Agent Assistance Module for Natural Language Customer Service
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current virtual assistance systems in customer care face challenges with error management and edge conditions, leading to frustration for consumers and increased costs for companies, as they require extensive re-prompting and clarification, and human agents struggle to multitask between managing conversations and interacting with computer systems, resulting in lower customer loyalty and higher operational costs.
Innovation Solution
A natural-language customer service interaction system that includes an agent assistance module which listens to conversations in real-time, recognizes and interprets them, and presents concepts graphically as actionable items on a dynamic interface, allowing human agents to manipulate these concepts to complete tasks efficiently, while also delegating tasks to automated systems when confident in interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated systems use extensive re-prompting and clarification to verify AI understanding, then understanding accuracy is improved, but consumer frustration increases and operational costs rise
Solution Approach 1:
The system performs preliminary actions by proactively presenting confidence indicators and alternative interpretations to agents before the customer experiences frustration. The agent assistance module analyzes the AI's understanding confidence level in advance and prepares visual cues that guide agents on when and how to intervene, preventing the harmful effect of customer frustration before it occurs.
Solution Approach 2:
The patent introduces an intermediary element - the visual interface with confidence indicators - that mediates between the automated AI system and the human agent. This intermediary provides transparent information about AI understanding quality, enabling agents to make informed decisions about when to take over the conversation, thus reducing direct customer frustration while maintaining understanding accuracy.
2Manufacturing precision
If human agents manually interact with computer systems to complete tasks, then task completion accuracy is improved, but cognitive strain increases and productivity decreases
Solution Approach 1:
The system enables self-service by allowing the AI to automatically complete tasks when confidence indicators show high understanding accuracy. The agent assistance module monitors task completion requirements and automatically executes actions without requiring manual agent intervention, thereby maintaining task completion accuracy while eliminating the cognitive strain and time loss associated with manual system interactions.
Solution Approach 2:
The patent applies partial action by having the AI system perform only the portion of task completion for which it has sufficient confidence, as indicated by the confidence indicators. When confidence is high, the AI completes the task partially or fully automatically; when confidence is low, the system invites agent intervention. This partial automation approach maintains accuracy for confident tasks while improving overall productivity.
3Reliability
If companies invest in skilled technical resources and data labeling to build AI systems, then AI system quality is improved, but operational costs increase
Solution Approach 1:
The patent implements feedback by using confidence indicators that provide real-time information about AI understanding quality. This feedback mechanism allows the system to automatically adjust its behavior - relying on AI when confidence is high and seeking human intervention when confidence is low - thereby maintaining AI system quality while reducing the need for continuous expensive human oversight and extensive data labeling operations.
Data Source
AI summary
A virtual assistant system for communicating with customers uses human intelligence to correct any errors in the system AI, while collecting data for machine learning and future improvements for more automation. The system may use a modular design, with separate components for carrying out different system functions and sub-functions, and with frameworks for selecting the component best able to respond to a given customer conversation. The system may have agent assistance functionality that uses natural language processing to identity concepts in a user conversation and to illustrate that concepts within a graphical user interface of a human agent so that the human agent can more accurately and more rapidly assist the user in accomplishing the user's conversational objectives.