Recall Request Mapping for Varied Life Sciences Terminology
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Facilities in the life sciences domain face challenges in efficiently managing recall requests due to variations in customer-submitted terminologies and content, leading to inefficient processing and resource utilization.
Innovation Solution
A system and method that employs artificial intelligence and machine learning algorithms to parse and map customer-submitted recall request objects with internal templates using large language models and fuzzy name matching, ensuring accurate mapping and efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing methods are used to handle recall requests, then flexibility in understanding varied customer terminologies is maintained, but processing efficiency and accuracy deteriorate due to manual errors and resource-intensive operations
Solution Approach 1:
The patent introduces an intermediary mapping system that includes a mapping engine with multiple algorithms (fuzzy name matching, phonetic matching, semantic analysis) to bridge customer-submitted recall request objects and internal template objects. This intermediary layer automatically translates varied customer terminologies into standardized internal formats, eliminating manual processing errors while maintaining high accuracy through multi-algorithm verification and validation mechanisms.
2Productivity
If automated mapping algorithms are implemented to handle varied customer terminologies, then processing speed and consistency improve, but system complexity increases due to multiple algorithms and validation mechanisms
Solution Approach 1:
The mapping system is segmented into distinct functional modules: a mapping engine containing multiple specialized algorithms (fuzzy name matching, phonetic matching, semantic analysis), a validation module, and a template management module. Each segment handles specific aspects of the mapping process, allowing the system to process complex varied terminologies through specialized sub-routines rather than a monolithic complex system, thereby improving maintainability and reducing operational complexity.
3Measurement precision
If multiple mapping algorithms are used to ensure accurate matching of recall request objects, then mapping precision improves, but processing time increases due to multiple validation steps
Solution Approach 1:
The mapping system implements partial validation by applying multiple algorithms selectively based on the confidence level of initial matching. When fuzzy name matching achieves high confidence, full multi-algorithm validation is skipped, processing time is reduced. When confidence is low or ambiguous cases are detected, the system activates additional phonetic and semantic analysis algorithms to ensure precision. This partial action approach maintains high mapping precision while minimizing unnecessary processing time for clear-cut cases.
Data Source
AI summary
Various embodiments described herein relate to systems and methods for managing recall requests in a facility. In this regard, first objects associated with one or more fields of a recall request is initially received in the facility. At least one first object of the first objects is parsed through a model. Also, second objects associated with the one or more fields of the recall request is then retrieved from a database. Based on the parsing, a first mapping for the first objects with the second objects are determined. Based at least on some algorithms and factors, a second mapping for the first objects with the second objects are also determined. A mapping for each of the first objects with the corresponding second objects is identified using the first and the second mappings. The mapping for each of the first objects is rendered via user interface using visual representations.