Multi-stage approval and controlled distribution of AI-generated derivative content
Herein disclosed is receiving predetermined content, receiving a request to transform the predetermined content into a derivative work, receiving a requested theme for the derivative work, using generative artificial intelligence to create the derivative work generated as a function of the predetermined content and the requested theme, determining if the generated derivative work is approved, in response to determining the generated derivative work is approved, applying a digital watermark to the approved derivative work, configuring an authorization server to govern use of the approved derivative work based on the digital watermark and providing user access to the authorized derivative work. The requested theme may be determined using a Large Language Model (LLM) and a chatbot interview. The generative artificial intelligence may comprise a diffusion model. The content may comprise music.
1 . A system for multi-stage approval and controlled distribution of a derivative work, the system comprising:
at least one processor; and
memory storing instructions that, when executed by the at least one processor, cause the system to:
receive, through a user interface presented on a client device, a request associated with source content for creating a derivative work based on the source content, wherein the request comprises a natural-language prompt;
parse, using a large-language-model parser, the request to extract a transformation theme from the natural-language prompt;
in response to evaluating the transformation theme using a machine-learning classifier, generate the derivative work from the source content in accordance with the transformation theme by applying a generative artificial-intelligence model;
in response to evaluating the derivative work against post-generation preference data associated with a content authority, generate a digital identifier based on the derivative work; and
enable at least one of distribution or usage of the derivative work in accordance with the digital identifier.
2 . The system of claim 1 , wherein the request is generated automatically by an application from contextual data without explicit user input.
3 . The system of claim 1 , wherein the machine-learning classifier is trained on historical approvals and rejections designated by the content authority.
4 . The system of claim 3 , wherein the instructions further cause the system to:
transmit the request to an external verification service designated by the content authority; and
approve the request for generation when both the machine-learning classifier and the external verification service return an approval indication.
5 . The system of claim 1 , wherein the instructions further cause the system to reject the request when fulfilment of the request would exceed at least one of a temporal, quantitative, or geographic limit on derivative-work creation.
6 . The system of claim 1 , wherein the generative artificial-intelligence model is a latent diffusion model.
7 . The system of claim 1 , wherein evaluating the derivative work against the post-generation preference data comprises generating a content-approval score and approving the derivative work when the content-approval score satisfies a binary pass/fail threshold.
8 . The system of claim 7 , wherein the instructions further cause the system to generate, when the derivative work fails a binary threshold, a remediation report that identifies portions of the derivative work responsible for failing the binary threshold.
9 . The system of claim 7 , wherein evaluating the derivative work further comprises:
comparing an audio fingerprint of the derivative work with a fingerprint library representing the source content; and
rejecting the derivative work when fingerprint similarity exceeds a similarity threshold.
10 . The system of claim 1 , wherein the digital identifier is at least one of:
a pointer that resolves to a remote record storing at least one of provenance data, attribution data or licensing data for the derivative work; or
a reference to external data stored in a database, the external data comprising metadata or rights information associated with the derivative work.
11 . The system of claim 10 , wherein the pointer comprises a cryptographically signed hash and the instructions further cause the system to verify the cryptographically signed hash before enabling distribution of the derivative work.
12 . The system of claim 1 , wherein the instructions further cause the system to detect the digital identifier and replace the digital identifier with a nested digital identifier that preserves provenance for successive derivative generations when the derivative work is subsequently received as source content.
13 . The system of claim 1 , wherein the digital identifier is associated with usage-constraint metadata comprising a time-to-live value and an authorization server automatically disables distribution of the derivative work upon expiry of the time-to-live value.
14 . The system of claim 1 , wherein the digital identifier is associated with usage-constraint metadata comprising at least one of: distribution channel restrictions; monetization parameters; attribution requirements; time-limited usage permissions; or geographical distribution limitations.
15 . The system of claim 1 , wherein the instructions further cause the system to execute a smart contract that automatically allocates revenue from each authorized distribution of the derivative work to stakeholders identified in a usage registry.
16 . The system of claim 1 , wherein evaluating the derivative work against post-generation preference data comprises:
analyzing the derivative work for unauthorized incorporation of third-party content;
comparing the derivative work against objectionable content criteria; and
scoring the derivative work based on quality thresholds defined by the content authority.
17 . The system of claim 1 , wherein a post-generation evaluation is performed by a third-party service independent from an entity generating the derivative work.
18 . The system of claim 1 , wherein the machine-learning classifier evaluates the transformation theme against exclusionary criteria identifying at least one of themes, topics, or content elements prohibited from inclusion in the derivative work by:
identifying at least one of keywords or semantic elements within the transformation theme;
comparing the identified keywords or semantic elements against the exclusionary criteria; and
rejecting the request if the identified keywords or semantic elements match the exclusionary criteria.
19 . The system of claim 1 , wherein generating the derivative work comprises maintaining predetermined elements of the source content while modifying other elements according to the transformation theme.
20 . The system of claim 19 , wherein the predetermined elements comprise at least one of original vocals, melody lines, rhythm patterns, or visual components.
21 . A system for approval and controlled distribution of a derivative work, the system comprising:
at least one processor; and
memory storing instructions that, when executed by the at least one processor, cause the system to:
receive, through a user interface presented on a client device, a request associated with source content for creating a derivative work based on the source content, wherein the request comprises a natural-language prompt;
parse, using a large-language-model parser, the request to extract a transformation theme from the natural-language prompt;
in response to evaluating the transformation theme using a machine-learning classifier, generate the derivative work from the source content in accordance with the transformation theme using a generative artificial-intelligence model;
generate a digital identifier based on the derivative work;
provide at least one of the digital identifier or data associated with the digital identifier to a usage registry; and
enable or disable at least one of distribution or usage of the derivative work based on usage constraints associated with the at least one of the digital identifier or data associated with the digital identifier.
22 . The system of claim 21 , wherein the machine-learning classifier confirms compliance with content authority guidelines before generating the derivative work.
23 . The system of claim 21 , wherein the instructions further cause the system to:
receive a subsequent request to access the derivative work;
verify the subsequent request by querying the usage registry using the digital identifier to retrieve associated usage constraints; and
selectively enable or disable at least one of playback, viewing, or distribution of the derivative work in accordance with the associated usage constraints.
24 . The system of claim 23 , wherein the subsequent request is received from a requestor, the requestor comprising at least one of:
a user device attempting to access the derivative work via a media application;
a content platform or distribution service requesting playback or download authorization;
an automated system performing periodic license verification; or
a third-party service querying usage permissions for compliance monitoring purposes.
25 . The system of claim 21 , wherein the machine-learning classifier evaluates the transformation theme against exclusionary criteria identifying at least one of themes, topics, or content elements prohibited from inclusion in the derivative work.
26 . The system of claim 21 , wherein the usage constraints include at least one of: distribution channel restrictions, monetization parameters, attribution requirements, time-limited usage permissions, or geographical distribution limitations.
27 . The system of claim 21 , wherein the digital identifier is at least one of:
a pointer that resolves to a remote record storing at least one of provenance data, attribution data, or licensing data for the derivative work; or
a reference to external data stored in a database, the external data comprising metadata or rights information associated with the derivative work.
28 . The system of claim 21 , wherein enabling or disabling at least one of distribution or usage of the derivative work based on usage constraints associated with the at least one of the digital identifier or data associated with the digital identifier is based on detection of at least one of a watermark or embedded data in the derivative work.
29 . The system of claim 1 , wherein the instructions further cause the system to include, in the derivative work, at least one of: a watermark; or embedded metadata comprising the digital identifier.
30 . The system of claim 21 , wherein the instructions further cause the system to include, in the derivative work, at least one of: a watermark; or embedded metadata comprising the digital identifier.