Large Language Model for Automated Virtual Scene Object Placement
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Solution Overview
Problem
Current gaming platforms require manual selection and placement of objects in virtual experiences, which is inefficient and cumbersome.
Innovation Solution
A computer-implemented method using a large language model to receive a natural language user prompt, identify objects with corresponding attributes, determine spatial placement information, and place objects in a virtual experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual selection and placement of objects is used, then users have control over object placement, but the process is inefficient and cumbersome
Solution Approach 1:
The patent replaces the manual mechanical process of selecting and placing objects with an automated AI system. The AI model processes natural language prompts and automatically generates object placement decisions, eliminating the need for users to manually navigate and place each object while maintaining operational ease through simple prompt input.
Solution Approach 2:
The system enables self-service scene creation where the AI automatically performs object identification, selection, and placement based on user prompts. The AI serves itself by processing the prompt, retrieving appropriate objects from its knowledge base, and executing placement actions without requiring user intervention in the technical operations.
2Productivity
If automated AI placement is used, then scene creation efficiency improves, but the system complexity increases
Solution Approach 1:
The AI system is designed with multi-functionality, handling object identification, selection, and placement operations through a single integrated model. This universal approach consolidates what would otherwise be separate complex subsystems into one cohesive AI service, managing system complexity while maintaining high productivity.
Solution Approach 2:
The natural language prompt serves as an intermediary between the user's simple intent and the complex AI processing required for automated placement. This intermediary layer translates user-friendly input into actionable instructions for the AI system, shielding users from underlying complexity while enabling efficient automated operation.
3Ease of operation
If natural language processing is used, then ease of operation improves, but processing time increases
Solution Approach 1:
The AI system performs preliminary processing by pre-identifying and preparing appropriate objects and placement locations before receiving the user prompt. This advance preparation allows the system to quickly execute placement operations once the prompt is processed, reducing overall time loss while maintaining simple user interaction.
Data Source
AI summary
A user prompt, such as a user prompt received by a client device and sent to an online game system, is provided into a trained large language model. The large language model identifies keywords corresponding to the user prompt. These keywords may be provided to a search engine that identifies corresponding object(s) to place in a virtual experience. The large language model further processes the user prompt to determine spatial placement information for the objects and places the objects accordingly. Subsequently, the system may iteratively receive more prompts and update the virtual experience based on the additional prompts. The placement may be facilitated using macros. The prompts may also affect other attributes of the objects. The knowledge built-into the LLM allows it to suggest which objects are relevant and what quantity and arrangements of these objects is consistent with a scene requested in the user prompt.