System

Abstract

An object of a system according to an embodiment is to enable a user to easily find makeup, a hairstyle, and skin care optimal for the user.SOLUTION: A system includes an image analysis unit, a simulation unit, and a proposal unit. The image analysis unit analyzes the photograph uploaded by the user. The simulation unit simulates makeup, a hairstyle, and a grooming option in real time on the basis of the facial features and the skin condition of the user analyzed by the image analysis unit. The proposal unit proposes personalized skin care or beauty treatment based on a result of the simulation by the simulation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology requires users to go through a lot of trial and error to find the makeup, hairstyle, and skincare that is best for them, which is time-consuming and costly.

[0005] The system according to the embodiment aims to enable users to easily find the best makeup, hairstyle, and skin care for themselves. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit, a simulation unit, and a suggestion unit. The image analysis unit analyzes photos uploaded by a user. The simulation unit simulates makeup, hairstyles, and grooming options in real time based on the user's facial features and skin condition analyzed by the image analysis unit. The suggestion unit suggests personalized skin care and beauty treatments based on the results of the simulation by the simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily find the best makeup, hairstyle, and skin care for themselves. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The Beauty Vision AI system according to an embodiment of the present invention automatically analyzes photos uploaded by users, and the generation AI simulates makeup, hairstyles, and grooming options in real time to suggest personalized skin care and beauty treatments, thereby providing users with optimal beauty solutions.

[0029] A beauty vision AI system according to an embodiment includes an image analysis unit, a simulation unit, and a suggestion unit. The image analysis unit analyzes photos uploaded by a user. For example, the image analysis unit analyzes the user's facial features using a facial recognition algorithm. The image analysis unit can also analyze skin tone and the presence or absence of blemishes to evaluate skin condition. The image analysis unit can also perform analysis taking into account attribute information such as the user's age, gender, and ethnicity. The simulation unit simulates makeup, hairstyles, and grooming options in real time based on the user's facial features and skin condition analyzed by the image analysis unit. For example, the simulation unit uses a generative AI to simulate and display in real time makeup options selected by the user. The simulation unit can also simulate and display in real time hair colors selected by the user. The simulation unit can also simulate and display in real time beard styling selected by the user. The suggestion unit suggests personalized skin care and beauty treatments based on the results of the simulation by the simulation unit. For example, the suggestion unit can suggest skin care products with high moisturizing effects based on the user's skin condition. The suggestion unit can also suggest beauty treatments according to the user's beauty needs. The suggestion unit can also analyze the user's emotional state and suggest beauty options according to the emotion. This allows the beauty vision AI system according to the embodiment to provide optimal beauty solutions to users. For example, users can upload their own photos and try out various beauty options. Users can also check the effects of makeup, hair color changes, beard styling, etc. in real time. Furthermore, users can receive personalized skin care and beauty treatment suggestions.

[0030] The image analysis unit reconstructs the user's facial features as a 3D model, enabling more realistic simulations. The image analysis unit reconstructs facial features as a 3D model, for example, based on a photo uploaded by the user. For example, it analyzes the position of the eyes, the shape of the nose, the contours of the mouth, and other details to generate a 3D model. The image analysis unit also uses the generated 3D model to simulate makeup, hairstyles, and grooming options in real time. For example, lip color and eye shadow can be applied to the 3D model to confirm the three-dimensional effect. The image analysis unit also displays simulation results from different angles based on the 3D model. For example, the user can rotate their face to check the effects of makeup and hairstyles from all directions. This allows the user's facial features to be reconstructed as a 3D model, enabling more realistic simulations.

[0031] The image analysis unit can provide a more personalized simulation by taking into account attribute information such as the user's age, gender, and ethnicity. The image analysis unit collects attribute information such as the user's age, gender, and ethnicity as input data and reflects it in facial feature analysis. For example, it takes into account skin texture and the presence or absence of wrinkles according to age. The image analysis unit also customizes the makeup and hairstyle simulation results based on the attribute information. For example, it suggests makeup options according to gender and hairstyles according to ethnicity. The image analysis unit also utilizes the attribute information to suggest beauty options that are optimal for the user. For example, it offers skin care products according to age and grooming options according to gender. This makes it possible to provide a more personalized simulation by taking into account the user's attribute information.

[0032] The simulation unit can also analyze a full-body photo of the user and simulate fashion and accessories. The simulation unit, for example, simulates fashion items and accessories based on a full-body photo uploaded by the user. For example, it displays the color and design of clothes and the placement of accessories in real time. The simulation unit also analyzes the full-body photo and makes fashion suggestions based on the user's body type and posture. For example, it suggests clothing styles that suit the body type and accessories that improve posture. The simulation unit also suggests optimal coordination for the user based on the simulation results of fashion items and accessories. For example, it displays fashion items that match a specific event or season. In this way, it is possible to simulate fashion and accessories by analyzing the user's full-body photo.

[0033] The simulation unit can enable collaboration with a virtual makeup artist or hairstylist based on the user's facial features. For example, the simulation unit uses generative AI to analyze the user's facial features and simulate beauty options suggested by the virtual makeup artist or hairstylist. For example, it displays makeup styles recommended by professional artists. The simulation unit also provides the user with optimal beauty options through collaboration with the virtual makeup artist or hairstylist. For example, the user can try on lip colors and hairstyles selected by the artists in real time. The simulation unit also proposes beauty options customized by the virtual makeup artist or hairstylist based on the user's facial features. For example, it displays makeup and hairstyles tailored to the user's face shape and skin tone. This enables collaboration with the virtual makeup artist or hairstylist based on the user's facial features.

[0034] The simulation unit can simulate the effect of makeup over time and predict the condition after long-term use. The simulation unit, for example, uses generative AI to simulate the effect of makeup over time. For example, it predicts how long makeup will last from morning to night and displays the condition after long-term use. The simulation unit also takes into account different times of day and activity status when simulating the effect of makeup. For example, it predicts the condition of makeup after exercise or after a meal and displays it in real time. The simulation unit also simulates the effect of makeup over time and suggests the most suitable makeup products for the user. For example, it displays lip colors that last a long time or foundations that don't easily come off. This makes it possible to simulate the effect of makeup over time and predict the condition after long-term use.

[0035] The simulation unit can provide more realistic results by taking into account different lighting conditions (natural light, indoor light, night light, etc.) when simulating makeup effects. For example, the simulation unit can provide more realistic results by taking into account different lighting conditions when simulating makeup effects. For example, the simulation unit can display in real time how makeup looks under lighting conditions such as natural light, indoor light, and night light. The simulation unit can also reflect lighting conditions in the simulation to suggest optimal makeup options to the user. For example, the simulation unit can display lip color under natural light and eye shadow under indoor light. The simulation unit can also simulate makeup effects based on different lighting conditions to provide realistic results to the user. For example, the simulation unit can display makeup options suitable for daytime outings and nighttime events. In this way, more realistic makeup simulation results can be provided by taking into account different lighting conditions.

[0036] The simulation unit can suggest related fashion items and accessories based on the makeup options selected by the user. For example, the simulation unit uses generative AI to suggest related fashion items and accessories based on the makeup options selected by the user. For example, earrings that match lip color and necklaces that match eye shadow are displayed. The simulation unit also simulates combinations of makeup options and fashion items to suggest optimal coordination for the user. For example, fashion items that match a specific event or season are displayed. The simulation unit also displays related fashion items and accessories in real time based on the user's makeup options. For example, a dress that matches lip color and a bag that matches eye shadow are suggested. This allows the simulation unit to suggest related fashion items and accessories based on the makeup options selected by the user.

[0037] When simulating the makeup effect, the simulation unit can consider the user's skin condition (dry, oily, sensitive skin, etc.) and suggest optimal makeup products. The simulation unit, for example, analyzes the user's skin condition and suggests makeup products based on the results. For example, it displays a moisturizing foundation suitable for dry skin or a matte lip color suitable for oily skin. The simulation unit also simulates the makeup effect by considering the skin condition. For example, it suggests a mild eye shadow suitable for sensitive skin or a moisturizing blush suitable for dry skin. The simulation unit also displays optimal makeup products in real time based on the user's skin condition. For example, it suggests a moisturizing lip balm suitable for dry skin or a matte foundation suitable for oily skin. In this way, optimal makeup products can be suggested by considering the user's skin condition.

[0038] The simulation unit can simulate hair color changes over time and predict color fading and the need for maintenance. The simulation unit, for example, uses generative AI to simulate hair color changes over time. For example, it predicts color fading and the need for maintenance after several weeks and displays the results in real time. The simulation unit also takes into account different times of day and activity conditions when simulating hair color changes. For example, it predicts the condition of hair color after swimming in a pool or sunbathing and displays the results in real time. The simulation unit also simulates hair color changes over time and recommends the most suitable hair color product for the user. For example, it displays hair colors that last a long time and hair colors that are less likely to fade. This makes it possible to simulate hair color changes over time and predict color fading and the need for maintenance.

[0039] The simulation unit can provide more realistic results by taking different lighting conditions (natural light, indoor light, night light, etc.) into account when simulating a hair color change. For example, the simulation unit can provide more realistic results by taking different lighting conditions into account when simulating a hair color change. For example, the simulation unit can display in real time how the hair color will look under lighting conditions such as natural light, indoor light, and night light. The simulation unit can also incorporate lighting conditions into the simulation to suggest optimal hair color options to the user. For example, the simulation unit can display blonde hair under natural light and brown hair under indoor light. The simulation unit can also provide realistic results by simulating a hair color change based on different lighting conditions. For example, the simulation unit can display hair color options suitable for daytime outings and nighttime events. This allows for more realistic hair color simulation results to be provided by taking different lighting conditions into account.

[0040] The simulation unit can suggest related hairstyles and hair accessories based on the hair color selected by the user. For example, the simulation unit uses a generative AI to suggest related hairstyles and hair accessories based on the hair color selected by the user. For example, it displays hairstyles that suit blonde hair and hair accessories that suit brown hair. The simulation unit also simulates combinations of hair colors and hairstyles to suggest optimal coordination for the user. For example, it displays hairstyles and hair accessories that suit a specific event or season. The simulation unit also displays related hairstyles and hair accessories in real time based on the user's hair color. For example, it suggests a curl style that suits blonde hair and a hairpin that suits brown hair. This makes it possible to suggest related hairstyles and hair accessories based on the hair color selected by the user.

[0041] When simulating a hair color change, the simulation unit can consider the user's hair type (straight hair, curly hair, thin hair, etc.) and suggest the most suitable hair color product. The simulation unit, for example, analyzes the user's hair type and suggests hair color products based on the results. For example, it displays hair colors suitable for straight hair and hair colors suitable for curly hair. The simulation unit also simulates a hair color change taking hair type into consideration. For example, it suggests hair colors suitable for thin hair and hair colors suitable for thick hair. The simulation unit also displays the most suitable hair color product in real time based on the user's hair type. For example, it suggests hair colors suitable for straight hair and hair colors suitable for curly hair. In this way, it is possible to suggest the most suitable hair color product by considering the user's hair type.

[0042] The simulation unit can simulate beard styling over time and predict the growth process and the need for maintenance. The simulation unit, for example, uses generative AI to simulate beard styling over time. For example, it predicts the beard growth process and maintenance need several weeks from now and displays it in real time. The simulation unit also takes into account different times of day and activity conditions when simulating beard styling. For example, it predicts the condition of the beard after exercise or after a meal and displays it in real time. The simulation unit also simulates beard styling over time and suggests beard styling products that are optimal for the user. For example, it displays beard styling products that last a long time and beard styling products that are easy to maintain. This makes it possible to simulate beard styling over time and predict the growth process and the need for maintenance.

[0043] The simulation unit can provide more realistic results by taking into account different lighting conditions (natural light, indoor light, night light, etc.) when simulating beard styling. For example, the simulation unit can provide more realistic results by taking into account different lighting conditions when simulating beard styling. For example, the simulation unit can display in real time how a beard will look under lighting conditions such as natural light, indoor light, and night light. The simulation unit can also incorporate lighting conditions into the simulation to suggest optimal beard styling options to the user. For example, the simulation unit can display a full beard under natural light and a goatee under indoor light. The simulation unit can also provide realistic results by simulating beard styling based on different lighting conditions. For example, the simulation unit can display beard styling options suitable for daytime outings and nighttime events. This allows for more realistic beard styling simulation results to be provided by taking into account different lighting conditions.

[0044] The simulation unit can suggest related grooming products and accessories based on the beard styling selected by the user. For example, the simulation unit uses generative AI to suggest related grooming products and accessories based on the beard styling selected by the user. For example, it displays beard oil that suits a full beard or a beard brush that suits a goatee. The simulation unit also simulates combinations of beard styling and grooming products to suggest optimal coordination for the user. For example, it displays grooming products and accessories that suit a specific event or season. The simulation unit also displays related grooming products and accessories in real time based on the user's beard styling. For example, it suggests beard oil that suits a full beard or a beard brush that suits a goatee. This makes it possible to suggest related grooming products and accessories based on the beard styling selected by the user.

[0045] When simulating beard styling, the simulation unit can consider the user's facial shape and bone structure and suggest optimal styling options. The simulation unit, for example, analyzes the user's facial shape and bone structure and suggests beard styling options based on the results. For example, it displays a goatee suitable for a round face or a full beard suitable for an angular face. The simulation unit also simulates beard styling taking the facial shape and bone structure into consideration. For example, it suggests a mustache suitable for a long face or a goatee suitable for a wide jaw. The simulation unit also displays optimal beard styling options in real time based on the user's facial shape and bone structure. For example, it suggests a goatee suitable for a round face or a full beard suitable for an angular face. In this way, it is possible to suggest optimal beard styling options by considering the user's facial shape and bone structure.

[0046] The suggestion unit can monitor the user's skin condition over time and make skin care suggestions according to changes in the season and environment. The suggestion unit, for example, uses a generative AI to monitor the user's skin condition over time and make skin care suggestions according to changes in the season and environment. For example, it suggests skin care products with high moisturizing effects during the dry winter months. The suggestion unit also analyzes the user's skin condition in real time and makes skin care suggestions according to changes in the season and environment. For example, it suggests sunscreen to protect against UV rays in the summer. The suggestion unit also monitors the user's skin condition over time and makes skin care suggestions according to changes in the season and environment in real time. For example, it suggests skin care products for sensitive skin to protect against pollen in the spring. In this way, it is possible to monitor the user's skin condition over time and make skin care suggestions according to changes in the season and environment.

[0047] When analyzing the user's skin condition, the suggestion unit takes into account lifestyle information such as diet and lifestyle habits, and can make more personalized skin care suggestions. The suggestion unit, for example, collects lifestyle information such as the user's diet and lifestyle habits, and makes skin care suggestions based on the results. For example, if the user has an unbalanced diet, the suggestion unit suggests skin care products containing vitamin C. The suggestion unit also analyzes the user's skin condition based on the lifestyle information and makes personalized skin care suggestions. For example, if the user is under a lot of stress, the suggestion unit suggests skin care products with a relaxing effect. The suggestion unit also takes into account the user's lifestyle information and suggests skin care products in real time. For example, if the user has been sleep-deprived, the suggestion unit suggests a night cream with a high moisturizing effect. In this way, more personalized skin care suggestions can be made by taking into account the user's lifestyle information.

[0048] The suggestion unit can suggest relevant makeup products and hair care products based on the user's skin condition. For example, the suggestion unit uses generative AI to suggest relevant makeup products and hair care products based on the user's skin condition. For example, it displays a moisturizing foundation suitable for dry skin or a hair mask suitable for damaged hair. The suggestion unit also analyzes the user's skin condition and suggests makeup products and hair care products based on the results. For example, it displays a matte lip color suitable for oily skin or a volume-boosting shampoo suitable for fine hair. The suggestion unit also suggests relevant makeup products and hair care products in real time based on the user's skin condition. For example, it displays a mild eye shadow suitable for sensitive skin or a moisturizing lip balm suitable for dry skin. This makes it possible to suggest relevant makeup products and hair care products based on the user's skin condition.

[0049] When analyzing the user's skin condition, the suggestion unit takes into account genetic information and past medical history, allowing for more accurate cosmetic treatment suggestions. The suggestion unit, for example, collects the user's genetic information and past medical history, and suggests cosmetic treatments based on the results. For example, if the user has a genetic tendency toward dry skin, the suggestion unit suggests cosmetic treatments with high moisturizing effects. The suggestion unit also analyzes the user's skin condition based on the genetic information and medical history, and suggests more accurate cosmetic treatments. For example, the suggestion unit suggests cosmetic treatments that avoid ingredients that have caused allergic reactions in the past. The suggestion unit also suggests cosmetic treatments in real time, taking into account the user's genetic information and medical history. For example, if the user is genetically prone to developing age spots, the suggestion unit suggests cosmetic treatments to address age spots. In this way, by taking into account the user's genetic information and medical history, more accurate cosmetic treatment suggestions can be made.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The simulation unit can enable collaboration with a virtual makeup artist or hairstylist based on the user's facial features. For example, it uses generative AI to analyze the user's facial features and simulate beauty options suggested by the virtual makeup artist or hairstylist. For example, it displays makeup styles recommended by professional artists. The simulation unit also provides the user with optimal beauty options through collaboration with the virtual makeup artist or hairstylist. For example, the user can try on lip colors and hairstyles selected by the artist in real time. The simulation unit also proposes beauty options customized by the virtual makeup artist or hairstylist based on the user's facial features. For example, it displays makeup and hairstyles tailored to the user's face shape and skin tone. This enables collaboration with the virtual makeup artist or hairstylist based on the user's facial features.

[0052] The simulation unit can also analyze a full-body photo of the user and simulate fashion and accessories. For example, it simulates fashion items and accessories based on a full-body photo uploaded by the user. For example, it displays the color and design of clothes and the placement of accessories in real time. The simulation unit also analyzes the full-body photo and makes fashion suggestions based on the user's body type and posture. For example, it suggests clothing styles that suit the body type and accessories that improve posture. The simulation unit also suggests optimal coordination for the user based on the simulation results of fashion items and accessories. For example, it displays fashion items that match a specific event or season. In this way, it is possible to simulate fashion and accessories by analyzing a full-body photo of the user.

[0053] The simulation unit can simulate the effect of makeup over time and predict the condition after extended use. For example, it uses generative AI to simulate the effect of makeup over time. For example, it predicts how long makeup will last from morning to night and displays the condition after extended use. The simulation unit also takes into account different times of day and activity status when simulating the effect of makeup. For example, it predicts the condition of makeup after exercise or a meal and displays it in real time. The simulation unit also simulates the effect of makeup over time and suggests the most suitable makeup products for the user. For example, it displays lip colors that last a long time or foundations that don't easily smudge. This makes it possible to simulate the effect of makeup over time and predict the condition after extended use.

[0054] The simulation unit can provide more realistic results by taking different lighting conditions (natural light, indoor light, night light, etc.) into account when simulating makeup effects. For example, different lighting conditions can be taken into account when simulating makeup effects. For example, the simulation unit displays in real time how the makeup will look under lighting conditions such as natural light, indoor light, and night light. The simulation unit also reflects lighting conditions in the simulation to suggest optimal makeup options to the user. For example, it displays lip color under natural light and eye shadow under indoor light. The simulation unit also simulates makeup effects based on different lighting conditions to provide realistic results to the user. For example, it displays makeup options suitable for going out in the daytime or for a nighttime event. In this way, more realistic makeup simulation results can be provided by taking different lighting conditions into account.

[0055] The simulation unit can suggest related fashion items and accessories based on the makeup options selected by the user. For example, using generative AI, it suggests related fashion items and accessories based on the makeup options selected by the user. For example, it displays earrings that match the lip color and a necklace that matches the eye shadow. The simulation unit also simulates combinations of makeup options and fashion items to suggest optimal coordination for the user. For example, it displays fashion items that match a specific event or season. The simulation unit also displays related fashion items and accessories in real time based on the user's makeup options. For example, it suggests a dress that matches the lip color and a bag that matches the eye shadow. This makes it possible to suggest related fashion items and accessories based on the makeup options selected by the user.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The image analysis unit analyzes the photo uploaded by the user. For example, it uses a facial recognition algorithm to analyze the user's facial features and evaluate skin tone and the presence or absence of blemishes. It can also take into account the user's demographic information, such as age, gender, and ethnicity. Step 2: The simulation unit simulates makeup, hairstyles, and grooming options in real time based on the user's facial features and skin condition analyzed by the image analysis unit. For example, using generative AI, the system displays the makeup options, hair color, and beard styling selected by the user in real time. Step 3: The suggestion unit suggests personalized skin care and beauty treatments based on the results of the simulation by the simulation unit. For example, the suggestion unit can suggest skin care products and beauty treatments with high moisturizing effects based on the user's skin condition, and can also suggest beauty options according to the user's emotional state.

[0058] (Example 2) The Beauty Vision AI system according to an embodiment of the present invention automatically analyzes photos uploaded by users, and the generation AI simulates makeup, hairstyles, and grooming options in real time to suggest personalized skin care and beauty treatments, thereby providing users with optimal beauty solutions.

[0059] A beauty vision AI system according to an embodiment includes an image analysis unit, a simulation unit, and a suggestion unit. The image analysis unit analyzes photos uploaded by a user. For example, the image analysis unit analyzes the user's facial features using a facial recognition algorithm. The image analysis unit can also analyze skin tone and the presence or absence of blemishes to evaluate skin condition. The image analysis unit can also perform analysis taking into account attribute information such as the user's age, gender, and ethnicity. The simulation unit simulates makeup, hairstyles, and grooming options in real time based on the user's facial features and skin condition analyzed by the image analysis unit. For example, the simulation unit uses a generative AI to simulate and display in real time makeup options selected by the user. The simulation unit can also simulate and display in real time hair colors selected by the user. The simulation unit can also simulate and display in real time beard styling selected by the user. The suggestion unit suggests personalized skin care and beauty treatments based on the results of the simulation by the simulation unit. For example, the suggestion unit can suggest skin care products with high moisturizing effects based on the user's skin condition. The suggestion unit can also suggest beauty treatments according to the user's beauty needs. The suggestion unit can also analyze the user's emotional state and suggest beauty options according to the emotion. This allows the beauty vision AI system according to the embodiment to provide optimal beauty solutions to users. For example, users can upload their own photos and try out various beauty options. Users can also check the effects of makeup, hair color changes, beard styling, etc. in real time. Furthermore, users can receive personalized skin care and beauty treatment suggestions.

[0060] The image analysis unit reconstructs the user's facial features as a 3D model, enabling more realistic simulations. The image analysis unit reconstructs facial features as a 3D model, for example, based on a photo uploaded by the user. For example, it analyzes the position of the eyes, the shape of the nose, the contours of the mouth, and other details to generate a 3D model. The image analysis unit also uses the generated 3D model to simulate makeup, hairstyles, and grooming options in real time. For example, lip color and eye shadow can be applied to the 3D model to confirm the three-dimensional effect. The image analysis unit also displays simulation results from different angles based on the 3D model. For example, the user can rotate their face to check the effects of makeup and hairstyles from all directions. This allows the user's facial features to be reconstructed as a 3D model, enabling more realistic simulations.

[0061] The image analysis unit can provide a more personalized simulation by taking into account attribute information such as the user's age, gender, and ethnicity. The image analysis unit collects attribute information such as the user's age, gender, and ethnicity as input data and reflects it in facial feature analysis. For example, it takes into account skin texture and the presence or absence of wrinkles according to age. The image analysis unit also customizes the makeup and hairstyle simulation results based on the attribute information. For example, it suggests makeup options according to gender and hairstyles according to ethnicity. The image analysis unit also utilizes the attribute information to suggest beauty options that are optimal for the user. For example, it offers skin care products according to age and grooming options according to gender. This makes it possible to provide a more personalized simulation by taking into account the user's attribute information.

[0062] The simulation unit analyzes the user's facial expressions and emotions and reflects them in the simulation results, thereby suggesting beauty options that correspond to the emotions. The simulation unit, for example, uses an emotion estimation function to analyze the user's facial expressions and emotions in real time. For example, it calculates an emotion score based on changes in facial expressions and suggests beauty options. The simulation unit also uses the emotion estimation function to monitor the user's emotions when trying out beauty options and makes suggestions that elicit positive emotions. For example, it prioritizes displaying makeup options that bring joy to the user. The simulation unit also customizes the simulation results based on the user's emotional data. For example, it suggests a hairstyle that suits the user when they are relaxed or a beard style that suits them when they are excited. In this way, by analyzing the user's facial expressions and emotions, it is possible to suggest beauty options that correspond to the user's emotions.

[0063] The simulation unit can also analyze a full-body photo of the user and simulate fashion and accessories. The simulation unit, for example, simulates fashion items and accessories based on a full-body photo uploaded by the user. For example, it displays the color and design of clothes and the placement of accessories in real time. The simulation unit also analyzes the full-body photo and makes fashion suggestions based on the user's body type and posture. For example, it suggests clothing styles that suit the body type and accessories that improve posture. The simulation unit also suggests optimal coordination for the user based on the simulation results of fashion items and accessories. For example, it displays fashion items that match a specific event or season. In this way, it is possible to simulate fashion and accessories by analyzing the user's full-body photo.

[0064] The simulation unit can enable collaboration with a virtual makeup artist or hairstylist based on the user's facial features. For example, the simulation unit uses generative AI to analyze the user's facial features and simulate beauty options suggested by the virtual makeup artist or hairstylist. For example, it displays makeup styles recommended by professional artists. The simulation unit also provides the user with optimal beauty options through collaboration with the virtual makeup artist or hairstylist. For example, the user can try on lip colors and hairstyles selected by the artists in real time. The simulation unit also proposes beauty options customized by the virtual makeup artist or hairstylist based on the user's facial features. For example, it displays makeup and hairstyles tailored to the user's face shape and skin tone. This enables collaboration with the virtual makeup artist or hairstylist based on the user's facial features.

[0065] The simulation unit can monitor the emotions of the user when trying out beauty options in real time and make suggestions that will elicit positive emotions. For example, the simulation unit can monitor the emotions of the user when trying out beauty options in real time and make suggestions that will elicit positive emotions. For example, it can prioritize the display of makeup options that bring joy to the user. The simulation unit can also use its emotion estimation function to analyze the user's emotional data and suggest beauty options that will elicit positive emotions. For example, it can suggest a hairstyle that suits the user when they are relaxed or a beard style that suits the user when they are excited. The simulation unit can also customize the suggested beauty options based on the user's emotional responses. For example, it can suggest a lip color that suits the user when they are smiling or an eye shadow that suits a serious expression. This allows the simulation unit to monitor the emotions of the user when trying out beauty options in real time and make suggestions that will elicit positive emotions.

[0066] The simulation unit can simulate the effect of makeup over time and predict the condition after long-term use. The simulation unit, for example, uses generative AI to simulate the effect of makeup over time. For example, it predicts how long makeup will last from morning to night and displays the condition after long-term use. The simulation unit also takes into account different times of day and activity status when simulating the effect of makeup. For example, it predicts the condition of makeup after exercise or after a meal and displays it in real time. The simulation unit also simulates the effect of makeup over time and suggests the most suitable makeup products for the user. For example, it displays lip colors that last a long time or foundations that don't easily come off. This makes it possible to simulate the effect of makeup over time and predict the condition after long-term use.

[0067] The simulation unit can provide more realistic results by taking into account different lighting conditions (natural light, indoor light, night light, etc.) when simulating makeup effects. For example, the simulation unit can provide more realistic results by taking into account different lighting conditions when simulating makeup effects. For example, the simulation unit can display in real time how makeup looks under lighting conditions such as natural light, indoor light, and night light. The simulation unit can also reflect lighting conditions in the simulation to suggest optimal makeup options to the user. For example, the simulation unit can display lip color under natural light and eye shadow under indoor light. The simulation unit can also simulate makeup effects based on different lighting conditions to provide realistic results to the user. For example, the simulation unit can display makeup options suitable for daytime outings and nighttime events. In this way, more realistic makeup simulation results can be provided by taking into account different lighting conditions.

[0068] The simulation unit can use the emotion estimation function to analyze the emotions of the user when trying out makeup and suggest makeup options according to the emotions. For example, the simulation unit can use the emotion estimation function to analyze the emotions of the user when trying out makeup and suggest makeup options based on the results. For example, the simulation unit can display lip colors that make the user feel happy or eye shadow that suits a relaxed user. The simulation unit can also customize makeup options based on the user's emotion data. For example, the simulation unit can suggest cheek colors that suit a user when they are excited or eyeliner that suits a serious expression. The simulation unit can also use the emotion estimation function to monitor the user's emotional reactions in real time and suggest makeup options that elicit positive emotions. For example, the simulation unit can display lip colors that suit a user when they are smiling or eye shadow that suits a user when they are relaxed. In this way, the simulation unit can suggest makeup options according to the emotions by analyzing the user's emotions.

[0069] The simulation unit can suggest related fashion items and accessories based on the makeup options selected by the user. For example, the simulation unit uses generative AI to suggest related fashion items and accessories based on the makeup options selected by the user. For example, earrings that match lip color and necklaces that match eye shadow are displayed. The simulation unit also simulates combinations of makeup options and fashion items to suggest optimal coordination for the user. For example, fashion items that match a specific event or season are displayed. The simulation unit also displays related fashion items and accessories in real time based on the user's makeup options. For example, a dress that matches lip color and a bag that matches eye shadow are suggested. This allows the simulation unit to suggest related fashion items and accessories based on the makeup options selected by the user.

[0070] When simulating the makeup effect, the simulation unit can consider the user's skin condition (dry, oily, sensitive skin, etc.) and suggest optimal makeup products. The simulation unit, for example, analyzes the user's skin condition and suggests makeup products based on the results. For example, it displays a moisturizing foundation suitable for dry skin or a matte lip color suitable for oily skin. The simulation unit also simulates the makeup effect by considering the skin condition. For example, it suggests a mild eye shadow suitable for sensitive skin or a moisturizing blush suitable for dry skin. The simulation unit also displays optimal makeup products in real time based on the user's skin condition. For example, it suggests a moisturizing lip balm suitable for dry skin or a matte foundation suitable for oily skin. In this way, optimal makeup products can be suggested by considering the user's skin condition.

[0071] The simulation unit can use the emotion estimation function to monitor the emotions of the user when trying out makeup in real time and make suggestions that will elicit positive emotions. For example, the simulation unit can monitor the emotions of the user when trying out makeup in real time and make suggestions that will elicit positive emotions. For example, it can display lip colors that make the user feel happy or eye shadow that suits a user when they are relaxed. The simulation unit can also use the emotion estimation function to analyze the user's emotion data and suggest makeup options that will elicit positive emotions. For example, it can suggest a cheek color that suits a user when they are excited or an eyeliner that suits a user with a serious expression. The simulation unit can also customize the suggested makeup options based on the user's emotional response. For example, it can suggest a lip color that suits a user when they are smiling or an eye shadow that suits a user when they are relaxed. In this way, the simulation unit can monitor the emotions of the user when trying out makeup in real time and make suggestions that will elicit positive emotions.

[0072] The simulation unit can simulate hair color changes over time and predict color fading and the need for maintenance. The simulation unit, for example, uses generative AI to simulate hair color changes over time. For example, it predicts color fading and the need for maintenance after several weeks and displays the results in real time. The simulation unit also takes into account different times of day and activity conditions when simulating hair color changes. For example, it predicts the condition of hair color after swimming in a pool or sunbathing and displays the results in real time. The simulation unit also simulates hair color changes over time and recommends the most suitable hair color product for the user. For example, it displays hair colors that last a long time and hair colors that are less likely to fade. This makes it possible to simulate hair color changes over time and predict color fading and the need for maintenance.

[0073] The simulation unit can provide more realistic results by taking different lighting conditions (natural light, indoor light, night light, etc.) into account when simulating a hair color change. For example, the simulation unit can provide more realistic results by taking different lighting conditions into account when simulating a hair color change. For example, the simulation unit can display in real time how the hair color will look under lighting conditions such as natural light, indoor light, and night light. The simulation unit can also incorporate lighting conditions into the simulation to suggest optimal hair color options to the user. For example, the simulation unit can display blonde hair under natural light and brown hair under indoor light. The simulation unit can also provide realistic results by simulating a hair color change based on different lighting conditions. For example, the simulation unit can display hair color options suitable for daytime outings and nighttime events. This allows for more realistic hair color simulation results to be provided by taking different lighting conditions into account.

[0074] The simulation unit can use the emotion estimation function to analyze the emotions of the user when trying out hair colors and suggest hair color options according to the emotions. For example, the simulation unit can use the emotion estimation function to analyze the emotions of the user when trying out hair colors and suggest hair color options based on the results. For example, it can display blonde hair that makes the user feel happy or brown hair that suits a relaxed user. The simulation unit also customizes hair color options based on the user's emotion data. For example, it can suggest red hair that suits a user when excited or black hair that suits a user with a serious expression. The simulation unit also uses the emotion estimation function to monitor the user's emotional responses in real time and suggest hair color options that elicit positive emotions. For example, it can display blonde hair that suits a user when smiling or brown hair that suits a user when relaxed. In this way, by analyzing the user's emotions, it can suggest hair color options according to the emotions.

[0075] The simulation unit can suggest related hairstyles and hair accessories based on the hair color selected by the user. For example, the simulation unit uses a generative AI to suggest related hairstyles and hair accessories based on the hair color selected by the user. For example, it displays hairstyles that suit blonde hair and hair accessories that suit brown hair. The simulation unit also simulates combinations of hair colors and hairstyles to suggest optimal coordination for the user. For example, it displays hairstyles and hair accessories that suit a specific event or season. The simulation unit also displays related hairstyles and hair accessories in real time based on the user's hair color. For example, it suggests a curl style that suits blonde hair and a hairpin that suits brown hair. This makes it possible to suggest related hairstyles and hair accessories based on the hair color selected by the user.

[0076] When simulating a hair color change, the simulation unit can consider the user's hair type (straight hair, curly hair, thin hair, etc.) and suggest the most suitable hair color product. The simulation unit, for example, analyzes the user's hair type and suggests hair color products based on the results. For example, it displays hair colors suitable for straight hair and hair colors suitable for curly hair. The simulation unit also simulates a hair color change taking hair type into consideration. For example, it suggests hair colors suitable for thin hair and hair colors suitable for thick hair. The simulation unit also displays the most suitable hair color product in real time based on the user's hair type. For example, it suggests hair colors suitable for straight hair and hair colors suitable for curly hair. In this way, it is possible to suggest the most suitable hair color product by considering the user's hair type.

[0077] The simulation unit can use the emotion estimation function to monitor the user's emotions in real time when trying out hair colors and make suggestions that will elicit positive emotions. For example, the simulation unit can monitor the user's emotions in real time when trying out hair colors and make suggestions that will elicit positive emotions. For example, it can display blonde hair that makes the user feel happy or brown hair that looks good when the user is relaxed. The simulation unit can also use the emotion estimation function to analyze the user's emotion data and suggest hair color options that will elicit positive emotions. For example, it can suggest red hair that suits the user when they are excited or black hair that suits a serious expression. The simulation unit can also customize the suggested hair color options based on the user's emotional response. For example, it can suggest blonde hair that looks good when the user is smiling or brown hair that looks good when the user is relaxed. This allows the simulation unit to monitor the user's emotions in real time when trying out hair colors and make suggestions that will elicit positive emotions.

[0078] The simulation unit can simulate beard styling over time and predict the growth process and the need for maintenance. The simulation unit, for example, uses generative AI to simulate beard styling over time. For example, it predicts the beard growth process and maintenance need several weeks from now and displays it in real time. The simulation unit also takes into account different times of day and activity conditions when simulating beard styling. For example, it predicts the condition of the beard after exercise or after a meal and displays it in real time. The simulation unit also simulates beard styling over time and suggests beard styling products that are optimal for the user. For example, it displays beard styling products that last a long time and beard styling products that are easy to maintain. This makes it possible to simulate beard styling over time and predict the growth process and the need for maintenance.

[0079] The simulation unit can provide more realistic results by taking into account different lighting conditions (natural light, indoor light, night light, etc.) when simulating beard styling. For example, the simulation unit can provide more realistic results by taking into account different lighting conditions when simulating beard styling. For example, the simulation unit can display in real time how a beard will look under lighting conditions such as natural light, indoor light, and night light. The simulation unit can also incorporate lighting conditions into the simulation to suggest optimal beard styling options to the user. For example, the simulation unit can display a full beard under natural light and a goatee under indoor light. The simulation unit can also provide realistic results by simulating beard styling based on different lighting conditions. For example, the simulation unit can display beard styling options suitable for daytime outings and nighttime events. This allows for more realistic beard styling simulation results to be provided by taking into account different lighting conditions.

[0080] The simulation unit can use the emotion estimation function to analyze the emotions of the user when trying out beard styling and suggest beard styling options according to the emotions. For example, the simulation unit can use the emotion estimation function to analyze the emotions of the user when trying out beard styling and suggest beard styling options based on the results. For example, it can display a full beard that makes the user feel happy or a goatee that suits when the user is relaxed. The simulation unit also customizes beard styling options based on the user's emotion data. For example, it can suggest a mustache that suits when the user is excited or a goatee that suits when the user has a serious expression. The simulation unit also uses the emotion estimation function to monitor the user's emotional reactions in real time and suggest beard styling options that elicit positive emotions. For example, it can display a full beard that suits when the user is smiling or a goatee that suits when the user is relaxed. In this way, by analyzing the user's emotions, it can suggest beard styling options according to the emotions.

[0081] The simulation unit can suggest related grooming products and accessories based on the beard styling selected by the user. For example, the simulation unit uses generative AI to suggest related grooming products and accessories based on the beard styling selected by the user. For example, it displays beard oil that suits a full beard or a beard brush that suits a goatee. The simulation unit also simulates combinations of beard styling and grooming products to suggest optimal coordination for the user. For example, it displays grooming products and accessories that suit a specific event or season. The simulation unit also displays related grooming products and accessories in real time based on the user's beard styling. For example, it suggests beard oil that suits a full beard or a beard brush that suits a goatee. This makes it possible to suggest related grooming products and accessories based on the beard styling selected by the user.

[0082] When simulating beard styling, the simulation unit can consider the user's facial shape and bone structure and suggest optimal styling options. The simulation unit, for example, analyzes the user's facial shape and bone structure and suggests beard styling options based on the results. For example, it displays a goatee suitable for a round face or a full beard suitable for an angular face. The simulation unit also simulates beard styling taking the facial shape and bone structure into consideration. For example, it suggests a mustache suitable for a long face or a goatee suitable for a wide jaw. The simulation unit also displays optimal beard styling options in real time based on the user's facial shape and bone structure. For example, it suggests a goatee suitable for a round face or a full beard suitable for an angular face. In this way, it is possible to suggest optimal beard styling options by considering the user's facial shape and bone structure.

[0083] The simulation unit can use the emotion estimation function to monitor the user's emotions in real time when trying out different beard styles and make suggestions that will elicit positive emotions. For example, the simulation unit can monitor the user's emotions in real time when trying out different beard styles and make suggestions that will elicit positive emotions. For example, it can display a full beard that makes the user feel happy or a goatee that suits the user when relaxed. The simulation unit can also use the emotion estimation function to analyze the user's emotion data and suggest beard styling options that will elicit positive emotions. For example, it can suggest a mustache that suits the user when they are excited or a goatee that suits a serious expression. The simulation unit can also customize the suggested beard styling options based on the user's emotional response. For example, it can suggest a full beard that suits the user when they are smiling or a goatee that suits the user when they are relaxed. This allows the simulation unit to monitor the user's emotions in real time when trying out different beard styles and make suggestions that will elicit positive emotions.

[0084] The suggestion unit can monitor the user's skin condition over time and make skin care suggestions according to changes in the season and environment. The suggestion unit, for example, uses a generative AI to monitor the user's skin condition over time and make skin care suggestions according to changes in the season and environment. For example, it suggests skin care products with high moisturizing effects during the dry winter months. The suggestion unit also analyzes the user's skin condition in real time and makes skin care suggestions according to changes in the season and environment. For example, it suggests sunscreen to protect against UV rays in the summer. The suggestion unit also monitors the user's skin condition over time and makes skin care suggestions according to changes in the season and environment in real time. For example, it suggests skin care products for sensitive skin to protect against pollen in the spring. In this way, it is possible to monitor the user's skin condition over time and make skin care suggestions according to changes in the season and environment.

[0085] When analyzing the user's skin condition, the suggestion unit takes into account lifestyle information such as diet and lifestyle habits, and can make more personalized skin care suggestions. The suggestion unit, for example, collects lifestyle information such as the user's diet and lifestyle habits, and makes skin care suggestions based on the results. For example, if the user has an unbalanced diet, the suggestion unit suggests skin care products containing vitamin C. The suggestion unit also analyzes the user's skin condition based on the lifestyle information and makes personalized skin care suggestions. For example, if the user is under a lot of stress, the suggestion unit suggests skin care products with a relaxing effect. The suggestion unit also takes into account the user's lifestyle information and suggests skin care products in real time. For example, if the user has been sleep-deprived, the suggestion unit suggests a night cream with a high moisturizing effect. In this way, more personalized skin care suggestions can be made by taking into account the user's lifestyle information.

[0086] The suggestion unit can use the emotion estimation function to analyze the user's emotional state and make skin care suggestions according to stress or fatigue. The suggestion unit, for example, uses the emotion estimation function to analyze the user's emotional state and make skin care suggestions according to stress or fatigue. For example, if the user is under a lot of stress, the suggestion unit suggests skin care products with a relaxing effect. The suggestion unit also customizes skin care products based on the user's emotional data. For example, if the user is feeling fatigued, the suggestion unit suggests skin care products with a refreshing effect. The suggestion unit also uses the emotion estimation function to monitor the user's emotional response in real time and suggest skin care products according to stress or fatigue. For example, skin care products suitable for when the user is relaxed are displayed. In this way, by analyzing the user's emotional state, skin care suggestions according to stress or fatigue can be made.

[0087] The suggestion unit can suggest relevant makeup products and hair care products based on the user's skin condition. For example, the suggestion unit uses generative AI to suggest relevant makeup products and hair care products based on the user's skin condition. For example, it displays a moisturizing foundation suitable for dry skin or a hair mask suitable for damaged hair. The suggestion unit also analyzes the user's skin condition and suggests makeup products and hair care products based on the results. For example, it displays a matte lip color suitable for oily skin or a volume-boosting shampoo suitable for fine hair. The suggestion unit also suggests relevant makeup products and hair care products in real time based on the user's skin condition. For example, it displays a mild eye shadow suitable for sensitive skin or a moisturizing lip balm suitable for dry skin. This makes it possible to suggest relevant makeup products and hair care products based on the user's skin condition.

[0088] When analyzing the user's skin condition, the suggestion unit takes into account genetic information and past medical history, allowing for more accurate cosmetic treatment suggestions. The suggestion unit, for example, collects the user's genetic information and past medical history, and suggests cosmetic treatments based on the results. For example, if the user has a genetic tendency toward dry skin, the suggestion unit suggests cosmetic treatments with high moisturizing effects. The suggestion unit also analyzes the user's skin condition based on the genetic information and medical history, and suggests more accurate cosmetic treatments. For example, the suggestion unit suggests cosmetic treatments that avoid ingredients that have caused allergic reactions in the past. The suggestion unit also suggests cosmetic treatments in real time, taking into account the user's genetic information and medical history. For example, if the user is genetically prone to developing age spots, the suggestion unit suggests cosmetic treatments to address age spots. In this way, by taking into account the user's genetic information and medical history, more accurate cosmetic treatment suggestions can be made.

[0089] The suggestion unit can use the emotion estimation function to monitor the user's emotional state in real time and make skin care suggestions that will elicit positive emotions. The suggestion unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time and make skin care suggestions that will elicit positive emotions. For example, it displays skin care products that are suitable for when the user is relaxed. The suggestion unit also customizes skin care products based on the user's emotional data. For example, it suggests skin care products with scents that the user finds pleasing or skin care products that have a refreshing effect. The suggestion unit also uses the emotion estimation function to monitor the user's emotional responses in real time and make skin care suggestions that will elicit positive emotions. For example, it displays skin care products that are suitable for when the user is relaxed. This makes it possible to monitor the user's emotional state in real time and make skin care suggestions that will elicit positive emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The simulation unit can enable collaboration with a virtual makeup artist or hairstylist based on the user's facial features. For example, it uses generative AI to analyze the user's facial features and simulate beauty options suggested by the virtual makeup artist or hairstylist. For example, it displays makeup styles recommended by professional artists. The simulation unit also provides the user with optimal beauty options through collaboration with the virtual makeup artist or hairstylist. For example, the user can try on lip colors and hairstyles selected by the artist in real time. The simulation unit also proposes beauty options customized by the virtual makeup artist or hairstylist based on the user's facial features. For example, it displays makeup and hairstyles tailored to the user's face shape and skin tone. This enables collaboration with the virtual makeup artist or hairstylist based on the user's facial features.

[0092] The simulation unit can also analyze a full-body photo of the user and simulate fashion and accessories. For example, it simulates fashion items and accessories based on a full-body photo uploaded by the user. For example, it displays the color and design of clothes and the placement of accessories in real time. The simulation unit also analyzes the full-body photo and makes fashion suggestions based on the user's body type and posture. For example, it suggests clothing styles that suit the body type and accessories that improve posture. The simulation unit also suggests optimal coordination for the user based on the simulation results of fashion items and accessories. For example, it displays fashion items that match a specific event or season. In this way, it is possible to simulate fashion and accessories by analyzing a full-body photo of the user.

[0093] The simulation unit can simulate the effect of makeup over time and predict the condition after extended use. For example, it uses generative AI to simulate the effect of makeup over time. For example, it predicts how long makeup will last from morning to night and displays the condition after extended use. The simulation unit also takes into account different times of day and activity status when simulating the effect of makeup. For example, it predicts the condition of makeup after exercise or a meal and displays it in real time. The simulation unit also simulates the effect of makeup over time and suggests the most suitable makeup products for the user. For example, it displays lip colors that last a long time or foundations that don't easily smudge. This makes it possible to simulate the effect of makeup over time and predict the condition after extended use.

[0094] The simulation unit can provide more realistic results by taking different lighting conditions (natural light, indoor light, night light, etc.) into account when simulating makeup effects. For example, different lighting conditions can be taken into account when simulating makeup effects. For example, the simulation unit displays in real time how the makeup will look under lighting conditions such as natural light, indoor light, and night light. The simulation unit also reflects lighting conditions in the simulation to suggest optimal makeup options to the user. For example, it displays lip color under natural light and eye shadow under indoor light. The simulation unit also simulates makeup effects based on different lighting conditions to provide realistic results to the user. For example, it displays makeup options suitable for going out in the daytime or for a nighttime event. In this way, more realistic makeup simulation results can be provided by taking different lighting conditions into account.

[0095] The simulation unit can suggest related fashion items and accessories based on the makeup options selected by the user. For example, using generative AI, it suggests related fashion items and accessories based on the makeup options selected by the user. For example, it displays earrings that match the lip color and a necklace that matches the eye shadow. The simulation unit also simulates combinations of makeup options and fashion items to suggest optimal coordination for the user. For example, it displays fashion items that match a specific event or season. The simulation unit also displays related fashion items and accessories in real time based on the user's makeup options. For example, it suggests a dress that matches the lip color and a bag that matches the eye shadow. This makes it possible to suggest related fashion items and accessories based on the makeup options selected by the user.

[0096] The simulation unit analyzes the user's facial expressions and emotions and reflects them in the simulation results, thereby suggesting beauty options that match those emotions. For example, the emotion estimation function is used to analyze the user's facial expressions and emotions in real time. For example, an emotion score is calculated based on changes in facial expressions, and beauty options are suggested. The simulation unit also uses the emotion estimation function to monitor the user's emotions when trying out beauty options and make suggestions that elicit positive emotions. For example, makeup options that bring joy to the user are preferentially displayed. The simulation unit also customizes the simulation results based on the user's emotional data. For example, it suggests a hairstyle that suits the user when they are relaxed, or a beard style that suits the user when they are excited. In this way, beauty options that match the user's emotions can be suggested by analyzing the user's facial expressions and emotions.

[0097] The simulation unit can monitor the user's emotions in real time when trying out beauty options and make suggestions that will elicit positive emotions. For example, the simulation unit can monitor the user's emotions in real time when trying out beauty options and make suggestions that will elicit positive emotions. For example, the simulation unit can prioritize displaying makeup options that bring joy to the user. The simulation unit can also use its emotion estimation function to analyze the user's emotional data and suggest beauty options that will elicit positive emotions. For example, the simulation unit can suggest a hairstyle that suits the user when they are relaxed or a beard style that suits the user when they are excited. The simulation unit can also customize the suggested beauty options based on the user's emotional responses. For example, the simulation unit can suggest a lip color that suits the user when they are smiling or an eyeshadow that suits a serious expression. This allows the simulation unit to monitor the user's emotions in real time when trying out beauty options and make suggestions that will elicit positive emotions.

[0098] The simulation unit can use the emotion estimation function to analyze the emotions of a user when trying out makeup and suggest makeup options according to the emotions. For example, the emotion estimation function can be used to analyze the emotions of a user when trying out makeup and suggest makeup options based on the results. For example, lip colors that make the user feel happy or eye shadow that suits a relaxed user can be displayed. The simulation unit also customizes makeup options based on the user's emotion data. For example, it can suggest a cheek color that suits a user when they are excited or an eyeliner that suits a serious expression. The simulation unit also uses the emotion estimation function to monitor the user's emotional reactions in real time and suggest makeup options that elicit positive emotions. For example, it can display lip colors that suit a user when they are smiling or eye shadow that suits a user when they are relaxed. In this way, makeup options according to the emotions can be suggested by analyzing the user's emotions.

[0099] The simulation unit can use the emotion estimation function to analyze the emotions of the user when trying out hair colors and suggest hair color options according to the emotions. For example, the emotion estimation function can be used to analyze the emotions of the user when trying out hair colors and suggest hair color options based on the results. For example, blonde hair that makes the user feel happy or brown hair that suits a relaxed user can be displayed. The simulation unit also customizes hair color options based on the user's emotional data. For example, red hair that suits a user when excited or black hair that suits a user with a serious expression can be displayed. The simulation unit also uses the emotion estimation function to monitor the user's emotional responses in real time and suggest hair color options that elicit positive emotions. For example, blonde hair that suits a user when smiling or brown hair that suits a user when relaxed can be displayed. In this way, by analyzing the user's emotions, hair color options according to the emotions can be suggested.

[0100] The suggestion unit can use the emotion estimation function to analyze the user's emotional state and make skin care suggestions according to stress or fatigue. For example, the emotion estimation function can be used to analyze the user's emotional state and make skin care suggestions according to stress or fatigue. For example, if the user is under a lot of stress, skin care products with a relaxing effect can be suggested. The suggestion unit also customizes skin care products based on the user's emotional data. For example, if the user is feeling fatigued, skin care products with a refreshing effect can be suggested. The suggestion unit also uses the emotion estimation function to monitor the user's emotional response in real time and suggest skin care products according to stress or fatigue. For example, skin care products suitable for when the user is relaxed can be displayed. In this way, by analyzing the user's emotional state, skin care suggestions according to stress or fatigue can be made.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The image analysis unit analyzes the photo uploaded by the user. For example, it uses a facial recognition algorithm to analyze the user's facial features and evaluate skin tone and the presence or absence of blemishes. It can also take into account the user's demographic information, such as age, gender, and ethnicity. Step 2: The simulation unit simulates makeup, hairstyles, and grooming options in real time based on the user's facial features and skin condition analyzed by the image analysis unit. For example, using generative AI, the system displays the makeup options, hair color, and beard styling selected by the user in real time. Step 3: The suggestion unit suggests personalized skin care and beauty treatments based on the results of the simulation by the simulation unit. For example, the suggestion unit can suggest skin care products and beauty treatments with high moisturizing effects based on the user's skin condition, and can also suggest beauty options according to the user's emotional state.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image analysis unit that analyzes photos uploaded by users; a simulation unit that simulates makeup, hairstyles, and grooming options in real time based on the facial features and skin condition of the user analyzed by the image analysis unit; a suggestion unit that suggests personalized skin care and beauty treatments based on the results of the simulation by the simulation unit. A system characterized by:

2. The image analysis unit Reconstruct the user's facial features as a 3D model to achieve a more realistic simulation.

2. The system of claim 1.

3. The image analysis unit Provide a more personalized simulation by taking into account the user's demographic information such as age, gender, and ethnicity.

2. The system of claim 1.

4. The simulation unit By analyzing the user's facial expressions and emotions and reflecting them in the simulation results, beauty options are suggested according to the user's emotions.

2. The system of claim 1.

5. The simulation unit It also analyzes the user's full-body photo and simulates fashion and accessories.

2. The system of claim 1.

6. The simulation unit Collaboration with a virtual makeup artist or hair stylist based on the user's facial features 2. The system of claim 1.

Images