Virtual Celebrity Avatar Licensing With Dynamic AI Interaction

The patent implements dynamic interaction levels that can be adjusted between autonomous (high automation) and manual control (low automation) modes. The system dynamically adapts the degree of AI autonomy in avatar interactions, allowing consumers to switch between fully autonomous celebrity avatars that independently interact with virtual consumers and manual control modes where consumers directly operate their avatars. This dynamic adjustment maintains long-term engagement value while managing system complexity through modular architecture.

The patent introduces a generative AI model as an intermediary between the consumer's virtual avatar and the celebrity avatar. This AI intermediary generates realistic interactions, dialogues, and group photos based on interaction levels, acting as a mediator that creates authentic-seeming celebrity interactions without requiring direct celebrity involvement. This intermediary layer adds interaction value while containing system complexity by abstracting the complex AI generation processes.

The patent implements preliminary action by pre-training generative AI models with extensive celebrity data, interaction patterns, and contextual knowledge before deployment. The system performs preliminary customization by generating unique virtual celebrity avatars based on selected celebrities' characteristics, appearances, and interaction styles. This preliminary preparation creates a foundation for sustained long-term engagement, as the pre-trained AI models can generate novel and varied interactions over time rather than relying solely on consumer customization choices.

The patent applies parameter changes by dynamically adjusting interaction levels as a key parameter that controls the degree of AI autonomy. This interaction level parameter transforms the system from static avatar customization to dynamic interaction control, where the same customized avatar can exhibit different behaviors and interaction qualities based on the selected autonomy level. This parameter-based control extends long-term engagement by providing continuous variability in interactions rather than relying on finite customization options.

The patent implements preliminary action by pre-training the generative AI models extensively before NFT generation. The models are pre-trained on large datasets of celebrity images, interactions, and contextual information, so that when NFT generation is triggered, the models are already optimized and ready to produce authentic-looking results quickly. This preliminary training phase separates the time-consuming preparation work from the actual NFT generation process, reducing the perceived delay for users while maintaining high authenticity standards.

The patent applies partial action by generating NFTs with varying degrees of detail and complexity based on interaction levels. At lower interaction levels, the system may generate simpler group photos with fewer details, while at higher interaction levels, more comprehensive and detailed images are produced. This partial generation approach balances authenticity requirements with time constraints, providing sufficient NFT value without always requiring maximum computational resources and time for every generation.