Skin Dryness Model for Personalized Cosmeceutical Recommendations
Solution Overview
Problem
Existing cosmeceutical products for addressing skin dryness lack personalized feedback and guidance, as they are formulated differently for various skin types and ages, leading to inconsistent effectiveness.
Innovation Solution
A digital imaging system and method that analyzes pixel data from user images using a trained skin dryness model to provide personalized electronic recommendations for addressing skin dryness issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple different cosmeceutical products are used for different skin types and ages, then the range of skin conditions covered is improved, but the effectiveness for individual users deteriorates due to lack of personalization
Solution Approach 1:
The system performs preliminary skin analysis by capturing images and determining skin dryness values before product recommendation. This preliminary assessment of the user's specific skin condition enables the system to provide personalized recommendations that are tailored to the individual's needs, thereby improving effectiveness while maintaining broad adaptability through the initial evaluation step.
Solution Approach 2:
The system provides feedback to users about their skin dryness levels and offers personalized product recommendations based on the analysis results. This feedback loop allows the system to adapt to individual user needs by continuously assessing skin conditions and adjusting recommendations, resolving the contradiction between broad product range and personalized effectiveness.
2Device complexity
If a single cosmeceutical product is used for all skin types, then the device complexity is reduced, but the adaptability to different skin conditions deteriorates
Solution Approach 1:
The system enables users to self-assess their skin dryness levels through image capture and automated analysis. This self-service approach allows a single product recommendation system to adapt to different skin types without requiring multiple complex formulations, as the system automatically adjusts recommendations based on individual user assessment results.
Solution Approach 2:
The system changes the parameter of skin dryness value determination based on individual user images and characteristics. By dynamically adjusting the assessment parameters for each user rather than using fixed product formulations, the system achieves high adaptability with a single recommendation engine, avoiding the need for multiple complex product lines.
3Measurement precision
If manual skin assessment is performed by professionals, then the measurement precision is improved, but the ease of operation and accessibility deteriorates
Solution Approach 1:
The system replaces manual professional skin assessment with an automated digital imaging and analysis system. This substitution maintains measurement precision through algorithmic analysis of skin images while dramatically improving ease of operation and accessibility, allowing users to perform skin assessments at home without requiring professional intervention.
Solution Approach 2:
The system creates a digital copy of the skin condition through image capture and analysis, replicating the function of manual professional assessment. This digital copying approach preserves the precision of expert evaluation while eliminating the need for physical professional involvement, thereby improving user accessibility and ease of operation.
Data Source
AI summary
Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin dryness. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective skin area of an individual. A skin dryness model, trained with the pixel data, is operable to output, across a range of a skin dryness scale, skin dryness values associated with a degree of skin dryness. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin dryness model, to determine a user-specific skin dryness value of the user skin area. A user-specific electronic recommendation addressing at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.