Tax Preparation System with Dynamic Relevancy Ranking
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Solution Overview
Problem
Traditional tax return preparation systems are inefficient and laborious, as they require taxpayers to navigate through rigidly defined user interfaces that ask irrelevant questions, leading to a time-consuming process that burdens individuals and businesses with significant compliance efforts.
Innovation Solution
A tax preparation system utilizing declarative data structures, such as completeness graphs and tax calculation graphs, allows for dynamic tax calculations based on user input, sourced data, or estimates, with a smart tax logic agent that identifies missing data fields and proposes relevant questions to fill in blanks, tailoring the user experience to the taxpayer's situation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional rigidly defined user interfaces are used to ask taxpayers questions, then all tax topics can be covered systematically, but irrelevant questions are presented to taxpayers leading to time-consuming processes
Solution Approach 1:
The system performs preliminary actions by analyzing taxpayer data profiles and determining relevancy rankings of tax topics before presenting questions. The tax logic agent evaluates which tax topics are most relevant to each taxpayer based on their specific situation, allowing the system to prioritize and present only necessary questions in advance, rather than following a rigid predetermined sequence.
Solution Approach 2:
The system implements dynamics by making the user interface adaptive and flexible rather than rigid. The tax topic relevancy module dynamically determines which tax topics to present and in what order based on real-time analysis of taxpayer data. This allows the question sequence to change dynamically according to each taxpayer's specific circumstances, eliminating irrelevant questions while ensuring all necessary topics are covered.
2Reliability
If a comprehensive set of tax questions is presented to all taxpayers, then all tax topics are covered, but the process becomes burdensome and laborious
Solution Approach 1:
The system applies local quality by customizing the tax preparation experience to each individual taxpayer rather than applying a uniform approach to all users. The tax logic agent analyzes specific taxpayer data profiles and determines which tax topics are locally relevant to each user's situation. This allows the system to present different sets of questions to different taxpayers based on their specific needs, ensuring completeness where necessary while reducing burden where not needed.
Solution Approach 2:
The system changes parameters by dynamically adjusting which tax topics are presented and in what order, rather than maintaining a fixed parameter set for all users. The tax topic relevancy module modifies the presentation parameters based on analysis of taxpayer data, changing the scope and sequence of questions to match each taxpayer's specific situation, thereby maintaining reliability while improving ease of operation.
3Adaptability or versatility
If irrelevant tax questions are asked of taxpayers, then all possible tax scenarios are considered, but the user experience is degraded and process time increases
Solution Approach 1:
The system performs preliminary analysis of taxpayer data profiles before presenting questions, determining which tax scenarios are relevant to each user's situation. This preliminary action allows the system to filter out irrelevant questions in advance while ensuring all necessary tax scenarios are covered, based on the specific characteristics of each taxpayer's data.
Solution Approach 2:
The system uses feedback mechanisms by continuously analyzing taxpayer responses and adjusting which tax topics are presented next. The tax logic agent monitors the taxpayer's situation and provides feedback-driven recommendations for which tax matters to address, ensuring comprehensive coverage of relevant scenarios while eliminating irrelevant questions based on real-time feedback from the taxpayer's data profile.
Data Source
AI summary
Systems, methods and articles of manufacture for determining the relevancy of tax matters to a particular taxpayer and prioritizing the tax matters in a tax preparation system for preparing an electronic tax return. A computerized tax preparation system accesses taxpayer data and generates a taxpayer data profile using the taxpayer data. The system executes at tax logic agent which evaluates missing tax data required to prepare the tax return for the taxpayer and outputs a plurality of suggested tax matters for obtaining the missing tax data to a user interface manager. The tax logic agent utilizes the taxpayer data profile and a tax matter relevancy module having a plurality of tax data profiles to determine a relevancy ranking for each suggested tax matter. A user interface manager receives the suggested tax matters and the corresponding relevancy rankings and determines one or more tax questions to present to the user.