Device Troubleshooting Workflow Using Natural-Language Adaptive Guidance
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Solution Overview
Problem
Existing troubleshooting guides for mechanical and electrical products require sequential step-by-step approaches that are inefficient and do not account for a user's previous actions, leading to repetitive and irrelevant steps.
Innovation Solution
A system utilizing artificial intelligence and machine learning to streamline troubleshooting workflows through natural language, voice, text, and click interfaces, providing customized recommendations based on user interactions and past experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a sequential step-by-step troubleshooting guide is used, then comprehensive coverage of all possible issues is achieved, but time consumption and inefficiency increase
Solution Approach 1:
The troubleshooting guide transitions from a static sequential format to a dynamic adaptive format that adjusts its recommendations based on real-time user responses. The system dynamically selects the next troubleshooting step based on the outcome of previous steps, allowing users to skip irrelevant steps and focus only on what is necessary for their specific issue.
Solution Approach 2:
The system incorporates feedback mechanisms where user responses to troubleshooting steps are used to refine subsequent recommendations. The adaptive guide learns from user actions and outcomes, continuously improving its ability to recommend relevant steps and avoiding repetitive or irrelevant troubleshooting actions.
2Stability of the object's composition
If a sequential troubleshooting guide is followed, then all steps are covered systematically, but relevance to user's specific situation decreases
Solution Approach 1:
The troubleshooting guide applies local quality by tailoring specific recommendations to the user's unique situation based on their device type, issue description, and previous actions. Instead of treating all users the same, the system customizes the troubleshooting path to match the specific local conditions of each user's problem.
Solution Approach 2:
The guide becomes dynamic and adaptive, adjusting its content and sequence based on real-time user interactions. The system continuously refines its recommendations to better match the user's specific situation, making each step highly relevant to their individual problem while maintaining systematic coverage of all possibilities.
3Stability of the object's composition
If a fixed sequential guide is used, then consistency across all users is maintained, but accountability for user's previous actions is lost
Solution Approach 1:
The system uses feedback from user actions and previous troubleshooting steps to personalize subsequent recommendations. This feedback loop allows the guide to remember and account for what the user has already done, avoiding repetition and adapting to their specific journey while maintaining consistent methodology.
Solution Approach 2:
The troubleshooting guide serves itself by automatically learning from user interactions and adjusting its own recommendations. The system self-adapts to user patterns and previous actions without requiring manual reconfiguration, maintaining consistency in its approach while personalizing the experience for each user's specific situation.
4Loss of information
If voluminous troubleshooting documentation is provided, then complete information is available, but user engagement and effectiveness decrease
Solution Approach 1:
The system extracts and presents only the most relevant information needed for the current troubleshooting step, rather than overwhelming users with complete documentation upfront. It selectively extracts and delivers information based on the current state of troubleshooting, making the process more engaging and effective while maintaining information completeness when needed.
Solution Approach 2:
The documentation becomes dynamic and adaptive, adjusting the amount and type of information presented based on user needs and troubleshooting progress. The system dynamically delivers information only when relevant to the current step, maintaining user engagement and effectiveness while ensuring complete information is available when required.
Data Source
AI summary
The system receives an input indicating a device and an indication of an issue associated with the device and a natural language input describing a troubleshooting procedure performed to resolve the issue. Based on the input indicating the device, and the indication of the issue associated with the device, the system maps the natural language input into a predetermined troubleshooting procedure associated with the device. The system receives an input indicating a result among the multiple results associated with the predetermined troubleshooting procedure. Based on the result of the predetermined troubleshooting procedure, the system determines a troubleshooting procedure to perform. The troubleshooting procedure includes a repair step associated with the issue or a testing step associated with the issue and is different from the troubleshooting procedure performed to resolve the issue. The system suggests the troubleshooting procedure to an operator associated with the device.