Modular Tax Topic Prediction Engine
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Solution Overview
Problem
Current tax preparation systems lack personalization and comprehensiveness, as they often rely on static question sets and do not effectively utilize prior year tax returns or additional data sources to identify relevant tax topics or questions for the current tax year.
Innovation Solution
A modular tax preparation system that separates tax logic from user interface functions, utilizing a topic engine to predict applicable tax topics based on prior year tax returns and secondary data sources, such as statistical data and predictive models, to generate non-binding suggestions for the user interface controller.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a static question set is used for tax preparation, then the system is simple to operate, but the personalization and comprehensiveness of tax topic identification deteriorates
Solution Approach 1:
The system transitions from a static question set to a dynamic question generation mechanism that adapts to each taxpayer's situation. The topic engine dynamically identifies relevant tax topics by analyzing prior year returns and secondary data sources, generating personalized questions rather than presenting a fixed questionnaire to all users.
Solution Approach 2:
The system performs preliminary analysis of prior year tax returns and secondary data sources before generating the current year questionnaire. By pre-identifying relevant tax topics and potential changes from previous years, the system prepares a customized question set in advance, improving both personalization and efficiency.
2Reliability
If prior year tax returns and secondary data sources are analyzed to identify relevant tax topics, then the comprehensiveness of tax preparation improves, but the complexity of the system increases
Solution Approach 1:
The system divides the complex task of tax preparation into separate modular components: a topic engine that identifies relevant tax topics, a primary data source (prior year returns), and secondary data sources (financial institution data, transaction data). This segmentation allows each component to handle specific functions independently, managing complexity while maintaining comprehensiveness.
Solution Approach 2:
The topic engine serves as an intermediary component that processes information from multiple data sources and generates personalized questions. This intermediary layer simplifies the overall system architecture by centralizing the complex analysis logic in a dedicated module, preventing complexity from propagating throughout the entire system.
3Adaptability or versatility
If a modular system with separate tax logic and user interface components is implemented, then the adaptability of the system improves, but the device complexity increases
Solution Approach 1:
The system is divided into distinct modular components including the topic engine, user interface controller, and data management modules. Each module has a specific function and can be independently developed, tested, and maintained. The topic engine handles tax logic while the user interface controller manages presentation, allowing flexible adaptation without increasing overall system complexity.
Solution Approach 2:
The modular architecture enables the topic engine to serve multiple functions: analyzing prior year returns, processing secondary data sources, identifying tax topics, and generating personalized questions. This multi-functionality within a single module reduces the number of components needed, balancing adaptability with structural simplicity.
Data Source
AI summary
Computer-implemented methods, systems and articles of manufacture for determining which questions to present to a user of a modular tax preparation application in which analysis of tax logic by the tax logic agent is separate from interview screens generated by the user interface controller. A topic engine of the modular tax preparation application is configured or programmed to predict which tax topics are applicable to the current electronic tax return based at least in part upon a data of a prior year tax return. Other sources besides the current tax return being prepared and the prior year tax return may also be utilized for topic determination. The tax logic agent generates a non-binding suggestion for the user interface controller based at least in part upon an output generated by the topic engine.