AI Chatbot for Troubleshooting Using Historical Resolution Data
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Solution Overview
Problem
Current systems for troubleshooting software issues are inefficient, time-consuming, and costly, particularly in complex and distributed environments, due to the need for manual searches across multiple data sources and duplication of efforts, and lack of intelligent resolution tools.
Innovation Solution
A chatbot assistant powered by artificial intelligence that utilizes historical resolution data from multiple data sources, such as Rally and Outlook, to provide instant and accurate troubleshooting solutions by analyzing user inputs and matching them with stored keywords and trained models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual searches across multiple data sources are performed to find issue resolutions, then comprehensive search coverage is achieved, but time consumption and labor effort increase significantly
Solution Approach 1:
The system enables self-service by allowing users to input their issues in natural language and automatically receiving resolutions without requiring manual searching. The AI chatbot autonomously queries historical data, matches patterns, and provides solutions, freeing users from manual search tasks while maintaining comprehensive search coverage through automated processing of multiple data sources.
Solution Approach 2:
The patent replaces manual mechanical searching processes with an automated AI-based system. Instead of humans manually searching across multiple data sources, the system uses natural language processing, machine learning models, and automated data querying to perform the search and analysis functions, significantly reducing time consumption while maintaining thoroughness.
2Adaptability or versatility
If traditional manual troubleshooting methods are used, then flexibility in handling unique issues is maintained, but productivity and cost-effectiveness deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where users can provide feedback on the accuracy and usefulness of AI-generated resolutions. This feedback loop allows the machine learning models to continuously improve their performance, adapting to unique issues while maintaining high productivity. The feedback also enables the system to learn from edge cases and refine its troubleshooting approach over time.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting the AI model's response based on the complexity and uniqueness of each issue. For common issues, the system provides direct automated resolutions, while for complex or unique issues, it adapts its approach to provide more detailed guidance or escalate to human experts, thereby maintaining both productivity and adaptability.
3Ease of operation
If distributed teams search for solutions independently, then autonomy is maintained, but redundant efforts and resource duplication occur
Solution Approach 1:
The system provides universal service to all distributed teams through a single centralized AI chatbot interface. Instead of each team member independently searching for solutions, the multi-functional AI system handles troubleshooting for all users, eliminating redundant search efforts while maintaining team autonomy through easy natural language interaction. The system serves multiple purposes: immediate resolution, knowledge sharing, and resource optimization across the distributed organization.
Data Source
AI summary
Systems and methods for troubleshooting issues based on historical resolution data. In one implementation, the disclosed system includes at least one processor and at least one non-transitory memory containing software code configured to cause the processor to: receive source data from a plurality of data sources; extract a plurality of keywords from the source data; store the plurality of keywords in a database; receive a natural-language user issue input from a chatbot; determine whether the received user input matches the keywords in the database, wherein a comparison result is inputted into a trained model; input the user issue input into the trained model; in response to the determining, transmit the resolution from the trained model to the chatbot.