Dynamic Tax Interview Question Rewording for Personalized User Experience
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Solution Overview
Problem
Traditional tax return preparation applications present questions in a fixed format, which may not be personalized or optimized for user responses, leading to less positive experiences and potential misinterpretation of user data.
Innovation Solution
A system that uses a rule engine and a question modification module to restructure or rephrase questions based on user data, such as demographic information and device type, to encourage positive responses and ensure answers align with the application's data model, while presenting modified questions to the user through an interview screen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional fixed-format questions are used in tax return preparation applications, then the system structure remains simple and data consistency is maintained, but user experience becomes less positive and response accuracy may deteriorate
Solution Approach 1:
The system separates the question generation process into distinct components: a rule engine that determines tax logic independently from a UI management module that handles question presentation. This segmentation allows the UI to be customized for better user experience without affecting the core tax logic, resolving the contradiction between simplified structure and improved ease of operation
Solution Approach 2:
An intermediary layer is introduced between the rule engine and the user interface, where questions are modified based on user characteristics before presentation. This intermediary transformation process enhances user experience while maintaining the integrity of the underlying tax logic and data consistency
2Measurement precision
If questions are modified based on user characteristics, then response accuracy and user experience improve, but the complexity of question management increases
Solution Approach 1:
The question presentation is made dynamic rather than static. Questions are automatically modified in real-time based on user characteristics such as demographic information and device type. This dynamic adaptation improves response accuracy without requiring manual management of multiple question versions, as the system automatically selects and modifies appropriate questions
Solution Approach 2:
The system changes parameters of question presentation based on user characteristics. Instead of creating entirely different questions, the system modifies existing questions by changing their parameters (wording, format, complexity) to better suit the user, thereby improving response accuracy while managing complexity through parameter-based transformations
3Ease of operation
If questions are rephrased to encourage positive responses, then user satisfaction improves, but potential misinterpretation of user data may occur
Solution Approach 1:
The system incorporates feedback mechanisms where the rule engine continuously monitors user responses and adjusts question modifications accordingly. This feedback loop ensures that while questions are rephrased to improve user satisfaction, the modifications do not lead to loss of information, as the system learns from user responses and refines its questioning strategy
Data Source
AI summary
Computer-implemented methods, systems and articles of manufacture for modifying the manner in which interview questions are presented to a user of a tax return preparation application to provide a more personalized experience during preparation of an electronic tax return. A selected question that is consistent with a data model or schema is modified or twisted such that the selected question is reworded or rephrased. The modified question, rather than the original question, is presented to the user. The user's answer to the modified question is converted, mapped or “untwisted” to derive a corresponding answer to the original question that is consistent with the data model or schema utilized by the tax return preparation application. The corresponding answer may then be read by a rule engine or logic agent that utilizes a decision table that defines rules to determine which additional or other questions can be presented to the user.