Conversational Agent Script Generation for Synchronous Conferencing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional synchronous conferencing systems (SCS) face challenges in efficiently managing live-agent workloads, as human agents are limited in handling multiple concurrent sessions and become less valuable for simple, repetitive inquiries, leading to inefficiencies in customer service.
Innovation Solution
A computer-implemented method using a processor to generate scripts that simulate live-agent actions, allowing a conversational agent to automate simple and repetitive tasks, thereby freeing up human agents to focus on more complex tasks, utilizing machine learning models trained on labeled data to understand and respond to customer inquiries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If live-agents handle customer inquiries manually, then customer service quality is maintained, but agent productivity is limited due to inability to process multiple concurrent sessions
Solution Approach 1:
The patent introduces a conversational agent as an intermediary between customers and live-agents. The CA handles initial inquiry routing and simple queries using NLP and machine learning models, while complex inquiries are escalated to live-agents. This mediator approach enables agents to focus on high-value interactions while maintaining overall service quality.
Solution Approach 2:
The patent segments customer inquiries into different categories based on complexity, topic, and urgency. Simple, routine inquiries are automatically handled by the conversational agent, while complex or sensitive inquiries are routed to human agents. This segmentation increases productivity by distributing workload appropriately across automated and human resources.
2Speed
If rule-based conversational agents are used, then response speed is improved, but adaptability to complex or novel inquiries deteriorates
Solution Approach 1:
The patent transitions from static rule-based systems to dynamic machine learning models that adapt to new inquiry patterns. The system uses trained ML models that can generalize to complex and novel situations, and continuously learns from new data. This dynamic approach maintains fast automated responses while improving adaptability to diverse customer needs.
Solution Approach 2:
The patent changes the fundamental parameters of the conversational agent from fixed rules to probabilistic machine learning models. This parameter change enables the system to handle uncertainty and variability in customer inquiries while maintaining rapid response times through efficient model inference.
3Productivity
If more live-agents are deployed to handle increased inquiry volume, then customer service coverage is improved, but operational cost increases
Solution Approach 1:
The patent implements self-service capabilities through the conversational agent that automatically handles routine customer inquiries without human intervention. The CA independently processes authentication, account information retrieval, and common troubleshooting tasks, eliminating the need to deploy additional live-agents for volume increases and controlling operational costs.
Data Source
AI summary
Embodiments of the invention are directed to a computer-implemented method of responding to an inquiry received electronically at a synchronous conferencing system (SCS). A non-limiting example of the computer-implemented method includes, based at least in part on the inquiry, using a processor of the SCS to generate a script having one or more script computer instructions. The processor is used to execute the script computer instructions to generate script computer instruction results, wherein the script computer instruction results include inquiry response information that is responsive to the inquiry. Based at least in part on the inquiry and the inquiry response information, the processor is used to generate an inquiry response that is responsive to the inquiry.