Multi-Modal Embedding Pipeline for Cross-Modal Document Rules
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Solution Overview
Problem
Traditional NLP techniques are limited to interpreting text and fail to integrate information from multiple modalities like images and tables, preventing comprehensive understanding of complex documents with multi-modal data structures.
Innovation Solution
A multi-stage pipeline that combines NLP, computer vision, and tabular data analysis to extract and fuse information from multi-modal documents, using a multi-modal embedding model to align and embed contextual information across different modalities, generating holistic structured rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional NLP techniques are used to interpret text, then text interpretation capability is maintained, but the ability to integrate information from multiple modalities (images, tables) is lost
Solution Approach 1:
The patent merges NLP, computer vision, and tabular data analysis techniques into a unified multi-stage pipeline. The system combines text processing, image analysis, and table data processing modules to handle multi-modal documents, enabling integration of information across different modalities while maintaining a structured and manageable architecture.
Solution Approach 2:
The multi-modal embedding model serves multiple functions by processing and embedding text, images, and table data into a shared embedding space. This universal model can handle various data types and modalities, providing a single framework that performs both text interpretation and visual analysis without requiring separate specialized systems.
2Reliability
If traditional single-modality techniques are used, then processing simplicity is maintained, but comprehensive understanding of complex multi-modal documents is prevented
Solution Approach 1:
The patent segments the document processing task into distinct stages: text extraction, image analysis, table data extraction, embedding generation, and rule generation. Each stage handles a specific modality or aspect of the document, allowing complex processing to be broken down into manageable steps that can be executed sequentially or in parallel.
Solution Approach 2:
The multi-modal embedding model acts as an intermediary that bridges different modalities. It processes text, images, and table data separately, then integrates them into a unified embedding space, enabling comprehensive understanding by mediating between the different data types and their relationships within the document.
3Measurement precision
If multi-modal embedding models are used to align and fuse information, then holistic understanding is improved, but computational resource requirements increase
Solution Approach 1:
The system performs preliminary processing of each modality separately before integration. Text is processed to extract semantic meaning, images are analyzed to identify content and relationships, and table data is parsed to extract structured information. These preliminary actions prepare the data for more efficient embedding and fusion, reducing the computational burden on subsequent steps.
Solution Approach 2:
The patent employs parameter changes in the embedding model to optimize the balance between precision and computational cost. By adjusting embedding dimensions, attention mechanisms, and fusion parameters, the system achieves high semantic alignment precision while controlling computational resource consumption based on the specific document complexity and processing requirements.
Data Source
AI summary
Various embodiments of the present disclosure provide an end-to-end multi-modal processing pipeline for improving computer comprehension of multi-modal documents. The techniques include extracting embedded media segments from the multi-modal document and identifying semantic section entities based on text segments within the document. The techniques include generating a multi-modal section embedding for each of the semantic section entities that fuses insights from both text and embedded media segments of the multi-modal document. The techniques include generating a multi-modal unstructured rule based on the multi-modal section embedding and then generating a multi-modal structured rule from the multi-modal unstructured rule. The performance of various prediction-based actions may be initiated based on the multi-modal structured rule.