Artifact Vector Tagging for Low-Compute Document Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing document management systems face challenges in efficiently generating metadata tags for new or revised electronic documents due to computational resource constraints, as automated mechanisms are either unavailable or computationally expensive.
Innovation Solution
A method involving image processing and machine learning techniques to generate a target artifact vector by comparing it to preexisting vectors, allowing metadata tags to be efficiently propagated from similar documents, with anomalies flagged for further processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated mechanisms are used to generate metadata tags, then productivity is improved, but computational resource consumption increases excessively
Solution Approach 1:
The system performs preliminary actions by converting documents to image collages and extracting text with bounding boxes in advance, storing these processed representations for later reuse. This preliminary processing enables faster metadata tag generation through artifact vector comparison without repeating the full processing pipeline for each new document.
Solution Approach 2:
The patent creates artifact vectors that serve as compressed copies of document representations, capturing essential visual and textual features. These vector copies enable efficient similarity comparison and metadata propagation without requiring full document reprocessing, significantly reducing computational resources while maintaining tagging accuracy.
2Reliability
If the entire knowledge base is processed to generate metadata tags, then completeness of tagging is improved, but the computational burden becomes unmanageable
Solution Approach 1:
Instead of processing the entire knowledge base uniformly, the system applies partial processing by comparing new documents against a curated set of artifact vectors. This selective approach processes only the necessary subset of documents, achieving sufficient tagging completeness without the prohibitive computational cost of full knowledge base processing.
Solution Approach 2:
The patent segments the knowledge base processing into two distinct phases: (1) preliminary conversion of representative documents into artifact vectors, and (2) comparison-based metadata generation for new documents. This segmentation allows the system to manage computational complexity by separating the heavy processing from the lightweight querying operations.
3Productivity
If automated metadata generation is implemented, then labor intensity is reduced, but consistency and accuracy of tags deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms by comparing new documents against existing artifact vectors and using the similarity results to propagate metadata tags. This feedback loop ensures that generated tags are consistent with existing documentation patterns while maintaining automation, as the system learns from and adapts to the characteristics of the knowledge base.
Solution Approach 2:
The patent creates a universal artifact vector representation that can serve multiple functions: document similarity comparison, metadata generation, and consistency validation. This multi-functional approach ensures that automated tagging maintains consistency across different document types and contexts while reducing labor intensity.
Data Source
AI summary
Image pages are generated from a document. Assembling the image pages generates a collage. Two-dimensional text and bounding boxes are extracted from the image pages. A structure verbalizer spatially formats the two-dimensional text in one-dimension with spatial information to generate spatial-formatted text. The spatial-formatted text is concatenated to generate a text extraction. A multimodal embedding model is applied to the collage and the text extraction to generate a target artifact vector. The target artifact vector is compared against a set of preexisting artifact vectors to identify a corresponding artifact vector associated with a corresponding document having a corresponding metadata tag. A distance is determined between the corresponding artifact vector and the target artifact vector. Responsive to the distance being within a threshold distance additional steps are performed, including performing both applying the corresponding metadata tag to the document to generate a modified document and outputting the modified document.