Abstract
Monetizing large media libraries, for example, archived content, may be challenging when targeting methods lack a granular understanding of context within a media asset. A system can employ a contextual intelligence engine to perform a multi-modal analysis of visual, auditory, and textual elements within media content, which can generate a detailed, time-stamped metadata profile for each asset. This structured data may be used for multiple targeting applications, such as linking user intent signals to relevant archived content, identifying trends for ad placements, or predicting potential viewership. This process can facilitate the dynamic creation of contextually relevant advertising opportunities. Such opportunities may augment existing advertisement inventories and provide additional monetization for new and archived media assets.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Pieracci, Caroline and Bufalini, Carlo, "Monetizing Media Inventories Using Multi-Modal Contextual Analysis and Predictive Ad Targeting", Technical Disclosure Commons, (February 09, 2026)
https://www.tdcommons.org/dpubs_series/9287