Dental Restoration Routing System Skill Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional CAD/CAM dental restoration systems fail to optimally assign dental restoration design cases to technicians based on their skill levels and capabilities, leading to mismatches that result in wasted resources, delays, and lack of quality control, and do not enable remote work station participation or automatic design proposal tools.
Innovation Solution
A computer-implemented method and system that routes dental restoration design cases to user accounts based on metadata analysis, evaluating design factors against user attribute data to match cases with suitable technicians, incorporating a queue management system for efficient resource allocation and quality control, and allowing remote participation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dental restoration design cases are assigned to technicians randomly or based only on availability, then the system operation is simple, but the mismatch between technician capability and design requirement causes wasted resources and delays
Solution Approach 1:
The system changes the assignment parameters from simple availability to a multi-dimensional skill profile matching system. Each technician has attribute data describing multiple design skill levels, and cases are evaluated against these attributes to determine optimal assignment, transforming the assignment mechanism from random/simple to parameter-based optimization
Solution Approach 2:
The manual or simple rule-based assignment system is replaced with an automated computer-implemented evaluation system that automatically parses case metadata, retrieves technician attribute data, evaluates design factors against skills, and routes cases to appropriate technicians, eliminating manual intervention and simple availability-based rules
2Reliability
If high capability technicians are assigned to all cases, then design quality is maintained, but human resources with high capabilities are wasted on simple cases
Solution Approach 1:
The system applies local quality by matching specific portions of technician capabilities to specific case requirements. Instead of uniformly assigning high-skill technicians to all cases, the system evaluates each case's design factors against technician skill profiles and assigns cases to technicians whose capabilities locally match the specific requirements of that case, optimizing the match between skill level and case complexity
Solution Approach 2:
The system uses partial action by assigning only the necessary level of technician capability required for each specific case. Rather than always deploying high-skill technicians, the system determines the minimum appropriate skill level needed for each case based on its design factors and assigns accordingly, avoiding excessive use of high-capability resources on simple cases while ensuring adequate capability for complex cases
3Productivity
If low skilled technicians handle complex cases, then resource utilization is maximized, but the order has to be re-assigned causing delays
Solution Approach 1:
The system performs preliminary action by evaluating and matching case requirements with technician capabilities before assignment occurs. The automated evaluation system parses case metadata, retrieves technician attribute data, and determines the optimal match in advance, ensuring that cases are assigned to technicians with appropriate skills from the start, thereby preventing the need for re-assignment and associated delays
4Adaptability or versatility
If conventional assignment systems are used, then remote work stations cannot participate, but the system cannot match different orders to different types of work stations
Solution Approach 1:
The system applies universality by creating a platform that works with any work station regardless of physical location or specific type. The cloud-based architecture with standardized attribute data retrieval and case evaluation allows diverse work stations (including remote ones) to participate in the assignment process, matching different orders to different types of work stations based on their capabilities rather than their location or conventional constraints
Data Source
AI summary
A computer-implemented method of routing a dental restoration design case to a user account includes receiving new dental restoration design cases associated with metadata describes design requirements. The method includes parsing the metadata to identify design factors indicating the requirements and storing the new cases in a main queue. The method also includes receiving login information, identifying the user account for the design user and retrieving attribute data associated with the account. The attribute data describes a design skill level of the design user. The method includes evaluating the design factors of the new dental restoration design cases in the main queue based on the attribute data of the account to determine a dental restoration design case in the main queue that satisfies the attribute data of the account and routing the determined dental restoration design case to a user queue associated with the user account.