Abstract
Brands struggle often to quickly identify and create brand led imagery and other associated diverse visual assets that can guarantee the best of user engagement and outcomes. These digital (creative) assets can be reused across apps, social media, websites and other immersive experiences the brands want to explore.
This disclosure describes ideas and techniques that start with creation of Gen AI led unique repositories of assets for brands. Retrieval Augmented Generation (RAG) and Deep Neural Networks, (which could further evolve through future AI architecture) is used as a technique for sourcing enhanced information from all external sources. This allows a brand asset to be as close to having the maximum ROI in terms of engagement, recall, a specific call to action, and even connecting to human interactions that can be further enriched with behavioral insights.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Kurup, Lavanya, "Owned Asset Models To Predict Brand Outcomes", Technical Disclosure Commons, (February 27, 2024)
https://www.tdcommons.org/dpubs_series/6716