AI for Cosmetic Ingredient Combination Prediction

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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