Digital Item Catalog Generation From Structured Document Import
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Solution Overview
Problem
Businesses struggle to create a digital presence due to the complexity of converting physical documents like menus into interactive online formats, and existing natural language processing techniques fail to accurately process structured and formatted documents.
Innovation Solution
A system utilizing computer vision and machine learning to analyze physical documents, generate digital versions with embedded functionality, and integrate them with e-commerce platforms, enabling seamless interaction and transaction capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language processing techniques are used to process documents, then text extraction is simplified, but accuracy deteriorates for structured and formatted documents
Solution Approach 1:
The patent introduces computer vision technology as an intermediary between the physical document and the digital processing system. This intermediary layer specifically processes structured and formatted documents to extract text, images, and layout information with high accuracy, overcoming the limitations of direct NLP processing while maintaining operational simplicity.
Solution Approach 2:
The system employs a multi-functional document processing approach that handles various document types (menus, catalogs, invoices) with different formats and structures. The computer vision-based system universally processes diverse document formats while maintaining high accuracy, unlike NLP techniques that struggle with structured formatting.
2Reliability
If manual input methods are used to create digital documents, then digital presence can be established, but time consumption increases
Solution Approach 1:
The system enables automatic document conversion where the physical document is scanned or photographed and the computer vision system automatically extracts information, generates digital versions, and integrates them with e-commerce platforms. This self-service process eliminates manual input requirements while maintaining reliable digital presence establishment.
Solution Approach 2:
The system performs preliminary processing by automatically extracting text, images, and structural information from physical documents before integration with e-commerce platforms. This preliminary action of automatic extraction and digital transformation significantly reduces the time required compared to manual input methods.
3Productivity
If simple document scanning is used, then digital conversion is rapid, but interactivity and functionality are lost
Solution Approach 1:
The patent replaces simple mechanical scanning with an intelligent computer vision system that processes documents to extract not only text but also images, layouts, and structural relationships. This substitution enables rapid digital conversion while preserving and enhancing document interactivity through features like clickable images, navigable layouts, and integrated e-commerce functionality.
Data Source
AI summary
Described herein are systems and techniques for generating a digital document from an original document. In some embodiments, such techniques may involve receiving the original document associated with a user, identifying one or more object categories within the original document, wherein the object categories are grouped based on items depicted in the original document, extracting one or more data values associated with the items depicted in the original document from the one or more object categories, generating document data as a digital version of the original document based on the one or more data values associated with the items depicted in the original document, and presenting the document data to at least one user device.