LLM Game Schema Cross-Referencing for Crash-Resistant Coding
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Solution Overview
Problem
Current coding devices for video games are intensive and error-prone, lacking adequate solutions to remedy these shortcomings.
Innovation Solution
Utilize a large language model (LLM) to generate and cross-reference structured data schemas for video game objects, allowing for the creation of schema instances and game actions, with the output in JavaScript Object Notation (JSON) format, and enable the writing of JavaScript code that refers to these schemas and instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional coding devices and methods are used for video game development, then developers have direct control over code creation, but the process becomes intensive and error-prone
Solution Approach 1:
The patent introduces an LLM-based intermediary system that acts as a mediator between natural language prompts and game code generation. This intermediary automatically generates schemas, validates cross-references, and produces instance data, thereby reducing errors while managing complexity through automation rather than manual processes.
Solution Approach 2:
The system enables self-service by allowing the LLM to autonomously generate schemas, validate their interconnections, and create instance data without requiring manual coding intervention. The automated validation and cross-referencing mechanisms serve themselves to ensure consistency and reduce errors in the game development process.
2Productivity
If manual schema creation and cross-referencing is performed, then developers maintain full control over data structures, but the process becomes time-consuming and intensive
Solution Approach 1:
The patent applies preliminary action by having the LLM pre-generate schemas and validate their cross-references before actual game development proceeds. This advance preparation of data structures and validation rules accelerates subsequent development while reducing time investment in manual schema creation and error correction.
Solution Approach 2:
The system replaces the mechanical process of manual schema creation and validation with an AI-based LLM system. This substitution automates the generation of structured data, cross-reference validation, and instance creation, dramatically improving productivity while reducing the time developers spend on repetitive coding tasks.
3Reliability
If AI-assisted schema generation is implemented, then development speed increases and errors reduce, but the system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the LLM-generated schemas are automatically validated for consistency and proper cross-referencing. This feedback loop ensures that generated code maintains high reliability by detecting and correcting potential errors before they cause crashes, while the automated nature of the feedback reduces the perceived system complexity for users.
Data Source
AI summary
In one aspect, schemas for interactive content creation may be created using large language models (LLMs). The schemas may refer to one another and may be used to build schema instances off the schemas themselves. The schema instances may also refer to each other. Computer code for actions in the interactive content can then be written in a given programming language to refer to the schemas and schema instances, enabling quick code creation with less chances of crashing due to the strongly-typed language being used, including the schema references to each other. The interactive content might be video game content or another type of interactive content.