Language Model Session Context Management for Selective Human Escalation
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Solution Overview
Problem
Companies face high costs and user dissatisfaction when transitioning users from inadequate automated support to human agents, necessitating improved automated support systems that reduce costs and enhance user experience.
Innovation Solution
A computer-implemented method using language models to manage session contexts, integrating automated and human agent interactions, where prompts are generated to guide responses, including API calls, user communications, and human agent requests, with feedback loops to enhance support quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If automated support agents are used to reduce costs, then support costs decrease, but support quality deteriorates requiring transfer to human agents
Solution Approach 1:
The patent introduces a context management system as an intermediary between automated support agents and human agents. This system maintains session context, user preferences, and interaction history, allowing automated agents to operate more effectively while enabling seamless handoff to human agents when needed, thus improving support quality without fully eliminating automated agent usage
Solution Approach 2:
The patent implements feedback mechanisms where the context management system continuously monitors automated agent performance and user satisfaction. When support quality thresholds are not met, the system triggers escalation to human agents and learns from these interactions to improve future automated agent responses, creating a closed-loop system that balances cost reduction with quality maintenance
2Reliability
If users are transferred to human agents for adequate support, then support quality improves, but support costs increase
Solution Approach 1:
The patent applies partial action by having human agents intervene only when necessary - specifically when automated agents fail to meet support quality thresholds or when complex issues require human judgment. The context management system enables this selective human involvement by maintaining sufficient context information, allowing human agents to step in with minimal onboarding time while reducing overall human agent utilization and associated costs
3Productivity
If automated support systems are enhanced to reduce human agent involvement, then operational efficiency improves, but system complexity increases
Solution Approach 1:
The patent segments the support system into distinct functional components: automated support agents, context management system, and human agent interface. The context management system is further divided into context maintenance, threshold monitoring, and escalation management functions. This segmentation allows each component to be optimized independently for operational efficiency while managing overall system complexity through modular design
Data Source
AI summary
User interactions may be managed through a combination of automated language model prompts and feedback from a human agent. An implementation may include a communication processing circuit that receives and updates the session context with a user's natural language communication. A prompt generator circuit creates prompts that include the user's communication and descriptions of response types, such as replying to the user or requesting human agent assistance. The language model processes these prompts and generates responses, which are routed accordingly by a response routing circuit-either to the user or to a human agent for assistance. Additionally, a feedback circuit collects feedback from the human agent to update the session context.