Lean Parsing for Tax Form Automation
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Solution Overview
Problem
Traditional electronic document preparation systems face challenges in efficiently updating and accurately populating fields in tax forms due to changes in tax laws, requiring significant human and computing resources, leading to delays, inaccuracies, and increased costs.
Innovation Solution
A method and system employing lean parsing algorithms and machine learning to analyze natural language text, determining operators, operands, and dependencies in tax forms to generate machine-executable functions, reducing the need for manual expert intervention and improving efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional electronic document preparation systems are used to update and populate tax form fields, then the system can process tax forms, but it requires significant human and computing resources leading to delays and increased costs
Solution Approach 1:
The system uses machine learning models to automatically analyze natural language text from tax forms, extract operators and operands, and generate machine-executable functions without requiring manual expert intervention for each update, enabling the system to adapt to tax law changes autonomously
Solution Approach 2:
The patent replaces manual expert analysis and traditional parsing methods with automated machine learning-based natural language processing systems that can rapidly interpret tax form instructions and generate corresponding computational functions
2Adaptability or versatility
If traditional parsing methods are used to analyze tax form text, then the system can understand form requirements, but it requires significant human expert intervention and computing resources
Solution Approach 1:
The natural language processing system segments the tax form text into discrete operators and operands, analyzing each component separately to build comprehensive machine-executable functions that capture the full complexity of tax calculations
Solution Approach 2:
The patent introduces natural language processing models as an intermediary layer between the human-readable tax form text and the machine-executable functions, automatically translating instructional text into computational logic without requiring manual coding
3Measurement precision
If manual expert intervention is used to update electronic tax forms, then accuracy can be maintained, but it increases costs and reduces efficiency
Solution Approach 1:
The system uses training datasets containing examples of correct tax form completions to train machine learning models, providing feedback mechanisms that continuously improve the accuracy of generated functions through iterative learning and validation
Data Source
AI summary
Systems and methods for lean parsing are disclosed. An example method is performed by one or more processors of a system and includes retrieving form data including first sentence segments and second sentence segments, determining a first predicate structure for each of the sentence segments based on a set of operators within the first set of sentence segments, identifying known tokens within the second set of sentence segments, each of the known tokens appearing on a list of predetermined tokens, identifying new tokens within the second set of sentence segments, each of the new tokens not on the list, mapping each known and new token to at least one operator, determining a second predicate structure for each sentence segment based on the mapping, and generating a predicate argument structure incorporating the first and second predicate structures, the predicate argument structure ready for mapping to at least one machine executable function.