GenAI Agent Guidance for Real-Time Contact Center Service
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Solution Overview
Problem
Contact center agents often lack access to historical interaction data, leading to inferior service and increased likelihood of unfavorable interactions, especially in cases of data incidents or breaches, as they cannot provide real-time guidance based on previous user experiences.
Innovation Solution
Utilizing a generative adversarial network (GAN) model trained with GPU and CPU processing to analyze user interactions, feedback, and social media data to provide real-time suggestions to agents for enhancing user experiences, including modifications to online tools and agent behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If contact center agents do not have access to historical interaction data, then system complexity is reduced, but service quality deteriorates
Solution Approach 1:
The patent introduces an intermediary system comprising a data warehouse and GenAI model that bridges the gap between historical interaction data and contact center agents. The data warehouse collects and stores interaction data from multiple sources, while the GenAI model processes this data and generates actionable insights, which are then presented to agents through a user interface. This intermediary architecture enables agents to access relevant historical information without requiring direct access to complex data infrastructure.
Solution Approach 2:
The patent replaces traditional mechanical information retrieval systems with a GenAI-based intelligent system. Instead of agents manually searching through databases or accessing raw data, the GenAI model automatically analyzes historical interactions, identifies patterns, and generates natural language insights. This substitution transforms the information delivery mechanism from manual/mechanical to intelligent/autonomous, improving service quality while managing system complexity.
2Reliability
If real-time access to interaction records is provided to agents, then service quality improves, but data processing complexity increases
Solution Approach 1:
The system performs preliminary data processing and analysis before interactions occur. The data warehouse pre-collects and organizes interaction data from multiple sources, and the GenAI model pre-processes this data to identify relevant patterns and insights. During live interactions, agents receive pre-generated insights and recommendations, eliminating the need for real-time data processing during critical customer service moments.
Solution Approach 2:
The patent extracts only the most relevant information from vast amounts of historical data and presents it to agents in a condensed, actionable format. The GenAI model filters through comprehensive interaction histories, social media feedback, and survey data to extract key insights such as user preferences, pain points, and contextual information. This extraction approach provides agents with essential information without overwhelming them with raw data complexity.
3Ease of operation
If comprehensive user data is collected and analyzed, then user experience enhancement improves, but data management complexity increases
Solution Approach 1:
The patent implements a universal data warehouse architecture that handles multiple types of data sources and processing requirements through a single integrated system. The data warehouse collects interaction data, social media feedback, survey results, and other user information using standardized schemas. The GenAI model universally processes diverse data types to generate insights applicable across different interaction scenarios, simplifying data management while enabling comprehensive user experience analysis.
Solution Approach 2:
The data warehouse serves as an intermediary layer between diverse data sources and the GenAI processing system. It standardizes data formats, manages data storage, and handles data quality issues, shielding the GenAI model from raw data complexity. This intermediary data management layer enables comprehensive data collection and analysis while maintaining system manageability and scalability.
Data Source
AI summary
A method that uses a graphics processing unit (“GPU”) to train and run a generative adversarial network (“GAN”) model to make enhancements to an online tool provided by the organization. The user may communicate with the organization to seek enhancements to the online tool. The method may include collecting a dataset and training the GAN model with records of previous contact between the user and the contact center relating to the online tool, the user's posts on social media relating to the online tool, and the user's answers to a survey provided by the organization relating to use of the online tool. A processor may run the GAN model to provide a suggestion to the agent before and during the contact center communication with the user for how to best interact to enhance the user's experience.