Dynamic Tax Interview Engine for Real-Time Error Detection
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Solution Overview
Problem
Tax preparation applications relying on pre-determined question-and-answer tree structures are inflexible and unable to identify data entry errors effectively, limiting their ability to adapt and provide real-time feedback to users.
Innovation Solution
A suggestion-based tax preparation application using a declarative knowledge base and modular interview engines, where a rule module generates non-binding suggestions based on runtime data, compliance rules, and user statistics, allowing the interface controller to dynamically determine the content and sequence of interview screens without relying on a pre-defined tree structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a pre-determined question-and-answer tree structure is used in tax preparation applications, then the application can systematically guide users through tax questions, but the application becomes rigid and lacks flexibility in adapting to different user needs and data conditions
Solution Approach 1:
The patent transforms the static question-and-answer tree structure into a dynamic system that adapts its behavior based on runtime conditions. The interview engine now dynamically determines which questions to ask next based on user responses, data already entered, and statistical patterns from other taxpayers, rather than following a fixed predetermined sequence. This allows the system to maintain systematic guidance while becoming flexible and adaptive to different user situations.
Solution Approach 2:
The system changes the parameters that control question sequencing from static tree structure definitions to dynamic parameters including user responses, runtime data analysis, and statistical patterns. By changing these controlling parameters, the system can adjust the interview flow in real-time to match actual user needs and data conditions, resolving the contradiction between structured guidance and adaptive flexibility.
2Ease of operation
If manual data entry fields are provided in interview screens, then users can input their tax information, but errors in data entry may not be identified by the application
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors entered data against statistical patterns from other taxpayers and compliance rules. When data entry errors or inconsistencies are detected, the system provides real-time feedback to users through suggestions and alerts, allowing them to correct errors before filing. This feedback loop maintains ease of data entry while significantly improving data accuracy and reliability.
Solution Approach 2:
The system performs preliminary error detection and validation checks before the user completes data entry. By analyzing entered data against statistical patterns and compliance requirements in real-time, the system identifies potential errors early in the process rather than waiting until the end, allowing users to correct issues while the context is still fresh and improving overall data reliability.
3Device complexity
If a pre-determined question sequence is followed, then the application structure remains simple and predictable, but the application cannot provide personalized experiences or adapt to changing data conditions
Solution Approach 1:
The patent segments the interview engine into independent modular components that can be dynamically assembled and executed. Rather than a single rigid question sequence, the system divides the interview into multiple question sets that can be selected and presented in different orders based on user needs. This modular segmentation maintains structural simplicity while enabling personalized and adaptive interview experiences.
Solution Approach 2:
The system creates a universal interview engine framework that can serve multiple functions: it can follow traditional question sequences when appropriate, adapt to user responses in real-time, provide personalized guidance based on statistical patterns, and enforce compliance rules. This multi-functional design allows a single system to maintain simplicity while providing adaptive, personalized experiences across different user scenarios.
Data Source
AI summary
Computer-implemented methods, system and computer program products for determining what to present to a user of a tax preparation application. A tax compliance or rule module receives runtime data of the electronic tax return and tax rules specifying tax authority requirements. The rule module executes at least one tax rule utilizing the runtime data to generate a non-binding suggestion, which is provided as an input to loosely coupled interface controller. The interface controller determines content of an interview screen for display to the user based at least in part upon factors including the non-binding suggestion, and presents the interview screen including the determined content to the user via a user interface. The content may or may not include content based on the non-binding suggestion depending on processing by the interface controller.