Neural Network CSR Query Processing for Customer Service Automation
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Solution Overview
Problem
Manual customer service operations in online environments are labor-intensive, prone to errors, and costly due to the large volume of questions and answers, affecting metrics, agent sentiment, and customer satisfaction.
Innovation Solution
A computerized method using neural networks to model CSR-customer relationships by processing user queries through contextualized word representation models, generating context-aware feature vectors, and implementing decision-making functions to provide real-time feedback and improve customer service delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual QA services are used for customer service, then human judgment and empathy can be provided, but the process becomes labor-intensive, costly, and prone to errors
Solution Approach 1:
The patent introduces an intermediary AI system that acts as a mediator between customer queries and human agents. The neural network model processes and analyzes customer questions, generating structured representations and suggested responses that human agents then review and refine. This intermediary layer handles the labor-intensive processing while maintaining human judgment for final decision-making, thereby improving both reliability and productivity simultaneously.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated neural network system. Instead of human agents manually analyzing every customer query, the system uses deep learning models to automatically process, understand, and generate responses for customer service interactions. This substitution dramatically increases processing volume while maintaining or improving accuracy through consistent application of learned patterns.
2Productivity
If only a small percentage of customer interactions are audited, then manual resources are conserved, but metrics, agent sentiment, and customer satisfaction measurements become unreliable
Solution Approach 1:
The AI system serves as an intermediary that enables comprehensive auditing of all customer interactions rather than sampling. By automatically processing and analyzing every query-response pair through neural networks, the system generates precise measurements of customer satisfaction, agent performance, and sentiment trends across the entire dataset, eliminating the need to rely on small manual samples.
Solution Approach 2:
The system performs self-service auditing by automatically evaluating its own performance and generating metrics without requiring external manual intervention. The neural network models continuously analyze interaction data, compute satisfaction scores, and provide feedback on agent performance, enabling the system to maintain high measurement precision across all interactions independently.
3Loss of information
If contextualized word representation models are used to process user queries, then understanding of customer intent improves, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary processing by pre-computing contextual embeddings for words and phrases that frequently appear in customer queries. These pre-computed representations are stored and reused when analyzing new queries, reducing the computational burden during real-time processing. This allows the system to maintain high contextual understanding while minimizing processing time for each individual query.
Solution Approach 2:
The patent applies contextualized word representation selectively based on the specific needs of each query. Rather than uniformly applying complex contextual analysis to all words, the system focuses computational resources on key terms and phrases that carry the most semantic weight for understanding customer intent. This localized application of contextual analysis maintains understanding quality while reducing overall processing time.
Data Source
AI summary
In one aspect, a computerized method for operating computerized neural networks for modelling CSR-customer relationships includes the step of receiving a user query. The user query comprises a set of digital text from a customer as input into an online CSR system. The method includes the step of filtering out unnecessary content of the user query. The method includes the step of splitting filtered user query in a sentence wise manner. The method includes the step of feeding the tokenized user query into a contextualized word representation model. The method includes the step of generating a set of context-aware feature vectors from the contextualized word representation model. With the set of context-aware feature vectors, the method implements a decision-making function to generate an identified customer query. The method includes the step of receiving an agent response, wherein the agent response is a response to the user query, and wherein the agent response comprises a set of digital text from an agent. With an LSTM network, the method generates a user query tensor vector. With the LSTM network, generating an agent query tensor vector. The method includes the step of concatenating the user query tensor vector and the agent query tensor vector to produce a single tensor, wherein the single tensor is processable by a neural network.