Conversational User Assistance with Error-Rate Fatigue Detection
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Solution Overview
Problem
Existing generative AI systems fail to recognize and address the human experience of fatigue and frustration in work environments, leading to potential disruptions in workforce productivity and quality of work.
Innovation Solution
A system and method that monitors user inputs for error rates, detects fatigue and frustration through natural language analysis, and invokes a conversational LLM to query the user about their mental state and task, providing personalized assistance such as emotional support or recommending breaks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a conversational LLM is invoked to detect and address user fatigue and frustration, then user well-being and work quality are improved, but system complexity and response time increase
Solution Approach 1:
The system segments the assistance function into discrete components: error rate monitoring, fatigue detection, frustration detection, and contextual pause injection. Each component operates independently but contributes to the overall assistance mechanism, managing complexity through modular design
Solution Approach 2:
The conversational LLM acts as an intermediary between the user and the system, mediating the detection and response to user state. The LLM translates user inputs into detectable patterns of fatigue and frustration, and delivers appropriate assistance messages without requiring direct system-user interaction complexity
2Loss of time
If the system monitors user inputs and detects error rates continuously, then user fatigue and frustration are identified earlier, but processing time and computational resources increase
Solution Approach 1:
The system merges multiple detection functions (error rate monitoring, fatigue detection, frustration detection) into a unified monitoring process that analyzes user inputs once and extracts multiple indicators simultaneously, reducing redundant computational overhead
Solution Approach 2:
The system applies partial monitoring by focusing on specific indicators (error rates, particular fatigue markers, frustration signals) rather than analyzing all possible user input characteristics, reducing computational resources while maintaining effective detection
3Adaptability or versatility
If the conversational LLM adapts persona to suit user context, then user experience and assistance effectiveness are improved, but processing time and system complexity increase
Solution Approach 1:
The system performs preliminary analysis of user context and state before invoking the conversational LLM, preparing adaptation parameters in advance. This preliminary action allows the LLM to quickly adopt an appropriate persona without extensive processing during the actual interaction
Solution Approach 2:
The system changes parameters of the conversational LLM configuration (persona, tone, assistance style) based on detected user state. By adjusting these parameters dynamically, the system adapts assistance effectiveness to user needs while maintaining efficient processing through parameter-based control rather than structural changes
Data Source
AI summary
In an approach for providing contextual conversational user assistance, a processor monitors a plurality of inputs by a user to a computing device. Responsive to determining, based on the monitoring, that an error rate associated with the plurality of inputs has exceeded a threshold level, a processor invokes a conversational large learning model (LLM). A processor queries the user regarding a mental state and a task of the user using the conversational LLM. A processor identifies one or more methods to assist the user based on a response of the user to the querying. A processor executes the one or more methods identified to assist the user.