System

Abstract

An object of a system according to an embodiment is to unitarily provide a specific proposal for approaching an appearance that is ideal for a user.SOLUTION: A system according to an embodiment includes a hair set proposal unit, a clothing proposal unit, a makeup proposal unit, a body shape improvement proposal unit, and an ideal medical chart setting unit. A hair set proposal part analyzes the face photograph of the user and proposes an optimum hair set. The clothing proposal unit analyzes the body shape data of the user and proposes optimal clothing. The makeup suggestion unit analyzes the face photograph of the user and suggests an optimal makeup method. The body shape improvement proposal unit analyzes the body shape data of the user and proposes an optimal body shape improvement method. The ideal medical chart setting unit sets an ideal medical chart for filling a gap between the user's ideal and reality.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 technologies have had the problem of making it difficult to provide users with specific, unified suggestions to help them achieve their ideal appearance.

[0005] The system according to the embodiment aims to provide specific suggestions in a centralized manner to help users achieve their ideal appearance. [Means for solving the problem]

[0006] The system according to the embodiment includes a hairstyling suggestion unit, an outfit suggestion unit, a makeup suggestion unit, a body shape improvement suggestion unit, and an ideal chart setting unit. The hairstyling suggestion unit analyzes a user's facial photo and suggests an optimal hairstyling. The outfit suggestion unit analyzes the user's body shape data and suggests optimal outfits. The makeup suggestion unit analyzes the user's facial photo and suggests optimal makeup methods. The body shape improvement suggestion unit analyzes the user's body shape data and suggests optimal body shape improvement methods. The ideal chart setting unit sets an ideal chart to bridge the gap between the user's ideal and reality. [Effects of the Invention]

[0007] The system according to the embodiment can provide specific suggestions to help the user get closer to their ideal appearance in a unified manner. [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 AR application according to the embodiment of the present invention is a system that utilizes generative AI to provide support for users to realize their ideal self, and thus the AR application can suggest specific ways for users to bridge the gap between their ideal and reality.

[0029] The AR application according to the embodiment includes a hair styling suggestion unit, an outfit suggestion unit, a makeup suggestion unit, a body shape improvement suggestion unit, and an ideal medical record setting unit. The hair styling suggestion unit analyzes a user's facial photo and suggests an optimal hairstyle. For example, when a user uploads a facial photo to the app, a generation AI analyzes the facial photo and suggests a hairstyle that suits the user's face shape and hair type. The generation AI uses a text generation AI (e.g., LLM) to suggest an optimal hairstyle based on the user's facial photo. The generation AI can also analyze a user's facial photo and suggest an optimal hairstyle using a multimodal generation AI. The generation AI suggests a hairstyle that suits the user, for example, based on the user's face shape and hair type. The outfit suggestion unit analyzes the user's body type data and suggests optimal outfits. For example, when a user inputs their body type data and preferred fashion style, the generation AI analyzes the data and suggests outfits that suit the user. The generation AI uses a text generation AI (e.g., LLM) to suggest optimal outfits based on the user's body type data. The generation AI can also use multimodal generation AI to analyze the user's body shape data and suggest optimal clothing. For example, the generation AI can suggest optimal clothing based on the user's body shape and preferences. The makeup suggestion unit analyzes the user's facial photo and suggests optimal makeup techniques. For example, when a user uploads a photo of their face to the app, the generation AI analyzes the facial photo and suggests makeup techniques that suit their facial features. The generation AI uses text generation AI (e.g., LLM) to suggest optimal makeup techniques based on the user's facial photo. The generation AI can also analyze the user's facial photo using multimodal generation AI and suggest optimal makeup techniques. For example, the generation AI suggests optimal makeup techniques based on facial features and skin tone. The body shape improvement suggestion unit analyzes the user's body shape data and suggests optimal body shape improvement techniques. For example, when a user enters their body shape data, the generation AI analyzes the data and suggests exercises and meal plans that suit the user. The generation AI uses text generation AI (e.g., LLM) to suggest optimal body shape improvement techniques based on the user's body shape data.The generation AI can also use the multimodal generation AI to analyze the user's body shape data and suggest optimal body shape improvement methods. The generation AI can, for example, suggest optimal exercise and meal plans based on the user's body shape and health condition. The ideal medical record setting unit sets an ideal medical record to bridge the gap between the user's ideal and reality. For example, when the user inputs their ideal appearance and goals, the generation AI analyzes the data and suggests specific methods to bridge the gap with reality. The generation AI can also use a text generation AI (e.g., LLM) to set an ideal medical record to bridge the gap between the user's ideal and reality. The generation AI can also use the multimodal generation AI to analyze the gap between the user's ideal and reality and set the ideal medical record. The generation AI can, for example, suggest specific steps based on the user's goals and real-life situation. This allows the AR application according to the embodiment to suggest specific methods to bridge the gap between the user's ideal and reality. For example, by receiving suggestions for hairstyles, clothing, and makeup, the user can improve their daily style. Furthermore, by receiving suggestions for body shape improvement, the user can maintain a healthy body shape. Furthermore, by setting an ideal chart, users can take specific steps towards their goals.

[0030] The hair styling suggestion unit can take into account the user's past hairstyle history and make suggestions based on the most successful style. For example, the hair styling suggestion unit allows the user to upload photos of hairstyles they have tried in the past, and the generation AI analyzes those photos and makes new suggestions based on the most highly rated style. For example, the hair styling suggestion unit selects the hairstyle that is best suited to the user based on evaluation data of past styles. The hair styling suggestion unit also stores the user's past hairstyle history in a database, and the generation AI refers to that history to make new suggestions based on the most successful style. For example, the success rate of past styles is analyzed to suggest the best style for the user. The hair styling suggestion unit also analyzes photos of hairstyles the user has tried in the past, and the generation AI identifies the most successful style from the photos and makes a new suggestion. For example, the generation AI selects the best hairstyle for the user based on evaluation data of past styles. This makes it possible to suggest the best hairstyle for the user.

[0031] The hair styling suggestion unit can monitor the user's hair type and scalp health in real time and recommend optimal hair care products based on that information. For example, the hair styling suggestion unit uses a sensor to monitor the user's hair type and scalp health in real time, and the generation AI analyzes the data to recommend optimal hair care products. For example, it measures the dryness of the hair and the amount of oil on the scalp and recommends shampoos and treatments based on that information. The hair styling suggestion unit can also develop an app to monitor the user's hair type and scalp health, and the generation AI analyzes the data to recommend optimal hair care products. For example, it measures the level of hair damage and the health of the scalp and recommends hair care products based on that information. The hair styling suggestion unit can also use a device to monitor the user's hair type and scalp health in real time, and the generation AI analyzes the data to recommend optimal hair care products. For example, it measures the dryness of the hair and the amount of oil on the scalp and recommends shampoos and treatments based on that information. This allows the hair styling suggestion unit to recommend optimal hair care products based on the user's hair type and scalp health.

[0032] When analyzing the user's body type data, the clothing suggestion unit can suggest optimal clothing by taking into consideration seasonal and weather information. For example, the clothing suggestion unit combines the user's body type data with seasonal and weather information, and the generation AI suggests optimal clothing. For example, it suggests clothing made of cool materials in summer and clothing made of warm materials in winter. The clothing suggestion unit also analyzes the user's body type data and builds a system that suggests optimal clothing by taking into consideration seasonal and weather information. For example, it suggests waterproof clothing for rainy days. The clothing suggestion unit also combines the user's body type data with seasonal and weather information, and the generation AI suggests optimal clothing. For example, it suggests clothing made of cool materials in summer and clothing made of warm materials in winter. In this way, it is possible to suggest optimal clothing by taking into consideration seasonal and weather information.

[0033] The clothing suggestion unit can suggest the most popular style based on the user's past fashion history. For example, the clothing suggestion unit stores the user's past fashion history in a database, and the generation AI analyzes that history to suggest the most popular style. For example, it selects the optimal style based on past evaluation data. The clothing suggestion unit also analyzes the user's past fashion history and builds a system that makes new suggestions based on the most popular style. For example, it analyzes the success rate of past styles and suggests the most suitable style for the user. The clothing suggestion unit also stores the user's past fashion history in a database, and the generation AI analyzes that history to suggest the most popular style. For example, it selects the optimal style based on past evaluation data. This makes it possible to suggest the most suitable fashion style for the user.

[0034] The makeup suggestion unit can monitor the skin condition and tone in real time when analyzing a user's facial photo and suggest optimal makeup products based on that. For example, the makeup suggestion unit analyzes the user's facial photo and uses a sensor that monitors the skin condition and tone in real time, and the generation AI analyzes that data to suggest optimal makeup products. For example, it can suggest a foundation that matches the dryness and tone of the skin. The makeup suggestion unit can also develop an app that analyzes the user's facial photo and monitors the skin condition and tone in real time, and the generation AI analyzes that data to suggest optimal makeup products. For example, it can suggest a foundation that matches the dryness and tone of the skin. The makeup suggestion unit can also analyze the user's facial photo and use a device that monitors the skin condition and tone in real time, and the generation AI analyzes that data to suggest optimal makeup products. For example, it can suggest a foundation that matches the dryness and tone of the skin. This makes it possible to suggest optimal makeup products based on the user's skin condition and tone.

[0035] The makeup suggestion unit can suggest the most successful makeup method based on the user's past makeup history. For example, the makeup suggestion unit stores the user's past makeup history in a database, and the generation AI analyzes that history to suggest the most successful makeup method. For example, the optimal makeup method is selected based on past evaluation data. The makeup suggestion unit also analyzes the user's past makeup history and builds a system that makes new suggestions based on the most successful makeup method. For example, the success rate of past makeup methods is analyzed to suggest the most optimal makeup method for the user. The makeup suggestion unit also stores the user's past makeup history in a database, and the generation AI analyzes that history to suggest the most successful makeup method. For example, the optimal makeup method is selected based on past evaluation data. This makes it possible to suggest the most optimal makeup method for the user.

[0036] When analyzing the user's body shape data, the body shape improvement suggestion unit can take into account the user's past exercise history and suggest the most effective method. For example, the body shape improvement suggestion unit stores the user's past exercise history in a database, and the generation AI analyzes that history to suggest the most effective exercise method. For example, the optimal method is selected based on the success rate of past exercises. The body shape improvement suggestion unit also analyzes the user's past exercise history and builds a system that makes new suggestions based on the most effective method. For example, the success rate of past exercises is analyzed to suggest the optimal method for the user. The body shape improvement suggestion unit also stores the user's past exercise history in a database, and the generation AI analyzes that history to suggest the most effective exercise method. For example, the optimal method is selected based on the success rate of past exercises. This makes it possible to suggest the optimal exercise method for the user.

[0037] The body shape improvement suggestion unit can suggest an optimal meal plan based on the user's meal history. For example, the body shape improvement suggestion unit stores the user's past meal history in a database, and the generation AI analyzes the history to suggest an optimal meal plan. For example, the optimal plan is selected based on the success rate of past meals. The body shape improvement suggestion unit also analyzes the user's past meal history and builds a system that makes new suggestions based on the optimal meal plan. For example, the success rate of past meals is analyzed and an optimal plan is suggested to the user. The body shape improvement suggestion unit also stores the user's past meal history in a database, and the generation AI analyzes the history to suggest an optimal meal plan. For example, the optimal plan is selected based on the success rate of past meals. This makes it possible to suggest an optimal meal plan to the user.

[0038] When analyzing the gap between the user's ideal and reality, the ideal chart setting unit can consider the user's past goal achievement history and suggest the most effective method. For example, the ideal chart setting unit stores the user's past goal achievement history in a database, and the generation AI analyzes that history to suggest the most effective method. For example, the optimal method is selected based on the past success rate of goal achievement. The ideal chart setting unit also analyzes the user's past goal achievement history and builds a system that makes new suggestions based on the most effective method. For example, the past success rate of goal achievement is analyzed and the optimal method is suggested to the user. The ideal chart setting unit also stores the user's past goal achievement history in a database, and the generation AI analyzes that history to suggest the most effective method. For example, the optimal method is selected based on the past success rate of goal achievement. This makes it possible to suggest the optimal goal achievement method to the user.

[0039] The ideal medical record setting unit can suggest specific steps that suit the user's lifestyle and occupation. For example, the ideal medical record setting unit collects data on the user's lifestyle and occupation, and the generation AI analyzes the data to suggest specific steps. For example, for a user who does a lot of desk work, stretching and light exercise are suggested. In addition, the ideal medical record setting unit analyzes the user's schedule and activities to suggest specific steps that suit the user's lifestyle and occupation. For example, for a user who plays sports, specific training is suggested. In addition, the ideal medical record setting unit collects data on the user's lifestyle and occupation, and the generation AI analyzes the data to suggest specific steps. For example, for a user who does a lot of desk work, stretching and light exercise are suggested. In this way, specific steps that are optimal for the user can be suggested.

[0040] In addition to setting the ideal medical record, the ideal medical record setting unit can be linked to a social feedback function that incorporates the opinions of the user's friends and family. For example, the ideal medical record setting unit incorporates a social feedback function that allows the user to share the ideal medical record set by the user with friends and family and collect their opinions. For example, the ideal medical record setting unit selects the optimal method for achieving goals based on the evaluations of friends and family. The ideal medical record setting unit also collects feedback from friends and family in real time about the set ideal medical record, and the generation AI suggests the optimal method based on that data. For example, it prioritizes suggesting methods that receive a lot of positive feedback. The ideal medical record setting unit also incorporates a social feedback function that allows the user to share the ideal medical record set by the user with friends and family and collect their opinions. For example, the ideal method for achieving goals based on the evaluations of friends and family. This makes it possible to set an ideal medical record that incorporates the opinions of the user's friends and family.

[0041] The ideal medical chart setting unit can break down the ideal medical chart settings into specific steps that correspond to the user's schedule. For example, the ideal medical chart setting unit collects the user's schedule data, and the generation AI analyzes the data to break down the ideal medical chart settings into specific steps. For example, it proposes goal achievement steps that match the daily schedule. Furthermore, in order to propose specific steps that correspond to the user's schedule, the generation AI analyzes the user's plans and activities. For example, it proposes weekly goal achievement steps. Furthermore, the ideal medical chart setting unit collects the user's schedule data, and the generation AI analyzes the data to break down the ideal medical chart settings into specific steps. For example, it proposes goal achievement steps that match the daily schedule. This makes it possible to break down the ideal medical chart settings into specific steps that correspond to the user's schedule.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] AR applications can suggest improvements to daily life based on a user's lifestyle. For example, they can suggest healthy lifestyle habits based on the user's daily activities and hobbies. Specifically, if a user does a lot of desk work, they can suggest regular stretching and light exercise. If a user prefers outdoor activities, they can suggest appropriate equipment and activity plans. Furthermore, they can suggest balanced meal plans based on the user's diet. This allows users to incorporate healthy habits into their daily lives.

[0044] AR applications can analyze a user's sleep patterns and suggest optimal sleeping environments. For example, a generating AI can collect the user's sleep data, analyze the data, and suggest optimal sleeping environments. Specifically, it can suggest appropriate bedding, room temperature, and lighting settings to improve the user's sleep quality. It can also suggest optimal bedtimes and wake-up times based on the user's sleep patterns. It can also suggest relaxation techniques and music to help the user relax and fall asleep. This allows users to get high-quality sleep and improve their daily performance.

[0045] AR applications can analyze a user's exercise data and suggest optimal fitness plans. For example, a generation AI can collect the user's exercise history and body shape data, analyze the data, and suggest optimal fitness plans. Specifically, appropriate exercises and training plans can be suggested based on the user's goals and physical fitness level. Effective exercise schedules can also be suggested based on the user's exercise habits. Furthermore, the application can monitor the user's exercise data in real time and provide advice to maximize the effects of exercise. This allows users to exercise effectively and maintain a healthy body shape.

[0046] AR applications can suggest new hobbies and activities based on a user's hobbies and interests. For example, by analyzing a user's past activity history and interests, the generative AI can suggest new hobbies and activities based on that data. Specifically, if a user enjoys outdoor activities, new hiking trails and campsites can be suggested. If a user is interested in arts and crafts, new art projects and craft workshops can be suggested. Furthermore, if a user is interested in music or dance, new music genres and dance classes can be suggested. This allows users to enjoy new hobbies and activities and improves their quality of life.

[0047] AR applications can analyze a user's travel history and suggest their next travel destination. For example, by analyzing a user's past travel history and preferences, the generation AI can suggest their next travel destination based on that data. Specifically, new travel destinations and tourist attractions can be suggested based on the places the user has visited in the past and the activities they are interested in. Optimal travel plans can also be suggested based on the user's budget and schedule. Furthermore, activities and restaurants during the trip can also be suggested based on the user's travel history. This allows users to obtain useful information when planning their next trip, helping them to enjoy a fulfilling trip.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The hair styling suggestion unit analyzes the user's facial photo and suggests the optimal hairstyle. For example, when a user uploads a photo of their face to the app, the generation AI analyzes the photo and suggests a hairstyle that suits their face shape and hair type. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to suggest the optimal hairstyle based on the user's facial photo. Step 2: The clothing suggestion unit analyzes the user's body data and suggests the most suitable clothing. For example, when a user inputs their body data and preferred fashion style, the generation AI analyzes the data and suggests clothing that suits the user. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to suggest the most suitable clothing based on the user's body data. Step 3: The makeup suggestion unit analyzes the user's facial photo and suggests the optimal makeup look. For example, when a user uploads a photo of their face to the app, the generation AI analyzes the photo and suggests makeup looks that suit their facial features. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to suggest the optimal makeup look based on the user's facial photo. Step 4: The body shape improvement suggestion unit analyzes the user's body shape data and suggests optimal body shape improvement methods. For example, when a user inputs their own body shape data, the generation AI analyzes the data and suggests exercise and meal plans that suit the user. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to suggest optimal body shape improvement methods based on the user's body shape data. Step 5: The ideal chart setting unit sets an ideal chart to bridge the gap between the user's ideal and reality. For example, when the user inputs their ideal appearance and goals, the generation AI analyzes the data and suggests specific ways to bridge the gap with reality. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the gap between the user's ideal and reality and set up an ideal chart.

[0050] (Example 2) The AR application according to the embodiment of the present invention is a system that utilizes generative AI to provide support for users to realize their ideal self, and thus the AR application can suggest specific ways for users to bridge the gap between their ideal and reality.

[0051] The AR application according to the embodiment includes a hair styling suggestion unit, an outfit suggestion unit, a makeup suggestion unit, a body shape improvement suggestion unit, and an ideal medical record setting unit. The hair styling suggestion unit analyzes a user's facial photo and suggests an optimal hairstyle. For example, when a user uploads a facial photo to the app, a generation AI analyzes the facial photo and suggests a hairstyle that suits the user's face shape and hair type. The generation AI uses a text generation AI (e.g., LLM) to suggest an optimal hairstyle based on the user's facial photo. The generation AI can also analyze a user's facial photo and suggest an optimal hairstyle using a multimodal generation AI. The generation AI suggests a hairstyle that suits the user, for example, based on the user's face shape and hair type. The outfit suggestion unit analyzes the user's body type data and suggests optimal outfits. For example, when a user inputs their body type data and preferred fashion style, the generation AI analyzes the data and suggests outfits that suit the user. The generation AI uses a text generation AI (e.g., LLM) to suggest optimal outfits based on the user's body type data. The generation AI can also use multimodal generation AI to analyze the user's body shape data and suggest optimal clothing. For example, the generation AI can suggest optimal clothing based on the user's body shape and preferences. The makeup suggestion unit analyzes the user's facial photo and suggests optimal makeup techniques. For example, when a user uploads a photo of their face to the app, the generation AI analyzes the facial photo and suggests makeup techniques that suit their facial features. The generation AI uses text generation AI (e.g., LLM) to suggest optimal makeup techniques based on the user's facial photo. The generation AI can also analyze the user's facial photo using multimodal generation AI and suggest optimal makeup techniques. For example, the generation AI suggests optimal makeup techniques based on facial features and skin tone. The body shape improvement suggestion unit analyzes the user's body shape data and suggests optimal body shape improvement techniques. For example, when a user enters their body shape data, the generation AI analyzes the data and suggests exercises and meal plans that suit the user. The generation AI uses text generation AI (e.g., LLM) to suggest optimal body shape improvement techniques based on the user's body shape data.The generation AI can also use the multimodal generation AI to analyze the user's body shape data and suggest optimal body shape improvement methods. The generation AI can, for example, suggest optimal exercise and meal plans based on the user's body shape and health condition. The ideal medical record setting unit sets an ideal medical record to bridge the gap between the user's ideal and reality. For example, when the user inputs their ideal appearance and goals, the generation AI analyzes the data and suggests specific methods to bridge the gap with reality. The generation AI can also use a text generation AI (e.g., LLM) to set an ideal medical record to bridge the gap between the user's ideal and reality. The generation AI can also use the multimodal generation AI to analyze the gap between the user's ideal and reality and set the ideal medical record. The generation AI can, for example, suggest specific steps based on the user's goals and real-life situation. This allows the AR application according to the embodiment to suggest specific methods to bridge the gap between the user's ideal and reality. For example, by receiving suggestions for hairstyles, clothing, and makeup, the user can improve their daily style. Furthermore, by receiving suggestions for body shape improvement, the user can maintain a healthy body shape. Furthermore, by setting an ideal chart, users can take specific steps towards their goals.

[0052] The hair styling suggestion unit can take into account the user's past hairstyle history and make suggestions based on the most successful style. For example, the hair styling suggestion unit allows the user to upload photos of hairstyles they have tried in the past, and the generation AI analyzes those photos and makes new suggestions based on the most highly rated style. For example, the hair styling suggestion unit selects the hairstyle that is best suited to the user based on evaluation data of past styles. The hair styling suggestion unit also stores the user's past hairstyle history in a database, and the generation AI refers to that history to make new suggestions based on the most successful style. For example, the success rate of past styles is analyzed to suggest the best style for the user. The hair styling suggestion unit also analyzes photos of hairstyles the user has tried in the past, and the generation AI identifies the most successful style from the photos and makes a new suggestion. For example, the generation AI selects the best hairstyle for the user based on evaluation data of past styles. This makes it possible to suggest the best hairstyle for the user.

[0053] The hair styling suggestion unit can monitor the user's hair type and scalp health in real time and recommend optimal hair care products based on that information. For example, the hair styling suggestion unit uses a sensor to monitor the user's hair type and scalp health in real time, and the generation AI analyzes the data to recommend optimal hair care products. For example, it measures the dryness of the hair and the amount of oil on the scalp and recommends shampoos and treatments based on that information. The hair styling suggestion unit can also develop an app to monitor the user's hair type and scalp health, and the generation AI analyzes the data to recommend optimal hair care products. For example, it measures the level of hair damage and the health of the scalp and recommends hair care products based on that information. The hair styling suggestion unit can also use a device to monitor the user's hair type and scalp health in real time, and the generation AI analyzes the data to recommend optimal hair care products. For example, it measures the dryness of the hair and the amount of oil on the scalp and recommends shampoos and treatments based on that information. This allows the hair styling suggestion unit to recommend optimal hair care products based on the user's hair type and scalp health.

[0054] The hair set suggestion unit can use the emotion estimation function to suggest a hairstyle that will make the user feel most confident. The hair set suggestion unit, for example, analyzes a facial photo of the user and uses the emotion estimation function to suggest a hairstyle that will make the user feel most confident. For example, the hair set suggestion unit selects a style that will make the user feel most confident based on the user's facial expression and emotion score. The hair set suggestion unit also analyzes the user's past hairstyle history and uses the emotion estimation function to suggest a style that will make the user feel most confident. For example, the hair set suggestion unit selects an optimal style for the user based on evaluation data of past styles. The hair set suggestion unit also analyzes a facial photo of the user and uses the emotion estimation function to suggest a hairstyle that will make the user feel most confident. For example, the hair set suggestion unit selects a style that will make the user feel most confident based on the user's facial expression and emotion score. This makes it possible to suggest a hairstyle that will make the user feel most confident.

[0055] When analyzing the user's body type data, the clothing suggestion unit can suggest optimal clothing by taking into consideration seasonal and weather information. For example, the clothing suggestion unit combines the user's body type data with seasonal and weather information, and the generation AI suggests optimal clothing. For example, it suggests clothing made of cool materials in summer and clothing made of warm materials in winter. The clothing suggestion unit also analyzes the user's body type data and builds a system that suggests optimal clothing by taking into consideration seasonal and weather information. For example, it suggests waterproof clothing for rainy days. The clothing suggestion unit also combines the user's body type data with seasonal and weather information, and the generation AI suggests optimal clothing. For example, it suggests clothing made of cool materials in summer and clothing made of warm materials in winter. In this way, it is possible to suggest optimal clothing by taking into consideration seasonal and weather information.

[0056] The clothing suggestion unit can suggest the most popular style based on the user's past fashion history. For example, the clothing suggestion unit stores the user's past fashion history in a database, and the generation AI analyzes that history to suggest the most popular style. For example, it selects the optimal style based on past evaluation data. The clothing suggestion unit also analyzes the user's past fashion history and builds a system that makes new suggestions based on the most popular style. For example, it analyzes the success rate of past styles and suggests the most suitable style for the user. The clothing suggestion unit also stores the user's past fashion history in a database, and the generation AI analyzes that history to suggest the most popular style. For example, it selects the optimal style based on past evaluation data. This makes it possible to suggest the most suitable fashion style for the user.

[0057] The clothing suggestion unit can use the emotion estimation function to suggest clothing that will most satisfy the user. The clothing suggestion unit, for example, analyzes a photo of the user's face and uses the emotion estimation function to suggest clothing that will most satisfy the user. For example, the most satisfying style is selected based on the user's facial expression and emotion score. The clothing suggestion unit also analyzes the user's past fashion history and uses the emotion estimation function to suggest a style that will most satisfy the user. For example, the most optimal style is selected based on evaluation data of past styles. The clothing suggestion unit also analyzes a photo of the user's face and uses the emotion estimation function to suggest clothing that will most satisfy the user. For example, the most satisfying style is selected based on the user's facial expression and emotion score. This makes it possible to suggest clothing that will most satisfy the user.

[0058] The makeup suggestion unit can monitor the skin condition and tone in real time when analyzing a user's facial photo and suggest optimal makeup products based on that. For example, the makeup suggestion unit analyzes the user's facial photo and uses a sensor that monitors the skin condition and tone in real time, and the generation AI analyzes that data to suggest optimal makeup products. For example, it can suggest a foundation that matches the dryness and tone of the skin. The makeup suggestion unit can also develop an app that analyzes the user's facial photo and monitors the skin condition and tone in real time, and the generation AI analyzes that data to suggest optimal makeup products. For example, it can suggest a foundation that matches the dryness and tone of the skin. The makeup suggestion unit can also analyze the user's facial photo and use a device that monitors the skin condition and tone in real time, and the generation AI analyzes that data to suggest optimal makeup products. For example, it can suggest a foundation that matches the dryness and tone of the skin. This makes it possible to suggest optimal makeup products based on the user's skin condition and tone.

[0059] The makeup suggestion unit can suggest the most successful makeup method based on the user's past makeup history. For example, the makeup suggestion unit stores the user's past makeup history in a database, and the generation AI analyzes that history to suggest the most successful makeup method. For example, the optimal makeup method is selected based on past evaluation data. The makeup suggestion unit also analyzes the user's past makeup history and builds a system that makes new suggestions based on the most successful makeup method. For example, the success rate of past makeup methods is analyzed to suggest the most optimal makeup method for the user. The makeup suggestion unit also stores the user's past makeup history in a database, and the generation AI analyzes that history to suggest the most successful makeup method. For example, the optimal makeup method is selected based on past evaluation data. This makes it possible to suggest the most optimal makeup method for the user.

[0060] The makeup suggestion unit can use the emotion estimation function to suggest a makeup method that will give the user the most confidence. The makeup suggestion unit, for example, analyzes a facial photo of the user and uses the emotion estimation function to suggest a makeup method that will give the user the most confidence. For example, the makeup suggestion unit selects a makeup method that will give the user the most confidence based on the user's facial expression and emotion score. The makeup suggestion unit also analyzes the user's past makeup history and uses the emotion estimation function to suggest a makeup method that will give the user the most confidence. For example, the makeup suggestion unit selects an optimal makeup method for the user based on evaluation data of past makeup methods. The makeup suggestion unit also analyzes a facial photo of the user and uses the emotion estimation function to suggest a makeup method that will give the user the most confidence. For example, the makeup suggestion unit selects a makeup method that will give the user the most confidence based on the user's facial expression and emotion score. This makes it possible to suggest a makeup method that will give the user the most confidence.

[0061] When analyzing the user's body shape data, the body shape improvement suggestion unit can take into account the user's past exercise history and suggest the most effective method. For example, the body shape improvement suggestion unit stores the user's past exercise history in a database, and the generation AI analyzes that history to suggest the most effective exercise method. For example, the optimal method is selected based on the success rate of past exercises. The body shape improvement suggestion unit also analyzes the user's past exercise history and builds a system that makes new suggestions based on the most effective method. For example, the success rate of past exercises is analyzed to suggest the optimal method for the user. The body shape improvement suggestion unit also stores the user's past exercise history in a database, and the generation AI analyzes that history to suggest the most effective exercise method. For example, the optimal method is selected based on the success rate of past exercises. This makes it possible to suggest the optimal exercise method for the user.

[0062] The body shape improvement suggestion unit can suggest an optimal meal plan based on the user's meal history. For example, the body shape improvement suggestion unit stores the user's past meal history in a database, and the generation AI analyzes the history to suggest an optimal meal plan. For example, the optimal plan is selected based on the success rate of past meals. The body shape improvement suggestion unit also analyzes the user's past meal history and builds a system that makes new suggestions based on the optimal meal plan. For example, the success rate of past meals is analyzed and an optimal plan is suggested to the user. The body shape improvement suggestion unit also stores the user's past meal history in a database, and the generation AI analyzes the history to suggest an optimal meal plan. For example, the optimal plan is selected based on the success rate of past meals. This makes it possible to suggest an optimal meal plan to the user.

[0063] The body shape improvement suggestion unit can use the emotion estimation function to suggest a body shape improvement method that will most motivate the user. For example, the body shape improvement suggestion unit analyzes the user's body shape data and uses the emotion estimation function to suggest a body shape improvement method that will most motivate the user. For example, it selects optimal exercises and meal plans based on the user's emotion score. The body shape improvement suggestion unit also analyzes the user's past body shape improvement history and uses the emotion estimation function to suggest a method that will most motivate the user. For example, it selects an optimal method based on past success rates and emotion data. The body shape improvement suggestion unit also analyzes the user's body shape data and uses the emotion estimation function to suggest a body shape improvement method that will most motivate the user. For example, it selects optimal exercises and meal plans based on the user's emotion score. This makes it possible to suggest a body shape improvement method that will most motivate the user.

[0064] When analyzing the gap between the user's ideal and reality, the ideal chart setting unit can consider the user's past goal achievement history and suggest the most effective method. For example, the ideal chart setting unit stores the user's past goal achievement history in a database, and the generation AI analyzes that history to suggest the most effective method. For example, the optimal method is selected based on the past success rate of goal achievement. The ideal chart setting unit also analyzes the user's past goal achievement history and builds a system that makes new suggestions based on the most effective method. For example, the past success rate of goal achievement is analyzed and the optimal method is suggested to the user. The ideal chart setting unit also stores the user's past goal achievement history in a database, and the generation AI analyzes that history to suggest the most effective method. For example, the optimal method is selected based on the past success rate of goal achievement. This makes it possible to suggest the optimal goal achievement method to the user.

[0065] The ideal medical record setting unit can suggest specific steps that suit the user's lifestyle and occupation. For example, the ideal medical record setting unit collects data on the user's lifestyle and occupation, and the generation AI analyzes the data to suggest specific steps. For example, for a user who does a lot of desk work, stretching and light exercise are suggested. In addition, the ideal medical record setting unit analyzes the user's schedule and activities to suggest specific steps that suit the user's lifestyle and occupation. For example, for a user who plays sports, specific training is suggested. In addition, the ideal medical record setting unit collects data on the user's lifestyle and occupation, and the generation AI analyzes the data to suggest specific steps. For example, for a user who does a lot of desk work, stretching and light exercise are suggested. In this way, specific steps that are optimal for the user can be suggested.

[0066] The ideal chart setting unit can use the emotion estimation function to suggest a goal achievement method that will most motivate the user. The ideal chart setting unit, for example, analyzes the user's goal data and uses the emotion estimation function to suggest a goal achievement method that will most motivate the user. For example, it selects optimal steps based on the user's emotion score. The ideal chart setting unit also analyzes the user's past goal achievement history and uses the emotion estimation function to suggest a goal achievement method that will most motivate the user. For example, it selects the optimal method based on past success rates and emotion data. The ideal chart setting unit also analyzes the user's goal data and uses the emotion estimation function to suggest a goal achievement method that will most motivate the user. For example, it selects optimal steps based on the user's emotion score. This makes it possible to suggest a goal achievement method that will most motivate the user.

[0067] In addition to setting the ideal medical record, the ideal medical record setting unit can be linked to a social feedback function that incorporates the opinions of the user's friends and family. For example, the ideal medical record setting unit incorporates a social feedback function that allows the user to share the ideal medical record set by the user with friends and family and collect their opinions. For example, the ideal medical record setting unit selects the optimal method for achieving goals based on the evaluations of friends and family. The ideal medical record setting unit also collects feedback from friends and family in real time about the set ideal medical record, and the generation AI suggests the optimal method based on that data. For example, it prioritizes suggesting methods that receive a lot of positive feedback. The ideal medical record setting unit also incorporates a social feedback function that allows the user to share the ideal medical record set by the user with friends and family and collect their opinions. For example, the ideal method for achieving goals based on the evaluations of friends and family. This makes it possible to set an ideal medical record that incorporates the opinions of the user's friends and family.

[0068] The ideal medical chart setting unit can break down the ideal medical chart settings into specific steps that correspond to the user's schedule. For example, the ideal medical chart setting unit collects the user's schedule data, and the generation AI analyzes the data to break down the ideal medical chart settings into specific steps. For example, it proposes goal achievement steps that match the daily schedule. Furthermore, in order to propose specific steps that correspond to the user's schedule, the generation AI analyzes the user's plans and activities. For example, it proposes weekly goal achievement steps. Furthermore, the ideal medical chart setting unit collects the user's schedule data, and the generation AI analyzes the data to break down the ideal medical chart settings into specific steps. For example, it proposes goal achievement steps that match the daily schedule. This makes it possible to break down the ideal medical chart settings into specific steps that correspond to the user's schedule.

[0069] The ideal chart setting unit uses the emotion estimation function to record the emotions the user feels in the process of achieving their goal and reflect them in the next suggestion. For example, the ideal chart setting unit uses the emotion estimation function to record the emotions the user feels in the process of achieving their goal and reflects that data in the next suggestion. For example, it prioritizes suggesting steps that elicit a large number of positive emotional responses. The ideal chart setting unit also records the emotions the user feels in the process of achieving their goal in real time, and the generation AI analyzes that data and reflects it in the next suggestion. For example, it selects the optimal step based on the emotion score. The ideal chart setting unit also uses the emotion estimation function to record the emotions the user feels in the process of achieving their goal and reflects that data in the next suggestion. For example, it prioritizes suggesting steps that elicit a large number of positive emotional responses. This allows the emotions the user feels in the process of achieving their goal to be recorded and reflected in the next suggestion.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] AR applications can suggest improvements to daily life based on a user's lifestyle. For example, they can suggest healthy lifestyle habits based on the user's daily activities and hobbies. Specifically, if a user does a lot of desk work, they can suggest regular stretching and light exercise. If a user prefers outdoor activities, they can suggest appropriate equipment and activity plans. Furthermore, they can suggest balanced meal plans based on the user's diet. This allows users to incorporate healthy habits into their daily lives.

[0072] AR applications can estimate a user's emotions and make suggestions for stress management. For example, they can analyze a user's facial expressions and voice data to estimate their stress level. Specifically, if a user is feeling high stress, they can suggest relaxation methods and activities to relieve stress. Also, if the user is relaxed, they can provide advice on how to maintain that state. Furthermore, they can suggest appropriate times to rest and refresh themselves based on the user's stress level. This allows users to effectively manage stress and maintain their physical and mental health.

[0073] AR applications can analyze a user's sleep patterns and suggest optimal sleeping environments. For example, a generating AI can collect the user's sleep data, analyze the data, and suggest optimal sleeping environments. Specifically, it can suggest appropriate bedding, room temperature, and lighting settings to improve the user's sleep quality. It can also suggest optimal bedtimes and wake-up times based on the user's sleep patterns. It can also suggest relaxation techniques and music to help the user relax and fall asleep. This allows users to get high-quality sleep and improve their daily performance.

[0074] AR applications can estimate a user's emotions and suggest music or entertainment that matches their emotions. For example, emotions can be estimated by analyzing the user's facial expressions and voice data. Specifically, if a user wants to relax, relaxation music or a meditation app can be suggested. If a user wants to cheer up, upbeat music or energetic entertainment can be suggested. Furthermore, if a user wants to express their emotions, art or creative activities can be suggested. This allows users to enjoy entertainment that matches their emotions and maintain their mental health.

[0075] AR applications can analyze a user's exercise data and suggest optimal fitness plans. For example, a generation AI can collect the user's exercise history and body shape data, analyze the data, and suggest optimal fitness plans. Specifically, appropriate exercises and training plans can be suggested based on the user's goals and physical fitness level. Effective exercise schedules can also be suggested based on the user's exercise habits. Furthermore, the application can monitor the user's exercise data in real time and provide advice to maximize the effects of exercise. This allows users to exercise effectively and maintain a healthy body shape.

[0076] AR applications can estimate a user's emotions and suggest meal plans based on those emotions. For example, emotions can be estimated by analyzing the user's facial expressions and voice data. Specifically, if a user is feeling stressed, a meal plan using ingredients with a relaxing effect can be suggested. Also, if a user needs energy, a meal plan using nutritious ingredients can be suggested. Furthermore, if a user is feeling a specific emotion, a meal plan that matches that emotion can be suggested. This allows users to enjoy meals that match their emotions and maintain their physical and mental health.

[0077] AR applications can suggest new hobbies and activities based on a user's hobbies and interests. For example, by analyzing a user's past activity history and interests, the generative AI can suggest new hobbies and activities based on that data. Specifically, if a user enjoys outdoor activities, new hiking trails and campsites can be suggested. If a user is interested in arts and crafts, new art projects and craft workshops can be suggested. Furthermore, if a user is interested in music or dance, new music genres and dance classes can be suggested. This allows users to enjoy new hobbies and activities and improves their quality of life.

[0078] AR applications can estimate a user's emotions and suggest relaxation methods according to their emotions. For example, emotions can be estimated by analyzing the user's facial expressions and voice data. Specifically, if a user is feeling stressed, meditation or deep breathing methods can be suggested. If a user wants to relax, aromatherapy or massage methods can be suggested. Furthermore, if a user wants to stabilize their emotions, yoga or stretching methods can be suggested. This allows users to practice relaxation methods that suit their emotions and maintain their physical and mental health.

[0079] AR applications can analyze a user's travel history and suggest their next travel destination. For example, by analyzing a user's past travel history and preferences, the generation AI can suggest their next travel destination based on that data. Specifically, new travel destinations and tourist attractions can be suggested based on the places the user has visited in the past and the activities they are interested in. Optimal travel plans can also be suggested based on the user's budget and schedule. Furthermore, activities and restaurants during the trip can also be suggested based on the user's travel history. This allows users to obtain useful information when planning their next trip, helping them to enjoy a fulfilling trip.

[0080] AR applications can estimate a user's emotions and suggest communication methods that correspond to those emotions. For example, emotions can be estimated by analyzing the user's facial expressions and voice data. Specifically, if a user is feeling stressed, the application can suggest relaxing communication methods and topics. Also, if a user is feeling happy, the application can suggest ways and topics for sharing those emotions. Furthermore, if a user wants to express their emotions, the application can suggest communication methods and topics that match those emotions. This allows users to communicate in a way that suits their emotions, which can smooth out human relationships.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The hair styling suggestion unit analyzes the user's facial photo and suggests the optimal hairstyle. For example, when a user uploads a photo of their face to the app, the generation AI analyzes the photo and suggests a hairstyle that suits their face shape and hair type. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to suggest the optimal hairstyle based on the user's facial photo. Step 2: The clothing suggestion unit analyzes the user's body data and suggests the most suitable clothing. For example, when a user inputs their body data and preferred fashion style, the generation AI analyzes the data and suggests clothing that suits the user. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to suggest the most suitable clothing based on the user's body data. Step 3: The makeup suggestion unit analyzes the user's facial photo and suggests the optimal makeup look. For example, when a user uploads a photo of their face to the app, the generation AI analyzes the photo and suggests makeup looks that suit their facial features. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to suggest the optimal makeup look based on the user's facial photo. Step 4: The body shape improvement suggestion unit analyzes the user's body shape data and suggests optimal body shape improvement methods. For example, when a user inputs their own body shape data, the generation AI analyzes the data and suggests exercise and meal plans that suit the user. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to suggest optimal body shape improvement methods based on the user's body shape data. Step 5: The ideal chart setting unit sets an ideal chart to bridge the gap between the user's ideal and reality. For example, when the user inputs their ideal appearance and goals, the generation AI analyzes the data and suggests specific ways to bridge the gap with reality. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the gap between the user's ideal and reality and set up an ideal chart.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. It is an AR application that utilizes generative AI. The generated AI is a hair styling suggestion unit that analyzes a user's facial photo and suggests an optimal hair styling; a clothing suggestion unit that analyzes the user's body type data and suggests optimal clothing; a makeup suggestion unit that analyzes a facial photo of the user and suggests an optimal makeup method; a body shape improvement suggestion unit that analyzes the body shape data of the user and suggests an optimal body shape improvement method; an ideal chart setting unit that sets an ideal chart to fill the gap between the user's ideal and reality. A system characterized by:

2. The hair styling suggestion unit Considering the user's past hairstyle history, suggestions are made based on the most successful styles 2. The system of claim 1.

3. The hair styling suggestion unit The system monitors the user's hair type and scalp health in real time and recommends optimal hair care products based on that information.

2. The system of claim 1.

4. The hair styling suggestion unit Suggest a hairstyle that the user feels most confident in 2. The system of claim 1.

5. The clothing suggestion unit When analyzing the user's body shape data, the system takes into account the season and weather information to suggest optimal clothing.

2. The system of claim 1.

6. The clothing suggestion unit Suggesting the most popular styles based on the user's past fashion history 2. The system of claim 1.

7. The clothing suggestion unit Suggest the most satisfying outfit for the user 2. The system of claim 1.

8. The makeup suggestion unit When analyzing the user's facial photo, the system monitors the skin condition and tone in real time and suggests the most suitable makeup products based on that.

2. The system of claim 1.

Images