AI Dispatch System for Imaging Device Maintenance
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Solution Overview
Problem
Traditional methods for maintaining and repairing imaging systems and other large machines are inefficient, requiring on-site visits and manual processing of service requests, which are time-consuming and costly, and often interrupt machine usage.
Innovation Solution
A computer-readable medium and apparatus that utilize an artificial intelligence model to analyze service requests, identify the necessary resources, and prioritize technicians based on skill level, tools, and location to efficiently dispatch maintenance teams, reducing downtime and improving response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual processing methods are used for service requests, then technicians can be dispatched to fix machines, but the process is time-consuming and results in machine downtime
Solution Approach 1:
The system enables self-service through automated AI diagnosis that analyzes service request data, identifies problems, and dispatches technicians without manual intervention. The imaging device itself can initiate service requests and the system automatically processes them, reducing dependency on manual triage and accelerating response time.
Solution Approach 2:
The patent replaces manual mechanical processing with an artificial intelligence model that automatically analyzes service request information, diagnoses problems, and determines optimal technician dispatch. This substitution of human decision-making with automated AI processing eliminates delays associated with manual evaluation and routing.
2Reliability
If on-site technician visits are scheduled to service imaging devices, then machine issues can be resolved, but machine usage is interrupted and costs increase
Solution Approach 1:
The system performs preliminary diagnosis and preparation by analyzing service request data before technician arrival. The AI model identifies the problem, determines required parts and tools, and prepares the technician with all necessary information in advance, enabling immediate resolution upon arrival and minimizing machine interruption time.
Solution Approach 2:
The system implements continuous feedback loops where service outcomes are captured and used to improve future dispatch decisions. The AI model learns from historical service data to optimize technician selection, part preparation, and routing, progressively reducing downtime and improving resolution efficiency with each service event.
3Adaptability or versatility
If comprehensive resource databases are maintained for servicing imaging devices, then appropriate technicians and parts can be identified, but system complexity increases
Solution Approach 1:
The system employs a universal AI model that handles multiple functions including problem diagnosis, technician identification, parts requirement determination, and dispatch optimization. This single multi-functional system replaces what would otherwise require separate specialized databases and processes for each function, managing complexity through consolidation.
Solution Approach 2:
The AI model dynamically adjusts dispatch parameters based on analyzed service request data, including technician skill levels, location, availability, and required expertise. The system changes parameters such as search radius, technician priority, and part procurement timing based on the specific problem characteristics, enabling adaptive resource allocation without rigid complex rules.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed. An example apparatus includes a technician selector to: identify at least one of a skill level, tools list, or replacement part list to fix a problem based on an identified problem corresponding to a service request from an imaging device; and access resources from a database of resources available to service the imaging device; a multiplier to weight resources based on at least one of the skill level, possessed tools in comparison to the tools list, possessed replacement parts in comparison to the replacement part list, distance to service location, or availability; and an interface to transmit the service request using a wireless communication to a repair device of the highest weighed resource, the service request to be augmented to include a configuration for the repair device to facilitate addressing of the service request by the highest weighted resource.