Automated Entity Information Collection System

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Solution Overview

Problem

Organizations face inefficiencies in manually collecting and analyzing information about multiple business entities, leading to inaccurate assessments and increased costs due to reliance on human workforce and limited data sources.

Innovation Solution

Automated systems that collect and analyze business entity information from disparate sources using machine learning models for semantic analysis, enabling the generation of uniform summaries and comparisons based on keywords, with optional human insights integration.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual collection and analysis of business entity information is used, then human workforce can provide insights, but efficiency is low and costs are increased

Engineering Contradiction:

Improveefficiency in collecting and analyzing entity informationVSAvoidtime required for manual information collection and analysis

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system enables automatic collection, parsing, and analysis of business entity information without human intervention. The automated entity information collection system performs semantic analysis, generates summaries, and stores data independently, eliminating the need for manual workforce involvement in these tasks.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processes with automated computational systems. Machine learning models and natural language processing algorithms substitute human analysts, automatically extracting insights from disparate sources and generating entity summaries through computational rather than manual means.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If information is collected from limited data sources, then collection process is simpler, but assessment accuracy is reduced

Engineering Contradiction:

Improveaccuracy in assessing business entitiesVSAvoidcomplexity of data collection system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system is designed to collect information from multiple disparate sources including web pages, social media, news articles, and proprietary databases. This multi-functional capability allows the same system to handle various data types and sources, comprehensively assessing business entities through diverse information channels.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent introduces an automated entity information collection system as an intermediary between disparate data sources and the organization's decision-making processes. This intermediary automatically parses, analyzes, and synthesizes information from multiple sources, managing the complexity of multi-source data collection while delivering accurate assessments.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If manual analysis of entity information is performed, then detailed insights can be obtained, but productivity is decreased

Engineering Contradiction:

Improvespeed of generating entity summaries and comparisonsVSAvoidquality and depth of entity information analysis

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The system replaces manual analytical processes with automated machine learning models that perform semantic analysis, topic classification, and sentiment analysis. These computational systems rapidly process and analyze entity information, generating comprehensive summaries and comparisons at speeds unattainable by human analysts while maintaining analytical depth.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent transforms unstructured entity information into structured summaries with standardized attributes and metrics. By changing the parameters of information representation from raw text to organized data with specific attributes, the system enables rapid processing and comparison while preserving analytical quality through consistent evaluation criteria.

Inventive Principle:

Principle #35Parameter changes

4Productivity

If automated systems with machine learning models are implemented, then efficiency and accuracy are improved, but system complexity increases

Engineering Contradiction:

Improveefficiency in entity information processingVSAvoidcomplexity of automated analysis system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system segments the complex task of entity information analysis into distinct functional modules: data collection from disparate sources, semantic analysis, topic classification, sentiment analysis, attribute extraction, and summary generation. This segmentation allows each component to be optimized independently while managing overall system complexity through modular architecture.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20240202756A1Automatic collection and processing of entity information

Publication Date: 2024.06.20 THE TORONTO DOMINION BANK

AI summary

This disclosure involves systems, software, and computer implemented methods for automatically generating and storing business entity summaries in a uniform format, including obtaining at least one identifier of an entity, and based on the at least one identifier obtaining information about the entity from two or more disparate sources. The obtained information can be parsed based on a semantic analysis of at least one source to generate a summary of the entity including one or more attributes associated with the entity. The summary of the entity is stored in a database.