Fleet Machine Maintenance Recommendations From Service Request Parsing
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Solution Overview
Problem
Current maintenance systems for fleets of machines, such as imaging systems, face inefficiencies due to manual processing of service requests, which leads to increased response times and costs, as they require on-site visits and manual data scanning by experts.
Innovation Solution
A system and method that utilize a service architecture with a service dictionary and classification schemes in a tree data structure, combined with text parsing and machine learning, to automatically process service requests, generate recommendations for online or on-site repairs, and minimize manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processing of service requests is used, then accuracy of service recommendations is maintained, but response time increases and productivity decreases
Solution Approach 1:
The patent introduces an intermediary system comprising a service dictionary, text parsing module, and classification scheme that automatically processes service requests. This intermediary translates unstructured service request text into structured data that can be matched against known service patterns, thereby maintaining accuracy while eliminating manual processing bottlenecks and improving response time.
Solution Approach 2:
The patent replaces the mechanical manual processing system with an automated computer-based system. The text parsing technique and classification schemes automatically analyze service request data, extract relevant features, and generate service recommendations without human intervention, thus improving productivity while maintaining measurement precision through systematic analysis.
2Reliability
If on-site visits by field engineers are performed, then comprehensive diagnosis and repair can be conducted, but cost and time consumption increase
Solution Approach 1:
The patent enables self-service through automated service request processing and recommendation generation. The system automatically analyzes service requests, matches them against classification schemes, and generates repair recommendations without requiring immediate field engineer intervention. This allows many issues to be diagnosed and resolved remotely, reducing time consumption while maintaining comprehensive diagnosis capability through systematic analysis.
Solution Approach 2:
The patent performs preliminary action by automatically processing service requests and generating recommendations before field engineers arrive. The text parsing and classification schemes pre-analyze the service request data, extract critical information, and prepare diagnostic recommendations in advance, allowing field engineers to focus on execution rather than initial analysis, thus reducing overall time consumption.
3Measurement precision
If manual scanning of service request data is performed, then accurate service recommendations can be generated, but processing overhead increases
Solution Approach 1:
The patent segments the complex task of service request analysis into manageable components through text parsing techniques that extract specific features and classification schemes that categorize different aspects of service requests. This segmentation automates the scanning process, reducing processing overhead while maintaining accuracy through systematic, rule-based analysis of each segment.
Solution Approach 2:
The patent transforms unstructured service request text into structured parameters through text parsing. By converting natural language descriptions into standardized data formats with defined parameters and categories, the system enables automated processing without manual intervention, reducing processing overhead while maintaining measurement precision through consistent parameter extraction.
Data Source
AI summary
A method for maintenance of a machine among a fleet of machines includes receiving a service request corresponding to the machine. The method also includes obtaining a service architecture corresponding to the fleet of machines. The service architecture includes a service dictionary and a plurality of classification schemes organized in a tree data structure. The method also includes processing the service request based on the service dictionary and a text parsing technique to generate a list of descriptive words. The method includes generating a recommendation based on the list of descriptive words and the service architecture. The recommendation includes at least one of an on-line repair activity, an on-site repair activity and a part replacement activity. The method also includes servicing the fault condition of the machine based on the recommendation.