Database-based automatic recommendation system for home care and personal care products through customized perfumes

Abstract

The present invention relates to an automatic recommendation system for home care and personal care products using customized perfumes based on a database. This system includes a product database, a data collection unit, a data analysis unit, a product recommendation unit, and a user interface unit, which collect and analyze the user's personal information, preferences, biosignals, environmental data, etc., to recommend customized products. Additionally, it includes a skin analysis module, an AI-based fragrance combination system, a health data analysis module, a subscription system, and a health effect analysis module to provide customized recommendation services to the user.

Description

Technology Field

[0001] The present invention relates to a database-based recommendation system and a method related thereto, and more specifically, to a system and method for automatically recommending home care and personal care products using customized perfumes. Background Technology

[0002] Recommendation systems are a machine learning method that provides content of interest based on users' preferences and past behaviors, and are being utilized across various industries to provide personalized services.

[0003] In particular, its importance is increasing in the cosmetics and perfume industries, and existing recommendation systems have primarily used content-based filtering and collaborative filtering methods.

[0004] However, recently, due to advancements in big data and artificial intelligence technologies, personalized recommendation systems are evolving into more sophisticated ones. These systems are improving recommendation accuracy by considering not only the user's basic information but also various contextual data such as emotions, mood, and situation.

[0005] Specifically, in the cosmetics and perfume industries, the need for such personalized recommendation services is becoming increasingly prominent because individual tastes, skin characteristics, and sensitivities have a significant impact on product preferences.

[0006] In addition, recently, deep neural network-based recommendation systems that utilize deep learning techniques to incorporate information such as product search history and user skin type have been proposed. Prior art literature

[0007] (Patent Document 0001) Korean Published Patent No. 10-2024-0005495 ​​(January 12, 2024)

[0008] (Patent Document 0002) Korean Published Patent No. 10-2022-0131590 (September 29, 2022) The problem to be solved

[0009] The present invention relates to a technology capable of providing accurate and personalized recommendations by comprehensively analyzing various characteristics and preferences of users using a recommendation system. By utilizing various data such as basic user information, purchase history, skin characteristics, and preferred scents to recommend the product most suitable for the user, the invention aims to increase user satisfaction and provide an efficient method for product selection. means of solving the problem

[0010] According to embodiments of the present invention for achieving the above-mentioned objectives, Effects of the invention

[0011] According to embodiments of the present invention, an automated recommendation system capable of significantly improving customer satisfaction can be implemented by comprehensively analyzing users' personal data to recommend customized perfumes and care products. Furthermore, according to embodiments of the present invention, an efficient system capable of reducing marketing costs, optimizing inventory management, and lowering return rates can be implemented through AI-based analysis and recommendations.

[0012] Therefore, by using the automatic recommendation system according to the embodiments, it is possible to increase customer loyalty and increase related sales, provide accurate customized services, and also achieve the effect of reducing operating costs.

[0013] Furthermore, through continuous feedback and data analysis, we can efficiently respond to changing customer preferences and lead technological innovation in the perfume and care product industry by leveraging AI and big data technologies.

[0014] However, the effects of the present invention are not limited to the above effects and can be extended in various ways without departing from the technical spirit and scope of the present invention. Brief explanation of the drawing

[0015] FIG. 1 is a configuration diagram showing an automatic product recommendation system according to one embodiment of the present invention. FIG. 2 is a flowchart illustrating a method of an automatic product recommendation system according to one embodiment of the present invention. FIG. 3 is a configuration diagram showing a cloud server according to one embodiment of the present invention. Specific details for implementing the invention

[0016] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. The sizes or thicknesses of the areas or parts depicted in the attached drawings may be slightly exaggerated for the clarity of the specification and convenience of explanation. Throughout the detailed description, the same reference numerals indicate the same components.

[0017] The embodiments of the present invention described below are provided to more clearly explain the present invention to those skilled in the art, and the scope of the present invention is not limited by the following embodiments, and the following embodiments may be modified in various other forms.

[0018] FIG. 1 is a configuration diagram showing an automatic product recommendation system according to one embodiment of the present invention.

[0019] Referring to FIG. 1, the device includes a data collection unit (101) that acquires user data through a heart rate sensor, skin temperature sensor, sweat secretion measurement sensor, etc. from a wearable device; a data analysis unit (301) that interprets the user's emotional state using a deep learning model and analyzes the collected data signal to derive a perfume matched to a specific state; a product recommendation unit (401) that creates a database of scents matched to the user's emotional state and recommends a perfume; and a user interface unit (601) that displays the recommended results through an interface.

[0020] The data collection unit (101) collects user information such as gender, age, preference, taste, etc., collects biosignal information such as heart rate, skin temperature, and sweat secretion amount through a wearable device, collects environmental data such as PM2.5, VOCs, CO2 and GPS-based location information through an air quality sensor, collects user's skin type information through a smartphone camera and a skin analysis application, analyzes facial expressions through facial expression recognition technology, collects emotional state information directly entered by the user, collects user's health information by linking with healthcare platforms such as Apple Health and Samsung Health, collects information directly entered by the user such as preferred scent family, intensity, and frequency of use, and stores collected information such as emotions and satisfaction recorded by the user after using perfume in a database (201).

[0021] The data analysis unit (301) structures various data transmitted from the data collection unit (101), comprehensively interprets the user's emotional state, biosignals, environmental data, etc. through a deep learning model, analyzes fragrance series, notes, full ingredients, brand, release date, perfumer information, etc., analyzes the user's skin type and condition based on the collected skin data, analyzes the collected environmental data, location information, climate, and weather data to identify the user's current situation, and health data analysis: analyzes the collected health information to identify the user's overall health condition and analyzes the correlation between the user's emotional state and the fragrance.

[0022] The product recommendation unit (401) selects the most suitable perfume and care products for the user based on the results of the data analysis unit (301), recommends perfumes that match the user's current emotional state and physical reaction by analyzing the user's real-time biosignal data, recommends perfumes that harmonize with the environment by analyzing the user's surrounding environment data, recommends suitable perfumes and care products by considering the user's skin type and health condition, recommends perfumes that match the user's current emotional state by identifying it, recommends safe and suitable perfumes and care products based on the user's health data, and learns the user's preferences so that the AI ​​suggests unique scent combinations.

[0023] It recommends customized products based on the user's scent profile data, recommends customized subscription services based on the user's usage patterns and preferences, and recommends suitable perfumes based on the user's health goals (e.g., stress relief, improved concentration).

[0024] The user interface section (601) provides an intuitive interface that receives basic information such as the user's gender, age, preferences, and tastes, visualizes biosignal data such as heart rate and skin temperature collected from a wearable device as graphs or charts, and displays environmental data such as air quality and GPS location in a form that is easy for the user to understand.

[0025] It visually displays the results of analyzing skin images captured by a smartphone camera, receives input on the user's current emotional state to display the analyzed state using emoticons, and provides an interface that manages the user's fragrance preferences and usage history by presenting AI-recommended perfumes and care products in a user-friendly manner; provides an interface that allows users to easily record emotions and satisfaction after product use; provides an interface for setting up and managing a customized perfume subscription service; and provides an interface for setting user health goals and tracking the effects of perfume use.

[0026] The database (201) lists detailed information on perfumes, home care products, and personal care products, fragrance notes, fragrance intensity levels, full ingredient lists, brand, release date, perfumer information, user evaluations and reviews of each product, fragrance note information associated with specific emotional states, and fragrance information suitable for various environments (e.g., forest, sea, city), and combines this with an AI algorithm to provide a customized product recommendation system that takes into account the user's personal characteristics, environment, and emotional state.

[0027] FIG. 2 is a flowchart illustrating a method of an automatic product recommendation system according to one embodiment of the present invention.

[0028] Referring to FIG. 2, data collection (100) inputs and provides various and comprehensive information, such as the user's personal information, biosignals, environmental data, skin condition, emotional condition, and health data, into the system.

[0029] Database construction (200) systematically classifies and stores detailed information about perfumes and care products, thereby creating a database of all characteristics and attributes of the products.

[0030] Data analysis (300) performs in-depth analysis of all collected data through an AI model and precisely interprets the user's biosignals, emotions, environment, health status, etc.

[0031] Personalized product recommendation (400) precisely matches analyzed user data and product data using a machine learning algorithm to select and recommend personalized products optimized for individuals.

[0032] The AI-based personalized scent combination (500) reflects the user's preferences and characteristics, and the AI ​​combines unique and personalized scents in real time.

[0033] The result provision and feedback (600) visually and clearly expresses the detailed information of the recommended product and the reason for the recommendation through the user interface and provides continuous service through the subscription system.

[0034] Furthermore, throughout the overall progress of this flowchart, the automated product recommendation system continuously improves recommendation algorithms and AI models based on feedback collected from users.

[0035] FIG. 3 is a configuration diagram showing a cloud server according to one embodiment of the present invention.

[0036] Referring to FIG. 3, the cloud server (110) includes biosignal data (111), environmental data (112), skin condition data (113), emotion data (114), health data (115), user preference data (116), and feedback data (117).

[0037] The biosignal data (111) includes information such as heart rate, skin temperature, sweat secretion amount, blood pressure, respiration, electromyography and acceleration (movement information), etc.

[0038] Environmental data (112) includes information such as air quality information (PM2.5, VOCs, CO2), GPS-based location information, current weather and climate information, etc.

[0039] Skin condition data (113) includes information such as skin type (dry, oily, sensitive), moisture, elasticity, pores, and wrinkles.

[0040] Emotion data (114) includes information such as facial expression analysis results and emotional states (happiness, depression, tension, etc.) directly entered by the user.

[0041] Health data (115) includes sensitive data such as weight, height, allergy information, and health information linked to an existing healthcare platform.

[0042] User preference data (116) includes information such as preferred scent family (floral, woody, spicy, etc.), preference for scent intensity, frequency of use, and history of previous purchases and use.

[0043] Feedback data (117) includes information such as emotional state after using perfume, product satisfaction, evaluation of scent persistence, and skin reaction information.

[0044] Therefore, by comprehensively analyzing this diverse data, we recommend customized perfumes and care products optimized for the user. Explanation of the symbols

[0045] * Explanation of symbols for major parts of the drawing * 111: Biosignal data 112 : Environment data 113: Skin condition data 114 : Sentiment Data 115 : Health Data 116: User preference data 117 : Feedback data

Claims

Claim 1 A customized product automatic recommendation system for home care and personal care products utilizing a database, characterized by comprising: a product database storing information on perfumes, home care products, and personal care products; a data collection unit collecting user's personal information, preferences, biosignals, and environmental data; a data analysis unit analyzing collected data to identify the user's condition and requirements; a product recommendation unit selecting suitable products from the product database based on the analysis results; and a user interface unit providing recommended product information to the user. Claim 2 A customized product automatic recommendation system according to claim 1, characterized in that the product database includes fragrance family, notes, full ingredients, brand, release date, and perfumer information. Claim 3 A customized product automatic recommendation system according to claim 1, wherein the data collection unit includes a biosignal collection module that collects a user's biosignal from a wearable device. Claim 4 A customized product automatic recommendation system according to claim 1, wherein the data analysis unit interprets the user's emotional state using a neural network-based deep learning model. Claim 5 A customized automatic product recommendation system according to claim 1, wherein the product recommendation unit further includes an environment data collection module that collects user's surrounding environment data, and recommends a product that harmonizes with the environment based on the collected environment data. Claim 6 A customized automatic product recommendation system according to claim 1, wherein the product recommendation unit further includes a skin analysis module that analyzes the user's skin condition and recommends a product suitable for the analyzed skin condition. Claim 7 A customized product automatic recommendation system according to claim 1, further comprising an AI-based fragrance combination system and characterized by manufacturing personalized perfumes according to user preferences. Claim 8 A customized automatic product recommendation system according to claim 1, further comprising a health management module that collects and analyzes user health data, wherein the product recommendation unit recommends products suitable for the user's health condition. Claim 9 A customized product automatic recommendation system according to claim 1, further comprising a subscription system that provides customized products on a regular basis, and characterized by continuously collecting and analyzing user feedback to adjust recommended products. Claim 10 A customized product automatic recommendation system according to claim 1, further comprising a health effect analysis module that analyzes the linkage between perfume and health, and characterized by recommending a suitable perfume according to the user's health goals.

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