System

Abstract

An object of a system according to an embodiment is to propose an optimal fashion style or lifestyle to a user.SOLUTION: A system includes a user information collection unit, an analysis unit, and a proposal unit. The user information collection unit collects user information. The analysis unit analyzes the user information collected by the user information collection unit. The proposal unit proposes a fashion style, a hairstyle, makeup, and a lifestyle that are optimal for the user on the basis of a result analyzed by the 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 had the problem of making it difficult to propose optimal fashion styles and lifestyles to individual users.

[0005] The system according to the embodiment aims to propose optimal fashion styles and lifestyles to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information collection unit, an analysis unit, and a suggestion unit. The user information collection unit collects user information. The analysis unit analyzes the user information collected by the user information collection unit. The suggestion unit suggests optimal fashion styles, hairstyles, makeup, and lifestyles for the user based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal fashion styles and lifestyles to users. [Brief explanation of the drawings]

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

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) The self-realization support system according to an embodiment of the present invention is a system that collects user information, analyzes it using a generation AI, and suggests optimal fashion, hairstyle, makeup, and lifestyle. This allows the user to find the style that best suits them and aim for maximum self-realization.

[0029] A self-realization support system according to an embodiment includes a user information collection unit, an analysis unit, and a suggestion unit. The user information collection unit collects user information, such as a user's facial photo, body type, skin color, hair texture, and preferred style. The user information collection unit can also collect information about the user's lifestyle, hobbies, and goals. The analysis unit analyzes the user information collected by the user information collection unit. For example, the generation AI analyzes the user's facial photo to identify the user's face shape and skin color. The generation AI also analyzes the user's body type and hair texture to obtain basic data for suggesting an optimal style. The generation AI also analyzes the user's lifestyle, hobbies, and goals to obtain basic data for lifestyle suggestions. The suggestion unit suggests optimal fashion styles, hairstyles, makeup, and lifestyles for the user based on the results of the analysis by the analysis unit. For example, the generation AI suggests clothing and accessories that match the user's body type and skin color. The generation AI can also suggest optimal hairstyles based on the user's facial photo, hair texture, and preferred style. Furthermore, the generation AI can suggest optimal makeup methods based on the user's facial photo, skin color, and preferred makeup style. Furthermore, the generation AI can also suggest optimal lifestyles based on the user's lifestyle habits, hobbies, and goals. This allows the self-realization support system according to the embodiment to help users find the style that best suits them and achieve their highest self-realization.

[0030] The suggestion unit can suggest clothes and accessories that suit the user's body type and skin color. For example, the suggestion unit suggests clothes with an optimal silhouette based on the user's body type. For example, it suggests fitted clothes to a user with a slender body type. The suggestion unit also suggests clothes and accessories with optimal colors based on the user's skin color. For example, it suggests pastel-colored clothes to a user with a light skin color. The suggestion unit can also suggest optimal coordination taking into consideration both the user's body type and skin color. For example, it suggests fitted pastel-colored clothes to a user with a slender body type and light skin color. This enables more personalized suggestions by suggesting styles that suit the user's body type and skin color.

[0031] The suggestion unit can suggest an optimal hairstyle based on the user's facial photo, hair quality, and preferred style. For example, the suggestion unit analyzes the user's facial photo and suggests a hairstyle that suits the shape of the face. For example, for a user with a round face, it suggests a hairstyle that makes the face look thinner. The suggestion unit also analyzes the user's hair quality and suggests a hairstyle that suits the hair quality. For example, for a user with thin hair, it suggests a hairstyle that adds volume. The suggestion unit can also suggest an optimal hairstyle taking into account the user's preferred style. For example, if the user prefers a casual style, it suggests a casual hairstyle. This makes it possible to suggest hairstyles based on the user's facial photo and hair quality.

[0032] The suggestion unit can suggest a haircut and coloring that suits the user's face shape. For example, the suggestion unit analyzes the user's face shape and suggests a haircut that suits the face shape. For example, for a user with a square face, the suggestion unit suggests a haircut that makes the face look softer. The suggestion unit also suggests coloring that suits the user's face shape. For example, for a user with a long face, the suggestion unit suggests coloring that makes the face look shorter. The suggestion unit can also suggest optimal haircuts and coloring by taking into consideration both the user's face shape and hair quality. For example, for a user with a square face and thin hair, the suggestion unit suggests a voluminous haircut and coloring that makes the face look softer. This makes it possible to suggest haircuts and coloring that suit the user's face shape.

[0033] The suggestion unit can suggest an optimal makeup method based on the user's facial photo, skin color, and preferred makeup style. The suggestion unit, for example, analyzes the user's facial photo and suggests a makeup method that suits the shape of the face. For example, for a user with a round face, it suggests a makeup method that makes the face look slimmer. The suggestion unit also analyzes the user's skin color and suggests a makeup method that suits the skin color. For example, for a user with a light skin color, it suggests a light-colored foundation or lip color. The suggestion unit can also suggest an optimal makeup method taking into account the user's preferred makeup style. For example, if the user prefers a natural makeup style, it suggests a natural makeup method. This makes it possible to suggest makeup methods based on the user's facial photo and skin color.

[0034] The suggestion unit can suggest foundation and lip colors that match the user's skin color. For example, the suggestion unit analyzes the user's skin color and suggests foundation that matches the skin color. For example, a dark-colored foundation is suggested for a user with dark skin color. The suggestion unit also suggests lip colors that match the user's skin color. For example, a light-colored lip color is suggested for a user with light skin color. The suggestion unit can also suggest optimal foundation and lip colors by taking into consideration both the user's skin color and preferred makeup style. For example, a dark-colored foundation and a natural-colored lip color is suggested for a user with dark skin color who prefers a natural makeup style. This makes it possible to suggest foundation and lip colors that match the user's skin color.

[0035] The suggestion unit can suggest an optimal lifestyle based on the user's lifestyle habits, hobbies, and goals. The suggestion unit, for example, analyzes the user's lifestyle habits and suggests a lifestyle that suits the lifestyle. For example, for a user with a healthy lifestyle, it suggests a healthy meal plan and exercise plan. The suggestion unit can also analyze the user's hobbies and suggest a lifestyle that suits the hobbies. For example, for a user who likes the outdoors, it suggests a lifestyle that incorporates outdoor activities. The suggestion unit can also analyze the user's goals and suggest a lifestyle that suits the goals. For example, for a user whose goal is to lose weight, it suggests a meal plan and exercise plan that is suitable for the diet. This makes it possible to suggest a lifestyle based on the user's lifestyle habits and hobbies.

[0036] The suggestion unit can suggest meal plans and exercise plans that suit the user's health condition and goals. The suggestion unit, for example, analyzes the user's health condition and suggests meal plans that suit the health condition. For example, a low-carbohydrate meal plan is suggested for a user with high blood sugar levels. The suggestion unit also suggests exercise plans that suit the user's health condition. For example, an exercise plan centered on aerobic exercise is suggested for a user with poor cardiopulmonary function. The suggestion unit can also suggest optimal meal plans and exercise plans taking into consideration both the user's health condition and goals. For example, a user with a high blood sugar level who is aiming to lose weight is suggested a low-carbohydrate meal plan and an exercise plan centered on aerobic exercise. This makes it possible to suggest meal plans and exercise plans based on the user's health condition and goals.

[0037] The analysis unit can incorporate an algorithm that analyzes a user's past selection history and behavioral patterns and predicts future selections. For example, the analysis unit analyzes a user's past fashion selection history and predicts the style the user is likely to choose next. For example, if the user has previously preferred casual clothing, the analysis unit can suggest a casual style next time. The analysis unit can also analyze a user's behavioral patterns and predict future selections. For example, if a user tends to choose a particular style in a particular season, the analysis unit can suggest a style that suits that season. The analysis unit can also analyze a user's purchase history and predict the item the user is likely to purchase next. For example, if the user has purchased many items from a particular brand in the past, the analysis unit can suggest new products from that brand. This makes it possible to predict future selections based on a user's past selection history and behavioral patterns.

[0038] The analysis unit analyzes the user's social media activity and understands trends and preferences, allowing it to make more personalized suggestions. For example, the analysis unit analyzes the user's social media posts to understand preferred styles and trends. For example, if the user posts many fashion-related items, the analysis unit will suggest those styles. The analysis unit also analyzes the styles of influencers the user follows and makes suggestions influenced by them. For example, if the user follows a specific influencer, the analysis unit will suggest the style of that influencer. The analysis unit can also analyze the user's history of "likes" and comments on social media to understand preferred styles. For example, the analysis unit will suggest styles that the user has liked the most. This enables more personalized suggestions to be made based on the user's social media activity.

[0039] The user information collection unit can perform image analysis of the user's living environment and suggest a style that suits it. For example, the user information collection unit can perform image analysis of the interior of the room where the user lives and suggest fashion and hairstyles that suit that style. For example, it can suggest a simple style that suits a modern interior. The user information collection unit can also perform image analysis of the scenery of the area where the user lives and suggest a style that suits that environment. For example, if the user lives in an area rich in nature, it can suggest a natural style. The user information collection unit can also analyze images of places the user frequently visits and suggest a style that suits that place. For example, if the user frequently goes to urban cafes, it can suggest an urban style. This makes it possible to suggest styles based on the user's living environment.

[0040] The analysis unit can analyze the user's health data and make style suggestions based on the health condition. The analysis unit, for example, analyzes the user's heart rate data and makes style suggestions based on the health condition. For example, if the heart rate is high, a relaxing style is suggested. The analysis unit can also analyze the user's sleep patterns and make style suggestions based on the health condition. For example, if the user is sleep deprived, a relaxing style is suggested. The analysis unit can also analyze the user's exercise data and make style suggestions based on the health condition. For example, a style that allows for relaxation after exercise is suggested. This makes it possible to make style suggestions based on the user's health data.

[0041] The analysis unit can analyze the user's cultural background and values ​​and make style suggestions based on them. For example, the analysis unit can analyze the user's cultural background and make style suggestions based on that. For example, it can suggest fashion styles rooted in a particular culture. The analysis unit can also analyze the user's values ​​and make style suggestions based on that. For example, it can suggest sustainable fashion to a user who has eco-friendly values. The analysis unit can also analyze the user's religious background and make style suggestions based on that. For example, it can suggest fashion styles suitable for a particular religion. This makes it possible to make style suggestions based on the user's cultural background and values.

[0042] The analysis unit can analyze the user's musical preferences and suggest styles that match the musical genre. For example, the analysis unit can analyze the user's music playlist and suggest fashion styles that match the user's preferred musical genre. For example, a rock style fashion can be suggested for a user who likes rock. The analysis unit can also analyze the styles of artists the user often listens to and suggest fashion styles influenced by them. For example, a style inspired by the fashion of a particular artist can be suggested. The analysis unit can also analyze the user's musical preferences and suggest hairstyles and makeup that match the genre. For example, an elegant style can be suggested for a user who likes classical music. This makes it possible to suggest styles based on the user's musical preferences.

[0043] The analysis unit can analyze the user's travel history and make style suggestions based on the places visited. For example, the analysis unit analyzes the user's travel history and suggests a fashion style based on the places visited. For example, if the user went to a beach resort, resort style fashion will be suggested. The analysis unit can also analyze trends in cities the user has visited and suggest a style that matches those trends. For example, if the user went to Paris, a style incorporating Parisian fashion trends will be suggested. The analysis unit can also analyze the user's travel history and suggest a style based on the culture of the places visited. For example, if the user visited Asian countries, a fashion style that matches that culture will be suggested. This makes it possible to suggest styles based on the user's travel history.

[0044] The suggestion unit can analyze the user's seasonal preferences and climate data to suggest a fashion style that suits the season. The suggestion unit, for example, analyzes the user's past fashion selection history to understand seasonal preferences. For example, for a user who prefers casual styles in summer, the suggestion unit suggests casual fashion that suits summer. The suggestion unit also analyzes climate data to suggest a fashion style that suits the season. For example, in winter, it suggests clothes made of warm materials. The suggestion unit can also analyze climate data for the user's region to suggest a fashion style that suits that region. For example, in rainy regions, it suggests waterproof fashion. This makes it possible to suggest fashion styles based on the user's seasonal preferences and climate data.

[0045] The suggestion unit can analyze the type of pet of the user and the user's relationship with the pet, and suggest a fashion style that can be enjoyed with the pet. For example, the suggestion unit can analyze the type of pet of the user and suggest a fashion style that can be enjoyed with the pet. For example, for a user who has a dog, the suggestion unit can suggest a casual style that is suitable for walking the dog. The suggestion unit can also analyze the relationship between the user and the pet and suggest a fashion style based on that relationship. For example, for a user who often takes photos with their pet, the suggestion unit can suggest a style that can be coordinated with the pet. The suggestion unit can also analyze the activities of the user's pet and suggest a fashion style that is suitable for that activity. For example, for a user who enjoys outdoors with their pet, the suggestion unit can suggest an outdoor style. This makes it possible to suggest fashion styles based on the type of pet of the user and the user's relationship with their pet.

[0046] The suggestion unit can analyze the user's hobbies and special skills and suggest a fashion style that suits them. For example, the suggestion unit can analyze the user's hobbies and suggest a fashion style that suits those hobbies. For example, for a user who likes music, the suggestion unit can suggest a style that is suitable for a music festival. The suggestion unit can also analyze the user's special skills and suggest a fashion style that suits those skills. For example, for a user who is good at dancing, the suggestion unit can suggest a style that is easy to move in. The suggestion unit can also analyze the user's hobbies and special skills and suggest a fashion style that suits those activities. For example, for a user who likes drawing, the suggestion unit can suggest a style that is suitable for an art studio. This makes it possible to suggest fashion styles based on the user's hobbies and special skills.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The user information collection unit collects the user's health data, and the analysis unit analyzes the data to suggest a style based on the user's health condition. For example, the unit can analyze the user's heart rate data and suggest a relaxing style if the heart rate is high. It can also analyze the user's sleep patterns and suggest a relaxing style if the user is sleep deprived. It can also analyze the user's exercise data and suggest a style that allows the user to relax after exercise. This makes it possible to suggest a style based on the user's health condition.

[0049] The suggestion unit can analyze a user's cultural background and values ​​and make style suggestions based on them. For example, it can analyze a user's cultural background and suggest fashion styles rooted in a specific culture. It can also analyze a user's values ​​and suggest sustainable fashion to a user with eco-friendly values. It can also analyze a user's religious background and suggest fashion styles suitable for a specific religion. This makes it possible to make style suggestions based on a user's cultural background and values.

[0050] The suggestion unit can analyze the user's musical preferences and suggest styles that match the music genre. For example, it can analyze the user's music playlist and suggest rock-style fashion to a user who likes rock. It can also analyze the styles of artists the user often listens to and suggest fashion styles influenced by them. Furthermore, it can suggest elegant styles to a user who likes classical music based on the user's musical preferences. This makes it possible to suggest styles based on the user's musical preferences.

[0051] The suggestion unit can analyze the user's travel history and make style suggestions based on the places visited. For example, if the user's travel history is analyzed and the user went to a beach resort, resort-style fashion can be suggested. It can also analyze trends in cities visited by the user and suggest styles that match those trends. Furthermore, it can analyze the user's travel history and suggest fashion styles that match the culture of Asian countries visited. This makes it possible to suggest styles based on the user's travel history.

[0052] The suggestion unit can analyze the type of pet the user has and their relationship with the pet, and suggest fashion styles that can be enjoyed together with the pet. For example, a casual style suitable for walking a dog can be suggested to a user who has a dog. The suggestion unit can also analyze the relationship between the user and their pet and suggest styles that can be coordinated with the pet. Furthermore, it can analyze the activities of the user's pet and suggest outdoor styles to a user who enjoys the outdoors. This makes it possible to suggest fashion styles based on the type of pet the user has and their relationship with the pet.

[0053] The analysis unit can incorporate an algorithm that analyzes a user's past selection history and behavioral patterns and predicts future selections. For example, the analysis unit can analyze a user's past fashion selection history to predict the style the user is likely to choose next. If the user has previously preferred casual clothing, it can suggest a casual style for the next purchase. Furthermore, by analyzing a user's behavioral patterns, if the user tends to choose a particular style in a particular season, it can suggest a style that suits that season. Furthermore, it can analyze a user's purchase history and predict the item the user is likely to purchase next. This makes it possible to predict future selections based on a user's past selection history and behavioral patterns.

[0054] The analysis unit analyzes the user's social media activity and identifies trends and preferences, enabling it to make more personalized suggestions. For example, it can analyze the user's social media posts to identify preferred styles and trends. If the user posts a lot of fashion-related content, it can suggest those styles. It can also analyze the styles of influencers the user follows and make suggestions influenced by them. It can also analyze the user's social media history of likes and comments to identify preferred styles. This enables it to make more personalized suggestions based on the user's social media activity.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The user information collection unit collects user information, such as the user's face photo, body type, skin color, hair quality, and preferred style. The user information collection unit can also collect information about the user's lifestyle, hobbies, goals, and so on. Step 2: The analysis unit analyzes the user information collected by the user information collection unit. For example, the generation AI analyzes a photo of the user's face to identify the shape of the face and skin color. The generation AI also analyzes the user's body type and hair quality to obtain basic data for suggesting the optimal style. Furthermore, the generation AI analyzes the user's lifestyle habits, hobbies, and goals to obtain basic data for lifestyle suggestions. Step 3: The suggestion unit suggests the optimal fashion style, hairstyle, makeup, and lifestyle for the user based on the results of the analysis by the analysis unit. For example, the generation AI suggests clothes and accessories that suit the user's body type and skin color. The generation AI can also suggest the optimal hairstyle based on the user's face photo, hair quality, and preferred style. Furthermore, the generation AI can also suggest the optimal makeup method based on the user's face photo, skin color, and preferred makeup style. Furthermore, the generation AI can also suggest the optimal lifestyle based on the user's lifestyle habits, hobbies, and goals.

[0057] (Example 2) The self-realization support system according to an embodiment of the present invention is a system that collects user information, analyzes it using a generation AI, and suggests optimal fashion, hairstyle, makeup, and lifestyle. This allows the user to find the style that best suits them and aim for maximum self-realization.

[0058] A self-realization support system according to an embodiment includes a user information collection unit, an analysis unit, and a suggestion unit. The user information collection unit collects user information, such as a user's facial photo, body type, skin color, hair texture, and preferred style. The user information collection unit can also collect information about the user's lifestyle, hobbies, and goals. The analysis unit analyzes the user information collected by the user information collection unit. For example, the generation AI analyzes the user's facial photo to identify the user's face shape and skin color. The generation AI also analyzes the user's body type and hair texture to obtain basic data for suggesting an optimal style. The generation AI also analyzes the user's lifestyle, hobbies, and goals to obtain basic data for lifestyle suggestions. The suggestion unit suggests optimal fashion styles, hairstyles, makeup, and lifestyles for the user based on the results of the analysis by the analysis unit. For example, the generation AI suggests clothing and accessories that match the user's body type and skin color. The generation AI can also suggest optimal hairstyles based on the user's facial photo, hair texture, and preferred style. Furthermore, the generation AI can suggest optimal makeup methods based on the user's facial photo, skin color, and preferred makeup style. Furthermore, the generation AI can also suggest optimal lifestyles based on the user's lifestyle habits, hobbies, and goals. This allows the self-realization support system according to the embodiment to help users find the style that best suits them and achieve their highest self-realization.

[0059] The suggestion unit can suggest clothes and accessories that suit the user's body type and skin color. For example, the suggestion unit suggests clothes with an optimal silhouette based on the user's body type. For example, it suggests fitted clothes to a user with a slender body type. The suggestion unit also suggests clothes and accessories with optimal colors based on the user's skin color. For example, it suggests pastel-colored clothes to a user with a light skin color. The suggestion unit can also suggest optimal coordination taking into consideration both the user's body type and skin color. For example, it suggests fitted pastel-colored clothes to a user with a slender body type and light skin color. This enables more personalized suggestions by suggesting styles that suit the user's body type and skin color.

[0060] The suggestion unit can suggest an optimal hairstyle based on the user's facial photo, hair quality, and preferred style. For example, the suggestion unit analyzes the user's facial photo and suggests a hairstyle that suits the shape of the face. For example, for a user with a round face, it suggests a hairstyle that makes the face look thinner. The suggestion unit also analyzes the user's hair quality and suggests a hairstyle that suits the hair quality. For example, for a user with thin hair, it suggests a hairstyle that adds volume. The suggestion unit can also suggest an optimal hairstyle taking into account the user's preferred style. For example, if the user prefers a casual style, it suggests a casual hairstyle. This makes it possible to suggest hairstyles based on the user's facial photo and hair quality.

[0061] The suggestion unit can suggest a haircut and coloring that suits the user's face shape. For example, the suggestion unit analyzes the user's face shape and suggests a haircut that suits the face shape. For example, for a user with a square face, the suggestion unit suggests a haircut that makes the face look softer. The suggestion unit also suggests coloring that suits the user's face shape. For example, for a user with a long face, the suggestion unit suggests coloring that makes the face look shorter. The suggestion unit can also suggest optimal haircuts and coloring by taking into consideration both the user's face shape and hair quality. For example, for a user with a square face and thin hair, the suggestion unit suggests a voluminous haircut and coloring that makes the face look softer. This makes it possible to suggest haircuts and coloring that suit the user's face shape.

[0062] The suggestion unit can suggest an optimal makeup method based on the user's facial photo, skin color, and preferred makeup style. The suggestion unit, for example, analyzes the user's facial photo and suggests a makeup method that suits the shape of the face. For example, for a user with a round face, it suggests a makeup method that makes the face look slimmer. The suggestion unit also analyzes the user's skin color and suggests a makeup method that suits the skin color. For example, for a user with a light skin color, it suggests a light-colored foundation or lip color. The suggestion unit can also suggest an optimal makeup method taking into account the user's preferred makeup style. For example, if the user prefers a natural makeup style, it suggests a natural makeup method. This makes it possible to suggest makeup methods based on the user's facial photo and skin color.

[0063] The suggestion unit can suggest foundation and lip colors that match the user's skin color. For example, the suggestion unit analyzes the user's skin color and suggests foundation that matches the skin color. For example, a dark-colored foundation is suggested for a user with dark skin color. The suggestion unit also suggests lip colors that match the user's skin color. For example, a light-colored lip color is suggested for a user with light skin color. The suggestion unit can also suggest optimal foundation and lip colors by taking into consideration both the user's skin color and preferred makeup style. For example, a dark-colored foundation and a natural-colored lip color is suggested for a user with dark skin color who prefers a natural makeup style. This makes it possible to suggest foundation and lip colors that match the user's skin color.

[0064] The suggestion unit can suggest an optimal lifestyle based on the user's lifestyle habits, hobbies, and goals. The suggestion unit, for example, analyzes the user's lifestyle habits and suggests a lifestyle that suits the lifestyle. For example, for a user with a healthy lifestyle, it suggests a healthy meal plan and exercise plan. The suggestion unit can also analyze the user's hobbies and suggest a lifestyle that suits the hobbies. For example, for a user who likes the outdoors, it suggests a lifestyle that incorporates outdoor activities. The suggestion unit can also analyze the user's goals and suggest a lifestyle that suits the goals. For example, for a user whose goal is to lose weight, it suggests a meal plan and exercise plan that is suitable for the diet. This makes it possible to suggest a lifestyle based on the user's lifestyle habits and hobbies.

[0065] The suggestion unit can suggest meal plans and exercise plans that suit the user's health condition and goals. The suggestion unit, for example, analyzes the user's health condition and suggests meal plans that suit the health condition. For example, a low-carbohydrate meal plan is suggested for a user with high blood sugar levels. The suggestion unit also suggests exercise plans that suit the user's health condition. For example, an exercise plan centered on aerobic exercise is suggested for a user with poor cardiopulmonary function. The suggestion unit can also suggest optimal meal plans and exercise plans taking into consideration both the user's health condition and goals. For example, a user with a high blood sugar level who is aiming to lose weight is suggested a low-carbohydrate meal plan and an exercise plan centered on aerobic exercise. This makes it possible to suggest meal plans and exercise plans based on the user's health condition and goals.

[0066] The user information collection unit can estimate the user's emotional state in real time and collect information based on the emotional state. The user information collection unit, for example, captures the user's facial expression with a camera and estimates the emotion in real time. For example, if the user is smiling, it is determined that the user has a positive emotion and collects information based on that emotion. The user information collection unit also analyzes the user's voice input and estimates the emotion from the tone and tempo of the voice. For example, if the user is excited, it collects information based on that emotion. The user information collection unit also analyzes the user's input content and estimates the emotion from the text. For example, if the user inputs "I had a lot of fun today," it is determined that the user has a positive emotion and collects information based on that emotion. This makes it possible to collect information based on the user's emotional state.

[0067] The analysis unit can incorporate an algorithm that analyzes a user's past selection history and behavioral patterns and predicts future selections. For example, the analysis unit analyzes a user's past fashion selection history and predicts the style the user is likely to choose next. For example, if the user has previously preferred casual clothing, the analysis unit can suggest a casual style next time. The analysis unit can also analyze a user's behavioral patterns and predict future selections. For example, if a user tends to choose a particular style in a particular season, the analysis unit can suggest a style that suits that season. The analysis unit can also analyze a user's purchase history and predict the item the user is likely to purchase next. For example, if the user has purchased many items from a particular brand in the past, the analysis unit can suggest new products from that brand. This makes it possible to predict future selections based on a user's past selection history and behavioral patterns.

[0068] The analysis unit analyzes the user's social media activity and understands trends and preferences, allowing it to make more personalized suggestions. For example, the analysis unit analyzes the user's social media posts to understand preferred styles and trends. For example, if the user posts many fashion-related items, the analysis unit will suggest those styles. The analysis unit also analyzes the styles of influencers the user follows and makes suggestions influenced by them. For example, if the user follows a specific influencer, the analysis unit will suggest the style of that influencer. The analysis unit can also analyze the user's history of "likes" and comments on social media to understand preferred styles. For example, the analysis unit will suggest styles that the user has liked the most. This enables more personalized suggestions to be made based on the user's social media activity.

[0069] The user information collection unit analyzes the user's voice input, infers emotions from the tone and tempo of the voice, and collects information based on the emotions. For example, if the user inputs "I had a lot of fun today," the user information collection unit infers positive emotions from the tone and tempo of the voice and collects information based on the emotions. Also, if the user inputs "I'm tired," the user information collection unit infers negative emotions from the tone and tempo of the voice and suggests a relaxing style based on the emotions. Also, if the user inputs "I want to try a new style," the user information collection unit infers emotions of excitement and anticipation from the tone and tempo of the voice and collects information based on the emotions. This makes it possible to infer emotions based on the user's voice input and collect information based on the emotions.

[0070] The user information collection unit can perform image analysis of the user's living environment and suggest a style that suits it. For example, the user information collection unit can perform image analysis of the interior of the room where the user lives and suggest fashion and hairstyles that suit that style. For example, it can suggest a simple style that suits a modern interior. The user information collection unit can also perform image analysis of the scenery of the area where the user lives and suggest a style that suits that environment. For example, if the user lives in an area rich in nature, it can suggest a natural style. The user information collection unit can also analyze images of places the user frequently visits and suggest a style that suits that place. For example, if the user frequently goes to urban cafes, it can suggest an urban style. This makes it possible to suggest styles based on the user's living environment.

[0071] The user information collecting unit can use the emotion estimation function to analyze the emotion of the user when entering text in real time, and provide an interface for eliciting positive emotions. For example, the user information collecting unit can use the emotion estimation function to analyze the emotion of the user when entering text in real time, and display a message for eliciting positive emotions. For example, a message such as "Great choice!" is displayed. The user information collecting unit can also use the emotion estimation function to analyze the emotion of the user when entering text in real time, and provide an interface for eliciting positive emotions. For example, an image or video that makes the user smile is displayed. The user information collecting unit can also use the emotion estimation function to analyze the emotion of the user when entering text in real time, and provide audio feedback for eliciting positive emotions. For example, audio feedback such as "That style looks great on you!" is provided. This makes it possible to provide an interface for eliciting positive emotions by analyzing the emotion of the user when entering text in real time.

[0072] The analysis unit can analyze the user's health data and make style suggestions based on the health condition. The analysis unit, for example, analyzes the user's heart rate data and makes style suggestions based on the health condition. For example, if the heart rate is high, a relaxing style is suggested. The analysis unit can also analyze the user's sleep patterns and make style suggestions based on the health condition. For example, if the user is sleep deprived, a relaxing style is suggested. The analysis unit can also analyze the user's exercise data and make style suggestions based on the health condition. For example, a style that allows for relaxation after exercise is suggested. This makes it possible to make style suggestions based on the user's health data.

[0073] The analysis unit can analyze the user's cultural background and values ​​and make style suggestions based on them. For example, the analysis unit can analyze the user's cultural background and make style suggestions based on that. For example, it can suggest fashion styles rooted in a particular culture. The analysis unit can also analyze the user's values ​​and make style suggestions based on that. For example, it can suggest sustainable fashion to a user who has eco-friendly values. The analysis unit can also analyze the user's religious background and make style suggestions based on that. For example, it can suggest fashion styles suitable for a particular religion. This makes it possible to make style suggestions based on the user's cultural background and values.

[0074] The analysis unit can analyze the user's musical preferences and suggest styles that match the musical genre. For example, the analysis unit can analyze the user's music playlist and suggest fashion styles that match the user's preferred musical genre. For example, a rock style fashion can be suggested for a user who likes rock. The analysis unit can also analyze the styles of artists the user often listens to and suggest fashion styles influenced by them. For example, a style inspired by the fashion of a particular artist can be suggested. The analysis unit can also analyze the user's musical preferences and suggest hairstyles and makeup that match the genre. For example, an elegant style can be suggested for a user who likes classical music. This makes it possible to suggest styles based on the user's musical preferences.

[0075] The analysis unit can analyze the user's travel history and make style suggestions based on the places visited. For example, the analysis unit analyzes the user's travel history and suggests a fashion style based on the places visited. For example, if the user went to a beach resort, resort style fashion will be suggested. The analysis unit can also analyze trends in cities the user has visited and suggest a style that matches those trends. For example, if the user went to Paris, a style incorporating Parisian fashion trends will be suggested. The analysis unit can also analyze the user's travel history and suggest a style based on the culture of the places visited. For example, if the user visited Asian countries, a fashion style that matches that culture will be suggested. This makes it possible to suggest styles based on the user's travel history.

[0076] The analysis unit can use the emotion estimation function to analyze the emotion of the user when entering text in real time, and provide an interface for eliciting positive emotions. For example, the analysis unit can use the emotion estimation function to analyze the emotion of the user when entering text in real time, and display a message for eliciting positive emotions. For example, a message such as "Great choice!" is displayed. The analysis unit can also use the emotion estimation function to analyze the emotion of the user when entering text in real time, and provide an interface for eliciting positive emotions. For example, an image or video that makes the user smile is displayed. The analysis unit can also use the emotion estimation function to analyze the emotion of the user when entering text in real time, and provide audio feedback for eliciting positive emotions. For example, audio feedback such as "That style looks great on you!" is provided. This makes it possible to provide an interface for eliciting positive emotions by analyzing the emotion of the user when entering text in real time.

[0077] The suggestion unit can estimate the user's emotions and suggest a fashion style based on the emotions. The suggestion unit, for example, analyzes the user's facial expression to estimate the emotions. For example, if the user is smiling, it is determined that the user has positive emotions and suggests a fashion style based on that emotion. The suggestion unit also analyzes the user's voice input and estimates the emotions from the tone and tempo of the voice. For example, if the user is excited, it suggests a fashion style based on that emotion. The suggestion unit can also analyze the user's input content and estimate the emotions from the text. For example, if the user inputs "I had a lot of fun today," it is determined that the user has positive emotions and suggests a fashion style based on that emotion. This makes it possible to suggest fashion styles based on the user's emotions.

[0078] The suggestion unit can analyze the user's seasonal preferences and climate data to suggest a fashion style that suits the season. The suggestion unit, for example, analyzes the user's past fashion selection history to understand seasonal preferences. For example, for a user who prefers casual styles in summer, the suggestion unit suggests casual fashion that suits summer. The suggestion unit also analyzes climate data to suggest a fashion style that suits the season. For example, in winter, it suggests clothes made of warm materials. The suggestion unit can also analyze climate data for the user's region to suggest a fashion style that suits that region. For example, in rainy regions, it suggests waterproof fashion. This makes it possible to suggest fashion styles based on the user's seasonal preferences and climate data.

[0079] The suggestion unit can analyze the type of pet of the user and the user's relationship with the pet, and suggest a fashion style that can be enjoyed with the pet. For example, the suggestion unit can analyze the type of pet of the user and suggest a fashion style that can be enjoyed with the pet. For example, for a user who has a dog, the suggestion unit can suggest a casual style that is suitable for walking the dog. The suggestion unit can also analyze the relationship between the user and the pet and suggest a fashion style based on that relationship. For example, for a user who often takes photos with their pet, the suggestion unit can suggest a style that can be coordinated with the pet. The suggestion unit can also analyze the activities of the user's pet and suggest a fashion style that is suitable for that activity. For example, for a user who enjoys outdoors with their pet, the suggestion unit can suggest an outdoor style. This makes it possible to suggest fashion styles based on the type of pet of the user and the user's relationship with their pet.

[0080] The suggestion unit can analyze the user's hobbies and special skills and suggest a fashion style that suits them. For example, the suggestion unit can analyze the user's hobbies and suggest a fashion style that suits those hobbies. For example, for a user who likes music, the suggestion unit can suggest a style that is suitable for a music festival. The suggestion unit can also analyze the user's special skills and suggest a fashion style that suits those skills. For example, for a user who is good at dancing, the suggestion unit can suggest a style that is easy to move in. The suggestion unit can also analyze the user's hobbies and special skills and suggest a fashion style that suits those activities. For example, for a user who likes drawing, the suggestion unit can suggest a style that is suitable for an art studio. This makes it possible to suggest fashion styles based on the user's hobbies and special skills.

[0081] The suggestion unit can use the emotion estimation function to analyze the emotion of the user when entering text in real time, and provide an interface for eliciting positive emotions. For example, the suggestion unit can use the emotion estimation function to analyze the emotion of the user when entering text in real time, and display a message for eliciting positive emotions. For example, it can display a message such as "Great choice!". The suggestion unit can also use the emotion estimation function to analyze the emotion of the user when entering text in real time, and provide an interface for eliciting positive emotions. For example, it can display an image or video that makes the user smile. The suggestion unit can also use the emotion estimation function to analyze the emotion of the user when entering text in real time, and provide audio feedback for eliciting positive emotions. For example, it can provide audio feedback such as "That style looks great on you!". This makes it possible to provide an interface for eliciting positive emotions by analyzing the emotion of the user when entering text in real time.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The user information collection unit collects the user's health data, and the analysis unit analyzes the data to suggest a style based on the user's health condition. For example, the unit can analyze the user's heart rate data and suggest a relaxing style if the heart rate is high. It can also analyze the user's sleep patterns and suggest a relaxing style if the user is sleep deprived. It can also analyze the user's exercise data and suggest a style that allows the user to relax after exercise. This makes it possible to suggest a style based on the user's health condition.

[0084] The suggestion unit can analyze a user's cultural background and values ​​and make style suggestions based on them. For example, it can analyze a user's cultural background and suggest fashion styles rooted in a specific culture. It can also analyze a user's values ​​and suggest sustainable fashion to a user with eco-friendly values. It can also analyze a user's religious background and suggest fashion styles suitable for a specific religion. This makes it possible to make style suggestions based on a user's cultural background and values.

[0085] The suggestion unit can analyze the user's musical preferences and suggest styles that match the music genre. For example, it can analyze the user's music playlist and suggest rock-style fashion to a user who likes rock. It can also analyze the styles of artists the user often listens to and suggest fashion styles influenced by them. Furthermore, it can suggest elegant styles to a user who likes classical music based on the user's musical preferences. This makes it possible to suggest styles based on the user's musical preferences.

[0086] The suggestion unit can analyze the user's travel history and make style suggestions based on the places visited. For example, if the user's travel history is analyzed and the user went to a beach resort, resort-style fashion can be suggested. It can also analyze trends in cities visited by the user and suggest styles that match those trends. Furthermore, it can analyze the user's travel history and suggest fashion styles that match the culture of Asian countries visited. This makes it possible to suggest styles based on the user's travel history.

[0087] The suggestion unit can analyze the type of pet the user has and their relationship with the pet, and suggest fashion styles that can be enjoyed together with the pet. For example, a casual style suitable for walking a dog can be suggested to a user who has a dog. The suggestion unit can also analyze the relationship between the user and their pet and suggest styles that can be coordinated with the pet. Furthermore, it can analyze the activities of the user's pet and suggest outdoor styles to a user who enjoys the outdoors. This makes it possible to suggest fashion styles based on the type of pet the user has and their relationship with the pet.

[0088] The suggestion unit can estimate the user's emotions and suggest a fashion style based on the emotions. For example, the suggestion unit can analyze the user's facial expression, and if the user is smiling, it can determine that the user has positive emotions and suggest a fashion style based on that emotion. It can also analyze the user's voice input, and if the user is excited, it can suggest a fashion style based on that emotion. It can also analyze the user's input content, and if the user enters "I had a lot of fun today," it can determine that the user has positive emotions and suggest a fashion style based on that emotion. This makes it possible to suggest fashion styles based on the user's emotions.

[0089] The user information collection unit can estimate the user's emotional state in real time and collect information based on the emotional state. For example, the user's facial expression is captured by a camera and emotions are estimated in real time. If the user is smiling, it is determined that the user has a positive emotion and information based on that emotion is collected. It can also analyze the user's voice input and estimate emotions from the tone and tempo of the voice. If the user is excited, it is also possible to collect information based on that emotion. Furthermore, it can analyze the user's input content and estimate emotions from the text. This makes it possible to collect information based on the user's emotional state.

[0090] The user information collection unit can analyze the user's voice input, infer emotions from the tone and tempo of the voice, and collect information based on that. For example, if the user inputs "I had a lot of fun today," the unit can infer positive emotions from the tone and tempo of the voice and collect information based on that emotion. Also, if the user inputs "I'm tired," the unit can infer negative emotions from the tone and tempo of the voice and suggest a relaxing style based on that emotion. Furthermore, if the user inputs "I want to try a new style," the unit can infer emotions of excitement and anticipation from the tone and tempo of the voice and collect information based on that emotion. This makes it possible to infer emotions based on the user's voice input and collect information based on that emotion.

[0091] The analysis unit can incorporate an algorithm that analyzes a user's past selection history and behavioral patterns and predicts future selections. For example, the analysis unit can analyze a user's past fashion selection history to predict the style the user is likely to choose next. If the user has previously preferred casual clothing, it can suggest a casual style for the next purchase. Furthermore, by analyzing a user's behavioral patterns, if the user tends to choose a particular style in a particular season, it can suggest a style that suits that season. Furthermore, it can analyze a user's purchase history and predict the item the user is likely to purchase next. This makes it possible to predict future selections based on a user's past selection history and behavioral patterns.

[0092] The analysis unit analyzes the user's social media activity and identifies trends and preferences, enabling it to make more personalized suggestions. For example, it can analyze the user's social media posts to identify preferred styles and trends. If the user posts a lot of fashion-related content, it can suggest those styles. It can also analyze the styles of influencers the user follows and make suggestions influenced by them. It can also analyze the user's social media history of likes and comments to identify preferred styles. This enables it to make more personalized suggestions based on the user's social media activity.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The user information collection unit collects user information, such as the user's face photo, body type, skin color, hair quality, and preferred style. The user information collection unit can also collect information about the user's lifestyle, hobbies, goals, and so on. Step 2: The analysis unit analyzes the user information collected by the user information collection unit. For example, the generation AI analyzes a photo of the user's face to identify the shape of the face and skin color. The generation AI also analyzes the user's body type and hair quality to obtain basic data for suggesting the optimal style. Furthermore, the generation AI analyzes the user's lifestyle habits, hobbies, and goals to obtain basic data for lifestyle suggestions. Step 3: The suggestion unit suggests the optimal fashion style, hairstyle, makeup, and lifestyle for the user based on the results of the analysis by the analysis unit. For example, the generation AI suggests clothes and accessories that suit the user's body type and skin color. The generation AI can also suggest the optimal hairstyle based on the user's face photo, hair quality, and preferred style. Furthermore, the generation AI can also suggest the optimal makeup method based on the user's face photo, skin color, and preferred makeup style. Furthermore, the generation AI can also suggest the optimal lifestyle based on the user's lifestyle habits, hobbies, and goals.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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 user information collection unit that collects user information; an analysis unit that analyzes the user information collected by the user information collection unit; a suggestion unit that suggests optimal fashion styles, hairstyles, makeup, and lifestyles to the user based on the results of the analysis by the analysis unit. A system characterized by:

2. The proposal unit Suggesting clothes and accessories that suit the user's body type and skin color 2. The system of claim 1.

3. The proposal unit The system suggests the best hairstyle based on the user's face photo, hair quality, and preferred style.

2. The system of claim 1.

4. The proposal unit Suggest haircuts and coloring that suit the user's face shape 2. The system of claim 1.

5. The proposal unit Suggests the best makeup method based on the user's face photo, skin color, and preferred makeup style.

2. The system of claim 1.

Images