Automated Document Quality Analysis for R&D Tax Credit Claims
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Solution Overview
Problem
The current process for claiming Research and Development (R&D) tax credits is burdensome and manual, lacking clear guidance and formal tracking, leading to inefficiencies and risks due to improper filings and complex eligibility requirements.
Innovation Solution
An automated document quality analysis and review tool using Natural Language Processing (NLP) and Machine Learning (ML) to identify key sections, assess readability, and generate scores, providing an interactive interface for users to navigate and improve document quality, with a knowledge base trained by Subject Matter Experts to ensure consistency, completeness, and correctness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual document review process is used for R&D tax credit claims, then flexibility in handling complex eligibility requirements is maintained, but productivity is reduced and errors increase
Solution Approach 1:
The document review system is segmented into multiple specialized analyzers including section identifier, pattern matcher, anti-pattern detector, readability analyzer, and eligibility determinor. Each analyzer handles specific aspects of document evaluation, allowing the system to process complex R&D tax credit documentation efficiently while maintaining modularity and manageability.
2Measurement precision
If automated analysis is implemented to improve document assessment consistency, then measurement precision is improved, but device complexity increases
Solution Approach 1:
A curated knowledge base serves as an intermediary between the document being analyzed and the evaluation criteria. This knowledge base contains pre-defined patterns, anti-patterns, and eligibility rules that mediate the analysis process, ensuring consistent and accurate assessment while simplifying the complexity by providing structured reference data.
Solution Approach 2:
The manual mechanical review process is replaced with automated computational analysis using natural language processing and pattern matching algorithms. This substitution enables precise, consistent document assessment without human variability while managing complexity through software-based solutions.
3Reliability
If comprehensive document analysis is performed to ensure completeness and correctness, then reliability is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary analysis by identifying document sections and matching them against curated patterns before conducting full eligibility determination. This preliminary action filters and prepares data in advance, ensuring comprehensive review for reliability while reducing overall processing time through efficient staged analysis.
Solution Approach 2:
The analysis process operates continuously through multiple parallel analyzers that work simultaneously on different aspects of the document. The section identifier, pattern matcher, anti-pattern detector, and readability analyzer operate in concert to provide continuous, comprehensive evaluation without idle time, improving both reliability and efficiency.
Data Source
AI summary
The invention relates to computer-implemented systems and methods for assessing the quality of a document or a technical memorandum written with a loosely defined template, stereotype and/or outline of key sections or headers. An embodiment of the present invention leverages Natural Language Processing (NLP) and Machine Learning (ML) techniques to identify key sections in a document using NLP text patterns and further establish, using ML, how closely a given section matches similar sections in other documents that are considered by human Subject Matter Experts (SMEs) to be “well-written” for the intended purpose of the overall document. An embodiment of the present invention further ascertains whether the overall flow of the document follows a general outline in terms of the order of sections.