Generative Neural Application Engine for Interactive Prototyping
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Solution Overview
Problem
Developing complex interactive applications, such as video games, remains an intensive process despite the use of application engines, which offload functions like graphics rendering and physics simulations. Existing technologies, like behavioral cloning and world modeling, do not fully capture the interactions between human users and applications in a unified manner.
Innovation Solution
A generative model is trained to predict application output together with the inputs that a human would provide, enabling the generation of new sequences of application and user interactions. This is achieved through user interface mechanisms that allow intuitive manipulation of the generative model at runtime, including graphical user interfaces for controlling input sequences and defining controller states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If application engines are used to offload functions like graphics rendering and physics simulations, then development complexity is reduced, but coding complex interactive applications remains an intensive process
Solution Approach 1:
The patent replaces traditional mechanical coding processes with a neural network-based generative system. The neural application engine uses trained neural networks to automatically generate application code and behavior from natural language descriptions, replacing the manual mechanical process of coding complex interactive applications while maintaining the benefits of application engines for rendering and physics
Solution Approach 2:
The neural application engine enables applications to generate their own code and behavior autonomously. The system trains neural networks to understand application requirements and automatically produce functional code, allowing the system to serve itself in the code generation process without requiring extensive manual programming for each application
2Reliability
If behavioral cloning and world modeling are used to model user interactions, then some aspects of user behavior are captured, but unified capture of human-user interactions is not achieved
Solution Approach 1:
The patent merges behavioral cloning and world modeling into a unified neural network framework. The system combines these previously separate approaches into a single neural application engine that can handle both user behavior prediction and environmental modeling simultaneously, achieving both reliability and versatility in interaction modeling
Solution Approach 2:
The neural application engine is designed as a universal system that can perform multiple functions: behavioral cloning, world modeling, code generation, and natural language processing. This multi-functional approach allows the system to capture unified human-user interactions across different contexts and application types
Data Source
AI summary
Various example user interface (UI) mechanisms are described herein, each of which enables a user to efficiently and intuitively manipulate a trained generative model at runtime. Among other things, the described techniques have applications in the field of game design or application design more generally, enabling a game or application developer to easily generate extended application (e.g., gameplay) sequences. Other applications include guided image or audio synthesis, or other forms of guided output generation (e.g., synthesized code, simulated or actual industrial outputs, engineering data, cybersecurity data etc.).