U.S. Patent No. 12,308,115
Automated Data Aggregation with File Analysis and Predictive Modeling
The granted patent covers the foundational architecture and processes behind PREFcards: the data aggregation server, the master-and-facility data form pattern, machine-learning-based file analysis and digitization, predictive modeling for inventory and procedural optimization, and the methods by which preference card data is ingested, structured, matched against medical device databases, and synchronized with facility inventory management systems.
The patent's claims address both the underlying technical architecture (cloud infrastructure, security measures, data flow) and the specific innovations applied to surgical preference card management — including the use of machine learning algorithms trained on aggregated historical data to generate amendment suggestions for surgical preference cards based on current inventory and procedural context.
Continuation-in-Part Application
PREFcards has filed a continuation-in-part (CIP) application that extends the original patent's coverage with additional claims. The CIP covers further innovations developed since the original filing, including refinements to the file analysis pipeline, expanded predictive modeling capabilities, new mechanisms for cross-facility data aggregation, and the QR-code-based access system with role-aware preference card delivery.
The CIP is currently pending before the U.S. Patent and Trademark Office. Until issued, the additional innovations are protected under "patent pending" status.
An overview of the protected processes.
Patents protect specific claims, not broad categories — but at a high level, the granted patent and pending CIP together cover the following families of processes that PREFcards uses to operate.
Aggregated data form architecture
The system by which a master preference card automatically generates facility-specific versions that sync, resolve discrepancies, and remain customizable — without losing alignment to the master.
AI-powered card optimization
Machine learning algorithms that analyze historical preference data, inventory levels, and procedural patterns to suggest amendments — including item additions, substitutions, and quantity changes — to surgical preference cards.
File analysis & document digitization
OCR and machine learning processes that ingest unstructured files (paper cards, scanned documents, PDFs, exports from legacy systems) and convert them into structured aggregated data forms with item-level fidelity.
Real-time inventory integration
Methods for electronically communicating with facility inventory management systems to alter preference cards in real time based on current item availability, with intelligent substitution of unavailable items.
QR code access with role-based versions
The system of generating QR codes that, when scanned, automatically deliver either a complete authenticated version or a limited clean version of a preference card based on the scanning user's authorization status.
Cross-facility data aggregation
The architecture for aggregating preference data, inventory data, and procedural outcomes across multiple healthcare facilities to train predictive models — while maintaining strict facility-level data customization and security.
This page is provided for informational purposes only and does not constitute legal advice or a complete enumeration of PREFcards' intellectual property. The full text of issued claims and pending applications is available through the USPTO. Licensing inquiries may be directed to our team via the contact page.