Bounding Box Vectors for Cross-Format Document Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Organizations using multiple software systems for enterprise data management face challenges in document consistency due to differences in format, semantic content, images, and other attributes, making it difficult for both humans and computers to identify if documents refer to the same content.
Innovation Solution
An apparatus and method that utilize a machine learning model to transform bounding boxes in images into vector space, allowing for the comparison of documents with different formats and attributes, by generating bounding boxes around text areas, converting them into bounding box vectors, and training a machine learning model to map these vectors into vector space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple different software systems are used for enterprise data management, then functional versatility is improved, but document consistency deteriorates
Solution Approach 1:
The patent implements a universal document processing framework that can handle documents from multiple different software systems. The system uses a common processing pipeline including image preprocessing, OCR, bounding box generation, and machine learning-based content analysis that works across different document formats and sources, enabling one system to serve multiple functions across diverse enterprise software ecosystems.
Solution Approach 2:
The patent transforms document comparison from visual/format-based matching to vector-space-based semantic matching. By converting document content into numerical vectors and using machine learning models to compare these vectors, the system changes the parameters of comparison from surface-level format attributes to deeper semantic meaning, thereby maintaining consistency across different document formats.
2Device complexity
If traditional document comparison methods are used, then simplicity is maintained, but identification accuracy deteriorates
Solution Approach 1:
The patent introduces bounding boxes as an intermediary element between the original document images and the machine learning model. These bounding boxes extract and localize specific content areas (text, images, tables) before feeding them into the ML model for comparison. This intermediary step bridges the gap between simple image input and complex semantic analysis, improving accuracy while maintaining a clear processing workflow.
Solution Approach 2:
The patent segments documents into distinct content areas using bounding boxes that identify and separate text regions, images, tables, and other elements. This segmentation allows the system to process and compare specific content areas independently, improving identification accuracy by focusing on meaningful content rather than treating documents as undifferentiated images.
3Manufacturing precision
If format-specific processing is used for each document type, then processing precision is improved, but system complexity deteriorates
Solution Approach 1:
The patent implements a universal bounding box processing approach that works across different document formats and types. The same bounding box generation, vector conversion, and machine learning comparison pipeline processes invoices, receipts, contracts, and other document types without requiring format-specific processing logic, thereby maintaining precision while reducing system complexity.
Data Source
AI summary
An example operation may include one or more of generating a plurality of bounding boxes at a plurality of content areas in an image corresponding to a plurality of pieces of text within the image, converting the plurality of bounding boxes into a plurality of bounding box vectors based on attributes of the plurality of bounding boxes, training a machine learning model to transform a bounding box into a location in vector space based on the plurality of bounding box vectors, and storing the trained machine learning model in memory.