Adaptive Analytics Models for Personalized Software User Flows
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Solution Overview
Problem
Traditional tax return preparation systems lack flexibility and personalization, leading to impersonal, confusing, and unnecessarily long user experiences, which can frustrate users and reduce the likelihood of becoming paying customers.
Innovation Solution
A method and system for dynamically updating analytics models to predict and adapt user preferences using artificial intelligence, analyzing user data with both production and offline models to improve user satisfaction and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional tax return preparation systems use fixed, predetermined question sequences, then system complexity is reduced and ease of manufacture is improved, but adaptability to individual user needs deteriorates and user satisfaction decreases
Solution Approach 1:
The patent implements dynamic user experience flows that adapt in real-time based on user responses and analytics model predictions. Instead of fixed predetermined sequences, the system dynamically selects and orders questions and content based on individual user characteristics, making the system both adaptable and personally relevant while maintaining manageable complexity through automated decision-making.
Solution Approach 2:
The system changes multiple parameters simultaneously including question selection, question ordering, content presentation, and interview flow based on analytics model outputs. This multi-parameter adaptation allows the system to personalize the user experience across multiple dimensions without requiring complete redesign of the underlying system architecture.
2Reliability
If traditional systems present all predetermined questions to all users, then completeness of information gathering is improved, but user experience quality deteriorates due to irrelevant and confusing questions
Solution Approach 1:
The patent segments the question set and user population into meaningful groups based on analytics model predictions. Instead of presenting all questions to all users, the system divides questions into relevant subsets for different user segments and presents only the appropriate subset to each user, ensuring both completeness of relevant information and quality of user experience.
Solution Approach 2:
The system applies partial action by presenting only the necessary subset of questions to each user based on their predicted needs and characteristics. Rather than exhaustively asking all possible questions, the analytics model identifies and presents only the relevant questions, achieving sufficient data collection without overwhelming the user with irrelevant content.
3Measurement precision
If analytics models are updated frequently to improve personalization accuracy, then user preference prediction accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and pre-training analytics models offline before deployment. User data is pre-analyzed and model updates are prepared in advance, so that during actual user interactions, the system can quickly apply pre-computed personalization strategies without extensive real-time processing, thus maintaining high accuracy while minimizing processing time.
Solution Approach 2:
The system introduces an intermediary layer of pre-computed analytics models and user segments that mediate between raw user data and personalized content delivery. This intermediary layer caches and pre-processes analytical results, allowing the system to maintain high prediction accuracy while reducing the computational burden and processing time during actual user interactions.
Data Source
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AI summary
A method and system adaptively improves potential customer conversion rates, revenue metrics, and/or other target metrics by providing effective user experience options to some users while concurrently testing user responses to other user experience options, according to one embodiment. The method and system selects the user experience options by applying user characteristics data to an analytics model, according to one embodiment. The method and system analyzes user responses to the user experience options to update the analytics model, and to dynamically adapt the personalization of the user experience options, according to one embodiment. The method and system dynamically and automatically defines, evaluates, and updates analytics models to provide progressively improving personalization of user experiences in a software system.