Virtual Model Interaction Using Event Triggering and Relationship Mapping
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Solution Overview
Problem
Existing methods for virtual interactions lack a realistic experience and are often complex and data-intensive, making them inefficient and time-consuming.
Innovation Solution
A system and method for rendering virtual model-based interactions using a processing subsystem that includes modules for virtual model generation, event creation, relationship searching, connection management, and event triggering, utilizing machine learning and image analysis to generate interactive virtual models and facilitate real-time events based on predefined schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing methods for virtual interactions are used, then interactions can be established, but the system becomes complex and time-consuming with huge data storage requirements
Solution Approach 1:
The patent extracts only the essential features needed for virtual interaction by using machine learning models that learn from limited input data (photos, videos, text) rather than requiring comprehensive datasets. The virtual model generation module selectively extracts relevant characteristics to create simplified yet realistic representations, reducing data storage requirements while maintaining interaction quality.
Solution Approach 2:
The system creates virtual copies (digital twins) of entities based on limited input data through machine learning models. These virtual models replicate essential characteristics and behaviors without requiring complete data replication, enabling realistic interactions with reduced computational and storage complexity compared to traditional methods.
2Productivity
If existing methods for virtual interactions are used, then interactions can be established, but they are time-consuming
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models with general knowledge and characteristics before specific virtual model generation. This pre-training enables the models to quickly adapt to new entities with minimal input data, significantly reducing the time required for virtual model generation and interaction setup compared to training from scratch for each entity.
Solution Approach 2:
The patent utilizes parameter changes in machine learning models to efficiently generate virtual representations. By adjusting model parameters based on input data characteristics and adapting the learning process dynamically, the system achieves fast virtual model generation while maintaining high interaction quality, reducing overall setup time.
3Quantity of substance
If existing methods for virtual interactions are used, then interactions can be established, but huge data storage is required
Solution Approach 1:
The system extracts only the necessary information from input data (photos, videos, text) to create virtual models, rather than storing complete datasets. The machine learning models selectively extract and retain essential features and characteristics, enabling interaction accessibility while dramatically reducing data storage requirements compared to traditional approaches.
Solution Approach 2:
Instead of storing complete original data, the system creates compact virtual copies that capture essential entity characteristics. These digital twins maintain interaction capabilities while occupying minimal storage space, making the system more accessible and easier to operate without requiring huge data storage infrastructure.
Data Source
AI summary
A system for rendering a virtual model-based interaction is provided. The system includes a processing subsystem which includes a virtual model generation module 40 which receives input(s), generates a learning model, and generates a virtual model. The processing subsystem also includes an event creating module 50 which creates an event. The processing subsystem also includes a relationship searching module 60 which receives multiple relationship-based details, and searches an account associated with the second user(s). The processing subsystem also includes a connection management module 70 which generates a connection request, establishes a connection between the first user and the second user(s), and provides multiple second user details to the first user. The processing subsystem also includes an event triggering module 80 which extracts event-related keyword(s) and triggers the event at a predefined schedule obtained based on mapping of the event-related keyword(s) with the second user detail(s), thereby rendering the virtual model-based interaction.