AI Repair Step Recommendation for Troubleshooting Workflows
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Solution Overview
Problem
Existing troubleshooting systems for electrical and mechanical products require users to follow sequential instructions, leading to inefficiencies and inconsistent results due to repeated steps and lack of personalization based on previous user actions.
Innovation Solution
A system utilizing artificial intelligence and machine learning to streamline and customize troubleshooting workflows through natural language, voice, text, and click interfaces, learning from user interactions to recommend relevant tests and repairs based on user history and demographics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If sequential troubleshooting instructions are provided, then users can follow step-by-step procedures to diagnose and repair issues, but users must repeat unnecessary steps and spend excessive time on irrelevant procedures
Solution Approach 1:
The troubleshooting guide dynamically adapts its content based on user inputs, device specifications, and troubleshooting history. The system reorders and filters steps in real-time, transforming a static sequential guide into a dynamic personalized workflow that responds to user actions and device characteristics.
Solution Approach 2:
The troubleshooting guide is segmented into modular components that can be independently selected and presented based on relevance. Instead of presenting all steps in a fixed sequence, the system divides the troubleshooting process into relevant subsets tailored to each user's specific situation.
2Reliability
If comprehensive troubleshooting documentation is provided, then all possible issues can be addressed, but the voluminous documentation becomes overwhelming and difficult to navigate
Solution Approach 1:
The system extracts and presents only the relevant troubleshooting steps needed for each specific situation, separating essential information from unnecessary content. This extraction process filters out voluminous irrelevant documentation while maintaining comprehensive coverage of necessary procedures.
Solution Approach 2:
The troubleshooting documentation provides different levels of detail and focus based on local conditions - user skill level, device type, and specific symptoms. Each user receives customized content with appropriate depth and scope for their particular situation rather than a one-size-fits-all approach.
3Reliability
If standardized troubleshooting procedures are used, then consistency can be maintained across different users, but the procedures cannot adapt to individual user actions or experiences
Solution Approach 1:
The system incorporates feedback loops that track user actions, inputs, and troubleshooting history. This feedback is used to continuously adapt and personalize the troubleshooting guide, maintaining consistency through structured procedures while adapting to individual user patterns and experiences.
Solution Approach 2:
The troubleshooting system serves multiple functions simultaneously - it provides standardized procedures for consistency while also personalizing content based on user demographics, history, and real-time inputs. The same system adapts to different user skill levels, device types, and troubleshooting contexts.
4Productivity
If users attempt troubleshooting before consulting published guides, then users can perform initial diagnostics, but they repeat steps already done and create inefficiencies in the troubleshooting process
Solution Approach 1:
The system captures and stores user actions and troubleshooting results as preliminary data before generating personalized recommendations. This preliminary action tracking ensures that subsequent troubleshooting steps build upon previous work rather than repeating it, preserving valuable diagnostic information.
Solution Approach 2:
The system uses feedback from user-reported troubleshooting actions to adjust and personalize the guide, ensuring that previously performed steps are not repeated. The feedback mechanism tracks what users have already done and uses this information to streamline the remaining troubleshooting process.
Data Source
AI summary
The system obtains a first representation of a troubleshooting guide associated with a first issue experienced by a first device. The first representation includes a first plurality of nodes and a first plurality of edges. A node indicates a troubleshooting procedure, and an edge indicates a result of the troubleshooting procedure, such as a testing step or repair step. The system determines a subset of the first plurality of edges indicating the repair step and converts the subset of the first plurality of edges into multiple repair nodes to obtain a second representation. A repair node indicates the repair step resolving the first issue. The second representation includes the first plurality of nodes and the multiple repair nodes. Based on the second representation, the system trains an artificial intelligence to provide a most likely repair step to resolve a second issue without requiring performance of the testing step.