AI Voice Label Printing for Natural Language Interpretation

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Solution Overview

Problem

Existing label printing systems fail to accurately and flexibly interpret voice commands due to limitations in rule-based algorithms, leading to errors and resource-intensive rule development, especially when handling variations in natural language.

Innovation Solution

A label printing system utilizing an AI model with transformer-based language capabilities, combined with a pre-processing module for transcription refinement and customizable replacement rules, to interpret voice inputs and generate accurate label designs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If rule-based algorithms are used to interpret voice commands, then the system structure is simple and easy to implement, but the system becomes rigid and prone to errors when handling variations in natural language

Engineering Contradiction:
Improveease of implementationVSAvoidability to handle natural language variations
Core Design Contradiction:
Ease of manufactureVSAdaptability or versatility

Solution Approach 1:

The patent replaces rule-based algorithms (mechanical system) with machine learning models that can naturally interpret and adapt to variations in spoken language. The machine learning model learns patterns from training data and generalizes to handle diverse natural language inputs without requiring explicit programming for each scenario.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If comprehensive rule-based systems are developed to handle all label design scenarios, then the system becomes more versatile, but the development and maintenance becomes resource-intensive

Engineering Contradiction:
Improvecoverage of label design scenariosVSAvoidcomplexity of rule development and maintenance
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent changes the fundamental parameter of the system from rule-based logic to machine learning-based pattern recognition. This allows the system to handle comprehensive label design scenarios through training on diverse data, eliminating the need for manual rule creation and maintenance while maintaining versatility.

Inventive Principle:
Principle #35Parameter changes

3Ease of manufacture

If predefined voice commands are used for label printing, then the system is easy to implement, but it becomes complex and less intuitive as the range of instructions expands

Engineering Contradiction:
Improveease of implementationVSAvoidintuitiveness of voice commands
Core Design Contradiction:
Ease of manufactureVSEase of operation

Solution Approach 1:

The patent replaces the mechanical system of predefined voice commands with a machine learning-based natural language interpretation system. This allows users to speak intuitively without being constrained by predefined command structures, while the system automatically understands and processes the intended meaning.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentEP4700641A1Systems and methods for voice-activated label printing using artificial intelligence
Publication Date: 2026.02.25 BRADY WORLDWIDE INC
  • EP4700641A1 patent drawingFigure 1
  • EP4700641A1 patent drawingFigure 2~3
  • EP4700641A1 patent drawingFigure 4

AI summary

In a label printing system that is configured for voice-activated label printing, a computing device includes a label printing module and a transcription module that generates a text transcription of voice input that includes spoken instructions for a label to be printed. The label printing system also includes a label intent module that generates a set of prompts for an Al model based on the text transcription. The set of prompts is structured to cause the Al model to interpret the text transcription and generate a data structure for printing the label. The label intent module provides the set of prompts to the Al model and receives the data structure from the Al model. The data structure includes label content and formatting instructions. The data structure is provided to the label printing module, which uses the data structure to cause the label to be printed on the printing device.