CLIP-GLS Virtual Object Generation with Utility Scoring
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Solution Overview
Problem
Current technologies for generating virtual objects in three-dimensional spaces lack the ability to effectively assess and improve the utility of these objects based on human interaction and perception, leading to suboptimal generation and iteration in virtual environments like the metaverse.
Innovation Solution
A computer-implemented method utilizing CLIP-guided Generative Latent Space analysis to identify, generate, and monitor virtual objects in 3D environments, inferring human perception data to assign a utility score, which is then used to enhance future generative iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to generate virtual objects in 3D spaces, then the generation process is simple and fast, but the utility and relevance of generated objects to human needs cannot be assessed or improved
Solution Approach 1:
The system implements feedback loops where human interaction data and perception information are continuously collected from virtual environments, processed through utility assessment models, and used to refine future object generation. This closed-loop feedback mechanism enables the system to learn from human responses and progressively improve the utility and relevance of generated virtual objects without requiring complete system redesign.
Solution Approach 2:
The patent introduces intermediary components including utility assessment models, perception inference systems, and feedback processing mechanisms that bridge the gap between simple object generation and human-centered utility evaluation. These intermediaries translate complex human interaction patterns into actionable feedback signals that guide iterative improvement of generated objects.
2Measurement precision
If utility assessment based on human perception is implemented, then the relevance and usefulness of virtual objects improve, but the computational complexity and data processing requirements increase
Solution Approach 1:
The utility assessment system is segmented into distinct functional modules: interaction monitoring components that collect raw data, perception inference models that interpret behavior patterns, utility calculation engines that compute relevance scores, and feedback synthesis systems that prepare improvement signals. This segmentation allows each component to specialize in specific tasks, improving measurement precision while managing overall system complexity through modular architecture.
Solution Approach 2:
The system employs partial monitoring strategies where only the most relevant interaction aspects are tracked in detail, while less critical behaviors are sampled or aggregated. This selective approach enables accurate utility assessment for key object properties without requiring complete monitoring of all user interactions, thereby reducing computational overhead while maintaining assessment accuracy.
Data Source
AI summary
In an approach to improve the generation of a virtual object in a three-dimensional virtual environment, embodiments of the present invention identify a virtual object to be generated in a three-dimensional virtual environment based on a natural language utterance. Additionally, embodiments generate the virtual object based on a CLIP-guided Generative Latent Space (CLIP-GLS) analysis, and monitor usage of the generated virtual object in the three-dimensional virtual space. Moreover, embodiments infer human perception data from the monitoring, and generate a utility score for the virtual object based on the human perception data.