Multimodal Document Extraction With Validation for Context Accuracy
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Solution Overview
Problem
Existing data extraction methods from loss runs documents in the insurance industry struggle with diverse document formats and layouts, failing to capture contextual relationships and adapt to evolving reporting standards, leading to errors and inefficiencies in underwriting and risk assessment.
Innovation Solution
A system and method using a large multimodal model (LMM) that integrates computer vision, optical character recognition, and a validation model to process diverse document formats, ensuring accurate extraction and normalization of metadata and data points, supported by priority-based load balancing and dynamic scaling for efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If basic OCR techniques combined with simple NLP models are used for data extraction, then the system is simple and easy to implement, but the contextual understanding and accuracy of extracted information deteriorates
Solution Approach 1:
The patent combines computer vision models, optical image recognition, large multimodal models, and validation models into an integrated data extraction system. This merging of multiple AI technologies enables the system to achieve high contextual understanding accuracy by processing both visual and textual aspects of documents simultaneously, resolving the contradiction between system simplicity and extraction accuracy.
Solution Approach 2:
The system employs a composite approach by integrating multiple types of AI models (computer vision, OCR, LMM, validation models) working together. This composite architecture allows the system to leverage the strengths of each model type to achieve superior contextual understanding while maintaining a structured and manageable system framework.
2Ease of operation
If traditional data extraction techniques are used, then the system is easy to operate, but the ability to handle diverse document formats and layouts deteriorates
Solution Approach 1:
The patent implements a universal data extraction system using large multimodal models that can process multiple document formats, layouts, and types through a single unified interface. The system's ability to handle diverse insurance documents (loss runs, claims, policies) with varying structures while maintaining consistent extraction quality demonstrates this multi-functionality, resolving the contradiction between ease of operation and format adaptability.
Solution Approach 2:
The system dynamically adapts to different document formats and layouts by using AI models that can automatically adjust their processing approach based on the input document characteristics. This dynamic capability allows the system to maintain high adaptability across diverse document types while preserving a simple, uniform user interface that ensures ease of operation.
3Adaptability or versatility
If frequent manual updates and reconfiguration are performed to keep pace with evolving reporting standards, then the system can adapt to changes, but the time consumption and error rate increase
Solution Approach 1:
The patent implements a self-updating system where the validation model is automatically retrained using newly extracted data and feedback from accuracy checks. This self-service capability allows the system to adapt to evolving reporting standards and document formats automatically over time, eliminating the need for frequent manual updates and reconfiguration while maintaining high adaptability to changing requirements.
Solution Approach 2:
The system incorporates continuous feedback loops where extracted data is validated against expected patterns and standards, with results fed back into the model training process. This feedback mechanism enables automatic adaptation to changing reporting standards, reducing manual intervention time while maintaining high adaptability to evolving industry requirements.
4Productivity
If parallel processing with dynamic scaling is implemented, then the processing speed and efficiency improve, but the system complexity and resource management difficulty increase
Solution Approach 1:
The patent implements dynamic scaling of processing resources based on the volume and complexity of incoming documents. The system can automatically adjust the number of parallel processing instances and allocate computational resources dynamically, enabling high processing speeds for large document volumes while maintaining simpler operation for smaller workloads, thus managing the complexity-productivity trade-off.
Data Source
AI summary
A system and method for automated extraction of contextual information from a set of data using an LMM is provided. The method begins by splitting the set of data into sub-data based on logical boundaries and converting each sub-data into an image. These images are then processed using a computer vision model and optical image recognition to extract metadata and positional coordinates. The images, extracted metadata, and positional coordinates are integrated into a custom prompt for the LMM. This prompt is processed to obtain relevant data points, which are subsequently validated for accuracy using a trained validation model that considers content density versus output records, regex pattern-based record matching scores, and template-based records approximation scores. An LLM is then used to normalize headers in the relevant data points. Finally, the method automatically extracts contextual information by generating responses to user queries on the normalized relevant data points using the LLM.