NLP Entity Attribute Extraction Across Diverse File Types

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Solution Overview

Problem

Analysts face time-intensive and error-prone tasks when aggregating and analyzing information associated with entities from diverse sources and file types to generate reports, requiring extensive manual searches and processing.

Innovation Solution

An intelligence platform employs natural language processing and machine learning techniques to extract and associate attributes from text documents, automatically generating reports by receiving information from various sources and file types, thereby reducing manual effort and increasing accuracy and speed.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual analysis of text documents is performed to extract information associated with an entity, then information can be extracted from diverse sources and file types, but the process is time-intensive and error-prone

Engineering Contradiction:

Improveaccuracy of information extractionVSAvoidtime required for manual analysis

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical analysis with automated natural language processing systems. The NLP system automatically processes text documents from diverse sources and file types to extract attributes associated with entities, eliminating the need for manual reading and analysis while maintaining high accuracy through sophisticated algorithms

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service information extraction where the NLP platform automatically processes documents without requiring human intervention. The automated system performs attribute extraction, association, and report generation independently, allowing users to simply input queries and receive processed information

Inventive Principle:

Principle #25Self-service

2Productivity

If manual aggregation of information from multiple sources is performed, then comprehensive information can be collected, but processing speed is slow and resources are consumed

Engineering Contradiction:

Improvespeed of report generationVSAvoidtime required for information aggregation

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The NLP system operates continuously to aggregate information from multiple sources without interruption. The automated process continuously monitors, extracts, and associates attributes from diverse document sources, maintaining continuous useful action rather than requiring discrete manual operations, thereby significantly increasing processing speed

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The patent substitutes manual information aggregation with automated NLP processing that can simultaneously handle multiple sources and file types. The system parallelly processes documents from various sources through automated text analysis, dramatically increasing productivity compared to sequential manual review

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If manual processing of diverse file types is performed, then flexibility in handling different formats is maintained, but complexity and error rates increase

Engineering Contradiction:

Improveability to process different file typesVSAvoidcomplexity of processing system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The NLP system provides universal processing capability that handles multiple file types and sources through a single integrated platform. The system can process various document formats (PDF, Word, text, etc.) and sources (web, databases, emails) using unified NLP algorithms, eliminating the need for separate processing systems for each format and reducing overall complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Reliability

If extensive manual searching is performed to ensure complete information extraction, then thoroughness is improved, but time consumption and labor requirements increase

Engineering Contradiction:

Improvecompleteness of information extractionVSAvoidtime required for manual searching

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The NLP system incorporates feedback mechanisms to ensure complete and accurate information extraction. The system continuously refines its attribute extraction through feedback loops that verify extracted information against source documents and adjust processing parameters, maintaining high reliability and completeness without requiring extensive manual verification time

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11030409B2Identifying attributes associated with an entity using natural language processing

Publication Date: 2021.06.08 ACCENTURE GLOBAL SOLUTIONS LTD

AI summary

A device may receive information associated with an entity. The information may include a first resource and a second resource. The first resource may be associated with a first file type, and the second resource may be associated with a second file type that is different than the first file type. The first resource may be associated with a first source, and the second resource may be associated with a second source that is different than the first source. The device may extract a plurality of attributes associated with the entity based on the information. The device may implement a natural language processing technique to extract the plurality of attributes. The device may associate the plurality of attributes with a plurality of elements based on extracting the plurality of attributes. The device may provide information that identifies the plurality of elements and the plurality of attributes to permit and/or cause an action to be performed.