Tax Information Data Structure Assembly via Semantic Heuristics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The manual review of income-tax documents for updating tax engines is cumbersome, time-consuming, and prone to errors, leading to infrequent updates and difficulties in providing value-added services in dynamic environments, which affects customer experience and profitability.
Innovation Solution
A computer system that extracts tax information from income-tax documents using semantic and structural heuristics, along with statistical identification techniques, to assemble a tax-information data structure, enabling continuous updates and improving the accuracy and relevance of tax knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of income-tax documents is used to update tax engines, then tax knowledge accuracy is maintained through expert review, but the process becomes extremely cumbersome, time-consuming and expensive
Solution Approach 1:
The system enables automatic extraction and assembly of tax information from documents using computational algorithms, allowing the tax engine to update itself without requiring manual expert review for every document, thus reducing time loss while maintaining accuracy through automated validation
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computer-based system that uses text extraction, semantic analysis, and statistical techniques to process tax documents, eliminating the time-consuming human review while maintaining information accuracy
2Reliability
If manual review process is used, then tax knowledge is carefully validated, but updates occur only infrequently (e.g., once a year)
Solution Approach 1:
The automated system enables continuous processing of tax documents as they become available, allowing the tax engine to be updated continuously rather than periodically, maintaining reliability through consistent validation while dramatically increasing update frequency
Solution Approach 2:
The system continuously monitors and processes new tax documents automatically, enabling the tax engine to self-update in real-time based on incoming documents, thus achieving both high reliability through automated validation and continuous updates
3Device complexity
If a quasistatic tax engine is used, then development costs are controlled through manual processes, but the system cannot adapt quickly to changing customer needs or provide value-added services in dynamic environments
Solution Approach 1:
The patent transforms the static tax engine into a dynamic system that automatically adapts to changing tax documents and customer needs through continuous automated processing, enabling the system to evolve with changing requirements while maintaining manageable complexity through standardized algorithms
Solution Approach 2:
The system dynamically adjusts its processing parameters and data structures based on the incoming tax documents and identified tax phrases, allowing the tax engine to adapt its behavior to different document formats and tax scenarios without increasing overall system complexity
4Loss of information
If extensive manual review of hundreds of documents is conducted, then comprehensive tax knowledge is captured, but the process becomes extremely expensive and resource-intensive
Solution Approach 1:
The patent replaces resource-intensive manual review with automated computational processing that can handle hundreds of documents efficiently, capturing comprehensive tax information through algorithmic extraction while minimizing human resource consumption
Solution Approach 2:
The system creates structured data copies and representations of tax information from source documents, allowing comprehensive information to be captured and processed in multiple formats without requiring repeated manual review of the original documents, thus reducing resource consumption
Data Source
AI summary
The disclosed embodiments relate to a tax-information assembly technique, which extracts tax information and associated context information from income-tax documents, where these income-tax documents are associated with an income-tax agency, and some of the income-tax documents include the same tax information in different document formats. During this technique, semantic and structural heuristics are used to identify tax phrases in the extracted tax information. Moreover, additional tax phrases in the extracted tax information are identified using a statistical identification technique. Next, relationships between the tax phrases and the additional tax phrases are determined, and the context information is used to consolidate the tax phrases and the additional tax phrases into a tax-information data structure.