Multi-stage approval and controlled distribution of AI-generated derivative content

A method for approval and controlled distribution of a derivative work, the method comprising:

• receiving a request associated with source content and including one or more user-specified parameters for creating a derivative work based on the source content;
• in response to evaluating, using an evaluation model comprising a machine-learning classifier, the request against pre-generation preference data, generating, using at least one processor, the derivative work based on the source content in accordance with the one or more user-specified parameters using a generative artificial-intelligence model comprising a machine-learning model configured to generate content based on the one or more user-specified parameters;
• generating, by a content approval machine-learning model, a content approval score based on an evaluation of the derivative work against post-generation preference data;
• in response to the content approval score satisfying a threshold, generating a digital identifier based on the derivative work;
• providing at least one of the digital identifier or data associated with the digital identifier to a usage registry;
• and
• 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.

A method for creating a derivative work, comprising:

• receiving source content from at least one of a database server or a user upload;
• receiving a request to transform the source content into a derivative work, wherein the request includes one or more user-specified parameters directed to one or more characteristics of the derivative work, and wherein at least one processor executes transformation operations based on the source content and the one or more user-specified parameters;
• evaluating, using an evaluation model comprising a machine-learning classifier, the request against pre-generation preference data;
• in response to the request satisfying the pre-generation preference data, creating the derivative work as a function of the source content and the one or more user-specified parameters using generative artificial intelligence comprising a generative machine-learning model configured to generate content based on the one or more user-specified parameters;
• evaluating, using a content approval machine-learning model, the derivative work against post-generation preference data to generate a content approval score;
• in response to the content approval score satisfying a threshold, generating a digital identifier based on the derivative work;
• providing at least one of the digital identifier or data associated with the digital identifier to a usage registry;
• enabling 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;
• and
• fixing at least a portion of the derivative work in computer storage media.