Facility Training Module Generation Using LLM Topic Extraction

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Solution Overview

Problem

Facilities face challenges in creating effective training modules for diverse workforces due to manual errors, time consumption, and inefficiencies in document classification and selection, leading to unoptimized resource utilization and reduced productivity.

Innovation Solution

A system and method utilizing Language Learning Models (LLMs) to automatically extract topics from facility documents, generate training templates, and create tailored training modules based on user inputs, reducing manual effort and improving accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If subject matter experts manually create training modules by collating and classifying documents, then training content can be customized for specific operations, but the process is error-prone, time-consuming, and labor-intensive

Engineering Contradiction:

Improveaccuracy of training module creationVSAvoidtime consumption in manual document classification

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical process of document classification and training module creation with an automated AI-based system. The system uses natural language processing and machine learning algorithms to automatically classify documents, extract relevant information, and generate training modules, eliminating the need for manual expert intervention in these repetitive tasks.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by allowing users to upload documents and specify training requirements through simple interfaces. The AI system automatically processes the documents, identifies relevant content, and generates training modules without requiring users to manually classify or curate content, making the process autonomous and efficient.

Inventive Principle:

Principle #25Self-service

2Productivity

If manual document classification is performed by experts, then relevant documents can be selected for training, but the process is cumbersome and consumes significant human resources

Engineering Contradiction:

Improvetraining module generation efficiencyVSAvoidcomplexity of manual classification process

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces the complex manual classification process with automated AI-based document processing. The system uses natural language processing to understand document content, machine learning models to classify documents based on training requirements, and automated information extraction to select relevant content, significantly simplifying the process and improving efficiency.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the parameters of document processing from manual human judgment to automated algorithmic processing. It transforms the classification process by using computational methods to analyze document metadata and content, automatically determining relevance based on predefined criteria and training objectives, thereby increasing productivity.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If manual training module creation is performed, then training can be tailored to specific operations, but it results in unoptimized resource utilization and inefficient operations

Engineering Contradiction:

Improveease of training module generationVSAvoidunoptimized utilization of human and electronic resources

Core Design Contradiction:

Ease of operationVSLoss of energy

Solution Approach 1:

The patent replaces manual expert labor with automated AI systems for training module generation. The system automatically processes documents, classifies content, and creates training modules, eliminating the need for experts to manually perform these tasks. This substitution optimizes resource utilization by deploying computational resources instead of human expertise for routine processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system provides multi-functionality by handling multiple tasks within a single automated framework: document upload, classification, information extraction, content selection, and training module generation. This universal approach consolidates what would otherwise require multiple separate manual processes into one efficient automated system, optimizing resource utilization.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250342411A1Systems and methods to generate training modules for a facility

Publication Date: 2025.11.06 HONEYWELL INTERNATIONAL INC

AI summary

Various embodiments described herein relate to systems and methods for generating training modules to train workforces in a facility. In this regard, one or more documents related to one or more operations in the facility is initially created. Then, a user in the facility provides a first input associated with the one or more documents. Based on the first input, one or more topics are extracted from the one or more documents. The extracted topics are then updated in a database as well. Further, one or more training templates are generated for the extracted topics. The user then provides a second input indicative of one or more specifications for the generation of the one or more training modules. Based on the one or more specifications, the one or more training modules are generated using the one or more training templates.