Digital Area Design System Using Image Recognition and User Profiles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in translating appealing room designs from images into their own physical spaces, as existing technologies struggle to emulate combinations of design elements not presented together in the images they see.
Innovation Solution
A computer-implemented method that uses user-input images and design profiles, built using machine learning, to generate digital designs for areas by identifying and incorporating design elements that match user preferences, including furniture, colors, lighting, and layout, for presentation via augmented reality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users save and reference design images from social media platforms, then they can preserve design inspiration, but they cannot automatically translate these images into their own physical space designs
Solution Approach 1:
The system creates digital copies of design elements from reference images and reproduces them in the user's physical space design. Image recognition technology extracts furniture, decor, and design elements from saved images, then generates digital replicas that can be virtually placed in the user's space for visualization and selection.
Solution Approach 2:
The system introduces an intermediary processing layer between the reference images and the user's physical space. This intermediary uses image recognition to analyze design elements, machine learning to understand user preferences from design profiles, and virtual rendering to present customized design combinations that bridge the gap between inspiration and implementation.
2Adaptability or versatility
If existing technologies attempt to emulate design combinations from images, then they can provide design suggestions, but they fail to accurately match user preferences and space constraints
Solution Approach 1:
The system performs preliminary actions by building detailed design profiles for users before generating design suggestions. These profiles capture user preferences, style preferences, and requirements through prior interactions and saved images. When users upload space images, the system already has their preference data ready to accurately match and customize design recommendations.
Solution Approach 2:
The system applies local quality by tailoring design suggestions to specific areas and user preferences rather than providing generic recommendations. Image recognition identifies specific design elements in reference images, and the system selectively combines these elements based on the user's localized preferences and the specific characteristics of their physical space.
3Adaptability or versatility
If the system generates multiple digital designs with different design elements, then users have more options, but the complexity of processing and presenting these designs increases
Solution Approach 1:
The system segments the design generation process into distinct modules: image recognition for extracting design elements, machine learning for preference matching, virtual rendering for design assembly, and augmented reality for presentation. This segmentation allows the system to handle multiple design options efficiently by processing each element independently and combining them through standardized interfaces.
Data Source
AI summary
Digital design of an area is provided by obtaining input parameters for designing the area, comparing the input parameters to a design profile for the user, in which the design profile indicates user preferences regarding design elements, identifying design element(s) to be included in digital design(s) for the area based on correlating the input parameters with design element(s) from the design profile, generating the digital design(s), each of which incorporates at least one of the identified design elements, and displaying a digital design for potential selection to guide designing the area.