Generative UI Parameter Controls for Fewer Repetitive Model Queries
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Solution Overview
Problem
Traditional user interactions with machine-learned generative models are predominantly one-shot, failing to surface the diverse capabilities of these models and leading to inefficient computing due to repetitive queries for satisfactory results.
Innovation Solution
A machine-learned generative user interface system generates user interface elements that allow real-time graduated control over machine-learned models, enabling users to explore and traverse the array of outcomes by providing computer-executable functional and interface code responsive to user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional one-shot user queries are used to interact with generative models, then users can receive generative content responses, but the diverse capabilities of the models are not sufficiently surfaced and repetitive queries are required
Solution Approach 1:
The system performs preliminary action by automatically generating and presenting multiple controlled variations of generative content along with their corresponding parameter settings before the user makes a final selection. The user interface presents options including modified content items with different parameters (e.g., style, composition, lighting) so the user can explore capabilities in advance without needing to submit multiple separate queries to the model
Solution Approach 2:
The system introduces an intermediary layer between the user and the generative model. This intermediary is the parameter-controlled user interface that translates user selections into precise model queries. The intermediary presents organized options and handles the complexity of generating multiple variations, acting as a mediator that reduces the need for direct repetitive querying of the model
2Reliability
If multiple queries are submitted to generate satisfactory results, then users can obtain desired content, but computing resources are consumed inefficiently
Solution Approach 1:
The system performs preliminary computation by pre-generating multiple controlled variations of content with different parameters before the user needs a final result. By preparing multiple options in advance through the parameter-controlled interface, the system reduces the total number of queries sent to the generative model, thereby conserving computing resources while ensuring satisfactory results are available
Solution Approach 2:
The system efficiently manages computing resources by changing parameters of content generation through a controlled interface. Instead of submitting multiple unrelated queries, the system modifies specific parameters (style, composition, lighting, etc.) systematically to generate variations. This parameter-based approach ensures satisfactory results while minimizing redundant computing by targeting specific aspect changes rather than regenerating entire content pieces
3Ease of operation
If one-shot queries are used, then interactions are simple, but users cannot explore or traverse the array of outcomes in a structured way
Solution Approach 1:
The system segments the exploration of generative outcomes by organizing parameters into distinct, controllable categories (e.g., style parameters, composition parameters, lighting parameters). Each parameter can be independently adjusted through dedicated interface elements, allowing users to explore outcomes in a structured manner. The segmentation of the output space into parameter-based categories makes the vast array of possible outcomes navigable and systematic
4Device complexity
If no parameter control interface is provided, then the user interface is simple, but users lack control over specific parameters of the generative content
Solution Approach 1:
The system implements a dynamic user interface that adapts based on the generation process. The interface includes parameter control elements that become active and accessible during the generation workflow, allowing users to modify parameters in real-time. The dynamic nature of the interface ensures it provides necessary control functionality without being statically complex, appearing simple during initial interaction but enabling detailed parameter control when needed
Data Source
AI summary
Aspects of the disclosed technology include machine-learning systems and methods for generating user interface elements that allow user control over generative content creation by machine-learned generative models. A generative user interface (UI) system is configured to generate, as output of one or more machine-learned sequence processing models, computer-executable functional code to process a user query in association with a content item. The system is configured to generate computer-executable interface code for a user interface that includes a user interface element associated with at least one parameter of the computer-executable functional code for modifying the content item. The system is configured to determine data for the at least one parameter of the computer-executable functional code based at least in part on a user input to the user interface element and generate a modified content item using the computer-executable functional code and the data for the at least one parameter.