Benjamin James Dean

US Patent:

20210352371, Nov 11, 2021

Filed:

Dec 21, 2020

Appl. No.:

17/129713

Inventors:

- Los Angeles CA, US
Benjamin Dean - Los Angeles CA, US
Pierre-Pascal Lamarche - Fredericton, CA

International Classification:

H04N 21/466
G06Q 30/02
H04N 21/478
H04N 21/234
G06N 20/00

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.

US Patent:

20200007934, Jan 2, 2020

Filed:

Jun 29, 2018

Appl. No.:

16/024511

Inventors:

- Los Angeles CA, US
Benjamin Dean - Los Angeles CA, US
Pierre-Pascal Lamarche - Fredericton, CA

International Classification:

H04N 21/466
G06Q 30/02
H04N 21/234
H04N 21/478
G06F 15/18

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.