Linguistic Graph Validation for Hidden Transaction Relationships

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Solution Overview

Problem

Existing suspicious transaction identification systems fail to identify transactions with low or no correlation to existing data, as they rely on statistical or machine learning methods that struggle with diverse transaction systems and hidden relationships between entities.

Innovation Solution

A method that constructs a graph modeling relationships between transaction elements using part of speech analysis to tag nodes, generate alternative tags, group nodes, validate against grammar, and add nodes to represent relationships, enabling the discovery of additional relationships within transaction and non-transactional data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If statistical or machine learning methods are used to identify suspicious transactions, then the system can process existing transaction data, but it fails to identify transactions with low or no correlation to existing data and hidden relationships between entities

Engineering Contradiction:

Improveability to identify suspicious transactionsVSAvoidability to handle diverse transaction systems and hidden relationships

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent introduces natural language processing as an intermediary layer between transaction data and analysis. By converting transaction elements into natural language statements and applying linguistic rules, the system can discover hidden relationships that statistical methods miss. The NLP component acts as a mediator that transforms structured data into semantic representations, enabling the detection of subtle patterns and relationships across diverse transaction systems.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent changes the analytical parameters from statistical correlations to linguistic relationships. Instead of measuring similarity based on numerical patterns, the system uses part-of-speech tagging, grammatical validation, and semantic analysis to identify relationships. This parameter transformation allows the system to detect hidden relationships between entities that do not exhibit statistical correlations, thereby improving both precision and adaptability.

Inventive Principle:

Principle #35Parameter changes

2Ease of manufacture

If traditional statistical analysis is used, then the system maintains simplicity in implementation, but it cannot uncover hidden relationships between entities in diverse transaction systems

Engineering Contradiction:

Improveease of implementationVSAvoidhidden relationships between entities

Core Design Contradiction:

Ease of manufactureVSLoss of information

Solution Approach 1:

The patent replaces the mechanical statistical analysis system with a linguistic processing system. Instead of applying mathematical formulas and statistical models, the system uses natural language processing techniques including part-of-speech tagging, grammatical rule validation, and semantic analysis. This substitution enables the discovery of hidden relationships while maintaining implementation feasibility through well-established NLP libraries and frameworks.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If the system focuses on apparent correlations in transaction data, then it can efficiently validate transactions, but it fails to identify suspicious transactions that lack obvious patterns

Engineering Contradiction:

Improvetransaction validation efficiencyVSAvoiddetection of suspicious transactions without obvious patterns

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent adds a linguistic dimension to transaction analysis. By representing transaction elements as natural language statements and analyzing their grammatical and semantic properties, the system creates a new dimension of analysis beyond numerical correlations. This dimensional expansion allows efficient processing while detecting subtle suspicious patterns that lack obvious statistical signatures, as the linguistic framework can identify anomalous relationship structures.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS12045825B2Linguistic transformation based relationship discovery for transaction validation

Publication Date: 2024.07.23 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

From transaction data, a graph modeling a set of relationships between transaction elements is constructed, a node of the graph representing a transaction element, an edge of the graph representing a relationship between two transaction elements. Using a part of speech analysis model, a subset of the nodes is tagged with a corresponding first part of speech tag. An alternative part of speech tag is generated for a tagged node. A set of tagged nodes is grouped into a word group including at least one tagged node having an alternative part of speech tag. The word group is validated against a grammar of a natural language. Responsive to the validating determining that the word group conforms to the grammar, a set of additional nodes representing the word group is added to the graph. Using the modified graph, a transaction in the transaction data is validated.