System

Abstract

An object of a system according to an embodiment is to enable a user to easily find an optimal skin care or makeup method for the user.SOLUTION: A system includes a question acquisition unit, an answer analysis unit, and a proposal generation unit. The question acquisition unit acquires a question from a user. The answer analysis unit analyzes the user's answer on the basis of the question acquired by the question acquisition unit. The proposal generation unit proposes an optimal skin care or makeup method based on the answer analyzed by the answer analysis 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 has made it difficult for users to find the skin care and makeup methods that are best suited to them.

[0005] The system according to the embodiment aims to enable users to easily find the skin care and makeup methods that are best suited to them. [Means for solving the problem]

[0006] The system according to the embodiment includes a question acquisition unit, an answer analysis unit, and a proposal generation unit. The question acquisition unit acquires a question from a user. The answer analysis unit analyzes the user's answer based on the question acquired by the question acquisition unit. The proposal generation unit proposes an optimal skin care or makeup method based on the answer analyzed by the answer analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily find the skin care and makeup methods that are best suited to them. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) A generative AI system according to an embodiment of the present invention is a system that helps users find skincare and makeup methods that suit them. Through a questionnaire format and facial photo analysis, this system allows anyone to easily achieve professional-looking makeup and expand their potential. As a result, the generative AI system provides users with optimal skincare and makeup methods, allowing them to maximize their potential.

[0029] The generative AI system according to the embodiment includes a question acquisition unit, an answer analysis unit, and a proposal generation unit. The question acquisition unit acquires a question from a user. For example, the user may input a question such as "What is your skin type?", and the question acquisition unit acquires the question. The question acquisition unit can also acquire a user's question using voice input. For example, the user may ask "What cosmetics do you usually use?" and the question acquisition unit acquires the question. The answer analysis unit analyzes the user's answer based on the question acquired by the question acquisition unit. For example, if the user answers "I have dry skin," the answer analysis unit analyzes the answer and identifies the user's skin type. The answer analysis unit can also analyze the user's answer using natural language processing technology. For example, if the user answers "My usual cosmetics are moisturizing cream," the answer analysis unit analyzes the answer and identifies the cosmetics used by the user. The proposal generation unit suggests optimal skin care and makeup methods based on the answer analyzed by the answer analysis unit. For example, if the user has dry skin, the proposal generation unit suggests a skin care method with high moisturizing effects. The suggestion generation unit can also suggest skin care and makeup methods that are tailored to the user's lifestyle and daily activities. For example, if the user spends a lot of time outdoors, the suggestion generation unit can suggest products with high sunscreen effects. This allows the AI ​​system according to the embodiment to suggest optimal skin care and makeup methods based on the user's questions.

[0030] The question acquisition unit dynamically changes questions based on the user's past answer history, allowing for more personalized suggestions. The question acquisition unit, for example, analyzes the history of questions the user has previously answered and dynamically generates new questions based on that history. For example, for a user who previously answered that they have dry skin, a detailed question about moisturizing is added. The question acquisition unit can also generate questions tailored to the user's preferences and needs based on the user's answer history. For example, if the user has previously preferred a particular brand of cosmetics, a question about that brand is added. This allows for dynamically changing questions based on the user's past answer history, allowing for more personalized suggestions.

[0031] The suggestion generation unit can suggest skin care or makeup methods that match the user's lifestyle or daily activities. For example, the suggestion generation unit estimates the user's lifestyle from the user's answers and suggests skin care or makeup methods that match that lifestyle. For example, for a user who spends a lot of time outdoors, the suggestion generation unit suggests products with high sun protection effects. The suggestion generation unit can also suggest skin care or makeup methods based on the user's daily activities. For example, if the user is busy, the suggestion generation unit suggests a makeup method that can be done in a short amount of time. This makes it possible to suggest skin care or makeup methods that match the user's lifestyle and daily activities.

[0032] The question acquisition unit supports voice input, and allows the user to receive suggestions by answering questions by voice. The question acquisition unit uses, for example, voice recognition technology to build a system in which the user answers questions by voice. For example, when a user answers by voice, "My skin type is dry skin," the question acquisition unit recognizes the voice and acquires the answer. The question acquisition unit can also convert voice into text in real time and make suggestions based on the text. For example, when a user answers by voice, "The cosmetic product I usually use is moisturizing cream," the question acquisition unit converts the voice into text and acquires the answer. This allows the user to receive suggestions by answering questions by voice.

[0033] The question acquisition unit is compatible with a chatbot format and can suggest skin care or makeup methods to the user through natural conversation. The question acquisition unit, for example, builds a chatbot-style question system that allows the user to receive suggestions of skin care or makeup methods through natural conversation. For example, if a user inputs "My skin has been dry lately" in a chat, the question acquisition unit analyzes the message and suggests an appropriate skin care method. The question acquisition unit can also smoothly progress a conversation with the user using a dialogue management system. For example, if a user asks, "What cosmetics do you usually use?", the question acquisition unit provides an appropriate answer to the question. This allows the user to receive suggestions of skin care or makeup methods through natural conversation.

[0034] The facial photo analysis unit can track changes in the user's skin over time and propose a long-term skin care plan. The facial photo analysis unit, for example, analyzes facial photos provided by the user periodically and builds a system that tracks changes in skin over time. For example, it analyzes changes in skin tone and blemishes and proposes a long-term skin care plan. The facial photo analysis unit can also continuously monitor the user's skin condition and update the skin care plan according to those changes. For example, it can analyze changes in skin by season and propose a skin care plan based on that. This makes it possible to track changes in the user's skin over time and propose a long-term skin care plan.

[0035] The facial photo analysis unit can create a 3D model of the user's facial features and suggest more detailed skin care or makeup methods. The facial photo analysis unit, for example, creates a 3D model of the user's facial features based on a facial photo, and builds a system that suggests detailed skin care and makeup methods based on that model. For example, it can suggest makeup methods that match the facial contours and bone structure. The facial photo analysis unit can also use 3D scanning technology to analyze the user's facial features in detail. For example, it can analyze the shape of the face and the texture of the skin, and suggest skin care and makeup methods based on that. This allows the user's facial features to be created as a 3D model, and more detailed skin care and makeup methods to be suggested.

[0036] The facial photo analysis unit can perform more accurate analysis using photos taken under different lighting conditions or angles. The facial photo analysis unit, for example, analyzes facial photos taken under different lighting conditions or angles to build a system that suggests more accurate skin care and makeup methods. For example, it analyzes photos taken under natural light and artificial light. The facial photo analysis unit can also analyze the user's facial features in detail based on photos taken from different angles. For example, it analyzes photos taken from the front and from an angle and suggests skin care and makeup methods based on the results. This allows for more accurate analysis using photos taken under different lighting conditions and angles.

[0037] The facial photo analysis unit can customize the analysis results of the facial photo to match the makeup style or fashion style selected by the user. For example, the facial photo analysis unit builds a system that suggests skin care and makeup methods that match the makeup style or fashion style selected by the user based on the analysis results of the facial photo. For example, it suggests makeup methods that match a casual style. The facial photo analysis unit can also customize skin care and makeup methods according to the user's preferences and needs. For example, if the user is attending a specific event, it suggests makeup methods that match the event. This allows the analysis results of the facial photo to be customized to match the makeup style or fashion style selected by the user.

[0038] The facial photo analysis unit can learn the techniques of professional makeup artists and perform makeup simulations in real time based on a facial photo of the user. The facial photo analysis unit, for example, builds a system that learns the techniques of professional makeup artists and performs makeup simulations in real time based on a facial photo of the user. For example, it simulates a specific makeup style and suggests it to the user. The facial photo analysis unit can also simulate makeup on the user's face using real-time rendering technology. For example, it simulates a makeup style selected by the user in real time and displays the results to the user. This allows the system to learn the techniques of professional makeup artists and perform makeup simulations in real time based on a facial photo of the user.

[0039] The facial photo analysis unit can analyze the user's facial features in detail and suggest makeup methods that match each individual feature. The facial photo analysis unit, for example, builds a system that analyzes the user's facial features in detail and suggests makeup methods that match those features. For example, it can suggest makeup methods that match the facial contours and bone structure. The facial photo analysis unit can also analyze the facial shape and skin texture and suggest makeup methods based on that. For example, it can suggest a foundation that matches the user's skin tone. This makes it possible to analyze the user's facial features in detail and suggest makeup methods that match each individual feature.

[0040] The proposal generation unit can propose a customized makeup method taking into consideration the user's preferences and past makeup history. The proposal generation unit, for example, considers the user's preferences and past makeup history and builds a system that proposes a customized makeup method based on that information. For example, the proposal generation unit re-proposes makeup styles that were popular in the past. The proposal generation unit can also customize makeup methods according to the user's preferences and needs. For example, if the user likes a particular color, the proposal generation unit proposes a makeup method based on that color. In this way, a customized makeup method can be proposed taking into consideration the user's preferences and past makeup history.

[0041] The suggestion generation unit can consider the user's skin care history and make consistent suggestions for skin care and makeup. The suggestion generation unit, for example, considers the user's skin care history and builds a system that makes consistent suggestions for skin care and makeup based on that information. For example, it suggests makeup techniques based on skin care products used in the past. The suggestion generation unit can also customize both skin care and makeup based on the user's skin care history. For example, if the user has sensitive skin, it suggests mild skin care products and makeup techniques. This makes it possible to consider the user's skin care history and make consistent suggestions for skin care and makeup.

[0042] The suggestion generation unit can dynamically customize skin care or makeup methods based on the user's lifestyle or daily activities. The suggestion generation unit, for example, analyzes the user's lifestyle and daily activities and builds a system that dynamically customizes skin care and makeup methods based on that information. For example, for a user who spends a lot of time outdoors, the suggestion generation unit can suggest products with high sun protection effects. The suggestion generation unit can also customize skin care and makeup methods according to the user's lifestyle and daily activities. For example, if the user is busy, the suggestion generation unit can suggest makeup methods that can be done in a short amount of time. This makes it possible to dynamically customize skin care and makeup methods based on the user's lifestyle and daily activities.

[0043] The suggestion generation unit can learn the user's past skincare and makeup history and make optimal customization suggestions. The suggestion generation unit, for example, builds a system that learns the user's past skincare and makeup history and makes optimal customization suggestions based on that information. For example, suggestions are made based on the effects of products used in the past. The suggestion generation unit can also customize skincare and makeup methods based on the user's past history. For example, if a user prefers products from a particular brand, suggestions are made based on products from that brand. This makes it possible to learn the user's past skincare and makeup history and make optimal customization suggestions.

[0044] The suggestion generation unit can suggest skin care or makeup methods taking into account the user's dietary habits or exercise habits. The suggestion generation unit, for example, analyzes the user's dietary habits and exercise habits and builds a system that suggests skin care or makeup methods based on that information. For example, a skin care product containing a specific vitamin is suggested to a user who eats a nutritionally balanced diet. The suggestion generation unit can also suggest skin care or makeup methods based on the user's exercise habits. For example, a post-exercise skin care method is suggested. This makes it possible to suggest skin care or makeup methods taking into account the user's dietary habits and exercise habits.

[0045] The suggestion generation unit can suggest skin care or makeup methods according to the season or climate of the user. The suggestion generation unit, for example, builds a system that considers seasonal and climate changes to suggest optimal skin care and makeup methods to the user. For example, it suggests moisturizing products suitable for the dry climate of winter. The suggestion generation unit can also suggest skin care and makeup methods based on the climate of the user's place of residence. For example, it suggests skin care methods with less oil to a user living in a humid region. This makes it possible to suggest skin care and makeup methods according to the season and climate of the user.

[0046] The proposal generation unit can continuously learn skincare and makeup methods based on user feedback and improve the accuracy of its proposals. For example, the proposal generation unit collects user feedback and builds a system that continuously learns skincare and makeup methods based on that information. For example, by providing feedback on the results of a user trying a proposed method, the proposal generation unit learns that information and reflects it in future proposals. The proposal generation unit can also develop algorithms to improve the accuracy of proposals based on user feedback. For example, it can analyze user ratings and comments and improve proposals based on that information. This allows the system to continuously learn skincare and makeup methods based on user feedback and improve the accuracy of its proposals.

[0047] The proposal generation unit can track a user's skincare and makeup history over the long term and make optimal improvement suggestions. The proposal generation unit can, for example, build a system that tracks a user's skincare and makeup history over the long term and makes optimal improvement suggestions based on that information. For example, suggestions can be made based on the effects of products used in the past. The proposal generation unit can also continuously improve skincare and makeup methods based on the user's history. For example, if a user has been using a particular product for a long period of time, the effect of that product can be analyzed and reflected in proposals from the next time onwards. This makes it possible to track a user's skincare and makeup history over the long term and make optimal improvement suggestions.

[0048] The proposal generation unit can compare data from different users and identify common areas for improvement. The proposal generation unit can, for example, build a system that compares data from different users and identifies common areas for improvement based on that data. For example, it can analyze the skin care and makeup histories of multiple users and identify common areas for improvement. The proposal generation unit can also normalize user data and extract common features based on that data. For example, it can identify common areas for improvement for a specific skin type and make suggestions based on that information. This makes it possible to compare data from different users and identify common areas for improvement.

[0049] The suggestion generation unit can suggest skin care or makeup methods taking into account the user's living environment or stress level. The suggestion generation unit, for example, considers the user's living environment and stress level and builds a system that suggests skin care or makeup methods based on that information. For example, it analyzes the climate and lifestyle habits of the user's place of residence and makes suggestions based on that information. The suggestion generation unit can also analyze the user's stress level and suggest skin care or makeup methods according to that level. For example, if the user is feeling high stress, the suggestion generation unit suggests a skin care method that has a relaxing effect. In this way, skin care or makeup methods can be suggested taking into account the user's living environment and stress level.

[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 suggestion generation unit can suggest skin care and makeup methods taking into account the user's diet and exercise habits. For example, if the user eats a nutritionally balanced diet, the suggestion generation unit can suggest skin care products containing vitamins and minerals based on the diet. Also, if the user exercises regularly, the suggestion generation unit can suggest post-exercise skin care methods. Furthermore, if the user is allergic to a specific food ingredient, the suggestion generation unit can take that information into account and suggest products that do not cause allergies.

[0052] The proposal generator can suggest skin care and makeup methods according to the user's season and climate. For example, it can suggest moisturizing products for dry winter climates and less oily products for hot summer climates. It can also suggest skin care methods that take into account the effects of humidity and ultraviolet rays based on the climate of the user's place of residence. It can also track seasonal changes in the skin and update the skin care plan accordingly.

[0053] The suggestion generator can dynamically customize skin care and makeup methods based on the user's lifestyle and daily activities. For example, it can suggest products with high sun protection to a user who spends a lot of time outdoors, and suggest makeup methods that can be done quickly to a busy user. It can also suggest nighttime skin care methods for a user who works the night shift. It can also suggest makeup methods for weekends or special events that suit the user's lifestyle.

[0054] The suggestion generator can learn the user's past skincare and makeup history and provide optimal customized suggestions based on that information. For example, suggestions can be made based on the effects of products used in the past, and if the user prefers products from a particular brand, suggestions can be made based on that brand's products. It can also customize makeup techniques according to the user's preferences and needs. It can also re-suggest makeup styles the user has tried in the past.

[0055] The suggestion generation unit can suggest skin care and makeup methods taking into account the user's living environment and stress level. For example, it can analyze the climate and lifestyle habits of the user's place of residence and make suggestions based on that information. It can also analyze the user's stress level and suggest skin care and makeup methods according to that level. Furthermore, if the user is feeling high stress, it can suggest skin care methods that have a relaxing effect.

[0056] The suggestion generator can compare data from different users and identify common areas for improvement based on that data. For example, it can analyze the skin care and makeup histories of multiple users to identify common areas for improvement. It can also normalize user data and extract common features based on that data. It can also identify common areas for improvement across specific skin types and make suggestions based on that information.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The question acquisition unit acquires a question from the user. For example, when the user inputs a question such as "What is your skin type?", the question acquisition unit acquires the question. The question acquisition unit can also acquire the user's question using voice input. For example, when the user asks by voice, "What cosmetics do you usually use?", the question acquisition unit acquires the question. Step 2: The answer analysis unit analyzes the user's answer based on the question acquired by the question acquisition unit. For example, if the user answers "I have dry skin," the answer analysis unit analyzes the answer and identifies the user's skin type. The answer analysis unit can also analyze the user's answer using natural language processing technology. For example, if the user answers "The cosmetic product I usually use is moisturizing cream," the answer analysis unit analyzes the answer and identifies the cosmetic product the user uses. Step 3: The suggestion generation unit suggests optimal skin care and makeup methods based on the answers analyzed by the answer analysis unit. For example, if the user has dry skin, the suggestion generation unit suggests skin care methods with high moisturizing effects. The suggestion generation unit can also suggest skin care and makeup methods that match the user's lifestyle and daily activities. For example, if the user spends a lot of time outdoors, the suggestion generation unit suggests products with high sunscreen effects.

[0059] (Example 2) A generative AI system according to an embodiment of the present invention is a system that helps users find skincare and makeup methods that suit them. Through a questionnaire format and facial photo analysis, this system allows anyone to easily achieve professional-looking makeup and expand their potential. As a result, the generative AI system provides users with optimal skincare and makeup methods, allowing them to maximize their potential.

[0060] The generative AI system according to the embodiment includes a question acquisition unit, an answer analysis unit, and a proposal generation unit. The question acquisition unit acquires a question from a user. For example, the user may input a question such as "What is your skin type?", and the question acquisition unit acquires the question. The question acquisition unit can also acquire a user's question using voice input. For example, the user may ask "What cosmetics do you usually use?" and the question acquisition unit acquires the question. The answer analysis unit analyzes the user's answer based on the question acquired by the question acquisition unit. For example, if the user answers "I have dry skin," the answer analysis unit analyzes the answer and identifies the user's skin type. The answer analysis unit can also analyze the user's answer using natural language processing technology. For example, if the user answers "My usual cosmetics are moisturizing cream," the answer analysis unit analyzes the answer and identifies the cosmetics used by the user. The proposal generation unit suggests optimal skin care and makeup methods based on the answer analyzed by the answer analysis unit. For example, if the user has dry skin, the proposal generation unit suggests a skin care method with high moisturizing effects. The suggestion generation unit can also suggest skin care and makeup methods that are tailored to the user's lifestyle and daily activities. For example, if the user spends a lot of time outdoors, the suggestion generation unit can suggest products with high sunscreen effects. This allows the AI ​​system according to the embodiment to suggest optimal skin care and makeup methods based on the user's questions.

[0061] The question acquisition unit dynamically changes questions based on the user's past answer history, allowing for more personalized suggestions. The question acquisition unit, for example, analyzes the history of questions the user has previously answered and dynamically generates new questions based on that history. For example, for a user who previously answered that they have dry skin, a detailed question about moisturizing is added. The question acquisition unit can also generate questions tailored to the user's preferences and needs based on the user's answer history. For example, if the user has previously preferred a particular brand of cosmetics, a question about that brand is added. This allows for dynamically changing questions based on the user's past answer history, allowing for more personalized suggestions.

[0062] The answer analysis unit can estimate the user's emotions and suggest skin care or makeup methods according to the emotions. The answer analysis unit, for example, analyzes the user's facial expressions and tone of voice when answering questions to estimate the emotions. For example, if the user feels tired, the answer analysis unit suggests a skin care method that has a relaxing effect. The answer analysis unit can also estimate the user's emotions using text analysis technology. For example, if the user answers, "I've been feeling a lot of stress lately," the answer analysis unit analyzes the text and estimates the user's emotions. This makes it possible to suggest skin care or makeup methods according to the user's emotions.

[0063] The suggestion generation unit can suggest skin care or makeup methods that match the user's lifestyle or daily activities. For example, the suggestion generation unit estimates the user's lifestyle from the user's answers and suggests skin care or makeup methods that match that lifestyle. For example, for a user who spends a lot of time outdoors, the suggestion generation unit suggests products with high sun protection effects. The suggestion generation unit can also suggest skin care or makeup methods based on the user's daily activities. For example, if the user is busy, the suggestion generation unit suggests a makeup method that can be done in a short amount of time. This makes it possible to suggest skin care or makeup methods that match the user's lifestyle and daily activities.

[0064] The question acquisition unit supports voice input, and allows the user to receive suggestions by answering questions by voice. The question acquisition unit uses, for example, voice recognition technology to build a system in which the user answers questions by voice. For example, when a user answers by voice, "My skin type is dry skin," the question acquisition unit recognizes the voice and acquires the answer. The question acquisition unit can also convert voice into text in real time and make suggestions based on the text. For example, when a user answers by voice, "The cosmetic product I usually use is moisturizing cream," the question acquisition unit converts the voice into text and acquires the answer. This allows the user to receive suggestions by answering questions by voice.

[0065] The question acquisition unit is compatible with a chatbot format and can suggest skin care or makeup methods to the user through natural conversation. The question acquisition unit, for example, builds a chatbot-style question system that allows the user to receive suggestions of skin care or makeup methods through natural conversation. For example, if a user inputs "My skin has been dry lately" in a chat, the question acquisition unit analyzes the message and suggests an appropriate skin care method. The question acquisition unit can also smoothly progress a conversation with the user using a dialogue management system. For example, if a user asks, "What cosmetics do you usually use?", the question acquisition unit provides an appropriate answer to the question. This allows the user to receive suggestions of skin care or makeup methods through natural conversation.

[0066] The question acquisition unit incorporates an emotion estimation function, and can analyze the emotions of the user when answering questions in real time and make suggestions that will elicit positive emotions. The question acquisition unit, for example, incorporates an emotion estimation function to build a system that analyzes the facial expressions and tone of voice of the user when answering questions in real time. For example, if the user feels tired, the question acquisition unit suggests a skin care method that has a relaxing effect. The question acquisition unit can also monitor the user's emotions in real time and make suggestions based on those emotions. For example, if the user is feeling stressed, the question acquisition unit suggests a skin care method that has a stress-relieving effect. This makes it possible to analyze the user's emotions in real time and make suggestions that will elicit positive emotions.

[0067] The facial photo analysis unit can track changes in the user's skin over time and propose a long-term skin care plan. The facial photo analysis unit, for example, analyzes facial photos provided by the user periodically and builds a system that tracks changes in skin over time. For example, it analyzes changes in skin tone and blemishes and proposes a long-term skin care plan. The facial photo analysis unit can also continuously monitor the user's skin condition and update the skin care plan according to those changes. For example, it can analyze changes in skin by season and propose a skin care plan based on that. This makes it possible to track changes in the user's skin over time and propose a long-term skin care plan.

[0068] The facial photo analysis unit can estimate the user's emotions based on the analysis results of the facial photo and suggest skin care or makeup methods according to the emotions. The facial photo analysis unit, for example, builds a system that estimates the user's emotions based on the analysis results of the facial photo. For example, if the user feels tired, the facial photo analysis unit suggests a skin care method that has a relaxing effect. The facial photo analysis unit can also estimate the user's emotions using facial expression analysis technology. For example, if the user is smiling, the facial photo analysis unit suggests a bright makeup method. In this way, the user's emotions can be estimated based on the analysis results of the facial photo and skin care or makeup methods according to the emotions can be suggested.

[0069] The facial photo analysis unit can create a 3D model of the user's facial features and suggest more detailed skin care or makeup methods. The facial photo analysis unit, for example, creates a 3D model of the user's facial features based on a facial photo, and builds a system that suggests detailed skin care and makeup methods based on that model. For example, it can suggest makeup methods that match the facial contours and bone structure. The facial photo analysis unit can also use 3D scanning technology to analyze the user's facial features in detail. For example, it can analyze the shape of the face and the texture of the skin, and suggest skin care and makeup methods based on that. This allows the user's facial features to be created as a 3D model, and more detailed skin care and makeup methods to be suggested.

[0070] The facial photo analysis unit can perform more accurate analysis using photos taken under different lighting conditions or angles. The facial photo analysis unit, for example, analyzes facial photos taken under different lighting conditions or angles to build a system that suggests more accurate skin care and makeup methods. For example, it analyzes photos taken under natural light and artificial light. The facial photo analysis unit can also analyze the user's facial features in detail based on photos taken from different angles. For example, it analyzes photos taken from the front and from an angle and suggests skin care and makeup methods based on the results. This allows for more accurate analysis using photos taken under different lighting conditions and angles.

[0071] The facial photo analysis unit can customize the analysis results of the facial photo to match the makeup style or fashion style selected by the user. For example, the facial photo analysis unit builds a system that suggests skin care and makeup methods that match the makeup style or fashion style selected by the user based on the analysis results of the facial photo. For example, it suggests makeup methods that match a casual style. The facial photo analysis unit can also customize skin care and makeup methods according to the user's preferences and needs. For example, if the user is attending a specific event, it suggests makeup methods that match the event. This allows the analysis results of the facial photo to be customized to match the makeup style or fashion style selected by the user.

[0072] The facial photo analysis unit incorporates an emotion estimation function, which can estimate emotions from the user's facial expression and suggest skin care or makeup methods according to the emotion. The facial photo analysis unit, for example, incorporates an emotion estimation function to build a system that estimates emotions from the user's facial expression. For example, if the user is smiling, the facial photo analysis unit suggests a bright makeup method. The facial photo analysis unit can also estimate the user's emotion using an expression analysis algorithm. For example, if the user feels tired, the facial photo analysis unit suggests a skin care method that has a relaxing effect. This makes it possible to estimate emotions from the user's facial expression and suggest skin care or makeup methods according to the emotion.

[0073] The facial photo analysis unit can learn the techniques of professional makeup artists and perform makeup simulations in real time based on a facial photo of the user. The facial photo analysis unit, for example, builds a system that learns the techniques of professional makeup artists and performs makeup simulations in real time based on a facial photo of the user. For example, it simulates a specific makeup style and suggests it to the user. The facial photo analysis unit can also simulate makeup on the user's face using real-time rendering technology. For example, it simulates a makeup style selected by the user in real time and displays the results to the user. This allows the system to learn the techniques of professional makeup artists and perform makeup simulations in real time based on a facial photo of the user.

[0074] The proposal generation unit can estimate the user's emotions and suggest a makeup style that matches the emotions when proposing a professional makeup technique. The proposal generation unit, for example, builds a system that estimates the user's emotions when proposing a professional makeup technique. For example, if the user is nervous, the proposal generation unit suggests a makeup style that has a relaxing effect. The proposal generation unit can also analyze the user's emotions using an emotion estimation function and suggest a makeup style that matches the emotions. For example, if the user is happy, the proposal generation unit suggests a glamorous makeup style. This makes it possible to estimate the user's emotions and suggest a makeup style that matches the emotions when proposing a professional makeup technique.

[0075] The facial photo analysis unit can analyze the user's facial features in detail and suggest makeup methods that match each individual feature. The facial photo analysis unit, for example, builds a system that analyzes the user's facial features in detail and suggests makeup methods that match those features. For example, it can suggest makeup methods that match the facial contours and bone structure. The facial photo analysis unit can also analyze the facial shape and skin texture and suggest makeup methods based on that. For example, it can suggest a foundation that matches the user's skin tone. This makes it possible to analyze the user's facial features in detail and suggest makeup methods that match each individual feature.

[0076] The proposal generation unit can propose a customized makeup method taking into consideration the user's preferences and past makeup history. The proposal generation unit, for example, considers the user's preferences and past makeup history and builds a system that proposes a customized makeup method based on that information. For example, the proposal generation unit re-proposes makeup styles that were popular in the past. The proposal generation unit can also customize makeup methods according to the user's preferences and needs. For example, if the user likes a particular color, the proposal generation unit proposes a makeup method based on that color. In this way, a customized makeup method can be proposed taking into consideration the user's preferences and past makeup history.

[0077] The suggestion generation unit can consider the user's skin care history and make consistent suggestions for skin care and makeup. The suggestion generation unit, for example, considers the user's skin care history and builds a system that makes consistent suggestions for skin care and makeup based on that information. For example, it suggests makeup techniques based on skin care products used in the past. The suggestion generation unit can also customize both skin care and makeup based on the user's skin care history. For example, if the user has sensitive skin, it suggests mild skin care products and makeup techniques. This makes it possible to consider the user's skin care history and make consistent suggestions for skin care and makeup.

[0078] The suggestion generation unit can monitor the user's emotions in real time and suggest a makeup style that corresponds to the emotion. The suggestion generation unit, for example, builds a system that monitors the user's emotions in real time and suggests a makeup style that corresponds to the emotion. For example, if the user is nervous, the suggestion generation unit suggests a makeup style that has a relaxing effect. The suggestion generation unit can also analyze the user's emotions using an emotion estimation function and suggest a makeup style that corresponds to the emotion. For example, if the user is happy, the suggestion generation unit suggests a glamorous makeup style. In this way, the user's emotions can be monitored in real time and a makeup style that corresponds to the emotion can be suggested.

[0079] The suggestion generation unit can dynamically customize skin care or makeup methods based on the user's lifestyle or daily activities. The suggestion generation unit, for example, analyzes the user's lifestyle and daily activities and builds a system that dynamically customizes skin care and makeup methods based on that information. For example, for a user who spends a lot of time outdoors, the suggestion generation unit can suggest products with high sun protection effects. The suggestion generation unit can also customize skin care and makeup methods according to the user's lifestyle and daily activities. For example, if the user is busy, the suggestion generation unit can suggest makeup methods that can be done in a short amount of time. This makes it possible to dynamically customize skin care and makeup methods based on the user's lifestyle and daily activities.

[0080] The suggestion generation unit can estimate the user's emotions and suggest skin care or makeup methods according to the emotions. The suggestion generation unit, for example, builds a system that estimates the user's emotions and suggests skin care or makeup methods according to the emotions. For example, if the user feels tired, the suggestion generation unit suggests a skin care method that has a relaxing effect. The suggestion generation unit can also analyze the user's emotions using the emotion estimation function and suggest skin care or makeup methods according to the emotions. For example, if the user is happy, the suggestion generation unit suggests a glamorous makeup method. In this way, the user's emotions can be estimated and skin care or makeup methods according to the emotions can be suggested.

[0081] The suggestion generation unit can learn the user's past skincare and makeup history and make optimal customization suggestions. The suggestion generation unit, for example, builds a system that learns the user's past skincare and makeup history and makes optimal customization suggestions based on that information. For example, suggestions are made based on the effects of products used in the past. The suggestion generation unit can also customize skincare and makeup methods based on the user's past history. For example, if a user prefers products from a particular brand, suggestions are made based on products from that brand. This makes it possible to learn the user's past skincare and makeup history and make optimal customization suggestions.

[0082] The suggestion generation unit can suggest skin care or makeup methods taking into account the user's dietary habits or exercise habits. The suggestion generation unit, for example, analyzes the user's dietary habits and exercise habits and builds a system that suggests skin care or makeup methods based on that information. For example, a skin care product containing a specific vitamin is suggested to a user who eats a nutritionally balanced diet. The suggestion generation unit can also suggest skin care or makeup methods based on the user's exercise habits. For example, a post-exercise skin care method is suggested. This makes it possible to suggest skin care or makeup methods taking into account the user's dietary habits and exercise habits.

[0083] The suggestion generation unit can suggest skin care or makeup methods according to the season or climate of the user. The suggestion generation unit, for example, builds a system that considers seasonal and climate changes to suggest optimal skin care and makeup methods to the user. For example, it suggests moisturizing products suitable for the dry climate of winter. The suggestion generation unit can also suggest skin care and makeup methods based on the climate of the user's place of residence. For example, it suggests skin care methods with less oil to a user living in a humid region. This makes it possible to suggest skin care and makeup methods according to the season and climate of the user.

[0084] The suggestion generation unit can monitor the user's emotions in real time and suggest skin care or makeup methods according to the emotions. The suggestion generation unit, for example, builds a system that monitors the user's emotions in real time and suggests skin care or makeup methods according to the emotions. For example, if the user feels tired, the suggestion generation unit suggests a skin care method that has a relaxing effect. The suggestion generation unit can also analyze the user's emotions using an emotion estimation function and suggest skin care or makeup methods according to the emotions. For example, if the user is happy, the suggestion generation unit suggests a glamorous makeup method. In this way, the user's emotions can be monitored in real time and skin care or makeup methods according to the emotions can be suggested.

[0085] The proposal generation unit can continuously learn skincare and makeup methods based on user feedback and improve the accuracy of its proposals. For example, the proposal generation unit collects user feedback and builds a system that continuously learns skincare and makeup methods based on that information. For example, by providing feedback on the results of a user trying a proposed method, the proposal generation unit learns that information and reflects it in future proposals. The proposal generation unit can also develop algorithms to improve the accuracy of proposals based on user feedback. For example, it can analyze user ratings and comments and improve proposals based on that information. This allows the system to continuously learn skincare and makeup methods based on user feedback and improve the accuracy of its proposals.

[0086] The suggestion generation unit can estimate the user's emotions during the learning and improvement process and make improvements according to the emotions. The suggestion generation unit, for example, builds a system that estimates the user's emotions during the learning and improvement process. For example, the suggestion generation unit analyzes the user's emotions when providing feedback on the results of trying a proposed method and makes improvements based on that information. The suggestion generation unit can also analyze the user's emotions using an emotion estimation function and make improvements according to those emotions. For example, if the user is feeling stressed, the suggestion generation unit suggests a skin care method that is effective in relieving stress. This makes it possible to estimate the user's emotions during the learning and improvement process and make improvements according to the emotions.

[0087] The proposal generation unit can track a user's skincare and makeup history over the long term and make optimal improvement suggestions. The proposal generation unit can, for example, build a system that tracks a user's skincare and makeup history over the long term and makes optimal improvement suggestions based on that information. For example, suggestions can be made based on the effects of products used in the past. The proposal generation unit can also continuously improve skincare and makeup methods based on the user's history. For example, if a user has been using a particular product for a long period of time, the effect of that product can be analyzed and reflected in proposals from the next time onwards. This makes it possible to track a user's skincare and makeup history over the long term and make optimal improvement suggestions.

[0088] The proposal generation unit can compare data from different users and identify common areas for improvement. The proposal generation unit can, for example, build a system that compares data from different users and identifies common areas for improvement based on that data. For example, it can analyze the skin care and makeup histories of multiple users and identify common areas for improvement. The proposal generation unit can also normalize user data and extract common features based on that data. For example, it can identify common areas for improvement for a specific skin type and make suggestions based on that information. This makes it possible to compare data from different users and identify common areas for improvement.

[0089] The suggestion generation unit can suggest skin care or makeup methods taking into account the user's living environment or stress level. The suggestion generation unit, for example, considers the user's living environment and stress level and builds a system that suggests skin care or makeup methods based on that information. For example, it analyzes the climate and lifestyle habits of the user's place of residence and makes suggestions based on that information. The suggestion generation unit can also analyze the user's stress level and suggest skin care or makeup methods according to that level. For example, if the user is feeling high stress, the suggestion generation unit suggests a skin care method that has a relaxing effect. In this way, skin care or makeup methods can be suggested taking into account the user's living environment and stress level.

[0090] The suggestion generation unit can monitor the user's emotions in real time and make improvement suggestions according to the emotions. The suggestion generation unit, for example, builds a system that monitors the user's emotions in real time and makes improvement suggestions according to those emotions. For example, the suggestion generation unit analyzes the user's emotions when providing feedback on the results of trying a proposed method and makes improvements based on that information. The suggestion generation unit can also analyze the user's emotions using an emotion estimation function and make improvement suggestions according to those emotions. For example, if the user is feeling stressed, the suggestion generation unit suggests a skin care method that is effective in relieving stress. This makes it possible to monitor the user's emotions in real time and make improvement suggestions according to the emotions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The suggestion generation unit can suggest skin care and makeup methods taking into account the user's diet and exercise habits. For example, if the user eats a nutritionally balanced diet, the suggestion generation unit can suggest skin care products containing vitamins and minerals based on the diet. Also, if the user exercises regularly, the suggestion generation unit can suggest post-exercise skin care methods. Furthermore, if the user is allergic to a specific food ingredient, the suggestion generation unit can take that information into account and suggest products that do not cause allergies.

[0093] The proposal generator can suggest skin care and makeup methods according to the user's season and climate. For example, it can suggest moisturizing products for dry winter climates and less oily products for hot summer climates. It can also suggest skin care methods that take into account the effects of humidity and ultraviolet rays based on the climate of the user's place of residence. It can also track seasonal changes in the skin and update the skin care plan accordingly.

[0094] The suggestion generation unit can estimate the user's emotions and suggest skin care and makeup methods according to those emotions. For example, if the user is feeling stressed, the suggestion generation unit can suggest skin care methods with a relaxing effect. If the user is happy, the suggestion generation unit can suggest glamorous makeup methods. Furthermore, if the user feels tired, the suggestion generation unit can suggest skin care products with a fatigue recovery effect.

[0095] The suggestion generator can dynamically customize skin care and makeup methods based on the user's lifestyle and daily activities. For example, it can suggest products with high sun protection to a user who spends a lot of time outdoors, and suggest makeup methods that can be done quickly to a busy user. It can also suggest nighttime skin care methods for a user who works the night shift. It can also suggest makeup methods for weekends or special events that suit the user's lifestyle.

[0096] The suggestion generation unit can monitor the user's emotions in real time and suggest skin care and makeup methods according to those emotions. For example, if the user is nervous, it can suggest skin care methods that have a relaxing effect. If the user is happy, it can also suggest glamorous makeup methods. Furthermore, if the user is sad, it can also suggest makeup methods that have a mood-lifting effect.

[0097] The suggestion generator can learn the user's past skincare and makeup history and provide optimal customized suggestions based on that information. For example, suggestions can be made based on the effects of products used in the past, and if the user prefers products from a particular brand, suggestions can be made based on that brand's products. It can also customize makeup techniques according to the user's preferences and needs. It can also re-suggest makeup styles the user has tried in the past.

[0098] The suggestion generator can estimate the user's emotions and make suggestions for improvement based on those emotions. For example, it can analyze the user's emotions when providing feedback on the results of trying a suggested method and make improvements based on that information. If the user is feeling stressed, it can also suggest a skin care method that is effective in relieving stress. Furthermore, if the user is satisfied, it can also make suggestions to maintain that emotion.

[0099] The suggestion generation unit can suggest skin care and makeup methods taking into account the user's living environment and stress level. For example, it can analyze the climate and lifestyle habits of the user's place of residence and make suggestions based on that information. It can also analyze the user's stress level and suggest skin care and makeup methods according to that level. Furthermore, if the user is feeling high stress, it can suggest skin care methods that have a relaxing effect.

[0100] The suggestion generator can monitor the user's emotions in real time and make suggestions for improvement based on those emotions. For example, it can analyze the user's emotions when providing feedback on the results of trying a suggested method and make improvements based on that information. If the user is feeling stressed, it can also suggest skin care methods that are effective in relieving stress. Furthermore, if the user is satisfied, it can make suggestions to maintain that emotion.

[0101] The suggestion generator can compare data from different users and identify common areas for improvement based on that data. For example, it can analyze the skin care and makeup histories of multiple users to identify common areas for improvement. It can also normalize user data and extract common features based on that data. It can also identify common areas for improvement across specific skin types and make suggestions based on that information.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The question acquisition unit acquires a question from the user. For example, when the user inputs a question such as "What is your skin type?", the question acquisition unit acquires the question. The question acquisition unit can also acquire the user's question using voice input. For example, when the user asks by voice, "What cosmetics do you usually use?", the question acquisition unit acquires the question. Step 2: The answer analysis unit analyzes the user's answer based on the question acquired by the question acquisition unit. For example, if the user answers "I have dry skin," the answer analysis unit analyzes the answer and identifies the user's skin type. The answer analysis unit can also analyze the user's answer using natural language processing technology. For example, if the user answers "The cosmetic product I usually use is moisturizing cream," the answer analysis unit analyzes the answer and identifies the cosmetic product the user uses. Step 3: The suggestion generation unit suggests optimal skin care and makeup methods based on the answers analyzed by the answer analysis unit. For example, if the user has dry skin, the suggestion generation unit suggests skin care methods with high moisturizing effects. The suggestion generation unit can also suggest skin care and makeup methods that match the user's lifestyle and daily activities. For example, if the user spends a lot of time outdoors, the suggestion generation unit suggests products with high sunscreen effects.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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. a question acquisition unit that acquires a question from a user; an answer analysis unit that analyzes an answer from a user based on the question acquired by the question acquisition unit; a suggestion generation unit that suggests an optimal skin care or makeup method based on the answers analyzed by the answer analysis unit; A system characterized by:

2. The question acquisition unit It supports voice input, allowing users to receive suggestions by answering questions by voice.

2. The system of claim 1.

3. The facial photo analysis section It can track changes in the user's skin over time and suggest long-term skin care plans.

2. The system of claim 1.

4. The facial photo analysis section It learns the techniques of professional makeup artists and can simulate makeup in real time based on the user's facial photo.

2. The system of claim 1.

5. The proposal generation unit Dynamically customizing skin care or makeup regimens based on a user's lifestyle or daily activities 2. The system of claim 1.

6. The answer analysis unit Estimates the user's emotions and suggests skin care or makeup methods according to the emotions 2. The system of claim 1.

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