System

Abstract

To provide a system capable of proposing optimal fashion coordination in consideration of a user's body shape, clothes on hand, weather, and the like.SOLUTION: The system includes an image input part, a figure data input part, a clothing image registration part, a weather data linkage part, a coordinate proposal part, a dressing simulation part, and an online shopping linkage part. The image input unit inputs an image of a user. The physique data input unit inputs physique data of a user. The clothing image registration unit registers an image of clothing on hand. The weather data coordination unit coordinates weather data. A coordinate proposal part proposes optimum fashion coordinates on the basis of the data. The dressing simulation part virtually simulates dressing of the coordination. An online shopping cooperation part performs cooperation and guidance to an online shopping site on the basis of the coordination.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to suggest optimal fashion coordination that takes into account a user's body type, clothing they already own, the weather, and other factors.

[0005] The system according to the embodiment aims to propose optimal fashion coordination taking into consideration the user's body type, clothing items in stock, the weather, and the like. [Means for solving the problem]

[0006] The system according to the embodiment includes an image input unit, a body type data input unit, a clothing image registration unit, a weather data linking unit, a coordination suggestion unit, a dress-up simulation unit, and an online shopping linking unit. The image input unit inputs an image of the user. The body type data input unit inputs the user's body type data. The clothing image registration unit registers images of clothing owned by the user. The weather data linking unit links weather data. The coordination suggestion unit suggests optimal fashion coordination based on data acquired by the image input unit, body type data input unit, clothing image registration unit, and weather data linking unit. The dress-up simulation unit virtually simulates dress-up of the coordination suggested by the coordination suggestion unit. The online shopping linking unit links and guides the user to online shopping sites based on the coordination suggested by the coordination suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal fashion coordination taking into consideration the user's body type, clothing they have, the weather, and the like. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A fashion coordinator system according to an embodiment of the present invention is a system that provides a fashion coordinator dedicated to a user by linking a user's image with body type data, images of clothing owned by the user, and weather data. As a result, the fashion coordinator system can provide a fashion coordinator dedicated to a user by linking a user's image with body type data, images of clothing owned by the user, and weather data.

[0029] A fashion coordinator system according to an embodiment includes an image input unit, a body type data input unit, a clothing image registration unit, a weather data linking unit, a coordination suggestion unit, a dress-up simulation unit, and an online shopping linking unit. The image input unit inputs a user's image. For example, the user uploads a full-body photo, and the generation AI analyzes the image to identify the user's posture and body type. The image input unit can also input a user's facial image, and the generation AI can analyze the facial features. The body type data input unit inputs the user's body type data. For example, the user inputs body type data such as height, weight, and waist size, and the generation AI analyzes the data to identify the user's body type. The body type data input unit can also input 3D scan data, and the generation AI can analyze the data. The clothing image registration unit registers images of the user's clothing. For example, the user uploads images of shirts, pants, skirts, jackets, etc., and the generation AI analyzes the images to recognize the type, color, and design of the clothing. The clothing image registration unit can also input clothing tag information, and the generation AI can analyze the information. The weather data linking unit links weather data. For example, the generation AI acquires weather data in real time based on the user's location information and suggests outfits according to the season and weather. The weather data linking unit can also acquire weather data such as temperature, humidity, precipitation, and wind speed, and the generation AI can analyze that data. The outfit suggestion unit suggests optimal fashion outfits based on the data acquired by the image input unit, body type data input unit, clothing image registration unit, and weather data linking unit. For example, the generation AI analyzes the user's body type, facial features, clothing in stock, and weather data to suggest optimal outfits. The outfit suggestion unit can also suggest outfits based on the user's preferred clothing and colors. The outfit simulation unit virtually simulates outfits suggested by the outfit suggestion unit. For example, the generation AI combines the user's image with images of clothing in stock to virtually change the outfit. The outfit suggestion unit can also perform outfit simulations in real time using 3D models.The online shopping linking unit links and directs users to online shopping sites based on the outfits proposed by the outfit suggestion unit. For example, a generation AI links and directs users to online shopping sites based on the user's preferred clothing and colors. The online shopping linking unit can also use API integration to design a purchase flow using a product database. This allows the fashion coordinator system according to the embodiment to provide users with a personalized fashion coordinator, allowing them to enjoy coordinating their outfits daily. For example, by receiving suggestions for optimal outfits depending on the weather, users can enjoy fashion that matches the season and weather. Furthermore, outfit suggestions that utilize existing clothing can also serve as a reference when purchasing new items. Furthermore, outfit simulations allow users to see how their outfits will look without actually trying them on, saving time and effort.

[0030] The image input unit can analyze the user's posture and walking style and suggest more suitable fashion items. For example, the user uploads a full-body photo, and the generation AI analyzes the image to identify the user's posture. For example, it captures characteristics such as a hunched back or a forward-leaning posture and suggests appropriate fashion items. The image input unit can also input video data to analyze the user's walking style, and the generation AI can analyze that data. For example, it can analyze stride length, walking speed, foot movement, etc. and suggest appropriate fashion items. The image input unit can also combine data on the user's posture and walking style for comprehensive analysis. For example, it can suggest more suitable fashion items based on posture characteristics and walking style patterns. This makes it possible to suggest more suitable fashion items based on the user's posture and walking style.

[0031] The body data input unit can analyze the user's past fashion history, learn changes in preferences, and predict future trends. For example, the body data input unit inputs data on fashion items purchased by the user in the past, and the generation AI analyzes the data to identify changes in the user's preferences. For example, color and design trends are learned from past purchase history. The body data input unit can also input the user's clothing history, and the generation AI can analyze the data. For example, the history of clothing worn during specific seasons or events can be analyzed to identify changes in preferences. The body data input unit can also input the user's social media data, and the generation AI can analyze the data. For example, the body data input unit can analyze fashion-related images and comments posted by the user to identify changes in preferences. This makes it possible to predict future trends based on the user's past fashion history.

[0032] The body data input unit can estimate the user's health and physical condition and suggest corresponding fashion items. For example, the body data input unit allows the user to upload a full-body photo, and the generation AI analyzes the image to estimate the user's health. For example, the unit can determine the user's physical condition based on factors such as skin color and dark circles under the eyes, and suggest appropriate fashion items. The body data input unit can also input the user's vital sign data, and the generation AI can analyze that data. For example, the health condition can be estimated based on data such as heart rate, blood pressure, and body temperature. The body data input unit can also input data from the user's fitness tracker, and the generation AI can analyze that data. For example, the physical condition can be estimated based on data such as the number of steps taken, amount of exercise, and quality of sleep. This allows the generation AI to suggest appropriate fashion items based on the user's health and physical condition.

[0033] The image input unit can also suggest hairstyles and makeup to the user. For example, the user uploads a photo of their face, and the generation AI analyzes the image to suggest hairstyles. For example, it suggests the optimal hairstyle based on the shape of the face and hair type. The image input unit can also input an image of the user's makeup, and the generation AI can analyze the image. For example, it suggests the optimal makeup based on the skin tone and eye shape. The image input unit can also input data on the user's preferred hairstyles and makeup, and the generation AI can analyze the data. For example, it can make suggestions that suit the user's preferences based on past hairstyle and makeup history. This allows for total coordination by suggesting hairstyles and makeup to the user.

[0034] The clothing image registration unit can recognize the material and texture of clothing in hand and suggest outfits based on that. For example, the clothing image registration unit allows a user to upload an image of their clothing, and the generation AI analyzes the image to identify the material and texture. For example, it can recognize materials such as cotton and silk and suggest outfits based on that. The clothing image registration unit can also input clothing tag information, and the generation AI can analyze that information. For example, it can suggest outfits based on information such as the composition of the material and washing instructions. The clothing image registration unit can also input image texture data to analyze the texture of clothing, and the generation AI can analyze that data. For example, it can recognize textures such as smoothness, roughness, and glossiness and suggest outfits based on that. This allows it to suggest appropriate outfits based on the material and texture of the clothing in hand.

[0035] The clothing image registration unit can grasp the frequency of use and condition of the user's clothing and suggest optimal maintenance methods based on that information. For example, the user can upload images of their clothing, and the generation AI can analyze the images to determine how often the clothing has been used. For example, the frequency of use can be determined from the degree of fading and wrinkles, and maintenance methods can be suggested based on that. The clothing image registration unit can also input the user's washing history, and the generation AI can analyze that data. For example, the condition of the clothing can be understood based on the number of times it has been washed and the washing method, and optimal maintenance methods can be suggested. The clothing image registration unit can also input the user's wearing history, and the generation AI can analyze that data. For example, the condition of a particular piece of clothing can be understood based on the number of times it has been worn and the period for which it has been worn, and maintenance methods can be suggested based on that. This makes it possible to suggest optimal maintenance methods based on the frequency of use and condition of the user's clothing.

[0036] The clothing image registration unit can suggest ideas for recycling and remaking clothes that the user already owns. For example, the clothing image registration unit allows the user to upload images of their own clothes, and the generation AI analyzes the images to suggest recycling ideas. For example, it can suggest ways to remake an old shirt into an eco-bag. The clothing image registration unit can also input the user's remake history, and the generation AI can analyze that data. For example, it can suggest new remake ideas based on data on items that have been remade in the past. The clothing image registration unit can also input data on the user's preferred designs and materials, and the generation AI can analyze that data. For example, it can suggest recycling and remake ideas that suit the user's preferences. This makes it possible to suggest ideas for recycling and remaking clothes that the user already owns.

[0037] The clothing image registration unit can suggest storage methods and organization techniques for existing clothes. For example, the clothing image registration unit allows a user to upload images of their existing clothes, and the generation AI analyzes the images to suggest storage methods. For example, it can suggest a method for storing clothes separately by season. The clothing image registration unit can also input data on the user's storage space, and the generation AI can analyze that data. For example, it can suggest the optimal storage method based on the size and arrangement of closets and drawers. The clothing image registration unit can also input data on the user's organization techniques, and the generation AI can analyze that data. For example, it can suggest the optimal organization method based on how clothes are classified and how storage items are used. This makes it possible to suggest storage methods and organization techniques for existing clothes.

[0038] The weather data linking unit can predict the user's skin and hair condition based on weather data and suggest corresponding fashion items. For example, the weather data linking unit allows the generation AI to predict the user's skin condition based on weather data and suggest corresponding fashion items. For example, on dry days, it suggests clothing made from moisturizing materials. The weather data linking unit can also input weather data to predict the user's hair condition, and the generation AI can analyze the data. For example, on humid days, it suggests a hat that helps keep hair in check. The weather data linking unit can also combine data on the user's skin and hair condition and perform a comprehensive analysis. For example, on dry days, it suggests clothing made from moisturizing materials and a hat that helps keep hair in check. This makes it possible to suggest fashion items that suit the user's skin and hair condition based on weather data.

[0039] The weather data linking unit can predict the user's planned activities based on weather data and suggest corresponding fashion items. For example, the weather data linking unit uses weather data to have the generation AI predict the user's planned activities and suggest corresponding fashion items. For example, on sunny days, it suggests clothing suitable for outdoor activities. The weather data linking unit can also input the user's calendar data and have the generation AI analyze that data. For example, based on the plans for a specific day, it suggests fashion items that suit the weather for that day. The weather data linking unit can also combine the user's planned activities with weather data and perform a comprehensive analysis. For example, it suggests clothing suitable for outdoor activities on sunny days and waterproof clothing on rainy days. This makes it possible to suggest fashion items that suit the user's planned activities based on weather data.

[0040] The weather data linking unit can suggest fashion items suitable for the user's travel destination or leisure activities based on weather data. For example, the weather data linking unit uses weather data to have the generation AI predict the weather at the user's travel destination and suggest fashion items accordingly. For example, swimsuits and sandals are suggested for beach resorts. The weather data linking unit can also input data on the user's travel destination, and the generation AI can analyze that data. For example, fashion items suitable for the climate and culture of the travel destination are suggested. The weather data linking unit can also input data on the user's leisure activities, and the generation AI can analyze that data. For example, fashion items suitable for outdoor activities and sports are suggested. This makes it possible to suggest fashion items suitable for the user's travel destination or leisure activities based on weather data.

[0041] The weather data linking unit can suggest interior items suitable for the user's living environment based on weather data. For example, the weather data linking unit uses weather data to have the generation AI predict the temperature and humidity of the user's living environment and suggest interior items accordingly. For example, a dehumidifier may be suggested for areas with high humidity. The weather data linking unit can also input data on the user's living environment, and the generation AI can analyze that data. For example, interior items suitable for the house's structure and surrounding environment may be suggested. The weather data linking unit can also input data on the user's lifestyle, and the generation AI can analyze that data. For example, interior items suitable for the user's living environment may be suggested based on weather data.

[0042] The dress-up simulation unit can simulate the user's movements and suggest how fashion items will look in accordance with the movements. For example, when a user performs a dress-up simulation, the generation AI simulates the user's movements and suggests how fashion items will look in accordance with the movements. For example, the generation AI can simulate how a skirt will sway when walking. The dress-up simulation unit can also simulate the user's movements when running, and the generation AI can analyze the data. For example, the generation AI can simulate how clothes will wrinkle and sway when running. The dress-up simulation unit can also simulate the user's sitting motion, and the generation AI can analyze the data. For example, the generation AI can simulate how clothes will look and how light will reflect when sitting. This makes it possible to suggest how fashion items will look in accordance with the user's movements.

[0043] The dress-up simulation unit can simulate the user's facial expressions and suggest how fashion items will look depending on the facial expression. For example, when a user performs a dress-up simulation, the generation AI simulates the user's facial expression and suggests how fashion items will look depending on the expression. For example, it simulates how clothes will look when the user is smiling. The dress-up simulation unit can also simulate the user's serious facial expression, and the generation AI can analyze the data. For example, it can simulate the impression and color changes of clothes when the user has a serious expression. The dress-up simulation unit can also simulate the user's surprised facial expression, and the generation AI can analyze the data. For example, it can simulate the light reflection of clothes when the user has a surprised facial expression. This makes it possible to suggest how fashion items will look depending on the user's facial expression.

[0044] The dress-up simulation unit can import images of the user's friends and family and suggest group coordinations. For example, the dress-up simulation unit allows the user to upload images of friends and family, and the generation AI analyzes the images to suggest group coordinations. For example, it may suggest matching outfits for the whole family. The dress-up simulation unit can also input preference data of the user's friends and family, and the generation AI can analyze that data. For example, it can suggest group coordinations based on the friends' and family's favorite colors and styles. The dress-up simulation unit can also input event data of the user's friends and family, and the generation AI can analyze that data. For example, it can suggest group coordinations to suit a specific event. This allows the user to enjoy coordinating outfits together with their friends and family.

[0045] The dress-up simulation unit can import an image of the user's pet and suggest outfits to coordinate with the pet. For example, the dress-up simulation unit allows the user to upload an image of the pet, and the generation AI analyzes the image to suggest outfits to coordinate with the pet. For example, it can suggest outfits that match the pet. The dress-up simulation unit can also input data on the user's pet's preferences, and the generation AI can analyze that data. For example, it can suggest outfits to coordinate with the pet based on the pet's favorite colors and styles. The dress-up simulation unit can also input data on events the user's pet has, and the generation AI can analyze that data. For example, it can suggest outfits to coordinate with the pet to match a specific event. This allows the user to enjoy coordinating outfits with their pet.

[0046] The online shopping linking unit can analyze the user's purchase history and suggest the optimal timing for purchases. For example, the online shopping linking unit analyzes the user's past purchase history and the generation AI suggests the optimal timing for purchases. For example, suggestions can be made to coincide with sales periods or new product release periods. The online shopping linking unit can also input data on the user's purchase history and the generation AI can analyze that data. For example, suggestions can be made to suggest the optimal timing for purchases based on purchase frequency and purchase amount. The online shopping linking unit can also input data on the user's budget and the generation AI can analyze that data. For example, suggestions can be made to suggest the optimal timing for purchases based on the budget. This makes it possible to suggest the optimal timing for purchases based on the user's purchase history.

[0047] The online shopping linking unit can suggest cost-effective items based on the user's budget. For example, the online shopping linking unit inputs the user's budget data, and the generation AI suggests cost-effective items based on that data. For example, suggestions are made using sale items or discount coupons. The online shopping linking unit can also input data on the user's purchase history, and the generation AI can analyze that data. For example, cost-effective items are suggested based on past purchase data. The online shopping linking unit can also input data on the user's preferences, and the generation AI can analyze that data. For example, cost-effective items that match the user's preferences are suggested. This makes it possible to suggest cost-effective items based on the user's budget.

[0048] The online shopping linking unit can suggest gift items taking into account the preferences of the user's friends and family. For example, the user inputs the preferences of their friends and family, and the generation AI suggests gift items based on that data. For example, suggestions can be made taking into account the friends' favorite brands and colors. The online shopping linking unit can also input the past gift history of the user's friends and family, and the generation AI can analyze that data. For example, new gift items can be suggested based on data on gifts given in the past. The online shopping linking unit can also input social media data of the user's friends and family, and the generation AI can analyze that data. For example, gift items can be suggested based on the favorite brands and items posted by friends and family. This makes it possible to suggest suitable gift items based on the preferences of the user's friends and family.

[0049] The online shopping linking unit can suggest interior items taking into consideration the user's living environment and lifestyle. For example, the user inputs data about their living environment and lifestyle into the online shopping linking unit, and the generation AI suggests interior items based on that data. For example, it can suggest storage furniture suitable for a small room. The online shopping linking unit can also input data about the user's living environment, and the generation AI can analyze that data. For example, it can suggest interior items that suit the climate and structure of the house. The online shopping linking unit can also input data about the user's lifestyle, and the generation AI can analyze that data. For example, it can suggest interior items that suit the user's daily activity patterns and hobbies. This makes it possible to suggest suitable interior items based on the user's living environment and lifestyle.

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

[0051] The fashion coordinator system can also input the user's lifestyle data, and the generation AI can analyze that data to suggest fashion items that suit the user's lifestyle. For example, if the user has an active lifestyle, it can suggest sportswear or outdoor wear. If the user has an office-based lifestyle, it can suggest business casual or formal items. Furthermore, if the user works remotely, it can suggest comfortable and relaxing home wear. This allows it to suggest the optimal fashion items to suit the user's lifestyle.

[0052] The fashion coordinator system can also suggest fashion items based on the user's hobbies and interests. For example, if the user likes to attend music festivals, the system can suggest casual and unique items suitable for festivals. If the user's hobby is visiting art galleries, the system can suggest chic and sophisticated items. Furthermore, if the user's hobby is traveling, the system can suggest fashion items that suit the climate and culture of the travel destination. This allows the system to suggest the most suitable fashion items based on the user's hobbies and interests.

[0053] The fashion coordinator system can also input the user's health data, and the generation AI can analyze that data to suggest fashion items that suit the user's health condition. For example, if the user has allergies, it can suggest items made with allergy-friendly materials. Also, if the user is prone to feeling cold, it can suggest items with high heat retention. Furthermore, if the user has sensitive skin, it can suggest items made with materials that are gentle on the skin. This allows it to suggest the most suitable fashion items according to the user's health condition.

[0054] The fashion coordinator system can also analyze a user's past fashion history, and the generation AI can use that data to predict future trends and suggest fashion items. For example, it can learn about changes in a user's preferences from past purchase history and suggest items that are likely to become popular next. It can also analyze a user's clothing history to predict and suggest seasonal trends. It can also analyze a user's social media data to predict and suggest future trends. This makes it possible to predict future trends and suggest optimal fashion items based on a user's past fashion history.

[0055] The fashion coordinator system can also input data about the user's travel destination, and the generation AI can analyze that data and suggest fashion items that match the destination. For example, if the user is traveling to a beach resort, it can suggest swimsuits and sandals. If the user is traveling to a cold region, it can also suggest warm coats and boots. Furthermore, if the user is traveling to an urban area, it can suggest chic and sophisticated items. This allows it to suggest the best fashion items for the user's travel destination.

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

[0057] Step 1: The image input unit inputs an image of the user. For example, the user can upload a full-body photo, and the generation AI can analyze the image to identify the user's posture and body shape. Alternatively, the generation AI can input an image of the user's face and analyze their facial features. Step 2: The body data input unit inputs the user's body data. For example, the user inputs body data such as height, weight, and waist size, and the generation AI analyzes that data to identify the user's body shape. 3D scan data can also be input and analyzed by the generation AI. Step 3: The clothing image registration unit registers images of the user's clothing. For example, the user can upload images of shirts, pants, skirts, jackets, etc., and the generation AI will analyze the images to recognize the type, color, and design of the clothing. Clothing tag information can also be entered, and the generation AI will analyze that information. Step 4: The weather data linking unit links the weather data. For example, the generation AI can obtain weather data in real time based on the user's location information and suggest outfits based on the season and weather. The generation AI can also obtain weather data such as temperature, humidity, precipitation, and wind speed and analyze that data. Step 5: The outfit suggestion unit proposes optimal fashion outfits based on the data acquired by the image input unit, body type data input unit, clothing image registration unit, and weather data linkage unit. For example, the generation AI analyzes the user's body type, facial features, clothing on hand, and weather data to propose optimal outfits. The generation AI can also propose outfits based on the user's preferred clothing and colors. Step 6: The dress-up simulation unit virtually simulates the outfits proposed by the outfit suggestion unit. For example, the generation AI combines the user's image with images of clothing they own to virtually dress them up. It is also possible to perform real-time dress-up simulations using 3D models. Step 7: The online shopping linking unit links and directs users to online shopping sites based on the outfits proposed by the outfit suggestion unit. For example, the generation AI links and directs users to online shopping sites based on the user's preferred clothing and colors. It is also possible to use API linking to access a product database and design a purchasing flow.

[0058] (Example 2) A fashion coordinator system according to an embodiment of the present invention is a system that provides a fashion coordinator dedicated to a user by linking a user's image with body type data, images of clothing owned by the user, and weather data. As a result, the fashion coordinator system can provide a fashion coordinator dedicated to a user by linking a user's image with body type data, images of clothing owned by the user, and weather data.

[0059] A fashion coordinator system according to an embodiment includes an image input unit, a body type data input unit, a clothing image registration unit, a weather data linking unit, a coordination suggestion unit, a dress-up simulation unit, and an online shopping linking unit. The image input unit inputs a user's image. For example, the user uploads a full-body photo, and the generation AI analyzes the image to identify the user's posture and body type. The image input unit can also input a user's facial image, and the generation AI can analyze the facial features. The body type data input unit inputs the user's body type data. For example, the user inputs body type data such as height, weight, and waist size, and the generation AI analyzes the data to identify the user's body type. The body type data input unit can also input 3D scan data, and the generation AI can analyze the data. The clothing image registration unit registers images of the user's clothing. For example, the user uploads images of shirts, pants, skirts, jackets, etc., and the generation AI analyzes the images to recognize the type, color, and design of the clothing. The clothing image registration unit can also input clothing tag information, and the generation AI can analyze the information. The weather data linking unit links weather data. For example, the generation AI acquires weather data in real time based on the user's location information and suggests outfits according to the season and weather. The weather data linking unit can also acquire weather data such as temperature, humidity, precipitation, and wind speed, and the generation AI can analyze that data. The outfit suggestion unit suggests optimal fashion outfits based on the data acquired by the image input unit, body type data input unit, clothing image registration unit, and weather data linking unit. For example, the generation AI analyzes the user's body type, facial features, clothing in stock, and weather data to suggest optimal outfits. The outfit suggestion unit can also suggest outfits based on the user's preferred clothing and colors. The outfit simulation unit virtually simulates outfits suggested by the outfit suggestion unit. For example, the generation AI combines the user's image with images of clothing in stock to virtually change the outfit. The outfit suggestion unit can also perform outfit simulations in real time using 3D models.The online shopping linking unit links and directs users to online shopping sites based on the outfits proposed by the outfit suggestion unit. For example, a generation AI links and directs users to online shopping sites based on the user's preferred clothing and colors. The online shopping linking unit can also use API integration to design a purchase flow using a product database. This allows the fashion coordinator system according to the embodiment to provide users with a personalized fashion coordinator, allowing them to enjoy coordinating their outfits daily. For example, by receiving suggestions for optimal outfits depending on the weather, users can enjoy fashion that matches the season and weather. Furthermore, outfit suggestions that utilize existing clothing can also serve as a reference when purchasing new items. Furthermore, outfit simulations allow users to see how their outfits will look without actually trying them on, saving time and effort.

[0060] The image input unit can analyze the user's posture and walking style and suggest more suitable fashion items. For example, the user uploads a full-body photo, and the generation AI analyzes the image to identify the user's posture. For example, it captures characteristics such as a hunched back or a forward-leaning posture and suggests appropriate fashion items. The image input unit can also input video data to analyze the user's walking style, and the generation AI can analyze that data. For example, it can analyze stride length, walking speed, foot movement, etc. and suggest appropriate fashion items. The image input unit can also combine data on the user's posture and walking style for comprehensive analysis. For example, it can suggest more suitable fashion items based on posture characteristics and walking style patterns. This makes it possible to suggest more suitable fashion items based on the user's posture and walking style.

[0061] The body data input unit can analyze the user's past fashion history, learn changes in preferences, and predict future trends. For example, the body data input unit inputs data on fashion items purchased by the user in the past, and the generation AI analyzes the data to identify changes in the user's preferences. For example, color and design trends are learned from past purchase history. The body data input unit can also input the user's clothing history, and the generation AI can analyze the data. For example, the history of clothing worn during specific seasons or events can be analyzed to identify changes in preferences. The body data input unit can also input the user's social media data, and the generation AI can analyze the data. For example, the body data input unit can analyze fashion-related images and comments posted by the user to identify changes in preferences. This makes it possible to predict future trends based on the user's past fashion history.

[0062] The image input unit uses the emotion estimation function to analyze the emotion a user expresses when uploading an image, and can adjust the suggested fashion style based on that emotion. For example, when a user uploads an image, the image input unit allows the generation AI to analyze the user's facial expression to identify the emotion. For example, it can detect a smile or a serious expression and suggest a fashion style that matches the emotion. The image input unit can also input the user's voice data, and the generation AI can analyze that data. For example, it can analyze the tone and speed of the voice to identify the emotion. The image input unit can also input the user's biometric data (heart rate and electrodermal activity), and the generation AI can analyze that data. For example, it can identify the emotion based on heart rate fluctuations and suggest a fashion style that matches the emotion. This allows the suggested fashion style to be adjusted based on the user's emotion.

[0063] The body data input unit can estimate the user's health and physical condition and suggest corresponding fashion items. For example, the body data input unit allows the user to upload a full-body photo, and the generation AI analyzes the image to estimate the user's health. For example, the unit can determine the user's physical condition based on factors such as skin color and dark circles under the eyes, and suggest appropriate fashion items. The body data input unit can also input the user's vital sign data, and the generation AI can analyze that data. For example, the health condition can be estimated based on data such as heart rate, blood pressure, and body temperature. The body data input unit can also input data from the user's fitness tracker, and the generation AI can analyze that data. For example, the physical condition can be estimated based on data such as the number of steps taken, amount of exercise, and quality of sleep. This allows the generation AI to suggest appropriate fashion items based on the user's health and physical condition.

[0064] The image input unit can also suggest hairstyles and makeup to the user. For example, the user uploads a photo of their face, and the generation AI analyzes the image to suggest hairstyles. For example, it suggests the optimal hairstyle based on the shape of the face and hair type. The image input unit can also input an image of the user's makeup, and the generation AI can analyze the image. For example, it suggests the optimal makeup based on the skin tone and eye shape. The image input unit can also input data on the user's preferred hairstyles and makeup, and the generation AI can analyze the data. For example, it can make suggestions that suit the user's preferences based on past hairstyle and makeup history. This allows for total coordination by suggesting hairstyles and makeup to the user.

[0065] The image input unit uses the emotion estimation function to analyze the emotion a user expresses when uploading an image and can suggest relaxing fashion items based on that emotion. For example, when a user uploads an image, the image input unit uses the generation AI to analyze the user's facial expression to identify the emotion and suggest relaxing fashion items. For example, if the user is nervous, a casual style is suggested. The image input unit can also input the user's voice data, and the generation AI can analyze that data. For example, the tone and speed of the voice can be analyzed to identify the emotion and suggest corresponding relaxing fashion items. The image input unit can also input the user's biometric data (heart rate and electrodermal activity), and the generation AI can analyze that data. For example, the emotion can be identified based on heart rate fluctuations and suggest corresponding relaxing fashion items. This makes it possible to suggest relaxing fashion items based on the user's emotions.

[0066] The clothing image registration unit can recognize the material and texture of clothing in hand and suggest outfits based on that. For example, the clothing image registration unit allows a user to upload an image of their clothing, and the generation AI analyzes the image to identify the material and texture. For example, it can recognize materials such as cotton and silk and suggest outfits based on that. The clothing image registration unit can also input clothing tag information, and the generation AI can analyze that information. For example, it can suggest outfits based on information such as the composition of the material and washing instructions. The clothing image registration unit can also input image texture data to analyze the texture of clothing, and the generation AI can analyze that data. For example, it can recognize textures such as smoothness, roughness, and glossiness and suggest outfits based on that. This allows it to suggest appropriate outfits based on the material and texture of the clothing in hand.

[0067] The clothing image registration unit can grasp the frequency of use and condition of the user's clothing and suggest optimal maintenance methods based on that information. For example, the user can upload images of their clothing, and the generation AI can analyze the images to determine how often the clothing has been used. For example, the frequency of use can be determined from the degree of fading and wrinkles, and maintenance methods can be suggested based on that. The clothing image registration unit can also input the user's washing history, and the generation AI can analyze that data. For example, the condition of the clothing can be understood based on the number of times it has been washed and the washing method, and optimal maintenance methods can be suggested. The clothing image registration unit can also input the user's wearing history, and the generation AI can analyze that data. For example, the condition of a particular piece of clothing can be understood based on the number of times it has been worn and the period for which it has been worn, and maintenance methods can be suggested based on that. This makes it possible to suggest optimal maintenance methods based on the frequency of use and condition of the user's clothing.

[0068] The clothing image registration unit uses the emotion estimation function to analyze the emotion a user expresses when registering a clothing image and can suggest clothing combinations based on that emotion. For example, when a user registers a clothing image, the clothing image registration unit allows the generation AI to analyze the user's facial expression to identify the emotion and suggest clothing combinations based on that emotion. For example, if the user expresses a happy expression, the generation AI can suggest casual combinations. The clothing image registration unit can also input the user's voice data, and the generation AI can analyze that data. For example, the tone and speed of the voice can be analyzed to identify the emotion and suggest clothing combinations based on that emotion. The clothing image registration unit can also input the user's biometric data (heart rate and electrodermal activity), and the generation AI can analyze that data. For example, the generation AI can identify the emotion based on heart rate fluctuations and suggest clothing combinations based on that emotion. This allows the generation AI to suggest appropriate clothing combinations based on the user's emotions.

[0069] The clothing image registration unit can suggest ideas for recycling and remaking clothes that the user already owns. For example, the clothing image registration unit allows the user to upload images of their own clothes, and the generation AI analyzes the images to suggest recycling ideas. For example, it can suggest ways to remake an old shirt into an eco-bag. The clothing image registration unit can also input the user's remake history, and the generation AI can analyze that data. For example, it can suggest new remake ideas based on data on items that have been remade in the past. The clothing image registration unit can also input data on the user's preferred designs and materials, and the generation AI can analyze that data. For example, it can suggest recycling and remake ideas that suit the user's preferences. This makes it possible to suggest ideas for recycling and remaking clothes that the user already owns.

[0070] The clothing image registration unit can suggest storage methods and organization techniques for existing clothes. For example, the clothing image registration unit allows a user to upload images of their existing clothes, and the generation AI analyzes the images to suggest storage methods. For example, it can suggest a method for storing clothes separately by season. The clothing image registration unit can also input data on the user's storage space, and the generation AI can analyze that data. For example, it can suggest the optimal storage method based on the size and arrangement of closets and drawers. The clothing image registration unit can also input data on the user's organization techniques, and the generation AI can analyze that data. For example, it can suggest the optimal organization method based on how clothes are classified and how storage items are used. This makes it possible to suggest storage methods and organization techniques for existing clothes.

[0071] The clothing image registration unit uses the emotion estimation function to analyze the emotion a user expresses when registering a clothing image and can suggest a clothing storage method based on that emotion. For example, when a user registers a clothing image, the clothing image registration unit uses the generation AI to analyze the user's facial expression to identify the emotion and then suggest a clothing storage method based on that emotion. For example, if the user expresses a happy expression, the generation AI can suggest a clothing storage method that shows the emotion. The clothing image registration unit can also input the user's voice data and analyze that data. For example, the generation AI can analyze the voice tone and speed to identify the emotion and then suggest a clothing storage method based on that emotion. The clothing image registration unit can also input the user's biometric data (heart rate and electrodermal activity) and analyze that data. For example, the generation AI can identify the emotion based on heart rate fluctuations and then suggest a clothing storage method based on that emotion. This allows the generation AI to suggest an appropriate clothing storage method based on the user's emotion.

[0072] The weather data linking unit can predict the user's skin and hair condition based on weather data and suggest corresponding fashion items. For example, the weather data linking unit allows the generation AI to predict the user's skin condition based on weather data and suggest corresponding fashion items. For example, on dry days, it suggests clothing made from moisturizing materials. The weather data linking unit can also input weather data to predict the user's hair condition, and the generation AI can analyze the data. For example, on humid days, it suggests a hat that helps keep hair in check. The weather data linking unit can also combine data on the user's skin and hair condition and perform a comprehensive analysis. For example, on dry days, it suggests clothing made from moisturizing materials and a hat that helps keep hair in check. This makes it possible to suggest fashion items that suit the user's skin and hair condition based on weather data.

[0073] The weather data linking unit can predict the user's planned activities based on weather data and suggest corresponding fashion items. For example, the weather data linking unit uses weather data to have the generation AI predict the user's planned activities and suggest corresponding fashion items. For example, on sunny days, it suggests clothing suitable for outdoor activities. The weather data linking unit can also input the user's calendar data and have the generation AI analyze that data. For example, based on the plans for a specific day, it suggests fashion items that suit the weather for that day. The weather data linking unit can also combine the user's planned activities with weather data and perform a comprehensive analysis. For example, it suggests clothing suitable for outdoor activities on sunny days and waterproof clothing on rainy days. This makes it possible to suggest fashion items that suit the user's planned activities based on weather data.

[0074] The weather data linking unit can use its emotion estimation function to analyze the user's emotions when checking weather data and suggest fashion items based on those emotions. For example, when a user checks weather data, the weather data linking unit uses the generation AI to analyze their facial expressions to identify their emotions and suggest fashion items based on those emotions. For example, if the user looks happy on a sunny day, the weather data linking unit can suggest bright-colored clothing. The weather data linking unit can also input the user's voice data, and the generation AI can analyze that data. For example, it can analyze the tone and speed of the voice to identify emotions and suggest corresponding fashion items. The weather data linking unit can also input the user's biometric data (heart rate and electrodermal activity), and the generation AI can analyze that data. For example, it can identify emotions based on heart rate fluctuations and suggest corresponding fashion items. This allows the generation AI to suggest appropriate fashion items based on the user's emotions.

[0075] The weather data linking unit can suggest fashion items suitable for the user's travel destination or leisure activities based on weather data. For example, the weather data linking unit uses weather data to have the generation AI predict the weather at the user's travel destination and suggest fashion items accordingly. For example, swimsuits and sandals are suggested for beach resorts. The weather data linking unit can also input data on the user's travel destination, and the generation AI can analyze that data. For example, fashion items suitable for the climate and culture of the travel destination are suggested. The weather data linking unit can also input data on the user's leisure activities, and the generation AI can analyze that data. For example, fashion items suitable for outdoor activities and sports are suggested. This makes it possible to suggest fashion items suitable for the user's travel destination or leisure activities based on weather data.

[0076] The weather data linking unit can suggest interior items suitable for the user's living environment based on weather data. For example, the weather data linking unit uses weather data to have the generation AI predict the temperature and humidity of the user's living environment and suggest interior items accordingly. For example, a dehumidifier may be suggested for areas with high humidity. The weather data linking unit can also input data on the user's living environment, and the generation AI can analyze that data. For example, interior items suitable for the house's structure and surrounding environment may be suggested. The weather data linking unit can also input data on the user's lifestyle, and the generation AI can analyze that data. For example, interior items suitable for the user's living environment may be suggested based on weather data.

[0077] The weather data linking unit can use its emotion estimation function to analyze the user's emotions when checking weather data and suggest relaxing fashion items based on those emotions. For example, when a user checks weather data, the weather data linking unit uses the generation AI to analyze their facial expressions to identify their emotions and suggest relaxing fashion items based on those emotions. For example, if the user looks depressed on a rainy day, the generation AI can suggest relaxing pajamas. The weather data linking unit can also input the user's voice data and analyze that data. For example, it can analyze the tone and speed of the voice to identify emotions and suggest corresponding relaxing fashion items. The weather data linking unit can also input the user's biometric data (heart rate and electrodermal activity) and analyze that data. For example, it can identify emotions based on heart rate fluctuations and suggest corresponding relaxing fashion items. This allows the generation AI to suggest relaxing fashion items based on the user's emotions.

[0078] The dress-up simulation unit can simulate the user's movements and suggest how fashion items will look in accordance with the movements. For example, when a user performs a dress-up simulation, the generation AI simulates the user's movements and suggests how fashion items will look in accordance with the movements. For example, the generation AI can simulate how a skirt will sway when walking. The dress-up simulation unit can also simulate the user's movements when running, and the generation AI can analyze the data. For example, the generation AI can simulate how clothes will wrinkle and sway when running. The dress-up simulation unit can also simulate the user's sitting motion, and the generation AI can analyze the data. For example, the generation AI can simulate how clothes will look and how light will reflect when sitting. This makes it possible to suggest how fashion items will look in accordance with the user's movements.

[0079] The dress-up simulation unit can simulate the user's facial expressions and suggest how fashion items will look depending on the facial expression. For example, when a user performs a dress-up simulation, the generation AI simulates the user's facial expression and suggests how fashion items will look depending on the expression. For example, it simulates how clothes will look when the user is smiling. The dress-up simulation unit can also simulate the user's serious facial expression, and the generation AI can analyze the data. For example, it can simulate the impression and color changes of clothes when the user has a serious expression. The dress-up simulation unit can also simulate the user's surprised facial expression, and the generation AI can analyze the data. For example, it can simulate the light reflection of clothes when the user has a surprised facial expression. This makes it possible to suggest how fashion items will look depending on the user's facial expression.

[0080] The dress-up simulation unit can use the emotion estimation function to analyze the emotions of a user when performing a dress-up simulation and suggest fashion items based on those emotions. For example, when a user performs a dress-up simulation, the generation AI analyzes the user's facial expression to identify the emotion and suggests fashion items based on that emotion. For example, if the user has a happy expression, a casual style is suggested. The dress-up simulation unit can also input the user's voice data, and the generation AI can analyze that data. For example, the tone and speed of the voice can be analyzed to identify the emotion and suggest corresponding fashion items. The dress-up simulation unit can also input the user's biometric data (heart rate and electrodermal activity), and the generation AI can analyze that data. For example, the emotion can be identified based on heart rate fluctuations and suggest corresponding fashion items. This makes it possible to suggest appropriate fashion items based on the user's emotions.

[0081] The dress-up simulation unit can import images of the user's friends and family and suggest group coordinations. For example, the dress-up simulation unit allows the user to upload images of friends and family, and the generation AI analyzes the images to suggest group coordinations. For example, it may suggest matching outfits for the whole family. The dress-up simulation unit can also input preference data of the user's friends and family, and the generation AI can analyze that data. For example, it can suggest group coordinations based on the friends' and family's favorite colors and styles. The dress-up simulation unit can also input event data of the user's friends and family, and the generation AI can analyze that data. For example, it can suggest group coordinations to suit a specific event. This allows the user to enjoy coordinating outfits together with their friends and family.

[0082] The dress-up simulation unit can import an image of the user's pet and suggest outfits to coordinate with the pet. For example, the dress-up simulation unit allows the user to upload an image of the pet, and the generation AI analyzes the image to suggest outfits to coordinate with the pet. For example, it can suggest outfits that match the pet. The dress-up simulation unit can also input data on the user's pet's preferences, and the generation AI can analyze that data. For example, it can suggest outfits to coordinate with the pet based on the pet's favorite colors and styles. The dress-up simulation unit can also input data on events the user's pet has, and the generation AI can analyze that data. For example, it can suggest outfits to coordinate with the pet to match a specific event. This allows the user to enjoy coordinating outfits with their pet.

[0083] The dress-up simulation unit can use the emotion estimation function to analyze the emotions of the user when performing the dress-up simulation and suggest relaxing fashion items based on those emotions. For example, when a user performs a dress-up simulation, the generation AI analyzes the user's facial expression to identify the emotion and suggests relaxing fashion items based on that emotion. For example, if the user has a happy expression, a casual style is suggested. The dress-up simulation unit can also input the user's voice data and have the generation AI analyze that data. For example, it can analyze the tone and speed of the voice to identify the emotion and suggest relaxing fashion items accordingly. The dress-up simulation unit can also input the user's biometric data (heart rate and electrodermal activity) and have the generation AI analyze that data. For example, it can identify the emotion based on heart rate fluctuations and suggest relaxing fashion items according to that emotion. This makes it possible to suggest relaxing fashion items based on the user's emotions.

[0084] The online shopping linking unit can analyze the user's purchase history and suggest the optimal timing for purchases. For example, the online shopping linking unit analyzes the user's past purchase history and the generation AI suggests the optimal timing for purchases. For example, suggestions can be made to coincide with sales periods or new product release periods. The online shopping linking unit can also input data on the user's purchase history and the generation AI can analyze that data. For example, suggestions can be made to suggest the optimal timing for purchases based on purchase frequency and purchase amount. The online shopping linking unit can also input data on the user's budget and the generation AI can analyze that data. For example, suggestions can be made to suggest the optimal timing for purchases based on the budget. This makes it possible to suggest the optimal timing for purchases based on the user's purchase history.

[0085] The online shopping linking unit can suggest cost-effective items based on the user's budget. For example, the online shopping linking unit inputs the user's budget data, and the generation AI suggests cost-effective items based on that data. For example, suggestions are made using sale items or discount coupons. The online shopping linking unit can also input data on the user's purchase history, and the generation AI can analyze that data. For example, cost-effective items are suggested based on past purchase data. The online shopping linking unit can also input data on the user's preferences, and the generation AI can analyze that data. For example, cost-effective items that match the user's preferences are suggested. This makes it possible to suggest cost-effective items based on the user's budget.

[0086] The online shopping linking unit can use the emotion estimation function to analyze the user's emotions when browsing an online shopping site and suggest items that encourage purchases based on those emotions. For example, when a user browses an online shopping site, the generation AI analyzes the user's facial expressions to identify the emotion and suggests items that encourage purchases based on that emotion. For example, if the user's facial expression looks happy, related items are suggested. The online shopping linking unit can also input the user's voice data, and the generation AI can analyze that data. For example, the tone and speed of the voice can be analyzed to identify the emotion and suggest items that encourage purchases accordingly. The online shopping linking unit can also input the user's biometric data (heart rate and electrodermal activity), and the generation AI can analyze that data. For example, the emotion can be identified based on heart rate fluctuations and items that encourage purchases accordingly. This makes it possible to suggest items that encourage purchases based on the user's emotions.

[0087] The online shopping linking unit can suggest gift items taking into account the preferences of the user's friends and family. For example, the user inputs the preferences of their friends and family, and the generation AI suggests gift items based on that data. For example, suggestions can be made taking into account the friends' favorite brands and colors. The online shopping linking unit can also input the past gift history of the user's friends and family, and the generation AI can analyze that data. For example, new gift items can be suggested based on data on gifts given in the past. The online shopping linking unit can also input social media data of the user's friends and family, and the generation AI can analyze that data. For example, gift items can be suggested based on the favorite brands and items posted by friends and family. This makes it possible to suggest suitable gift items based on the preferences of the user's friends and family.

[0088] The online shopping linking unit can suggest interior items taking into consideration the user's living environment and lifestyle. For example, the user inputs data about their living environment and lifestyle into the online shopping linking unit, and the generation AI suggests interior items based on that data. For example, it can suggest storage furniture suitable for a small room. The online shopping linking unit can also input data about the user's living environment, and the generation AI can analyze that data. For example, it can suggest interior items that suit the climate and structure of the house. The online shopping linking unit can also input data about the user's lifestyle, and the generation AI can analyze that data. For example, it can suggest interior items that suit the user's daily activity patterns and hobbies. This makes it possible to suggest suitable interior items based on the user's living environment and lifestyle.

[0089] The online shopping linkage unit can use the emotion estimation function to analyze a user's emotions when browsing an online shopping site and suggest relaxing items based on those emotions. For example, when a user browses an online shopping site, the generation AI analyzes the user's facial expressions to identify the user's emotions and suggests relaxing items based on those emotions. For example, if the user is feeling stressed, the generation AI can suggest a relaxing aroma candle. The online shopping linkage unit can also input the user's voice data and analyze the data. For example, the tone and speed of the voice can be analyzed to identify the user's emotions and suggest relaxing items accordingly. The online shopping linkage unit can also input the user's biometric data (heart rate and electrodermal activity) and the generation AI can analyze the data. For example, the generation AI can identify the user's emotions based on heart rate fluctuations and suggest relaxing items accordingly. This allows the generation AI to suggest relaxing items based on the user's emotions.

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

[0091] The fashion coordinator system can also input the user's lifestyle data, and the generation AI can analyze that data to suggest fashion items that suit the user's lifestyle. For example, if the user has an active lifestyle, it can suggest sportswear or outdoor wear. If the user has an office-based lifestyle, it can suggest business casual or formal items. Furthermore, if the user works remotely, it can suggest comfortable and relaxing home wear. This allows it to suggest the optimal fashion items to suit the user's lifestyle.

[0092] The fashion coordinator system can also suggest fashion items based on the user's hobbies and interests. For example, if the user likes to attend music festivals, the system can suggest casual and unique items suitable for festivals. If the user's hobby is visiting art galleries, the system can suggest chic and sophisticated items. Furthermore, if the user's hobby is traveling, the system can suggest fashion items that suit the climate and culture of the travel destination. This allows the system to suggest the most suitable fashion items based on the user's hobbies and interests.

[0093] The fashion coordinator system can also estimate the user's emotions and suggest fashion items based on the estimated emotions. For example, if the user is feeling stressed, it can suggest casual items that will help them relax. If the user is feeling happy or excited, it can suggest items with bright colors or gorgeous designs. Furthermore, if the user is feeling down, it can suggest items printed with positive messages to lift their spirits. In this way, it is possible to suggest the most suitable fashion items based on the user's emotions.

[0094] The fashion coordinator system can also input the user's health data, and the generation AI can analyze that data to suggest fashion items that suit the user's health condition. For example, if the user has allergies, it can suggest items made with allergy-friendly materials. Also, if the user is prone to feeling cold, it can suggest items with high heat retention. Furthermore, if the user has sensitive skin, it can suggest items made with materials that are gentle on the skin. This allows it to suggest the most suitable fashion items according to the user's health condition.

[0095] The fashion coordinator system can also estimate the user's emotions and suggest relaxing fashion items based on the estimated emotions. For example, if the user is tense, it can suggest a relaxing casual style. If the user is tired, it can suggest items made of comfortable, soft materials. Furthermore, if the user is feeling stressed, it can suggest items with colors and designs that have a relaxing effect. In this way, it is possible to suggest relaxing fashion items based on the user's emotions.

[0096] The fashion coordinator system can also analyze a user's past fashion history, and the generation AI can use that data to predict future trends and suggest fashion items. For example, it can learn about changes in a user's preferences from past purchase history and suggest items that are likely to become popular next. It can also analyze a user's clothing history to predict and suggest seasonal trends. It can also analyze a user's social media data to predict and suggest future trends. This makes it possible to predict future trends and suggest optimal fashion items based on a user's past fashion history.

[0097] The fashion coordinator system can also estimate the user's emotions and suggest fashion items suitable for special events based on the estimated emotions. For example, if the user is nervous about attending a wedding, the system can suggest relaxed formal items. If the user looks happy about attending a party, the system can suggest a glamorous party dress. If the user looks serious about attending a business meeting, the system can suggest a professional business suit. In this way, the system can suggest fashion items suitable for special events based on the user's emotions.

[0098] The fashion coordinator system can also estimate the user's emotions and suggest seasonal fashion items based on the estimated emotions. For example, if the user is feeling anxious about the cold in winter, a warm coat or sweater can be suggested. If the user is feeling stressed about the heat in summer, items made from cool materials can be suggested. Furthermore, if the user is feeling uncomfortable about hay fever in spring, items to combat pollen can be suggested. In this way, the system can suggest the most suitable fashion items for each season based on the user's emotions.

[0099] The fashion coordinator system can also input data about the user's travel destination, and the generation AI can analyze that data and suggest fashion items that match the destination. For example, if the user is traveling to a beach resort, it can suggest swimsuits and sandals. If the user is traveling to a cold region, it can also suggest warm coats and boots. Furthermore, if the user is traveling to an urban area, it can suggest chic and sophisticated items. This allows it to suggest the best fashion items for the user's travel destination.

[0100] The fashion coordinator system can also estimate the user's emotions and suggest fashion items for special occasions based on the estimated emotions. For example, if the user looks happy on their birthday, a gorgeous party dress can be suggested. If the user is feeling emotional about an anniversary, an item with a romantic design can be suggested. Furthermore, if the user is excited about the New Year, a gorgeous New Year's outfit can be suggested. In this way, the system can suggest the most suitable fashion items for special occasions based on the user's emotions.

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

[0102] Step 1: The image input unit inputs an image of the user. For example, the user can upload a full-body photo, and the generation AI can analyze the image to identify the user's posture and body shape. Alternatively, the generation AI can input an image of the user's face and analyze their facial features. Step 2: The body data input unit inputs the user's body data. For example, the user inputs body data such as height, weight, and waist size, and the generation AI analyzes that data to identify the user's body shape. 3D scan data can also be input and analyzed by the generation AI. Step 3: The clothing image registration unit registers images of the user's clothing. For example, the user can upload images of shirts, pants, skirts, jackets, etc., and the generation AI will analyze the images to recognize the type, color, and design of the clothing. Clothing tag information can also be entered, and the generation AI will analyze that information. Step 4: The weather data linking unit links the weather data. For example, the generation AI can obtain weather data in real time based on the user's location information and suggest outfits based on the season and weather. The generation AI can also obtain weather data such as temperature, humidity, precipitation, and wind speed and analyze that data. Step 5: The outfit suggestion unit proposes optimal fashion outfits based on the data acquired by the image input unit, body type data input unit, clothing image registration unit, and weather data linkage unit. For example, the generation AI analyzes the user's body type, facial features, clothing on hand, and weather data to propose optimal outfits. The generation AI can also propose outfits based on the user's preferred clothing and colors. Step 6: The dress-up simulation unit virtually simulates the outfits proposed by the outfit suggestion unit. For example, the generation AI combines the user's image with images of clothing they own to virtually dress them up. It is also possible to perform real-time dress-up simulations using 3D models. Step 7: The online shopping linking unit links and directs users to online shopping sites based on the outfits proposed by the outfit suggestion unit. For example, the generation AI links and directs users to online shopping sites based on the user's preferred clothing and colors. It is also possible to use API linking to access a product database and design a purchasing flow.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

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

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

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

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

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

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

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

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

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

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

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

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

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

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

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

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

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

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

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

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

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

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

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

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

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

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

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

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

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

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

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

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

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

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

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

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

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

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

Claims

1. an image input unit for inputting an image of a user; a body type data input unit for inputting body type data of a user; a clothing image registration unit for registering images of clothing owned by the user; a weather data linking unit that links weather data; a coordinate suggestion unit that suggests an optimal fashion coordinate based on the data acquired by the image input unit, the body type data input unit, the clothing image registration unit, and the weather data linking unit; a dress-up simulation unit that virtually simulates dress-up of the coordination suggested by the coordination suggestion unit; an online shopping linking unit that links to and guides users to an online shopping site based on the coordination suggested by the coordination suggesting unit; A system characterized by:

2. The image input unit Analyzing the emotions of the user when uploading an image and adjusting the suggested fashion style based on the emotions.

2. The system of claim 1.

3. The body type data input unit Estimate the user's health condition and physical condition and suggest fashion items accordingly 2. The system of claim 1.

4. The clothing image registration unit Recognizes the material and texture of the clothing and suggests outfits based on that 2. The system of claim 1.

5. The weather data linking unit Predicting the user's skin condition and hair condition based on the weather data and suggesting fashion items accordingly 2. The system of claim 1.

Images