Replaceable AI Models for Voice and Text Channels
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Solution Overview
Problem
Current customer experience quality validation is manual, prone to selection bias, imprecise metrics, and subjective variations, leading to inaccurate self-appraisal and limited automation.
Innovation Solution
A system and method that utilize interchangeable and tunable Artificial Intelligence Models for automated analysis and reporting of customer service experiences, including speech-to-text and text-to-speech conversions, real-time interaction assistance, and batch analysis for producing quality interaction reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual customer experience quality validation is used, then subjective review and human judgment are applied, but the process is prone to selection bias, imprecise metrics, and inaccurate self-appraisal
Solution Approach 1:
The patent replaces manual human review processes with automated AI-based analysis systems. Specifically, it uses speech-to-text conversion, text-to-speech conversion, and AI model-generated summaries to automatically analyze customer interactions, replacing the mechanical process of human reviewers listening to and evaluating recorded interactions.
Solution Approach 2:
The system enables self-service through automated self-appraisal mechanisms where the AI models independently evaluate customer experience quality without human intervention. The automated system performs its own quality validation through programmed algorithms that assess interaction quality metrics.
2Reliability
If recorded or real-time customer interactions are reviewed manually, then detailed analysis is possible, but the process is difficult and subject to variations and mistakes by human reviewers
Solution Approach 1:
The patent replaces the complex manual review process with automated AI systems that consistently apply the same evaluation criteria. The system uses standardized algorithms for assessing interaction quality, eliminating the variability and errors inherent in human review while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The system changes the parameters of the review process by transitioning from subjective human judgment to objective algorithmic evaluation. It defines specific measurable parameters for interaction quality such as response time, accuracy metrics, and customer satisfaction indicators that can be consistently measured and compared.
3Adaptability or versatility
If multiple AI models with different capabilities are used, then complex customer requests can be handled appropriately, but the system complexity increases
Solution Approach 1:
The patent segments the AI model system into multiple specialized models with different capability levels. Each AI model is designed to handle specific types or complexities of customer requests, allowing the system to match request complexity with appropriate model capability rather than using a single monolithic system.
Solution Approach 2:
The system dynamically selects and switches between different AI models based on the complexity and type of customer request. The model selection is not static but adapts in real-time to the specific interaction requirements, optimizing resource usage while maintaining versatility.
Data Source
AI summary
A method for training AI models and interacting with customers during customer service sessions using one or more of the AI models is described. The method can be implemented on a cloud architecture where processing and storage resources used to support the AI models can be scaled based on demand. Customer interactions can be recorded and/or monitored in order to provide data for additional training for the AI models. In some embodiments, customer interactions are monitored in real-time in order to make decisions about whether to switch to an AI model more likely to provide a customer more accurate answers and/or a higher level of customer satisfaction.