Advice Engine for Personalized Tax Return Preparation
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Solution Overview
Problem
The process of preparing tax returns is complex and error-prone, with users often unaware of necessary tips or help articles that could positively impact their returns, leading to potential errors or reduced refunds.
Innovation Solution
A system that profiles taxpayers as they prepare their returns, automatically presenting relevant tax tips and help articles based on the impact of previously viewed information, using a typecasting engine to classify returns and determine indicator variables for personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all tips and help articles are provided to users, then comprehensive coverage of tax information is achieved, but users are overwhelmed with too much information
Solution Approach 1:
The system extracts and selects only the most relevant tips and help articles from the complete tax information database based on the user's specific tax situation and profile. This extraction process filters out irrelevant information while maintaining comprehensive coverage of applicable tax concepts, thereby preventing user overload.
Solution Approach 2:
The system applies local quality by tailoring the presentation of tax tips and help articles to match the specific characteristics of each user's tax situation. Different users receive customized information sets based on their profiles, ensuring that each user receives only the locally relevant information needed for their specific case rather than a generic overview.
2Ease of operation
If tips and help articles are manually selected by users, then user control over information is maintained, but users may not know which information is most relevant
Solution Approach 1:
The system performs preliminary action by automatically analyzing the user's tax situation and pre-selecting the most relevant tips and help articles before the user needs them. This preliminary classification and selection process ensures that when users review information, the most relevant items are already highlighted and ready, reducing the burden of manual search.
Solution Approach 2:
The system incorporates feedback mechanisms where users can indicate which tips and help articles are most helpful for their specific situations. This feedback is used to refine and improve the automated selection algorithm over time, enhancing the system's ability to accurately identify and present relevant information without requiring extensive manual user input.
3Loss of information
If personalized tax information is provided based on user profiles, then information relevance is improved, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the tax information database into distinct categories and segments based on tax topics, user profiles, and relevance criteria. This segmentation allows the complex task of personalized information delivery to be broken down into manageable steps: classifying users by profile, segmenting information by topic, and matching the two through automated algorithms.
Solution Approach 2:
The system introduces an intermediary layer in the form of an automated classification and selection engine that mediates between the user profiles and the vast tax information database. This intermediary component handles the complexity of matching and filtering, translating user-specific criteria into relevant information sets without requiring direct complex interactions between the user and the entire database.
Data Source
AI summary
System, method and media for the provision of relevant information, tax regulations and other advice for the preparation of the return to the user of the tax preparation system. This can be done by developing a profile of the taxpayer as the user moves through the process of preparing the tax return. When a user views a tip or help article, the impact on the outcome of the return is noted. If the impact is a positive one, then the tip or help article may be automatically presented to future users with similar taxpayer profiles. In this way, users can maximize the benefit of the return for the taxpayer while increasing the accuracy of the prepared return.