Machine Learning Image Selection System
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Solution Overview
Problem
The challenge lies in efficiently selecting imagery that resonates with target audiences for brand presentation, as existing methods require manual sifting through vast amounts of data, consuming significant time and resources, and lack precision in identifying desirable attributes.
Innovation Solution
A machine learning system is trained using a collection of images with defined attributes to predict which images are likely to engage viewers, reducing the time and resources needed for image selection by determining whether images satisfy brand-specific criteria, including graphical and content attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to sift through vast amounts of imagery data, then human judgment and creativity can be applied to select images, but significant time and resources are consumed
Solution Approach 1:
The patent replaces manual mechanical image selection processes with an automated machine learning system. The system uses trained neural networks to automatically evaluate images against brand guidelines and select appropriate imagery, eliminating the need for manual sifting through vast datasets while maintaining selection quality through AI-driven assessment
Solution Approach 2:
The machine learning system performs self-service by automatically evaluating and selecting images without requiring continuous human intervention. The system trains on brand guidelines and autonomously makes selection decisions, reducing time consumption while maintaining reliable image selection through automated judgment
2Reliability
If manual methods are used to identify desirable image attributes, then human expertise can be applied, but precision in identifying attributes is insufficient
Solution Approach 1:
The patent replaces manual attribute identification with automated machine learning systems that use trained neural networks to precisely detect and measure image attributes. The system can accurately identify graphical attributes (colors, composition) and content attributes (objects, text) with higher precision than human judgment through systematic AI analysis
3Productivity
If machine learning systems are trained on large datasets of images with attributes, then image selection efficiency improves, but system complexity increases
Solution Approach 1:
The patent segments the complex machine learning system into distinct functional components: data collection modules, attribute extraction subsystems, training mechanisms, and selection output systems. This segmentation allows the complex productivity-enhancing system to be managed through modular components, reducing overall system complexity while maintaining high image selection efficiency
Data Source
AI summary
A system includes a computing device that includes a memory configured to store instructions. The system also includes a processor to execute the instructions to perform operations that include receiving data representing an image, the image being represented in the data by a collection of visual elements. Operations also include determining whether to select the image for presentation by one or more entities using a machine learning system, the machine learning system being trained using data representing a plurality of training images and data representing one or more attributes regarding image presentation by the one or more entities.