AI Article Recommender for Service Agent Response Efficiency
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Solution Overview
Problem
Service agents in on-demand apps face challenges in providing real-time support due to the need to sift through irrelevant information and lack of familiarity with products or services, leading to delayed responses and reduced user experience.
Innovation Solution
An article recommender system that uses AI and NLP to identify and recommend relevant articles from a multi-tenant database, leveraging collaborative knowledge from all service agents to aid in responding to customer inquiries, enhancing the service agent's understanding and response efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If service agents manually review articles to find relevant information, then they can provide accurate responses, but response time increases and user experience degrades
Solution Approach 1:
The patent introduces an AI-powered intermediary system that acts as a mediator between service agents and the knowledge base. This intermediary automatically retrieves, filters, and ranks relevant articles based on customer inquiries, eliminating the need for agents to manually search through irrelevant material while maintaining high accuracy in information retrieval.
Solution Approach 2:
The patent replaces the mechanical manual search process with an automated AI-based information retrieval system. The system uses natural language processing and machine learning algorithms to substitute the manual review process, significantly reducing response time while maintaining or improving the accuracy of information provided to customers.
2Productivity
If service agents spend time formulating responses without AI assistance, then they maintain independence, but productivity decreases
Solution Approach 1:
The patent enables service agents to access pre-processed, AI-generated response suggestions and relevant articles through an intuitive interface. The system performs complex information retrieval, filtering, and ranking operations automatically, allowing agents to quickly review and customize responses without needing to understand the underlying complex AI processes.
3Ease of operation
If service agents lack familiarity with products or services, then onboarding is easier, but their ability to handle nuanced requests decreases
Solution Approach 1:
The patent implements a system that performs preliminary actions by pre-processing customer inquiries and automatically retrieving relevant knowledge base articles before agents need to respond. The AI system prepares context-aware suggestions and relevant information in advance, allowing agents with varying levels of product knowledge to provide high-quality support for even the most nuanced requests.
Data Source
AI summary
A method and system for recommending articles including: receiving a customer request from the customer during the session; generating case data for a case, by an article recommender app; configuring a training set based on the subject and description data of the customer request; identifying, by an artificial intelligence (AI) app, a first pool of articles from a knowledge database; identifying by at least one query, a second pool of articles from a case article database to into a merged pool of articles; assigning, by the AI app, an implicit label to one of the first pool and the second pool of the articles; applying a model derived by the AI app based on customer behavior and a set of features related to the case to classify each article of the merged pool of articles based at least in part on the predicted relevance of the article.