AI Communications Agent for Dynamic Customer Service Adaptation
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Solution Overview
Problem
Existing customer service systems, such as IVR and internet-based systems, are rigid and costly to adapt to changing business or customer needs, requiring human operators for high-quality support and efficient task handling.
Innovation Solution
A smart communications agent system using machine learning models to replicate human customer service agent behaviors, capturing and clustering communication transcripts and human-computer interactions to generate feature vectors, which are then used to create artificial intelligence communications agents capable of handling various customer inquiries across multiple communication mediums.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If rigid IVR or internet-based systems are used, then automation extent increases, but adaptability deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from static, pre-programmed IVR systems to a dynamic machine learning model that continuously learns and adapts from customer-agent interaction data. The system evolves its behavior patterns over time, allowing it to maintain high automation while adapting to changing customer needs and business requirements without manual reconfiguration.
Solution Approach 2:
The patent uses copying by capturing and replicating the behavioral patterns of human customer service agents through machine learning. The AI communications agent learns from observed human-agent interactions and copies effective communication strategies, task handling approaches, and decision-making patterns, enabling automated systems to perform complex customer service tasks that previously required human judgment.
2Reliability
If human operators are used, then service quality improves, but productivity deteriorates
Solution Approach 1:
The system copies the high-quality service behaviors of human operators by training machine learning models on extensive interaction data. This allows the AI to replicate the nuanced decision-making, empathy, and problem-solving skills of human agents while operating at automated speeds, thereby maintaining service quality without the productivity limitations of human workers.
Solution Approach 2:
The patent replaces the mechanical, rule-based operation of traditional IVR systems with an intelligent system based on machine learning and natural language processing. This substitution enables the system to handle complex, unstructured customer inquiries with human-like understanding and response quality, achieving both high service quality and automated productivity.
3Ease of manufacture
If traditional IVR systems are configured, then ease of manufacture improves for basic tasks, but adaptability deteriorates
Solution Approach 1:
The system applies self-service by enabling automatic configuration and adaptation through machine learning. Instead of requiring manual programming for each new task or business change, the system automatically learns from interaction data and adjusts its behavior patterns. This self-configuring capability maintains the ease of deployment for basic tasks while providing seamless adaptability to changing requirements.
Solution Approach 2:
The patent utilizes parameter changes by transforming the system from fixed, hard-coded configurations to dynamic, data-driven parameters. The machine learning models adjust their internal parameters based on learned patterns from customer interactions, allowing the system to adapt to new tasks and business needs by changing its behavioral parameters rather than its fundamental structure.
Data Source
AI summary
Systems and methods for artificial intelligence communications agents are disclosed. Implementations relate to capturing individual agent's behaviors and modelling them in artificial intelligence (AI) learning models so that the agent's behavior can be easily replicated. Some implementations further relate to systems and methods for capturing human-computer interactions (HCl) performed by agents and using robotic process automation (RPA) to automate tasks that would otherwise require human interaction. The combination of AI learning models and RPA are used to provide artificial intelligence communications agents capable of responding to a variety of topics of conversation over a variety of communication mediums.