Tiered AI Model Deployment for Satisfaction Prediction
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Solution Overview
Problem
Existing methods for predicting human satisfaction are inaccurate and lack repeatability due to reliance on non-technical measurements.
Innovation Solution
Utilizing multiple AI models at different times with distinct data sets to improve prediction accuracy by iteratively refining satisfaction predictions based on collected data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single AI model is used for satisfaction prediction, then the system complexity is low, but the prediction accuracy is insufficient
Solution Approach 1:
The patent divides the satisfaction prediction task into multiple sequential stages, each handled by a different AI model. The first model processes initial data at time T1, and the second model processes additional data at time T2, with each model specialized for its respective time point and data type, thereby improving overall prediction accuracy while maintaining manageable complexity through functional segmentation
Solution Approach 2:
The system dynamically selects and switches between different AI models based on the available data and time point. The prediction process transitions from using only the first model to using the second model when additional data becomes available, allowing the system to adapt its complexity and accuracy based on real-time conditions
2Measurement precision
If multiple AI models are used at different times with distinct data sets, then the prediction accuracy is improved, but the computational resources and system complexity increase
Solution Approach 1:
The first AI model performs preliminary satisfaction prediction using initial data collected at time T1, providing an early indication of customer satisfaction before all relevant data is available. This preliminary action allows for early interventions while reducing overall computational burden by processing data in stages rather than waiting for complete data sets
Solution Approach 2:
The system maintains continuous prediction capability by deploying the second AI model to process additional data collected at time T2, building upon the first model's prediction. This continuous action ensures that satisfaction assessment is ongoing and updated as new information becomes available, maximizing the utility of computational resources throughout the customer experience timeline
Data Source
AI summary
Systems, methods, and computer-readable storage media for tiered deployment of multiple Artificial Intelligence (AI) models. The system receives first data associated with a human undergoing an experience, and executes a first artificial intelligence model using that data, with the result being a first satisfaction prediction for the human regarding the experience. At a later time, the system receives second data associated with the human undergoing the experience, and executes a second artificial intelligence model using at least a portion of the first data and the second data, where the second artificial intelligence model has a higher prediction accuracy than the first artificial intelligence model.