US 12,013,891 B2
Model-based attribution for content generated by an artificial intelligence (AI)
Tamay Aykut, Pacifica, CA (US); and Christopher Benjamin Kuhn, Munich (DE)
Assigned to Sureel Inc., Pacifica, CA (US)
Filed by Sureel Inc., Pacifica, CA (US)
Filed on Oct. 30, 2023, as Appl. No. 18/384,899.
Application 18/384,899 is a continuation of application No. 18/242,898, filed on Sep. 6, 2023.
Application 18/242,898 is a continuation of application No. 18/231,551, filed on Aug. 8, 2023.
Claims priority of provisional application 63/521,066, filed on Jun. 14, 2023.
Claims priority of provisional application 63/422,885, filed on Nov. 4, 2022.
Prior Publication US 2024/0152544 A1, May 9, 2024
Int. Cl. G06F 16/45 (2019.01); G06F 16/438 (2019.01)
CPC G06F 16/45 (2019.01) [G06F 16/438 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
training, by one or more processors, an artificial intelligence to create a trained artificial intelligence to generate a particular type of derivative content, the training comprising:
selecting a content creator from multiple content creators to create a selected content creator;
selecting a plurality of content items associated with the content creator to create selected content items;
training the artificial intelligence using the selected content items;
determining a content item influence of individual content items of the plurality of content items on a plurality of training techniques used to train the artificial intelligence based at least in part on determining a difference between:
a first set of parameters associated with the artificial intelligence, and
a second set of parameters associated with the trained artificial intelligence, wherein the second set of parameters comprises a plurality of weights, a plurality of biases, or any combination thereof;
aggregating the content item influence of individual content items of the plurality of content items on the plurality of training techniques used to train the artificial intelligence;
determining a creator influence of the selected content creator on the trained artificial intelligence based at least in part on the aggregating; and
including the creator influence in a static attribution vector;
after determining that the training of the artificial intelligence has been completed, determining, by the one or more processors, that the trained artificial intelligence has received an input;
generating, by the artificial intelligence, an output based on the input;
creating, by the one or more processors, an attribution determination based at least in part on the static attribution vector; and
initiating, by the one or more processors, providing compensation to one or more of the multiple content creators based at least in part on the attribution determination.