NLP Maintenance Assistant for Machine Request Routing
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Solution Overview
Problem
Existing maintenance systems for processing machines are inefficient in managing user requests for assistance, as they either generate new responses for every new request or are limited by the machine's sensor capabilities and software, lacking in user freedom and Internet connection quality, and do not effectively manage diversified user requests.
Innovation Solution
A maintenance system utilizing a natural language processing (NLP) engine to receive and process user requests, generate input vectors, and select solution texts from a database, with a machine-learned model for automatic request management, enabling efficient routing and response generation based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a service system generates new responses for every new request, then the response is always fresh and relevant, but the system efficiency deteriorates due to redundant processing
Solution Approach 1:
The system pre-processes and stores standardized response templates in the database before they are needed. When a request arrives, the NLP engine compares it against these pre-prepared templates to find matches, avoiding the need to generate entirely new responses from scratch for every request.
Solution Approach 2:
Instead of creating unique responses for each request, the system creates copies of standardized response templates that have been previously validated and stored. The NLP engine selects and retrieves appropriate template copies based on the request characteristics, significantly reducing processing overhead while maintaining response quality.
2Ease of operation
If the machine automatically generates service requests based on sensor capabilities, then the request management is simplified, but the user freedom deteriorates as users cannot formulate requests at their own discretion
Solution Approach 1:
The system serves multiple functions through a single interface: it can process both automatically generated requests from machine sensors and manually formulated requests from users. The NLP engine is designed to handle diverse input formats and sources, making the request management system universal and adaptable to different user needs.
Solution Approach 2:
The NLP engine acts as an intermediary layer between the user (or machine sensor) and the service request processing system. It translates various input formats into a standardized internal representation, allowing both automatic sensor data and free-form user requests to be processed through the same efficient pathway without compromising user freedom.
3Reliability
If the system provides real-time evaluation of service requests, then the request quality is improved, but the system complexity increases due to additional processing requirements
Solution Approach 1:
The system merges the request evaluation function with the existing NLP processing pipeline. The same NLP engine that processes and understands user requests also performs the evaluation by comparing requests against stored templates and criteria, eliminating the need for separate evaluation hardware or software components.
Solution Approach 2:
The system performs self-evaluation by automatically comparing incoming requests against the stored template database and predefined quality criteria. The NLP engine autonomously determines request quality without requiring external human review or additional complex validation systems, enabling real-time quality assessment with minimal added complexity.
Data Source
AI summary
A method for providing assistance for a user of a processing machine comprises the following steps, performed by a server computer: receiving a request text, representing the request for assistance from the user; processing the request text through a natural language processing engine, trained to extract values for a plurality of predetermined parameters, so as to generate an input vector containing the values thus extracted from the request text; accessing a database containing a plurality of solution texts, each solution text constituting a predetermined reply to a possible request for assistance or to a type of request for assistance; selecting a solution text from the plurality of solution texts, dependently on the input vector, to make the selected solution text available to the user.