US 12,455,918 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 May 1, 2024, as Appl. No. 18/652,223.
Application 18/652,223 is a continuation of application No. 18/384,899, filed on Oct. 30, 2023, granted, now 12,013,891.
Application 18/384,899 is a continuation of application No. 18/242,898, filed on Sep. 6, 2023, granted, now 12,314,308.
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 2024/0281463 A1, Aug. 22, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 16/45 (2019.01); G06F 16/438 (2019.01); G06N 3/0455 (2023.01); G06N 3/0475 (2023.01); G06Q 30/0208 (2023.01)
CPC G06F 16/45 (2019.01) [G06F 16/438 (2019.01); G06N 3/0455 (2023.01); G06N 3/0475 (2023.01); G06Q 30/0208 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
determining, by one or more processors, that a training of an artificial intelligence has been completed, to create a trained artificial intelligence;
determining, by the one or more processors, an attribution vector created during the training of the artificial intelligence;
determining, by the one or more processors, that the trained artificial intelligence has received an input;
generating, by the trained artificial intelligence, an output based on the input;
while generating the output based on the input, determining:
activations of neural pathways in the trained artificial intelligence; and
an amount of individual activations;
after determining that the trained artificial intelligence has completed generating the output, creating a dynamic attribution vector based on the activations of the neural pathways and the amount of the individual activations, the dynamic attribution vector indicating an influence of individual content creators on the output;
modifying the attribution vector based at least in part on the dynamic attribution vector;
determining, by the one or more processors, an attribution determination for individual content creators of multiple content creators based on the attribution vector and based on determining one or more of the multiple content creators that contributed at least a threshold amount during the training, wherein a particular creator having a contribution less than the threshold amount does not receive a creator attribution; 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;
wherein training the artificial intelligence to create the trained artificial intelligence comprises:
selecting a content creator from multiple content creators to create a selected content creator;
selecting a plurality of content items associated with the selected content creator to create selected content items;
training the artificial intelligence using the selected content items;
determining a measure of a proximity between:
the artificial intelligence trained using the selected content items, and the artificial intelligence prior to initiating the training;
determining a creator influence of the content creator on the trained artificial intelligence based at least in part on the measure of the proximity; and
including the creator influence in the attribution vector.