Automated Recall Detection Using Fuzzy Logic and NLP
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Solution Overview
Problem
Current online product procurement systems lack automated methods for identifying recalled products from unstructured data, which can lead to patients receiving unsafe or unavailable items, especially in time-sensitive healthcare situations.
Innovation Solution
The system employs fuzzy logic and natural language algorithms with a data grammar dictionary to extract item identifiers and manufacturer names from unstructured recall data, creating a temporary table with corrected attributes, and identifies substitute products for recalled items based on contract thresholds and availability status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data administration is used to maintain catalog content, then data accuracy can be maintained, but productivity and response time to recall events deteriorate
Solution Approach 1:
The system automatically monitors recall events and updates product data without requiring manual intervention. The automated recall monitoring system continuously checks recall databases, extracts relevant information, and updates the electronic catalog automatically, allowing the system to maintain itself without human involvement.
Solution Approach 2:
The patent replaces manual data administration with automated computer-based systems. The mechanical process of manual data entry and verification is substituted with electronic automated monitoring, data extraction algorithms, and database updates, significantly improving both speed and consistency of recall response.
2Productivity
If automated recall monitoring is implemented, then productivity and response time improve, but device complexity increases
Solution Approach 1:
The system integrates multiple functions into a single automated recall monitoring platform. The same system handles recall detection, data extraction, product matching, catalog updates, and user notification, eliminating the need for separate manual processes and reducing overall system complexity despite automation.
Solution Approach 2:
The patent introduces an intermediary automated monitoring system between external recall databases and the internal electronic catalog. This intermediary layer automatically translates and processes recall information, simplifying the integration complexity and providing a clear interface between different system components.
3Ease of operation
If out-of-date product data is used, then ease of operation is maintained, but harmful factors increase due to unsafe or unavailable items being ordered
Solution Approach 1:
The system continuously monitors recall events and provides real-time feedback to the electronic catalog. When a recall is detected, the system automatically updates product availability status and notifies users, ensuring that the catalog always reflects current product safety and availability information without requiring user intervention.
Solution Approach 2:
The automated monitoring system performs preliminary actions by continuously checking recall databases before users place orders. This proactive approach ensures that recalled or unavailable products are identified and flagged before they can be accidentally ordered, preventing harmful outcomes while maintaining simple user interaction.
Data Source
AI summary
Systems and methods for automatically determining recalled products from unstructured recall data. In exemplary embodiments, fuzzy logic is used to determine recalled products from unstructured recall data. In exemplary embodiments, natural language algorithms with an embedded data grammar dictionary are used to extract item identifiers and manufacturer name from unstructured recall data.