Image Style Feature Modeling for Content Performance Prediction
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Solution Overview
Problem
Existing methods for determining how content items will perform among a target audience are inefficient and inaccurate, often requiring costly and time-consuming 'A/B testing, and traditional research techniques are limited by small sample sizes and human bias.
Innovation Solution
A machine learning model is trained using extracted stylistic features from images, such as object and scene tensors, to predict performance scores for candidate images, enabling real-time evaluation of content across multiple audiences without human feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional research techniques and A/B testing are used to determine content performance, then measurement precision can be achieved, but loss of time and loss of energy increase significantly
Solution Approach 1:
The system performs preliminary action by training a machine learning model in advance using historical content performance data and audience engagement metrics. This pre-trained model can then predict content performance for new candidates without requiring time-consuming A/B testing, thus resolving the contradiction between measurement precision and time loss.
Solution Approach 2:
The patent replaces the mechanical A/B testing process with an automated machine learning prediction system. Instead of physically conducting controlled experiments with content variations, the system uses computational models to simulate and predict performance outcomes, eliminating the time and energy costs associated with traditional testing while maintaining prediction accuracy.
2Measurement precision
If A/B testing is conducted to evaluate content performance, then measurement precision improves, but use of energy increases
Solution Approach 1:
The system substitutes energy-intensive A/B testing with computationally efficient machine learning inference. Once the model is trained, generating performance predictions requires minimal computational resources compared to conducting actual A/B tests, thus maintaining measurement precision while significantly reducing energy consumption.
Solution Approach 2:
The patent creates a virtual copy of the content evaluation process through machine learning models. Instead of physically testing content variations with real audiences (which consumes energy), the system uses trained models to simulate and evaluate content performance virtually, achieving the same measurement goals with fractionated energy usage.
3Device complexity
If traditional research methods are used with small sample sizes, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system changes the fundamental parameters of the evaluation approach by transitioning from small-sample traditional research to large-scale machine learning training. The model processes vast amounts of historical content performance data and audience engagement metrics, transforming the input parameters from limited samples to comprehensive datasets, thereby achieving high measurement precision without excessive device complexity.
Solution Approach 2:
The machine learning model serves multiple functions: it predicts performance scores, identifies audience preferences, and evaluates content across diverse categories. This multi-functionality allows the system to achieve high measurement precision for various content types using a single unified model, avoiding the need for separate simple research methods for each content category.
Data Source
AI summary
Systems, methods, and storage media for training a machine learning model are disclosed. Exemplary implementations may select a set of training images for a machine learning model, extract object features from each training image to generate an object tensor for each training image, extract stylistic features from each training image to generate a stylistic feature tensor for each training image, determine an engagement metric for each training image, and train a neural network comprising a plurality of nodes arranged in a plurality of sequential layers.