Custom Schema Document Classification for Accurate AI Extraction
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Solution Overview
Problem
Enterprises face challenges in efficiently and accurately classifying and extracting data from diverse documents, leading to inefficiencies and errors in document management processes.
Innovation Solution
A document processing service that integrates generative AI and extraction models, allowing users to define custom schemas for document classification and data extraction, leveraging a fine-tuned Large Language Model (LLM) to enhance accuracy and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional document classification methods are used, then the system is simpler to implement, but classification accuracy and adaptability to diverse document types deteriorate
Solution Approach 1:
The patent introduces a schema as an intermediary layer between the document processing system and the diverse document types. The schema defines structured templates with fields, data types, and validation rules that mediate between raw documents and classification results, enabling accurate classification without increasing overall system complexity
Solution Approach 2:
The classification system is made dynamic through configurable schemas that can be adapted to different document types. The system can dynamically select and apply appropriate schemas based on document characteristics, allowing high accuracy across diverse document formats without requiring a fixed complex structure
2Productivity
If manual data extraction methods are used, then the system is easier to manage, but extraction efficiency and accuracy deteriorate
Solution Approach 1:
The patent replaces manual mechanical extraction processes with automated computational methods. Extraction models and schema-based validation automatically identify and extract data fields from documents, significantly improving efficiency while the modular schema design keeps processing complexity manageable
Solution Approach 2:
The system enables self-service data extraction through schema-defined templates that automatically guide the extraction process. The schemas themselves serve as the extraction logic, eliminating the need for complex external processing while maintaining high accuracy through structured field definitions
3Measurement precision
If generic document processing approaches are used, then the system is more universal, but accuracy for specific document types deteriorates
Solution Approach 1:
The patent applies local quality by allowing different schemas to be defined for different document types or sections. Each schema can be optimized for specific document characteristics (e.g., invoices vs. contracts) while the overall system remains versatile through the ability to select and combine multiple schemas as needed
4Reliability
If extensive processing is performed on all documents, then extraction completeness improves, but computing resource consumption increases
Solution Approach 1:
The patent extracts only the essential fields and characteristics needed for classification and data extraction through schema-defined templates. By focusing extraction efforts on specific relevant fields rather than processing all document content equally, the system achieves high completeness for critical data while minimizing unnecessary computing resource consumption
Data Source
AI summary
Systems and methods described herein provide a document processing service using customizable schemas. First user input includes a document and second user input identifies a classification schema. The classification schema is retrieved to obtain first prompt content. Prompt data is generated. The prompt data includes the first prompt content and second prompt content that includes a document classification instruction. The prompt data and document content of the document are processed by a large language model to obtain a classification result for the document. The classification result is presented via a user interface at a user device.