Semantic Dependency Analysis for Adaptive Tax Interview Sequencing

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Solution Overview

Problem

Tax preparation applications relying on pre-determined question-and-answer tree structures are inflexible and unable to identify data entry errors effectively, limiting their ability to adapt and ensure data accuracy during the tax return preparation process.

Innovation Solution

A modular interview engine framework that uses a rule module and interface controller, loosely coupled to generate non-binding suggestions based on runtime data, compliance rules, and user-specific attributes, allowing for real-time error identification and dynamic question presentation without relying on a pre-defined hierarchical tree structure.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a pre-determined question-and-answer tree structure is used, then the application structure is simple and easy to implement, but the application lacks flexibility and adaptability in question identification

Engineering Contradiction:

Improveflexibility in question identificationVSAvoidapplication structure complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent replaces the rigid mechanical tree structure with a semantic network model that uses natural language processing and semantic analysis. This allows the system to identify questions based on semantic relationships in the user's input rather than following predetermined paths, achieving flexibility without proportionally increasing structural complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system dynamically changes parameters such as question selection, interview path, and data validation rules based on semantic analysis of runtime data. This allows the application to adapt to different user inputs and contexts while maintaining a manageable underlying structure through parameter dynamicization rather than structural complexity.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If data entry fields are provided without error detection, then ease of operation is improved, but data accuracy and reliability deteriorate

Engineering Contradiction:

Improvedata accuracyVSAvoiduser input simplicity

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system implements real-time feedback mechanisms that analyze user inputs semantically and provide immediate error detection and correction suggestions. The semantic analysis engine continuously monitors data entries against tax rules and semantic consistency requirements, alerting users to potential errors while allowing flexible input methods.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary semantic analysis and validation before final data submission. By pre-checking data for semantic consistency, rule compliance, and potential errors during the interview process rather than only at the end, the system maintains ease of operation while ensuring data accuracy through advance error detection.

Inventive Principle:

Principle #10Preliminary action

3Ease of operation

If manual data entry is allowed, then ease of operation is improved, but error introduction increases

Engineering Contradiction:

Improvedata entry convenienceVSAvoiddata entry errors

Core Design Contradiction:

Ease of operationVSObject-generated harmful factors

Solution Approach 1:

The patent employs natural language processing and semantic analysis to replace traditional mechanical validation methods. The system understands the semantic meaning of user inputs and automatically detects errors such as inconsistent data, missing information, or logically contradictory entries, reducing human error while maintaining the convenience of manual entry.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system provides self-service error detection and correction capabilities that guide users through the data entry process. Through semantic analysis, the system automatically identifies potential errors and offers corrective suggestions, enabling users to correct their own mistakes without external assistance while maintaining ease of operation.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS10475132B1Computer implemented methods systems and articles of manufacture for identifying tax return preparation application questions based on semantic dependency

Publication Date: 2019.11.12 INTUIT INC

AI summary

Computer-implemented methods, system and computer program products for determining questions or potential questions to present to a user of a tax preparation application based at least in part upon analysis of pre-determined semantic dependencies of interview questions. Questions that are determined to be independent or free of semantic dependency can be selected as questions to be presented to the user or questions that are the subject of non-binding suggestions generated by a rule module and provided to an interface controller, which processes the non-binding suggestions.