ML-Based Virtual Object Insertion in Dynamic Environments
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing virtual environment systems face challenges in seamlessly integrating supplemental content, often resulting in incongruous or unsuitable additions due to limited predefined object placements and lack of adaptability to dynamic environments.
Innovation Solution
The implementation of a machine learning model that identifies and predicts suitable virtual objects for insertion by training on persistent and temporary objects within the environment, ensuring accurate and contextually appropriate placement of supplemental content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predefined objects are placed in explicitly marked locations for supplemental content display, then the insertion process is simple and controlled, but the adaptability to dynamic environments and contextual appropriateness is limited
Solution Approach 1:
The patent replaces the manual, rule-based mechanical system of predefined object placement with an automated machine learning system. The ML model analyzes virtual environment frames, identifies suitable locations and objects, and automatically inserts supplemental content without explicit programmer rules, thereby achieving both ease of operation and adaptability to dynamic contexts.
Solution Approach 2:
The system enables the virtual environment to self-assess suitability for supplemental content insertion through the ML model. The model independently analyzes environment characteristics, temporary objects, and contextual factors to determine appropriate insertion points, allowing the system to adapt autonomously without external intervention.
2Adaptability or versatility
If new virtual objects are inserted as supplemental content, then content variety is increased, but the coherence and contextual appropriateness of the environment may be compromised
Solution Approach 1:
The ML model incorporates feedback loops where it continuously learns from environment analysis results. The model evaluates candidate objects against multiple criteria including visual consistency, contextual relevance, and environmental characteristics, using this feedback to refine selections and ensure contextual appropriateness while maintaining content variety.
Solution Approach 2:
The patent applies local quality by analyzing specific regions and characteristics of the virtual environment to determine suitability for supplemental content. Different areas of the environment are evaluated based on their local properties, allowing the system to insert diverse content types in appropriate contexts while maintaining overall environmental coherence.
3Manufacturing precision
If manual selection and placement of supplemental content is performed, then precise control over insertion is achieved, but automation and efficiency are reduced
Solution Approach 1:
The patent replaces manual selection and placement operations with an automated machine learning system that performs environment analysis, candidate generation, and insertion decisions. The ML model achieves precise placement by learning from environment characteristics and temporal patterns, eliminating the need for manual intervention while maintaining high precision.
Solution Approach 2:
The ML model serves as an intermediary between the virtual environment and supplemental content insertion process. It analyzes environment frames, evaluates candidate objects, and determines optimal insertion points, acting as an intelligent mediator that combines automation with precise control without requiring direct manual input.
4Productivity
If supplemental content is inserted without environmental analysis, then insertion speed is increased, but accuracy and suitability of placement are reduced
Solution Approach 1:
The system performs preliminary analysis of virtual environment frames before insertion decisions are made. The ML model pre-processes environment data, identifies characteristic patterns, and evaluates suitability metrics in advance, enabling fast insertion decisions without sacrificing accuracy. This preliminary action allows the system to maintain high productivity while ensuring precise and suitable placement.
Data Source
AI summary
Insertion of supplemental content into a virtual environment is automated using a machine learning model. The machine learning model is trained to calculate a confidence value that a candidate virtual object fits into a virtual environment based on an input that includes a candidate virtual object, a list of persistent virtual objects, and a list of temporary virtual objects. The machine learning model is trained using the persistent and temporary objects displayed in the current virtual environment until it predicts that a selected virtual object fits into the current virtual environment. The trained machine learning model is then used to select a virtual object comprising supplemental content to be inserted as a new virtual object in the virtual environment.