Topic-Routed Generative AI for Accurate Customer Response Automation
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Solution Overview
Problem
Existing systems for managing and responding to customer requests are inefficient, costly, and prone to human error, leading to unnecessary resource usage and suboptimal computer functionality.
Innovation Solution
Implementing a system that utilizes generative AI models to automatically generate responses by analyzing queries, determining relevant topics, and accessing updatable knowledge bases, with multiple AI models for data retrieval and semantic memory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual individuals or teams are used to provide responses to customer requests, then accuracy and knowledge expertise can be maintained, but cost and time consumption increase significantly
Solution Approach 1:
The patent replaces manual human processing with an automated AI-based system that uses natural language processing, machine learning models, and knowledge graphs to generate responses to customer requests automatically, eliminating the need for manual intervention while maintaining accuracy
Solution Approach 2:
The system enables self-service by allowing the AI model to autonomously process customer requests, retrieve information from knowledge bases, and generate appropriate responses without human intervention, thereby reducing time consumption while maintaining reliability
2Reliability
If manual processing is used for customer requests, then human expertise can be leveraged, but system scalability is limited
Solution Approach 1:
The patent segments the system into multiple specialized AI models and knowledge graphs, each trained on specific domains or topics. This segmentation allows the system to scale by adding more specialized models for different domains without affecting existing ones, while maintaining expertise quality through specialized training data
Solution Approach 2:
The system achieves universality by creating a multi-functional AI platform that can handle various types of customer requests across different domains through a common architecture of NLP processors, machine learning models, and knowledge graphs, enabling scalability while maintaining expertise through modular domain-specific components
3Loss of information
If traditional search and retrieval processes are used, then human specialists can access information, but system resource usage increases
Solution Approach 1:
The patent implements preliminary action by pre-processing and indexing information into knowledge graphs and vector databases before actual queries are received. This allows the system to quickly retrieve relevant information without performing comprehensive search operations during actual user interactions, reducing real-time resource consumption while maintaining information retrieval accuracy
Solution Approach 2:
The system replaces traditional mechanical search processes with AI-based information retrieval using neural networks, vector embeddings, and knowledge graphs, which are more efficient at retrieving accurate information with lower computational resources compared to conventional search algorithms
Data Source
AI summary
Various methods and processes, apparatuses or systems, and media for using generative AI models to automatically generate responses to customer requests in an efficient and accurate manner are disclosed. The method includes: receiving a query from a user; analyzing the query to determine a topic that is relevant to the query; publishing the query to a topic queue that corresponds to the determined topic; identifying a generative artificial intelligence (AI) model that is trained by using data that corresponds to the determined topic; submitting the query to the generative AI model; receiving an answer to the first query from the generative AI model; storing the received answer to the query in a semantic memory; and transmitting the answer to the user.