Tax Preparation Automation via Completeness Graphs
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Solution Overview
Problem
Tax preparation is a time-consuming and laborious process, with individuals and businesses spending significant hours complying with filing requirements, and existing tax preparation software often requires manual entry of extensive data through rigid user interfaces.
Innovation Solution
The use of declarative data structures, such as completeness graphs and tax calculation graphs, allows for a more dynamic and automated tax preparation process by connecting tax rules and calculations, enabling a data capture utility to gather and transfer user-specific tax data from various sources, reducing manual input and streamlining the preparation of tax returns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data entry is used through rigid user interfaces, then data can be collected, but the process is time-consuming and laborious
Solution Approach 1:
The system performs preliminary actions by automatically gathering tax-related data from multiple sources (employers, financial institutions, government agencies) before the user needs to input anything. Data capture utilities are executed to collect information in advance, populating data structures with tax data, income information, and other relevant details, thereby eliminating the need for manual entry during the actual tax preparation process.
Solution Approach 2:
The system enables self-service by using data capture utilities that automatically connect to external data sources and retrieve necessary tax information without human intervention. The completeness graph and tax calculation graph structures self-evaluate to determine what data is missing and automatically pursue that data from available sources, reducing reliance on manual user input and accelerating the overall process.
2Reliability
If extensive manual data entry is required, then comprehensive tax data can be collected, but the complexity of the user interface increases
Solution Approach 1:
The system introduces intermediary data structures (completeness graph and tax calculation graph) that mediate between the user and the complex tax calculation engine. These graphical structures serve as simplified interfaces that automatically manage data collection requirements, tracking what data is needed and pursuing it automatically, thereby shielding users from the underlying complexity while ensuring data completeness.
Solution Approach 2:
The completeness graph provides continuous feedback by automatically evaluating what tax data is missing and notifying the system accordingly. This feedback mechanism triggers automatic data pursuit from external sources, creating a closed-loop system that ensures comprehensive data collection without requiring users to manually track or input every piece of information, thus maintaining reliability while reducing interface complexity.
3Ease of operation
If automated data capture is implemented, then manual data entry is reduced, but the system must integrate with multiple external data sources
Solution Approach 1:
The system implements universality by designing data capture utilities that can interact with multiple types of external data sources (employers, financial institutions, government agencies) through a unified interface. The completeness graph and tax calculation graph structures serve universal purposes of tracking data requirements and coordinating collection efforts across diverse sources, allowing the system to maintain ease of operation while handling complex multi-source integration through standardized mechanisms.
Data Source
AI summary
A computer-implemented method for inferring or estimating user-related data for use with tax preparation software is disclosed. The method uses a computer that connects to the one or more remotely located data sources and executing a data capture utility, the data capture utility capturing at least some tax data pertaining to the user. The computing device executes an estimation module that receives the captured tax data pertaining to the user and generates one or more estimates and stores the one or more estimates in a data store associated with the tax preparation software, the data store configured to store user-specific tax data therein. The computing device executes a tax calculation engine of the tax preparation software configured to read the user-specific tax data contained in the data store and compute an intermediate or final tax liability or refund amount.