Supply Chain Graph Generation via Automated Data Mining

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Solution Overview

Problem

Current supply chain management systems lack effective methods to access and analyze data from various sources to generate comprehensive supply chain visualizations, leading to untimely and ineffective risk alerts, and manual processing of information is inefficient, hindering informed decision-making in the financial services sector.

Innovation Solution

A system that automatically processes and interprets data from structured and unstructured sources to generate supply chain visualizations, using an input and identification module, instantiated query generation module, and supply chain generation module to create supply chain graphs, enabling the analysis of commodity flow, risk prediction, and informed decision-making.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual processing of supply chain information is used, then data accuracy may be maintained through human judgment, but processing efficiency and speed are significantly reduced

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidtime delay in risk alerts

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical processing with automated computer-based systems including natural language processing, machine learning algorithms, and automated data extraction tools to process supply chain information, thereby significantly improving processing efficiency while maintaining analytical depth

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces automated software agents and algorithms as intermediaries between raw supply chain data and risk analysis, enabling continuous automated processing that bridges the gap between data collection and actionable risk alerts without manual intervention delays

Inventive Principle:

Principle #24Intermediary (Mediator)

2Loss of information

If comprehensive data from multiple sources is collected to improve risk analysis accuracy, then the completeness of supply chain information increases, but the complexity of data processing and integration increases

Engineering Contradiction:

Improvecompleteness of supply chain dataVSAvoidcomplexity of data processing system

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent segments the complex data processing system into specialized modules including natural language processing components, entity extraction modules, relationship mapping algorithms, and risk analysis engines, each handling specific aspects of supply chain data to manage overall system complexity

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a multi-functional integrated platform that simultaneously performs data collection from diverse sources, natural language processing, entity extraction, relationship mapping, and risk analysis through a unified system architecture, reducing the need for separate specialized systems

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Productivity

If automated systems are used to process supply chain data, then processing speed and efficiency improve, but the ability to interpret nuanced information and contextual understanding may deteriorate

Engineering Contradiction:

Improvedata processing speedVSAvoidaccuracy of risk assessment

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent implements feedback mechanisms where automated processing results are continuously refined through machine learning algorithms that learn from analyst corrections and outcomes, improving both speed and accuracy over time through iterative optimization

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent employs natural language processing algorithms and semantic analysis tools as intermediaries that bridge automated processing and contextual understanding, enabling machines to interpret nuanced supply chain information while maintaining high processing speeds

Inventive Principle:

Principle #24Intermediary (Mediator)

4Loss of information

If supply chain information is made accessible to third parties for competitive analysis, then market transparency and risk awareness improve, but data security and proprietary information protection are compromised

Engineering Contradiction:

Improveaccessibility of supply chain intelligenceVSAvoidrisk of data breach and competitive disadvantage

Core Design Contradiction:

Loss of informationVSObject-affected harmful factors

Solution Approach 1:

The patent applies different levels of data access and privacy protection to different portions of supply chain information, allowing selective sharing of non-sensitive data while protecting critical proprietary information through role-based access controls and data classification

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS10896392B2Methods and systems for generating supply chain representations

Publication Date: 2021.01.19 REFINITIV US ORGANIZATION LLC

AI summary

A computer implemented method for mining supply chain information to produce supply chain graphs includes receiving by a computer a set of data; identifying a supplier, a commodity, and a customer from the set of data; generating a query comprising the identified data; determining the absence of any of the supplier, the commodity, or the customer from the set of data; if any items are determined to be absent from the set of data, substituting a placeholder for the missing item from the data set; sending the query; receiving a set of supply chain graph information; generating a supply chain graph signal based upon the set of supply chain graph information; and transmitting the supply chain graph signal. A computing device or system includes a processor an electronic memory; and a program for mining supply chain information to produce supply chain graphs stored in the electronic memory.