Autoencoder Image Analysis for Bandwidth Reduction

The patent segments the image processing workflow by first generating multiple candidate image variations at full resolution, then using machine learning models to predict performance metrics for each candidate. Only the top-performing candidates are processed and delivered at full resolution, while lower-performing ones are discarded or delivered at reduced resolution. This segmentation reduces overall bandwidth consumption by avoiding transmission of low-quality images that would not engage users.

The system performs preliminary actions by pre-generating multiple candidate image variations and pre-evaluating them using trained machine learning models before actual ad deployment. This preliminary evaluation predicts which images will perform best with target audiences, allowing the system to pre-select optimal candidates and avoid wasting bandwidth on poor-performing images during live serving.

The patent creates multiple candidate copies/variations of advertising images with different visual characteristics. Instead of processing one high-quality image through complex evaluation, the system generates multiple simplified copies and uses machine learning models to predict their performance. This copying approach distributes the computational burden across parallel model evaluations rather than requiring complex real-time analysis of single high-resolution images.

The system changes parameters by generating image candidates with varied visual parameters (colors, compositions, text elements) and using machine learning models to predict performance based on these parameter variations. This allows the system to evaluate multiple parameter combinations efficiently without requiring complex real-time processing of each full-resolution image, reducing computing resource burden while maintaining visual impact optimization.

The patent implements feedback mechanisms by using machine learning models that are trained on historical ad performance data. These models predict which image candidates will engage audiences based on learned patterns from past successes and failures. The feedback loop continuously improves prediction accuracy, allowing the system to quickly identify high-performing images without extensive processing time, thus reducing waste while maintaining content quality.

The system replaces mechanical/manual image evaluation processes with automated machine learning models. Instead of relying on time-consuming human review or complex rule-based filtering, the patent uses trained neural networks to rapidly evaluate image candidates and predict their engagement potential. This substitution dramatically reduces processing time while maintaining or improving content quality assessment accuracy.