AI Diagnostic Prompt Summarization for Technical Support Triage
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Solution Overview
Problem
Users often struggle to independently diagnose technical problems, requiring support technicians to perform diagnostic tasks before resolving issues, which can be inefficient and require extensive training.
Innovation Solution
A system utilizing a network-connected device with a language model to generate diagnostic plans by receiving user prompts, summarizing them, and providing a natural-language response to elicit additional information, which is then used by a support technician to diagnose and resolve technical issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If support technicians perform manual diagnostic tasks to resolve user technical problems, then diagnostic accuracy can be maintained through human expertise, but the time required and training complexity increase significantly
Solution Approach 1:
The patent introduces an AI language model as an intermediary between the user's technical problem and the support technician. The model generates a summarized prompt from user inputs that contains structured diagnostic information, which the technician then uses to perform accurate diagnostics more efficiently. This intermediary processing resolves the contradiction by automating the initial information gathering and structuring while preserving human expertise for the actual diagnostic reasoning.
2Reliability
If support technicians perform manual diagnostic tasks to resolve user technical problems, then comprehensive diagnostic coverage can be achieved through human judgment, but the training requirements and operational complexity increase
Solution Approach 1:
The AI language model performs self-service by automatically generating the summarized prompt containing relevant diagnostic information from user inputs. This eliminates the need for technicians to manually gather and structure diagnostic information, reducing training requirements while maintaining comprehensive diagnostic coverage through the model's ability to identify and organize relevant details.
3Measurement precision
If AI language models generate detailed diagnostic plans directly without summarization, then diagnostic accuracy may improve, but the information overload and processing complexity increase
Solution Approach 1:
The system extracts only the essential diagnostic information from user inputs to create a condensed summarized prompt. This extraction process removes redundant and irrelevant details while preserving the core diagnostic elements, thereby reducing information processing complexity while maintaining the accuracy needed for effective technical support.
Data Source
AI summary
A method of hybrid technical support includes receiving, by a network-connected device, a first user prompt including at least one technical support query and generating, by a language model executed by the network-connected device, a first natural-language response to the first user prompt, the first natural-language response configured to elicit first additional information describing the at least one technical support query. The method further includes receiving, by the network-connected device, a second user prompt including the first additional information describing the at least one technical support query, generating a pre-summarization prompt based on the first user prompt and the second user prompt, generating a summarization of the pre-summarization prompt using a language summarization model executed by the network-connected device, and providing the summarization to a support technician device configured to be operated by a support technician. The language summarization model is configured to generate summaries of text prompts.