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.

Systems and immersive methods are provided for aggregation and organization of image, video and digital rights data. In an exemplary embodiment, a system and method for aggregation and organization of media data acquired from a plurality of sources is provided. The system and method include a data collection element, a brand commercial (POPmercial) placement for sponsorship element, a stake and mining rewards (Futures In Popular) against placed media element and a user interface that allows publishing of a unique curated media stream as a first-time-ever NFT BROADCAST, (POPcast).

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.

Systems and methods described herein involve improving the accuracy of artificial intelligence (AI) by providing an AI engine with related data in response to a query, including a main site comprising one or more servers that manage an AI augmented engine configured to provide the AI engine with related data in response to the query, the AI engine configured to provide output in response to input queries and the related data; a storage system that manages data according to data regulation rules; and one or more remote sites, each comprising another storage system that manages data according to data regulation rules configured to restrict copying of the managed data according to location restriction and user privilege; wherein the AI augmented engine is configured to, in response to the query, copy query related data from the one or more remote sites to the storage system of the main site, when permitted by the data regulation rules of the one or more remote sites.

Disclosed is method for preventing fraud in application executed in device, method including providing application to device, wherein application has a first data model module and viewer module; using viewer module rendering on user interface of display of device, first user view of application, first user view including set of visible graphical objects, wherein first user view is rendered based on data of first data module; detecting triggering event; based on detected triggering event, providing update data module to application for updating first data model module; detecting control event; and based on detected control event, rendering updated user view of application on user interface of device using updated first data model module.

A computer-implemented method for identifying proprietary content in composite media comprising a plurality of media components, the method comprising: identifying a first media component of the composite media as proprietary content; identifying a second media component of the composite media as a context indicator; and determining a context for the proprietary content based on the context indicator.

Apparatus and associated methods relate to an artwork authenticity owner validation system. In an illustrative example, an artwork remote identification system (ARIS) may include an artwork companion chip (ACC) and a certificate of authenticity (COA). The ACC, for example, may be physically attached to an artwork. The ACC and the COA, for example, may each include a unique identifier. For example, a centralized server may be configured to automatically authenticate an ownership of the artwork attached to the ACC by validating the ACC and the COA. For example, the user may scan the COA and then the ACC within a predetermined time limit to gain access to modify an ownership record in an artwork profile of the artwork. Various embodiments may advantageously allow a modification to the ownership information only when the centralized authentication server validates the COA and the ACC within the predetermined time period.

The present technology significantly enhances digital media piracy detection by integrating personalized viewer interactions into the broadcasting content. Through the unique customization of SMS, QR codes, trivia shuffling, and voice commands, the present technology not only detects piracy with high precision but also enhances viewer engagement and maintains robust privacy and security through advanced blockchain and zero-knowledge proof technologies.

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.

A system for generating derivative works comprises a content derivation platform with at least one processor configured to receive predetermined content and a request to transform it into a derivative work with a requested theme. The system uses generative artificial intelligence to create the derivative work comprising audio, video, or images based on the requested theme. A content approval machine learning model with a neural network determines an approval score based on content owner preferences and text embeddings identifying items in the derivative work. When the score exceeds a predetermined minimum, the system applies a digital watermark, configures an authorization server to govern use based on the digital watermark, and provides access to the approved derivative work. The requested theme may be determined through a chatbot interview using a Large Language Model. The generative artificial intelligence may comprise a diffusion model. The content may comprise music or audio.