Neural Network Preference Index for Image Content
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Solution Overview
Problem
Current methods for determining the favorability of marketing materials, such as images, are time-consuming and resource-intensive, requiring control-group studies that consume significant time and financial resources.
Innovation Solution
A neural network-based approach is employed to generate a preference index for image content, utilizing a distributed computing system and transfer learning to process large datasets efficiently, allowing for the assessment of sentiment and attributes in digital images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If control-group studies are used to determine favorability of marketing materials, then measurement precision is improved, but loss of time and loss of financial resources worsen
Solution Approach 1:
The patent creates a computational model that copies the functionality of control-group studies by training a machine learning system on labeled image data. The model learns to predict favorability metrics without requiring actual human subject studies, thus replicating the measurement capability while eliminating the time and resource costs of conducting physical studies.
Solution Approach 2:
The patent performs preliminary training of the favorability prediction model using a dataset of images with known favorability labels. This preliminary action creates a pre-trained system that can immediately assess new marketing materials without requiring time-consuming control-group studies for each new image, enabling rapid evaluation while maintaining measurement precision.
2Measurement precision
If control-group studies are used to determine favorability of marketing materials, then measurement precision is improved, but loss of financial resources worsens
Solution Approach 1:
The patent replaces expensive control-group studies with a computational model that has been trained on labeled data. Once trained, the model can assess favorability at minimal computational cost, eliminating the need to repeatedly conduct costly studies while maintaining measurement precision through the model's learned predictions.
Solution Approach 2:
The patent changes the measurement parameters from requiring human subject responses to using automated image analysis features. By transforming the favorability assessment into a computational problem based on image characteristics, the system achieves comparable measurement precision while dramatically reducing financial resource requirements.
3Productivity
If neural networks are used to generate preference indices, then productivity is improved, but device complexity worsens
Solution Approach 1:
The patent extracts the complex neural network processing into a separate favorability prediction system that can be pre-trained and deployed independently. This extraction allows the main application system to simply call the prediction function without needing to understand or manage the internal complexity of the neural network, thus improving productivity while managing device complexity through modular architecture.
Data Source
AI summary
Briefly, embodiments of methods and/or systems of generating preference indices for contiguous portions of digital images are disclosed. For one embodiment, as an example, parameters of a neural network may be developed to generate object labels for digital images. The developed parameters may be transferred to a neural network utilized to generate signal sample value levels corresponding to preference indices for contiguous portions of digital images.