Abstract: A system for multi-stage approval and controlled distribution of a derivative work includes at least one processor and memory storing instructions that, when executed by the at least one processor, cause the system to receive a request associated with source content and generate the derivative work based on the source content in accordance with the one or more user-specified parameters by applying a generative artificial-intelligence model. The instructions further cause the system to generate a content approval score based on an evaluation of the derivative work against post-generation preference data, generate a digital identifier, provide at least one of the digital identifier or data associated with the digital identifier to a usage registry, and enable 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.

Abstract: Herein disclosed is a non-transitory computer-readable medium having stored thereon program instructions that, when executed by at least one processor, cause the at least one processor to receive, at a generative artificial intelligence system, a request to generate a generative audio work based on predetermined content, create the generative audio work as a function of the predetermined content and a requested theme, and embed a watermark into the generative audio work by distributing the watermark across multiple frequency bands and across a plurality of temporal segments of the generative audio work. Distributing the watermark across multiple frequency bands includes modifying frequency components of the multiple frequency bands, which are less perceptible to a human ear. The watermark includes a payload, and presence of the payload in the generative audio work indicates that the generative audio work was generated by the generative artificial intelligence system.

Abstract: Aspects are directed to methods and systems for managing derivative work creation through pre-approved themes. In some aspects, a content derivation platform receives a request to create a derivative work based on predetermined content. The platform may analyze the request against one or more stored approved themes using semantic analysis. The semantic analysis may comprise converting the request into a text embedding and finding alignment between the text embedding and content embeddings associated with the stored approved themes. The platform may identify a matched approved theme based on the analysis and obtains transformation parameters associated with the matched approved theme. The platform may generate the derivative work using generative artificial intelligence based on the predetermined content and the transformation parameters.

Abstract: A system for multi-stage approval and controlled distribution of a derivative work includes at least one processor and memory storing instructions that, when executed by the at least one processor, cause the system to receive a request associated with source content and generate the derivative work based on the source content in accordance with the one or more user-specified parameters by applying a generative artificial-intelligence model. The instructions further cause the system to generate a content approval score based on an evaluation of the derivative work against post-generation preference data, generate a digital identifier, provide at least one of the digital identifier or data associated with the digital identifier to a usage registry, and enable 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.

Abstract: Herein disclosed is a system having at least one processor and memory storing instructions that, when executed by the at least one processor, cause the system to receive a request to generate a generative audio work based on predetermined content, create the generative audio work, and embed a watermark into the generative audio work by distributing the watermark across multiple frequency bands of the generative audio work and across a plurality of temporal segments of the generative audio work. The watermark includes a payload, and presence of the payload in the generative audio work is determined by a generative artificial intelligence system and indicates that the generative audio work was generated by the generative artificial intelligence system. The payload includes metadata that facilitates tracking of the generative audio work and management of access to the generative audio work.

Abstract: Herein disclosed is a method performed by at least one processor that includes receiving a user request to create generated media content, generating coherent and contextually relevant text, creating the generated media content based on the user request using generative artificial intelligence, generating supplemental content, augmenting the generated media content with additional information, and applying a digital watermark to the generated media content. The user request includes a prompt and a theme. The generated media content relates to one or more copyrighted works approved for use in relation to the generated media content. The generated media content includes at least one human voice sound. The supplemental content includes at least one image relating to the user request. The additional information includes at least one metadata tag identifying a genre of the generated media content, at least one metadata tag identifying a length of the generated media content, and music lyrics.

Abstract: Herein disclosed is a method for generating content using a generative artificial-intelligence model and associating provenance data with the generated content via a digital identifier. The digital identifier may comprise at least one of a watermark, a fingerprint, embedded metadata, a hash, an external metadata payload, or an entry in a content database. Usage-constraint metadata, including provenance data indicating that the content was generated using artificial intelligence, is associated with the digital identifier and provided to a partner platform. The partner platform may resolve the usage-constraint metadata and selectively enable or disable distribution, recommendation, or playback of the generated content. The provenance data may be presented to a consumer via a user interface associated with the partner platform, indicating one or more aspects of the content in which artificial intelligence was used.

Abstract: Herein disclosed is a system having at least one processor and memory storing instructions that, when executed by the at least one processor, cause the system to receive a request to generate a generative audio work based on predetermined content, create the generative audio work, and embed a watermark into the generative audio work by distributing the watermark across multiple frequency bands of the generative audio work and across a plurality of temporal segments of the generative audio work. The watermark includes a payload, and presence of the payload in the generative audio work is determined by a generative artificial intelligence system and indicates that the generative audio work was generated by the generative artificial intelligence system. The payload includes metadata that facilitates tracking of the generative audio work and management of access to the generative audio work.

Abstract: Herein disclosed is a system having at least one processor and memory storing instructions that, when executed by the at least one processor, cause the system to receive a request to create generated content and to evaluate, using an evaluation model comprising a machine-learning classifier, the request against pre-generation preference data. The instructions further cause the system to create the generated content as a function of a requested theme using generative artificial intelligence comprising a generative machine-learning model configured to generate content based on the requested theme. The instructions further cause the system to determine if the generated content is approved based on a content approval machine-learning model configured to generate a content approval output based on the generated content and at least one preference parameter defining approval criteria for the generated content. The instructions further cause the system to conditionally provide access to the generated content.

Abstract: Herein disclosed is a method for generating and managing derivative works using generative artificial intelligence. A request to generate a derivative work based on predetermined content is received, the request comprising a requested theme. The derivative work is created as a function of the predetermined content and the requested theme using generative artificial intelligence. A watermark is embedded into the derivative work by distributing the watermark across multiple frequency bands of the derivative work, the frequency bands being less perceptible to a human ear. The watermark may comprise a unique identifier associated with the derivative work that references rights information stored in a database. Use of the derivative work may be tracked based on the unique identifier. An authorization server may be configured to govern use of the derivative work based on the watermark, including validating user requests for access based on the rights information associated with the derivative work.

Abstract: A method includes receiving content from at least one of a database server or a user upload, receiving a request to transform the content into a derivative work, executing transformation operations based on the content and the requested theme, and evaluating, using an evaluation model, the request against pre-generation preference data. The method further includes, in response to the request satisfying the pre-generation preference data, creating the derivative work as a function of the content and the requested theme using generative artificial intelligence. The method further includes providing access to the derivative work. The artificial intelligence includes a generative machine learning model configured to generate content based on the requested theme. The request includes a requested theme directed to a characteristic of the derivative work.

Abstract: Herein disclosed is a method for generating content using a generative artificial-intelligence model and associating provenance data with the generated content via a digital identifier. The digital identifier may comprise at least one of a watermark, a fingerprint, embedded metadata, a hash, an external metadata payload, or an entry in a content database. Usage-constraint metadata, including provenance data indicating that the content was generated using artificial intelligence, is associated with the digital identifier and provided to a partner platform. The partner platform may resolve the usage-constraint metadata and selectively enable or disable distribution, recommendation, or playback of the generated content. The provenance data may be presented to a consumer via a user interface associated with the partner platform, indicating one or more aspects of the content in which artificial intelligence was used.

Abstract: Aspects are directed to methods and systems for managing derivative work creation through pre-approved themes. In some aspects, a content derivation platform receives a request to create a derivative work based on predetermined content. The platform may analyze the request against one or more stored approved themes using semantic analysis. The semantic analysis may comprise converting the request into a text embedding and finding alignment between the text embedding and content embeddings associated with the stored approved themes. The platform may identify a matched approved theme based on the analysis and obtains transformation parameters associated with the matched approved theme. The platform may generate the derivative work using generative artificial intelligence based on the predetermined content and the transformation parameters.

Abstract: Herein disclosed is a method for generating and managing derivative works using generative artificial intelligence. A request to generate a derivative work based on predetermined content is received, the request comprising a requested theme. The derivative work is created as a function of the predetermined content and the requested theme using generative artificial intelligence. A watermark is embedded into the derivative work by distributing the watermark across multiple frequency bands of the derivative work, the frequency bands being less perceptible to a human ear. The watermark may comprise a unique identifier associated with the derivative work that references rights information stored in a database. Use of the derivative work may be tracked based on the unique identifier. An authorization server may be configured to govern use of the derivative work based on the watermark, including validating user requests for access based on the rights information associated with the derivative work.

Abstract: Herein disclosed is a method performed by at least one processor. The method includes receiving request to create generated content, creating the generated content as a function of the requested theme using generative artificial intelligence, determining if the generated content is approved based on a content approval machine learning model configured to generate a content approval output based on the generated content and at least one preference parameter defining approval criteria for the generated content, and providing access to the generated content in response to the content approval output indicating approval. The request includes a requested theme directed to a target characteristic of the generated content. The content approval machine learning model includes a neural network. The content approval output may include a content approval score, and the content approval output may indicate approval when the content approval score satisfies an approval threshold.

Abstract: Herein disclosed is a method performed by at least one processor. The method includes receiving a request to create generated content, creating the generated content as a function of the requested theme using generative artificial intelligence, determining if the generated content is approved based on a content approval machine learning model configured to generate a content approval output based on the generated content and at least one preference parameter defining approval criteria for the generated content, and providing access to the generated content in response to the content approval output indicating approval. The request includes a requested theme directed to a target characteristic of the generated content. The content approval machine learning model includes a neural network. The content approval output may include a content approval score, and the content approval output may indicate approval when the content approval score satisfies an approval threshold.

Abstract: A request is received associated with source content and including one or more user-specified parameters for creating a derivative work based on the source content. The derivative work is generated in response to evaluating the request against pre-generation preference data using an evaluation model, and is generated based on the source content in accordance with the one or more user-specified parameters by applying a generative artificial-intelligence model. The generative artificial-intelligence model includes a machine learning model configured to generate content based on the one or more user-specified parameters. A content approval score is generated by a content approval model based on an evaluation of the derivative work against post-generation preference data. In response to the content approval score satisfying a threshold, a digital identifier is generated based on the derivative work. At least one of distribution or usage of the derivative work is enabled in accordance with the digital identifier.

Abstract: Program instructions on a non-transitory computer-readable medium are executable by at least one processor to receive predetermined content, a request to transform the predetermined content into a derivative work, and one or more user-specified parameters. Generative artificial intelligence is used to generate the derivative work as a function of the predetermined content and the one or more user-specified parameters. The derivative work includes audio, video, or images. Based on a content approval machine learning model, a determination is made as to whether the derivative work is approved based on a content approval score. The content approval score is based on at least one content owner preference and the derivative work. In response to determining the content approval score exceeds a threshold, a digital watermark is applied to the derivative work, an authorization server is configured to govern use of the derivative work, and access to the derivative work is provided.

Abstract: A request is received associated with source content and including one or more user-specified parameters for creating a derivative work based on the source content. The derivative work is generated in response to evaluating the request against pre-generation preference data using an evaluation model, and is generated based on the source content in accordance with the one or more user-specified parameters by applying a generative artificial-intelligence model. The generative artificial-intelligence model includes a machine learning model configured to generate content based on the one or more user-specified parameters. A content approval score is generated by a content approval model based on an evaluation of the derivative work against post-generation preference data. In response to the content approval score satisfying a threshold, a digital identifier is generated based on the derivative work. At least one of distribution or usage of the derivative work is enabled in accordance with the digital identifier.

Abstract: A system and method for creating AI-generated derivative works from predetermined content with copyright compliance and content owner control. In some aspects, the system receives predetermined content and a user-requested transformation theme, then employs generative artificial intelligence to create a derivative work. The system may enable scalable rights management for AI-generated content across music, video, text, and other media formats.