermediary's automated analysis.
2Productivity
If automated AI/ML models are used to determine document relationships, then processing speed and scalability improve, but the complexity of the system increases
Solution Approach 1:
The automated verification system is segmented into distinct functional modules: metadata extraction module that pulls relevant attributes from documents, AI/ML model module that performs relationship classification, and rule-based verification module that applies business logic. This modular segmentation manages system complexity by organizing functions into independent, manageable components that can be developed and maintained separately.
Solution Approach 2:
The AI/ML model serves multiple functions: it classifies parent-child relationships, validates metadata consistency, and identifies document hierarchies across different document types. This multi-functionality reduces overall system complexity by using a single versatile component rather than separate specialized systems for each task.
3Measurement precision
If multiple verification levels are implemented, then accuracy in determining parent-child relationships improves, but the process complexity increases
Solution Approach 1:
The verification process is divided into two distinct levels: first-level automated verification using AI/ML models for rapid initial assessment, and second-level manual verification by specialists for confirmation and handling of edge cases. This segmentation improves measurement precision by combining automated pattern recognition with human expert judgment, while managing complexity through clear separation of verification stages.
Solution Approach 2:
The first-level automated verification performs preliminary action by pre-classifying document relationships and pre-validating metadata before manual review. This preliminary action filters out obvious cases that don't require human review, allowing second-level verification to focus only on complex or uncertain cases, thereby improving accuracy while controlling the complexity of manual intervention.
Data Source
AI summary
A system for determining hierarchical relationship in batches of documents is disclosed. A data collection module receives one or more batches structured and unstructured documents. A metadata configuration module defines the plurality of metadata fields, enabling structured metadata extraction. The metadata extraction module identifies and extracts metadata attributes using an artificial intelligence model or extraction techniques. A first level verification module classifies documents as parent or child based on extracted attributes. A reviewer module allows specialists to manually validate classifications. A rule defining module enables user-defined match-up rules for parent-child and peer-to-peer relationships. A second level verification module refines classification using contextual analysis and generates structured data including the metadata attribute and the corresponding plurality of metadata fields, the parent-child relationship and the peer-to-peer. The structured data is reviewed, and confirmed, by the one or more specialist. A display module to provide reviewed structured data to the user.