Tax Logic Agent Completeness Graph Explanation Engine
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Solution Overview
Problem
Electronic tax return preparation systems often confuse users with seemingly misplaced or unreasonable tax questions, leading to anxiety and a higher likelihood of abandoning the preparation process due to lack of explanations for the necessity of specific tax data.
Innovation Solution
A system and method that utilize a tax logic agent and explanation engine to analyze completeness graphs, identify variables requiring explanations, and generate natural language explanations indexed to these variables, providing users with clear reasons for asked tax questions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the tax return preparation system asks detailed tax questions to collect necessary data, then the completeness of tax data collection is improved, but user anxiety and likelihood of abandoning the process increase due to lack of explanations
Solution Approach 1:
The patent introduces an explanation engine as an intermediary component that generates natural language explanations for tax questions. This mediator translates the system's data collection needs into user-friendly explanations, reducing user anxiety while maintaining data completeness. The explanation engine acts as a bridge between the tax logic agent and the user interface, providing context for why each question is asked.
Solution Approach 2:
The system implements feedback by providing explanations to users about why specific tax questions are asked. This feedback loop reduces user uncertainty and anxiety by clarifying the purpose of each data collection request. The explanations are generated dynamically based on the current state of the completeness graph and the specific variable being queried.
2Ease of operation
If the system provides explanations for all tax questions to reduce user anxiety, then user experience is improved, but system complexity and processing time increase
Solution Approach 1:
The patent applies local quality by generating explanations only for specific variables that require clarification, rather than providing uniform explanations for all tax questions. The explanation engine analyzes the completeness graph to identify which variables need explanations based on their context and importance, creating a tailored explanation strategy that reduces overall system complexity while maintaining user experience quality where needed.
Solution Approach 2:
The system changes the parameter of explanation generation from a static, all-encompassing approach to a dynamic, context-dependent approach. The explanation engine adjusts its behavior based on the current state of the completeness graph, the specific variable being queried, and the user's progress through the tax return preparation process. This parameter-based approach reduces unnecessary processing while providing explanations when most beneficial.
3Loss of information
If the system generates explanations for all variables, then completeness of explanations is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements partial action by generating explanations only for a subset of variables that most benefit from clarification. The explanation engine prioritizes variables based on their importance, context, and the user's likely confusion points, rather than generating explanations for all variables uniformly. This selective approach maintains sufficient explanation completeness while significantly reducing processing time and computational resource requirements.
Solution Approach 2:
The system performs preliminary analysis of the completeness graph to identify which variables are most likely to require explanations before actually generating them. The tax logic agent and explanation engine work together to pre-identify high-priority variables based on the current state of data collection, allowing the system to prepare explanations efficiently only when needed rather than generating all explanations upfront or on demand.
Data Source
AI summary
A system for explaining tax questions for an electronic tax return preparation program includes a computing device having a tax logic agent and a user interface controller. The computing device executes the tax logic agent, which analyzes a completeness graph to identify a required variable. The tax logic agent also determines whether an explanation asset is indexed to the required variable in the completeness graph, and sends an identity of the required variable to a user interface controller. The user interface controller generates a tax question corresponding to the required variable. When the tax logic agent determines that an explanation asset is indexed to the required variable in the completeness graph, the computing device presents the tax question and the explanation asset. When an explanation asset is not indexed to the required variable in the completeness graph, the computing device presents the tax question without the explanation asset.