system

Abstract

We provide the system. [Solution] A means for analyzing skin condition information and image data received from the user and generating personalized beauty care suggestions, Image recognition means for identifying skin type and problems, In order to suggest products and skincare routines suitable for the user, means of referring to beauty trends and scientific data, A means of tracking the progress of the proposed skincare routine, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, there is a problem that information related to beauty and skin care is enormous, and it is difficult for individual users to select the most suitable beauty care for themselves. In particular, finding appropriate products and routines according to the skin type and troubles of each user requires indispensable professional knowledge. Many users are overwhelmed by information and are in a situation where they cannot make appropriate choices. In addition, existing beauty care is based on generalized advice, and there is also a problem that it is difficult to sufficiently respond to individual needs.

Means for Solving the Problems

[0005] To address this challenge, the present invention provides a system that identifies skin type and problems based on skin condition information and image data collected from users, and proposes optimal beauty care and products to the user. This system includes means for analyzing individual skin conditions using image recognition technology and means for referencing beauty trends and scientific data to generate personalized suggestions tailored to the user. Furthermore, it provides means for tracking the progress of the proposed skincare routine, thereby supporting users in effectively continuing their beauty care. In addition, it includes means for re-analyzing the routine to optimize it based on user feedback, aiming to improve the quality of continuous care.

[0006] A "user" is someone who uses the system to input information about their skin condition and receive personalized beauty care suggestions.

[0007] "Skin condition information" refers to data about the user's skin type and current skin problems.

[0008] "Image data" refers to photographic data of the user's skin appearance, which is used to analyze the skin condition through image recognition.

[0009] "Personalized beauty care suggestions" refer to information that recommends the optimal beauty products and skincare routine based on the user's skin condition.

[0010] "Image recognition means" refers to technology and equipment for analyzing image data received from a user to identify skin type and problems.

[0011] "Beauty trends" refer to general trends and popular technologies regarding products, methods, and ingredients in the latest beauty industry.

[0012] "Scientific data" refers to a collection of information based on the latest research findings and evidence related to beauty and skincare.

[0013] A "skincare routine" is a general term for the methods and procedures that a user uses to care for their skin on a daily basis.

[0014] "Tracking means" refers to technologies and devices for recording and managing the progress of beauty care performed by a user.

[0015] "Feedback" refers to information provided by users about their skin condition, their experiences using beauty products, and the results they achieved.

[0016] "Re-analysis means" refers to technologies and devices that utilize feedback data to optimize skincare routines. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Embodiments for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention is configured as a system that allows users to analyze their skin condition and receive optimal beauty care. This system includes a process in which a server generates personalized beauty care suggestions based on skin condition information and image data provided by the user through a terminal.

[0039] Specifically, users input their skin type, problem areas, and past skincare history into their device. They also use the device to take photos of their skin, sending more detailed information to the server. The server receives this data and uses its built-in image recognition technology to identify skin type and problems.

[0040] The server further references accumulated beauty trends and scientific data to select the most suitable products and skincare routines for each user. Based on this, the server generates personalized beauty care suggestions for each user and delivers them to their device.

[0041] For example, if a user has dry skin and redness, the server will use image recognition to check the condition, recommend highly moisturizing products containing hyaluronic acid and ceramides, and then suggest a morning and evening skincare routine using those products.

[0042] Subsequently, the user practices the skincare routine suggested by the device, and the device tracks this. The tracking data and user feedback information are sent back to the server for further analysis. Through this process, the system aims to continuously improve the quality of beauty care and assist users in performing the most optimal care for their individual skin.

[0043] Therefore, the present invention aims to provide reliable information and support that enables users to confidently bring out their own beauty.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user inputs skin condition information into the device and takes photos of their skin using the camera as needed. This allows the device to accumulate the user's data.

[0047] Step 2:

[0048] The device packages skin condition information and image data collected from the user and sends it to the server.

[0049] Step 3:

[0050] The server prepares the data received from the terminal for analysis. For image data, analysis is initiated using image recognition to identify skin type and problems.

[0051] Step 4:

[0052] Based on the analysis results, the server references beauty trends and scientific data to determine the optimal beauty care products and skincare routines for each user. This information is then generated as personalized beauty care recommendations.

[0053] Step 5:

[0054] The server generates beauty care suggestions, which are then sent back to the terminal as a data package.

[0055] Step 6:

[0056] The device analyzes beauty care suggestions received from the server and displays them visually to the user. The user then uses this information to implement their daily skincare routine.

[0057] Step 7:

[0058] The user executes the proposed routine and tracks the routine's progress on their terminal as needed. This tracking information is later resent to the server.

[0059] Step 8:

[0060] When a user enters feedback after using skincare products via their device, the feedback data is sent from the device to the server. The server then re-analyzes this data and uses it to improve the skincare routine.

[0061] Step 9:

[0062] The server uses feedback and tracking data to generate adjustments to the skincare routine as needed, or to generate new suggestions, which are then sent back to the device and presented to the user.

[0063] As a result, users are supported in continuously monitoring changes in their skin and implementing the optimal beauty care routine.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] In today's world, there is a demand for beauty care tailored to each individual user, but conventional systems have struggled to provide personalized care suggestions based on the detailed condition of each user's skin. Furthermore, there has been a lack of mechanisms for continuously evaluating the effectiveness of the suggested care and making improvements based on feedback.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for analyzing skin condition information and image data collected from users to generate individual aesthetic treatment suggestions; means for using image recognition technology to identify skin characteristics and problems; means for referencing trend and scientific information to suggest products and treatment procedures suitable for the user; and means for generating suggestion content using a generative artificial intelligence model. This makes it possible to provide accurate beauty care suggestions based on the user's skin condition and to continuously improve those suggestions.

[0069] A "user" is an individual who uses the system to receive beauty care suggestions based on their skin condition.

[0070] "Skin condition information" refers to information about the user's skin type, problem areas, and past care history.

[0071] "Image data" refers to photographic data taken to understand the detailed condition of the user's skin.

[0072] "Analysis" is the process of processing collected skin condition information and image data to identify the user's skin characteristics and problems.

[0073] "Individualized aesthetic treatment suggestions" refer to suggestions for optimal cosmetic care methods and products identified based on the user's skin condition.

[0074] "Image recognition technology" is a technology that uses captured image data to identify skin characteristics and problems.

[0075] "Trend and scientific information" refers to the latest trends and science-based data in the beauty industry.

[0076] A "generative artificial intelligence model" is a machine learning model that generates appropriate beauty care suggestions based on input information.

[0077] "Tracking" is the process of recording and analyzing the progress of the proposed treatment procedures.

[0078] This system is designed as a platform for users to precisely analyze their skin condition and receive optimal beauty care. Users first input their skin type, problem areas, and past skincare history via a terminal. They then use the terminal to take images of their skin and send them to a server. The server receives this data and uses built-in image recognition technology to identify skin characteristics. This image recognition technology utilizes advanced image analysis software with machine learning algorithms.

[0079] The server then accesses accumulated beauty trend information and scientific databases, and uses a generative AI model to select the most suitable products and skincare routines for the user. A concrete example of a prompt message is, "Please suggest a routine to use with moisturizing products suitable for a user with dry skin." This prompt allows the server to generate personalized beauty care suggestions tailored to each individual user and send them to the device.

[0080] For example, if a user has dry skin with noticeable redness, the server can analyze the image data to identify this condition and recommend products containing highly moisturizing hyaluronic acid and ceramides. Furthermore, a specific morning and evening skincare routine using these products can also be suggested.

[0081] Ultimately, the user performs the suggested care, and the device tracks the progress. The tracking data is sent back to the server for further analysis along with feedback. Through this process, the system continuously improves the quality of beauty care and helps users perform the most appropriate care. A key feature of this system is its ability to always provide the latest and most effective care by using a generative AI model.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user inputs skin condition data into the device. The user enters their skin type, problem areas, and past skincare history through the device's interface. This input data is temporarily stored on the device before being sent to the server. The entered information is used as basic analysis data in subsequent processing steps.

[0085] Step 2:

[0086] The user takes an image of their skin with their device. The user uses the device's camera to capture a high-resolution image of their skin and sends it to the server. This image data is crucial input data for detailed skin analysis. The image serves as the basic data necessary for processing by image recognition technology on the server side.

[0087] Step 3:

[0088] The server receives the data and performs analysis using image recognition technology. The server receives skin condition information and image data sent by the user and uses its built-in image recognition technology to identify skin characteristics and problems. In this process, image analysis utilizing machine learning algorithms is performed to identify skin type and problem areas. As output, the analysis results are stored in an internal database and used in the subsequent care suggestion generation step.

[0089] Step 4:

[0090] The server uses a generative AI model to create beauty care suggestions. Based on the analyzed data, the server refers to accumulated beauty trends and scientific data, and utilizes the generative AI model to suggest the most suitable products and skincare routines for the user. By giving instructions to the generative AI model in the prompt message, such as "Suggest moisturizing products and usage methods suitable for a user with dry skin," appropriate care suggestions are made. As output, personalized beauty care suggestions are generated.

[0091] Step 5:

[0092] The server generates suggestions and sends them to the terminal, presenting them visually to the user. The beauty care suggestions created by the server are sent to the terminal and visually displayed on the terminal's screen. The user can review these suggestions and learn about specific care procedures and recommended products.

[0093] Step 6:

[0094] The user performs the suggested care, and the device tracks the progress. The user performs the suggested skincare routine daily, and the device records the status of this routine. As input, the user's care routine is collected and stored as tracking data.

[0095] Step 7:

[0096] The device sends tracking data and user feedback to the server, which then performs a re-analysis. Based on the tracked data and feedback, the server performs another analysis and generates optimized beauty care suggestions. This process allows the user to continuously receive the most suitable beauty care. As output, improved suggestions are provided.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] Traditional beauty consultations made it difficult for users to accurately understand their own skin condition and to select and purchase appropriate products. Furthermore, selecting products in stores was time-consuming, and receiving personalized, optimized beauty care suggestions required considerable effort and expense.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes means for analyzing skin condition information and image data received from the user and generating personalized beauty care suggestions; image recognition means for identifying skin type and problems; means for referencing beauty trends and scientific data to suggest products and skincare routines suitable for the user; and visual display means for displaying product placement information based on the analysis results to promote purchase. This allows the user to quickly select and purchase products suitable for them and easily implement effective beauty care.

[0102] "Skin condition information received from the user" refers to information about the user's skin type and problem areas that they input through their device.

[0103] "Image data" refers to photographic data of skin taken by the user using their device.

[0104] "Personalized beauty care suggestions" refer to information that recommends beauty products and skincare routines tailored to a specific user, based on their analyzed skin condition.

[0105] "Image recognition means" refers to a technology used to identify skin type and skin problems, and includes an algorithm for analyzing image data.

[0106] "Beauty trends and scientific data" refers to market data and chemical / medical research findings related to the latest beauty products and care methods.

[0107] A "skincare routine" refers to a series of skincare steps using specific beauty products, with the aim of maintaining and improving skin health.

[0108] "Means of tracking progress" refers to a system that records and manages the progress of the skincare routine that the user is following.

[0109] "Displaying product placement information" means visually guiding users to the recommended locations of beauty products within the store.

[0110] "Visual display means" refers to devices and technologies for providing information to users visually, and includes displays and UI design.

[0111] The system implementing this invention consists of a user, a terminal, and a server. The terminal is a device equipped with a camera and an internet connection. The user uses this terminal to take a photograph of their skin and input skin condition information. This data is transmitted to the server in real time.

[0112] The server first analyzes the data received from the user. Image recognition libraries such as OpenCV and TENSORFLOW® are used for image analysis. This identifies skin type and problem areas. The server then uses the collected beauty trends and scientific data to generate optimal beauty care products and skincare routines for each individual user.

[0113] The generated suggestions are visually displayed to the user through the terminal's screen. Furthermore, the location of recommended products is shown to encourage in-store purchases. This allows users to easily find and purchase products within the store.

[0114] As a concrete example, consider a user using this system in a cosmetics store. The user takes a photo of their skin with a terminal and receives an analysis result indicating dry skin. The system recommends a moisturizing cream containing hyaluronic acid and also displays information on where that cream is located on the store's shelves. In this way, the user can quickly find the necessary beauty products and implement appropriate care.

[0115] An example of a prompt message is: "Analyze the skin image taken by the user and suggest the most suitable skincare products. Refer to the database and select and display recommended items within the range of products currently available in the store." By using this prompt message, the generating AI model can generate optimal suggestions in real time based on each user's individual data.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] Users take photos of their skin using their device and input information about their skin condition. This input includes selecting their skin type (such as dry or oily skin) and specifying areas of skin problems. This information, along with the image file, is sent from the device to the server.

[0119] Step 2:

[0120] The server analyzes the received image file and skin condition information. Using image recognition libraries (such as OpenCV or TensorFlow), it automatically extracts skin type and problem areas from the image data. This process outputs the skin type and specific problems.

[0121] Step 3:

[0122] Based on the analysis results, the server references beauty trends and scientific data to generate the optimal beauty care products and skincare routine for the user. It uses skin type and problem information as input, searches the database for relevant product information, and outputs recommended items and usage instructions.

[0123] Step 4:

[0124] The server sends the generated beauty care suggestions to the user's device. It also references product placement data from the store the user is visiting and generates data that visually displays the location of the suggested products within the store.

[0125] Step 5:

[0126] The terminal displays beauty care suggestions and product placement information received from the server. Specifically, it displays a list of suggested products along with the shelf location of each product as a store map, making it easy for users to find products within the store.

[0127] Step 6:

[0128] The user uses the product based on the suggested skincare routine. The device records this usage information and can accept feedback after use. The feedback information is sent to the server for later analysis.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] This invention combines an emotional engine with a beauty care system to realize personalized beauty care that takes into account the user's emotional state. The system includes a process for generating optimal beauty care suggestions based on skin condition information, image data, and emotional information provided by the user.

[0131] When a user provides information and images about their skin condition using their device, the device sends that data to a server. The server receives the data and uses image recognition to analyze the user's skin type and problems. The analysis results are then compared with beauty trends and scientific data to select the most suitable skincare routine and products for the user.

[0132] The emotion engine, a key feature of this invention, recognizes emotions from the user's voice, facial expressions, and entered text. This emotional information is also reflected in beauty care suggestions, generating encouraging and supportive messages according to the user's emotional state. By utilizing this emotional information, the invention aims to enhance user motivation and support continuous care.

[0133] For example, if a user types "I'm tired today" through their device, the emotion engine recognizes that emotion as "fatigue." The server takes this information into account, suggests relaxing beauty products, and delivers a message in a gentle tone to the user through their device.

[0134] Furthermore, continuous user feedback and emotional data are stored and re-analyzed on the server. Based on this data, the server adjusts long-term skincare routines and provides strategies to enhance the user's inner and outer beauty.

[0135] Thus, the present invention aims to achieve more effective and personalized beauty care by comprehensively considering the user's mental and physical state through an emotional engine.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The user uses their device to input information about their skin condition and, if necessary, take photos of their skin. They also input information about their emotions (voice, facial expressions, text, etc.).

[0139] Step 2:

[0140] The device packages skin condition information, image data, and emotional information collected from the user and sends them to the server.

[0141] Step 3:

[0142] The server analyzes the received data. The image data then enters a process where image recognition tools identify skin type and problems.

[0143] Step 4:

[0144] The server analyzes the received emotional information using an emotion engine. It identifies the user's emotions from voice data, facial expressions, and text, and records the results as emotional data.

[0145] Step 5:

[0146] The server combines skin condition analysis results with emotional data, and selects the optimal beauty care by referring to beauty trends and scientific data. Based on the emotional data, the content and delivery method of the beauty care are adjusted.

[0147] Step 6:

[0148] The server generates personalized beauty care suggestions and sends them to the device. These suggestions include encouraging and supportive messages, as well as products tailored to the user's mood.

[0149] Step 7:

[0150] The device visually displays beauty care suggestions from the server and provides them to the user. Based on the information received, the user implements their daily skincare routine.

[0151] Step 8:

[0152] The user performs the suggested routine and inputs feedback on the results and their feelings via the device. The device then resends this feedback data to the server.

[0153] Step 9:

[0154] The server re-analyzes the feedback and emotional data, and generates adjustments to the long-term skincare routine or new suggestions as needed, which are then resent to the device.

[0155] Through these processes, the system provides comprehensive beauty care that takes into account the user's emotional state, effectively supporting continuous care.

[0156] (Example 2)

[0157] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0158] Traditional beauty care systems primarily rely on user skin condition information and beauty trends for their recommendations, but they lack the ability to provide personalized care suggestions that take into account individual emotional states. This often results in inadequate recommendations tailored to the user's emotional state, negatively impacting the long-term sustainability of their care. Furthermore, the insufficient use of feedback for skincare optimization makes it difficult to propose individually optimized care.

[0159] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0160] In this invention, the server includes means for analyzing skin condition information and image data received from the user, image recognition means for identifying skin type and problems, and emotion recognition means for recognizing emotions from the user's voice, facial expressions, and text, and reflecting them in beauty care suggestions. This enables personalized beauty care suggestions tailored to the user's emotional state, ensuring more personalized care and long-term skincare continuity.

[0161] "Skin condition information received from the user" refers to data indicating the user's skin condition, and is received in image or text format.

[0162] "Image data" refers to visual data that captures the current state of the user's skin, and is sent to the server for analysis and recognition.

[0163] "Personalized beauty care suggestions" refer to personalized beauty product and skincare routine suggestions generated based on the user's specific skin condition and emotions.

[0164] "Image recognition means" refers to technologies used to identify skin type and problems, and includes algorithms and software for analyzing image data.

[0165] "Means of referencing beauty trends and scientific data" refers to technologies for selecting products and routines suitable for the user based on the latest beauty information and scientific findings.

[0166] "Means of tracking progress" refers to a function for checking and recording how far a proposed beauty care routine has progressed.

[0167] "Emotion recognition means" refers to technologies and devices for analyzing and recognizing a user's emotional state from their voice, facial expressions, and text.

[0168] A "generative AI model" is a machine learning model used to output optimal care suggestions based on the user's skin condition and emotional information.

[0169] The embodiments for carrying out this invention are shown below.

[0170] Users take photos of their skin and input information about their skin condition using a mobile device or computer. They may also utilize dedicated application software for this process.

[0171] The terminal transmits the input image data and text information to the server via a secure internet connection. The HTTPS protocol is commonly used for this purpose.

[0172] The server analyzes the received image data using image recognition software (e.g., OpenCV or TensorFlow) to identify skin type and problems. The analysis results are referenced against the latest beauty trends and scientific data stored in a database. A database management system (DBMS) may be used for database comparison.

[0173] Furthermore, emotion recognition software on the server analyzes voice, facial expressions, or text data from the user using NLP techniques to identify the user's emotions. This technique utilizes common voice and image analysis libraries.

[0174] The server uses a generative AI model based on analyzed skin condition and emotional information to generate optimal beauty care suggestions for the user. The generative AI model is constantly retrained based on the latest data and user feedback to improve its accuracy.

[0175] For example, if a user enters "I'm too busy today to spend time on skincare" into their device, the server will suggest an "easy-to-use all-in-one cream" and display an encouraging message: "Let's aim for effective care even in a short amount of time."

[0176] An example of a prompt message would be, "What are the best skincare products for when you're feeling stressed?"

[0177] In this way, the invention makes it possible to provide more personalized and effective beauty care tailored to the individual skin condition and emotional state of each user.

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] Users input information and images about their skin condition using a mobile device or computer. The input data is prepared as image files and text data by a dedicated application. Users take a picture of their skin using the camera function and input text information such as "My skin is dry today."

[0181] Step 2:

[0182] The terminal sends the input image data and text data to the server. The data is transferred securely using the HTTPS protocol. The image file and text are sent to the server as input data, and this becomes the source data for the next analysis process.

[0183] Step 3:

[0184] The server analyzes the received image data using image recognition software. It utilizes algorithms from OpenCV and TensorFlow to identify the user's skin type and problems. The image data is analyzed pixel by pixel, and the output provides results indicating oily skin, dry skin, or specific skin problem areas.

[0185] Step 4:

[0186] The server analyzes the text data using natural language processing technology. It understands the context related to the user's skin condition from the input text information and extracts relevant keywords. For example, it might extract the keyword "dryness" and output information indicating that the skin needs moisturizing.

[0187] Step 5:

[0188] The server compares the analysis results with beauty trends and scientific data. Using a database management system, it selects beauty products and routines suitable for the user based on the analysis results. Using the analysis results and trend data as input, it obtains "recommended moisturizing creams" as output.

[0189] Step 6:

[0190] The server analyzes the user's voice, facial expressions, and text data using emotion recognition software to identify their emotions. Using NLP (Neuro-Linguistic Programming) technology, it recognizes the emotion "stress" from the user's input text, "I'm feeling down today." It takes voice and text data as input and obtains emotional information as output.

[0191] Step 7:

[0192] The server uses a generation AI model to generate optimal beauty care suggestions based on the user's skin condition and emotional information. The analysis results are input to the generation AI model as prompts, and it outputs a suggestion such as, "Using a moisturizing face mask is good for stress relief."

[0193] Step 8:

[0194] The server generates a proposed care plan and a message of encouragement, and sends it to the user via the device. The server sends the generated message, "You've worked hard today. Let's give your skin some relaxation time," along with recommendations for beauty products, to the device. The user can view this information on the device screen.

[0195] Step 9:

[0196] The user implements the proposed care plan and sends feedback to the server via their device. This feedback is used to optimize future skincare suggestions. The user also enters their feedback on the care product, such as "It was very effective," and sends it to the server.

[0197] The above outlines the specific processing flow within this system.

[0198] (Application Example 2)

[0199] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0200] In providing optimal beauty care tailored to an individual's skin condition and emotions, conventional systems have struggled to offer suggestions that take into account the user's psychological state. Furthermore, there is a lack of concrete means to improve the in-store customer experience.

[0201] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0202] In this invention, the server includes means for analyzing skin condition information and image data received from the user and generating individualized beauty care suggestions; image recognition means for identifying skin type and problems; means for referencing beauty trends and scientific data to suggest products and skincare routines suitable for the user; emotion recognition means for analyzing the user's emotional information and optimizing beauty care suggestions according to their emotional state; and means for presenting customized beauty products and care advice based on emotional information using a digital device. This makes it possible to provide more effective and personalized beauty care that comprehensively considers the user's physical and mental state.

[0203] A "user" refers to an individual who uses this system and is the entity that provides information on skin condition and emotions.

[0204] "Skin condition information" refers to data that represents the detailed condition of the user's skin, and includes image data and text information.

[0205] "Image data" refers to visual information that shows the condition of the user's skin, and through analysis, it can be used to identify skin type and problems.

[0206] "Beauty care suggestions" refer to recommendations that suggest the optimal skincare routine and beauty products based on the user's skin condition and emotional information.

[0207] "Image recognition means" refers to technologies and algorithms used to analyze image data and identify skin type and problems.

[0208] "Beauty trends" refer to information about the latest beauty methods and products, and are data that is referenced when making beauty care suggestions.

[0209] "Scientific data" refers to information based on scientific evidence regarding skincare, and is used to improve the accuracy of recommendations.

[0210] "Emotional information" refers to data that indicates the user's psychological state, and includes voice, facial expressions, and text input.

[0211] "Emotion recognition means" refers to technologies that analyze a user's emotional information and recognize their psychological state.

[0212] A "digital device" is an electronic device that enables interaction with the user and is used for presenting and collecting information.

[0213] "Customized beauty products and care advice" refers to information that suggests beauty products and care methods suitable for specific conditions based on the user's skin condition and emotional information.

[0214] The system implementing this invention is a complex system that provides personalized beauty care suggestions based on the user's skin condition and emotional information. Optimal care is achieved through data exchange between the server, terminal, and user.

[0215] The server first analyzes skin condition information and image data received from the user via the terminal. This analysis uses an image recognition algorithm, and TensorFlow is a possible example. Furthermore, natural language processing technology is used as an emotion recognition engine to extract emotional information from the user's voice and text input. OpenAI® GPT is cited as an example here.

[0216] Based on this data, the server selects the optimal skincare products and care advice and sends them back to the device. During this process, it references beauty trends and scientific data to ensure that the selected information is up-to-date and scientifically based. The user's psychological state is also considered, and personalized suggestions are provided based on emotional information.

[0217] Users can visually access beauty care information through their device's digital equipment (e.g., smart glasses or smartphones). These digital devices utilize AR functionality and displays to support user information acquisition.

[0218] As a concrete example, consider a scenario where a user enters a store wearing smart glasses. An example of a prompt message in this case would be: "If fatigue or stress is detected from the customer's current facial expression or voice, quickly generate a message recommending appropriate relaxation beauty products." In this way, advanced care that is tailored to the user's physical and mental state becomes possible.

[0219] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0220] Step 1:

[0221] In a process initiated from a terminal, the user uses a smart device (e.g., smart glasses or a smartphone) to input photos of their skin and audio data. This data is then sent from the terminal to a server. The data input here includes information about skin condition and emotions.

[0222] Step 2:

[0223] The server analyzes the received image data using an image recognition algorithm (e.g., TensorFlow) to identify the user's skin type and any skin problems. This data processing includes pixel-level pigmentation analysis and skin texture recognition. The output is information about skin type and skin problems.

[0224] Step 3:

[0225] The server uses an emotion recognition engine to analyze user emotion information from voice data and input text. This step involves detecting specific keywords and analyzing voice tone to categorize emotional states. The output is an evaluation of the user's emotional state.

[0226] Step 4:

[0227] The server combines analyzed skin condition information and emotional information, and generates beauty care suggestions using a generative AI model. Here, skincare products and advice optimized for the user are selected by cross-referencing with beauty trends and scientific databases. The output consists of a series of beauty care suggestions.

[0228] Step 5:

[0229] The server sends the generated beauty care suggestions to the terminal. The terminal visualizes this information and displays it via the user's smart device using AR functionality or a display screen. This display includes customized product information and specific care procedures. The output is the visual information presented to the user.

[0230] Step 6:

[0231] Users can refer to the provided information and select specific skincare routines and products. Their selections and feedback are sent to the server via their device and stored as additional data to further optimize future suggestions. The output consists of updated user information and feedback.

[0232] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0233] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0234] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0235] [Second Embodiment]

[0236] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0237] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0238] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0239] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0240] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0241] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0242] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0243] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0244] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0245] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0246] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0247] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0248] This invention is configured as a system that allows users to analyze their skin condition and receive optimal beauty care. This system includes a process in which a server generates personalized beauty care suggestions based on skin condition information and image data provided by the user through a terminal.

[0249] Specifically, users input their skin type, problem areas, and past skincare history into their device. They also use the device to take photos of their skin, sending more detailed information to the server. The server receives this data and uses its built-in image recognition technology to identify skin type and problems.

[0250] The server further references accumulated beauty trends and scientific data to select the most suitable products and skincare routines for each user. Based on this, the server generates personalized beauty care suggestions for each user and delivers them to their device.

[0251] For example, if a user has dry skin and redness, the server will use image recognition to check the condition, recommend highly moisturizing products containing hyaluronic acid and ceramides, and then suggest a morning and evening skincare routine using those products.

[0252] Subsequently, the user practices the skincare routine suggested by the device, and the device tracks this. The tracking data and user feedback information are sent back to the server for further analysis. Through this process, the system aims to continuously improve the quality of beauty care and assist users in performing the most optimal care for their individual skin.

[0253] Therefore, the present invention aims to provide reliable information and support that enables users to confidently bring out their own beauty.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] The user inputs skin condition information into the device and takes photos of their skin using the camera as needed. This allows the device to accumulate the user's data.

[0257] Step 2:

[0258] The device packages skin condition information and image data collected from the user and sends it to the server.

[0259] Step 3:

[0260] The server prepares the data received from the terminal for analysis. For image data, analysis is initiated using image recognition to identify skin type and problems.

[0261] Step 4:

[0262] Based on the analysis results, the server references beauty trends and scientific data to determine the optimal beauty care products and skincare routines for each user. This information is then generated as personalized beauty care recommendations.

[0263] Step 5:

[0264] The server generates beauty care suggestions, which are then sent back to the terminal as a data package.

[0265] Step 6:

[0266] The device analyzes beauty care suggestions received from the server and displays them visually to the user. The user then uses this information to implement their daily skincare routine.

[0267] Step 7:

[0268] The user executes the proposed routine and tracks the routine's progress on their terminal as needed. This tracking information is later resent to the server.

[0269] Step 8:

[0270] When a user enters feedback after using skincare products via their device, the feedback data is sent from the device to the server. The server then re-analyzes this data and uses it to improve the skincare routine.

[0271] Step 9:

[0272] The server uses feedback and tracking data to generate adjustments to the skincare routine as needed, or to generate new suggestions, which are then sent back to the device and presented to the user.

[0273] As a result, users are supported in continuously monitoring changes in their skin and implementing the optimal beauty care routine.

[0274] (Example 1)

[0275] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] In today's world, there is a demand for beauty care tailored to each individual user, but conventional systems have struggled to provide personalized care suggestions based on the detailed condition of each user's skin. Furthermore, there has been a lack of mechanisms for continuously evaluating the effectiveness of the suggested care and making improvements based on feedback.

[0277] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0278] In this invention, the server includes means for analyzing skin condition information and image data collected from users to generate individual aesthetic treatment suggestions; means for using image recognition technology to identify skin characteristics and problems; means for referencing trend and scientific information to suggest products and treatment procedures suitable for the user; and means for generating suggestion content using a generative artificial intelligence model. This makes it possible to provide accurate beauty care suggestions based on the user's skin condition and to continuously improve those suggestions.

[0279] A "user" is an individual who uses the system to receive beauty care suggestions based on their skin condition.

[0280] "Skin condition information" refers to information about the user's skin type, problem areas, and past care history.

[0281] "Image data" refers to photo data taken to grasp the detailed condition of the user's skin.

[0282] "Analysis" is a process of processing the collected skin condition information and image data to identify the characteristics and problems of the user's skin.

[0283] "Individual aesthetic treatment proposal" refers to the proposal of the optimal beauty care methods and products identified based on the user's skin condition.

[0284] "Image recognition technology" is a technology for identifying the characteristics and problems of the skin using the captured image data.

[0285] "Fashion and scientific information" refers to the latest trends and science-based data in the beauty field.

[0286] "Generative artificial intelligence model" is a machine learning model that generates appropriate beauty care proposals based on input information.

[0287] "Tracking" is a process for recording and analyzing the progress of the proposed treatment procedure.

[0288] This system is configured as a platform for the user to precisely analyze their own skin condition and further receive optimal beauty care. The user can first input their skin type, problem areas, and past skin care history through the terminal. Furthermore, use the terminal to take a picture of their skin and send the image to the server. The server receives this data and uses the built-in image recognition technology to identify the characteristics of the skin. This image recognition technology utilizes advanced image analysis software using machine learning algorithms.

[0289] The server then accesses accumulated beauty trend information and scientific databases, and uses a generative AI model to select the most suitable products and skincare routines for the user. A concrete example of a prompt message is, "Please suggest a routine to use with moisturizing products suitable for a user with dry skin." This prompt allows the server to generate personalized beauty care suggestions tailored to each individual user and send them to the device.

[0290] For example, if a user has dry skin with noticeable redness, the server can analyze the image data to identify this condition and recommend products containing highly moisturizing hyaluronic acid and ceramides. Furthermore, a specific morning and evening skincare routine using these products can also be suggested.

[0291] Ultimately, the user performs the suggested care, and the device tracks the progress. The tracking data is sent back to the server for further analysis along with feedback. Through this process, the system continuously improves the quality of beauty care and helps users perform the most appropriate care. A key feature of this system is its ability to always provide the latest and most effective care by using a generative AI model.

[0292] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0293] Step 1:

[0294] The user inputs skin condition data into the device. The user enters their skin type, problem areas, and past skincare history through the device's interface. This input data is temporarily stored on the device before being sent to the server. The entered information is used as basic analysis data in subsequent processing steps.

[0295] Step 2:

[0296] The user takes an image of their skin with their device. The user uses the device's camera to capture a high-resolution image of their skin and sends it to the server. This image data is crucial input data for detailed skin analysis. The image serves as the basic data necessary for processing by image recognition technology on the server side.

[0297] Step 3:

[0298] The server receives the data and performs analysis using image recognition technology. The server receives skin condition information and image data sent by the user and uses its built-in image recognition technology to identify skin characteristics and problems. In this process, image analysis utilizing machine learning algorithms is performed to identify skin type and problem areas. As output, the analysis results are stored in an internal database and used in the subsequent care suggestion generation step.

[0299] Step 4:

[0300] The server uses a generative AI model to create beauty care suggestions. Based on the analyzed data, the server refers to accumulated beauty trends and scientific data, and utilizes the generative AI model to suggest the most suitable products and skincare routines for the user. By giving instructions to the generative AI model in the prompt message, such as "Suggest moisturizing products and usage methods suitable for a user with dry skin," appropriate care suggestions are made. As output, personalized beauty care suggestions are generated.

[0301] Step 5:

[0302] The server generates suggestions and sends them to the terminal, presenting them visually to the user. The beauty care suggestions created by the server are sent to the terminal and visually displayed on the terminal's screen. The user can review these suggestions and learn about specific care procedures and recommended products.

[0303] Step 6:

[0304] The user implements the proposed care, and the terminal tracks its progress. The user performs the proposed skin care daily, and the terminal records the implementation status. As input, the user's care implementation details are collected and stored as tracking data.

[0305] Step 7:

[0306] The terminal sends the tracking data and the user's feedback to the server, and the server performs re-analysis. Based on the tracked data and feedback, the server performs re-analysis again to generate an optimized beauty care proposal. Through this process, the user can continuously receive optimal beauty care. As output, an improved proposal is provided.

[0307] (Application Example 1)

[0308] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0309] In conventional beauty counseling, it was difficult for users to accurately understand their own skin conditions, and it was also difficult to select and purchase appropriate products. Furthermore, it took a long time to select products in stores, and a lot of effort and cost were required to receive individually optimized beauty care proposals.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0311] In this invention, the server includes means for analyzing skin condition information and image data received from the user and generating personalized beauty care suggestions; image recognition means for identifying skin type and problems; means for referencing beauty trends and scientific data to suggest products and skincare routines suitable for the user; and visual display means for displaying product placement information based on the analysis results to promote purchase. This allows the user to quickly select and purchase products suitable for them and easily implement effective beauty care.

[0312] "Skin condition information received from the user" refers to information about the user's skin type and problem areas that they input through their device.

[0313] "Image data" refers to photographic data of skin taken by the user using their device.

[0314] "Personalized beauty care suggestions" refer to information that recommends beauty products and skincare routines tailored to a specific user, based on their analyzed skin condition.

[0315] "Image recognition means" refers to a technology used to identify skin type and skin problems, and includes an algorithm for analyzing image data.

[0316] "Beauty trends and scientific data" refers to market data and chemical / medical research findings related to the latest beauty products and care methods.

[0317] A "skincare routine" refers to a series of skincare steps using specific beauty products, with the aim of maintaining and improving skin health.

[0318] "Means of tracking progress" refers to a system that records and manages the progress of the skincare routine that the user is following.

[0319] "Displaying product placement information" means visually guiding users to the recommended locations of beauty products within the store.

[0320] "Visual display means" refers to devices and technologies for providing information to users visually, and includes displays and UI design.

[0321] The system implementing this invention consists of a user, a terminal, and a server. The terminal is a device equipped with a camera and an internet connection. The user uses this terminal to take a photograph of their skin and input skin condition information. This data is transmitted to the server in real time.

[0322] The server first analyzes the data received from the user. Image recognition libraries such as OpenCV and TensorFlow are used for image analysis. This identifies skin type and problem areas. Then, the server uses the collected beauty trends and scientific data to generate optimal beauty care products and skincare routines for each individual user.

[0323] The generated suggestions are visually displayed to the user through the terminal's screen. Furthermore, the location of recommended products is shown to encourage in-store purchases. This allows users to easily find and purchase products within the store.

[0324] As a concrete example, consider a user using this system in a cosmetics store. The user takes a photo of their skin with a terminal and receives an analysis result indicating dry skin. The system recommends a moisturizing cream containing hyaluronic acid and also displays information on where that cream is located on the store's shelves. In this way, the user can quickly find the necessary beauty products and implement appropriate care.

[0325] An example of a prompt message is: "Analyze the skin image taken by the user and suggest the most suitable skincare products. Refer to the database and select and display recommended items within the range of products currently available in the store." By using this prompt message, the generating AI model can generate optimal suggestions in real time based on each user's individual data.

[0326] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0327] Step 1:

[0328] Users take photos of their skin using their device and input information about their skin condition. This input includes selecting their skin type (such as dry or oily skin) and specifying areas of skin problems. This information, along with the image file, is sent from the device to the server.

[0329] Step 2:

[0330] The server analyzes the received image file and skin condition information. Using image recognition libraries (such as OpenCV or TensorFlow), it automatically extracts skin type and problem areas from the image data. This process outputs the skin type and specific problems.

[0331] Step 3:

[0332] Based on the analysis results, the server references beauty trends and scientific data to generate the optimal beauty care products and skincare routine for the user. It uses skin type and problem information as input, searches the database for relevant product information, and outputs recommended items and usage instructions.

[0333] Step 4:

[0334] The server sends the generated beauty care suggestions to the user's device. It also references product placement data from the store the user is visiting and generates data that visually displays the location of the suggested products within the store.

[0335] Step 5:

[0336] The terminal displays beauty care suggestions and product placement information received from the server. Specifically, it displays a list of suggested products along with the shelf location of each product as a store map, making it easy for users to find products within the store.

[0337] Step 6:

[0338] The user uses the product based on the suggested skincare routine. The device records this usage information and can accept feedback after use. The feedback information is sent to the server for later analysis.

[0339] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0340] This invention combines an emotional engine with a beauty care system to realize personalized beauty care that takes into account the user's emotional state. The system includes a process for generating optimal beauty care suggestions based on skin condition information, image data, and emotional information provided by the user.

[0341] When a user provides information and images about their skin condition using their device, the device sends that data to a server. The server receives the data and uses image recognition to analyze the user's skin type and problems. The analysis results are then compared with beauty trends and scientific data to select the most suitable skincare routine and products for the user.

[0342] The emotion engine, a key feature of this invention, recognizes emotions from the user's voice, facial expressions, and entered text. This emotional information is also reflected in beauty care suggestions, generating encouraging and supportive messages according to the user's emotional state. By utilizing this emotional information, the invention aims to enhance user motivation and support continuous care.

[0343] For example, if a user types "I'm tired today" through their device, the emotion engine recognizes that emotion as "fatigue." The server takes this information into account, suggests relaxing beauty products, and delivers a message in a gentle tone to the user through their device.

[0344] Furthermore, continuous user feedback and emotional data are stored and re-analyzed on the server. Based on this data, the server adjusts long-term skincare routines and provides strategies to enhance the user's inner and outer beauty.

[0345] Thus, the present invention aims to achieve more effective and personalized beauty care by comprehensively considering the user's mental and physical state through an emotional engine.

[0346] The following describes the processing flow.

[0347] Step 1:

[0348] The user uses their device to input information about their skin condition and, if necessary, take photos of their skin. They also input information about their emotions (voice, facial expressions, text, etc.).

[0349] Step 2:

[0350] The device packages skin condition information, image data, and emotional information collected from the user and sends them to the server.

[0351] Step 3:

[0352] The server analyzes the received data. The image data then enters a process where image recognition tools identify skin type and problems.

[0353] Step 4:

[0354] The server analyzes the received emotional information using an emotion engine. It identifies the user's emotions from voice data, facial expressions, and text, and records the results as emotional data.

[0355] Step 5:

[0356] The server combines skin condition analysis results with emotional data, and selects the optimal beauty care by referring to beauty trends and scientific data. Based on the emotional data, the content and delivery method of the beauty care are adjusted.

[0357] Step 6:

[0358] The server generates personalized beauty care suggestions and sends them to the device. These suggestions include encouraging and supportive messages, as well as products tailored to the user's mood.

[0359] Step 7:

[0360] The device visually displays beauty care suggestions from the server and provides them to the user. Based on the information received, the user implements their daily skincare routine.

[0361] Step 8:

[0362] The user performs the suggested routine and inputs feedback on the results and their feelings via the device. The device then resends this feedback data to the server.

[0363] Step 9:

[0364] The server re-analyzes the feedback and emotional data, and generates adjustments to the long-term skincare routine or new suggestions as needed, which are then resent to the device.

[0365] Through these processes, the system provides comprehensive beauty care that takes into account the user's emotional state, effectively supporting continuous care.

[0366] (Example 2)

[0367] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0368] Traditional beauty care systems primarily rely on user skin condition information and beauty trends for their recommendations, but they lack the ability to provide personalized care suggestions that take into account individual emotional states. This often results in inadequate recommendations tailored to the user's emotional state, negatively impacting the long-term sustainability of their care. Furthermore, the insufficient use of feedback for skincare optimization makes it difficult to propose individually optimized care.

[0369] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0370] In this invention, the server includes means for analyzing skin condition information and image data received from the user, image recognition means for identifying skin type and problems, and emotion recognition means for recognizing emotions from the user's voice, facial expressions, and text, and reflecting them in beauty care suggestions. This enables personalized beauty care suggestions tailored to the user's emotional state, ensuring more personalized care and long-term skincare continuity.

[0371] "Skin condition information received from the user" refers to data indicating the user's skin condition, and is received in image or text format.

[0372] "Image data" refers to visual data that captures the current state of the user's skin, and is sent to the server for analysis and recognition.

[0373] "Personalized beauty care suggestions" refer to personalized beauty product and skincare routine suggestions generated based on the user's specific skin condition and emotions.

[0374] "Image recognition means" refers to technologies used to identify skin type and problems, and includes algorithms and software for analyzing image data.

[0375] "Means of referencing beauty trends and scientific data" refers to technologies for selecting products and routines suitable for the user based on the latest beauty information and scientific findings.

[0376] "Means of tracking progress" refers to a function for checking and recording how far a proposed beauty care routine has progressed.

[0377] "Emotion recognition means" refers to technologies and devices for analyzing and recognizing a user's emotional state from their voice, facial expressions, and text.

[0378] A "generative AI model" is a machine learning model used to output optimal care suggestions based on the user's skin condition and emotional information.

[0379] The embodiments for carrying out this invention are shown below.

[0380] Users take photos of their skin and input information about their skin condition using a mobile device or computer. They may also utilize dedicated application software for this process.

[0381] The terminal transmits the input image data and text information to the server via a secure internet connection. The HTTPS protocol is commonly used for this purpose.

[0382] The server analyzes the received image data using image recognition software (e.g., OpenCV or TensorFlow) to identify skin type and problems. The analysis results are referenced against the latest beauty trends and scientific data stored in a database. A database management system (DBMS) may be used for database comparison.

[0383] Furthermore, emotion recognition software on the server analyzes voice, facial expressions, or text data from the user using NLP techniques to identify the user's emotions. This technique utilizes common voice and image analysis libraries.

[0384] The server uses a generative AI model based on analyzed skin condition and emotional information to generate optimal beauty care suggestions for the user. The generative AI model is constantly retrained based on the latest data and user feedback to improve its accuracy.

[0385] For example, if a user enters "I'm too busy today to spend time on skincare" into their device, the server will suggest an "easy-to-use all-in-one cream" and display an encouraging message: "Let's aim for effective care even in a short amount of time."

[0386] An example of a prompt message would be, "What are the best skincare products for when you're feeling stressed?"

[0387] In this way, the invention makes it possible to provide more personalized and effective beauty care tailored to the individual skin condition and emotional state of each user.

[0388] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0389] Step 1:

[0390] Users input information and images about their skin condition using a mobile device or computer. The input data is prepared as image files and text data by a dedicated application. Users take a picture of their skin using the camera function and input text information such as "My skin is dry today."

[0391] Step 2:

[0392] The terminal sends the input image data and text data to the server. The data is transferred securely using the HTTPS protocol. The image file and text are sent to the server as input data, and this becomes the source data for the next analysis process.

[0393] Step 3:

[0394] The server analyzes the received image data using image recognition software. It utilizes algorithms from OpenCV and TensorFlow to identify the user's skin type and problems. The image data is analyzed pixel by pixel, and the output provides results indicating oily skin, dry skin, or specific skin problem areas.

[0395] Step 4:

[0396] The server analyzes the text data using natural language processing technology. It understands the context related to the user's skin condition from the input text information and extracts relevant keywords. For example, it might extract the keyword "dryness" and output information indicating that the skin needs moisturizing.

[0397] Step 5:

[0398] The server compares the analysis results with beauty trends and scientific data. Using a database management system, it selects beauty products and routines suitable for the user based on the analysis results. Using the analysis results and trend data as input, it obtains "recommended moisturizing creams" as output.

[0399] Step 6:

[0400] The server analyzes the user's voice, facial expressions, and text data using emotion recognition software to identify their emotions. Using NLP (Neuro-Linguistic Programming) technology, it recognizes the emotion "stress" from the user's input text, "I'm feeling down today." It takes voice and text data as input and obtains emotional information as output.

[0401] Step 7:

[0402] The server uses a generation AI model to generate optimal beauty care suggestions based on the user's skin condition and emotional information. The analysis results are input to the generation AI model as prompts, and it outputs a suggestion such as, "Using a moisturizing face mask is good for stress relief."

[0403] Step 8:

[0404] The server generates a proposed care plan and a message of encouragement, and sends it to the user via the device. The server sends the generated message, "You've worked hard today. Let's give your skin some relaxation time," along with recommendations for beauty products, to the device. The user can view this information on the device screen.

[0405] Step 9:

[0406] The user implements the proposed care plan and sends feedback to the server via their device. This feedback is used to optimize future skincare suggestions. The user also enters their feedback on the care product, such as "It was very effective," and sends it to the server.

[0407] The above outlines the specific processing flow within this system.

[0408] (Application Example 2)

[0409] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0410] In providing optimal beauty care tailored to an individual's skin condition and emotions, conventional systems have struggled to offer suggestions that take into account the user's psychological state. Furthermore, there is a lack of concrete means to improve the in-store customer experience.

[0411] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0412] In this invention, the server includes means for analyzing skin condition information and image data received from the user and generating individualized beauty care suggestions; image recognition means for identifying skin type and problems; means for referencing beauty trends and scientific data to suggest products and skincare routines suitable for the user; emotion recognition means for analyzing the user's emotional information and optimizing beauty care suggestions according to their emotional state; and means for presenting customized beauty products and care advice based on emotional information using a digital device. This makes it possible to provide more effective and personalized beauty care that comprehensively considers the user's physical and mental state.

[0413] A "user" refers to an individual who uses this system and is the entity that provides information on skin condition and emotions.

[0414] "Skin condition information" refers to data that represents the detailed condition of the user's skin, and includes image data and text information.

[0415] "Image data" refers to visual information that shows the condition of the user's skin, and through analysis, it can be used to identify skin type and problems.

[0416] "Beauty care suggestions" refer to recommendations that suggest the optimal skincare routine and beauty products based on the user's skin condition and emotional information.

[0417] "Image recognition means" refers to technologies and algorithms used to analyze image data and identify skin type and problems.

[0418] "Beauty trends" refer to information about the latest beauty methods and products, and are data that is referenced when making beauty care suggestions.

[0419] "Scientific data" refers to information based on scientific evidence regarding skincare, and is used to improve the accuracy of recommendations.

[0420] "Emotional information" refers to data that indicates the user's psychological state, and includes voice, facial expressions, and text input.

[0421] "Emotion recognition means" refers to technologies that analyze a user's emotional information and recognize their psychological state.

[0422] A "digital device" is an electronic device that enables interaction with the user and is used for presenting and collecting information.

[0423] "Customized beauty products and care advice" refers to information that suggests beauty products and care methods suitable for specific conditions based on the user's skin condition and emotional information.

[0424] The system implementing this invention is a complex system that provides personalized beauty care suggestions based on the user's skin condition and emotional information. Optimal care is achieved through data exchange between the server, terminal, and user.

[0425] The server first analyzes skin condition information and image data received from the user via the terminal. This analysis uses image recognition algorithms, with TensorFlow being a possible example. Furthermore, natural language processing technology is used as an emotion recognition engine to extract emotional information from the user's voice and text input. OpenAI GPT is an example of this.

[0426] Based on this data, the server selects the optimal skincare products and care advice and sends them back to the device. During this process, it references beauty trends and scientific data to ensure that the selected information is up-to-date and scientifically based. The user's psychological state is also considered, and personalized suggestions are provided based on emotional information.

[0427] Users can visually access beauty care information through their device's digital equipment (e.g., smart glasses or smartphones). These digital devices utilize AR functionality and displays to support user information acquisition.

[0428] As a concrete example, consider a scenario where a user enters a store wearing smart glasses. An example of a prompt message in this case would be: "If fatigue or stress is detected from the customer's current facial expression or voice, quickly generate a message recommending appropriate relaxation beauty products." In this way, advanced care that is tailored to the user's physical and mental state becomes possible.

[0429] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0430] Step 1:

[0431] In a process initiated from a terminal, the user uses a smart device (e.g., smart glasses or a smartphone) to input photos of their skin and audio data. This data is then sent from the terminal to a server. The data input here includes information about skin condition and emotions.

[0432] Step 2:

[0433] The server analyzes the received image data using an image recognition algorithm (e.g., TensorFlow) to identify the user's skin type and any skin problems. This data processing includes pixel-level pigmentation analysis and skin texture recognition. The output is information about skin type and skin problems.

[0434] Step 3:

[0435] The server uses an emotion recognition engine to analyze user emotion information from voice data and input text. This step involves detecting specific keywords and analyzing voice tone to categorize emotional states. The output is an evaluation of the user's emotional state.

[0436] Step 4:

[0437] The server combines analyzed skin condition information and emotional information, and generates beauty care suggestions using a generative AI model. Here, skincare products and advice optimized for the user are selected by cross-referencing with beauty trends and scientific databases. The output consists of a series of beauty care suggestions.

[0438] Step 5:

[0439] The server sends the generated beauty care suggestions to the terminal. The terminal visualizes this information and displays it via the user's smart device using AR functionality or a display screen. This display includes customized product information and specific care procedures. The output is the visual information presented to the user.

[0440] Step 6:

[0441] Users can refer to the provided information and select specific skincare routines and products. Their selections and feedback are sent to the server via their device and stored as additional data to further optimize future suggestions. The output consists of updated user information and feedback.

[0442] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0443] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0444] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0445] [Third Embodiment]

[0446] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0447] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0448] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0449] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0450] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0451] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0452] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0453] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0454] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0455] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0456] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0457] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0458] This invention is configured as a system that allows users to analyze their skin condition and receive optimal beauty care. This system includes a process in which a server generates personalized beauty care suggestions based on skin condition information and image data provided by the user through a terminal.

[0459] Specifically, users input their skin type, problem areas, and past skincare history into their device. They also use the device to take photos of their skin, sending more detailed information to the server. The server receives this data and uses its built-in image recognition technology to identify skin type and problems.

[0460] The server further references accumulated beauty trends and scientific data to select the most suitable products and skincare routines for each user. Based on this, the server generates personalized beauty care suggestions for each user and delivers them to their device.

[0461] For example, if a user has dry skin and redness, the server will use image recognition to check the condition, recommend highly moisturizing products containing hyaluronic acid and ceramides, and then suggest a morning and evening skincare routine using those products.

[0462] Subsequently, the user practices the skincare routine suggested by the device, and the device tracks this. The tracking data and user feedback information are sent back to the server for further analysis. Through this process, the system aims to continuously improve the quality of beauty care and assist users in performing the most optimal care for their individual skin.

[0463] Therefore, the present invention aims to provide reliable information and support that enables users to confidently bring out their own beauty.

[0464] The following describes the processing flow.

[0465] Step 1:

[0466] The user inputs skin condition information into the device and takes photos of their skin using the camera as needed. This allows the device to accumulate the user's data.

[0467] Step 2:

[0468] The device packages skin condition information and image data collected from the user and sends it to the server.

[0469] Step 3:

[0470] The server prepares the data received from the terminal for analysis. For image data, analysis is initiated using image recognition to identify skin type and problems.

[0471] Step 4:

[0472] Based on the analysis results, the server references beauty trends and scientific data to determine the optimal beauty care products and skincare routines for each user. This information is then generated as personalized beauty care recommendations.

[0473] Step 5:

[0474] The server generates beauty care suggestions, which are then sent back to the terminal as a data package.

[0475] Step 6:

[0476] The device analyzes beauty care suggestions received from the server and displays them visually to the user. The user then uses this information to implement their daily skincare routine.

[0477] Step 7:

[0478] The user executes the proposed routine and tracks the routine's progress on their terminal as needed. This tracking information is later resent to the server.

[0479] Step 8:

[0480] When a user enters feedback after using skincare products via their device, the feedback data is sent from the device to the server. The server then re-analyzes this data and uses it to improve the skincare routine.

[0481] Step 9:

[0482] The server uses feedback and tracking data to generate adjustments to the skincare routine as needed, or to generate new suggestions, which are then sent back to the device and presented to the user.

[0483] As a result, users are supported in continuously monitoring changes in their skin and implementing the optimal beauty care routine.

[0484] (Example 1)

[0485] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0486] In today's world, there is a demand for beauty care tailored to each individual user, but conventional systems have struggled to provide personalized care suggestions based on the detailed condition of each user's skin. Furthermore, there has been a lack of mechanisms for continuously evaluating the effectiveness of the suggested care and making improvements based on feedback.

[0487] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0488] In this invention, the server includes means for analyzing skin condition information and image data collected from users to generate individual aesthetic treatment suggestions; means for using image recognition technology to identify skin characteristics and problems; means for referencing trend and scientific information to suggest products and treatment procedures suitable for the user; and means for generating suggestion content using a generative artificial intelligence model. This makes it possible to provide accurate beauty care suggestions based on the user's skin condition and to continuously improve those suggestions.

[0489] A "user" is an individual who uses the system to receive beauty care suggestions based on their skin condition.

[0490] "Skin condition information" refers to information about the user's skin type, problem areas, and past care history.

[0491] "Image data" refers to photographic data taken to understand the detailed condition of the user's skin.

[0492] "Analysis" is the process of processing collected skin condition information and image data to identify the user's skin characteristics and problems.

[0493] "Individualized aesthetic treatment suggestions" refer to suggestions for optimal cosmetic care methods and products identified based on the user's skin condition.

[0494] "Image recognition technology" is a technology that uses captured image data to identify skin characteristics and problems.

[0495] "Trend and scientific information" refers to the latest trends and science-based data in the beauty industry.

[0496] A "generative artificial intelligence model" is a machine learning model that generates appropriate beauty care suggestions based on input information.

[0497] "Tracking" is the process of recording and analyzing the progress of the proposed treatment procedures.

[0498] This system is designed as a platform for users to precisely analyze their skin condition and receive optimal beauty care. Users first input their skin type, problem areas, and past skincare history via a terminal. They then use the terminal to take images of their skin and send them to a server. The server receives this data and uses built-in image recognition technology to identify skin characteristics. This image recognition technology utilizes advanced image analysis software with machine learning algorithms.

[0499] The server then accesses accumulated beauty trend information and scientific databases, and uses a generative AI model to select the most suitable products and skincare routines for the user. A concrete example of a prompt message is, "Please suggest a routine to use with moisturizing products suitable for a user with dry skin." This prompt allows the server to generate personalized beauty care suggestions tailored to each individual user and send them to the device.

[0500] For example, if a user has dry skin with noticeable redness, the server can analyze the image data to identify this condition and recommend products containing highly moisturizing hyaluronic acid and ceramides. Furthermore, a specific morning and evening skincare routine using these products can also be suggested.

[0501] Ultimately, the user performs the suggested care, and the device tracks the progress. The tracking data is sent back to the server for further analysis along with feedback. Through this process, the system continuously improves the quality of beauty care and helps users perform the most appropriate care. A key feature of this system is its ability to always provide the latest and most effective care by using a generative AI model.

[0502] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0503] Step 1:

[0504] The user inputs skin condition data into the device. The user enters their skin type, problem areas, and past skincare history through the device's interface. This input data is temporarily stored on the device before being sent to the server. The entered information is used as basic analysis data in subsequent processing steps.

[0505] Step 2:

[0506] The user takes an image of their skin with their device. The user uses the device's camera to capture a high-resolution image of their skin and sends it to the server. This image data is crucial input data for detailed skin analysis. The image serves as the basic data necessary for processing by image recognition technology on the server side.

[0507] Step 3:

[0508] The server receives the data and performs analysis using image recognition technology. The server receives skin condition information and image data sent by the user and uses its built-in image recognition technology to identify skin characteristics and problems. In this process, image analysis utilizing machine learning algorithms is performed to identify skin type and problem areas. As output, the analysis results are stored in an internal database and used in the subsequent care suggestion generation step.

[0509] Step 4:

[0510] The server uses a generative AI model to create beauty care suggestions. Based on the analyzed data, the server refers to accumulated beauty trends and scientific data, and utilizes the generative AI model to suggest the most suitable products and skincare routines for the user. By giving instructions to the generative AI model in the prompt message, such as "Suggest moisturizing products and usage methods suitable for a user with dry skin," appropriate care suggestions are made. As output, personalized beauty care suggestions are generated.

[0511] Step 5:

[0512] The server generates suggestions and sends them to the terminal, presenting them visually to the user. The beauty care suggestions created by the server are sent to the terminal and visually displayed on the terminal's screen. The user can review these suggestions and learn about specific care procedures and recommended products.

[0513] Step 6:

[0514] The user performs the suggested care, and the device tracks the progress. The user performs the suggested skincare routine daily, and the device records the status of this routine. As input, the user's care routine is collected and stored as tracking data.

[0515] Step 7:

[0516] The device sends tracking data and user feedback to the server, which then performs a re-analysis. Based on the tracked data and feedback, the server performs another analysis and generates optimized beauty care suggestions. This process allows the user to continuously receive the most suitable beauty care. As output, improved suggestions are provided.

[0517] (Application Example 1)

[0518] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0519] Traditional beauty consultations made it difficult for users to accurately understand their own skin condition and to select and purchase appropriate products. Furthermore, selecting products in stores was time-consuming, and receiving personalized, optimized beauty care suggestions required considerable effort and expense.

[0520] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0521] In this invention, the server includes means for analyzing skin condition information and image data received from the user and generating personalized beauty care suggestions; image recognition means for identifying skin type and problems; means for referencing beauty trends and scientific data to suggest products and skincare routines suitable for the user; and visual display means for displaying product placement information based on the analysis results to promote purchase. This allows the user to quickly select and purchase products suitable for them and easily implement effective beauty care.

[0522] "Skin condition information received from the user" refers to information about the user's skin type and problem areas that they input through their device.

[0523] "Image data" refers to photographic data of skin taken by the user using their device.

[0524] "Personalized beauty care suggestions" refer to information that recommends beauty products and skincare routines tailored to a specific user, based on their analyzed skin condition.

[0525] "Image recognition means" refers to a technology used to identify skin type and skin problems, and includes an algorithm for analyzing image data.

[0526] "Beauty trends and scientific data" refers to market data and chemical / medical research findings related to the latest beauty products and care methods.

[0527] A "skincare routine" refers to a series of skincare steps using specific beauty products, with the aim of maintaining and improving skin health.

[0528] "Means of tracking progress" refers to a system that records and manages the progress of the skincare routine that the user is following.

[0529] "Displaying product placement information" means visually guiding users to the recommended locations of beauty products within the store.

[0530] "Visual display means" refers to devices and technologies for providing information to users visually, and includes displays and UI design.

[0531] The system implementing this invention consists of a user, a terminal, and a server. The terminal is a device equipped with a camera and an internet connection. The user uses this terminal to take a photograph of their skin and input skin condition information. This data is transmitted to the server in real time.

[0532] The server first analyzes the data received from the user. Image recognition libraries such as OpenCV and TensorFlow are used for image analysis. This identifies skin type and problem areas. Then, the server uses the collected beauty trends and scientific data to generate optimal beauty care products and skincare routines for each individual user.

[0533] The generated suggestions are visually displayed to the user through the terminal's screen. Furthermore, the location of recommended products is shown to encourage in-store purchases. This allows users to easily find and purchase products within the store.

[0534] As a concrete example, consider a user using this system in a cosmetics store. The user takes a photo of their skin with a terminal and receives an analysis result indicating dry skin. The system recommends a moisturizing cream containing hyaluronic acid and also displays information on where that cream is located on the store's shelves. In this way, the user can quickly find the necessary beauty products and implement appropriate care.

[0535] An example of a prompt message is: "Analyze the skin image taken by the user and suggest the most suitable skincare products. Refer to the database and select and display recommended items within the range of products currently available in the store." By using this prompt message, the generating AI model can generate optimal suggestions in real time based on each user's individual data.

[0536] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0537] Step 1:

[0538] Users take photos of their skin using their device and input information about their skin condition. This input includes selecting their skin type (such as dry or oily skin) and specifying areas of skin problems. This information, along with the image file, is sent from the device to the server.

[0539] Step 2:

[0540] The server analyzes the received image file and skin condition information. Using image recognition libraries (such as OpenCV or TensorFlow), it automatically extracts skin type and problem areas from the image data. This process outputs the skin type and specific problems.

[0541] Step 3:

[0542] Based on the analysis results, the server references beauty trends and scientific data to generate the optimal beauty care products and skincare routine for the user. It uses skin type and problem information as input, searches the database for relevant product information, and outputs recommended items and usage instructions.

[0543] Step 4:

[0544] The server sends the generated beauty care suggestions to the user's device. It also references product placement data from the store the user is visiting and generates data that visually displays the location of the suggested products within the store.

[0545] Step 5:

[0546] The terminal displays beauty care suggestions and product placement information received from the server. Specifically, it displays a list of suggested products along with the shelf location of each product as a store map, making it easy for users to find products within the store.

[0547] Step 6:

[0548] The user uses the product based on the suggested skincare routine. The device records this usage information and can accept feedback after use. The feedback information is sent to the server for later analysis.

[0549] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0550] This invention combines an emotional engine with a beauty care system to realize personalized beauty care that takes into account the user's emotional state. The system includes a process for generating optimal beauty care suggestions based on skin condition information, image data, and emotional information provided by the user.

[0551] When a user provides information and images about their skin condition using their device, the device sends that data to a server. The server receives the data and uses image recognition to analyze the user's skin type and problems. The analysis results are then compared with beauty trends and scientific data to select the most suitable skincare routine and products for the user.

[0552] The emotion engine, a key feature of this invention, recognizes emotions from the user's voice, facial expressions, and entered text. This emotional information is also reflected in beauty care suggestions, generating encouraging and supportive messages according to the user's emotional state. By utilizing this emotional information, the invention aims to enhance user motivation and support continuous care.

[0553] For example, if a user types "I'm tired today" through their device, the emotion engine recognizes that emotion as "fatigue." The server takes this information into account, suggests relaxing beauty products, and delivers a message in a gentle tone to the user through their device.

[0554] Furthermore, continuous user feedback and emotional data are stored and re-analyzed on the server. Based on this data, the server adjusts long-term skincare routines and provides strategies to enhance the user's inner and outer beauty.

[0555] Thus, the present invention aims to achieve more effective and personalized beauty care by comprehensively considering the user's mental and physical state through an emotional engine.

[0556] The following describes the processing flow.

[0557] Step 1:

[0558] The user uses their device to input information about their skin condition and, if necessary, take photos of their skin. They also input information about their emotions (voice, facial expressions, text, etc.).

[0559] Step 2:

[0560] The device packages skin condition information, image data, and emotional information collected from the user and sends them to the server.

[0561] Step 3:

[0562] The server analyzes the received data. The image data then enters a process where image recognition tools identify skin type and problems.

[0563] Step 4:

[0564] The server analyzes the received emotional information using an emotion engine. It identifies the user's emotions from voice data, facial expressions, and text, and records the results as emotional data.

[0565] Step 5:

[0566] The server combines skin condition analysis results with emotional data, and selects the optimal beauty care by referring to beauty trends and scientific data. Based on the emotional data, the content and delivery method of the beauty care are adjusted.

[0567] Step 6:

[0568] The server generates personalized beauty care suggestions and sends them to the device. These suggestions include encouraging and supportive messages, as well as products tailored to the user's mood.

[0569] Step 7:

[0570] The device visually displays beauty care suggestions from the server and provides them to the user. Based on the information received, the user implements their daily skincare routine.

[0571] Step 8:

[0572] The user performs the suggested routine and inputs feedback on the results and their feelings via the device. The device then resends this feedback data to the server.

[0573] Step 9:

[0574] The server re-analyzes the feedback and emotional data, and generates adjustments to the long-term skincare routine or new suggestions as needed, which are then resent to the device.

[0575] Through these processes, the system provides comprehensive beauty care that takes into account the user's emotional state, effectively supporting continuous care.

[0576] (Example 2)

[0577] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0578] Traditional beauty care systems primarily rely on user skin condition information and beauty trends for their recommendations, but they lack the ability to provide personalized care suggestions that take into account individual emotional states. This often results in inadequate recommendations tailored to the user's emotional state, negatively impacting the long-term sustainability of their care. Furthermore, the insufficient use of feedback for skincare optimization makes it difficult to propose individually optimized care.

[0579] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0580] In this invention, the server includes means for analyzing skin condition information and image data received from the user, image recognition means for identifying skin type and problems, and emotion recognition means for recognizing emotions from the user's voice, facial expressions, and text, and reflecting them in beauty care suggestions. This enables personalized beauty care suggestions tailored to the user's emotional state, ensuring more personalized care and long-term skincare continuity.

[0581] "Skin condition information received from the user" refers to data indicating the user's skin condition, and is received in image or text format.

[0582] "Image data" refers to visual data that captures the current state of the user's skin, and is sent to the server for analysis and recognition.

[0583] "Personalized beauty care suggestions" refer to personalized beauty product and skincare routine suggestions generated based on the user's specific skin condition and emotions.

[0584] "Image recognition means" refers to technologies used to identify skin type and problems, and includes algorithms and software for analyzing image data.

[0585] "Means of referencing beauty trends and scientific data" refers to technologies for selecting products and routines suitable for the user based on the latest beauty information and scientific findings.

[0586] "Means of tracking progress" refers to a function for checking and recording how far a proposed beauty care routine has progressed.

[0587] "Emotion recognition means" refers to technologies and devices for analyzing and recognizing a user's emotional state from their voice, facial expressions, and text.

[0588] A "generative AI model" is a machine learning model used to output optimal care suggestions based on the user's skin condition and emotional information.

[0589] The embodiments for carrying out this invention are shown below.

[0590] Users take photos of their skin and input information about their skin condition using a mobile device or computer. They may also utilize dedicated application software for this process.

[0591] The terminal transmits the input image data and text information to the server via a secure internet connection. The HTTPS protocol is commonly used for this purpose.

[0592] The server analyzes the received image data using image recognition software (e.g., OpenCV or TensorFlow) to identify skin type and problems. The analysis results are referenced against the latest beauty trends and scientific data stored in a database. A database management system (DBMS) may be used for database comparison.

[0593] Furthermore, emotion recognition software on the server analyzes voice, facial expressions, or text data from the user using NLP techniques to identify the user's emotions. This technique utilizes common voice and image analysis libraries.

[0594] The server uses a generative AI model based on analyzed skin condition and emotional information to generate optimal beauty care suggestions for the user. The generative AI model is constantly retrained based on the latest data and user feedback to improve its accuracy.

[0595] For example, if a user enters "I'm too busy today to spend time on skincare" into their device, the server will suggest an "easy-to-use all-in-one cream" and display an encouraging message: "Let's aim for effective care even in a short amount of time."

[0596] An example of a prompt message would be, "What are the best skincare products for when you're feeling stressed?"

[0597] In this way, the invention makes it possible to provide more personalized and effective beauty care tailored to the individual skin condition and emotional state of each user.

[0598] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0599] Step 1:

[0600] Users input information and images about their skin condition using a mobile device or computer. The input data is prepared as image files and text data by a dedicated application. Users take a picture of their skin using the camera function and input text information such as "My skin is dry today."

[0601] Step 2:

[0602] The terminal sends the input image data and text data to the server. The data is transferred securely using the HTTPS protocol. The image file and text are sent to the server as input data, and this becomes the source data for the next analysis process.

[0603] Step 3:

[0604] The server analyzes the received image data using image recognition software. It utilizes algorithms from OpenCV and TensorFlow to identify the user's skin type and problems. The image data is analyzed pixel by pixel, and the output provides results indicating oily skin, dry skin, or specific skin problem areas.

[0605] Step 4:

[0606] The server analyzes the text data using natural language processing technology. It understands the context related to the user's skin condition from the input text information and extracts relevant keywords. For example, it might extract the keyword "dryness" and output information indicating that the skin needs moisturizing.

[0607] Step 5:

[0608] The server compares the analysis results with beauty trends and scientific data. Using a database management system, it selects beauty products and routines suitable for the user based on the analysis results. Using the analysis results and trend data as input, it obtains "recommended moisturizing creams" as output.

[0609] Step 6:

[0610] The server analyzes the user's voice, facial expressions, and text data using emotion recognition software to identify their emotions. Using NLP (Neuro-Linguistic Programming) technology, it recognizes the emotion "stress" from the user's input text, "I'm feeling down today." It takes voice and text data as input and obtains emotional information as output.

[0611] Step 7:

[0612] The server uses a generation AI model to generate optimal beauty care suggestions based on the user's skin condition and emotional information. The analysis results are input to the generation AI model as prompts, and it outputs a suggestion such as, "Using a moisturizing face mask is good for stress relief."

[0613] Step 8:

[0614] The server generates a proposed care plan and a message of encouragement, and sends it to the user via the device. The server sends the generated message, "You've worked hard today. Let's give your skin some relaxation time," along with recommendations for beauty products, to the device. The user can view this information on the device screen.

[0615] Step 9:

[0616] The user implements the proposed care plan and sends feedback to the server via their device. This feedback is used to optimize future skincare suggestions. The user also enters their feedback on the care product, such as "It was very effective," and sends it to the server.

[0617] The above outlines the specific processing flow within this system.

[0618] (Application Example 2)

[0619] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0620] In providing optimal beauty care tailored to an individual's skin condition and emotions, conventional systems have struggled to offer suggestions that take into account the user's psychological state. Furthermore, there is a lack of concrete means to improve the in-store customer experience.

[0621] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0622] In this invention, the server includes means for analyzing skin condition information and image data received from the user and generating individualized beauty care suggestions; image recognition means for identifying skin type and problems; means for referencing beauty trends and scientific data to suggest products and skincare routines suitable for the user; emotion recognition means for analyzing the user's emotional information and optimizing beauty care suggestions according to their emotional state; and means for presenting customized beauty products and care advice based on emotional information using a digital device. This makes it possible to provide more effective and personalized beauty care that comprehensively considers the user's physical and mental state.

[0623] A "user" refers to an individual who uses this system and is the entity that provides information on skin condition and emotions.

[0624] "Skin condition information" refers to data that represents the detailed condition of the user's skin, and includes image data and text information.

[0625] "Image data" refers to visual information that shows the condition of the user's skin, and through analysis, it can be used to identify skin type and problems.

[0626] "Beauty care suggestions" refer to recommendations that suggest the optimal skincare routine and beauty products based on the user's skin condition and emotional information.

[0627] "Image recognition means" refers to technologies and algorithms used to analyze image data and identify skin type and problems.

[0628] "Beauty trends" refer to information about the latest beauty methods and products, and are data that is referenced when making beauty care suggestions.

[0629] "Scientific data" refers to information based on scientific evidence regarding skincare, and is used to improve the accuracy of recommendations.

[0630] "Emotional information" refers to data that indicates the user's psychological state, and includes voice, facial expressions, and text input.

[0631] "Emotion recognition means" refers to technologies that analyze a user's emotional information and recognize their psychological state.

[0632] A "digital device" is an electronic device that enables interaction with the user and is used for presenting and collecting information.

[0633] "Customized beauty products and care advice" refers to information that suggests beauty products and care methods suitable for specific conditions based on the user's skin condition and emotional information.

[0634] The system implementing this invention is a complex system that provides personalized beauty care suggestions based on the user's skin condition and emotional information. Optimal care is achieved through data exchange between the server, terminal, and user.

[0635] The server first analyzes skin condition information and image data received from the user via the terminal. This analysis uses image recognition algorithms, with TensorFlow being a possible example. Furthermore, natural language processing technology is used as an emotion recognition engine to extract emotional information from the user's voice and text input. OpenAI GPT is an example of this.

[0636] Based on this data, the server selects the optimal skincare products and care advice and sends them back to the device. During this process, it references beauty trends and scientific data to ensure that the selected information is up-to-date and scientifically based. The user's psychological state is also considered, and personalized suggestions are provided based on emotional information.

[0637] Users can visually access beauty care information through their device's digital equipment (e.g., smart glasses or smartphones). These digital devices utilize AR functionality and displays to support user information acquisition.

[0638] As a concrete example, consider a scenario where a user enters a store wearing smart glasses. An example of a prompt message in this case would be: "If fatigue or stress is detected from the customer's current facial expression or voice, quickly generate a message recommending appropriate relaxation beauty products." In this way, advanced care that is tailored to the user's physical and mental state becomes possible.

[0639] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0640] Step 1:

[0641] In a process initiated from a terminal, the user uses a smart device (e.g., smart glasses or a smartphone) to input photos of their skin and audio data. This data is then sent from the terminal to a server. The data input here includes information about skin condition and emotions.

[0642] Step 2:

[0643] The server analyzes the received image data using an image recognition algorithm (e.g., TensorFlow) to identify the user's skin type and any skin problems. This data processing includes pixel-level pigmentation analysis and skin texture recognition. The output is information about skin type and skin problems.

[0644] Step 3:

[0645] The server uses an emotion recognition engine to analyze user emotion information from voice data and input text. This step involves detecting specific keywords and analyzing voice tone to categorize emotional states. The output is an evaluation of the user's emotional state.

[0646] Step 4:

[0647] The server combines analyzed skin condition information and emotional information, and generates beauty care suggestions using a generative AI model. Here, skincare products and advice optimized for the user are selected by cross-referencing with beauty trends and scientific databases. The output consists of a series of beauty care suggestions.

[0648] Step 5:

[0649] The server sends the generated beauty care suggestions to the terminal. The terminal visualizes this information and displays it via the user's smart device using AR functionality or a display screen. This display includes customized product information and specific care procedures. The output is the visual information presented to the user.

[0650] Step 6:

[0651] Users can refer to the provided information and select specific skincare routines and products. Their selections and feedback are sent to the server via their device and stored as additional data to further optimize future suggestions. The output consists of updated user information and feedback.

[0652] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0653] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0654] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0655] [Fourth Embodiment]

[0656] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0657] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0658] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0659] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0660] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0661] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0662] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0663] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0664] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0665] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0666] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0667] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0668] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0669] This invention is configured as a system that allows users to analyze their skin condition and receive optimal beauty care. This system includes a process in which a server generates personalized beauty care suggestions based on skin condition information and image data provided by the user through a terminal.

[0670] Specifically, users input their skin type, problem areas, and past skincare history into their device. They also use the device to take photos of their skin, sending more detailed information to the server. The server receives this data and uses its built-in image recognition technology to identify skin type and problems.

[0671] The server further references accumulated beauty trends and scientific data to select the most suitable products and skincare routines for each user. Based on this, the server generates personalized beauty care suggestions for each user and delivers them to their device.

[0672] For example, if a user has dry skin and redness, the server will use image recognition to check the condition, recommend highly moisturizing products containing hyaluronic acid and ceramides, and then suggest a morning and evening skincare routine using those products.

[0673] Subsequently, the user practices the skincare routine suggested by the device, and the device tracks this. The tracking data and user feedback information are sent back to the server for further analysis. Through this process, the system aims to continuously improve the quality of beauty care and assist users in performing the most optimal care for their individual skin.

[0674] Therefore, the present invention aims to provide reliable information and support that enables users to confidently bring out their own beauty.

[0675] The following describes the processing flow.

[0676] Step 1:

[0677] The user inputs skin condition information into the device and takes photos of their skin using the camera as needed. This allows the device to accumulate the user's data.

[0678] Step 2:

[0679] The device packages skin condition information and image data collected from the user and sends it to the server.

[0680] Step 3:

[0681] The server prepares the data received from the terminal for analysis. For image data, analysis is initiated using image recognition to identify skin type and problems.

[0682] Step 4:

[0683] Based on the analysis results, the server references beauty trends and scientific data to determine the optimal beauty care products and skincare routines for each user. This information is then generated as personalized beauty care recommendations.

[0684] Step 5:

[0685] The server generates beauty care suggestions, which are then sent back to the terminal as a data package.

[0686] Step 6:

[0687] The device analyzes beauty care suggestions received from the server and displays them visually to the user. The user then uses this information to implement their daily skincare routine.

[0688] Step 7:

[0689] The user executes the proposed routine and tracks the routine's progress on their terminal as needed. This tracking information is later resent to the server.

[0690] Step 8:

[0691] When a user enters feedback after using skincare products via their device, the feedback data is sent from the device to the server. The server then re-analyzes this data and uses it to improve the skincare routine.

[0692] Step 9:

[0693] The server uses feedback and tracking data to generate adjustments to the skincare routine as needed, or to generate new suggestions, which are then sent back to the device and presented to the user.

[0694] As a result, users are supported in continuously monitoring changes in their skin and implementing the optimal beauty care routine.

[0695] (Example 1)

[0696] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0697] In today's world, there is a demand for beauty care tailored to each individual user, but conventional systems have struggled to provide personalized care suggestions based on the detailed condition of each user's skin. Furthermore, there has been a lack of mechanisms for continuously evaluating the effectiveness of the suggested care and making improvements based on feedback.

[0698] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0699] In this invention, the server includes means for analyzing skin condition information and image data collected from users to generate individual aesthetic treatment suggestions; means for using image recognition technology to identify skin characteristics and problems; means for referencing trend and scientific information to suggest products and treatment procedures suitable for the user; and means for generating suggestion content using a generative artificial intelligence model. This makes it possible to provide accurate beauty care suggestions based on the user's skin condition and to continuously improve those suggestions.

[0700] A "user" is an individual who uses the system to receive beauty care suggestions based on their skin condition.

[0701] "Skin condition information" refers to information about the user's skin type, problem areas, and past care history.

[0702] "Image data" refers to photographic data taken to understand the detailed condition of the user's skin.

[0703] "Analysis" is the process of processing collected skin condition information and image data to identify the user's skin characteristics and problems.

[0704] "Individualized aesthetic treatment suggestions" refer to suggestions for optimal cosmetic care methods and products identified based on the user's skin condition.

[0705] "Image recognition technology" is a technology that uses captured image data to identify skin characteristics and problems.

[0706] "Trend and scientific information" refers to the latest trends and science-based data in the beauty industry.

[0707] A "generative artificial intelligence model" is a machine learning model that generates appropriate beauty care suggestions based on input information.

[0708] "Tracking" is the process of recording and analyzing the progress of the proposed treatment procedures.

[0709] This system is designed as a platform for users to precisely analyze their skin condition and receive optimal beauty care. Users first input their skin type, problem areas, and past skincare history via a terminal. They then use the terminal to take images of their skin and send them to a server. The server receives this data and uses built-in image recognition technology to identify skin characteristics. This image recognition technology utilizes advanced image analysis software with machine learning algorithms.

[0710] The server then accesses accumulated beauty trend information and scientific databases, and uses a generative AI model to select the most suitable products and skincare routines for the user. A concrete example of a prompt message is, "Please suggest a routine to use with moisturizing products suitable for a user with dry skin." This prompt allows the server to generate personalized beauty care suggestions tailored to each individual user and send them to the device.

[0711] For example, if a user has dry skin with noticeable redness, the server can analyze the image data to identify this condition and recommend products containing highly moisturizing hyaluronic acid and ceramides. Furthermore, a specific morning and evening skincare routine using these products can also be suggested.

[0712] Ultimately, the user performs the suggested care, and the device tracks the progress. The tracking data is sent back to the server for further analysis along with feedback. Through this process, the system continuously improves the quality of beauty care and helps users perform the most appropriate care. A key feature of this system is its ability to always provide the latest and most effective care by using a generative AI model.

[0713] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0714] Step 1:

[0715] The user inputs skin condition data into the device. The user enters their skin type, problem areas, and past skincare history through the device's interface. This input data is temporarily stored on the device before being sent to the server. The entered information is used as basic analysis data in subsequent processing steps.

[0716] Step 2:

[0717] The user takes an image of their skin with their device. The user uses the device's camera to capture a high-resolution image of their skin and sends it to the server. This image data is crucial input data for detailed skin analysis. The image serves as the basic data necessary for processing by image recognition technology on the server side.

[0718] Step 3:

[0719] The server receives the data and performs analysis using image recognition technology. The server receives skin condition information and image data sent by the user and uses its built-in image recognition technology to identify skin characteristics and problems. In this process, image analysis utilizing machine learning algorithms is performed to identify skin type and problem areas. As output, the analysis results are stored in an internal database and used in the subsequent care suggestion generation step.

[0720] Step 4:

[0721] The server uses a generative AI model to create beauty care suggestions. Based on the analyzed data, the server refers to accumulated beauty trends and scientific data, and utilizes the generative AI model to suggest the most suitable products and skincare routines for the user. By giving instructions to the generative AI model in the prompt message, such as "Suggest moisturizing products and usage methods suitable for a user with dry skin," appropriate care suggestions are made. As output, personalized beauty care suggestions are generated.

[0722] Step 5:

[0723] The server generates suggestions and sends them to the terminal, presenting them visually to the user. The beauty care suggestions created by the server are sent to the terminal and visually displayed on the terminal's screen. The user can review these suggestions and learn about specific care procedures and recommended products.

[0724] Step 6:

[0725] The user performs the suggested care, and the device tracks the progress. The user performs the suggested skincare routine daily, and the device records the status of this routine. As input, the user's care routine is collected and stored as tracking data.

[0726] Step 7:

[0727] The device sends tracking data and user feedback to the server, which then performs a re-analysis. Based on the tracked data and feedback, the server performs another analysis and generates optimized beauty care suggestions. This process allows the user to continuously receive the most suitable beauty care. As output, improved suggestions are provided.

[0728] (Application Example 1)

[0729] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0730] Traditional beauty consultations made it difficult for users to accurately understand their own skin condition and to select and purchase appropriate products. Furthermore, selecting products in stores was time-consuming, and receiving personalized, optimized beauty care suggestions required considerable effort and expense.

[0731] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0732] In this invention, the server includes means for analyzing skin condition information and image data received from the user and generating personalized beauty care suggestions; image recognition means for identifying skin type and problems; means for referencing beauty trends and scientific data to suggest products and skincare routines suitable for the user; and visual display means for displaying product placement information based on the analysis results to promote purchase. This allows the user to quickly select and purchase products suitable for them and easily implement effective beauty care.

[0733] "Skin condition information received from the user" refers to information about the user's skin type and problem areas that they input through their device.

[0734] "Image data" refers to photographic data of skin taken by the user using their device.

[0735] "Personalized beauty care suggestions" refer to information that recommends beauty products and skincare routines tailored to a specific user, based on their analyzed skin condition.

[0736] "Image recognition means" refers to a technology used to identify skin type and skin problems, and includes an algorithm for analyzing image data.

[0737] "Beauty trends and scientific data" refers to market data and chemical / medical research findings related to the latest beauty products and care methods.

[0738] A "skincare routine" refers to a series of skincare steps using specific beauty products, with the aim of maintaining and improving skin health.

[0739] "Means of tracking progress" refers to a system that records and manages the progress of the skincare routine that the user is following.

[0740] "Displaying product placement information" means visually guiding users to the recommended locations of beauty products within the store.

[0741] "Visual display means" refers to devices and technologies for providing information to users visually, and includes displays and UI design.

[0742] The system implementing this invention consists of a user, a terminal, and a server. The terminal is a device equipped with a camera and an internet connection. The user uses this terminal to take a photograph of their skin and input skin condition information. This data is transmitted to the server in real time.

[0743] The server first analyzes the data received from the user. Image recognition libraries such as OpenCV and TensorFlow are used for image analysis. This identifies skin type and problem areas. Then, the server uses the collected beauty trends and scientific data to generate optimal beauty care products and skincare routines for each individual user.

[0744] The generated suggestions are visually displayed to the user through the terminal's screen. Furthermore, the location of recommended products is shown to encourage in-store purchases. This allows users to easily find and purchase products within the store.

[0745] As a concrete example, consider a user using this system in a cosmetics store. The user takes a photo of their skin with a terminal and receives an analysis result indicating dry skin. The system recommends a moisturizing cream containing hyaluronic acid and also displays information on where that cream is located on the store's shelves. In this way, the user can quickly find the necessary beauty products and implement appropriate care.

[0746] An example of a prompt message is: "Analyze the skin image taken by the user and suggest the most suitable skincare products. Refer to the database and select and display recommended items within the range of products currently available in the store." By using this prompt message, the generating AI model can generate optimal suggestions in real time based on each user's individual data.

[0747] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0748] Step 1:

[0749] Users take photos of their skin using their device and input information about their skin condition. This input includes selecting their skin type (such as dry or oily skin) and specifying areas of skin problems. This information, along with the image file, is sent from the device to the server.

[0750] Step 2:

[0751] The server analyzes the received image file and skin condition information. Using image recognition libraries (such as OpenCV or TensorFlow), it automatically extracts skin type and problem areas from the image data. This process outputs the skin type and specific problems.

[0752] Step 3:

[0753] Based on the analysis results, the server references beauty trends and scientific data to generate the optimal beauty care products and skincare routine for the user. It uses skin type and problem information as input, searches the database for relevant product information, and outputs recommended items and usage instructions.

[0754] Step 4:

[0755] The server sends the generated beauty care suggestions to the user's device. It also references product placement data from the store the user is visiting and generates data that visually displays the location of the suggested products within the store.

[0756] Step 5:

[0757] The terminal displays beauty care suggestions and product placement information received from the server. Specifically, it displays a list of suggested products along with the shelf location of each product as a store map, making it easy for users to find products within the store.

[0758] Step 6:

[0759] The user uses the product based on the suggested skincare routine. The device records this usage information and can accept feedback after use. The feedback information is sent to the server for later analysis.

[0760] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0761] This invention combines an emotional engine with a beauty care system to realize personalized beauty care that takes into account the user's emotional state. The system includes a process for generating optimal beauty care suggestions based on skin condition information, image data, and emotional information provided by the user.

[0762] When a user provides information and images about their skin condition using their device, the device sends that data to a server. The server receives the data and uses image recognition to analyze the user's skin type and problems. The analysis results are then compared with beauty trends and scientific data to select the most suitable skincare routine and products for the user.

[0763] The emotion engine, a key feature of this invention, recognizes emotions from the user's voice, facial expressions, and entered text. This emotional information is also reflected in beauty care suggestions, generating encouraging and supportive messages according to the user's emotional state. By utilizing this emotional information, the invention aims to enhance user motivation and support continuous care.

[0764] For example, if a user types "I'm tired today" through their device, the emotion engine recognizes that emotion as "fatigue." The server takes this information into account, suggests relaxing beauty products, and delivers a message in a gentle tone to the user through their device.

[0765] Furthermore, continuous user feedback and emotional data are stored and re-analyzed on the server. Based on this data, the server adjusts long-term skincare routines and provides strategies to enhance the user's inner and outer beauty.

[0766] Thus, the present invention aims to achieve more effective and personalized beauty care by comprehensively considering the user's mental and physical state through an emotional engine.

[0767] The following describes the processing flow.

[0768] Step 1:

[0769] The user uses their device to input information about their skin condition and, if necessary, take photos of their skin. They also input information about their emotions (voice, facial expressions, text, etc.).

[0770] Step 2:

[0771] The device packages skin condition information, image data, and emotional information collected from the user and sends them to the server.

[0772] Step 3:

[0773] The server analyzes the received data. The image data then enters a process where image recognition tools identify skin type and problems.

[0774] Step 4:

[0775] The server analyzes the received emotional information using an emotion engine. It identifies the user's emotions from voice data, facial expressions, and text, and records the results as emotional data.

[0776] Step 5:

[0777] The server combines skin condition analysis results with emotional data, and selects the optimal beauty care by referring to beauty trends and scientific data. Based on the emotional data, the content and delivery method of the beauty care are adjusted.

[0778] Step 6:

[0779] The server generates personalized beauty care suggestions and sends them to the device. These suggestions include encouraging and supportive messages, as well as products tailored to the user's mood.

[0780] Step 7:

[0781] The device visually displays beauty care suggestions from the server and provides them to the user. Based on the information received, the user implements their daily skincare routine.

[0782] Step 8:

[0783] The user performs the suggested routine and inputs feedback on the results and their feelings via the device. The device then resends this feedback data to the server.

[0784] Step 9:

[0785] The server re-analyzes the feedback and emotional data, and generates adjustments to the long-term skincare routine or new suggestions as needed, which are then resent to the device.

[0786] Through these processes, the system provides comprehensive beauty care that takes into account the user's emotional state, effectively supporting continuous care.

[0787] (Example 2)

[0788] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0789] Traditional beauty care systems primarily rely on user skin condition information and beauty trends for their recommendations, but they lack the ability to provide personalized care suggestions that take into account individual emotional states. This often results in inadequate recommendations tailored to the user's emotional state, negatively impacting the long-term sustainability of their care. Furthermore, the insufficient use of feedback for skincare optimization makes it difficult to propose individually optimized care.

[0790] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0791] In this invention, the server includes means for analyzing skin condition information and image data received from the user, image recognition means for identifying skin type and problems, and emotion recognition means for recognizing emotions from the user's voice, facial expressions, and text, and reflecting them in beauty care suggestions. This enables personalized beauty care suggestions tailored to the user's emotional state, ensuring more personalized care and long-term skincare continuity.

[0792] "Skin condition information received from the user" refers to data indicating the user's skin condition, and is received in image or text format.

[0793] "Image data" refers to visual data that captures the current state of the user's skin, and is sent to the server for analysis and recognition.

[0794] "Personalized beauty care suggestions" refer to personalized beauty product and skincare routine suggestions generated based on the user's specific skin condition and emotions.

[0795] "Image recognition means" refers to technologies used to identify skin type and problems, and includes algorithms and software for analyzing image data.

[0796] "Means of referencing beauty trends and scientific data" refers to technologies for selecting products and routines suitable for the user based on the latest beauty information and scientific findings.

[0797] "Means of tracking progress" refers to a function for checking and recording how far a proposed beauty care routine has progressed.

[0798] "Emotion recognition means" refers to technologies and devices for analyzing and recognizing a user's emotional state from their voice, facial expressions, and text.

[0799] A "generative AI model" is a machine learning model used to output optimal care suggestions based on the user's skin condition and emotional information.

[0800] The embodiments for carrying out this invention are shown below.

[0801] Users take photos of their skin and input information about their skin condition using a mobile device or computer. They may also utilize dedicated application software for this process.

[0802] The terminal transmits the input image data and text information to the server via a secure internet connection. The HTTPS protocol is commonly used for this purpose.

[0803] The server analyzes the received image data using image recognition software (e.g., OpenCV or TensorFlow) to identify skin type and problems. The analysis results are referenced against the latest beauty trends and scientific data stored in a database. A database management system (DBMS) may be used for database comparison.

[0804] Furthermore, emotion recognition software on the server analyzes voice, facial expressions, or text data from the user using NLP techniques to identify the user's emotions. This technique utilizes common voice and image analysis libraries.

[0805] The server uses a generative AI model based on analyzed skin condition and emotional information to generate optimal beauty care suggestions for the user. The generative AI model is constantly retrained based on the latest data and user feedback to improve its accuracy.

[0806] For example, if a user enters "I'm too busy today to spend time on skincare" into their device, the server will suggest an "easy-to-use all-in-one cream" and display an encouraging message: "Let's aim for effective care even in a short amount of time."

[0807] An example of a prompt message would be, "What are the best skincare products for when you're feeling stressed?"

[0808] In this way, the invention makes it possible to provide more personalized and effective beauty care tailored to the individual skin condition and emotional state of each user.

[0809] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0810] Step 1:

[0811] Users input information and images about their skin condition using a mobile device or computer. The input data is prepared as image files and text data by a dedicated application. Users take a picture of their skin using the camera function and input text information such as "My skin is dry today."

[0812] Step 2:

[0813] The terminal sends the input image data and text data to the server. The data is transferred securely using the HTTPS protocol. The image file and text are sent to the server as input data, and this becomes the source data for the next analysis process.

[0814] Step 3:

[0815] The server analyzes the received image data using image recognition software. It utilizes algorithms from OpenCV and TensorFlow to identify the user's skin type and problems. The image data is analyzed pixel by pixel, and the output provides results indicating oily skin, dry skin, or specific skin problem areas.

[0816] Step 4:

[0817] The server analyzes the text data using natural language processing technology. It understands the context related to the user's skin condition from the input text information and extracts relevant keywords. For example, it might extract the keyword "dryness" and output information indicating that the skin needs moisturizing.

[0818] Step 5:

[0819] The server compares the analysis results with beauty trends and scientific data. Using a database management system, it selects beauty products and routines suitable for the user based on the analysis results. Using the analysis results and trend data as input, it obtains "recommended moisturizing creams" as output.

[0820] Step 6:

[0821] The server analyzes the user's voice, facial expressions, and text data using emotion recognition software to identify their emotions. Using NLP (Neuro-Linguistic Programming) technology, it recognizes the emotion "stress" from the user's input text, "I'm feeling down today." It takes voice and text data as input and obtains emotional information as output.

[0822] Step 7:

[0823] The server uses a generation AI model to generate optimal beauty care suggestions based on the user's skin condition and emotional information. The analysis results are input to the generation AI model as prompts, and it outputs a suggestion such as, "Using a moisturizing face mask is good for stress relief."

[0824] Step 8:

[0825] The server generates a proposed care plan and a message of encouragement, and sends it to the user via the device. The server sends the generated message, "You've worked hard today. Let's give your skin some relaxation time," along with recommendations for beauty products, to the device. The user can view this information on the device screen.

[0826] Step 9:

[0827] The user implements the proposed care plan and sends feedback to the server via their device. This feedback is used to optimize future skincare suggestions. The user also enters their feedback on the care product, such as "It was very effective," and sends it to the server.

[0828] The above outlines the specific processing flow within this system.

[0829] (Application Example 2)

[0830] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0831] In providing optimal beauty care tailored to an individual's skin condition and emotions, conventional systems have struggled to offer suggestions that take into account the user's psychological state. Furthermore, there is a lack of concrete means to improve the in-store customer experience.

[0832] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0833] In this invention, the server includes means for analyzing skin condition information and image data received from the user and generating individualized beauty care suggestions; image recognition means for identifying skin type and problems; means for referencing beauty trends and scientific data to suggest products and skincare routines suitable for the user; emotion recognition means for analyzing the user's emotional information and optimizing beauty care suggestions according to their emotional state; and means for presenting customized beauty products and care advice based on emotional information using a digital device. This makes it possible to provide more effective and personalized beauty care that comprehensively considers the user's physical and mental state.

[0834] A "user" refers to an individual who uses this system and is the entity that provides information on skin condition and emotions.

[0835] "Skin condition information" refers to data that represents the detailed condition of the user's skin, and includes image data and text information.

[0836] "Image data" refers to visual information that shows the condition of the user's skin, and through analysis, it can be used to identify skin type and problems.

[0837] "Beauty care suggestions" refer to recommendations that suggest the optimal skincare routine and beauty products based on the user's skin condition and emotional information.

[0838] "Image recognition means" refers to technologies and algorithms used to analyze image data and identify skin type and problems.

[0839] "Beauty trends" refer to information about the latest beauty methods and products, and are data that is referenced when making beauty care suggestions.

[0840] "Scientific data" refers to information based on scientific evidence regarding skincare, and is used to improve the accuracy of recommendations.

[0841] "Emotional information" refers to data that indicates the user's psychological state, and includes voice, facial expressions, and text input.

[0842] "Emotion recognition means" refers to technologies that analyze a user's emotional information and recognize their psychological state.

[0843] A "digital device" is an electronic device that enables interaction with the user and is used for presenting and collecting information.

[0844] "Customized beauty products and care advice" refers to information that suggests beauty products and care methods suitable for specific conditions based on the user's skin condition and emotional information.

[0845] The system implementing this invention is a complex system that provides personalized beauty care suggestions based on the user's skin condition and emotional information. Optimal care is achieved through data exchange between the server, terminal, and user.

[0846] The server first analyzes skin condition information and image data received from the user via the terminal. This analysis uses image recognition algorithms, with TensorFlow being a possible example. Furthermore, natural language processing technology is used as an emotion recognition engine to extract emotional information from the user's voice and text input. OpenAI GPT is an example of this.

[0847] Based on this data, the server selects the optimal skincare products and care advice and sends them back to the device. During this process, it references beauty trends and scientific data to ensure that the selected information is up-to-date and scientifically based. The user's psychological state is also considered, and personalized suggestions are provided based on emotional information.

[0848] Users can visually access beauty care information through their device's digital equipment (e.g., smart glasses or smartphones). These digital devices utilize AR functionality and displays to support user information acquisition.

[0849] As a concrete example, consider a scenario where a user enters a store wearing smart glasses. An example of a prompt message in this case would be: "If fatigue or stress is detected from the customer's current facial expression or voice, quickly generate a message recommending appropriate relaxation beauty products." In this way, advanced care that is tailored to the user's physical and mental state becomes possible.

[0850] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0851] Step 1:

[0852] In a process initiated from a terminal, the user uses a smart device (e.g., smart glasses or a smartphone) to input photos of their skin and audio data. This data is then sent from the terminal to a server. The data input here includes information about skin condition and emotions.

[0853] Step 2:

[0854] The server analyzes the received image data using an image recognition algorithm (e.g., TensorFlow) to identify the user's skin type and any skin problems. This data processing includes pixel-level pigmentation analysis and skin texture recognition. The output is information about skin type and skin problems.

[0855] Step 3:

[0856] The server uses an emotion recognition engine to analyze user emotion information from voice data and input text. This step involves detecting specific keywords and analyzing voice tone to categorize emotional states. The output is an evaluation of the user's emotional state.

[0857] Step 4:

[0858] The server combines analyzed skin condition information and emotional information, and generates beauty care suggestions using a generative AI model. Here, skincare products and advice optimized for the user are selected by cross-referencing with beauty trends and scientific databases. The output consists of a series of beauty care suggestions.

[0859] Step 5:

[0860] The server sends the generated beauty care suggestions to the terminal. The terminal visualizes this information and displays it via the user's smart device using AR functionality or a display screen. This display includes customized product information and specific care procedures. The output is the visual information presented to the user.

[0861] Step 6:

[0862] Users can refer to the provided information and select specific skincare routines and products. Their selections and feedback are sent to the server via their device and stored as additional data to further optimize future suggestions. The output consists of updated user information and feedback.

[0863] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0864] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0865] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0866] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0867] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0868] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0869] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0870] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0871] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0872] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0873] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0874] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0875] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0876] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0877] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0878] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0879] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0880] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0881] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0882] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0883] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0884] The following is further disclosed regarding the embodiments described above.

[0885] (Claim 1)

[0886] A means for analyzing skin condition information and image data received from a user and generating personalized beauty care suggestions,

[0887] Image recognition means for identifying skin type and problems,

[0888] In order to suggest products and skincare routines suitable for the user, means of referring to beauty trends and scientific data,

[0889] A means of tracking the progress of the proposed skincare routine,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, comprising means for receiving user feedback and reanalyzing it as data for optimizing the skincare routine.

[0893] (Claim 3)

[0894] The system according to claim 1, comprising means for visually displaying suggested beauty care information to the user.

[0895] "Example 1"

[0896] (Claim 1)

[0897] A means for analyzing skin condition information and image data collected from users and generating individual aesthetic treatment suggestions,

[0898] A means of using image recognition technology to identify skin characteristics and problems,

[0899] In order to suggest products and treatment procedures suitable for the user, means of referring to epidemic and scientific information,

[0900] A means of tracking the progress of the proposed treatment procedure,

[0901] A means of generating proposals using a generative artificial intelligence model,

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The system according to claim 1, comprising means for receiving feedback from users and re-analyzing it as information for optimizing treatment procedures.

[0905] (Claim 3)

[0906] The system according to claim 1, comprising means for visually displaying proposed aesthetic treatment information to the user.

[0907] "Application Example 1"

[0908] (Claim 1)

[0909] A means for analyzing skin condition information and image data received from a user and generating personalized beauty care suggestions,

[0910] Image recognition means for identifying skin type and problems,

[0911] In order to suggest products and skincare routines suitable for the user, means of referring to beauty trends and scientific data,

[0912] A means of tracking the progress of the proposed skincare routine,

[0913] A visual display means for displaying item placement information based on analysis results to promote purchase,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, comprising means for receiving user feedback and reanalyzing it as data for optimizing the skincare routine.

[0917] (Claim 3)

[0918] The system according to claim 1, comprising means for visually displaying proposed beauty care information and related article information.

[0919] "Example 2 of combining an emotion engine"

[0920] (Claim 1)

[0921] A means for analyzing skin condition information and image data received from a user and generating personalized beauty care suggestions,

[0922] Image recognition means for identifying skin type and problems,

[0923] In order to suggest products and routines suitable for the user, means of referring to beauty trends and scientific data,

[0924] A means of tracking the progress of the proposed routine,

[0925] A means of recognizing emotions from the user's voice, facial expressions, and text, and reflecting them in beauty care suggestions,

[0926] A means for generating care suggestions based on a generative AI model,

[0927] A system that includes this.

[0928] (Claim 2)

[0929] The system according to claim 1, comprising means for receiving user feedback and re-analyzing it as information for optimizing routines.

[0930] (Claim 3)

[0931] The system according to claim 1, comprising means for visually displaying suggested beauty care information to the user.

[0932] "Application example 2 when combining with an emotional engine"

[0933] (Claim 1)

[0934] A means for analyzing skin condition information and image data received from a user and generating personalized beauty care suggestions,

[0935] Image recognition means for identifying skin type and problems,

[0936] In order to suggest products and skincare routines suitable for the user, means of referring to beauty trends and scientific data,

[0937] A means of tracking the progress of the proposed skincare routine,

[0938] An emotion recognition method for analyzing user emotional information and optimizing beauty care suggestions according to their emotional state,

[0939] A means of presenting customized beauty products and care advice based on emotional information using a digital device,

[0940] A system that includes this.

[0941] (Claim 2)

[0942] The system according to claim 1, comprising means for receiving user feedback and reanalyzing it as data for optimizing the skincare routine.

[0943] (Claim 3)

[0944] The system according to claim 1, comprising means for visually displaying suggested beauty care information to the user. [Explanation of symbols]

[0945] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for analyzing skin condition information and image data received from a user and generating personalized beauty care suggestions, Image recognition means for identifying skin type and problems, In order to suggest products and skincare routines suitable for the user, means of referring to beauty trends and scientific data, A means of tracking the progress of the proposed skincare routine, A system that includes this.

2. The system according to claim 1, comprising means for receiving user feedback and reanalyzing it as data for optimizing the skincare routine.

3. The system according to claim 1, comprising means for visually displaying suggested beauty care information to the user.

Images