ML Query Response System for Support Inquiries
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Solution Overview
Problem
Conventional support service processes, such as call centers and online chats, face challenges in managing high volumes of user inquiries and providing accurate responses due to limited access to data, leading to inefficiencies and low customer satisfaction.
Innovation Solution
A smart support service agent system utilizing natural language processing (NLP) and machine learning algorithms to parse user inquiries, generate search strings, and search a comprehensive knowledge base, including third-party and public data, to provide accurate and efficient responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional call centers and online chat services are used to handle user inquiries, then human support agents can provide personalized assistance, but the system cannot manage high volumes of inquiries efficiently and incurs high infrastructure and labor costs
Solution Approach 1:
The system enables self-service by training a machine learning model to autonomously parse user inquiries, generate search strings, and retrieve answers from a knowledge base without human intervention. The model independently processes high volumes of inquiries, eliminating the need for extensive call center infrastructure and labor-intensive support operations.
Solution Approach 2:
The patent replaces the mechanical system of human agents manually searching knowledge bases with an automated machine learning-based information retrieval system. The machine learning model substitutes human cognitive processes with algorithmic operations, dramatically increasing inquiry handling capacity while reducing infrastructure complexity.
2Measurement precision
If support agents manually search knowledge bases to find answers, then they can access relevant information, but the process is time-consuming and reduces customer satisfaction
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning model on extensive support data before deployment. The model learns to accurately parse inquiries and generate effective search strings in advance, enabling rapid and accurate answer retrieval during actual support operations without time-consuming manual searching.
Solution Approach 2:
The patent applies parameter changes by transforming the support process from manual keyword searching to machine learning-based semantic parsing. The system changes the parameters of inquiry analysis by using trained models to extract meaningful features and generate optimized search strings, dramatically improving both response time and answer accuracy simultaneously.
3Reliability
If comprehensive knowledge bases including third-party data are searched to provide accurate responses, then answer quality improves, but the complexity of data management and processing increases
Solution Approach 1:
The machine learning model serves as an intermediary between user inquiries and the comprehensive knowledge base. It translates natural language inquiries into optimized search strings that effectively query diverse data sources including third-party and public data, managing the complexity of data integration while improving response accuracy.
Solution Approach 2:
The system creates a copied and simplified representation of complex knowledge base data through the machine learning model's learned embeddings and features. This copied representation enables accurate information retrieval without directly managing the full complexity of the underlying comprehensive knowledge base structure.
Data Source
AI summary
Systems and methods are provided for training a machine learning model using a plurality of data related to a product and services support system to determine a plurality of parameters to be used to search for one or more results for an input stream and storing the plurality of parameters in one or more databases. Systems and methods further provide for receiving an input stream from a user computing device, parsing the input stream to generate a parsed input stream, translating the parsed input stream into one or more of the plurality of parameters output from the machine learning model to generate a search string, searching a knowledge base using the search string to determine one or more results associated with the parsed input stream, and providing at least one result of the one or more results associated with the parsed input stream to the user computing device.