Granted Patent B2

US 12,632,516 · App. 19/312,216 · Granted May 19, 2026

Inventor: Daniel A. Drolet (Charleston, SC)

Assignee: Music IP Holdings, Inc.

G06F21/106G06F16/632G06F21/1084G06F40/205G06F2221/2137

Abstract

Claims (39)

1 . A system comprising:

a content derivation platform comprising at least one processor configured to:

receive predetermined content using a database server;

receive a request to transform the predetermined content into a derivative work;

receive a requested theme for the derivative work;

create the derivative work as a function of the predetermined content and the requested theme, using generative artificial intelligence, wherein to create the derivative work further comprises to transform the predetermined content into the derivative work comprising audio, video or images, based on an embedding for the requested theme;

determine if the generated derivative work is approved based on a content approval machine learning model configured to determine a content approval score as a function of at least one content owner preference and the generated derivative work, wherein the content approval machine learning model further comprises a neural network configured to determine a score identifying a degree of like or dislike by the content owner for the derivative work, the score determined as a function of a text embedding identifying an item in the derivative work; and

in response to the content approval score being greater than a predetermined minimum:

apply a digital watermark to the approved derivative work;

configure an authorization server to govern use of the approved derivative work based on the digital watermark; and

provide access to the approved derivative work.

2 . The system of claim 1 , wherein the content comprises music.

3 . The system of claim 1 , wherein the content comprises audio.

4 . The system of claim 3 , wherein the audio further comprises a human voice sound.

5 . The system of claim 4 , wherein the at least one processor is further configured to detect the human voice based on a technique comprising autocorrelation.

6 . The system of claim 5 , wherein the autocorrelation further comprises frequency domain autocorrelation.

7 . The system of claim 3 , wherein the audio further comprises a musical instrument sound.

8 . The system of claim 1 , wherein the requested theme is determined based on an interview with a user.

9 . The system of claim 8 , wherein the interview with the user is performed by a chatbot.

10 . The system of claim 8 , wherein the requested theme is determined based on a response from the user that is matched with a semantically similar predetermined theme identified by a Large Language Model (LLM) as a function of the response from the user.

11 . The system of claim 10 , wherein the predetermined theme is pre-approved by the content owner.

12 . The system of claim 1 , wherein the generative artificial intelligence comprises a diffusion model.

13 . The system of claim 12 , wherein the diffusion model is a latent diffusion model.

14 . The system of claim 12 , wherein the at least one processor is further configured to encode the content to a latent space, using an encoder network.

15 . The system of claim 14 , wherein the encoder network further comprises a convolutional neural network (CNN) configured to extract mel-frequency cepstral coefficients (MFCCs) from the content.

16 . The system of claim 14 , wherein the encoder network further comprises a CNN configured to extract a spatial or temporal feature from the content.

17 . The system of claim 14 , wherein the encoder network further comprises a recurrent neural network (RNN) or a transformer, configured to extract a word embedding from the content.

18 . The system of claim 14 , wherein the at least one processor is further configured to decode the content from the latent space, using a decoder network.

19 . The system of claim 1 , wherein the at least one processor is further configured to determine a text embedding identifying an item, using a CLIP model.

20 . The system of claim 1 , wherein the at least one processor is further configured to convert the requested theme to a text embedding in a shared latent space.

21 . The system of claim 1 , wherein to apply the digital watermark further comprises to embed the digital watermark in the derivative work.

22 . The system of claim 21 , wherein the at least one processor is further configured to embed the digital watermark using frequency domain embedding.

23 . The system of claim 1 , wherein the at least one processor is further configured to:

update the derivative work with a new digital watermark that is valid for a limited time; and

provide access to the updated derivative work.

24 . The system of claim 1 , wherein to govern use of the approved derivative work further comprises to configure a tracking system to determine authenticity of the derivative work, verified as a function of the digital watermark by the tracking system.

25 . The system of claim 1 , wherein to govern use of the approved derivative work further comprises to validate user requests for access to the derivative work authorized as a function of the digital watermark.

26 . The system of claim 1 , wherein to govern use of the approved derivative work further comprises to automatically request an automated payment via a smart contract execution triggered based on use of the derivative work detected as a function of the digital watermark.

27 . The system of claim 1 , wherein to govern use of the approved derivative work further comprises to revoke access to the derivative work upon determining a time-sensitive watermark has expired.

Continuity (4)

References Cited (150)