System

Abstract

An object of a system according to an embodiment is to propose optimal coordination based on clothes of a user and to realize efficient fashion management.SOLUTION: A system includes a clothing photograph input unit, an analysis unit, a coordination proposal unit, a purchase proposal unit, and a sale proposal unit. The clothing photograph input unit inputs a clothing photograph of a user. The analysis unit analyzes the clothing photograph input by the clothing photograph input unit. The coordination proposing section proposes optimum coordination based on the data analyzed by the analyzing section. The purchase proposal unit proposes purchase of a new fashion item on the basis of the coordination proposed by the coordination proposal unit. The sale proposal unit proposes sale of unnecessary clothes on the basis of the coordination proposed by the coordination proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for users to find the optimal outfit based on their own clothes, making efficient fashion management difficult.

[0005] The system according to the embodiment aims to propose optimal outfits based on the user's clothes and realize efficient fashion management. [Means for solving the problem]

[0006] The system according to the embodiment includes a clothing photo input unit, an analysis unit, a coordination suggestion unit, a purchase suggestion unit, and a sale suggestion unit. The clothing photo input unit inputs clothing photos of the user. The analysis unit analyzes the clothing photos input by the clothing photo input unit. The coordination suggestion unit suggests optimal coordination based on the data analyzed by the analysis unit. The purchase suggestion unit suggests purchasing new fashion items based on the coordination suggested by the coordination suggestion unit. The sale suggestion unit suggests selling unwanted clothing based on the coordination suggested by the coordination suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal outfits based on the user's clothes and realize efficient fashion management. [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) The fashion coordination system according to an embodiment of the present invention automatically analyzes photos of clothes taken by a user, and a generation AI proposes optimal outfits, supporting the purchase of new fashion items and the sale of unwanted clothes. This makes the fashion coordination system more efficient for users in managing their fashion and makes choosing outfits every day more enjoyable.

[0029] A fashion coordination system according to an embodiment includes a clothing photo input unit, an analysis unit, a coordination suggestion unit, a purchase suggestion unit, and a sales suggestion unit. The clothing photo input unit inputs a user's clothing photo. For example, the user uploads a photo of clothing taken with a smartphone. The clothing photo input unit can also import photos taken with a digital camera. The clothing photo input unit can also select and upload existing image files. The analysis unit analyzes the clothing photo input by the clothing photo input unit. For example, the analysis unit can identify the type, color, and shape of clothing using image recognition technology. The analysis unit can also extract clothing features using generative AI. The analysis unit can also analyze clothing materials and designs. For example, the analysis unit can analyze detailed clothing features using deep learning technology. The coordination suggestion unit suggests optimal coordination based on the data analyzed by the analysis unit. For example, the coordination suggestion unit uses generative AI to suggest coordination based on the user's preferences and trends. The coordination suggestion unit can also make suggestions taking into account the user's past coordination history. The coordination suggestion unit can also suggest coordination according to the season and weather. For example, the coordination suggestion unit suggests clothes made of light materials in spring and clothes made of warm materials in winter. The purchase suggestion unit suggests the purchase of new fashion items based on the coordination suggested by the coordination suggestion unit. For example, the purchase suggestion unit utilizes data from an online shopping platform to suggest new items that go well with clothes the user already owns. The purchase suggestion unit can also suggest items based on the user's budget and preferences. The purchase suggestion unit can also make suggestions taking into account the user's purchase history. For example, the purchase suggestion unit suggests new items that go well with items the user has previously purchased. The sale suggestion unit suggests the sale of unwanted clothes based on the coordination suggested by the coordination suggestion unit. For example, the sale suggestion unit utilizes data from an online flea market to suggest ways to sell unwanted clothes the user owns.The sales suggestion unit can also suggest recycling and donation options. The sales suggestion unit can also analyze the user's emotional response and prioritize emotionally positive suggestions. For example, the sales suggestion unit prioritizes suggestions that will satisfy the user. This allows the fashion coordination system according to the embodiment to streamline the user's fashion management and make choosing daily outfits more enjoyable. For example, the user can easily purchase new items or sell unwanted clothes through the system. The user can also enjoy coordinating outfits according to the system's suggestions.

[0030] The analysis unit can analyze background information of clothing photos and suggest outfits suitable for specific scenes or events. The analysis unit can, for example, analyze background information of clothing photos uploaded by a user and suggest outfits suitable for specific scenes or events. For example, if a beach is shown in the background, a casual resort style can be suggested. The analysis unit can also suggest a business casual style if an office is shown in the background. The analysis unit can also suggest a formal style if a party venue is shown in the background. In this way, by taking background information into consideration, it is possible to suggest outfits suitable for specific scenes or events.

[0031] The analysis unit can analyze the user's body type and skin color and, based on that, suggest clothing in the most appropriate color and shape. For example, the analysis unit uses a generative AI to analyze the user's body type and suggest clothing shapes and styles that suit that body type. For example, it can suggest a long coat for a tall user and a short jacket for a short user. The analysis unit can also analyze the user's skin color and, based on that, suggest clothing in the most appropriate color. For example, it can suggest pastel-colored clothing for a user with light skin, and vivid-colored clothing for a user with dark skin. The analysis unit can also comprehensively analyze the user's body type and skin color and suggest the most appropriate coordination. This makes it possible to suggest the most appropriate clothing based on the user's body type and skin color.

[0032] The analysis unit can convert photos of clothes uploaded by users into 3D models, enabling virtual try-on. The analysis unit, for example, can build a system that converts photos of clothes uploaded by users into 3D models and enables virtual try-on. For example, the user can try on clothes on their own avatar. The analysis unit can also use 3D scanning technology to generate a 3D model tailored to the user's body shape. The analysis unit can also use real-time rendering technology to instantly display the results of the virtual try-on. This allows users to virtually try on clothes and check their outfits before actually trying them on.

[0033] The analysis unit can input not only photos of clothes but also information about the user's lifestyle and hobbies, and suggest outfits based on that. The analysis unit, for example, inputs the user's lifestyle information (e.g., type of work and daily activities) and suggests optimal outfits based on that. For example, it might suggest a business casual style for an office worker. The analysis unit can also input information about the user's hobbies and suggest outfits based on that. For example, it might suggest an active style for a user whose hobby is sports. The analysis unit can also suggest outfits suitable for specific scenes or events based on the user's lifestyle and hobbies. This makes it possible to suggest outfits based on the user's lifestyle and hobbies.

[0034] The coordination suggestion unit can take seasonal and weather information into consideration when suggesting coordinations by the generation AI. For example, the coordination suggestion unit takes seasonal information into consideration when suggesting coordinations by the generation AI and suggests a style that suits the season. For example, it suggests clothes made of light materials in summer and clothes made of warm materials in winter. The coordination suggestion unit can also take weather information into consideration and suggest a style that suits the weather. For example, it suggests clothes made of waterproof materials on rainy days and breathable clothes on sunny days. The coordination suggestion unit can also suggest accessories and shoes that suit the season and weather. This makes it possible to suggest coordinations that suit the season and weather.

[0035] The coordination suggestion unit allows the generation AI to learn the user's past coordination history and make more personalized suggestions. For example, the coordination suggestion unit allows the generation AI to learn the user's past coordination history and make personalized suggestions based on that data. For example, it suggests coordinations that reflect the user's preferred styles and colors. The coordination suggestion unit can also make suggestions that match trends based on the user's past coordination history. The coordination suggestion unit can also make suggestions according to the season or weather based on the user's past coordination history. This allows more personalized suggestions to be made based on the user's past coordination history.

[0036] The coordination suggestion unit can add a function for incorporating the opinions of the user's friends and family into the coordination suggestions. For example, the coordination suggestion unit adds a function for incorporating the opinions of the user's friends and family into the coordination suggestions and collects feedback on the proposed coordination. For example, it provides a function that allows friends and family to comment and rate. The coordination suggestion unit can also modify the coordination based on the opinions of the user's friends and family. For example, it can propose more personalized coordination by reflecting the opinions of friends and family. This makes it possible to propose coordination that incorporates the opinions of the user's friends and family.

[0037] The coordination suggestion unit can customize the suggested coordination according to different situations. For example, the coordination suggestion unit adds a function to customize the suggested coordination according to different situations, allowing the user to select. For example, it can suggest coordination for work, dates, and casual wear. The coordination suggestion unit can also automatically determine the situation based on the user's schedule and suggest the optimal coordination. For example, it can analyze the user's calendar information and suggest coordination that matches a specific event. This makes it possible to suggest coordination that suits different situations.

[0038] The purchase suggestion unit can take the user's budget and purchase history into consideration when suggesting new items to the generation AI. For example, the purchase suggestion unit considers the user's budget information and suggests new fashion items that can be purchased within that range. For example, it selects the most suitable items within the budget set by the user. The purchase suggestion unit can also consider the user's purchase history and suggest new items that go well with items purchased in the past. The purchase suggestion unit can also suggest items based on the user's preferences and trends. This makes it possible to suggest new items that take the user's budget and purchase history into consideration.

[0039] The purchase suggestion unit can provide customization options based on the user's preferences and past purchase history for items suggested by the generation AI. For example, the purchase suggestion unit provides customization options based on the user's preferences and past purchase history. For example, the generation AI can suggest items that match the user's preferred colors and styles. The purchase suggestion unit can also update the customization options based on user feedback. For example, the customization options selected by the user can be recorded and reflected in the next suggestion. This makes it possible to provide customization options based on the user's preferences and past purchase history.

[0040] The purchase suggestion unit can add a function to share suggested items with the user's friends and family and collect their opinions. The purchase suggestion unit can add a function to share suggested items with the user's friends and family and collect their opinions. For example, it can provide a function that allows friends and family to leave comments and ratings. The purchase suggestion unit can also modify items based on the opinions of the user's friends and family. For example, it can suggest more personalized items by reflecting the opinions of friends and family. This makes it possible to suggest items that incorporate the opinions of the user's friends and family.

[0041] The purchase suggestion unit can customize the suggested items based on different styles or themes. For example, the purchase suggestion unit can add a function to customize the suggested items based on different styles or themes, allowing the user to select. For example, eco-friendly items or luxury items can be suggested. The purchase suggestion unit can also suggest styles or themes based on the user's preferences or trends. For example, items that match the user's preferred style can be suggested. This makes it possible to suggest items based on different styles or themes.

[0042] The sales suggestion unit can suggest the optimal timing for the generation AI to sell the user's unwanted clothes. For example, the generation AI analyzes market data and suggests the optimal timing for the user to sell their unwanted clothes. For example, it can suggest selling at a time that coincides with a season or event when demand is high. The sales suggestion unit can also analyze the user's emotional response and suggest an emotionally positive timing for selling. For example, it can prioritize suggesting a timing for selling that will satisfy the user. This makes it possible to suggest the optimal timing for selling the user's unwanted clothes.

[0043] The sales suggestion unit allows the generation AI to suggest options for recycling or donating the user's unwanted clothes. For example, the sales suggestion unit may suggest options for recycling the user's unwanted clothes. For example, it may introduce recycle shops or recycling programs. The sales suggestion unit may also suggest options for donating the user's unwanted clothes. For example, it may suggest the selection of a donation destination and a donation method. The sales suggestion unit may also analyze the user's emotional response and suggest emotionally positive recycling or donation options. This allows it to suggest options for recycling or donating the user's unwanted clothes.

[0044] The sales proposal unit can customize the sales proposal according to different platforms. For example, the sales proposal unit adds a function to customize the sales proposal according to different platforms, allowing the user to select. For example, the sales proposal unit can suggest selling methods such as auction sites and recycle shops. The sales proposal unit can also suggest platforms based on the user's preferences and sales history. For example, the sales proposal unit can suggest the optimal selling method based on platforms the user has used in the past. This makes it possible to make sales proposals according to different platforms.

[0045] The coordination suggestion unit allows the generation AI to learn the user's coordination history and make more efficient suggestions. For example, the coordination suggestion unit allows the generation AI to learn the user's past coordination history and make efficient suggestions based on that data. For example, it may suggest coordinations based on items the user often wears. The coordination suggestion unit can also make suggestions that match the trends based on the user's coordination history. The coordination suggestion unit can also make suggestions according to the season or weather based on the user's coordination history. This allows more efficient suggestions to be made based on the user's coordination history.

[0046] The coordination suggestion unit allows the generation AI to propose optimal coordination by taking into consideration the user's lifestyle and daily schedule. For example, the generation AI considers the user's lifestyle information (e.g., type of work and daily activities) and proposes optimal coordination based on that. For example, it may propose a business casual style for office workers. The coordination suggestion unit can also consider the user's daily schedule and propose coordination based on that. For example, it may analyze the user's calendar information and propose coordination that matches a specific event. This makes it possible to propose optimal coordination based on the user's lifestyle and schedule.

[0047] The coordination suggestion unit can add a function for incorporating the opinions of the user's friends and family into the coordination suggestions. For example, the coordination suggestion unit adds a function for incorporating the opinions of the user's friends and family into the coordination suggestions and collects feedback on the proposed coordination. For example, it provides a function that allows friends and family to comment and rate. The coordination suggestion unit can also modify the coordination based on the opinions of the user's friends and family. For example, it can propose more personalized coordination by reflecting the opinions of friends and family. This makes it possible to propose coordination that incorporates the opinions of the user's friends and family.

[0048] The coordination suggestion unit can customize the suggested coordination according to different situations. For example, the coordination suggestion unit adds a function to customize the suggested coordination according to different situations, allowing the user to select. For example, it can suggest coordination for work, dates, and casual wear. The coordination suggestion unit can also automatically determine the situation based on the user's schedule and suggest the optimal coordination. For example, it can analyze the user's calendar information and suggest coordination that matches a specific event. This makes it possible to suggest coordination that suits different situations.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The fashion coordination system can also analyze the user's health data and suggest outfits that match their health condition. For example, it can suggest comfortable clothing for active days based on the user's step count and exercise volume. It can also analyze the user's sleep data and suggest relaxing clothing for tired days. It can also take the user's dietary data into account and suggest outfits that match specific nutritional conditions. This makes it possible to provide optimal outfits based on the user's health condition.

[0051] The fashion coordination system can also analyze the user's travel plans and suggest outfits that match the travel destination. For example, if the user is traveling to a beach resort, resort-style clothing can be suggested. If the user is traveling to a cold region, warm clothing can be suggested. Furthermore, if the user is traveling to an urban area, city-style clothing can be suggested. This makes it possible to provide optimal outfits based on the user's travel plans.

[0052] The fashion coordination system can also analyze the user's musical preferences and suggest coordinations according to the music genre. For example, if the user likes rock music, rock style clothing can be suggested. If the user likes classical music, elegant clothing can be suggested. Furthermore, if the user likes hip hop music, street style clothing can be suggested. This makes it possible to provide optimal coordination based on the user's musical preferences.

[0053] The fashion coordination system can also analyze the user's hobbies and interests and suggest coordination based on the results. For example, if the user likes outdoor activities, it can suggest active style clothing. If the user is interested in art, it can also suggest clothing with creative designs. Furthermore, if the user's hobby is cooking, it can also suggest clothing suitable for activities in the kitchen. This makes it possible to provide optimal coordination based on the user's hobbies and interests.

[0054] The fashion coordination system can also suggest outfits based on the user's social events and occasions. For example, if the user is attending a wedding, formal attire can be suggested. If the user is attending a casual party, relaxed attire can be suggested. Furthermore, if the user is attending a business meeting, professional attire can be suggested. This allows the system to provide optimal outfits based on the user's social events and occasions.

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

[0056] Step 1: The clothing photo input unit inputs a user's clothing photo. For example, the user can upload a photo of the clothing taken with a smartphone. It is also possible to import a photo taken with a digital camera, or to select and upload an existing image file. Step 2: The analysis unit analyzes the clothing photos input by the clothing photo input unit. For example, image recognition technology is used to identify the type, color, and shape of the clothing, and generative AI is used to extract the clothing's features. Deep learning technology can also be used to analyze the detailed characteristics of the clothing. Step 3: The outfit suggestion unit proposes optimal outfits based on the data analyzed by the analysis unit. For example, it uses a generation AI to propose outfits based on the user's preferences and trends, and also makes suggestions based on the user's past outfit history, the season, and the weather. Step 4: The purchase suggestion unit suggests new fashion items to purchase based on the coordination suggested by the coordination suggestion unit. For example, it may use data from an online shopping platform to suggest items based on the user's budget and preferences. It may also make suggestions taking into account the user's purchase history. Step 5: The sales suggestion unit suggests selling unwanted clothes based on the outfits suggested by the outfit suggestion unit. For example, it may use data from online flea markets to suggest recycling or donation options. It also analyzes the user's emotional responses and prioritizes emotionally positive suggestions.

[0057] (Example 2) The fashion coordination system according to an embodiment of the present invention automatically analyzes photos of clothes taken by a user, and a generation AI proposes optimal outfits, supporting the purchase of new fashion items and the sale of unwanted clothes. This makes the fashion coordination system more efficient for users in managing their fashion and makes choosing outfits every day more enjoyable.

[0058] A fashion coordination system according to an embodiment includes a clothing photo input unit, an analysis unit, a coordination suggestion unit, a purchase suggestion unit, and a sales suggestion unit. The clothing photo input unit inputs a user's clothing photo. For example, the user uploads a photo of clothing taken with a smartphone. The clothing photo input unit can also import photos taken with a digital camera. The clothing photo input unit can also select and upload existing image files. The analysis unit analyzes the clothing photo input by the clothing photo input unit. For example, the analysis unit can identify the type, color, and shape of clothing using image recognition technology. The analysis unit can also extract clothing features using generative AI. The analysis unit can also analyze clothing materials and designs. For example, the analysis unit can analyze detailed clothing features using deep learning technology. The coordination suggestion unit suggests optimal coordination based on the data analyzed by the analysis unit. For example, the coordination suggestion unit uses generative AI to suggest coordination based on the user's preferences and trends. The coordination suggestion unit can also make suggestions taking into account the user's past coordination history. The coordination suggestion unit can also suggest coordination according to the season and weather. For example, the coordination suggestion unit suggests clothes made of light materials in spring and clothes made of warm materials in winter. The purchase suggestion unit suggests the purchase of new fashion items based on the coordination suggested by the coordination suggestion unit. For example, the purchase suggestion unit utilizes data from an online shopping platform to suggest new items that go well with clothes the user already owns. The purchase suggestion unit can also suggest items based on the user's budget and preferences. The purchase suggestion unit can also make suggestions taking into account the user's purchase history. For example, the purchase suggestion unit suggests new items that go well with items the user has previously purchased. The sale suggestion unit suggests the sale of unwanted clothes based on the coordination suggested by the coordination suggestion unit. For example, the sale suggestion unit utilizes data from an online flea market to suggest ways to sell unwanted clothes the user owns.The sales suggestion unit can also suggest recycling and donation options. The sales suggestion unit can also analyze the user's emotional response and prioritize emotionally positive suggestions. For example, the sales suggestion unit prioritizes suggestions that will satisfy the user. This allows the fashion coordination system according to the embodiment to streamline the user's fashion management and make choosing daily outfits more enjoyable. For example, the user can easily purchase new items or sell unwanted clothes through the system. The user can also enjoy coordinating outfits according to the system's suggestions.

[0059] The analysis unit can analyze background information of clothing photos and suggest outfits suitable for specific scenes or events. The analysis unit can, for example, analyze background information of clothing photos uploaded by a user and suggest outfits suitable for specific scenes or events. For example, if a beach is shown in the background, a casual resort style can be suggested. The analysis unit can also suggest a business casual style if an office is shown in the background. The analysis unit can also suggest a formal style if a party venue is shown in the background. In this way, by taking background information into consideration, it is possible to suggest outfits suitable for specific scenes or events.

[0060] The analysis unit can analyze the user's body type and skin color and, based on that, suggest clothing in the most appropriate color and shape. For example, the analysis unit uses a generative AI to analyze the user's body type and suggest clothing shapes and styles that suit that body type. For example, it can suggest a long coat for a tall user and a short jacket for a short user. The analysis unit can also analyze the user's skin color and, based on that, suggest clothing in the most appropriate color. For example, it can suggest pastel-colored clothing for a user with light skin, and vivid-colored clothing for a user with dark skin. The analysis unit can also comprehensively analyze the user's body type and skin color and suggest the most appropriate coordination. This makes it possible to suggest the most appropriate clothing based on the user's body type and skin color.

[0061] The analysis unit can use the emotion estimation function to analyze the emotion a user expresses when uploading a photo and suggest outfits that match that emotion. For example, the analysis unit can use the emotion estimation function to analyze the facial expression of a user when uploading a photo and suggest outfits that match positive emotions. For example, it can suggest bright-colored clothing for a photo of a smiling face. The analysis unit can also analyze the voice of a user when uploading a photo to estimate the emotion. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also analyze the user's biometric data (heart rate and electrodermal activity) to estimate the emotion. For example, it can calculate an emotion score based on fluctuations in heart rate. This makes it possible to suggest outfits that match the user's emotion.

[0062] The analysis unit can convert photos of clothes uploaded by users into 3D models, enabling virtual try-on. The analysis unit, for example, can build a system that converts photos of clothes uploaded by users into 3D models and enables virtual try-on. For example, the user can try on clothes on their own avatar. The analysis unit can also use 3D scanning technology to generate a 3D model tailored to the user's body shape. The analysis unit can also use real-time rendering technology to instantly display the results of the virtual try-on. This allows users to virtually try on clothes and check their outfits before actually trying them on.

[0063] The analysis unit can input not only photos of clothes but also information about the user's lifestyle and hobbies, and suggest outfits based on that. The analysis unit, for example, inputs the user's lifestyle information (e.g., type of work and daily activities) and suggests optimal outfits based on that. For example, it might suggest a business casual style for an office worker. The analysis unit can also input information about the user's hobbies and suggest outfits based on that. For example, it might suggest an active style for a user whose hobby is sports. The analysis unit can also suggest outfits suitable for specific scenes or events based on the user's lifestyle and hobbies. This makes it possible to suggest outfits based on the user's lifestyle and hobbies.

[0064] The analysis unit can use the emotion estimation function to analyze the emotions of a user when uploading a photo of clothing in real time and make suggestions that will elicit positive emotions. For example, the analysis unit can use the emotion estimation function to analyze the emotions of a user when uploading a photo of clothing in real time and make suggestions that will elicit positive emotions. For example, the analysis unit can suggest outfits that will make the user smile. The analysis unit can also analyze the user's voice in real time to estimate emotions. For example, the analysis unit can analyze the tone and speed of the voice to calculate an emotion score. The analysis unit can also analyze the user's biometric data in real time to estimate emotions. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. This makes it possible to suggest positive outfits that match the user's emotions.

[0065] The coordination suggestion unit can take seasonal and weather information into consideration when suggesting coordinations by the generation AI. For example, the coordination suggestion unit takes seasonal information into consideration when suggesting coordinations by the generation AI and suggests a style that suits the season. For example, it suggests clothes made of light materials in summer and clothes made of warm materials in winter. The coordination suggestion unit can also take weather information into consideration and suggest a style that suits the weather. For example, it suggests clothes made of waterproof materials on rainy days and breathable clothes on sunny days. The coordination suggestion unit can also suggest accessories and shoes that suit the season and weather. This makes it possible to suggest coordinations that suit the season and weather.

[0066] The coordination suggestion unit allows the generation AI to learn the user's past coordination history and make more personalized suggestions. For example, the coordination suggestion unit allows the generation AI to learn the user's past coordination history and make personalized suggestions based on that data. For example, it suggests coordinations that reflect the user's preferred styles and colors. The coordination suggestion unit can also make suggestions that match trends based on the user's past coordination history. The coordination suggestion unit can also make suggestions according to the season or weather based on the user's past coordination history. This allows more personalized suggestions to be made based on the user's past coordination history.

[0067] The coordination suggestion unit can add a function for incorporating the opinions of the user's friends and family into the coordination suggestions. For example, the coordination suggestion unit adds a function for incorporating the opinions of the user's friends and family into the coordination suggestions and collects feedback on the proposed coordination. For example, it provides a function that allows friends and family to comment and rate. The coordination suggestion unit can also modify the coordination based on the opinions of the user's friends and family. For example, it can propose more personalized coordination by reflecting the opinions of friends and family. This makes it possible to propose coordination that incorporates the opinions of the user's friends and family.

[0068] The coordination suggestion unit can customize the suggested coordination according to different situations. For example, the coordination suggestion unit adds a function to customize the suggested coordination according to different situations, allowing the user to select. For example, it can suggest coordination for work, dates, and casual wear. The coordination suggestion unit can also automatically determine the situation based on the user's schedule and suggest the optimal coordination. For example, it can analyze the user's calendar information and suggest coordination that matches a specific event. This makes it possible to suggest coordination that suits different situations.

[0069] The coordination suggestion unit uses the emotion estimation function to collect the user's emotional reactions to the proposed coordination in real time, and can continuously make optimal suggestions. The coordination suggestion unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the proposed coordination in real time, and can continuously make optimal suggestions based on that data. For example, it prioritizes suggestions that have a high number of positive emotional reactions. The coordination suggestion unit can also analyze the user's emotional reactions and improve the accuracy of suggestions. For example, it customizes suggestions based on the user's preferences and emotions. This allows the coordination suggestion unit to collect the user's emotional reactions in real time and continuously make optimal coordination suggestions.

[0070] The purchase suggestion unit can take the user's budget and purchase history into consideration when suggesting new items to the generation AI. For example, the purchase suggestion unit considers the user's budget information and suggests new fashion items that can be purchased within that range. For example, it selects the most suitable items within the budget set by the user. The purchase suggestion unit can also consider the user's purchase history and suggest new items that go well with items purchased in the past. The purchase suggestion unit can also suggest items based on the user's preferences and trends. This makes it possible to suggest new items that take the user's budget and purchase history into consideration.

[0071] The purchase suggestion unit can provide customization options based on the user's preferences and past purchase history for items suggested by the generation AI. For example, the purchase suggestion unit provides customization options based on the user's preferences and past purchase history. For example, the generation AI can suggest items that match the user's preferred colors and styles. The purchase suggestion unit can also update the customization options based on user feedback. For example, the customization options selected by the user can be recorded and reflected in the next suggestion. This makes it possible to provide customization options based on the user's preferences and past purchase history.

[0072] The purchase suggestion unit can use the emotion estimation function to analyze the user's emotional response to the suggested items and prioritize emotionally positive suggestions. The purchase suggestion unit can, for example, use the emotion estimation function to analyze the user's emotional response to the suggested items and prioritize suggestions with a high number of positive emotional responses. For example, it can prioritize suggestions of items that the user will enjoy. The purchase suggestion unit can also analyze the user's voice to estimate the emotional response. For example, it can analyze the tone and speed of the voice to calculate an emotional score. The purchase suggestion unit can also analyze the user's biometric data to estimate the emotional response. For example, it can calculate an emotional score based on heart rate fluctuations. This makes it possible to prioritize positive suggestions based on the user's emotional response.

[0073] The purchase suggestion unit can add a function to share suggested items with the user's friends and family and collect their opinions. The purchase suggestion unit can add a function to share suggested items with the user's friends and family and collect their opinions. For example, it can provide a function that allows friends and family to leave comments and ratings. The purchase suggestion unit can also modify items based on the opinions of the user's friends and family. For example, it can suggest more personalized items by reflecting the opinions of friends and family. This makes it possible to suggest items that incorporate the opinions of the user's friends and family.

[0074] The purchase suggestion unit can customize the suggested items based on different styles or themes. For example, the purchase suggestion unit can add a function to customize the suggested items based on different styles or themes, allowing the user to select. For example, eco-friendly items or luxury items can be suggested. The purchase suggestion unit can also suggest styles or themes based on the user's preferences or trends. For example, items that match the user's preferred style can be suggested. This makes it possible to suggest items based on different styles or themes.

[0075] The purchase suggestion unit uses the emotion estimation function to collect the user's emotional responses to the suggested items in real time, and can continuously make optimal suggestions. The purchase suggestion unit, for example, uses the emotion estimation function to collect the user's emotional responses to the suggested items in real time, and can continuously make optimal suggestions based on that data. For example, it prioritizes suggestions with a high number of positive emotional responses. The purchase suggestion unit can also analyze the user's emotional responses and improve the accuracy of suggestions. For example, it customizes suggestions based on the user's preferences and emotions. This allows the user's emotional responses to be collected in real time, and can continuously make optimal suggestions.

[0076] The sales suggestion unit can suggest the optimal timing for the generation AI to sell the user's unwanted clothes. For example, the generation AI analyzes market data and suggests the optimal timing for the user to sell their unwanted clothes. For example, it can suggest selling at a time that coincides with a season or event when demand is high. The sales suggestion unit can also analyze the user's emotional response and suggest an emotionally positive timing for selling. For example, it can prioritize suggesting a timing for selling that will satisfy the user. This makes it possible to suggest the optimal timing for selling the user's unwanted clothes.

[0077] The sales suggestion unit allows the generation AI to suggest options for recycling or donating the user's unwanted clothes. For example, the sales suggestion unit may suggest options for recycling the user's unwanted clothes. For example, it may introduce recycle shops or recycling programs. The sales suggestion unit may also suggest options for donating the user's unwanted clothes. For example, it may suggest the selection of a donation destination and a donation method. The sales suggestion unit may also analyze the user's emotional response and suggest emotionally positive recycling or donation options. This allows it to suggest options for recycling or donating the user's unwanted clothes.

[0078] The sales proposal unit can use the emotion estimation function to analyze the user's emotional response to the sales proposal and prioritize emotionally positive proposals. For example, the sales proposal unit can use the emotion estimation function to analyze the user's emotional response to the sales proposal and prioritize proposals with a higher number of positive emotional responses. For example, the sales proposal unit can prioritize proposals for sales methods that will satisfy the user. The sales proposal unit can also analyze the user's voice to estimate the emotional response. For example, the sales proposal unit can analyze the tone and speed of the voice to calculate an emotional score. The sales proposal unit can also analyze the user's biometric data to estimate the emotional response. For example, the emotional score can be calculated based on fluctuations in heart rate. This makes it possible to prioritize positive sales proposals based on the user's emotional response.

[0079] The sales proposal unit can customize the sales proposal according to different platforms. For example, the sales proposal unit adds a function to customize the sales proposal according to different platforms, allowing the user to select. For example, the sales proposal unit can suggest selling methods such as auction sites and recycle shops. The sales proposal unit can also suggest platforms based on the user's preferences and sales history. For example, the sales proposal unit can suggest the optimal selling method based on platforms the user has used in the past. This makes it possible to make sales proposals according to different platforms.

[0080] The sales proposal unit uses the emotion estimation function to collect the user's emotional reactions to the sales proposal in real time, and can continuously make optimal proposals. The sales proposal unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the sales proposal in real time, and can continuously make optimal proposals based on that data. For example, it prioritizes proposals with a high number of positive emotional reactions. The sales proposal unit can also analyze the user's emotional reactions and improve the accuracy of proposals. For example, it customizes proposals based on the user's preferences and emotions. This allows the user's emotional reactions to be collected in real time, and can continuously make optimal sales proposals.

[0081] The coordination suggestion unit allows the generation AI to learn the user's coordination history and make more efficient suggestions. For example, the coordination suggestion unit allows the generation AI to learn the user's past coordination history and make efficient suggestions based on that data. For example, it may suggest coordinations based on items the user often wears. The coordination suggestion unit can also make suggestions that match the trends based on the user's coordination history. The coordination suggestion unit can also make suggestions according to the season or weather based on the user's coordination history. This allows more efficient suggestions to be made based on the user's coordination history.

[0082] The coordination suggestion unit allows the generation AI to propose optimal coordination by taking into consideration the user's lifestyle and daily schedule. For example, the generation AI considers the user's lifestyle information (e.g., type of work and daily activities) and proposes optimal coordination based on that. For example, it may propose a business casual style for office workers. The coordination suggestion unit can also consider the user's daily schedule and propose coordination based on that. For example, it may analyze the user's calendar information and propose coordination that matches a specific event. This makes it possible to propose optimal coordination based on the user's lifestyle and schedule.

[0083] The coordination suggestion unit can use the emotion estimation function to analyze the user's emotional response to the coordination proposal and prioritize emotionally positive suggestions. The coordination suggestion unit can, for example, use the emotion estimation function to analyze the user's emotional response to the coordination proposal and prioritize suggestions that have a high number of positive emotional responses. For example, it can prioritize suggesting coordinations that make the user smile. The coordination suggestion unit can also analyze the user's voice to estimate the emotional response. For example, it can analyze the tone and speed of the voice to calculate an emotion score. The coordination suggestion unit can also analyze the user's biometric data to estimate the emotional response. For example, it can calculate an emotion score based on heart rate fluctuations. This makes it possible to prioritize positive coordination suggestions based on the user's emotional response.

[0084] The coordination suggestion unit can add a function for incorporating the opinions of the user's friends and family into the coordination suggestions. For example, the coordination suggestion unit adds a function for incorporating the opinions of the user's friends and family into the coordination suggestions and collects feedback on the proposed coordination. For example, it provides a function that allows friends and family to comment and rate. The coordination suggestion unit can also modify the coordination based on the opinions of the user's friends and family. For example, it can propose more personalized coordination by reflecting the opinions of friends and family. This makes it possible to propose coordination that incorporates the opinions of the user's friends and family.

[0085] The coordination suggestion unit can customize the suggested coordination according to different situations. For example, the coordination suggestion unit adds a function to customize the suggested coordination according to different situations, allowing the user to select. For example, it can suggest coordination for work, dates, and casual wear. The coordination suggestion unit can also automatically determine the situation based on the user's schedule and suggest the optimal coordination. For example, it can analyze the user's calendar information and suggest coordination that matches a specific event. This makes it possible to suggest coordination that suits different situations.

[0086] The coordination suggestion unit uses the emotion estimation function to collect the user's emotional reactions to the proposed coordination in real time, and can continuously make optimal suggestions. The coordination suggestion unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the proposed coordination in real time, and can continuously make optimal suggestions based on that data. For example, it prioritizes suggestions that have a high number of positive emotional reactions. The coordination suggestion unit can also analyze the user's emotional reactions and improve the accuracy of suggestions. For example, it customizes suggestions based on the user's preferences and emotions. This allows the coordination suggestion unit to collect the user's emotional reactions in real time and continuously make optimal coordination suggestions.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The fashion coordination system can also analyze the user's health data and suggest outfits that match their health condition. For example, it can suggest comfortable clothing for active days based on the user's step count and exercise volume. It can also analyze the user's sleep data and suggest relaxing clothing for tired days. It can also take the user's dietary data into account and suggest outfits that match specific nutritional conditions. This makes it possible to provide optimal outfits based on the user's health condition.

[0089] The fashion coordination system can also analyze the user's travel plans and suggest outfits that match the travel destination. For example, if the user is traveling to a beach resort, resort-style clothing can be suggested. If the user is traveling to a cold region, warm clothing can be suggested. Furthermore, if the user is traveling to an urban area, city-style clothing can be suggested. This makes it possible to provide optimal outfits based on the user's travel plans.

[0090] The fashion coordination system can further estimate the user's emotions and suggest coordination based on the estimated emotions. For example, if the user is feeling stressed, it can suggest relaxing clothing. If the user is excited, it can also suggest energetic clothing. Furthermore, if the user is sad, it can also suggest brightly colored clothing to lift the user's spirits. In this way, it is possible to provide optimal coordination based on the user's emotions.

[0091] The fashion coordination system can also analyze the user's musical preferences and suggest coordinations according to the music genre. For example, if the user likes rock music, rock style clothing can be suggested. If the user likes classical music, elegant clothing can be suggested. Furthermore, if the user likes hip hop music, street style clothing can be suggested. This makes it possible to provide optimal coordination based on the user's musical preferences.

[0092] The fashion coordination system can further estimate the user's emotions and make purchase suggestions based on the estimated emotions. For example, if the user is happy, new items in bright colors can be suggested. If the user is depressed, special items to lift the user's spirits can be suggested. Furthermore, if the user is excited, items with energetic designs can be suggested. This makes it possible to provide optimal purchase suggestions based on the user's emotions.

[0093] The fashion coordination system can also analyze the user's hobbies and interests and suggest coordination based on the results. For example, if the user likes outdoor activities, it can suggest active style clothing. If the user is interested in art, it can also suggest clothing with creative designs. Furthermore, if the user's hobby is cooking, it can also suggest clothing suitable for activities in the kitchen. This makes it possible to provide optimal coordination based on the user's hobbies and interests.

[0094] The fashion coordination system can further estimate the user's emotions and make sales proposals based on the estimated emotions. For example, if the user is satisfied, a special offer to promote sales can be proposed. Also, if the user is anxious, support to give the user a sense of security can be proposed. Furthermore, if the user is excited, a quick sale can be proposed. In this way, optimal sales proposals can be provided based on the user's emotions.

[0095] The fashion coordination system can also estimate the user's emotions and suggest recycling or donations based on the estimated emotions. For example, if the user feels grateful, the system can suggest donations. Also, if the user is environmentally conscious, the system can suggest recycling. Furthermore, if the user is thinking about contributing to society, the system can suggest donations to specific charities. This makes it possible to provide optimal recycling and donation suggestions based on the user's emotions.

[0096] The fashion coordination system can also estimate the user's emotions and collect feedback on coordination based on the estimated emotions. For example, if the user expresses positive emotions about a suggested coordination, that style can be reflected in future suggestions. On the other hand, if the user expresses negative emotions, that style can be avoided. Furthermore, it is possible to accumulate user emotion data and analyze long-term trends. This makes it possible to provide optimal coordination suggestions based on the user's emotions.

[0097] The fashion coordination system can also suggest outfits based on the user's social events and occasions. For example, if the user is attending a wedding, formal attire can be suggested. If the user is attending a casual party, relaxed attire can be suggested. Furthermore, if the user is attending a business meeting, professional attire can be suggested. This allows the system to provide optimal outfits based on the user's social events and occasions.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The clothing photo input unit inputs a user's clothing photo. For example, the user can upload a photo of the clothing taken with a smartphone. It is also possible to import a photo taken with a digital camera, or to select and upload an existing image file. Step 2: The analysis unit analyzes the clothing photos input by the clothing photo input unit. For example, image recognition technology is used to identify the type, color, and shape of the clothing, and generative AI is used to extract the clothing's features. Deep learning technology can also be used to analyze the detailed characteristics of the clothing. Step 3: The outfit suggestion unit proposes optimal outfits based on the data analyzed by the analysis unit. For example, it uses a generation AI to propose outfits based on the user's preferences and trends, and also makes suggestions based on the user's past outfit history, the season, and the weather. Step 4: The purchase suggestion unit suggests new fashion items to purchase based on the coordination suggested by the coordination suggestion unit. For example, it may use data from an online shopping platform to suggest items based on the user's budget and preferences. It may also make suggestions taking into account the user's purchase history. Step 5: The sales suggestion unit suggests selling unwanted clothes based on the outfits suggested by the outfit suggestion unit. For example, it may use data from online flea markets to suggest recycling or donation options. It also analyzes the user's emotional responses and prioritizes emotionally positive suggestions.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a clothes photo input unit for inputting a user's clothes photo; An analysis unit that analyzes the clothing photos input by the clothing photo input unit; a coordinate suggestion unit that suggests an optimal coordinate based on the data analyzed by the analysis unit; a purchase suggestion unit that suggests the purchase of a new fashion item based on the coordination suggested by the coordination suggestion unit; a sales proposal unit that proposes the sale of unnecessary clothes based on the coordination proposed by the coordination proposal unit. A system characterized by:

2. The analysis unit The photos of the clothes uploaded by the user are converted into 3D models, allowing for virtual try-on.

2. The system of claim 1.

3. The coordination suggestion unit The AI ​​takes into account seasonal and weather information when proposing outfits.

2. The system of claim 1.

4. The purchase proposal unit The generative AI takes into account the user's budget and purchasing history when suggesting new items.

2. The system of claim 1.

5. The sales proposal unit The generation AI suggests the best time to sell the user's unwanted clothes.

2. The system of claim 1.

6. The analysis unit Analyze the emotions of the user when they upload a photo and suggest outfits that match those emotions.

2. The system of claim 1.

Images