Ad Framework Diversity via Demographic Image Classification

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Solution Overview

Problem

Current advertisement and customer journey frameworks lack diversity and inclusivity in their imagery, failing to effectively represent various demographics, which can lead to non-compliance with representation standards and reduced engagement with target audiences.

Innovation Solution

The system evaluates and modifies advertisement frameworks by using demographic metadata and an inclusivity policy to ensure diverse representation, utilizing a graphical user interface to select and adjust images based on attributes like gender, race, and age, and employs machine learning to predict engaging content, thereby optimizing framework performance and personalization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If advertisement frameworks use traditional image selection methods, then the framework development process is simple, but the representation diversity and inclusivity are insufficient

Engineering Contradiction:

Improverepresentation diversityVSAvoidframework development complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary classification of images based on demographic attributes (gender, race, age, ability) before they are needed in advertisement frameworks. This advance preparation allows the framework to quickly assemble diverse representations without complex real-time analysis, resolving the contradiction between diversity and complexity.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary classification system that acts as a bridge between raw image data and advertisement framework requirements. This intermediary layer pre-processes images into categorized groups, enabling the framework to achieve diverse representation without directly handling the complexity of individual image analysis.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If the system manually selects images for advertisement frameworks, then the selection process is simple to control, but the time consumption and labor resources increase

Engineering Contradiction:

Improveframework generation speedVSAvoidimage selection time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system enables self-service automation where the classification system automatically selects and organizes images based on predefined demographic criteria without requiring manual intervention. This allows rapid framework generation while maintaining control through automated decision-making based on classification data.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system changes the parameter of image selection from manual qualitative assessment to automated quantitative classification based on demographic attributes. This parameter transformation enables high-speed automated selection while maintaining diversity requirements through structured classification parameters.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If advertisement frameworks lack demographic representation data, then the computational resources required are reduced, but the ability to target specific demographics and track metrics is diminished

Engineering Contradiction:

Improvedemographic targeting capabilityVSAvoidcomputational resource consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system segments the image database into distinct demographic categories (gender, race, age, ability) using classification. This segmentation allows the framework to efficiently query and assemble targeted demographics without processing entire image libraries, reducing computational overhead while maintaining targeting capability.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The classification of images into demographic categories is performed in advance before framework assembly. This preliminary action creates ready-to-use categorized groups, enabling efficient demographic targeting during framework generation without requiring intensive real-time computational resources.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12254672B1Policy based representation based on classification information

Publication Date: 2025.03.18 CITIGROUP

AI summary

A system or technique can analyze metadata from a first set of images using a neural network to identify classification information about individuals depicted in the images. The system generates data based on this classification information, including attributes of the individuals, and compares this data against predefined criteria for image presentations. If any attributes are found to be non-compliant with the criteria, the system queries a database of second image metadata linked to a set of images. This database includes data generated from prior presentations and classification information provided by individuals depicted in the images. The system locates compliant attributes in the second image metadata, retrieves an image linked to these compliant attributes, and modifies a presentation to include the retrieved image.