Gesture-Based Generative Output Refinement Without Full Regeneration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing generative models require redundant regenerating of entire outputs in response to user modifications, wasting computational resources and necessitating multiple user interactions for refinement.
Innovation Solution
Facilitating modification of generative outputs through intuitive user gestures, such as pinch-to-zoom or touch inputs, allowing refinement of specific portions without regenerating the entire output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the application regenerates the entirety of the initial generative output in response to a subsequent input query, then the user can receive a more detailed output, but computational resources are wasted and the refinement process becomes redundant
Solution Approach 1:
The patent divides the generative output into distinct segments or portions that can be independently identified and modified. When a user provides a follow-up input, the system selectively processes only the relevant segment rather than regenerating the entire output, thereby reducing computational resource waste while maintaining refinement precision.
Solution Approach 2:
The system applies local quality by allowing different portions of the generative output to have different refinement treatments. Each segment can be independently enhanced or modified based on user feedback, rather than applying uniform regeneration to the entire output, which optimizes resource usage while achieving precise local refinements.
2Manufacturing precision
If the application regenerates the entirety of the initial generative output, then the user can receive refined output, but multiple user interactions are required and the process becomes tedious
Solution Approach 1:
By segmenting the output into manageable portions, the system enables users to provide targeted feedback on specific segments rather than requiring multiple full regenerations. This reduces the number of interactions needed while maintaining refinement quality.
Solution Approach 2:
The system performs preliminary processing by identifying and isolating the specific segments that need refinement before user feedback is provided. This preliminary segmentation action prepares the system to apply refinements more efficiently in subsequent interactions, reducing overall interaction complexity.
3Adaptability or versatility
If standard GUI elements are used for refining generative output, then the interface is familiar to users, but the refinement options are constrained and require multiple tedious interactions
Solution Approach 1:
The patent implements a universal gesture-based interface that can perform multiple refinement functions (summarization, expansion, translation, formatting) through a single set of gestures. This multi-functional approach increases adaptability without requiring separate GUI elements for each refinement type, thereby maintaining interface simplicity while expanding functionality.
Solution Approach 2:
The system replaces traditional mechanical GUI elements (buttons, menus, sliders) with gesture-based interactions. This substitution allows for more versatile refinement options to be accessed through natural gestures rather than navigating complex menu structures, increasing adaptability while reducing perceived interface complexity.
Data Source
AI summary
Implementations set forth herein relate to modifying a generative output of an application according to an input gesture that is performed without necessarily interacting with a GUI element that is rendered separate from a generative output (e.g., a GUI element separate from a natural language output generated using an LLM or an image generated using an image diffusion model). Various different input gestures can be performed by a user to refine a generative output to be simpler, more complex, to include an image, to modify a generated image, and/or otherwise modify the generative output. In some implementations, an input gesture can be processed as one or more predetermined gestures, and/or an input gesture can be interpreted per case using an available model for interpreting such gestures. In this way, models for interpreting gestures and/or refining generative output can be enhanced through further training of such models.