Machine Learning User Dissatisfaction Prediction
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Solution Overview
Problem
Transportation networks face challenges in accurately predicting user dissatisfaction and preventing users from ceasing service usage due to poor experiences, as existing methods lack effective ad hoc rules and require manual data examination.
Innovation Solution
A system using machine learning algorithms to automatically compute user dissatisfaction scores based on historical trip data, training models without manual training sets, and identifying positive and negative trip sequences to predict user dissatisfaction and perform remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual examination of user data is used to predict user dissatisfaction, then measurement precision can be improved, but loss of time and productivity deteriorate due to resource-intensive manual analysis
Solution Approach 1:
The patent replaces manual mechanical analysis of user data with an automated machine learning system. The machine learning model automatically processes trip data, identifies patterns, and predicts user dissatisfaction without human intervention, thereby maintaining prediction accuracy while eliminating time-consuming manual examination.
Solution Approach 2:
The system performs self-service by automatically training on historical data and continuously improving its predictions without requiring manual retraining or human analysis. The machine learning model autonomously processes new trip data and updates its understanding of user behavior patterns, eliminating the need for manual resource investment.
2Measurement precision
If ad hoc rules are formulated to capture user satisfaction factors, then measurement precision improves, but device complexity increases due to difficulty in capturing subtle interactions
Solution Approach 1:
The patent substitutes complex manual rule formulation with an automated machine learning system that automatically discovers patterns in data. The model handles subtle interactions between multiple satisfaction factors without requiring explicit programming of interaction rules, thereby maintaining high measurement precision while reducing formulation complexity.
Solution Approach 2:
The system transforms the problem from formulating complex interaction rules to changing parameters in a machine learning model. Instead of manually creating rules to capture subtle interactions, the system adjusts model parameters and training data to automatically learn these complex relationships, simplifying the overall approach while maintaining accuracy.
3Productivity
If automated machine learning models are used to compute user dissatisfaction scores, then productivity improves, but measurement precision may deteriorate due to model accuracy dependencies
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning model continuously learns from actual user behavior data. By comparing predicted dissatisfaction with actual user cessation patterns, the model refines its accuracy over time, ensuring that automated predictions improve rather than deteriorate measurement precision through iterative learning and adaptation.
Solution Approach 2:
The system performs preliminary action by pre-training the machine learning model on extensive historical data before deployment. This preliminary training ensures the model achieves high accuracy in predicting user dissatisfaction, so when automated computation is used for productivity gains, measurement precision is already established through thorough pre-training on representative datasets.
Data Source
AI summary
A server of a transportation network determines characteristics of trips provided to users, as well as the usage of the services by users. Using the determined characteristics, the server trains a model that, for a given set of trips for a user, estimates a degree of likely user dissatisfaction with the trips. Based on the estimated degree of user dissatisfaction, the system can estimate user dissatisfaction in real time, directly after completion of a trip, and can take remedial actions should the user be estimated to be likely dissatisfied.