Zone-Based Metadata Extraction System with Plugin Queue
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Solution Overview
Problem
Existing systems for metadata extraction and processing lack an architecture for pre-processing and post-processing documents based on document type, limiting their functionality in performing actions related to custom zones.
Innovation Solution
A system and method that maps zone-based metadata into referenceable objects, allowing for custom zone definitions associated with actions, and utilizes plugins for OCR and AI/Computer Vision to extract and generate metadata, with scalable processing through a process queue, supporting various document formats and input methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing metadata extraction systems are used, then basic OCR and metadata storage functions are provided, but pre-processing and post-processing capabilities based on document type are missing
Solution Approach 1:
The system segments document processing into distinct phases: pre-processing (document type identification, zone definition), metadata extraction (OCR processing), and post-processing (actions and triggers). This segmentation enables targeted processing at each stage while maintaining overall system organization through modular plugin architecture.
Solution Approach 2:
The patent introduces intermediary components including a document type identification module that bridges document input and processing, and a trigger module that mediates between extracted metadata and post-processing actions. These intermediaries enable sophisticated document-type-specific processing without requiring complete system redesign.
2Adaptability or versatility
If custom zones with associated actions are implemented, then document processing functionality is enhanced, but system complexity increases
Solution Approach 1:
The system implements universal zone objects that can be configured with multiple attributes (coordinates, data type, associated actions) and serve different document types. The same zone object structure is used across invoices, receipts, forms, and other document types, reducing complexity through standardized multi-functional components.
Solution Approach 2:
The system manages complexity by parameterizing zone configurations rather than creating separate structures for each document type. Zones are defined with configurable parameters including coordinates, data types, and associated actions, allowing flexible adaptation to different document formats through parameter changes rather than structural changes.
3Adaptability or versatility
If multiple processing plugins are added for different document formats, then processing versatility improves, but system management complexity increases
Solution Approach 1:
The patent introduces a queue service as an intermediary that manages plugin execution. The queue service receives processing requests, determines appropriate plugins based on document type and format, and coordinates their execution. This intermediary layer shields users from plugin management complexity while enabling versatile multi-format processing.
Solution Approach 2:
The system implements self-service through automatic plugin selection and execution based on document characteristics. The queue service automatically identifies the appropriate OCR and interpretation plugins for uploaded documents without requiring manual configuration, allowing the system to serve itself in managing processing complexity.
4Productivity
If a queue service is implemented for scalable processing, then processing capacity improves, but system architecture complexity increases
Solution Approach 1:
The queue service segments the processing workflow into discrete, manageable tasks that can be independently executed. By dividing document processing into queue-managed units with clear input/output definitions, the system achieves scalability while maintaining architectural simplicity through standardized task handling procedures.
Data Source
AI summary
The present disclosure provides a system and method for mapping zone-based metadata into one or more reference-able objects. The system and method extracts and/or generates metadata from a document according to custom zones defined in a document type. Each custom zone is associated to actions such as generating events, invoking triggers and/or invoking callback methods. One or more of these actions may be triggered when pre-processing or post-processing a document. According to an inventive concept of the present application, when a document is stored in a data storage and placed in a process queue to be processed by a suitable processing plugin according to the custom zones defined in the document. For instance, an image file comprising textual custom zones may be processed by an OCR plugin which extracts text metadata from the document. Other processing plugins include plugins to extract metadata from proprietary document formats such as CAD, PPTX, PDF, PSD, or others. Plugins are also available for generation of metadata by interpreting an image (i.e. detection or identification of objects and/or scenery in an image) through artificial intelligence and computer vision means. The process queue can be used for scalability of the quantity of actions to process.