Dynamic Tax Interview Sequencing via Predictive Analytics
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Solution Overview
Problem
Traditional tax return preparation systems are inflexible and generic, leading to irrelevant and confusing user experiences, which results in user frustration and a low conversion rate of potential customers to paying customers.
Innovation Solution
A personalized tax return preparation system using cascaded analytics models to iteratively present relevant questions based on user data and responses, improving user confidence and reducing irrelevant inquiries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional tax return preparation systems use fixed, pre-determined interview questions for all users, then the system structure is simple and easy to implement, but the user experience becomes generic and irrelevant, leading to user frustration and abandonment
Solution Approach 1:
The patent implements dynamic question sequencing where the interview questions adapt in real-time based on user responses. The system transitions from a static, fixed question list to a dynamic structure where question selection and ordering change according to user characteristics and answers, making the interview process adaptive and relevant to each user while maintaining manageable system complexity through rule-based adaptation
Solution Approach 2:
The system changes parameters such as question selection, question ordering, and interview path based on user data and responses. By dynamically adjusting these parameters rather than using fixed values, the system achieves personalized user experiences without requiring complete system redesign, balancing relevance with implementation feasibility
2Productivity
If traditional systems present all interview questions to every user, then the system is simple to implement, but users experience unnecessarily long and confusing interview processes
Solution Approach 1:
The patent extracts and removes irrelevant questions from the interview process by analyzing user data and responses in real-time. Questions that are not relevant to the specific user's situation are dynamically excluded from the interview sequence, reducing interview duration and confusion while maintaining the ability to ask all necessary questions for users who need them
Solution Approach 2:
The system applies partial action by selectively presenting only the necessary subset of questions to each user based on their specific needs and characteristics. Rather than presenting the complete question set to every user (excessive action), the system tailors the question sequence to include only what is needed for each individual case, improving efficiency without sacrificing completeness
3Adaptability or versatility
If traditional tax preparation systems use hard-coded, static analysis features, then the system is stable and reliable, but the analytics cannot evolve to meet changing user needs or situations
Solution Approach 1:
The patent transforms static, hard-coded analytics into dynamic analytics that can adapt to changing user needs while maintaining system stability. The analytics engine processes user data and responses in real-time to dynamically determine question sequencing and presentation, allowing the system to evolve its analytical approach without requiring complete system redeployment, thus balancing adaptability with reliability
4Ease of operation
If traditional systems present irrelevant interview questions to users, then the system follows a generic template, but potential customers become frustrated and abandon the process
Solution Approach 1:
The patent applies local quality by customizing the interview experience for each user based on their specific characteristics, responses, and needs. Rather than applying a uniform generic template to all users, the system adjusts question selection, ordering, and presentation locally for each user interaction, improving satisfaction while managing complexity through targeted personalization rather than complete customization
Data Source
AI summary
A method and system improve retention of a user of a tax return preparation system by personalizing a tax return preparation interview with questions that are at least partially based on user data processed by one or more predictive models, according to one embodiment. The method and system include receiving user data that is associated with a user, and applying the user data to one or more predictive models to cause the one or more predictive models to generate predictive output data, according to one embodiment. The predictive output data are scores for a subset of questions, and scores represent a relevance to the user of each of the subset of questions, according to one embodiment. The method and system include presenting selected ones of the subset of questions to the user, at least partially based on the scores, to personalize a tax return preparation interview for the user.