system

Abstract

To provide a system capable of proposing optimum coordination on the basis of user's attribute information and clothes in hand.SOLUTION: The system includes an attribute information acquisition unit, a photographing unit, a coordination suggestion unit, a calendar cooperation unit, a feedback collection unit, an item suggestion unit, and an exhibition support unit. The attribute information acquisition unit acquires attribute information of a user. The photograph taking unit takes a photograph of clothes and small articles held by the user. The coordination proposal unit proposes coordination for one week based on the TPO. The calendar cooperation part proposes coordination based on a future schedule. The feedback collection unit collects the wearing record and the favorite degree of the user and reflects the collected wearing record and favorite degree in the next and subsequent coordinate proposals. The item suggester suggests new items based on climate, preferences, and trends. The exhibition support unit generates an explanation sentence and a market price amount of an item having a low degree of use, and supports exhibition in the Furima application.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately proposed optimal outfits based on a user's attribute information and the clothes they own, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal outfits based on the user's attribute information and clothes they own. [Means for solving the problem]

[0006] The system according to the embodiment includes an attribute information acquisition unit, a photography unit, a coordination suggestion unit, a calendar linkage unit, a feedback collection unit, an item suggestion unit, and a listing support unit. The attribute information acquisition unit acquires the user's attribute information. The photography unit takes photos of the user's clothing and accessories. The coordination suggestion unit suggests a week's worth of coordination based on the TPO (Time, Place, Occasion, and Occasion) based on the attribute information and photos. The calendar linkage unit suggests coordination based on future plans in conjunction with the calendar. The feedback collection unit collects the user's wearing history and favorite level as feedback and reflects this in future coordination suggestions. The item suggestion unit suggests new items based on the climate, preferences, and trends other than the user's clothing and accessories. The listing support unit generates descriptions and market prices from photo data of items that are rarely used, and supports listing on the flea market app. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal outfits based on the user's attribute information and clothes they own. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The coordination suggestion system according to an embodiment of the present invention automatically suggests a week's worth of coordinations that take into account the time, place, and occasion based on the user's attribute information and photos of their clothes and accessories, and also performs calendar integration, feedback collection, item suggestions, and item listing support. As a result, the coordination suggestion system makes it easy for users to find coordinations that suit their style, and also allows them to smoothly purchase new items and list unwanted items.

[0029] The coordination suggestion system according to the embodiment includes an attribute information acquisition unit, a photography unit, a coordination suggestion unit, a calendar linkage unit, a feedback collection unit, an item suggestion unit, and a listing support unit. The attribute information acquisition unit acquires user attribute information. For example, information such as age, gender, occupation, and hobbies can be input. The photography unit takes photos of the user's clothes and accessories. For example, photos can be taken using a smartphone camera and uploaded to the system. The coordination suggestion unit proposes a week's worth of coordinations taking into account the time, place, and occasion based on the attribute information and photos. For example, it proposes coordinations based on schedules such as office casual on Monday, remote work on Tuesday, and lunch with friends on Wednesday. The calendar linkage unit proposes coordinations based on future plans in cooperation with a calendar. For example, it proposes coordinations based on schedules in cooperation with Google (registered trademark) Calendar or Outlook Calendar. The feedback collection unit collects the user's wearing history and favorite rating as feedback and reflects this in future coordination suggestions. For example, since this coordination was highly favorited, it will propose a similar style next time. The item suggestion unit suggests new items based on the weather, preferences, and trends, in addition to the clothes and accessories the user already owns. For example, since there has been a lot of rain this week, it would suggest a waterproof jacket. The listing support unit generates a description and a market price from photo data of items that are rarely used, and supports listing on a flea market app. For example, since this jacket is not often worn, it generates a description and a market price for listing. This allows the coordination suggestion system to easily find coordinations that suit the user's style, and also allows the user to smoothly purchase new items and list unwanted items.

[0030] The attribute information acquisition unit analyzes a user's past fashion history and fashion posts on social media to suggest more accurate outfits. The attribute information acquisition unit, for example, analyzes a user's past fashion history to understand preferences for specific styles and brands. For example, the unit makes suggestions that reflect the user's preferences based on previously purchased items and outfits worn. The attribute information acquisition unit also analyzes fashion posts on social media to understand the user's preferred styles. For example, it analyzes posts on Instagram (registered trademark) or Pinterest to identify the items and styles the user often wears. The attribute information acquisition unit also integrates the user's past fashion history and social media posts to suggest more accurate outfits. For example, it automatically generates outfits that match the user's preferences based on past purchase history and social media posts. This allows for more accurate outfit suggestions by analyzing a user's past fashion history and fashion posts on social media.

[0031] The attribute information acquisition unit can periodically monitor changes in the user's body shape and suggest optimal outfits that match the changes in the body shape. The attribute information acquisition unit, for example, uses smart clothing or a wearable device to periodically monitor changes in the user's body shape. For example, it automatically records changes in weight and waist size and suggests outfits based on the changes. The attribute information acquisition unit also suggests optimal outfits that match the body shape by having the user periodically input body shape data. For example, the user inputs weight and waist size once a month and adjusts outfits based on that data. The attribute information acquisition unit also monitors changes in the user's body shape and suggests styles and items that match a specific body shape. For example, when the body shape changes, it automatically suggests items and styles that fit well. In this way, by monitoring changes in the user's body shape, it is possible to suggest optimal outfits that match the body shape.

[0032] The attribute information acquisition unit can provide a fashion matching function with other users based on the user's attribute information, allowing users with similar styles to exchange items. The attribute information acquisition unit, for example, matches users with similar styles based on the user's attribute information and promotes the exchange of items. For example, users who prefer the same casual style exchange items. The attribute information acquisition unit also uses the fashion matching function to provide a platform on which users can exchange items with other users. For example, users can list unwanted items and exchange them with other users. The attribute information acquisition unit also develops a fashion matching algorithm based on the user's attribute information and automatically suggests optimal exchange partners. For example, users who prefer the same size or style can be matched. This allows a fashion matching function with other users to be provided based on the user's attribute information, allowing users with similar styles to exchange items.

[0033] The attribute information acquisition unit can automatically generate a seasonal fashion calendar based on the user's attribute information and suggest coordination that matches the season. The attribute information acquisition unit, for example, builds a system that automatically generates a seasonal fashion calendar based on the user's attribute information. For example, it suggests coordination that matches each season: spring, summer, autumn, and winter. The attribute information acquisition unit also suggests optimal coordination for the user based on the seasonal fashion calendar. For example, it suggests items made of light materials in spring and items made of warm materials in winter. The attribute information acquisition unit also integrates the user's attribute information with the seasonal fashion calendar to automatically generate coordination that matches the season. For example, it suggests new coordination that matches the change of seasons. In this way, it is possible to automatically generate a seasonal fashion calendar based on the user's attribute information and suggest coordination that matches the season.

[0034] The coordination suggestion unit can analyze the user's past schedules and coordination history, and learn and suggest optimal coordination patterns. The coordination suggestion unit, for example, analyzes the user's past schedules and coordination history to build a system that learns optimal coordination patterns. For example, it suggests optimal coordination based on past data. The coordination suggestion unit also learns specific patterns based on the user's past schedules and coordination history and reflects them in the next coordination suggestion. For example, it suggests coordination to suit a specific event. The coordination suggestion unit also analyzes the user's past schedules and coordination history to suggest optimal coordination patterns that match the user's preferences and style. For example, it suggests similar styles based on past data. In this way, it is possible to analyze the user's past schedules and coordination history, and learn and suggest optimal coordination patterns.

[0035] The coordination suggestion unit can propose optimal coordination by taking into account the user's travel route and environmental information of the destination. The coordination suggestion unit, for example, builds a system that analyzes the user's travel route and proposes optimal coordination by taking into account environmental information of the destination. For example, it proposes coordination based on travel distance and the climate of the destination. The coordination suggestion unit also proposes optimal coordination based on environmental information of the user's destination. For example, it proposes items taking into account the temperature and humidity of the destination. The coordination suggestion unit also integrates the user's travel route and environmental information of the destination to develop a system that proposes optimal coordination. For example, it proposes coordination that takes into account comfort during travel. This makes it possible to propose optimal coordination by taking into account the user's travel route and environmental information of the destination.

[0036] The coordination suggestion unit can provide a coordination guide specialized for a specific event or situation in accordance with the user's schedule. The coordination suggestion unit, for example, builds a system that provides a coordination guide specialized for a specific event or situation in accordance with the user's schedule. For example, it suggests coordination suitable for a wedding or a business meeting. The coordination suggestion unit also provides a coordination guide specialized for a specific event or situation, allowing the user to select the most suitable coordination. For example, it suggests coordination suitable for a casual party or a formal dinner. The coordination suggestion unit also develops a system that automatically generates a coordination guide specialized for a specific event or situation based on the user's schedule. For example, it suggests coordination suitable for travel or outdoor activities. In this way, it is possible to provide a coordination guide specialized for a specific event or situation in accordance with the user's schedule.

[0037] The coordination suggestion unit provides a function for comparing coordination with other users based on the user's schedule, allowing the user to gain fashion inspiration. The coordination suggestion unit, for example, builds a system that provides a function for comparing coordination with other users based on the user's schedule. For example, the coordination suggestion unit refers to the coordination of other users who will be attending the same event. The coordination suggestion unit also uses the coordination comparison function to allow the user to refer to the coordination of other users and gain fashion inspiration. For example, it displays the coordination of users who prefer the same style. The coordination suggestion unit also provides a function for comparing coordination with other users based on the user's schedule, allowing the user to select the optimal coordination. For example, it compares the coordination of users who will be attending the same event. This allows the coordination suggestion unit to provide a function for comparing coordination with other users based on the user's schedule, allowing the user to gain fashion inspiration.

[0038] The feedback collection unit can analyze reactions and comments on social media in addition to user feedback and reflect them in the next outfit suggestion. The feedback collection unit, for example, builds a system that analyzes reactions and comments on social media in addition to user feedback and reflects them in the next outfit suggestion. For example, it analyzes comments on Instagram (registered trademark) and Twitter (registered trademark) to understand the user's preferences. The feedback collection unit also complements the user's feedback based on reactions on social media and reflects them in the next outfit suggestion. For example, it analyzes "likes" and comments on social media to identify the user's preferences. The feedback collection unit also develops a system that integrates the user's feedback and reactions on social media and reflects them in the next outfit suggestion. For example, it proposes an outfit that suits the user's preferences based on reactions on social media. This makes it possible to analyze reactions and comments on social media in addition to user feedback and reflect them in the next outfit suggestion.

[0039] The feedback collection unit automatically adjusts the evaluation criteria for coordination based on user feedback, allowing for more accurate suggestions. The feedback collection unit, for example, builds a system that automatically adjusts the evaluation criteria for coordination based on user feedback. For example, it analyzes the content of the feedback and dynamically changes the evaluation criteria. The feedback collection unit also analyzes user feedback and automatically adjusts the evaluation criteria for coordination. For example, it adjusts the next suggestion based on the evaluation of a specific style or item. The feedback collection unit also develops a system that automatically adjusts the evaluation criteria for coordination based on user feedback, allowing for more accurate suggestions. For example, it changes the evaluation criteria based on the feedback score. This allows for the automatic adjustment of the evaluation criteria for coordination based on user feedback, allowing for more accurate suggestions.

[0040] The feedback collection unit provides a function for sharing outfits with other users based on the user's feedback, allowing them to give each other advice. The feedback collection unit, for example, builds a system that provides a function for sharing outfits with other users based on the user's feedback. For example, a user shares their outfits with other users and receives advice. The feedback collection unit also uses the outfit sharing function to allow a user to share outfits with other users and give each other advice. For example, a user posts their outfit and receives feedback from other users. The feedback collection unit also develops a system that provides a function for sharing outfits with other users based on the user's feedback, allowing them to give each other advice. For example, a user shares their outfits and receives advice from other users. This allows a function for sharing outfits with other users based on the user's feedback, allowing them to give each other advice.

[0041] The feedback collection unit can automatically generate seasonal coordination trends based on user feedback and reflect them in suggestions. The feedback collection unit, for example, builds a system that automatically generates seasonal coordination trends based on user feedback. For example, it analyzes the content of the feedback and identifies the seasonal trends. The feedback collection unit also automatically generates seasonal coordination trends and reflects them in suggestions based on user feedback. For example, it proposes trends tailored to each season: spring, summer, autumn, and winter. The feedback collection unit also develops a system that automatically generates seasonal coordination trends based on user feedback and reflects them in suggestions. For example, it identifies seasonal trends based on feedback scores. This allows seasonal coordination trends to be automatically generated based on user feedback and reflected in suggestions.

[0042] The item suggestion unit can analyze a user's past purchase history and trend data to suggest optimal items. The item suggestion unit, for example, builds a system that analyzes a user's past purchase history and combines it with trend data to suggest optimal items. For example, suggestions are made based on items purchased in the past and current trends. The item suggestion unit also suggests optimal items to a user based on the past purchase history and trend data. For example, it suggests new items in a style similar to items purchased in the past. The item suggestion unit also integrates a user's past purchase history with trend data to develop a system that suggests optimal items. For example, it suggests items that match the current trend based on the user's past purchase history. This makes it possible to analyze a user's past purchase history and trend data to suggest optimal items.

[0043] The item suggestion unit can suggest customizable items based on the user's preferences and trends, thereby realizing an individual style. The item suggestion unit, for example, builds a system that suggests customizable items based on the user's preferences and trends. For example, it suggests items whose colors and designs can be selected. The item suggestion unit also suggests customizable items, allowing the user to realize an individual style. For example, the user customizes items to suit their preferences. The item suggestion unit also develops a system that suggests customizable items based on the user's preferences and trends. For example, it allows the user to select items to suit their style. This allows the user to suggest customizable items based on the user's preferences and trends, thereby realizing an individual style.

[0044] The item suggestion unit provides an item exchange function with other users based on the user's preferences and trends, allowing the user to enjoy a variety of fashion. The item suggestion unit, for example, builds a system that provides an item exchange function with other users based on the user's preferences and trends. For example, users who like the same style exchange items with each other. The item suggestion unit also uses the item exchange function to allow users to exchange items with other users and enjoy a variety of fashion. For example, a user puts up an item that they no longer need and exchanges it with other users. The item suggestion unit also develops a system that provides an item exchange function with other users based on the user's preferences and trends, allowing the user to enjoy a variety of fashion. For example, users who like the same size or style exchange items with each other. This provides an item exchange function with other users based on the user's preferences and trends, allowing the user to enjoy a variety of fashion.

[0045] The item suggestion unit can automatically generate a seasonal fashion catalog based on the user's preferences and trends and reflect the suggestions. The item suggestion unit, for example, builds a system that automatically generates a seasonal fashion catalog based on the user's preferences and trends. For example, it suggests items that suit each season: spring, summer, autumn, and winter. The item suggestion unit also suggests items that are best suited to the user based on the seasonal fashion catalog. For example, it suggests items made of light materials in spring and items made of warm materials in winter. The item suggestion unit also develops a system that automatically generates a seasonal fashion catalog based on the user's preferences and trends and reflects the suggestions. For example, it suggests new items in line with the change of seasons. In this way, a seasonal fashion catalog can be automatically generated based on the user's preferences and trends and reflected in the suggestions.

[0046] The listing support unit can analyze past sales data when listing an item with low usage, and propose the optimal listing timing and price. For example, the listing support unit builds a system that analyzes past sales data when listing an item with low usage, and proposes the optimal listing timing and price. For example, the optimal listing timing is identified based on past sales data. The listing support unit also proposes the optimal listing timing and price for an item with low usage, based on past sales data. For example, listing the item in time for a specific season or event. The listing support unit also develops a system that analyzes past sales data when listing an item with low usage, and proposes the optimal listing timing and price. For example, the optimal price is set based on past sales data. As a result, past sales data can be analyzed when listing an item with low usage, and the optimal listing timing and price can be proposed.

[0047] The listing support unit can automatically evaluate the condition and characteristics of an item and generate a detailed description when an item with low usage is listed. The listing support unit, for example, builds a system that automatically evaluates the condition and characteristics of an item and generates a detailed description when an item with low usage is listed. For example, it analyzes a photo of the item and automatically evaluates the condition and characteristics. The listing support unit also automatically evaluates the condition and characteristics of the item and generates a detailed description. For example, it generates a description based on the material, design, frequency of use, etc. of the item. The listing support unit also develops a system that automatically evaluates the condition and characteristics of an item and generates a detailed description when an item with low usage is listed. For example, it analyzes a photo of the item and automatically generates a detailed description. This makes it possible to automatically evaluate the condition and characteristics of an item and generate a detailed description when an item with low usage is listed.

[0048] The listing support unit provides an item exchange function with other users when an item that is not used frequently is put up for sale, thereby enabling effective use of unnecessary items. The listing support unit, for example, builds a system that provides an item exchange function with other users when an item that is not used frequently is put up for sale. For example, items are exchanged between users who prefer the same style. The listing support unit also uses the item exchange function to allow users to exchange items with other users and make effective use of unnecessary items. For example, a user puts up an unnecessary item for sale and exchanges it with other users. The listing support unit also develops a system that provides an item exchange function with other users when an item that is not used frequently is put up for sale, thereby making effective use of unnecessary items. For example, items are exchanged between users who prefer the same size or style. This provides an item exchange function with other users when an item that is not used frequently is put up for sale, thereby making effective use of unnecessary items.

[0049] The listing support unit can provide a listing guide that takes seasonal demand into consideration when listing an item that is less popular. For example, the listing support unit builds a system that provides a listing guide that takes seasonal demand into consideration when listing an item that is less popular. For example, it suggests items that are in high demand during specific seasons. The listing support unit also suggests the optimal listing timing for an item that is less popular based on seasonal demand. For example, it lists an item that is in high demand during winter in winter. The listing support unit also develops a system that provides a listing guide that takes seasonal demand into consideration when listing an item that is less popular. For example, it suggests the optimal listing timing to coincide with the change of seasons. This makes it possible to provide a listing guide that takes seasonal demand into consideration when listing an item that is less popular.

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

[0051] The outfit suggestion system may further include a voice assistant unit. The voice assistant unit enables the user to receive outfit suggestions and provide feedback via voice. For example, when the user makes a voice request such as "Tell me what outfit I'm going to wear today," the voice assistant unit will suggest an appropriate outfit. When the user asks, "What do you think of this outfit?", the voice assistant unit can collect feedback and reflect it in the next suggestion. Furthermore, when the user asks about an item they own, the voice assistant unit can provide detailed information about the item and outfit advice. This allows the user to easily receive outfit suggestions and provide feedback via voice.

[0052] The outfit suggestion system may further include a virtual try-on unit. The virtual try-on unit allows the user to virtually try on clothes they own or suggested items. For example, the user can take a full-body photo of themselves using a smartphone camera and overlay the suggested outfit on the photo. The virtual try-on unit may also help the user find the best outfit by trying on different items. Furthermore, the virtual try-on unit may also provide a function that allows the user to save images of outfits tried on by the user and review them later. This allows the user to virtually try on outfits without actually trying them on.

[0053] The outfit suggestion system can further include a weather forecast linkage unit. The weather forecast linkage unit acquires weather forecast information for the user's area and suggests optimal outfits based on that information. For example, it suggests waterproof jackets and boots for rainy days, and clothes made of breathable materials for hot days. The weather forecast linkage unit can also suggest outfits suitable for each time period, taking into account the weather forecast for the time period when the user will be out. Furthermore, the weather forecast linkage unit can also provide advice to the user on which items to purchase depending on the weather. This allows the user to easily find the optimal outfit to match the weather.

[0054] The outfit suggestion system can further acquire lifestyle information about the user and suggest optimal outfits based on that information. The lifestyle information acquisition unit collects information such as the user's exercise habits, diet, and sleep patterns. For example, it can suggest sportswear that is easy to move in to a user who exercises frequently, and comfortable business casual to a user who does a lot of office work. The lifestyle information acquisition unit can also suggest items that match the user's lifestyle. For example, it can suggest clothes made from organic materials to a health-conscious user. This makes it possible to suggest optimal outfits that suit the user's lifestyle.

[0055] The outfit suggestion system can further acquire the user's health data and suggest optimal outfits based on that data. The health data acquisition unit collects data such as the user's heart rate, blood pressure, and stress level. For example, it can suggest clothes made of relaxing materials to a user with a high stress level, and suggest comfortable, breathable clothes to a user with a high heart rate. The health data acquisition unit can also suggest items tailored to the user's health condition. For example, it can suggest items with high heat retention to a user who is prone to feeling cold. This makes it possible to suggest optimal outfits that suit the user's health condition.

[0056] The coordination suggestion system can further acquire the user's hobbies and interests and suggest optimal coordination based on them. The hobby information acquisition unit, for example, collects information about the user's hobbies and interests. For example, it suggests coordination suitable for outdoor activities to a user whose hobby is outdoors, and suggests coordination suitable for a music event to a user whose hobby is music. The hobby information acquisition unit can also suggest items that match the user's hobbies and interests. For example, it suggests items that are suitable for an art event to a user whose hobby is art. This makes it possible to suggest optimal coordination that matches the user's hobbies and interests.

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

[0058] Step 1: The attribute information acquisition unit acquires attribute information of the user. For example, information such as age, gender, occupation, and hobbies can be input. Step 2: The photography unit takes photos of the user's clothes and accessories. For example, photos can be taken using a smartphone camera and uploaded to the system. Step 3: The outfit suggestion module proposes outfits for the week, taking into account the time, place, and occasion based on the user's attributes and photos. For example, it suggests outfits that fit the user's schedule, such as office casual on Monday, remote work on Tuesday, and lunch with friends on Wednesday. Step 4: The calendar linking unit links with the calendar to suggest outfits based on future plans. For example, it links with Google (registered trademark) Calendar or Outlook Calendar to suggest outfits based on future plans. Step 5: The feedback collection unit collects the user's wearing record and favorite rating as feedback, and reflects this in future coordination suggestions. For example, since this coordination was highly favored, a similar style will be suggested next time. Step 6: The item suggestion department suggests new items based on the weather, preferences, and trends, other than the clothes and accessories you already own. For example, since it's been raining a lot this week, they might suggest a waterproof jacket. Step 7: The listing support unit generates a description and a market price from the photo data of an item that is not often used, and supports listing on the flea market app. For example, since this jacket is not often worn, a description and a market price are generated for listing.

[0059] (Example 2) The coordination suggestion system according to an embodiment of the present invention automatically suggests a week's worth of coordinations that take into account the time, place, and occasion based on the user's attribute information and photos of their clothes and accessories, and also performs calendar integration, feedback collection, item suggestions, and item listing support. As a result, the coordination suggestion system makes it easy for users to find coordinations that suit their style, and also allows them to smoothly purchase new items and list unwanted items.

[0060] The coordination suggestion system according to the embodiment includes an attribute information acquisition unit, a photography unit, a coordination suggestion unit, a calendar linkage unit, a feedback collection unit, an item suggestion unit, and a listing support unit. The attribute information acquisition unit acquires user attribute information. For example, information such as age, gender, occupation, and hobbies can be input. The photography unit takes photos of the user's clothes and accessories. For example, photos can be taken using a smartphone camera and uploaded to the system. The coordination suggestion unit proposes a week's worth of coordinations taking into account the time, place, and occasion based on the attribute information and photos. For example, it proposes coordinations based on schedules such as office casual on Monday, remote work on Tuesday, and lunch with friends on Wednesday. The calendar linkage unit proposes coordinations based on future plans in cooperation with a calendar. For example, it proposes coordinations based on schedules in cooperation with Google (registered trademark) Calendar or Outlook Calendar. The feedback collection unit collects the user's wearing history and favorite rating as feedback and reflects this in future coordination suggestions. For example, since this coordination was highly favorited, it will propose a similar style next time. The item suggestion unit suggests new items based on the weather, preferences, and trends, in addition to the clothes and accessories the user already owns. For example, since there has been a lot of rain this week, it would suggest a waterproof jacket. The listing support unit generates a description and a market price from photo data of items that are rarely used, and supports listing on a flea market app. For example, since this jacket is not often worn, it generates a description and a market price for listing. This allows the coordination suggestion system to easily find coordinations that suit the user's style, and also allows the user to smoothly purchase new items and list unwanted items.

[0061] The attribute information acquisition unit analyzes a user's past fashion history and fashion posts on social media to suggest more accurate outfits. The attribute information acquisition unit, for example, analyzes a user's past fashion history to understand preferences for specific styles and brands. For example, the unit makes suggestions that reflect the user's preferences based on previously purchased items and outfits worn. The attribute information acquisition unit also analyzes fashion posts on social media to understand the user's preferred styles. For example, it analyzes posts on Instagram (registered trademark) or Pinterest to identify the items and styles the user often wears. The attribute information acquisition unit also integrates the user's past fashion history and social media posts to suggest more accurate outfits. For example, it automatically generates outfits that match the user's preferences based on past purchase history and social media posts. This allows for more accurate outfit suggestions by analyzing a user's past fashion history and fashion posts on social media.

[0062] The attribute information acquisition unit can periodically monitor changes in the user's body shape and suggest optimal outfits that match the changes in the body shape. The attribute information acquisition unit, for example, uses smart clothing or a wearable device to periodically monitor changes in the user's body shape. For example, it automatically records changes in weight and waist size and suggests outfits based on the changes. The attribute information acquisition unit also suggests optimal outfits that match the body shape by having the user periodically input body shape data. For example, the user inputs weight and waist size once a month and adjusts outfits based on that data. The attribute information acquisition unit also monitors changes in the user's body shape and suggests styles and items that match a specific body shape. For example, when the body shape changes, it automatically suggests items and styles that fit well. In this way, by monitoring changes in the user's body shape, it is possible to suggest optimal outfits that match the body shape.

[0063] The attribute information acquisition unit uses the emotion estimation function to analyze the emotion of the user when taking a photo and provide advice to elicit positive emotions. For example, the attribute information acquisition unit analyzes facial expressions and voice when the user takes a photo and estimates the emotion in real time. For example, the attribute information acquisition unit analyzes the user's emotion using a camera or microphone and provides advice to elicit positive emotions. The attribute information acquisition unit also uses the emotion estimation function to provide an interface for eliciting positive emotions when the user takes a photo. For example, it presents encouraging messages and success stories. The attribute information acquisition unit also provides feedback in real time based on the emotion estimation data when the user takes a photo and provides advice to strengthen positive emotions. For example, it displays appropriate encouragement or compliments based on the input content. This makes it possible to analyze the emotion of the user when taking a photo and provide advice to elicit positive emotions.

[0064] The attribute information acquisition unit can provide a fashion matching function with other users based on the user's attribute information, allowing users with similar styles to exchange items. The attribute information acquisition unit, for example, matches users with similar styles based on the user's attribute information and promotes the exchange of items. For example, users who prefer the same casual style exchange items. The attribute information acquisition unit also uses the fashion matching function to provide a platform on which users can exchange items with other users. For example, users can list unwanted items and exchange them with other users. The attribute information acquisition unit also develops a fashion matching algorithm based on the user's attribute information and automatically suggests optimal exchange partners. For example, users who prefer the same size or style can be matched. This allows a fashion matching function with other users to be provided based on the user's attribute information, allowing users with similar styles to exchange items.

[0065] The attribute information acquisition unit can automatically generate a seasonal fashion calendar based on the user's attribute information and suggest coordination that matches the season. The attribute information acquisition unit, for example, builds a system that automatically generates a seasonal fashion calendar based on the user's attribute information. For example, it suggests coordination that matches each season: spring, summer, autumn, and winter. The attribute information acquisition unit also suggests optimal coordination for the user based on the seasonal fashion calendar. For example, it suggests items made of light materials in spring and items made of warm materials in winter. The attribute information acquisition unit also integrates the user's attribute information with the seasonal fashion calendar to automatically generate coordination that matches the season. For example, it suggests new coordination that matches the change of seasons. In this way, it is possible to automatically generate a seasonal fashion calendar based on the user's attribute information and suggest coordination that matches the season.

[0066] The attribute information acquisition unit uses the emotion estimation function to analyze emotional reactions to attribute information selected by the user and can support the selection of optimal attribute information. For example, when the user selects attribute information, the attribute information acquisition unit uses the emotion estimation function to analyze emotional reactions in real time. For example, it provides support for eliciting positive emotions toward the selected attribute information. The attribute information acquisition unit also analyzes emotional reactions to the attribute information selected by the user based on the emotion estimation data and supports the selection of optimal attribute information. For example, it preferentially suggests attribute information with a strong positive emotion. The attribute information acquisition unit also uses the emotion estimation function to provide feedback when the user selects attribute information and supports the selection of optimal attribute information. For example, it displays an emotion score for the selected attribute information. This makes it possible to analyze emotional reactions to the attribute information selected by the user and support the selection of optimal attribute information.

[0067] The coordination suggestion unit can analyze the user's past schedules and coordination history, and learn and suggest optimal coordination patterns. The coordination suggestion unit, for example, analyzes the user's past schedules and coordination history to build a system that learns optimal coordination patterns. For example, it suggests optimal coordination based on past data. The coordination suggestion unit also learns specific patterns based on the user's past schedules and coordination history and reflects them in the next coordination suggestion. For example, it suggests coordination to suit a specific event. The coordination suggestion unit also analyzes the user's past schedules and coordination history to suggest optimal coordination patterns that match the user's preferences and style. For example, it suggests similar styles based on past data. In this way, it is possible to analyze the user's past schedules and coordination history, and learn and suggest optimal coordination patterns.

[0068] The coordination suggestion unit can propose optimal coordination by taking into account the user's travel route and environmental information of the destination. The coordination suggestion unit, for example, builds a system that analyzes the user's travel route and proposes optimal coordination by taking into account environmental information of the destination. For example, it proposes coordination based on travel distance and the climate of the destination. The coordination suggestion unit also proposes optimal coordination based on environmental information of the user's destination. For example, it proposes items taking into account the temperature and humidity of the destination. The coordination suggestion unit also integrates the user's travel route and environmental information of the destination to develop a system that proposes optimal coordination. For example, it proposes coordination that takes into account comfort during travel. This makes it possible to propose optimal coordination by taking into account the user's travel route and environmental information of the destination.

[0069] The coordination suggestion unit can use the emotion estimation function to analyze the user's emotions regarding their plans and suggest coordination that will elicit positive emotions. For example, the coordination suggestion unit analyzes the user's emotions regarding their plans using the emotion estimation function and suggests coordination that will elicit positive emotions. For example, it suggests coordination that will make the user feel confident on the day of an important presentation. The coordination suggestion unit also analyzes the user's emotions regarding their plans based on the emotion estimation data and suggests coordination that will elicit positive emotions. For example, it suggests coordination for a day when the user wants to relax. The coordination suggestion unit also builds a system that analyzes the user's emotions regarding their plans in real time and suggests coordination that will elicit positive emotions. For example, it suggests optimal coordination based on the emotion score. This makes it possible to analyze the user's emotions regarding their plans and suggest coordination that will elicit positive emotions.

[0070] The coordination suggestion unit can provide a coordination guide specialized for a specific event or situation in accordance with the user's schedule. The coordination suggestion unit, for example, builds a system that provides a coordination guide specialized for a specific event or situation in accordance with the user's schedule. For example, it suggests coordination suitable for a wedding or a business meeting. The coordination suggestion unit also provides a coordination guide specialized for a specific event or situation, allowing the user to select the most suitable coordination. For example, it suggests coordination suitable for a casual party or a formal dinner. The coordination suggestion unit also develops a system that automatically generates a coordination guide specialized for a specific event or situation based on the user's schedule. For example, it suggests coordination suitable for travel or outdoor activities. In this way, it is possible to provide a coordination guide specialized for a specific event or situation in accordance with the user's schedule.

[0071] The coordination suggestion unit provides a function for comparing coordination with other users based on the user's schedule, allowing the user to gain fashion inspiration. The coordination suggestion unit, for example, builds a system that provides a function for comparing coordination with other users based on the user's schedule. For example, the coordination suggestion unit refers to the coordination of other users who will be attending the same event. The coordination suggestion unit also uses the coordination comparison function to allow the user to refer to the coordination of other users and gain fashion inspiration. For example, it displays the coordination of users who prefer the same style. The coordination suggestion unit also provides a function for comparing coordination with other users based on the user's schedule, allowing the user to select the optimal coordination. For example, it compares the coordination of users who will be attending the same event. This allows the coordination suggestion unit to provide a function for comparing coordination with other users based on the user's schedule, allowing the user to gain fashion inspiration.

[0072] The coordination suggestion unit can use the emotion estimation function to analyze the user's emotional response to their plans and suggest optimal coordination. For example, the coordination suggestion unit uses the emotion estimation function to analyze the user's emotional response to their plans in real time and suggest optimal coordination. For example, it can suggest an outfit that will make the user feel confident on the day of an important meeting. The coordination suggestion unit can also analyze the user's emotional response to their plans based on the emotion estimation data and suggest an outfit that will elicit positive emotions. For example, it can suggest an outfit for a day when the user wants to relax. The coordination suggestion unit can also build a system that analyzes the user's emotions regarding their plans in real time and suggests an outfit that will elicit positive emotions. For example, it can suggest optimal coordination based on an emotion score. This makes it possible to analyze the user's emotional response to their plans and suggest optimal coordination.

[0073] The feedback collection unit can analyze reactions and comments on social media in addition to user feedback and reflect them in the next outfit suggestion. The feedback collection unit, for example, builds a system that analyzes reactions and comments on social media in addition to user feedback and reflects them in the next outfit suggestion. For example, it analyzes comments on Instagram (registered trademark) and Twitter (registered trademark) to understand the user's preferences. The feedback collection unit also complements the user's feedback based on reactions on social media and reflects them in the next outfit suggestion. For example, it analyzes "likes" and comments on social media to identify the user's preferences. The feedback collection unit also develops a system that integrates the user's feedback and reactions on social media and reflects them in the next outfit suggestion. For example, it proposes an outfit that suits the user's preferences based on reactions on social media. This makes it possible to analyze reactions and comments on social media in addition to user feedback and reflect them in the next outfit suggestion.

[0074] The feedback collection unit automatically adjusts the evaluation criteria for coordination based on user feedback, allowing for more accurate suggestions. The feedback collection unit, for example, builds a system that automatically adjusts the evaluation criteria for coordination based on user feedback. For example, it analyzes the content of the feedback and dynamically changes the evaluation criteria. The feedback collection unit also analyzes user feedback and automatically adjusts the evaluation criteria for coordination. For example, it adjusts the next suggestion based on the evaluation of a specific style or item. The feedback collection unit also develops a system that automatically adjusts the evaluation criteria for coordination based on user feedback, allowing for more accurate suggestions. For example, it changes the evaluation criteria based on the feedback score. This allows for the automatic adjustment of the evaluation criteria for coordination based on user feedback, allowing for more accurate suggestions.

[0075] The feedback collection unit can use the emotion estimation function to analyze the user's emotions at the time of feedback and suggest an outfit that will elicit positive emotions. The feedback collection unit, for example, uses the emotion estimation function to analyze the user's emotions at the time of feedback in real time and suggest an outfit that will elicit positive emotions. For example, it analyzes facial expressions and voice at the time of feedback. The feedback collection unit also analyzes the user's emotions at the time of feedback based on the emotion estimation data and suggests an outfit that will elicit positive emotions. For example, it suggests an optimal outfit depending on the content of the feedback. The feedback collection unit also builds a system that analyzes the user's emotions at the time of feedback in real time and suggests an outfit that will elicit positive emotions. For example, it suggests an optimal outfit based on an emotion score. This makes it possible to analyze the user's emotions at the time of feedback and suggest an outfit that will elicit positive emotions.

[0076] The feedback collection unit provides a function for sharing outfits with other users based on the user's feedback, allowing them to give each other advice. The feedback collection unit, for example, builds a system that provides a function for sharing outfits with other users based on the user's feedback. For example, a user shares their outfits with other users and receives advice. The feedback collection unit also uses the outfit sharing function to allow a user to share outfits with other users and give each other advice. For example, a user posts their outfit and receives feedback from other users. The feedback collection unit also develops a system that provides a function for sharing outfits with other users based on the user's feedback, allowing them to give each other advice. For example, a user shares their outfits and receives advice from other users. This allows a function for sharing outfits with other users based on the user's feedback, allowing them to give each other advice.

[0077] The feedback collection unit can automatically generate seasonal coordination trends based on user feedback and reflect them in suggestions. The feedback collection unit, for example, builds a system that automatically generates seasonal coordination trends based on user feedback. For example, it analyzes the content of the feedback and identifies the seasonal trends. The feedback collection unit also automatically generates seasonal coordination trends and reflects them in suggestions based on user feedback. For example, it proposes trends tailored to each season: spring, summer, autumn, and winter. The feedback collection unit also develops a system that automatically generates seasonal coordination trends based on user feedback and reflects them in suggestions. For example, it identifies seasonal trends based on feedback scores. This allows seasonal coordination trends to be automatically generated based on user feedback and reflected in suggestions.

[0078] The feedback collection unit can use the emotion estimation function to analyze the user's emotional response at the time of feedback and suggest an optimal outfit. The feedback collection unit, for example, uses the emotion estimation function to analyze the user's emotional response at the time of feedback in real time and suggest an optimal outfit. For example, it analyzes facial expressions and voice at the time of feedback. The feedback collection unit also analyzes the user's emotional response at the time of feedback based on the emotion estimation data and suggests an optimal outfit. For example, it suggests an optimal outfit based on the content of the feedback. The feedback collection unit also analyzes the user's emotional response at the time of feedback in real time and builds a system that suggests an optimal outfit. For example, it suggests an optimal outfit based on an emotion score. This makes it possible to analyze the user's emotional response at the time of feedback and suggest an optimal outfit.

[0079] The item suggestion unit can analyze a user's past purchase history and trend data to suggest optimal items. The item suggestion unit, for example, builds a system that analyzes a user's past purchase history and combines it with trend data to suggest optimal items. For example, suggestions are made based on items purchased in the past and current trends. The item suggestion unit also suggests optimal items to a user based on the past purchase history and trend data. For example, it suggests new items in a style similar to items purchased in the past. The item suggestion unit also integrates a user's past purchase history with trend data to develop a system that suggests optimal items. For example, it suggests items that match the current trend based on the user's past purchase history. This makes it possible to analyze a user's past purchase history and trend data to suggest optimal items.

[0080] The item suggestion unit can suggest customizable items based on the user's preferences and trends, thereby realizing an individual style. The item suggestion unit, for example, builds a system that suggests customizable items based on the user's preferences and trends. For example, it suggests items whose colors and designs can be selected. The item suggestion unit also suggests customizable items, allowing the user to realize an individual style. For example, the user customizes items to suit their preferences. The item suggestion unit also develops a system that suggests customizable items based on the user's preferences and trends. For example, it allows the user to select items to suit their style. This allows the user to suggest customizable items based on the user's preferences and trends, thereby realizing an individual style.

[0081] The item suggestion unit can use the emotion estimation function to analyze the user's emotion toward the proposed item and suggest an item that elicits positive emotion. The item suggestion unit, for example, uses the emotion estimation function to analyze the user's emotion toward the proposed item in real time and suggest an item that elicits positive emotion. For example, it analyzes facial expressions and voices regarding the proposed item. The item suggestion unit also analyzes the user's emotion toward the proposed item based on the emotion estimation data and suggests an item that elicits positive emotion. For example, it suggests an optimal item based on the emotion score for the proposed item. The item suggestion unit also builds a system that analyzes the user's emotion toward the proposed item in real time and suggests an item that elicits positive emotion. For example, it suggests an optimal item based on the emotion score. In this way, the user's emotion toward the proposed item can be analyzed and an item that elicits positive emotion can be suggested.

[0082] The item suggestion unit provides an item exchange function with other users based on the user's preferences and trends, allowing the user to enjoy a variety of fashion. The item suggestion unit, for example, builds a system that provides an item exchange function with other users based on the user's preferences and trends. For example, users who like the same style exchange items with each other. The item suggestion unit also uses the item exchange function to allow users to exchange items with other users and enjoy a variety of fashion. For example, a user puts up an item that they no longer need and exchanges it with other users. The item suggestion unit also develops a system that provides an item exchange function with other users based on the user's preferences and trends, allowing the user to enjoy a variety of fashion. For example, users who like the same size or style exchange items with each other. This provides an item exchange function with other users based on the user's preferences and trends, allowing the user to enjoy a variety of fashion.

[0083] The item suggestion unit can automatically generate a seasonal fashion catalog based on the user's preferences and trends and reflect the suggestions. The item suggestion unit, for example, builds a system that automatically generates a seasonal fashion catalog based on the user's preferences and trends. For example, it suggests items that suit each season: spring, summer, autumn, and winter. The item suggestion unit also suggests items that are best suited to the user based on the seasonal fashion catalog. For example, it suggests items made of light materials in spring and items made of warm materials in winter. The item suggestion unit also develops a system that automatically generates a seasonal fashion catalog based on the user's preferences and trends and reflects the suggestions. For example, it suggests new items in line with the change of seasons. In this way, a seasonal fashion catalog can be automatically generated based on the user's preferences and trends and reflected in the suggestions.

[0084] The item suggestion unit can use the emotion estimation function to analyze the user's emotional response to the proposed item and suggest the optimal item. The item suggestion unit, for example, uses the emotion estimation function to analyze the user's emotional response to the proposed item in real time and suggest the optimal item. For example, it analyzes facial expressions and voices in response to the proposed item. The item suggestion unit also analyzes the user's emotional response to the proposed item based on the emotion estimation data and suggests the optimal item. For example, it suggests the optimal item based on the emotion score for the proposed item. The item suggestion unit also builds a system that analyzes the user's emotional response to the proposed item in real time and suggests the optimal item. For example, it suggests the optimal item based on the emotion score. In this way, the user's emotional response to the proposed item can be analyzed and the optimal item can be suggested.

[0085] The listing support unit can analyze past sales data when listing an item with low usage, and propose the optimal listing timing and price. For example, the listing support unit builds a system that analyzes past sales data when listing an item with low usage, and proposes the optimal listing timing and price. For example, the optimal listing timing is identified based on past sales data. The listing support unit also proposes the optimal listing timing and price for an item with low usage, based on past sales data. For example, listing the item in time for a specific season or event. The listing support unit also develops a system that analyzes past sales data when listing an item with low usage, and proposes the optimal listing timing and price. For example, the optimal price is set based on past sales data. As a result, past sales data can be analyzed when listing an item with low usage, and the optimal listing timing and price can be proposed.

[0086] The listing support unit can automatically evaluate the condition and characteristics of an item and generate a detailed description when an item with low usage is listed. The listing support unit, for example, builds a system that automatically evaluates the condition and characteristics of an item and generates a detailed description when an item with low usage is listed. For example, it analyzes a photo of the item and automatically evaluates the condition and characteristics. The listing support unit also automatically evaluates the condition and characteristics of the item and generates a detailed description. For example, it generates a description based on the material, design, frequency of use, etc. of the item. The listing support unit also develops a system that automatically evaluates the condition and characteristics of an item and generates a detailed description when an item with low usage is listed. For example, it analyzes a photo of the item and automatically generates a detailed description. This makes it possible to automatically evaluate the condition and characteristics of an item and generate a detailed description when an item with low usage is listed.

[0087] The listing support unit can use the emotion estimation function to analyze the user's emotion at the time of listing and provide listing advice that elicits positive emotions. The listing support unit, for example, uses the emotion estimation function to analyze the user's emotion at the time of listing in real time and provide listing advice that elicits positive emotions. For example, it analyzes facial expressions and voice at the time of listing. The listing support unit also analyzes the user's emotion at the time of listing based on the emotion estimation data and provides listing advice that elicits positive emotions. For example, it provides optimal advice based on the emotion score at the time of listing. The listing support unit also builds a system that analyzes the user's emotion at the time of listing in real time and provides listing advice that elicits positive emotions. For example, it provides optimal advice based on the emotion score. This makes it possible to analyze the user's emotion at the time of listing and provide listing advice that elicits positive emotions.

[0088] The listing support unit provides an item exchange function with other users when an item that is not used frequently is put up for sale, thereby enabling effective use of unnecessary items. The listing support unit, for example, builds a system that provides an item exchange function with other users when an item that is not used frequently is put up for sale. For example, items are exchanged between users who prefer the same style. The listing support unit also uses the item exchange function to allow users to exchange items with other users and make effective use of unnecessary items. For example, a user puts up an unnecessary item for sale and exchanges it with other users. The listing support unit also develops a system that provides an item exchange function with other users when an item that is not used frequently is put up for sale, thereby making effective use of unnecessary items. For example, items are exchanged between users who prefer the same size or style. This provides an item exchange function with other users when an item that is not used frequently is put up for sale, thereby making effective use of unnecessary items.

[0089] The listing support unit can provide a listing guide that takes seasonal demand into consideration when listing an item that is less popular. For example, the listing support unit builds a system that provides a listing guide that takes seasonal demand into consideration when listing an item that is less popular. For example, it suggests items that are in high demand during specific seasons. The listing support unit also suggests the optimal listing timing for an item that is less popular based on seasonal demand. For example, it lists an item that is in high demand during winter in winter. The listing support unit also develops a system that provides a listing guide that takes seasonal demand into consideration when listing an item that is less popular. For example, it suggests the optimal listing timing to coincide with the change of seasons. This makes it possible to provide a listing guide that takes seasonal demand into consideration when listing an item that is less popular.

[0090] The listing support unit can use the emotion estimation function to analyze the user's emotional response when listing an item and propose an optimal listing method. The listing support unit, for example, uses the emotion estimation function to analyze the user's emotional response when listing an item in real time and propose an optimal listing method. For example, it analyzes facial expressions and voice at the time of listing. The listing support unit also analyzes the user's emotional response when listing an item based on the emotion estimation data and proposes an optimal listing method. For example, it proposes an optimal method based on an emotion score at the time of listing. The listing support unit also builds a system that analyzes the user's emotional response when listing an item in real time and proposes an optimal listing method. For example, it proposes an optimal method based on an emotion score. In this way, it is possible to analyze the user's emotional response when listing an item and propose an optimal listing method.

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

[0092] The outfit suggestion system may further include a voice assistant unit. The voice assistant unit enables the user to receive outfit suggestions and provide feedback via voice. For example, when the user makes a voice request such as "Tell me what outfit I'm going to wear today," the voice assistant unit will suggest an appropriate outfit. When the user asks, "What do you think of this outfit?", the voice assistant unit can collect feedback and reflect it in the next suggestion. Furthermore, when the user asks about an item they own, the voice assistant unit can provide detailed information about the item and outfit advice. This allows the user to easily receive outfit suggestions and provide feedback via voice.

[0093] The outfit suggestion system may further include a virtual try-on unit. The virtual try-on unit allows the user to virtually try on clothes they own or suggested items. For example, the user can take a full-body photo of themselves using a smartphone camera and overlay the suggested outfit on the photo. The virtual try-on unit may also help the user find the best outfit by trying on different items. Furthermore, the virtual try-on unit may also provide a function that allows the user to save images of outfits tried on by the user and review them later. This allows the user to virtually try on outfits without actually trying them on.

[0094] The outfit suggestion system can further include a weather forecast linkage unit. The weather forecast linkage unit acquires weather forecast information for the user's area and suggests optimal outfits based on that information. For example, it suggests waterproof jackets and boots for rainy days, and clothes made of breathable materials for hot days. The weather forecast linkage unit can also suggest outfits suitable for each time period, taking into account the weather forecast for the time period when the user will be out. Furthermore, the weather forecast linkage unit can also provide advice to the user on which items to purchase depending on the weather. This allows the user to easily find the optimal outfit to match the weather.

[0095] The outfit suggestion system can also use an emotion estimation function to analyze the user's emotions toward the outfit selected and provide advice to elicit positive emotions. For example, if the user smiles or shows satisfaction with the selected outfit, the system can recommend that outfit. Alternatively, if the user shows dissatisfaction or anxiety about the selected outfit, the system can suggest an alternative outfit. Furthermore, the emotion estimation function can be used to analyze the user's emotions toward specific items and suggest items based on those emotions. This allows the user to find an outfit that matches their emotions.

[0096] The outfit suggestion system can further acquire lifestyle information about the user and suggest optimal outfits based on that information. The lifestyle information acquisition unit collects information such as the user's exercise habits, diet, and sleep patterns. For example, it can suggest sportswear that is easy to move in to a user who exercises frequently, and comfortable business casual to a user who does a lot of office work. The lifestyle information acquisition unit can also suggest items that match the user's lifestyle. For example, it can suggest clothes made from organic materials to a health-conscious user. This makes it possible to suggest optimal outfits that suit the user's lifestyle.

[0097] The outfit suggestion system can further use its emotion estimation function to analyze the emotions a user has toward a specific event and suggest the optimal outfit based on those emotions. For example, if a user is nervous about an important presentation, the system can suggest an outfit that will help them feel confident. It can also suggest an outfit that will help the user relax on a day off when they want to relax. Furthermore, the emotion estimation function can be used to analyze the emotions a user has toward a specific event in real time and adjust the outfit based on those emotions. This allows the user to find the optimal outfit for a specific event.

[0098] The outfit suggestion system can further acquire the user's health data and suggest optimal outfits based on that data. The health data acquisition unit collects data such as the user's heart rate, blood pressure, and stress level. For example, it can suggest clothes made of relaxing materials to a user with a high stress level, and suggest comfortable, breathable clothes to a user with a high heart rate. The health data acquisition unit can also suggest items tailored to the user's health condition. For example, it can suggest items with high heat retention to a user who is prone to feeling cold. This makes it possible to suggest optimal outfits that suit the user's health condition.

[0099] The outfit suggestion system can further use an emotion estimation function to analyze the emotions a user has toward a specific item and suggest items based on those emotions. For example, if a user has positive emotions toward a specific item, the system can suggest outfits that include that item. On the other hand, if a user has negative emotions toward a specific item, the system can suggest outfits that avoid that item. Furthermore, the emotion estimation function can be used to analyze the emotions a user has toward a specific item in real time and adjust the item suggestions based on those emotions. This allows users to find items that match their emotions.

[0100] The coordination suggestion system can further acquire the user's hobbies and interests and suggest optimal coordination based on them. The hobby information acquisition unit, for example, collects information about the user's hobbies and interests. For example, it suggests coordination suitable for outdoor activities to a user whose hobby is outdoors, and suggests coordination suitable for a music event to a user whose hobby is music. The hobby information acquisition unit can also suggest items that match the user's hobbies and interests. For example, it suggests items that are suitable for an art event to a user whose hobby is art. This makes it possible to suggest optimal coordination that matches the user's hobbies and interests.

[0101] The outfit suggestion system can further use an emotion estimation function to analyze the user's emotions toward a specific season and suggest optimal outfits based on those emotions. For example, if the user has positive emotions toward winter, that emotion can be taken into consideration when suggesting winter outfits. Also, if the user has negative emotions toward summer, that emotion can be taken into consideration when suggesting summer outfits. Furthermore, the emotion estimation function can be used to analyze the user's emotions toward a specific season in real time and adjust the outfit based on that emotion. This allows the user to find the optimal outfit for a specific season.

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

[0103] Step 1: The attribute information acquisition unit acquires attribute information of the user. For example, information such as age, gender, occupation, and hobbies can be input. Step 2: The photography unit takes photos of the user's clothes and accessories. For example, photos can be taken using a smartphone camera and uploaded to the system. Step 3: The outfit suggestion module proposes outfits for the week, taking into account the time, place, and occasion based on the user's attributes and photos. For example, it suggests outfits that fit the user's schedule, such as office casual on Monday, remote work on Tuesday, and lunch with friends on Wednesday. Step 4: The calendar linking unit links with the calendar to suggest outfits based on future plans. For example, it links with Google (registered trademark) Calendar or Outlook Calendar to suggest outfits based on future plans. Step 5: The feedback collection unit collects the user's wearing record and favorite rating as feedback, and reflects this in future coordination suggestions. For example, since this coordination was highly favored, a similar style will be suggested next time. Step 6: The item suggestion department suggests new items based on the weather, preferences, and trends, other than the clothes and accessories you already own. For example, since it's been raining a lot this week, they might suggest a waterproof jacket. Step 7: The listing support unit generates a description and a market price from the photo data of an item that is not often used, and supports listing on the flea market app. For example, since this jacket is not often worn, a description and a market price are generated for listing.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0138] 7, 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an attribute information acquisition unit that acquires attribute information of a user; a photography unit that takes photos of the user's clothes and accessories; a coordination suggestion unit that suggests a week's worth of coordination based on the attribute information and the photograph and in accordance with TPO; The calendar integration section works with the calendar to suggest coordination based on future plans, a feedback collection unit that collects the user's wearing record and favorite degree as feedback and reflects the feedback in future coordination suggestions; an item suggestion unit that suggests new items based on the climate, preferences, and trends other than the clothes and accessories that the user already owns; and a listing support unit that generates a description and a market price from the photo data of an item that is not frequently used, and supports listing the item on the flea market app. A system characterized by:

2. The attribute information acquisition unit Analyzing the user's past fashion history and fashion posts on social media, and proposing more accurate coordination 2. The system of claim 1.

3. The attribute information acquisition unit Regularly monitor changes in the user's body shape and suggest the optimal coordination in accordance with the changes in the body shape.

2. The system of claim 1.

4. The attribute information acquisition unit Analyzing the emotions felt by the user when taking the photo and providing advice to elicit positive emotions 2. The system of claim 1.

5. The attribute information acquisition unit Based on the attribute information of the user, a fashion matching function is provided with other users, allowing users with similar styles to exchange items.

2. The system of claim 1.

6. The attribute information acquisition unit Based on the attribute information of the user, a seasonal fashion calendar is automatically generated and coordination according to the season is suggested.

2. The system of claim 1.

7. The attribute information acquisition unit Analyzing emotional responses to the attribute information selected by the user and supporting selection of optimal attribute information 2. The system of claim 1.

Images