NLP Interaction Clustering for Traceable Pattern Recognition

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing systems struggle to accurately identify and manage large numbers of interactions, lack traceability between interactions, and fail to link interactions with evaluation metrics, leading to inefficient resource consumption and lack of traceability.

Innovation Solution

A system using NLP-based tokenizing and clustering models to identify and link primary interactions with secondary interactions, employing NFTs to tag interactions and a deep-learning model to generate pattern-recognition clusters, predicting missing interactions using unsupervised deep-learning technology.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If existing systems handle interactions separately, then each interaction can be processed individually, but processing and networking resources are consumed due to the large number of interactions

Engineering Contradiction:

ImproveIndividual interaction processingVSAvoidProcessing and networking resources

Core Design Contradiction:

Ease of operationVSUse of energy by moving object

Solution Approach 1:

The patent merges multiple related interactions into a single batch operation by identifying primary interactions and their associated secondary interactions. Instead of processing each interaction separately, the system groups them together and processes them in one batch, reducing the number of separate processing operations and networking communications required.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent creates a universal interaction group structure that can handle multiple types of interactions (primary and secondary) through a single processing framework. This multi-functional approach allows the system to process different interaction types together using the same batch processing mechanism, improving resource efficiency.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If existing systems generate various interaction entries, then interactions can be categorized, but traceability between interactions is lost

Engineering Contradiction:

ImproveInteraction categorizationVSAvoidTraceability between interactions

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The patent implements a nested structure where secondary interactions are nested within the context of primary interactions through interaction groups. Each interaction group contains a primary interaction and its associated secondary interactions, creating a hierarchical nesting that preserves the relationships between interactions while maintaining categorization flexibility.

Inventive Principle:

Principle #7Nested doll (Nesting)

Solution Approach 2:

The patent introduces interaction groups as intermediary structures that link primary interactions with secondary interactions. These groups serve as mediators that maintain traceability by explicitly defining the relationships between interactions, allowing the system to categorize interactions adaptively while preserving the ability to trace connections between them.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Device complexity

If no tracing method is available, then system complexity is reduced, but linkages between interactions and evaluation metrics cannot be established

Engineering Contradiction:

ImproveSystem structureVSAvoidLinkage between interactions and evaluation metrics

Core Design Contradiction:

Device complexityVSLoss of information

Solution Approach 1:

The patent performs preliminary action by pre-establishing interaction groups that define the relationships between primary and secondary interactions before evaluation occurs. This advance grouping structure is created and stored, so when evaluation metrics need to be applied, the traceability links are already in place, eliminating the need for complex real-time tracing mechanisms.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12511480B2Pattern recognition using NLP-based tokenizing and clustering models

Publication Date: 2025.12.30 BANK OF AMERICA CORP

AI summary

A system for interaction pattern recognition receives an input primary interaction and accesses clusters indicating interaction group patterns. Each cluster includes a respective primary interaction and secondary interactions linked to that primary interaction. Each cluster is identified by a respective non-fungible token. The system then determines a non-fungible token of the input primary interaction, compares it to the non-fungible tokens of the clusters, selects a first cluster based on a match between the non-fungible token of the input primary interaction and a first non-fungible token identifying the first cluster, determines the secondary interactions in the first cluster as linked to the input primary interaction, retrieves the secondary interactions from the clusters, generates a recommended group of interactions including the input primary interaction and the retrieved secondary interactions, and provides the recommended group of interactions and an indication that the retrieved secondary interactions are linked to the input primary interaction.