Virtual Content Creation Method Using AI Emotional State Detection
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Solution Overview
Problem
Existing methods fail to provide customized real-time communication with models when users cannot access them in person, leading to one-sided communication and inability to seek advice due to geographical distance or unavailability.
Innovation Solution
A method using a server that receives and extracts features from model content, such as text, voice, and video, and applies deep learning or artificial intelligence to transform this content into virtual content matching the user's emotional state, allowing for interactive and contextual communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the model is kept in a remote position or inaccessible location, then the user's privacy and the model's security are protected, but the user cannot communicate with the model or seek advice in real-time
Solution Approach 1:
A server acts as an intermediary between the user and the model. The server receives model content (text, voice, video), extracts features using deep learning, and generates virtual content that represents the model's responses. This mediator enables real-time communication while keeping the model physically inaccessible and secure.
Solution Approach 2:
The system creates virtual copies of the model through AI-generated content. Instead of providing direct access to the model, the server generates virtual representations (text responses, synthesized voice, generated video) that replicate the model's communication capabilities, allowing users to interact with these copies rather than the original model.
2Adaptability or versatility
If traditional communication methods are used with inaccessible models, then the communication process is simple, but the communication is one-sided and users cannot receive personalized advice
Solution Approach 1:
The server changes multiple parameters of the model content including text content, voice characteristics, video appearance, and emotional tone based on the user's detected emotional state. This transforms static model content into dynamic, personalized virtual content that adapts to each user's specific situation and emotional needs.
Solution Approach 2:
The system implements a feedback loop where the server detects the user's emotional state from their input, analyzes this feedback, and adjusts the generated virtual content accordingly. This allows the communication to be interactive and adaptive rather than one-sided, enabling the system to respond to user needs in real-time.
3Adaptability or versatility
If the server processes and transforms model content using deep learning, then personalized and interactive virtual content is generated, but the processing time and computational resources increase
Solution Approach 1:
The server performs preliminary processing by pre-extracting features from model content (text, voice, video) and storing them in an organized manner. This preliminary action prepares the data for rapid retrieval and transformation during user interactions, reducing the processing time required when actual communication occurs.
Data Source
AI summary
A virtual content creation method according to an embodiment of the present invention includes, by a server, receiving a model content including at least one of a text, an SMS, a voice-recorded MP3 file, a picture, and a video of a model; by the server, extracting a model feature including at least one of a text feature, a voice feature, an image feature, and a video feature from the model content; and when a user wants to communicate with the model, by the server, being operated based on deep learning or artificial intelligence to allow the user to input a user content to the server, determine a user state by detecting an emotional state of the user from the user content, and transform the model content into the virtual content using the model feature or the user state.