System

Abstract

An object of a system according to an embodiment is to efficiently manage clothes of a user and propose optimal clothes according to a season, weather, or an event.SOLUTION: A system according to an embodiment includes an image acquisition unit, a digital wardrobe generation unit, a proposal unit, and a feedback processing unit. The image acquisition unit acquires an image of clothes of a user. The digital wardrobe generation unit generates a digital wardrobe based on the image acquired by the image acquisition unit. The proposal unit proposes clothes according to the season, the weather, or the event on the basis of the digital wardrobe generated by the digital wardrobe generation unit. The feedback processing unit processes the user's feedback on the clothes proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently manage a user's clothing and suggest optimal outfits according to the season, weather, and event.

[0005] The system according to the embodiment aims to efficiently manage a user's clothes and suggest the most suitable outfits according to the season, weather, and event. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, a digital wardrobe generation unit, a suggestion unit, and a feedback processing unit. The image acquisition unit acquires images of a user's clothes. The digital wardrobe generation unit generates a digital wardrobe based on the images acquired by the image acquisition unit. The suggestion unit suggests outfits according to the season, weather, or event based on the digital wardrobe generated by the digital wardrobe generation unit. The feedback processing unit processes the user's feedback on the outfits suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage a user's clothes and suggest the most suitable outfits according to the season, weather, and event. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​system according to the embodiment of the present invention creates a digital wardrobe by scanning images of the clothes a user owns and saving them in an app, and then suggests outfits according to the season, weather, and events. This allows the AI ​​system to improve the accuracy of outfit suggestions for the user.

[0029] The AI ​​system according to the embodiment includes an image acquisition unit, a digital wardrobe generation unit, a suggestion unit, and a feedback processing unit. The image acquisition unit acquires images of a user's clothes. For example, the user takes photos of the clothes with a smartphone and uploads the images to an app. The image acquisition unit can also directly acquire existing digital images. For example, the user uploads images downloaded from an online shop. The digital wardrobe generation unit generates a digital wardrobe based on the images acquired by the image acquisition unit. For example, the generation AI analyzes the uploaded images and automatically recognizes information such as the type, color, and design of the clothes, and registers them in the digital wardrobe. The digital wardrobe generation unit can also generate a digital wardrobe based on information manually entered by the user. The suggestion unit suggests outfits appropriate for the season, weather, or event based on the digital wardrobe generated by the digital wardrobe generation unit. For example, the generation AI uses weather forecast data to suggest waterproof jackets and boots for rainy days and light clothing for sunny days. The suggestion unit also suggests outfits suitable for specific events (e.g., weddings and parties). The feedback processing unit processes user feedback on the outfits proposed by the suggestion unit. For example, if the user provides feedback such as "I don't like this outfit," the generation AI learns that information and reflects it in the next suggestion. The feedback processing unit also collects data to improve the accuracy of suggestions based on the user's feedback. This allows the AI ​​system according to the embodiment to improve the accuracy of outfit suggestions to the user. For example, when the user provides feedback on the suggested outfits, the generation AI learns the user's preferences and reflects them in the next suggestion. If the user provides feedback such as "I don't like this outfit," the generation AI learns that information and reflects it in the next suggestion. If the user provides feedback such as "I like this outfit," the generation AI learns that information and reflects it in the next suggestion.

[0030] The image acquisition unit can automatically recognize material and brand information from images of the user's clothes and register it in the digital wardrobe generation unit. The image acquisition unit, for example, uses a generation AI to automatically extract material information from uploaded images of clothes and register it in the digital wardrobe. For example, it identifies materials such as cotton, wool, and polyester, allowing the user to search for clothes by material. The image acquisition unit also uses a generation AI to automatically extract brand information from uploaded images of clothes and register it in the digital wardrobe. For example, it identifies brand names and logos, allowing the user to search for clothes by brand. The image acquisition unit also uses a generation AI to automatically extract design information from uploaded images of clothes and register it in the digital wardrobe. For example, it identifies designs such as dresses, shirts, and pants, allowing the user to search for clothes by design. This allows the automatic recognition of material and brand information of the user's clothes and registration of them in the digital wardrobe, providing more detailed information.

[0031] The digital wardrobe generation unit can convert images of clothes acquired by the image acquisition unit into 3D models, allowing the user to virtually try on the clothes. For example, the digital wardrobe generation unit can use a generation AI to convert uploaded images of clothes into 3D models, allowing the user to virtually try on the clothes. For example, the user can dress their avatar in the clothes and rotate it 360 degrees to check the look. The digital wardrobe generation unit can also use a generation AI to convert uploaded images of clothes into 3D models, allowing the user to virtually try on the clothes. For example, the user can dress their avatar in the clothes and check the look against different backgrounds. The digital wardrobe generation unit can also use a generation AI to convert uploaded images of clothes into 3D models, allowing the user to virtually try on the clothes. For example, the user can dress their avatar in the clothes and check the look in different situations. This allows the user to virtually try on the clothes, allowing them to check how the clothes will look before actually trying them on.

[0032] The suggestion unit can suggest items that can be purchased online based on the user's height and body type information. The suggestion unit, for example, uses a generation AI to suggest items that can be purchased online based on the user's height and body type information. For example, it suggests clothes of a size and design that suit the user's body type and provides a purchase link. The suggestion unit also uses a generation AI to suggest items that can be purchased online based on the user's height and body type information. For example, it suggests shoes of a size and design that suit the user's body type and provides a purchase link. The suggestion unit also uses a generation AI to suggest items that can be purchased online based on the user's height and body type information. For example, it suggests accessories of a size and design that suit the user's body type and provides a purchase link. This improves purchasing convenience by suggesting appropriate items based on the user's height and body type information.

[0033] The feedback processing unit recognizes personal preferences based on user feedback and can improve the accuracy of suggestions made by the suggestion unit. For example, when the user provides feedback on suggested clothing, the feedback processing unit allows the generation AI to learn the user's preferences and reflect them in the next suggestion. For example, if the user provides feedback such as "I don't like this outfit," the generation AI learns that information and reflects it in the next suggestion. Also, when the user provides feedback on suggested clothing, the feedback processing unit allows the generation AI to learn the user's preferences and reflect it in the next suggestion. For example, if the user provides feedback such as "I like this outfit," the generation AI learns that information and reflects it in the next suggestion. Also, when the user provides feedback on suggested clothing, the feedback processing unit allows the generation AI to learn the user's preferences and reflect it in the next suggestion. For example, if the user provides feedback such as "I don't like this color," the generation AI learns that information and reflects it in the next suggestion. This allows the generation AI to recognize personal preferences based on user feedback, improve the accuracy of suggestions, and make suggestions that are more satisfying.

[0034] When suggesting clothing appropriate for a season, weather, or event, the suggestion unit can analyze past weather data or event history and reflect the results in future suggestions. The suggestion unit, for example, uses a generation AI to analyze past weather data and learn seasonal weather patterns. For example, it suggests clothing appropriate for a specific season based on weather forecast data from the past few years. The suggestion unit also uses a generation AI to analyze past event history and suggest clothing appropriate for a specific event. For example, it suggests appropriate clothing based on the history of past weddings and parties. The suggestion unit also uses a generation AI to analyze past weather data and event history and reflect the results in future suggestions. For example, it suggests clothing appropriate for future weather or events based on past weather data and event history. This makes it possible to make more appropriate clothing suggestions by analyzing past weather data and event history and reflecting the results in future suggestions.

[0035] The suggestion unit can reflect user feedback on proposed outfits in real time and instantly update the suggestion content. The suggestion unit, for example, builds a system that collects user feedback on proposed outfits in real time and instantly updates the suggestion content based on the results. For example, if a user provides feedback such as "I don't like this outfit," the next suggestion is instantly changed. The suggestion unit also builds a system that collects user feedback on proposed outfits in real time and instantly updates the suggestion content based on the results. For example, if a user provides feedback such as "I like this outfit," the next suggestion is instantly changed. The suggestion unit also builds a system that collects user feedback on proposed outfits in real time and instantly updates the suggestion content based on the results. For example, if a user provides feedback such as "I don't like this color," the next suggestion is instantly changed. This allows for more appropriate outfit suggestions by reflecting user feedback in real time and instantly updating the suggestion content.

[0036] The suggestion unit can make clothing suggestions based not only on the season or weather but also on the user's schedule or activity. The suggestion unit, for example, uses a generation AI to analyze the user's schedule data and make clothing suggestions based on the specific activity. For example, if the user plans to go to the gym, sportswear is suggested. The suggestion unit also uses a generation AI to analyze the user's schedule data and make clothing suggestions based on the specific activity. For example, if the user plans to attend a business meeting, formal clothing is suggested. The suggestion unit also uses a generation AI to analyze the user's schedule data and make clothing suggestions based on the specific activity. For example, if the user plans to attend a casual event, casual clothing is suggested. This makes it possible to make more appropriate clothing suggestions by making clothing suggestions based on the user's schedule and activity.

[0037] The suggestion unit can display other users' ratings and comments on the proposed outfit for reference. The suggestion unit, for example, builds a system that displays other users' ratings and comments on the proposed outfit in real time. For example, if another user comments, "This outfit is great!", the comment is displayed. The suggestion unit also builds a system that displays other users' ratings and comments on the proposed outfit in real time. For example, if another user comments, "I don't like this color," the comment is displayed. The suggestion unit also builds a system that displays other users' ratings and comments on the proposed outfit in real time. For example, if another user comments, "This design is great," the comment is displayed. This makes it possible to make more appropriate outfit suggestions by referring to other users' ratings and comments.

[0038] The suggestion unit can add a function that allows the user to suggest and order custom-made clothes based on the user's body type information. The suggestion unit, for example, uses a generation AI to add a function that allows the user to suggest and order custom-made clothes based on the user's body type information. For example, it suggests suits and dresses that fit the user's body type perfectly. The suggestion unit also uses a generation AI to add a function that allows the user to suggest and order custom-made clothes based on the user's body type information. For example, it suggests shirts and pants that fit the user's body type perfectly. The suggestion unit also uses a generation AI to add a function that allows the user to suggest and order custom-made clothes based on the user's body type information. For example, it suggests jackets and coats that fit the user's body type perfectly. This allows the user to suggest and order custom-made clothes based on the user's body type information, making it possible to make more appropriate clothing suggestions.

[0039] The suggestion unit can provide fitness and health management advice based on the user's body type information. The suggestion unit, for example, uses a generation AI to build a system that provides fitness and health management advice based on the user's body type information. For example, it proposes exercise plans and meal plans tailored to the user's body type. The suggestion unit also uses a generation AI to build a system that provides fitness and health management advice based on the user's body type information. For example, it proposes training plans and nutrition plans tailored to the user's body type. The suggestion unit also uses a generation AI to build a system that provides fitness and health management advice based on the user's body type information. For example, it proposes stretching plans and relaxation plans tailored to the user's body type. In this way, it is possible to support the user in maintaining their health by providing fitness and health management advice based on the user's body type information.

[0040] The suggestion unit can add a function that allows a user to share body type information with other users and exchange item suggestions between users with similar body types. The suggestion unit, for example, adds a function that allows a user to share their own body type information with other users, allowing users with similar body types to exchange item suggestions between them. For example, the suggestion unit refers to items purchased by users with the same body type. The suggestion unit also adds a function that allows a user to share their own body type information with other users, allowing users with similar body types to exchange item suggestions between them. For example, the suggestion unit refers to reviews of items purchased by users with the same body type. The suggestion unit also adds a function that allows a user to share their own body type information with other users, allowing users with similar body types to exchange item suggestions between them. For example, the suggestion unit refers to reviews of items purchased by users with the same body type. This allows body type information to be shared with other users, allowing users with similar body types to exchange item suggestions between them, making it possible to suggest more appropriate items.

[0041] When generating an image of outfits that match the user's own clothes, the suggestion unit can generate coordinated outfit images with different backgrounds and situations and present them to the user. The suggestion unit, for example, uses a generation AI to generate coordinated outfit images that combine the user's own clothes with suggested items against different backgrounds. For example, outfits for situations such as the office, the beach, and a party are displayed. The suggestion unit also uses a generation AI to generate coordinated outfit images that combine the user's own clothes with suggested items against different backgrounds. For example, outfits for situations such as a cafe, a park, and a restaurant are displayed. The suggestion unit also uses a generation AI to generate coordinated outfit images that combine the user's own clothes with suggested items against different backgrounds. For example, outfits for situations such as a travel destination, a sporting event, and a shopping mall are displayed. In this way, by generating coordinated outfit images with different backgrounds and situations, the user can check outfits for various situations.

[0042] The suggestion unit can add a function that allows coordination images to be shared with other users and that enables the exchange of ratings and comments. For example, the suggestion unit adds a function that allows coordination images to be shared with other users and that enables the exchange of ratings and comments. For example, coordination images are shared with friends and family and that enables the exchange of ratings and comments. The suggestion unit also adds a function that allows coordination images to be shared with other users and that enables the exchange of ratings and comments. For example, coordination images are shared between users with the same hobbies and that enables the exchange of ratings and comments. The suggestion unit also adds a function that allows coordination images to be shared with other users and that enables the exchange of ratings and comments. For example, coordination images are shared within a fashion community and that enables the exchange of ratings and comments. This allows coordination images to be shared with other users and that enables the exchange of ratings and comments, thereby enabling more appropriate coordination suggestions.

[0043] The suggestion unit can add a function that allows coordinated outfit images to be posted directly to social media, thereby encouraging users to share their outfits. For example, the suggestion unit can add a function that allows coordinated outfit images to be posted directly to social media, thereby enabling users to easily share their outfits. For example, a button can be provided that allows users to post to Instagram or Facebook with one click. The suggestion unit can also add a function that allows coordinated outfit images to be posted directly to social media, thereby enabling users to easily share their outfits. For example, a button can be provided that allows users to post to Twitter or Pinterest with one click. The suggestion unit can also add a function that allows coordinated outfit images to be posted directly to social media, thereby enabling users to easily share their outfits. For example, a button can be provided that allows users to post to LinkedIn or Snapchat with one click. In this way, adding a function that allows coordinated outfit images to be posted directly to social media can encourage users to share their outfits and obtain more feedback.

[0044] The feedback processing unit can use the generation AI to analyze the user's feedback history and track long-term changes in preferences. The feedback processing unit, for example, uses the generation AI to build a system that analyzes the user's feedback history and tracks long-term changes in preferences. For example, it analyzes trends in user preferences based on past feedback data. The feedback processing unit also uses the generation AI to build a system that analyzes the user's feedback history and tracks long-term changes in preferences. For example, it predicts changes in user preferences based on past feedback data. The feedback processing unit also uses the generation AI to build a system that analyzes the user's feedback history and tracks long-term changes in preferences. For example, it visualizes changes in user preferences based on past feedback data. This makes it possible to analyze the user's feedback history and track long-term changes in preferences, thereby making it possible to make more appropriate suggestions.

[0045] The feedback processing unit can provide personalized styling advice to the user based on the content of the feedback. The feedback processing unit, for example, uses a generation AI to analyze the content of the user's feedback and build a system that provides personalized styling advice. For example, if the user provides feedback such as "I don't like this color," a different color is suggested. The feedback processing unit also uses a generation AI to analyze the content of the user's feedback and build a system that provides personalized styling advice. For example, if the user provides feedback such as "I don't like this design," a different design is suggested. The feedback processing unit also uses a generation AI to analyze the content of the user's feedback and build a system that provides personalized styling advice. For example, if the user provides feedback such as "I don't like this brand," a different brand is suggested. In this way, by providing personalized styling advice based on the content of the feedback, user satisfaction can be improved.

[0046] The feedback processing unit can add a function that allows users with common preferences to exchange item suggestions with each other, taking into account feedback from other users. The feedback processing unit, for example, adds a function that allows users with common preferences to exchange item suggestions with each other, taking into account feedback from other users. For example, users who like the same style or color can suggest items to each other. The feedback processing unit also adds a function that allows users with common preferences to exchange item suggestions with each other, taking into account feedback from other users. For example, users who like the same brand or design can suggest items to each other. The feedback processing unit also adds a function that allows users with common preferences to exchange item suggestions with each other, taking into account feedback from other users. For example, users who like items to be used for the same purpose or situation can suggest items to each other. This allows users with common preferences to exchange item suggestions with each other, taking into account feedback from other users, making it possible to make more appropriate item suggestions.

[0047] The feedback processing unit can suggest new brands and designers that match the user's preferences based on the feedback. The feedback processing unit, for example, uses a generation AI to analyze the content of the user's feedback and build a system that suggests new brands and designers that match the user's preferences. For example, if a user gives feedback that they "don't like this brand," a different brand is suggested. The feedback processing unit also uses a generation AI to analyze the content of the user's feedback and build a system that suggests new brands and designers that match the user's preferences. For example, if a user gives feedback that they "don't like this designer," a different designer is suggested. The feedback processing unit also uses a generation AI to analyze the content of the user's feedback and build a system that suggests new brands and designers that match the user's preferences. For example, if a user gives feedback that they "don't like this style," a different style is suggested. In this way, by suggesting new brands and designers based on the feedback, user satisfaction can be improved.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The suggestion unit can analyze the user's past purchase history and suggest new products of similar styles or brands. For example, it can suggest new items from the same brand based on data on items the user has previously purchased. Also, if the user has a preference for a particular style, it can suggest new products that match that style. It can also suggest new products that are easy to combine with items the user has previously purchased. This makes it possible to make more personalized suggestions by utilizing the user's past purchase history.

[0050] The suggestion unit can suggest clothing suitable for a specific time period or activity based on the user's lifestyle. For example, if the user plans to jog in the morning, sportswear can be suggested. If the user plans to go out for dinner in the evening, formal attire can be suggested. Furthermore, casual attire for the user to relax in on the weekend can be suggested. This makes it possible to suggest clothing that matches the user's lifestyle.

[0051] The suggestion unit can suggest clothing that emphasizes fit based on the user's body type information. For example, it can suggest shirts and pants in sizes that fit the user's body type. It can also suggest dresses and skirts with designs that fit the user's body type. It can also suggest jackets and coats that fit the user's body type. This makes it possible to suggest clothing that emphasizes fit based on the user's body type information.

[0052] The suggestion unit can suggest clothes made from eco-friendly materials and brands based on the user's preferences. For example, it can suggest clothes made from organic cotton or recycled materials. It can also suggest new items from eco-friendly brands. Furthermore, if the user prefers an eco-friendly lifestyle, it can also suggest clothes that suit that lifestyle. This makes it possible to suggest eco-friendly clothes based on the user's preferences.

[0053] The suggestion unit can suggest appropriate activewear based on the user's activity level. For example, if the user runs, running wear is suggested. If the user does yoga, yoga wear is suggested. Furthermore, if the user trains at the gym, training wear can be suggested. This makes it possible to suggest appropriate activewear based on the user's activity level.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The image acquisition unit acquires an image of the user's clothes. For example, the user takes a photo of the clothes with their smartphone and uploads the image to the app. The image acquisition unit can also directly acquire existing digital images. For example, the user can upload an image downloaded from an online shop. Step 2: The digital wardrobe generator generates a digital wardrobe based on the images acquired by the image acquisition unit. For example, the generation AI analyzes the uploaded images and automatically recognizes information such as the type, color, and design of the clothing, and registers it in the digital wardrobe. The digital wardrobe generator can also generate a digital wardrobe based on information manually entered by the user. Step 3: The suggestion unit suggests clothing appropriate for the season, weather, or event based on the digital wardrobe generated by the digital wardrobe generation unit. For example, the generation AI uses weather forecast data to suggest waterproof jackets and boots for rainy days and light clothing for sunny days. It also suggests clothing suitable for specific events (such as weddings and parties). Step 4: The feedback processor processes the user's feedback on the outfits suggested by the suggestion processor. For example, if the user provides feedback such as "I don't like this outfit," the generation AI learns that information and reflects it in the next suggestion. The feedback processor also collects data based on the user's feedback to improve the accuracy of the suggestions.

[0056] (Example 2) The AI ​​system according to the embodiment of the present invention creates a digital wardrobe by scanning images of the clothes a user owns and saving them in an app, and then suggests outfits according to the season, weather, and events. This allows the AI ​​system to improve the accuracy of outfit suggestions for the user.

[0057] The AI ​​system according to the embodiment includes an image acquisition unit, a digital wardrobe generation unit, a suggestion unit, and a feedback processing unit. The image acquisition unit acquires images of a user's clothes. For example, the user takes photos of the clothes with a smartphone and uploads the images to an app. The image acquisition unit can also directly acquire existing digital images. For example, the user uploads images downloaded from an online shop. The digital wardrobe generation unit generates a digital wardrobe based on the images acquired by the image acquisition unit. For example, the generation AI analyzes the uploaded images and automatically recognizes information such as the type, color, and design of the clothes, and registers them in the digital wardrobe. The digital wardrobe generation unit can also generate a digital wardrobe based on information manually entered by the user. The suggestion unit suggests outfits appropriate for the season, weather, or event based on the digital wardrobe generated by the digital wardrobe generation unit. For example, the generation AI uses weather forecast data to suggest waterproof jackets and boots for rainy days and light clothing for sunny days. The suggestion unit also suggests outfits suitable for specific events (e.g., weddings and parties). The feedback processing unit processes user feedback on the outfits proposed by the suggestion unit. For example, if the user provides feedback such as "I don't like this outfit," the generation AI learns that information and reflects it in the next suggestion. The feedback processing unit also collects data to improve the accuracy of suggestions based on the user's feedback. This allows the AI ​​system according to the embodiment to improve the accuracy of outfit suggestions to the user. For example, when the user provides feedback on the suggested outfits, the generation AI learns the user's preferences and reflects them in the next suggestion. If the user provides feedback such as "I don't like this outfit," the generation AI learns that information and reflects it in the next suggestion. If the user provides feedback such as "I like this outfit," the generation AI learns that information and reflects it in the next suggestion.

[0058] The image acquisition unit can automatically recognize material and brand information from images of the user's clothes and register it in the digital wardrobe generation unit. The image acquisition unit, for example, uses a generation AI to automatically extract material information from uploaded images of clothes and register it in the digital wardrobe. For example, it identifies materials such as cotton, wool, and polyester, allowing the user to search for clothes by material. The image acquisition unit also uses a generation AI to automatically extract brand information from uploaded images of clothes and register it in the digital wardrobe. For example, it identifies brand names and logos, allowing the user to search for clothes by brand. The image acquisition unit also uses a generation AI to automatically extract design information from uploaded images of clothes and register it in the digital wardrobe. For example, it identifies designs such as dresses, shirts, and pants, allowing the user to search for clothes by design. This allows the automatic recognition of material and brand information of the user's clothes and registration of them in the digital wardrobe, providing more detailed information.

[0059] The digital wardrobe generation unit can convert images of clothes acquired by the image acquisition unit into 3D models, allowing the user to virtually try on the clothes. For example, the digital wardrobe generation unit can use a generation AI to convert uploaded images of clothes into 3D models, allowing the user to virtually try on the clothes. For example, the user can dress their avatar in the clothes and rotate it 360 degrees to check the look. The digital wardrobe generation unit can also use a generation AI to convert uploaded images of clothes into 3D models, allowing the user to virtually try on the clothes. For example, the user can dress their avatar in the clothes and check the look against different backgrounds. The digital wardrobe generation unit can also use a generation AI to convert uploaded images of clothes into 3D models, allowing the user to virtually try on the clothes. For example, the user can dress their avatar in the clothes and check the look in different situations. This allows the user to virtually try on the clothes, allowing them to check how the clothes will look before actually trying them on.

[0060] The suggestion unit can suggest items that can be purchased online based on the user's height and body type information. The suggestion unit, for example, uses a generation AI to suggest items that can be purchased online based on the user's height and body type information. For example, it suggests clothes of a size and design that suit the user's body type and provides a purchase link. The suggestion unit also uses a generation AI to suggest items that can be purchased online based on the user's height and body type information. For example, it suggests shoes of a size and design that suit the user's body type and provides a purchase link. The suggestion unit also uses a generation AI to suggest items that can be purchased online based on the user's height and body type information. For example, it suggests accessories of a size and design that suit the user's body type and provides a purchase link. This improves purchasing convenience by suggesting appropriate items based on the user's height and body type information.

[0061] The feedback processing unit recognizes personal preferences based on user feedback and can improve the accuracy of suggestions made by the suggestion unit. For example, when the user provides feedback on suggested clothing, the feedback processing unit allows the generation AI to learn the user's preferences and reflect them in the next suggestion. For example, if the user provides feedback such as "I don't like this outfit," the generation AI learns that information and reflects it in the next suggestion. Also, when the user provides feedback on suggested clothing, the feedback processing unit allows the generation AI to learn the user's preferences and reflect it in the next suggestion. For example, if the user provides feedback such as "I like this outfit," the generation AI learns that information and reflects it in the next suggestion. Also, when the user provides feedback on suggested clothing, the feedback processing unit allows the generation AI to learn the user's preferences and reflect it in the next suggestion. For example, if the user provides feedback such as "I don't like this color," the generation AI learns that information and reflects it in the next suggestion. This allows the generation AI to recognize personal preferences based on user feedback, improve the accuracy of suggestions, and make suggestions that are more satisfying.

[0062] When suggesting clothing appropriate for a season, weather, or event, the suggestion unit can analyze past weather data or event history and reflect the results in future suggestions. The suggestion unit, for example, uses a generation AI to analyze past weather data and learn seasonal weather patterns. For example, it suggests clothing appropriate for a specific season based on weather forecast data from the past few years. The suggestion unit also uses a generation AI to analyze past event history and suggest clothing appropriate for a specific event. For example, it suggests appropriate clothing based on the history of past weddings and parties. The suggestion unit also uses a generation AI to analyze past weather data and event history and reflect the results in future suggestions. For example, it suggests clothing appropriate for future weather or events based on past weather data and event history. This makes it possible to make more appropriate clothing suggestions by analyzing past weather data and event history and reflecting the results in future suggestions.

[0063] The suggestion unit can reflect user feedback on proposed outfits in real time and instantly update the suggestion content. The suggestion unit, for example, builds a system that collects user feedback on proposed outfits in real time and instantly updates the suggestion content based on the results. For example, if a user provides feedback such as "I don't like this outfit," the next suggestion is instantly changed. The suggestion unit also builds a system that collects user feedback on proposed outfits in real time and instantly updates the suggestion content based on the results. For example, if a user provides feedback such as "I like this outfit," the next suggestion is instantly changed. The suggestion unit also builds a system that collects user feedback on proposed outfits in real time and instantly updates the suggestion content based on the results. For example, if a user provides feedback such as "I don't like this color," the next suggestion is instantly changed. This allows for more appropriate outfit suggestions by reflecting user feedback in real time and instantly updating the suggestion content.

[0064] The suggestion unit can use the emotion estimation function to analyze the emotion the user feels toward the proposed outfit and make suggestions that elicit positive emotions. For example, the suggestion unit can use the emotion estimation function to analyze the emotion the user feels toward the proposed outfit in real time and adjust the suggestion content based on the data. For example, if the user accepts the suggestion with a smile, the suggestion unit can suggest a similar style. The suggestion unit can also use the emotion estimation function to analyze the emotion the user feels toward the proposed outfit in real time and adjust the suggestion content based on the data. For example, if the user looks dissatisfied, the suggestion unit can suggest a different style. The suggestion unit can also use the emotion estimation function to analyze the emotion the user feels toward the proposed outfit in real time and adjust the suggestion content based on the data. For example, if the user looks surprised, the suggestion unit can suggest a different style. In this way, by analyzing the user's emotions and making suggestions that elicit positive emotions, it is possible to improve user satisfaction.

[0065] The suggestion unit can make clothing suggestions based not only on the season or weather but also on the user's schedule or activity. The suggestion unit, for example, uses a generation AI to analyze the user's schedule data and make clothing suggestions based on the specific activity. For example, if the user plans to go to the gym, sportswear is suggested. The suggestion unit also uses a generation AI to analyze the user's schedule data and make clothing suggestions based on the specific activity. For example, if the user plans to attend a business meeting, formal clothing is suggested. The suggestion unit also uses a generation AI to analyze the user's schedule data and make clothing suggestions based on the specific activity. For example, if the user plans to attend a casual event, casual clothing is suggested. This makes it possible to make more appropriate clothing suggestions by making clothing suggestions based on the user's schedule and activity.

[0066] The suggestion unit can display other users' ratings and comments on the proposed outfit for reference. The suggestion unit, for example, builds a system that displays other users' ratings and comments on the proposed outfit in real time. For example, if another user comments, "This outfit is great!", the comment is displayed. The suggestion unit also builds a system that displays other users' ratings and comments on the proposed outfit in real time. For example, if another user comments, "I don't like this color," the comment is displayed. The suggestion unit also builds a system that displays other users' ratings and comments on the proposed outfit in real time. For example, if another user comments, "This design is great," the comment is displayed. This makes it possible to make more appropriate outfit suggestions by referring to other users' ratings and comments.

[0067] The suggestion unit can use the emotion estimation function to identify the outfit that evokes the most positive emotion for the user for each event and reflect this tendency in the suggestions. The suggestion unit, for example, uses the emotion estimation function to build a system that identifies the outfit that evokes the most positive emotion for the user for each specific event. For example, the suggestion unit suggests outfits suitable for events such as weddings and parties. The suggestion unit also uses the emotion estimation function to build a system that identifies the outfit that evokes the most positive emotion for the user for each specific event. For example, the suggestion unit suggests outfits suitable for events such as business meetings and casual gatherings. The suggestion unit also uses the emotion estimation function to build a system that identifies the outfit that evokes the most positive emotion for the user for each specific event. For example, the suggestion unit suggests outfits suitable for events such as sporting events and leisure activities. In this way, the suggestion unit can identify the outfit that evokes the most positive emotion for the user for each event and reflect this tendency in the suggestions, thereby improving user satisfaction.

[0068] The suggestion unit can add a function that allows the user to suggest and order custom-made clothes based on the user's body type information. The suggestion unit, for example, uses a generation AI to add a function that allows the user to suggest and order custom-made clothes based on the user's body type information. For example, it suggests suits and dresses that fit the user's body type perfectly. The suggestion unit also uses a generation AI to add a function that allows the user to suggest and order custom-made clothes based on the user's body type information. For example, it suggests shirts and pants that fit the user's body type perfectly. The suggestion unit also uses a generation AI to add a function that allows the user to suggest and order custom-made clothes based on the user's body type information. For example, it suggests jackets and coats that fit the user's body type perfectly. This allows the user to suggest and order custom-made clothes based on the user's body type information, making it possible to make more appropriate clothing suggestions.

[0069] The suggestion unit uses the emotion estimation function to analyze the emotion the user feels toward the proposed item and make suggestions that elicit positive emotions. For example, the suggestion unit uses the emotion estimation function to analyze the emotion the user feels toward the proposed item in real time and adjust the suggestion content based on the data. For example, if the user accepts the suggestion with a smile, the suggestion unit suggests a similar style. The suggestion unit also uses the emotion estimation function to analyze the emotion the user feels toward the proposed item in real time and adjust the suggestion content based on the data. For example, if the user looks dissatisfied, the suggestion unit suggests a different style. The suggestion unit also uses the emotion estimation function to analyze the emotion the user feels toward the proposed item in real time and adjust the suggestion content based on the data. For example, if the user looks surprised, the suggestion unit suggests a different style. In this way, by analyzing the user's emotions and making suggestions that elicit positive emotions, it is possible to improve user satisfaction.

[0070] The suggestion unit can provide fitness and health management advice based on the user's body type information. The suggestion unit, for example, uses a generation AI to build a system that provides fitness and health management advice based on the user's body type information. For example, it proposes exercise plans and meal plans tailored to the user's body type. The suggestion unit also uses a generation AI to build a system that provides fitness and health management advice based on the user's body type information. For example, it proposes training plans and nutrition plans tailored to the user's body type. The suggestion unit also uses a generation AI to build a system that provides fitness and health management advice based on the user's body type information. For example, it proposes stretching plans and relaxation plans tailored to the user's body type. In this way, it is possible to support the user in maintaining their health by providing fitness and health management advice based on the user's body type information.

[0071] The suggestion unit can add a function that allows a user to share body type information with other users and exchange item suggestions between users with similar body types. The suggestion unit, for example, adds a function that allows a user to share their own body type information with other users, allowing users with similar body types to exchange item suggestions between them. For example, the suggestion unit refers to items purchased by users with the same body type. The suggestion unit also adds a function that allows a user to share their own body type information with other users, allowing users with similar body types to exchange item suggestions between them. For example, the suggestion unit refers to reviews of items purchased by users with the same body type. The suggestion unit also adds a function that allows a user to share their own body type information with other users, allowing users with similar body types to exchange item suggestions between them. For example, the suggestion unit refers to reviews of items purchased by users with the same body type. This allows body type information to be shared with other users, allowing users with similar body types to exchange item suggestions between them, making it possible to suggest more appropriate items.

[0072] The suggestion unit uses the emotion estimation function to collect other users' emotional reactions to an item proposed by a user and can identify popular items. The suggestion unit, for example, uses the emotion estimation function to analyze the emotions other users have toward the proposed item in real time and collect the data. For example, the suggestion unit analyzes the facial expressions of other users when they press the "Like!" button. The suggestion unit also uses the emotion estimation function to analyze the emotions other users have toward the proposed item in real time and collect the data. For example, the suggestion unit analyzes the facial expressions of other users when they comment, "This item is great." The suggestion unit also uses the emotion estimation function to analyze the emotions other users have toward the proposed item in real time and collect the data. For example, the suggestion unit analyzes the facial expressions of other users when they comment, "I don't like this item." This allows the suggestion unit to collect other users' emotional reactions and identify popular items, thereby enabling more appropriate item suggestions.

[0073] When generating an image of outfits that match the user's own clothes, the suggestion unit can generate coordinated outfit images with different backgrounds and situations and present them to the user. The suggestion unit, for example, uses a generation AI to generate coordinated outfit images that combine the user's own clothes with suggested items against different backgrounds. For example, outfits for situations such as the office, the beach, and a party are displayed. The suggestion unit also uses a generation AI to generate coordinated outfit images that combine the user's own clothes with suggested items against different backgrounds. For example, outfits for situations such as a cafe, a park, and a restaurant are displayed. The suggestion unit also uses a generation AI to generate coordinated outfit images that combine the user's own clothes with suggested items against different backgrounds. For example, outfits for situations such as a travel destination, a sporting event, and a shopping mall are displayed. In this way, by generating coordinated outfit images with different backgrounds and situations, the user can check outfits for various situations.

[0074] The suggestion unit uses the emotion estimation function to analyze the emotion the user feels toward the generated image and generate an image that elicits positive emotions. For example, the suggestion unit uses the emotion estimation function to analyze the emotion the user feels toward the generated image in real time and adjust the image based on the data. For example, if the user looks at the image with a smile, the suggestion unit generates a similar style. The suggestion unit also uses the emotion estimation function to analyze the emotion the user feels toward the generated image in real time and adjust the image based on the data. For example, if the user looks dissatisfied, the suggestion unit generates a different style. The suggestion unit also uses the emotion estimation function to analyze the emotion the user feels toward the generated image in real time and adjust the image based on the data. For example, if the user looks surprised, the suggestion unit generates a different style. In this way, by analyzing the user's emotions and generating an image that elicits positive emotions, user satisfaction can be improved.

[0075] The suggestion unit can add a function that allows coordination images to be shared with other users and that enables the exchange of ratings and comments. For example, the suggestion unit adds a function that allows coordination images to be shared with other users and that enables the exchange of ratings and comments. For example, coordination images are shared with friends and family and that enables the exchange of ratings and comments. The suggestion unit also adds a function that allows coordination images to be shared with other users and that enables the exchange of ratings and comments. For example, coordination images are shared between users with the same hobbies and that enables the exchange of ratings and comments. The suggestion unit also adds a function that allows coordination images to be shared with other users and that enables the exchange of ratings and comments. For example, coordination images are shared within a fashion community and that enables the exchange of ratings and comments. This allows coordination images to be shared with other users and that enables the exchange of ratings and comments, thereby enabling more appropriate coordination suggestions.

[0076] The suggestion unit can add a function that allows coordinated outfit images to be posted directly to social media, thereby encouraging users to share their outfits. For example, the suggestion unit can add a function that allows coordinated outfit images to be posted directly to social media, thereby enabling users to easily share their outfits. For example, a button can be provided that allows users to post to Instagram or Facebook with one click. The suggestion unit can also add a function that allows coordinated outfit images to be posted directly to social media, thereby enabling users to easily share their outfits. For example, a button can be provided that allows users to post to Twitter or Pinterest with one click. The suggestion unit can also add a function that allows coordinated outfit images to be posted directly to social media, thereby enabling users to easily share their outfits. For example, a button can be provided that allows users to post to LinkedIn or Snapchat with one click. In this way, adding a function that allows coordinated outfit images to be posted directly to social media can encourage users to share their outfits and obtain more feedback.

[0077] The suggestion unit can use the emotion estimation function to collect emotional reactions to coordination images created by other users and identify popular coordinations. The suggestion unit, for example, uses the emotion estimation function to analyze in real time the emotions felt by other users toward the coordination images created by other users and collect the data. For example, the suggestion unit analyzes the facial expressions of other users when they press the "Like!" button. The suggestion unit also uses the emotion estimation function to analyze in real time the emotions felt by other users toward the coordination images created by other users and collect the data. For example, the suggestion unit analyzes the facial expressions of other users when they comment, "This coordination is great." The suggestion unit also uses the emotion estimation function to analyze in real time the emotions felt by other users toward the coordination images created by other users and collect the data. For example, the suggestion unit analyzes the facial expressions of other users when they comment, "I don't like this coordination." This allows the suggestion unit to collect the emotional reactions of other users and identify popular coordinations, thereby enabling more appropriate coordination suggestions.

[0078] The feedback processing unit can use the generation AI to analyze the user's feedback history and track long-term changes in preferences. The feedback processing unit, for example, uses the generation AI to build a system that analyzes the user's feedback history and tracks long-term changes in preferences. For example, it analyzes trends in user preferences based on past feedback data. The feedback processing unit also uses the generation AI to build a system that analyzes the user's feedback history and tracks long-term changes in preferences. For example, it predicts changes in user preferences based on past feedback data. The feedback processing unit also uses the generation AI to build a system that analyzes the user's feedback history and tracks long-term changes in preferences. For example, it visualizes changes in user preferences based on past feedback data. This makes it possible to analyze the user's feedback history and track long-term changes in preferences, thereby making it possible to make more appropriate suggestions.

[0079] The feedback processing unit can provide personalized styling advice to the user based on the content of the feedback. The feedback processing unit, for example, uses a generation AI to analyze the content of the user's feedback and build a system that provides personalized styling advice. For example, if the user provides feedback such as "I don't like this color," a different color is suggested. The feedback processing unit also uses a generation AI to analyze the content of the user's feedback and build a system that provides personalized styling advice. For example, if the user provides feedback such as "I don't like this design," a different design is suggested. The feedback processing unit also uses a generation AI to analyze the content of the user's feedback and build a system that provides personalized styling advice. For example, if the user provides feedback such as "I don't like this brand," a different brand is suggested. In this way, by providing personalized styling advice based on the content of the feedback, user satisfaction can be improved.

[0080] The feedback processing unit uses the emotion estimation function to analyze the emotion of the user when providing feedback and can provide a feedback interface that elicits positive emotions. The feedback processing unit, for example, uses the emotion estimation function to analyze the emotion of the user when providing feedback in real time and adjust the feedback interface based on the data. For example, if the user provides feedback with a smile, a positive message is displayed. The feedback processing unit also uses the emotion estimation function to analyze the emotion of the user when providing feedback in real time and adjust the feedback interface based on the data. For example, if the user has a dissatisfied expression, an encouraging message is displayed. The feedback processing unit also uses the emotion estimation function to analyze the emotion of the user when providing feedback in real time and adjust the feedback interface based on the data. For example, if the user has a surprised expression, a message of gratitude is displayed. In this way, by analyzing the user's emotion and providing a feedback interface that elicits positive emotions, user satisfaction can be improved.

[0081] The feedback processing unit can add a function that allows users with common preferences to exchange item suggestions with each other, taking into account feedback from other users. The feedback processing unit, for example, adds a function that allows users with common preferences to exchange item suggestions with each other, taking into account feedback from other users. For example, users who like the same style or color can suggest items to each other. The feedback processing unit also adds a function that allows users with common preferences to exchange item suggestions with each other, taking into account feedback from other users. For example, users who like the same brand or design can suggest items to each other. The feedback processing unit also adds a function that allows users with common preferences to exchange item suggestions with each other, taking into account feedback from other users. For example, users who like items to be used for the same purpose or situation can suggest items to each other. This allows users with common preferences to exchange item suggestions with each other, taking into account feedback from other users, making it possible to make more appropriate item suggestions.

[0082] The feedback processing unit can suggest new brands and designers that match the user's preferences based on the feedback. The feedback processing unit, for example, uses a generation AI to analyze the content of the user's feedback and build a system that suggests new brands and designers that match the user's preferences. For example, if a user gives feedback that they "don't like this brand," a different brand is suggested. The feedback processing unit also uses a generation AI to analyze the content of the user's feedback and build a system that suggests new brands and designers that match the user's preferences. For example, if a user gives feedback that they "don't like this designer," a different designer is suggested. The feedback processing unit also uses a generation AI to analyze the content of the user's feedback and build a system that suggests new brands and designers that match the user's preferences. For example, if a user gives feedback that they "don't like this style," a different style is suggested. In this way, by suggesting new brands and designers based on the feedback, user satisfaction can be improved.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The suggestion unit can analyze the user's past purchase history and suggest new products of similar styles or brands. For example, it can suggest new items from the same brand based on data on items the user has previously purchased. Also, if the user has a preference for a particular style, it can suggest new products that match that style. It can also suggest new products that are easy to combine with items the user has previously purchased. This makes it possible to make more personalized suggestions by utilizing the user's past purchase history.

[0085] The suggestion unit can suggest clothing suitable for a specific time period or activity based on the user's lifestyle. For example, if the user plans to jog in the morning, sportswear can be suggested. If the user plans to go out for dinner in the evening, formal attire can be suggested. Furthermore, casual attire for the user to relax in on the weekend can be suggested. This makes it possible to suggest clothing that matches the user's lifestyle.

[0086] The suggestion unit can estimate the user's emotions and suggest relaxing clothing to reduce stress. For example, if the user is feeling stressed, the suggestion unit can suggest comfortable, relaxing casual clothing. If the user feels like relaxing, the suggestion unit can suggest clothing made of soft materials. Furthermore, if the user feels like relaxing, the suggestion unit can suggest clothing with colors and designs that have a relaxing effect. This makes it possible to suggest clothing to reduce stress based on the user's emotions.

[0087] The suggestion unit can estimate the user's emotions and suggest clothing that will make the user feel confident for a specific event. For example, if the user has an important presentation coming up, the suggestion unit can suggest formal clothing that will make the user feel confident. If the user is planning to go on a date, the suggestion unit can suggest stylish clothing that will make the user feel confident. Furthermore, if the user is planning to have an interview, the suggestion unit can suggest professional clothing that will make the user feel confident. This makes it possible to suggest clothing that will make the user feel confident based on the user's emotions.

[0088] The suggestion unit can estimate the user's emotions and suggest clothing with bright colors and designs to lift the user's mood. For example, if the user is feeling down, bright colored clothing can be suggested. If the user feels like they want to cheer up, clothing with a positive design can be suggested. Furthermore, if the user feels like they want to lift their spirits, clothing with an uplifting pattern or print can be suggested. This makes it possible to suggest clothing to lift the user's mood based on the user's emotions.

[0089] The suggestion unit can estimate the user's emotions and suggest comfortable loungewear or pajamas when the user feels like relaxing. For example, if the user is tired, pajamas made of soft material can be suggested. Also, if the user feels like relaxing, comfortable loungewear can be suggested. Furthermore, if the user feels like relaxing, loungewear with a color or design that has a relaxing effect can be suggested. This makes it possible to suggest clothing suitable for when the user wants to relax based on the user's emotions.

[0090] The suggestion unit can estimate the user's emotions and suggest clothing that will elicit positive emotions for a specific event. For example, if the user is attending a wedding, a gorgeous dress that will elicit positive emotions can be suggested. If the user is attending a party, colorful clothing that will elicit a fun mood can be suggested. Furthermore, if the user is attending a business meeting, a professional suit that will elicit positive emotions can be suggested. This makes it possible to suggest clothing that will elicit positive emotions for a specific event based on the user's emotions.

[0091] The suggestion unit can suggest clothing that emphasizes fit based on the user's body type information. For example, it can suggest shirts and pants in sizes that fit the user's body type. It can also suggest dresses and skirts with designs that fit the user's body type. It can also suggest jackets and coats that fit the user's body type. This makes it possible to suggest clothing that emphasizes fit based on the user's body type information.

[0092] The suggestion unit can suggest clothes made from eco-friendly materials and brands based on the user's preferences. For example, it can suggest clothes made from organic cotton or recycled materials. It can also suggest new items from eco-friendly brands. Furthermore, if the user prefers an eco-friendly lifestyle, it can also suggest clothes that suit that lifestyle. This makes it possible to suggest eco-friendly clothes based on the user's preferences.

[0093] The suggestion unit can suggest appropriate activewear based on the user's activity level. For example, if the user runs, running wear is suggested. If the user does yoga, yoga wear is suggested. Furthermore, if the user trains at the gym, training wear can be suggested. This makes it possible to suggest appropriate activewear based on the user's activity level.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The image acquisition unit acquires an image of the user's clothes. For example, the user takes a photo of the clothes with their smartphone and uploads the image to the app. The image acquisition unit can also directly acquire existing digital images. For example, the user can upload an image downloaded from an online shop. Step 2: The digital wardrobe generator generates a digital wardrobe based on the images acquired by the image acquisition unit. For example, the generation AI analyzes the uploaded images and automatically recognizes information such as the type, color, and design of the clothing, and registers it in the digital wardrobe. The digital wardrobe generator can also generate a digital wardrobe based on information manually entered by the user. Step 3: The suggestion unit suggests clothing appropriate for the season, weather, or event based on the digital wardrobe generated by the digital wardrobe generation unit. For example, the generation AI uses weather forecast data to suggest waterproof jackets and boots for rainy days and light clothing for sunny days. It also suggests clothing suitable for specific events (such as weddings and parties). Step 4: The feedback processor processes the user's feedback on the outfits suggested by the suggestion processor. For example, if the user provides feedback such as "I don't like this outfit," the generation AI learns that information and reflects it in the next suggestion. The feedback processor also collects data based on the user's feedback to improve the accuracy of the suggestions.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] 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.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0140] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0141] 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.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0162] 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]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image acquisition unit that acquires an image of the user's clothes; a digital wardrobe generation unit that generates a digital wardrobe based on the images acquired by the image acquisition unit; a suggestion unit that suggests clothing according to a season, weather, or event based on the digital wardrobe generated by the digital wardrobe generation unit; a feedback processing unit that processes user feedback regarding the clothing suggested by the suggestion unit. A system characterized by:

2. The digital wardrobe generator comprises: The image of the clothes acquired by the image acquisition unit is converted into a 3D model, allowing the user to virtually try on the clothes.

2. The system of claim 1.

3. The proposal unit Suggesting items for purchase on the web based on the user's height or body type 2. The system of claim 1.

4. The proposal unit When suggesting clothing according to the season, weather, or event, analyze past weather data or event history and reflect this in future suggestions.

2. The system of claim 1.

5. The proposal unit When generating images to match with existing clothes, coordinated images are generated in different backgrounds and situations and presented to the user.

2. The system of claim 1.

6. The proposal unit Analyzes the user's feelings about the suggested outfits and makes suggestions that elicit positive emotions 2. The system of claim 1.

7. The feedback processing unit Analyze the emotions users feel when giving feedback and provide a feedback interface that elicits positive emotions.

2. The system of claim 1.

8. The feedback processing unit Collect emotional responses to other users' feedback to identify popular suggestions 2. The system of claim 1.

Images