INPUT-BASED ATTRIBUTION FOR CONTENT GENERATED BY AN ARTIFICIAL INTELLIGENCE (AI)

1 . A method comprising:

determining, by one or more processors, an input provided to a generative artificial intelligence to generate an output;

parsing, by the one or more processors, the input to determine:

a type of content to generate;

a content description; and

one or more creator identifiers;

embedding, by the one or more processors, the input into a shared language-image space using an encoder to create an input embedding;

determining, by the one or more processors, a creator description comprising a creator-based embedding associated with individual creators identified by the one or more creator identifiers;

performing, by the one or more processors, a comparison of the input embedding to the creator-based embedding associated with individual creators;

determining, by the one or more processors and based on the comparison, a distance between a creator embedding of the individual creators and the input embedding;

determining, by the one or more processors, one or more creator attributions based on the distance of an amount of the embedding of the individual creators in the input embedding;

determining, by the one or more processors, a creator attribution vector that includes the one or more creator attributions; and

initiating providing compensation to one or more creators based on the creator attribution vector.

2 . The method of claim 1 , wherein the generative artificial intelligence comprises:

a latent diffusion model;

a generative adversarial network;

a generative pre-trained transformer;

a variational autoencoders;

a multimodal model; or

any combination thereof.

3 . The method of claim 1 , further comprising:

selecting a particular creator of the one or more creators;

performing, using a neural network, an analysis of content items created by the particular creator;

determining, based on the analysis, a plurality of captions describing the content items;

creating, based on the plurality of captions, a particular creator description; and

associating the particular creator description with the particular creator.

4 . The method of claim 3 , wherein:

the neural network is implemented using a Contrastive Language Image Pretraining encoder; and

the encoder comprises a transformer neural network.

5 . The method of claim 1 , wherein the type of content comprises:

a digital image having an appearance of a work of art;

a digital visual image;

a digital text-based book;

a digital music composition;

a digital video; or

any combination thereof.

6 . The method of claim 1 , wherein the distance comprises:

a cosine similarity,

a contrastive learning encoding distance;

a simple matching coefficient,

a Hamming distance,

a Jaccard index,

an Orchini similarity,

a Sorensen-Dice coefficient,

a Tanimoto distance,

a Tucker coefficient of congruence,

a Tversky index, or

any combination thereof.

7 . A server comprising:

one or more processors;

a non-transitory memory device to store instructions executable by the one or more processors to perform operations comprising:

determining an input provided to a generative artificial intelligence to generate an output;

parsing the input to determine:

a type of content to generate;

a content description; and

one or more creator identifiers;

embedding the input into a shared language-image space using an encoder to create an input embedding;

determining a creator description comprising a creator-based embedding associated with individual creators identified by the one or more creator identifiers;

performing a comparison of the input embedding to the creator-based embedding associated with individual creators;

determining, based on the comparison, a distance of an amount of an embedding of the individual creators in the input embedding;

determining one or more creator attributions based on the distance of the amount of the embedding of the individual creators in the input embedding;

determining a creator attribution vector that includes the one or more creator attributions; and

initiating providing compensation to one or more creators based on the creator attribution vector.

8 . The server of claim 7 , wherein the generative artificial intelligence comprises:

a latent diffusion model;

a generative adversarial network;

a generative pre-trained transformer;

a variational autoencoders;

a multimodal model; or

any combination thereof.

9 . The server of claim 7 , further comprising:

selecting a particular creator of the one or more creators;

performing, using a neural network, an analysis of content items created by the particular creator;

determining, based on the analysis, a plurality of captions describing the content items;

creating, based on the plurality of captions, a particular creator description; and

associating the particular creator description with the particular creator.

10 . The server of claim 9 , wherein:

the neural network is implemented using a Contrastive Language Image Pretraining encoder; and

the encoder comprises a transformer neural network.

11 . The server of claim 7 , wherein:

the one or more creators comprise one or more artists;

the one or more creators comprise one or more authors;

the one or more creators comprise one or more musicians;

the one or more creators comprise one or more visual content creators; or

any combination thereof.

12 . The server of claim 7 , wherein the content description comprises:

a noun comprising a name of a living creature, an object, a place, or any combination thereof; and

zero or more adjectives to qualify the noun.

13 . The server of claim 7 , wherein the distance comprises:

a cosine similarity,

a contrastive learning encoding distance,

a simple matching coefficient,

a Hamming distance,

a Jaccard index,

an Orchini similarity,

a Sorensen-Dice coefficient,

a Tanimoto distance,

Tucker coefficient of congruence,

a Tversky index, or

any combination thereof.

14 . A non-transitory computer-readable memory device to store instructions executable by one or more processors to perform operations comprising:

determining an input provided to a generative artificial intelligence to generate an output;

parsing the input to determine:

a type of content to generate;

a content description; and

one or more creator identifiers;

embedding the input into a shared language-image space using an encoder to create an input embedding;

determining a creator description comprising a creator-based embedding associated with individual creators identified by the one or more creator identifiers;

performing a comparison of the input embedding to the creator-based embedding associated with individual creators;

determining, based on the comparison, a distance of an amount of an embedding of the individual creators in the input embedding;

determining one or more creator attributions based on the distance of the amount of the embedding of the individual creators in the input embedding;

determining a creator attribution vector that includes the one or more creator attributions; and

initiating providing compensation to one or more creators based on the creator attribution vector.

15 . The non-transitory computer-readable memory device of claim 14 , wherein the generative artificial intelligence comprises:

a latent diffusion model;

a generative adversarial network;

a generative pre-trained transformer;

a variational autoencoders;

a multimodal model; or

any combination thereof.

16 . The non-transitory computer-readable memory device of claim 14 , further comprising:

selecting a particular creator of the one or more creators;

performing, using a neural network, an analysis of content items created by the particular creator;

determining, based on the analysis, a plurality of captions describing the content items;

creating, based on the plurality of captions, a particular creator description; and

associating the particular creator description with the particular creator.

17 . The non-transitory computer-readable memory device of claim 14 , wherein:

the type of content comprises a digital image having an appearance of a work of art and the one or more creators comprise one or more artists.

18 . The non-transitory computer-readable memory device of claim 14 , wherein:

the type of content comprises a digital book and the one or more creators comprise one or more authors.

19 . The non-transitory computer-readable memory device of claim 14 , wherein:

the type of content comprises a digital music composition and the one or more creators comprise one or more musicians.

20 . The non-transitory computer-readable memory device of claim 14 , wherein:

the type of content comprises visual content and the one or more creators comprise one or more visual content creators.