AI Document Processing Schema Generation
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Solution Overview
Problem
Conventional machine learning document processing requires significant time, effort, and computing capacity for manual template creation and annotation, which is inflexible and prone to human errors, making it unsuitable for large-scale document processing tasks with varied formats and layouts.
Innovation Solution
A data processing and extraction system (DES) that leverages an AI system to automatically generate a schema for document processing, reducing user involvement and resource consumption by reformulating user inputs into a format understandable by the AI, allowing for intelligent document processing without manual template creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual template creation and annotation are used for machine learning document processing, then processing accuracy can be improved, but time consumption and computing capacity requirements increase significantly
Solution Approach 1:
The system enables self-service by allowing users to define document processing requirements through high-level concepts rather than manual template creation. The AI system automatically generates templates and annotates documents based on these concepts, eliminating the need for users to manually create and refine templates while maintaining accurate processing results.
Solution Approach 2:
The patent replaces the mechanical process of manual template creation and annotation with an AI-based automated system. Instead of requiring users to manually define templates and annotate documents, the system uses natural language processing and machine learning to automatically generate templates and extract information, significantly reducing time consumption while maintaining accuracy.
2Reliability
If manual template creation is used for document processing, then processing reliability can be improved, but device complexity and operational difficulty increase
Solution Approach 1:
The system provides self-service functionality where users simply define what information they need in natural language, and the AI system automatically generates the appropriate templates and processing configurations. This eliminates the need for users to manually create complex templates while maintaining reliable processing results.
Solution Approach 2:
The patent changes the parameters of template creation from manual specification to AI-generated based on natural language inputs. Instead of requiring users to manually define template structures, the system transforms high-level conceptual inputs into detailed processing templates automatically, reducing complexity while maintaining reliability.
3Measurement precision
If manual template updates are required for new document layouts, then processing accuracy is maintained, but adaptability and productivity decrease
Solution Approach 1:
The system introduces dynamic adaptability by using AI models that can automatically adjust to new document layouts without requiring manual template updates. The AI system learns from new document formats and automatically generates appropriate processing templates, enabling the system to adapt to changing document structures while maintaining accuracy and improving productivity.
Solution Approach 2:
The patent implements feedback mechanisms where the AI system continuously learns from processed documents and automatically refines its templates. When new document layouts are introduced, the system can learn from these examples and automatically update its processing templates, maintaining accuracy without requiring manual intervention and thereby improving productivity.
Data Source
AI summary
Various embodiments for intelligent document processing and information extraction using artificial intelligence are described herein. An embodiment operates by receiving user input comprising a plurality of entries indicating what values are to be extracted from one or more documents by an artificial intelligence (AI) language processing system. A schema for extracting the values is generated, and text versions of the one or more documents are received. The schema and the text versions of the one or more documents are converted into a prompt for the AI language processing system. A text-based output from the AI language processing system is received, and converted into a format to be stored on a database or displayed on the user interface.