Abstract: The present invention is directed to machine-learning based methods and systems related to dynamically inserting items multimedia content into media broadcasts. By using machine-learning based models, the performance of different items of multimedia content with different audiences can be automatically simulated, resulting in recommendations for where, when and how to optimally distribute those items of multimedia content. The multimedia content can be distributed by dynamically integrating that multimedia content into a streaming video feed. The reaction of an audience to the multimedia content is then automatically monitored, collected, and analyzed using machine-learning techniques, allowing the reaction of the audience to the multimedia content to be automatically determined. This reaction can then be input back into the machine-learning based simulator, further refining future predictions for the performance of items of multimedia content with audiences.

Abstract: The present invention is directed to machine-learning based methods and systems related to dynamically inserting items multimedia content into media broadcasts. By using machine-learning based models, the performance of different items of multimedia content with different audiences can be automatically simulated, resulting in recommendations for where, when and how to optimally distribute those items of multimedia content. The multimedia content can be distributed by dynamically integrating that multimedia content into a streaming video feed. The reaction of an audience to the multimedia content is then automatically monitored, collected, and analyzed using machine-learning techniques, allowing the reaction of the audience to the multimedia content to be automatically determined. This reaction can then be input back into the machine-learning based simulator, further refining future predictions for the performance of items of multimedia content with audiences.