AI Troubleshooting Dialog Branch Jumping for Complex Flowcharts
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Solution Overview
Problem
Existing troubleshooting systems are inefficient and prone to errors due to large, complex flowcharts and the need for expert anticipation of user interactions, often leading to tedious and inaccurate problem-solving processes.
Innovation Solution
A system utilizing an AI-based dialog model with structured representations, including acyclic flowcharts and influence diagrams, that facilitates efficient troubleshooting by identifying semantic matches in user utterances and allowing for flexible traversal through the structured representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a flowchart is used to facilitate automated troubleshooting, then the system can provide structured guidance, but the process becomes tedious and inefficient due to the large size and complexity of the flowchart
Solution Approach 1:
The patent extracts the essential decision logic from the large flowchart and represents it using a compact neural network model. The neural network learns from the flowchart's decision structure and can directly determine the next troubleshooting step without traversing the entire flowchart, thereby reducing the time required for troubleshooting while maintaining structured guidance.
Solution Approach 2:
The patent replaces the mechanical flowchart traversal system with an AI-based neural network system. Instead of manually or automatically following a rigid flowchart path, the neural network processes user inputs and flowchart data to intelligently determine the next step, substituting the mechanical navigation process with an adaptive AI-driven approach that reduces troubleshooting time.
2Adaptability or versatility
If the flowchart is made comprehensive to cover all possible issues, then the system can handle more problem types, but the complexity and size of the flowchart increases
Solution Approach 1:
The patent extracts the decision-making logic from the comprehensive but complex flowchart and encodes it into a neural network model. This allows the system to maintain coverage for multiple problem types while using a more compact and manageable representation that reduces the apparent complexity of the troubleshooting system.
Solution Approach 2:
The neural network model serves as a universal representation that can handle multiple types of troubleshooting problems without requiring separate flowchart paths for each issue type. The model learns from the comprehensive flowchart data and can adapt to different problem scenarios, providing multi-functionality that reduces overall system complexity while maintaining versatility.
3Reliability
If the flowchart includes all possible observation paths, then the system can account for all scenarios, but the troubleshooter may think a step is completed when it is not
Solution Approach 1:
The patent implements a feedback mechanism where the neural network continuously monitors user inputs and compares them against the expected observations from the flowchart. When the user's input does not match the anticipated observation, the system provides feedback to correct the user's misunderstanding, ensuring accurate observation tracking without requiring complex manual verification processes.
Solution Approach 2:
The patent replaces complex manual observation tracking with an AI-based verification system. The neural network automatically determines whether the user's observation matches the expected outcome and can guide the user through incorrect observations, substituting the need for complex tracking mechanisms with an intelligent verification approach that improves reliability.
4Ease of operation
If the system follows the flowchart step-by-step, then the troubleshooting process is systematic, but it cannot efficiently handle user inputs that jump to different branches
Solution Approach 1:
The patent makes the troubleshooting system dynamic by using a neural network that can adapt its response based on user inputs. Instead of rigidly following the flowchart sequence, the neural network processes user inputs and dynamically determines the next appropriate step, allowing the system to handle both systematic progression and flexible user inputs that jump to different branches of the troubleshooting logic.
Data Source
AI summary
Embodiments described herein provide a system for facilitating efficient troubleshooting for a product. During operation, the system can identify an artificial-intelligence-(AI-) based dialog model operating based on a structured representation, which can indicate sequential troubleshooting steps to be performed by a user. The system can provide a machine utterance of the dialog model corresponding to a troubleshooting step to the user. The system can then search the structured representation for a semantic match for a user utterance obtained in accordance with the dialog model from the user. If the semantic match indicates an anticipated option associated with the machine utterance, the system can traverse a current branch of the structured representation using the dialog model based on the anticipated option. Otherwise, if the semantic match indicates an option on a different branch, the system can jump to the option on the different branch for subsequent traversal.