Medical Device Service Case Triage Using Malfunction Probability Vectors

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Solution Overview

Problem

Current methods for identifying malfunction areas in medical imaging systems are labor-intensive and require significant expert effort, as they rely on manual mapping of maintenance activities and error codes, and provide retrospective identification, lacking real-time information for service technicians.

Innovation Solution

An automated system using classifiers to generate probability vectors from text descriptions of parts and service reports, enabling real-time identification of probable malfunction areas during service calls, reducing downtime and improving maintenance efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual mapping of maintenance activities and error codes to malfunction areas is used, then identification accuracy can be maintained through expert knowledge, but labor intensity and time consumption increase significantly

Engineering Contradiction:

Improvemalfunction area identification accuracyVSAvoidtime for maintaining mappings and processing service cases

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system enables automatic self-service by using machine learning classifiers to autonomously identify malfunction areas from service case data, eliminating the need for manual expert mapping and processing while maintaining high accuracy through automated pattern recognition

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual process of expert mapping and classification with an automated information processing system using machine learning algorithms that analyze service case data, error codes, and maintenance activities to determine malfunction areas automatically

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of information

If retrospective identification of malfunction areas is performed after service case closure, then complete information is available for analysis, but real-time maintenance decision-making is hindered

Engineering Contradiction:

Improvecompleteness of service case informationVSAvoiddowntime of medical device

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system performs preliminary action by automatically identifying malfunction areas during the service case processing itself, using real-time analysis of available data such as error codes and maintenance activities, enabling maintenance decisions to be made immediately without waiting for case closure

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements continuous feedback by automatically analyzing service case data as it becomes available and providing real-time malfunction area identification, allowing the maintenance process to be guided by automated insights throughout the service case lifecycle rather than only after completion

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If extensive mapping between parts, error codes, and malfunction areas is maintained, then comprehensive coverage of all possible issues is achieved, but system complexity and maintenance burden increase

Engineering Contradiction:

Improvecoverage of malfunction areasVSAvoidcomplexity of mapping maintenance

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system extracts the essential diagnostic information directly from service case data, error codes, and maintenance activities without requiring a comprehensive pre-established mapping between all parts, error codes, and malfunction areas, thereby simplifying the system while maintaining diagnostic capability

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent transforms the approach from static mapping tables to dynamic machine learning models that adapt to different service cases, changing the parameter representation from fixed categorical mappings to probabilistic predictions based on analyzed features, thereby reducing complexity while improving versatility

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12259801B2System and method for automated or semi-automated identification of malfunction area(s) for maintenance cases

Publication Date: 2025.03.25 KONINKLIJKE PHILIPS NV

AI summary

A method (100) of automated identification of one or more malfunction areas of a set (S) of malfunction areas for a service case in which a medical device (12) is serviced includes: generating an output probability vector (40) of probabilities for the set of malfunction areas of the medical device by operations including applying at least one classifier (42, 46) to at least one of (1) text descriptions of parts ordered for the service case and/or (2) a text description of the service case; and displaying a list (56) of one or more most probable malfunction areas for the service case wherein the one or more most probable malfunction areas are the one or more most malfunction areas of the set of malfunction areas having highest probability in the output probability vector.