Agnostic Image Digitizer for Legacy Database Compatibility
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Solution Overview
Problem
Legacy databases face challenges in compatibility with modern document formats, leading to difficulties in extracting data and metadata, which hinders the extension of their usable lifetime and user experience enhancement, especially when submissions are in formats like scanned images or pictures.
Innovation Solution
Digitizing documents from non-digital formats into a compatible digital format using computer hardware processors, allowing for data and metadata extraction and population into a database, and utilizing statistical models like AI systems to pre-populate fields in documents and update the model based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If documents are stored in non-digital formats (scans, pictures) in a legacy database, then the database can maintain compatibility with old systems, but the ability to extract data and metadata is hindered
Solution Approach 1:
The patent introduces an intermediary processing system that includes optical character recognition (OCR) engines and conversion modules. These intermediaries translate non-digital document formats into machine-readable digital formats, enabling data extraction from scanned images and pictures while maintaining the legacy database structure. The intermediary layer bridges the gap between old storage formats and modern data access requirements.
Solution Approach 2:
The system dynamically changes the format parameters of stored documents by converting them between different representations. Documents are transformed from image-based formats (scans, pictures) into structured digital formats with extractable metadata and text content. This parameter transformation occurs on-demand during data retrieval operations, allowing the same document to serve both legacy compatibility and modern data extraction needs.
2Adaptability or versatility
If a legacy database is replaced with a new system, then modern technologies can be adopted, but the cost of replacement and certification requirements increase
Solution Approach 1:
The patent segments the database system into two distinct layers: a legacy database layer that maintains historical compatibility and a modern processing layer that handles data extraction, statistical modeling, and AI operations. This segmentation allows each layer to be optimized independently - the legacy layer remains unchanged for stability, while the modern layer adopts current technologies without requiring full system replacement.
Solution Approach 2:
The enhanced database system performs multiple functions simultaneously: it maintains legacy document storage, enables modern data extraction, supports statistical modeling, and facilitates AI training. This multi-functionality is achieved through integrated processing modules that work with both old and new data formats, eliminating the need to choose between legacy compatibility and modern capabilities.
3Ease of operation
If statistical models are trained on legacy database data, then user experience can be enhanced, but data must first be converted from incompatible formats
Solution Approach 1:
The system performs preliminary conversion of non-digital documents into machine-readable formats during initial data ingestion and storage operations. By pre-processing documents as they are added to the legacy database, the data is already in an extractable format when needed for statistical modeling and AI training, eliminating the need for time-consuming conversion at query time.
Solution Approach 2:
The data conversion and extraction process operates continuously in the background rather than as discrete batch operations. As documents are added, modified, or accessed, the system continuously converts and extracts data, maintaining a ready-state dataset for statistical modeling. This continuous processing ensures data is always available for analysis without interrupting user operations.
Data Source
AI summary
Methods for enhancing compatibility of a document of an entity with an organization's database on a computer server. Methods may include using a computer hardware processor to digitize a document from a first format into a digital format, such as bytes, when the first format may not be compatible with the database. Methods may further include using a computer hardware processor to convert the document from a digital format into a second format, where the second format of the document may be compatible with the organization's database. Methods may include using a computer hardware processor to populate the database on the computer server with data from the document in the second format. Methods may further include storing the populated database and the document in the second format on the computer server.