Director Service for Interactive Elements in Immersive Environments
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Solution Overview
Problem
Current technologies face challenges in effectively managing interactive elements in immersive environments, such as video games and virtual reality, due to the complexity of dialogue and action trees, which can lead to inconsistent user interactions and limited creative potential for content designers.
Innovation Solution
A director service acts as an intermediary to integrate interactive elements between developers, users, and generative machine learning models, processing inputs to recognize semantic context and intent, generating prompts that ensure responsive outputs within environment guidelines, thereby reducing the need for extensive manual development and fine-tuning of ML models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive dialogue and action trees are used to control interactive elements, then the interactive content can function correctly within the content's context, but the developer burden and complexity of management increases enormously
Solution Approach 1:
The patent introduces a director service as an intermediary layer between the developer's content and the ML model. This director service manages the interaction by receiving user inputs, determining appropriate ML model responses based on content context, and coordinating the overall interaction flow. This mediator approach reduces the developer burden of directly managing complex dialogue trees while ensuring interactive content functions correctly within its context.
Solution Approach 2:
The patent replaces the traditional mechanical system of hand-crafted dialogue trees and action graphs with a generative ML model. Instead of programming explicit response paths, the system uses an ML model that can generate appropriate responses dynamically, guided by prompts from the director service. This substitution dramatically reduces the complexity of managing interactive elements while maintaining functional reliability.
2Ease of manufacture
If ML models are used to manage interactive elements, then the developer burden is reduced, but the ML models cannot effectively interact with users to produce relevant, repeatable, and consistent results independently of designer or system management
Solution Approach 1:
The director service acts as a mediator that bridges the ML model's generative capabilities with the need for consistent, context-appropriate interactions. It receives user inputs, determines the appropriate response based on content context and guidelines, and formats prompts for the ML model. This intermediary ensures that the ML model produces relevant, repeatable, and consistent results while reducing developer burden.
Solution Approach 2:
The system incorporates feedback mechanisms where the director service evaluates ML model outputs against content context and guidelines before presenting them to users. This feedback loop ensures consistency and relevance while allowing the system to learn from interactions. The director service can adjust prompts based on previous interactions, ensuring repeatable and reliable results.
3Reliability
If extensive manual development and fine-tuning of ML models is performed, then the ML model can be optimized for specific interactions, but the time and resource requirements increase significantly
Solution Approach 1:
The patent replaces extensive manual fine-tuning with prompt engineering and a director service that manages interactions dynamically. Instead of training the ML model specifically for each content context, the system uses general-purpose prompts that can be adapted to different contexts through the director service. This approach significantly reduces training time and resource requirements while maintaining optimized performance.
Solution Approach 2:
The system uses a universal ML model that can handle multiple interaction types through context-aware prompting. The director service adapts the same base model to different content contexts by adjusting prompts and guidelines, eliminating the need for separate fine-tuning for each scenario. This multi-functionality approach reduces time and resource requirements while maintaining high performance.
4Adaptability or versatility
If interactive elements are designed to respond to a broader range of inputs in a creative manner, then user experience is enhanced, but the complexity of managing dialogue and action trees increases
Solution Approach 1:
The patent replaces rigid dialogue trees with a generative ML model that can creatively respond to diverse inputs. The director service manages complexity by handling prompt construction and context management, while the ML model generates appropriate responses without requiring explicit programming for each scenario. This enables broader input responsiveness and creative interactions.
Solution Approach 2:
The system transitions from static dialogue trees to dynamic interactions where the ML model adapts its responses based on real-time context. The director service dynamically adjusts prompts and guidelines based on the interaction state, enabling the system to handle a broader range of inputs creatively while managing complexity through dynamic adaptation rather than static structure.
Data Source
AI summary
The present disclosure relates to systems and methods for using a director service as an intermediary management system to integrate interactive elements between a developer, a user, a generative machine learning (ML) model, and/or an interactive environment. In examples, the director service may receive input from a user or developer device relating to an interactive element from an interactive environment. The director service may process input from one or more of the developer, the user, and the interactive environment to recognize semantic context and intent objectives associated with the input. The director service may generate one or more prompts based on such input, which is processed by an ML model to generate output. In examples, the prompts may be provided to the ML model to direct it towards providing an output that is responsive to the input and one or more environment guidelines. The input and/or output may be multimodal.