Tax Return Completion Graph Parser for Dynamic Tax Law Adaptation
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Solution Overview
Problem
Current virtual agents tasked with tax return preparation face challenges in handling complex tasks due to the need for exhaustive enumeration of numerous use cases and are not easily adaptable to changes in tax laws and regulations, making them inefficient and time-consuming to update.
Innovation Solution
Implementing a tax knowledge virtual agent system that utilizes a completion graph parser, query object creator, natural language synthesis and analysis engines, and a runner interface to interact with users through natural language questions, allowing for dynamic modification of tax return completion graphs based on user input and updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If virtual agents use exhaustive enumeration of use cases for tax return preparation, then they can handle complex tasks, but the system becomes difficult to update when tax laws change
Solution Approach 1:
The system transitions from static exhaustive enumeration to dynamic completion graphs that can be automatically updated. The completion graph parser dynamically interprets tax law changes and updates the graph structure, allowing the virtual agent to adapt to new tax laws without re-enumerating all use cases, thus maintaining both reliability for complex tasks and adaptability to changes.
Solution Approach 2:
The system changes the fundamental parameter of how tax knowledge is represented - from fixed use case enumerations to dynamic completion graphs. This parameter change enables the system to maintain comprehensive task handling while becoming adaptable to tax law changes through graph updates rather than re enumeration.
2Adaptability or versatility
If virtual agents update tax return preparation systems to adapt to law changes, then adaptability improves, but the time and effort required for updates increases
Solution Approach 1:
The system performs preliminary action by pre-building a comprehensive completion graph that captures all possible tax return scenarios. When tax laws change, the system only needs to update the relevant portions of the completion graph rather than rebuilding the entire system, significantly reducing update time while maintaining adaptability.
Solution Approach 2:
The completion graph is segmented into modular components representing different tax topics and scenarios. This segmentation allows the system to update only the affected segments when tax laws change, rather than updating the entire system, thus reducing the time and effort required for updates while maintaining comprehensive adaptability.
3Ease of operation
If traditional interfaces are used for tax return preparation, then user interaction is straightforward, but the system lacks natural language understanding and flexibility
Solution Approach 1:
The system introduces a natural language processing intermediary layer between the user and the completion graph. This intermediary translates natural language queries into completion graph traversals, allowing users to interact simply with natural language while the system maintains comprehensive adaptability through the structured graph representation.
Solution Approach 2:
The completion graph serves as a universal data structure that handles multiple types of tax return scenarios and user queries simultaneously. This multi-functionality allows the system to maintain ease of operation through unified natural language interaction while providing adaptability across diverse tax topics and changing laws.
Data Source
AI summary
A system for facilitating user interaction with a tax return preparation program includes a completion graph parser configured to identify a portion of a tax return completion graph corresponding to a tax topic. The system also includes a query object creator configured to analyze and assign a node in the portion as a query object property. The system further includes a natural language synthesis engine configured to map the property to a first natural language question. Moreover, the system includes a natural language analysis engine configured to analyze user input and to identify a characteristic that maps to the property based on the user input. In addition, the system includes a runner interface configured to modify the query object based on the characteristic, update the tax return completion graph based on the modified query object, and analyze the updated tax return completion graph to determine whether it is complete.