Dynamic Question Selection for User Engagement
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Solution Overview
Problem
Interactive response systems face challenges in maintaining user engagement, as users may disengage due to boredom or distraction, leading to a high risk of churn.
Innovation Solution
A method using a machine learning model to generate a churn risk from user interaction data, selecting fields with high prediction confidences to optimize question presentation, thereby tailoring interactions and increasing engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system asks questions in a fixed sequence to gather information efficiently, then productivity is improved, but user engagement deteriorates as users become bored or distracted
Solution Approach 1:
The patent dynamically adjusts the question sequence based on real-time engagement metrics and predicted user responses. Instead of a fixed sequence, the system uses machine learning models to predict which questions the user is most likely to answer accurately and maintains a flexible question pool that adapts to user behavior patterns, thereby maintaining both efficiency and engagement.
Solution Approach 2:
The system changes the parameters of question presentation by adjusting difficulty, timing, and sequencing based on user performance and engagement levels. Machine learning models predict user responses and modify question parameters in real-time to optimize both information gathering efficiency and user engagement, preventing boredom and distraction.
2Reliability
If the system uses machine learning models to predict user responses and customize questions, then user engagement is improved, but device complexity increases
Solution Approach 1:
The patent segments the machine learning functionality into separate, specialized models: one for predicting user responses and another for assessing engagement levels. This modular approach allows each model to be optimized independently and reduces overall system complexity while maintaining high engagement through customized question sequences.
3Reliability
If the system processes user interaction data in real-time to assess engagement and adjust questions, then user engagement is maintained, but use of energy increases
Solution Approach 1:
The system performs partial processing by focusing computational resources only on the most critical engagement metrics and high-priority questions. Instead of analyzing all user interactions equally, the machine learning models identify and process only the most significant data points for engagement assessment, reducing energy consumption while maintaining effective engagement maintenance.
Data Source
AI summary
A method optimizes questions to retain engagement. The method includes generating, using a machine learning model, a churn risk from user interaction data. The method includes selecting, when the churn risk satisfies a threshold, a field, from multiple fields, using multiple prediction confidences corresponding to multiple prediction values generated for the multiple fields. The method includes obtaining a prediction value for the field and obtaining a question, corresponding to the field, using the prediction value. The method includes presenting the question and receiving a user input in response to the question.