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 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: 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: Aspects are directed receiving a request for media content from a user device, querying a plurality of content provider application programming interfaces (APIs) to retrieve media catalog data, generating a list of media recommendations based on content obtained from one or more of the plurality of content provider APIs, receiving a selection of a media item from the list of media recommendations, providing an acquisition request to a content provider system associated with the selected media item, and retrieving the selected media item from a hosting system.

Abstract: Aspects of the present disclosure are directed to a method for providing differentiated access in a media subscription platform. The method may comprise: maintaining a data store that associates subscribers with subscription tiers and stores a permission policy defining allowed subscriber interactions with media items supplied by creators. The method comprises receiving, from a client device of a premium tier subscriber, a request relating to a referenced media item. The method comprises determining, from the permission policy, at least one content-interaction mode permitted for the subscriber with respect to the referenced media item. The method comprises generating, by an AI model selected based on the content-interaction mode and permission policy, modified content, supplemental content, or generative content.

Abstract: The present disclosure provides a system for generating artist-governed merchandise derivative works, comprising at least one processor and memory storing instructions that, when executed, cause the system to receive a request associated with source content and a transformation theme for creating a merchandise derivative work, evaluate the request against pre-generation preference data associated with a content authority using a knowledge agent maintaining brand guidelines, generate the merchandise derivative work from the source content according to the transformation theme by applying a generative artificial-intelligence model through an action agent, perform post-generation validation against the brand guidelines, generate a digital identifier based on the merchandise derivative work, and enable distribution through a physical merchandise path for tangible products or a virtual merchandise path for digital assets.

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.

Abstract: 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.

Abstract: Aspects of the present disclosure are directed to a system having at least one processor and memory instructions executable by the at least one processor to maintain a data store that associates subscribers with subscription tiers and stores a permission policy defining allowed subscriber interactions with media items supplied by creators. The instructions further cause the system to receive, from a client device of a premium tier subscriber, a request relating to a referenced media item. The instructions further cause the system to determine, from the permission policy, at least one content-interaction mode permitted for the subscriber with respect to the referenced media item. The instructions further cause the system to generate, by an AI model selected based on the content-interaction mode and permission policy, modified content, supplemental content, or generative content. The system may generate interactive visualizations that respond to one or more aspects of the referenced media item.

Abstract: The present disclosure provides a system for generating artist-governed merchandise derivative works, comprising at least one processor and memory storing instructions that, when executed, cause the system to receive a request associated with source content and a transformation theme for creating a merchandise derivative work, evaluate the request against pre-generation preference data associated with a content authority using a knowledge agent maintaining brand guidelines, generate the merchandise derivative work from the source content according to the transformation theme by applying a generative artificial-intelligence model through an action agent, perform post-generation validation against the brand guidelines, generate a digital identifier based on the merchandise derivative work, and enable distribution through a physical merchandise path for tangible products or a virtual merchandise path for digital assets.

Abstract: 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.

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.

Abstract: 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.

Abstract: 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.

Abstract: A system for the playback of content files includes a memory storing a content file including a plurality of stems, each stem encoding a portion of the audio of a sound recording, multiple stems in the plurality of stems representing different portions of the sound recording for the same time period, the content file also including a set of instructions controlling playback of the stems. A decoder is configured to decode the stems according to the set of instructions to create an audio output signal.

Abstract: Example embodiments allow for the creation, distribution, and use of flexible media formats. Example embodiments may allow individual content files to be rendered in multiple formats and versions. In addition, example embodiments may provide for granular rights management, which may allow users to access content files on a feature-by-feature basis.

Abstract: Example embodiments allow for the creation, distribution, and use of flexible media formats. Example embodiments may allow individual content files to be rendered in multiple formats and versions. In addition, example embodiments may provide for granular rights management, which may allow users to access content files on a feature-by-feature basis.

Abstract: Example embodiments allow for the creation, distribution, and use of flexible media formats. Example embodiments may allow individual content files to be rendered in multiple formats and versions. In addition, example embodiments may provide for granular rights management, which may allow users to access content files on a feature-by-feature basis.