Interview Topic Autogeneration Using Completeness Graphs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The process of updating tax software to reflect changes in tax forms and laws is manual, labor-intensive, prone to errors, and inefficient, requiring human analysts to interpret changes and code them into user interfaces, which is time-consuming and resource-intensive.
Innovation Solution
A system and method that automates the creation of an interview experience for document preparation using a completeness graph and field guide, leveraging AI to generate and update user interfaces dynamically, reducing the need for manual coding and ensuring compliance with changing tax laws.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual coding is used to update user interfaces for tax form changes, then flexibility and customization are improved, but productivity and time efficiency deteriorate
Solution Approach 1:
The system performs self-updating by automatically generating interview screens when tax form definitions change. The completeness graph and field guide enable the system to autonomously adapt to new tax forms without requiring manual programmer intervention, thus maintaining flexibility while dramatically improving update speed
Solution Approach 2:
The system pre-defines interview screens and user interface elements based on tax form specifications before actual use. When tax forms are updated, the pre-configured system automatically generates the necessary interface changes in advance, eliminating the need for manual coding during critical update periods
2Reliability
If manual interpretation and coding by human analysts is used, then accuracy in understanding tax laws is improved, but loss of time and resource intensity worsen
Solution Approach 1:
The system replaces the mechanical process of manual interpretation and coding with an automated computational system. The completeness graph and field guide enable algorithms to automatically translate tax form definitions into functional interview screens, maintaining accuracy through structured data models while eliminating time-consuming manual processes
Solution Approach 2:
The completeness graph serves as an intermediary data structure that bridges tax form definitions and user interface generation. This intermediary layer automatically translates between the two domains, ensuring accuracy through structured representation while eliminating the need for manual translation by human analysts
3Ease of operation
If complex interview layers are created for online customers, then user experience quality is improved, but device complexity and resource requirements worsen
Solution Approach 1:
The system segments the interview creation process into distinct components: tax form definitions, completeness graphs, field guides, and interview screen generation. This segmentation allows each component to be independently managed and generated, simplifying the overall system architecture while enabling high-quality, customized user experiences through modular assembly
Data Source
AI summary
A system for automating creation of an interview experience for document preparation. The system creates a completeness graph by interpreting current rules for the document preparation, the completeness graph indicates informational elements for completing the document preparation. A guide generator creates a field guide using data from the completeness graph including questions to pose to a user for requesting user input for the informational elements for completing the document preparation. An autogenerating process generates the interview experience using the completeness graph and the field guide, and automatically updates a user interface code and a resultant user interface screen displayed to the user in response to modifications to the completeness graph and the field guide.