Analytics Model Tuning for Personalized Tax Software
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Solution Overview
Problem
Traditional tax return preparation systems are inflexible and generic, leading to impersonal, lengthy, and confusing user experiences due to their static analytics and hard-coded features, which fail to adapt to individual user needs.
Innovation Solution
The software system adjusts analytics model characteristics to reduce overfitting and improve accuracy in determining user preferences by using techniques such as local stop probabilities, decision tree pruning, and dynamic allocation of user experience options, allowing for personalized experiences based on user characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional tax return preparation systems use static, hard-coded features and predetermined question sequences, then the system structure is simple and easy to implement, but the user experience becomes generic, impersonal, and inflexible
Solution Approach 1:
The patent implements dynamic user experience by allowing the system to adapt question sequences, presentation formats, and interview flows based on real-time user characteristics and responses. The analytics model dynamically adjusts the user experience rather than following a fixed predetermined path, making the system flexible and responsive to individual user needs.
Solution Approach 2:
The system changes key parameters of the user experience based on analytics model predictions, including question sequencing, presentation format, level of guidance, and interview flow. These parameter changes are driven by user characteristics and model predictions, allowing the system to optimize the user experience for different user types without requiring complete system redesign.
2Measurement precision
If the system collects and analyzes more user data to personalize experiences, then user experience personalization improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining user segments and experience options before actual user interaction. The analytics model is pre-trained with historical data to recognize patterns and make predictions about user preferences. This preliminary preparation allows the system to quickly personalize experiences without extensive real-time processing during user sessions.
Solution Approach 2:
The patent replaces traditional mechanical A/B testing methods with an analytics-driven prediction system. Instead of randomly assigning users to different experience versions and waiting for statistical results, the system uses trained analytics models to predict user preferences and directly apply optimized experiences, significantly reducing processing time and computational overhead.
3Productivity
If traditional systems present all predetermined questions to all users, then the interview process is complete and thorough, but users experience unnecessary length and confusion
Solution Approach 1:
The patent segments the user base into different groups based on characteristics such as tax complexity, technical proficiency, and learning preferences. Each segment receives a customized question sequence and presentation format tailored to their needs. This segmentation allows the system to present only relevant information to each user group, eliminating unnecessary questions and reducing confusion while maintaining completeness of essential information collection.
Solution Approach 2:
The system applies local quality by providing different levels and types of information to different user segments based on their specific needs and characteristics. Rather than uniform treatment, each user receives customized content, guidance level, and question sequencing appropriate to their situation, ensuring that all users receive sufficient relevant information without being overwhelmed by unnecessary details.
Data Source
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, from a variety of different 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 characteristics of the analytics model are adjusted and/or tuned to control/reduce uncertainty in identifying effective user experience options, 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, at least partially based on feedback from users, according to one embodiment.