Visual Property Detection for Color Blindness
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Solution Overview
Problem
Current fashion recommendation systems fail to effectively assist individuals with visual impairments, such as color blindness or complete blindness, in evaluating and selecting clothing options from their closet or elsewhere, as they lack the ability to incorporate actual clothing items and expert recommendations for real-time feedback.
Innovation Solution
A system and method that utilizes a data storage system, machine learning algorithms, and a portable communication device to score and recommend clothing sets based on user input, incorporating expert opinions and social network feedback, while allowing users to import digital renditions of garments and accessories, and providing audio or visual feedback on color and pattern identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static fashion style matrix with set attributes is used, then the system structure is simple, but the system cannot access variable information such as expert opinions or actual clothing items
Solution Approach 1:
The system segments fashion evaluation into multiple independent modules: image processing module for analyzing clothing items, data storage module for storing clothing databases and user profiles, machine learning module for pattern recognition and fashion trend analysis, and recommendation module for generating personalized suggestions. This segmentation allows each module to access and process variable information independently while maintaining overall system manageability.
Solution Approach 2:
The system implements a universal fashion evaluation platform that can handle multiple functions: analyzing actual clothing items from user closets, accessing expert opinions through integrated databases, providing real-time feedback, and generating recommendations. The machine learning algorithms serve multiple purposes including fashion trend detection, color matching, style analysis, and personalization, making the system highly versatile without proportionally increasing complexity.
2Adaptability or versatility
If mathematical functions are used to compare article attributes, then the comparison process is objective, but the system cannot incorporate practical variables like actual user clothing or expert recommendations
Solution Approach 1:
The system introduces an intermediary machine learning layer that bridges objective mathematical comparisons with practical fashion context. The ML algorithms process mathematical attribute data (colors, patterns, materials) and transform it into practical fashion evaluations by incorporating expert opinions, current trends, and contextual information. This intermediary layer preserves the objectivity of mathematical comparison while adding the necessary practical variables and contextual understanding.
Solution Approach 2:
The system implements feedback mechanisms where user interactions with fashion recommendations, expert opinions, and actual clothing items are continuously fed back into the machine learning models. This feedback loop allows the system to learn from practical variables and real-world fashion contexts, progressively improving its ability to incorporate practical information while maintaining objective comparison capabilities through structured data processing.
3Ease of operation
If visual evaluation methods are used for fashion selection, then the selection process is intuitive, but it becomes inaccessible to individuals with color blindness or visual impairments
Solution Approach 1:
The system replaces the mechanical visual evaluation process with an automated machine learning-based analysis system. Instead of requiring users to visually assess clothing items and make selections, the system uses image processing algorithms to automatically analyze clothing attributes, match them with user preferences and fashion trends, and generate recommendations. This substitution eliminates the visual impairment barrier while maintaining intuitive selection through automated processing.
Solution Approach 2:
The system creates digital copies and representations of physical clothing items through image processing and machine learning models. These digital representations capture essential fashion attributes, colors, patterns, and styles, allowing the system to process and compare clothing items without requiring direct visual perception. The digital copies enable the system to provide recommendations to visually impaired users based on accurate representation of physical garments.
4Productivity
If real-time fashion evaluation is provided, then the feedback is timely and useful, but the system requires complex processing of multiple data sources
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing fashion data, clothing item databases, and user profile information in advance. The machine learning models are pre-trained on extensive fashion datasets, and the system maintains ready-access databases of clothing attributes, color palettes, and fashion trends. This preliminary preparation enables rapid real-time evaluation by avoiding the need to process all data from scratch during each user interaction.
Solution Approach 2:
The system maintains continuous operation through persistent data structures and continuously updated machine learning models. The data storage system continuously stores and indexes fashion information, and the ML algorithms continuously learn from new data inputs. This continuity allows the system to provide timely feedback by accessing pre-processed information and applying trained models without interruption or delay, efficiently handling multiple data sources through ongoing processing rather than batch operations.
Data Source
AI summary
Novel system, methods, which include machine learning, and device for providing color, mapping and fashion recommendations, including for persons with visual impairment such as color blindness or complete blindness. Also described are methods for assisting an individual with the task of interpreting at least one map, the method comprising: providing a portable communication device to identify and extract color and/or pattern from the at least one map through use of a camera and at least one algorithm; providing a processor capable of accessing locally stored and/or remote information about the at least one map; assigning colors on the at least one map with a red green blue (RGB) value through use of a color assignment algorithm; wherein the individual can touch a spot on digital renditions of the at least one map to determine the RGB value; providing a database for storing the digital renditions of the at least one map; wherein the individual imports the at least one map, through a series of photos or videos, for bulk imports into the database; providing revisions to the at least one map or designs for at least one novel map, for use in real-world or virtual environments, wherein the at least one map can be scored relative to a backround in the virtual environment such that map colors or map backrounds or both the map colors and the map backrounds are adjusted resulting in alternate color combinations in virtual contexts.