Automated Document Quality Assessment via NLP Layout Detection

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing systems fail to efficiently identify appropriate document templates for compliance monitoring and risk assessment, particularly when documents do not follow standardized formats, leading to manual inefficiencies and potential errors in document review processes.

Innovation Solution

A method and system utilizing Natural Language Processing (NLP) and trained neural networks to identify document types and sub-types, detect content layout, and determine compliance scores by comparing input documents to predefined templates, with feedback learning to retrain models based on Subject Matter Expert feedback.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual review processes are used for document compliance assessment, then thorough risk identification can be achieved, but time consumption and operational complexity increase significantly

Engineering Contradiction:

Improverisk identification accuracyVSAvoiddocument review time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical review processes with an automated system comprising NLP techniques, neural network models, and machine learning algorithms. The system automatically performs document type identification, layout detection, template matching, and compliance assessment, eliminating the need for manual gathering and analysis of document information while maintaining thorough risk identification capabilities

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an intermediary automated processing layer between the document and the compliance assessment outcome. This intermediary system uses trained neural networks and layout detection models to bridge the gap between raw document input and compliance evaluation, enabling automated yet accurate risk identification without direct manual intervention

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If multiple specialized reviews are conducted for different document aspects, then comprehensive compliance coverage is achieved, but process complexity and resource requirements increase

Engineering Contradiction:

Improvecompliance coverageVSAvoidreview process complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent merges multiple specialized review functions into a single integrated automated system. The system combines document type identification, layout analysis, template matching, and compliance assessment into one unified process that handles legal, financial, human resource, and regulatory compliance aspects simultaneously, eliminating the need for separate specialized reviews

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent creates a universal document review system that can handle multiple document types and compliance requirements through a single platform. The system uses trained neural network models and configurable templates to perform diverse compliance assessments across different domains (legal, financial, HR, regulatory) without requiring separate specialized processes for each

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Ease of operation

If existing systems are used for document review, then basic processing can be performed, but they fail to identify appropriate templates and monitor risks in non-standardized documents

Engineering Contradiction:

Improvedocument processing capabilityVSAvoidtemplate identification accuracy

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The patent performs preliminary document type identification and layout detection before template matching and compliance assessment. By using trained neural network models to pre-analyze document characteristics and structure, the system prepares the document data in advance, enabling accurate template identification and risk monitoring even for non-standardized documents

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent changes the parameters of document analysis by using machine learning models that can adapt to varying document structures and formats. The system adjusts its analysis parameters based on detected document types and layouts, enabling reliable template identification across standardized and non-standardized documents by dynamically modifying how documents are processed

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12106044B2Method and system for determining quality of a document during risk and compliance assessment

Publication Date: 2024.10.01 WIPRO LTD

AI summary

Disclosed herein is a method and system for determining quality of an input document during risk and compliance assessment. The method includes receiving input document for risk and compliance assessment, identifying a document type, and at least one sub-type of the input document using a Natural Language Processing (NLP) technique and a trained neural network model. Layout of content present in the input document is detected based on each of a plurality of segments extracted from content and structural parameters associated with respective segments. A document review model is identified from a plurality of document review models based on type and at least one sub-type of input document. Thereafter, the quality of the input document and a compliance score is determined by identifying one or more deviations of content of the input document from content of a predefined template for the input document.