Discover New Cosmetic Formulations using AI

Accurate and efficient method for generating chemical formulations using predictive modeling techniques. The method involves determining the functional category of an ingredient in a requested new formulation, identifying a representative molecular structure for that category, generating predicted properties for both the full formulation and the identified structure, and recommending a formulation with the representative structure that meets the desired properties. This allows decoupling of characteristic effects within functional categories and analyzing them separately to improve formulation recommendations.

Source

Automated chemical formulation using AI to efficiently find optimized formulations for targeted properties without extensive experimentation. It involves training machine learning models on existing chemical data, predicting formulations for a target property, filtering results based on a separate model's predictions, and synthesizing the predicted formulation. This allows deriving and validating formulations with high accuracy and efficiency by leveraging AI instead of trial-and-error.

Source

A machine learning-based recommendation system for skin care goods is an individualized tool that makes product recommendations to consumers based on their type and preferences. First, we give the user's face as input, examines several aspects of face. This analysis enables comprehend particular wants preferences attractiveness while also revealing insights into distinct traits. Following determined, such serum, wash, moisturizer, sunscreen are suggested according type. The system's extremely tailored unique, taking account each requirements, preferences, features. Through constant learning from user interactions feedback, algorithms able update optimize suggestions. desire customized beauty treatments rising consumer awareness have propelled skincare industry's exponential rise. To improve pleasure engagement, a Skin Care Products Recommendation System in this scenario use advanced data tools provide recommendations. strategy merges properties like ingredients, efficacy, ratings with user-specific type, concerns, environmental conditions. content-based filtering, hybrid models, coll... Read More

Source

Recommendation system for customized cosmetics that leverages AI-driven skin analysis to match users with tailored product formulations. The system employs a pre-trained AI model to analyze user skin images and determine their skin type and condition. Based on this analysis, the system generates personalized product recommendations, including both pre-existing and customized formulations, to address specific skin concerns. The recommendations are then transmitted to a cosmetics producer, enabling the production of customized products that cater to individual skin characteristics.

Source

Personalized cosmetic matching system that optimizes skin care solutions based on individual skin concerns. The system estimates skin problem resolution using a comprehensive skin analysis, then recommends specific cosmetic combinations to address identified concerns. The system incorporates user feedback through application and evaluation, ensuring the selected products effectively address skin issues. This approach enables users to achieve optimal skin health through customized cosmetic pairing.

Source

Predicting chemical mixture properties using machine learning to enable automated alerts and real-time optimization. The method employs a deep learning approach that integrates molecular structure embeddings with predictive models to generate comprehensive property predictions for chemical mixtures. The embeddings capture molecular interactions and chemical structure relationships, enabling the system to predict sensory properties such as odor intensity, stability, and compatibility. The predictions are then used to generate alerts and control measures for mixture composition, reaction conditions, and product development.

Source

A method for developing personalized skin care products that addresses the limitations of computer-driven formula optimization. The method combines multiple established formulas with specific ingredient requirements from existing databases to create optimized formulations tailored to individual skin care needs. By leveraging a comprehensive database of established formulas, the method generates new formulations by combining optimal ingredient ratios from these established formulas, ensuring that the final product meets both safety standards and specific care requirements.

Source

Characterizing product formulations through deep learning-based representation learning, enabling automated formula optimization and prediction. The method employs a neural network architecture to extract meaningful features from product formulations, allowing for efficient prediction and recommendation of optimized formulations across various fields. The system includes data collection, preprocessing, model training, evaluation, and generation components, enabling the creation of standardized and optimized formulations for formula design and optimization applications.

Source

AI-based platform for personalized matching of cosmetics and skincare products to individuals' skin and hair. The platform uses AI algorithms to analyze subject data like questionnaires, images, environment, medical background, etc. to extract skin/hair features. It then applies AI to match products based on molecular properties derived from ingredient analysis. The matched products are displayed to the user. The platform also clusters users into skin/hair types based on features, and recommends products based on those clusters.

Source

A personalized cosmetics manufacturing recipe recommendation system that tailors ampoule combinations based on user skin characteristics and behavior patterns. The system conducts a comprehensive skin analysis survey, stores user data, and generates customized ampoule recipes by combining ingredients based on their skin type, condition, and environmental exposure. The system continuously monitors user movement patterns to dynamically adjust ingredient ratios and create personalized formulations.

Source

Automatically suggesting product formulas through machine learning-based optimization of ingredient combinations. The system analyzes user preferences and desired product characteristics, then generates and refines primary and secondary formulas through a collaborative machine learning approach. The system leverages causal relationships between ingredients and their performance characteristics to predict optimal formula formulations, eliminating the need for manual trial-and-error testing.

Source

A cosmetic product analysis system to help users analyze, assess safety, predict performance, and get blending recommendations for cosmetic products. The system uses machine learning algorithms to analyze ingredients, compatibility, color, texture, and application techniques. It provides users with feedback on potential issues, alternative products, and tips to improve blending. The system also offers safety reports and allergen flagging. The analysis is accessible via web and mobile apps.

Source

A predictive system for optimizing cosmetic formulations based on skin condition analysis. The system uses facial image analysis to determine the optimal cosmetic ingredients for a user's skin type, leveraging a causal inference process that maps product characteristics to skin changes. By analyzing the relationship between product features and skin responses, the system identifies the most effective ingredients for each skin type, enabling personalized product recommendations.

Source

Training a machine learning model to predict formulation properties and identify optimized formulations through permutation-invariant representation learning. The model learns to compress formulation compositions into a compact representation that captures their structural and chemical properties, enabling efficient prediction of formulation properties and formulation optimization. The model is trained on a comprehensive dataset of formulations, including their compositions and corresponding properties, and uses permutation-invariant encoding to preserve the relationships between formulation components. This approach enables the model to identify promising formulations and predict their properties without requiring extensive property testing, while maintaining the structural integrity of the formulation components.

Source

Method for determining personalized formulations of personal care products through automated optimization of ingredient and constraint relationships. The method integrates chemist-designed active ingredient and constraint models with a database of ingredient interactions and regulatory requirements, enabling the creation of customized formulations that match individual user characteristics.

Source

Cosmetics manufacturing support device that can be used for a variety of purposes, including developing new cosmetic products. The device includes a receiving means, a learning model, a graph generation unit, a link prediction unit, a learning unit, a property prediction unit, an estimation unit, and an output unit.

Source

A method for determining personalized cosmetic formulations through smart mirrors that analyzes user skin conditions through facial scanning. The method collects and analyzes user facial images, comparing them against a database of skin conditions and corresponding formulations. The system then generates a customized cosmetic recommendation based on the user's specific skin characteristics, including active ingredients tailored to their condition. This personalized approach enables users to find the optimal cosmetic products for their unique skin needs.

Source

Predicting chemical mixture properties using machine learning models trained on composition-specific clusters. The method identifies key molecular characteristics of components like resins and additives, then combines these clusters to predict mixture properties. The trained models can be used to validate formulation strategies and predict performance characteristics of chemical mixtures, enabling more efficient development of formulations.

Source

Determining optimal compound recipe formulations through a genetic algorithm-based approach that iteratively refines initial component ratios through predictive modeling. The method employs a genetic algorithm to generate candidate formulations from initial component ratios, trains a machine learning model to predict product attributes, and iteratively evaluates the model's predictions against predefined quality criteria until the best formulation is identified.

Source

Cosmetic development support method for streamlining raw material composition optimization through machine learning. The method enables rapid evaluation of cosmetic formulations by combining multiple evaluation criteria (sensory and objective) with predefined blending parameters. It trains a classification model on training data specific to each cosmetic class, generating optimal blending ratios for each class. The model outputs evaluation scores for each blend, allowing users to select the most suitable formulation for their specific cosmetic product.

Source

Chemical formulation automation system that enables rapid and accurate prediction of chemical formulations based on target properties. The system employs machine learning models trained on a dataset of existing formulations to predict new formulations that meet specific physical properties. This predictive capability is combined with a validation process to refine the predictions, enabling the automation of formulation synthesis. The system can be integrated with existing chemical processing infrastructure to enable efficient synthesis of target chemicals.

Source

Computer program for determining performance properties of personal care products containing oil and surfactant mixtures. The program enables the prediction of product performance characteristics, such as sensory properties and formulation parameters, through a data-driven model and composition analysis. It allows users to input composition parameters and perform calculations to determine specific performance properties of oil-containing and surfactant-containing products. The program provides detailed formulation parameters, including oil ratios and surfactant concentrations, as well as formulation output, enabling the optimization of product formulations for specific performance requirements.

Source

Generating optimized chemical formulations for skin care products using neural networks. The method employs a neural network to predict formulation outcomes based on target attributes and ingredient properties. It creates a library of formulations by iteratively refining a set of initial recipes using the neural network, evaluating their performance against predefined target attributes. The network optimizes ingredient combinations and formulation sequences to achieve the desired properties, while maintaining compatibility with external subjects. This approach enables the creation of de novo formulations that meet specific product requirements, reducing the need for empirical testing and improving product stability.

Source

Using artificial neural networks to develop cosmetics tailored to specific consumers and to identify groups of consumers likely to prefer new cosmetic products. The method involves building a cosmetic map database by applying raw material, product, and preference data to a neural network. To find tailored products, the database is searched based on consumer preferences. To find similar products, the database is searched based on product ingredients. This allows recommending customized cosmetics and consumer groups for new products.

Source

Predicting formulation properties using machine learning models trained on reference data from known substances. The method employs a supervised learning approach to predict formulation properties based on reference formulations, enabling the rapid development of formulations for specific applications by leveraging existing formulation knowledge. The predictive model learns relationships between formulation properties and reference substances, allowing users to generate formulations for biologically active substances by inputting their desired formulation properties.

Source

Personalized inner beauty service using AI to recommend products and ingredients that improve skin health. The system analyzes user skin conditions, genetics, and user input on skin evaluation areas. It determines active ingredients and products based on matching scores. The scores are calculated from ingredient rankings, user scores, and genetic scores. Higher matching scores indicate better ingredient fits for recommended products.

Source

Customized cosmetics through machine learning that enables personalized product recommendations and manufacturing through user data. The system analyzes both subjective and objective makeup preferences to generate optimized cosmetic formulations, formulations, and manufacturing processes. By combining these data points, the system provides users with tailored cosmetic recommendations that address their specific skin concerns and preferences.

Source

A personalized cosmetic formulation recommendation system that leverages advanced data analytics to match users with customized raw materials tailored to their specific skin types and needs. The system collects user data, cosmetic raw material data, and skin improvement information, then applies machine learning algorithms to create optimized formulation sets. These sets are then matched against user profiles to deliver precise, effective products.

Source

Cosmetic recommendation method and device that uses AI to personalize makeup recommendations based on user feedback. The method analyzes user feedback from multiple makeup events, determines satisfaction levels, and constructs a recommendation model that predicts the effectiveness of each makeup combination. The model incorporates user preferences, product characteristics, and environmental conditions to generate personalized makeup recommendations.

Source

A computer-aided skin care product formulation method and system that optimizes formulation development through automated aggregation of complementary ingredients. The method employs machine learning-based aggregation of supplementary formulations to identify optimal combinations of ingredients, eliminating redundant formulations while maintaining formulation integrity. The system uses a polymerization algorithm to combine complementary ingredients into aggregated groups, with each group's relevance score calculated based on component correlations. The highest-scoring aggregated group is selected as the optimized formulation, ensuring comprehensive coverage of skin care requirements while minimizing formulation redundancy.

Source

A personalized cosmetic recommendation system that analyzes ingredient interactions across multiple products to prevent compatibility issues. The system collects ingredient data from all cosmetics used by a user and analyzes whether any ingredients are incompatible across products. Based on this compatibility analysis, the system recommends a single, optimal cosmetic combination that ensures consistent skin benefits across all products. The system eliminates unnecessary packaging by automatically selecting the recommended combination for users.

Source

A method for personalized skin care product formulation that integrates skin detection data to optimize product development. The method employs machine learning algorithms to analyze user skin characteristics and environmental conditions across diverse geographic regions, enabling the development of customized formulations tailored to individual skin types and regional needs. This approach eliminates the need for traditional formula development, where products are often formulated based on generic criteria rather than user-specific requirements. The method enables the creation of products that deliver optimal performance in specific regions, addressing the unique skin care needs of diverse populations.

Source

Predicting cosmetic efficacy and product characteristics using artificial neural networks. The method employs a database of cosmetic maps constructed by integrating raw material and attribute information from various cosmetics. When external device sends characteristic data, the database automatically retrieves and transmits matching cosmetic properties or ingredients. This enables rapid analysis of cosmetic formulations and characteristics without the need for extensive experimentation.

Source

A system for creating customized cosmetic products by automating the formulation process. The system enables users to select and combine ingredients to achieve specific product characteristics, such as texture, stability, and skin type. It calculates the required ingredient quantities and product formulation parameters, including phase transitions, to produce a customized product. The system also includes a social network component where users can share and compare their formulations.

Source

Predicting chemical composition properties using machine learning models that integrate ingredient interactions. The approach leverages a composition's chemical properties to determine its predicted value, which is derived from the chemical composition's identity and ingredient interactions. The model learns from a training set of compositions and their corresponding properties, enabling rapid prediction of chemical composition properties without experimental measurement. This approach enables the rapid identification of chemical compositions with specific properties, such as pH or solubility, without the need for extensive experimental testing.

Source

A method for optimizing cosmetic blending that ensures uniform quality through automated blending order determination. The method analyzes the chemical properties of each raw material and their interactions to predict optimal blending sequences. It uses machine learning algorithms to identify the most effective order of blending based on the chemical properties of each ingredient, ensuring consistent product quality.

Source

Recommending personalized cosmetics to users by analyzing their evaluation of previously used products. The method involves calculating ingredient indexes for each component based on user feedback, determining conformity and nonconformity indexes for ingredients, and using these to recommend optimal cosmetics. It goes beyond just skin type matching to provide tailored product recommendations based on individual ingredient evaluations.

Source

Product recommendation system that provides ingredient analysis function for cosmetics, foods, and drugs. The system accurately analyzes the components of products like cosmetics to estimate their content and analyze effects, side effects, and characteristics. It recommends products that are best suited for individual users based on their physical characteristics, preferences, and environmental factors. The system uses a library of product ingredient models, calculates component percentages, scores for analysis items, and overall suitability. It prioritizes products for recommendation based on factors like age, skin type, allergies, and season.

Source

Intelligent matching of cosmetics and skin through chemical analysis and AI-driven processing. The method collects and analyzes chemical data from various cosmetics products, including skin care, cleansing, makeup removers, and color cosmetics, to create a comprehensive database of their chemical compositions. This database is then used to provide personalized matching recommendations between cosmetics and skin types through AI-driven processing.

Source

Cosmetic recommendation system that leverages user history to deliver personalized product recommendations. The system analyzes user cosmetic usage patterns and matches them against a database of products containing specific functional ingredients. When a user selects a product with a matching ingredient combination, the system generates a list of recommended products that incorporate those ingredients. The system uses machine learning to predict optimal ingredient combinations based on user skin conditions and preferences, ensuring targeted product recommendations that address specific skin concerns.

Source

Customized cosmetics manufacturing system that enables personalized product formulation based on individual skin type and environmental conditions. The system divides the face into distinct regions and measures skin characteristics across these areas. It then combines these measurements with environmental data to determine optimal formulation components. The system uses a skin measurement score to determine the most suitable formulation components for each region, providing customized products that address specific skin concerns and environmental factors.

Source

Customized skin care system that provides targeted active ingredients for treating specific skin conditions. The system involves analyzing user's skin conditions, selecting compatible ingredients, and generating stable formulations with base and boosters. It uses diagnostic tools like questionnaires or images to determine skin needs. The selected ingredients are checked for compatibility. The formulation is communicated to a dispensing system. The customized skin care product contains a base and multiple boosters that mix to provide efficacious concentrations of active ingredients for the user's skin conditions.

Source

A platform for customized skin care products that enables precise formulation matching through an automated batching system. The platform integrates a network, a comprehensive raw material database, and a control unit to manage the formulation process. It stores and manages active and antioxidant ingredients, as well as other key components, in a centralized database. The platform's control unit coordinates the formulation process, automatically matching the optimal ingredient ratios and formulations for specific skin types and preferences. The system enables production of customized skin care products that perfectly match individual skin needs, eliminating the need for trial and error.

Source

A method and device for personalized cosmetic formulation optimization that enables users to customize their products based on specific requirements. The method generates initial formulation parameters, calculates their corresponding optimized values, and applies a cost-based filtering process to select the optimal combination. This approach streamlines the formulation process by automatically determining the best formulation parameters based on user input, eliminating the need for manual configuration.

Source

A data-driven approach for product development that combines machine learning, expert feedback, and computational tools to improve product offerings. The system characterizes the vast product space through ingredient combinations, generates product space data, and analyzes product distances to identify clusters and patterns. It then provides expert guidance through automated product recommendations, quality control, and pricing optimization, enabling companies to create innovative products while avoiding redundant iterations.

Source