NLP Payroll Tax Notice Extraction

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Solution Overview

Problem

The complexity and variability in payroll tax notices from different taxing agencies pose a challenge for taxed entities to ensure compliance, as existing methods rely on spatially specific templates that require frequent updates to accommodate format changes, leading to a processing burden.

Innovation Solution

A computer-implemented method using natural language processing (NLP) and named entity recognition (NER) to extract attributes and named entities from payroll tax notices, generating a structured summary that identifies the taxed entity, tax jurisdiction, and taxing agency, independent of spatial relationships, thus allowing for format changes without requiring template updates.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If spatially specific templates are used to extract information from payroll tax notices, then information extraction can be achieved, but the system requires frequent template updates to accommodate format changes from different taxing agencies

Engineering Contradiction:

Improveadaptability to different notice formatsVSAvoidtemplate update complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent replaces the mechanical template-matching system with an AI-based natural language processing system. Instead of using fixed spatial templates that require manual updates when formats change, the system uses NLP models to automatically understand and extract information from varied notice formats, eliminating the need for frequent template updates while maintaining adaptability.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the approach from fixed spatial parameters (template positions) to semantic parameters (meaning-based extraction). By using NLP to identify entities and relationships based on their semantic meaning rather than their position in the document, the system can adapt to format changes without requiring parameter updates.

Inventive Principle:

Principle #35Parameter changes

2Productivity

If conventional template-based extraction is used, then processing can be standardized, but time and resources are consumed for frequent template updates

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidtime for template updates

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces the manual template maintenance process with an automated NLP-based extraction system. This substitution eliminates the time-consuming task of frequently updating templates while maintaining standardized processing, thereby improving productivity without incurring the time loss associated with template updates.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If spatial templates are used to identify information in payroll tax notices, then extraction can be performed, but the system cannot easily accommodate format changes without template updates

Engineering Contradiction:

Improveextraction accuracyVSAvoidflexibility to format changes

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent replaces the rigid spatial template system with a flexible NLP-based system that maintains extraction accuracy through semantic understanding. The NLP model can adapt to format changes from different taxing agencies while maintaining reliable extraction by focusing on the meaning and context of the information rather than its spatial position.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11023982B2Method to efficiently categorize, extract and setup of payroll tax notices

Publication Date: 2021.06.01 ADP INC

AI summary

Aspects of the present invention provide devices that process payroll tax notices by extracting payroll tax notice attributes and named entities from text of a payroll tax notice document using natural language processing, named entity recognition, and the labels of entities identified by way of machine comprehension. The devices generate a structured payroll tax notice summary based on the extracted payroll tax notice attributes and user context that identify a receiving taxed entity, a tax jurisdiction, and a taxing agency.