AI Document Processing System with Multi-Model Adaptation
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Solution Overview
Problem
Current intelligent document processing platforms are limited in handling unstructured documents, relying on a single machine learning model and struggling to adapt to different document types and user feedback, making it difficult to extract and transform data effectively for various business needs.
Innovation Solution
A computer-implemented system using a plurality of machine learning models and algorithms to process documents, allowing for the ingestion of unstructured documents, extraction and classification of data, reconstruction into structured formats, and transformation into customized presentations, with the ability to optimize models based on user feedback for improved data extraction and presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single machine learning model is used for document processing, then the system is simpler to implement, but it cannot effectively handle diverse document types and adapt to different business requirements
Solution Approach 1:
The patent implements a universal document processing system that can handle multiple document types (invoices, receipts, forms, contracts) and perform various processing tasks (extraction, classification, validation, transformation) through a single integrated platform. The system uses a pluarality of machine learning models that can be selected and combined based on the specific document type and processing requirements, allowing one system to serve multiple functions across different business scenarios.
Solution Approach 2:
The patent divides the document processing system into distinct modular components: document intake module, machine learning model selection layer, data extraction module, validation module, and output generation module. Each component performs a specific function and can be independently configured. The system segments processing logic into reusable functions that can be combined in different ways to handle various document types and business requirements.
2Adaptability or versatility
If a single machine learning model is used, then the system is easier to maintain, but it cannot learn from feedback or adapt to new document types
Solution Approach 1:
The patent implements feedback mechanisms where processing results are validated against expected outcomes and business rules. The system incorporates validation modules that check extracted data for accuracy and completeness, and uses this feedback to refine and retrain machine learning models. The system can learn from correction feedback when documents are manually reviewed or when validation fails, continuously improving its performance across different document types.
Solution Approach 2:
The patent creates a dynamic system where the machine learning model selection and configuration can change based on the input document type and processing requirements. The system dynamically selects appropriate models from the plurality available, and can adapt model parameters and processing pipelines in real-time based on the specific characteristics of each document being processed.
3Measurement precision
If multiple machine learning models are used to handle diverse document types, then processing accuracy improves, but system complexity increases
Solution Approach 1:
The patent performs preliminary document classification and analysis before selecting the appropriate machine learning model for processing. The system pre-configures model selection rules and document type categorizations, so that when a document is received, the appropriate model is automatically selected based on pre-established criteria. This preliminary action simplifies the complexity of managing multiple models by automating the selection process.
Solution Approach 2:
The patent introduces an intermediary layer between the document input and the machine learning models that handles model selection, configuration, and coordination. This intermediary component analyzes the document characteristics and automatically selects the most appropriate model from the plurality available, managing the complexity of having multiple models by providing a unified interface and automated decision-making logic.
4Reliability
If the system processes unstructured documents with multiple models, then extraction reliability improves, but processing time increases
Solution Approach 1:
The patent applies partial processing strategies where not all machine learning models are applied to every document. Instead, the system selects and applies only the necessary models based on document type and processing requirements. For example, simple structured documents may require minimal processing with fewer models, while complex unstructured documents receive more comprehensive multi-model processing, optimizing the balance between reliability and processing time.
Data Source
AI summary
Computer-implemented systems and methods for applying rules via artificial intelligence for document processing are disclosed. The computer-implemented system comprises a database, a memory storing instructions, and at least one processor configured to receive a plurality of documents from a customer, validate a number and type of the plurality of documents, identify a file type and format of the plurality of documents based on the selection of a plurality of machine learning models, extract and classify a first data set from the plurality of documents based on the selection of the plurality of machine learning models using the identification of the file type and the format, reconstruct the first data set into a structured data set, transform the structured data set into a customized new presentation, receive a change from a user, optimize the selection of the plurality of machine learning models, and display the modified customized new presentation.