AI Troubleshooting Dialogs With Semantic Branch Jumping
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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 requiring tedious and erroneous traversals, and are limited by pre-trained language models that struggle with diverse user utterances.
Innovation Solution
An AI-based interactive system that uses a structured representation, such as a service flowchart, with a trainable dialogue model and in-context embeddings to facilitate efficient troubleshooting by allowing semantic matching and jumping to relevant steps based on user utterances, incorporating expert knowledge for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a flowchart-based automated troubleshooting system is used, then the system can provide structured troubleshooting guidance, but the system becomes tedious and inefficient due to large flowcharts and complex problem spaces
Solution Approach 1:
The patent replaces the mechanical flowchart traversal system with an AI-based semantic search system. Instead of following rigid flowchart paths, the system uses natural language processing and semantic embeddings to understand user problems and directly navigate to relevant troubleshooting steps, eliminating the tedious mechanical traversal process while maintaining structured guidance.
Solution Approach 2:
The system changes the search parameter from structured flowchart paths to semantic meaning representations. By transforming troubleshooting steps into semantic embeddings and using these for search, the system can efficiently navigate the problem space based on semantic similarity rather than following fixed procedural paths, significantly improving productivity.
2Adaptability or versatility
If pre-trained language models are used for understanding user inputs, then the system can process user utterances, but the models struggle with diverse user inputs and lack accuracy
Solution Approach 1:
The patent performs preliminary action by creating domain-specific embeddings for all troubleshooting steps and observations before user interaction. These pre-computed embeddings serve as reference standards, allowing the system to accurately measure and match user utterances against known troubleshooting scenarios, significantly improving understanding accuracy for diverse inputs.
Solution Approach 2:
The system implements feedback by continuously comparing user utterances against the pre-computed embeddings and adjusting the search and matching process. This feedback mechanism allows the system to refine its understanding of diverse user inputs by measuring similarity against established troubleshooting patterns, improving accuracy over time.
3Stability of the object's composition
If the system follows the flowchart sequentially, then the troubleshooting path is structured, but the system cannot efficiently jump to relevant steps when problems are not initially understood
Solution Approach 1:
The patent segments the troubleshooting flowchart into discrete steps with associated semantic embeddings. This segmentation allows the system to independently evaluate each step's semantic meaning and efficiently search for relevant steps based on user problems, enabling the system to jump to appropriate segments rather than sequentially traversing the entire flowchart.
Solution Approach 2:
The system introduces semantic embeddings as an intermediary between user problems and troubleshooting steps. These embeddings serve as a mediator that translates diverse user inputs into comparable representations, allowing the system to efficiently match user problems with relevant troubleshooting steps without following the rigid sequential structure, thus reducing time to solution.
4Adaptability or versatility
If the flowchart is made comprehensive to cover all possible issues, then the system can handle complex problems, but the flowchart becomes large and the traversal process becomes tedious
Solution Approach 1:
The patent replaces the mechanical flowchart traversal system with an AI-based semantic search system. Instead of following rigid flowchart paths, the system uses natural language processing and semantic embeddings to understand user problems and directly navigate to relevant troubleshooting steps, eliminating the tedious mechanical traversal process while maintaining comprehensive problem coverage through semantic search capability.
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.