Personalized Skincare Formulation via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current skincare products are often formulated for a mass market, making it difficult to accommodate diverse skin types effectively, as they rely on unreliable self-reported data and limited product options, leading to ineffective customized solutions.
Innovation Solution
A computing system and machine learning algorithms that collect objective dermal, visual, and environmental data to provide personalized skincare product recommendations and formulations, which can be refined over time based on user feedback, incorporating data on skin health, lifestyle, and environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If skincare products are formulated for a mass market, then manufacturing efficiency and cost-effectiveness are improved, but the ability to accommodate diverse skin types and provide personalized solutions deteriorates
Solution Approach 1:
The patent segments the mass market into distinct skin type categories (oily, dry, combination, sensitive, acne-prone, etc.) and formulates specific products for each segment. This allows the company to maintain efficient manufacturing of targeted products while accommodating diverse skin types through structured product lines rather than attempting to create a single universal product.
Solution Approach 2:
The patent varies formulation parameters (ingredient concentrations, base compositions, active ingredients) to create different product versions tailored to specific skin types. By systematically changing these parameters, the company can produce personalized solutions for different skin concerns while maintaining manufacturing efficiency through standardized production processes for each product type.
2Ease of operation
If self-reported data is used for product customization, then implementation simplicity is improved, but data reliability and accuracy deteriorate
Solution Approach 1:
The patent introduces trained estheticians as intermediaries between customers and the customization system. These professionals administer standardized assessments, interpret results, and guide product selection, thereby improving data reliability through expert evaluation while maintaining ease of operation through professional service delivery. The esthetician acts as a mediator who translates customer responses into accurate skin type classifications.
3Device complexity
If limited existing products are recommended based on self-reported data, then product selection process is simplified, but effectiveness of customized solutions deteriorates
Solution Approach 1:
The patent segments the product catalog into clearly defined categories corresponding to different skin types and concerns. This structured segmentation allows the system to recommend specific product combinations based on assessed skin type, maintaining process simplicity through clear categorization while improving effectiveness by matching products to specific skin needs rather than offering generic recommendations.
4Manufacturing precision
If customized formulations are created for individual customers, then personalization accuracy is improved, but manufacturing complexity and cost deteriorate
Solution Approach 1:
The patent divides the customization process into discrete segments: skin type assessment, formulation selection, and product assembly. By segmenting customized formulations into standardized modules (base products, active ingredients, additives), the system can achieve high personalization accuracy through systematic combination of pre-formulated components while controlling manufacturing complexity through modular assembly rather than complete custom formulation for each customer.
Data Source
AI summary
Systems and methods for formulating a personalized skincare product for a user. Data inputs reflecting dermal information of the user (e.g., hydration level measurements, oil level measurements, and a photograph of the user's skin reflecting a set of skin concerns) are collected by a computing device and used to determine a set of normalized scores. A skin health data set is generated based on the normalized scores and stored in memory. A skin health metric is determined based on the skin health data set and is stored in memory. The computing device determines, using a machine learning framework, one or more first skincare product formulations based on the user skin health data set. The formulation(s) can be used to manufacture one or more customized skincare products for the user and can be iteratively refined over time, e.g., by collecting additional data from the user over time.