Algorithmic Product Labeling via Machine Learning

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Solution Overview

Problem

Conventional product labeling and distribution methods often prioritize efficiency and economies of scale, leading to inconsistent consumer feedback and subjective brand/label design, which may not accurately reflect consumer preferences.

Innovation Solution

A platform utilizing machine learning processes, including Gaussian Mixture Models and deep learning, to correlate user ratings with product label characteristics, allowing for systematic generation of 'algorithmic' branding and labeling that adapts to individual user preferences by randomly distributing different label versions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional labeling methods are used with large production runs, then manufacturing efficiency and cost are improved, but consumer preference accuracy and adaptability deteriorate

Engineering Contradiction:

Improvelabeling efficiencyVSAvoidconsumer preference adaptability

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent segments the product line into multiple version designations (v1, v2, v3, etc.) based on label characteristics. Each version corresponds to different label variations that have been tested and optimized for specific consumer preferences. This segmentation allows the company to offer multiple tailored versions rather than a single standardized product, thereby improving adaptability to consumer preferences while maintaining efficient production through standardized version categories.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent changes the parameters of label characteristics (design elements, colors, text arrangements, etc.) to create different version designations. By systematically varying these parameters and testing them with consumer feedback, the company identifies optimal parameter combinations for different consumer segments. This allows adaptation to consumer preferences through parameter optimization while maintaining production efficiency through standardized version templates.

Inventive Principle:

Principle #35Parameter changes

2Ease of operation

If subjective brand/label design approaches are used, then decision maker preferences are satisfied, but objective consumer preference measurement deteriorates

Engineering Contradiction:

Improvebranding decision processVSAvoidconsumer preference measurement

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent implements a feedback mechanism where consumer ratings and preferences are systematically collected and analyzed. Consumer feedback on different label versions is aggregated to determine which characteristics resonate most with target audiences. This objective feedback loop replaces subjective decision-maker preferences with data-driven insights, allowing the branding process to be both easy to operate (through automated analysis) and precisely measured (through consumer rating aggregation).

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces the mechanical/subjective process of human decision-making in branding with an automated computational system. Machine learning algorithms analyze consumer feedback data and objectively determine optimal label characteristics, substituting human subjectivity with algorithmic precision. This substitution maintains ease of operation through automated processing while dramatically improving measurement precision of consumer preferences.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11232498B2Method, system, and computer readable medium for labeling and distributing products having multiple versions with recipient version correlation on a per user basis

Publication Date: 2022.01.25 PENROSE HILL

AI summary

Embodiments are directed to labeling and distributing products having multiple versions while providing correlation of user ratings with a received product version on a per user basis.