AI Post Editing With Suggested Titles and User Confirmation

The patent uses pre-trained generative AI models as intermediaries between user input and desired output. These models handle the complex tasks of title generation, grammar correction, and content personalization, allowing the electronic device to achieve high productivity without directly implementing complex AI algorithms. The models act as mediators that translate user content into polished posts with suggested titles, grammar corrections, and tone adjustments.

The system performs preliminary actions by pre-training AI models with extensive data before deployment. The models are pre-trained to understand grammar rules, content patterns, and tone variations, so when deployed in the electronic device, they can immediately perform editing tasks without requiring the device itself to contain complex training mechanisms. This separates the complexity of model training from the simplicity of model deployment.

The patent segments the editing system into multiple specialized AI models, each handling specific tasks: one model for title generation, another for grammar correction, another for content personalization, and another for tone adjustment. This segmentation allows each model to be optimized for its specific function while maintaining overall system versatility. The electronic device can selectively invoke only the models needed for each editing task, managing complexity through modular organization.

The patent implements a universal editing system where multiple AI models work together to provide comprehensive editing capabilities. The same framework supports title generation, grammar correction, content personalization, and tone adjustment across different types of posts (text, images, videos). This multi-functional approach allows a single integrated system to handle diverse editing needs without requiring separate specialized systems for each task.