System and Method for Optimizing Digital Assets

11) A system for iterative media asset generation and optimization, comprising, by a Processor and Memory Circuitry (PMC) configured to:

• provide a pre-trained generative model and a media asset in a media space;
• encode, by the generative model, the media asset into a latent space vector within a smooth or semi-smooth latent space such that controlled variations in the latent vector correspond to controlled semantic or visual variations in a corresponding media asset;
• create, in the latent space, at least two controlled latent variations of the latent vector, each variation being a latent space vector at a maximal controlled distance epsilon from the latent vector and generated along an exploration direction;
• generate, using the generative model, at least two media assets corresponding to the controlled variations, the generated assets exhibiting controlled asset variations relative to the media asset or a previously selected media asset;
• conduct an A/B test by presenting the generated media assets to users and measuring KPI performance associated with a KPI type;
• select the generated media asset having the best measured KPI performance;
• when the measured KPI of the selected media asset fails to satisfy an exit criterion, repeat the encode, create, generate, conduct and select operations with respect to the selected media asset as the media asset for the next iteration;
• and
• output the selected media asset when the exit criterion is satisfied.

12) A non-transitory computer readable storage medium tangibly embodying a program of instructions that, when executed by a computer, cause the computer to perform a method of iterative media asset generation and optimization, comprising:

• (i) providing a pre-trained generative model and a media asset in a media space;
• (ii) encoding, by the generative model, the media asset into a latent space vector within a smooth or semi-smooth latent space such that controlled variations in the latent vector correspond to controlled semantic or visual variations in a corresponding media asset;
• (iii) creating, in the latent space, at least two controlled latent variations of the latent vector, each variation being a latent space vector at a maximal controlled distance epsilon from the latent vector and generated along an exploration direction;
• (iv) generating, using the generative model, at least two media assets corresponding to the controlled variations, the generated assets exhibiting controlled asset variations relative to the media asset or a previously selected media asset;
• (v) conducting an A/B test by presenting the generated media assets to users and measuring KPI performance associated with a KPI type;
• (vi) selecting the generated media asset having the best measured KPI performance;
• when the measured KPI of the selected media asset fails to satisfy an exit criterion,
• (vii) repeating stages (ii)-(vi) using the selected media asset as the media asset for the next iteration;
• and
• (viii) outputting the selected media asset when the exit criterion is satisfied.