Generative Neural Application Engine for Interactive Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional application development for interactive applications, such as video games, is intensive and complex due to the need for extensive hand-coding, despite the use of application engines that simplify certain functions like graphics rendering and physics simulations.
Innovation Solution
Employing a generative model, like a neural dreaming model, to predict application outputs and user inputs based on training sequences, allowing for the generation of new sequences of interactions without separate user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If application engines are used to simplify development, then ease of manufacture is improved, but device complexity increases
Solution Approach 1:
The patent replaces traditional application engines with a generative AI model that uses machine learning to predict application outputs. This substitution eliminates the need for complex engine architectures while maintaining development simplicity, as the AI model learns patterns directly from training data rather than requiring explicit programming of physics engines, rendering systems, and other complex components.
Solution Approach 2:
The generative AI model serves multiple functions that traditionally required separate components: it performs physics simulation, graphics rendering, and game logic simultaneously by predicting the next application output based on current state and input. This multi-functionality reduces overall system complexity while maintaining ease of development.
2Manufacturing precision
If hand-coding is used for entire applications, then manufacturing precision is improved, but productivity deteriorates
Solution Approach 1:
The system performs preliminary action by training the generative AI model on extensive datasets that capture the desired application behavior and outputs. This pre-training phase encodes the manufacturing precision requirements into the model's learned parameters, allowing rapid generation of accurate application outputs during runtime without requiring hand-coding of each scenario.
Solution Approach 2:
Instead of hand-coding each application output, the system uses the trained generative model to copy and generalize patterns from training data. The model learns from examples of correct application outputs and reproduces similar outputs for new inputs, maintaining functional accuracy while dramatically increasing development productivity.
3Productivity
If generative models are used to predict application outputs, then productivity is improved, but reliability may worsen
Solution Approach 1:
The system implements feedback mechanisms where the generative model's outputs are evaluated against ground truth data during training, and the model parameters are adjusted to minimize errors. This continuous feedback loop ensures that the model learns accurate mappings from inputs to outputs, maintaining reliability while enabling rapid prototyping and generation.
Data Source
AI summary
The disclosed concepts relate to implementation of application and application engine functionality using machine learning. One example method involves obtaining a seed image representing a seeded application state and mapping the seed image to at least one seed image token using an image encoder. The example method also involves inputting the at least one seed image token as a prompt to a neural dreaming model that has been trained to predict training sequences obtained from one or more executions of one or more applications, the training sequences including images output by the one more applications during the one or more executions and inputs to the one or more applications during the one or more executions. The example method also involves generating subsequent image tokens with the neural dreaming model, and decoding the subsequent image tokens with an image decoder to obtain subsequent images.