system

Abstract

The system according to the embodiment aims to enable users to easily try on clothes that suit them and to create custom-made clothes even if there are no existing products available. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a provision unit, a purchase unit, and a custom-made unit. The reception unit receives an image of a user. The generation unit generates a plurality of different types of clothing based on the image received by the reception unit. The provision unit provides the image generated by the generation unit to the user. The purchase unit carries out a purchase procedure if the clothing selected from the image provided by the provision unit is an existing product. The custom-made unit creates a custom-made outfit if the clothing selected from the image provided by the provision unit is not an existing product.

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 that it is difficult for users to try out clothes that suit them, and that custom-made options are limited if users are not satisfied with existing products.

[0005] The system according to the embodiment aims to enable users to easily try on clothes that suit them and to create custom-made clothes even if there are no existing products available. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, a purchase unit, and a custom-made unit. The reception unit receives an image of a user. The generation unit generates a plurality of different types of clothing based on the image received by the reception unit. The provision unit provides the image generated by the generation unit to the user. The purchase unit performs a purchase procedure if the clothing selected from the image provided by the provision unit is an existing product. The custom-made unit creates a custom-made outfit if the clothing selected from the image provided by the provision unit is not an existing product. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily try out clothes that suit them and to create custom-made clothes even if there are no existing products available. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses an image generation AI to generate images of various outfits featuring the user as a model, allowing the user to purchase their favorite outfits as existing products or have them custom-made. The system allows users to upload their own images, and the image generation AI generates images of the user wearing various outfits based on the user's image. The user then browses the generated images and selects an outfit they like. If the selected outfit is an existing product, it can be purchased as is. If it is not available, it can be custom-made. For example, a user uploads an image of themselves. It is recommended that the user upload an image taken from the front. For example, an image taken with a device can be uploaded. This image is then input into the image generation AI. The image generation AI then analyzes the input image of the user and generates images of the user wearing various outfits. The image generation AI analyzes the user's body type and facial features and generates outfits that match the user's appearance. For example, various styles of outfits, such as casual, formal, and seasonal, can be generated. The generated images can be viewed on the user's device. The user can then select an outfit they like by viewing the generated images. For example, they can select a specific dress or suit from the generated images. If the selected outfit is an existing product, it can be purchased as is. For example, if the selected dress is an existing product, the purchase procedure can be carried out at an online shop. If there is no existing product, it can be made to order. In the case of made to order, the outfit is created to be customized according to the user's body type and preferences. In this way, the system allows the user to try on outfits using themselves as models, and to easily purchase or have made to order outfits that they like. In this way, the system improves user satisfaction and allows users to enjoy fashion that meets their individual needs.

[0029] A fashion generation system according to an embodiment includes a reception unit, a generation unit, a provision unit, a purchase unit, and a custom-made unit. The reception unit receives a user's image. The user's image may include, but is not limited to, a face photo, a full-body photo, or a specific pose. The reception unit receives, for example, an image taken by the user on a device. The generation unit uses a generation AI to generate multiple different types of outfits based on the image received by the reception unit. The generated outfits may include, but are not limited to, casual, formal, and sportswear. The generation unit analyzes, for example, the user's body type and facial features and generates outfits that match the outfits. The generation AI generates appropriate outfits based on the user's body type and facial features using a text generation AI (e.g., LLM) or a multimodal generation AI. The provision unit provides the image generated by the generation unit to the user. The provision unit, for example, makes the generated image viewable on the user's device. The provision unit provides the image via, for example, a website, a mobile app, or email. The purchasing unit performs a purchasing procedure at an online shop if the outfit selected from the image provided by the providing unit is an existing product. The purchasing procedure includes, but is not limited to, online payment and delivery procedures, for example. For example, if the selected outfit is an existing product, the purchasing unit performs a purchasing procedure at an online shop. For example, if the outfit selected from the image provided by the providing unit is not an existing product, the customization unit creates outfits adjusted based on the user's body type and preferences. Customization includes, but is not limited to, size adjustments and design changes, for example. The customization unit creates outfits customized to suit the user's body type and preferences, for example. In this way, the fashion generation system according to the embodiment generates various outfits based on the user's images, allowing the user to purchase or have custom-made outfits that they like.

[0030] The reception unit can receive images taken by a user with a device. Examples of devices include, but are not limited to, smartphones, tablets, and digital cameras. For example, the reception unit can receive images taken by a user with a smartphone. The reception unit can also receive images taken by a user with a tablet. Furthermore, the reception unit can also receive images taken by a user with a digital camera. This allows the reception unit to receive images taken by a user with a device.

[0031] The generation unit can analyze the user's body shape and facial features and generate appropriate clothing based on the user's body shape and facial features. Body shape and facial features include, but are not limited to, height, weight, and facial contours. For example, the generation unit can analyze the user's height and generate clothing that matches the height. The generation unit can also analyze the user's weight and generate clothing that matches the weight. Furthermore, the generation unit can analyze the user's facial contours and generate clothing that matches the weight. This makes it possible to generate clothing that matches the user's body shape and facial features. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's body shape and facial features into the generation AI and cause the generation AI to generate appropriate clothing.

[0032] The providing unit can make the generated image viewable on the user's device. Examples of viewable devices include, but are not limited to, a web browser, a dedicated app, and the like. For example, the providing unit can make the generated image viewable on a web browser. The providing unit can also make the generated image viewable on a dedicated app. Furthermore, the providing unit can send the generated image by email, allowing the user to view the image by email. This allows the user to view the generated image on the device.

[0033] If the selected outfit is an existing product, the purchasing unit can complete the purchase process at an online shop. Examples of online shops include, but are not limited to, Yahoo! (registered trademark) Shopping and ZOZOTOWN (registered trademark). For example, if the selected outfit is available for purchase at Yahoo! Shopping, the purchasing unit can complete the purchase process at Yahoo! Shopping. Also, if the selected outfit is available for purchase at ZOZOTOWN, the purchasing unit can also complete the purchase process at ZOZOTOWN. Furthermore, if the selected outfit is available at another online shop, the purchasing unit can also complete the purchase process at that online shop. This allows the purchasing process to be completed at an online shop if the selected outfit is an existing product.

[0034] If the selected clothing is not an existing product, the customization unit can create clothing that is adjusted based on the user's body type and preferences. Examples of adjustments include, but are not limited to, size adjustments and design changes. For example, the customization unit can adjust the size to fit the user's body type. The customization unit can also change the design to fit the user's preferences. Furthermore, the customization unit can create clothing that is balanced overall based on the user's body type and preferences. In this way, if the selected clothing is not an existing product, clothing customized to fit the user's body type and preferences can be created. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input data on the user's body type and preferences into a generation AI and cause the generation AI to generate customized clothing.

[0035] The reception unit can analyze the user's past image upload history and select the optimal reception method. For example, the reception unit can analyze the time periods during which the user frequently uploaded in the past and send notifications during those time periods. The reception unit can also preferentially suggest devices that the user has used in the past. Furthermore, the reception unit can also encourage uploading on specific days of the week based on the user's past upload history. This makes it possible to analyze the user's past image upload history and select the optimal reception method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past upload history data into the generation AI and have the generation AI select the optimal reception method.

[0036] When receiving images, the reception unit may perform filtering based on the user's current project or area of ​​interest. For example, the reception unit may only accept images related to the user's ongoing project. The reception unit may also preferentially accept related images based on the user's area of ​​interest. Furthermore, the reception unit may refer to the user's past project history to accept highly relevant images. This allows filtering based on the user's current project or area of ​​interest. Examples of filtering include, but are not limited to, keyword filtering and category filtering. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit may input data on the user's project or area of ​​interest into a generation AI and have the generation AI perform filtering.

[0037] When accepting an image, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user selects voice input, the acceptance unit can accept the image using voice recognition technology. Furthermore, if the user selects text input, the acceptance unit can also accept the image using text analysis technology. Furthermore, if the user selects image input, the acceptance unit can also accept the image using image analysis technology. This allows the optimal acceptance means to be selected depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's input data to a generation AI and cause the generation AI to select the optimal acceptance means.

[0038] When accepting images, the reception unit can prioritize accepting highly relevant images in consideration of the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize accepting images related to that area. Furthermore, if the user is traveling, the reception unit can prioritize accepting images related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize accepting images related to the area around the user's home. This allows highly relevant images to be prioritized in consideration of the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to select highly relevant images.

[0039] When receiving an image, the reception unit can analyze the user's social media activity and receive related images. For example, the reception unit can preferentially receive images shared by the user on social media. The reception unit can also analyze the user's social media activity and receive related images. Furthermore, the reception unit can also receive related images by referring to the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related images can be received. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to such examples. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related images.

[0040] When accepting an image, the reception unit can customize the reception method by reflecting the user's past feedback. For example, the reception unit preferentially suggests a reception method that the user has previously preferred. The reception unit can also improve the reception method based on the user's past feedback. Furthermore, the reception unit can also suggest an optimal reception method by referring to the user's past feedback. This allows the reception method to be customized by reflecting the user's past feedback. Feedback includes, for example, survey results, user reviews, etc., but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data into the generation AI and cause the generation AI to customize the reception method.

[0041] When generating clothing, the generation unit can adjust the level of detail of the generation based on the user's body type and facial features. For example, the generation unit generates clothing that fits well to the user's body type. The generation unit can also adjust the design around the face to match the user's facial features. Furthermore, the generation unit can generate clothing that achieves overall balance by taking the user's body type and facial features into consideration. This allows the level of detail of the generation to be adjusted based on the user's body type and facial features. The level of detail includes, but is not limited to, resolution and level of detail. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the user's body type and facial features into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0042] When generating clothing, the generation unit can apply different generation algorithms depending on the season or event. For example, the generation unit applies an algorithm that generates cool clothing in summer. The generation unit can also apply an algorithm that generates warm clothing in winter. Furthermore, the generation unit can apply an algorithm that generates clothing suited to a specific event (such as a wedding or a party). This makes it possible to apply different generation algorithms depending on the season or event. Examples of generation algorithms include, but are not limited to, deep learning and rule-based generation. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input data on the season or event into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0043] When generating outfits, the generation unit can improve the accuracy of the generation by referring to the user's past outfit selections. For example, the generation unit generates outfits of a similar style by referring to the style of outfits selected by the user in the past. The generation unit can also reflect the user's preferred colors and designs based on the user's past selections. Furthermore, the generation unit can analyze the user's past selections and generate optimal outfits. This can improve the accuracy of the generation by referring to the user's past outfit selections. Accuracy includes, but is not limited to, accuracy and reproducibility. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's past selection data into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0044] When generating outfits, the generation unit can determine the generation priority based on the user's submission time. For example, if the user is in a hurry, the generation unit can generate outfits immediately. Furthermore, if the user has set a specific deadline, the generation unit can also generate outfits to meet that deadline. Furthermore, if the user has not specified a submission time, the generation unit can generate outfits with normal priority. This allows the generation priority to be determined based on the user's submission time. The submission time includes, but is not limited to, the submission date and time, submission frequency, etc. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's submission time data into the generation AI and have the generation AI determine the generation priority.

[0045] When generating outfits, the generation unit can adjust the order of generation based on the user's relevance. For example, the generation unit prioritizes generating outfits related to a specific event. The generation unit can also prioritize generating highly relevant outfits based on the user's field of interest. Furthermore, the generation unit can prioritize generating highly relevant outfits by referring to the user's past selection results. This allows the order of generation to be adjusted based on the user's relevance. Relevance includes, for example, a user profile, a past selection history, etc., but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0046] When generating an outfit, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user is knowledgeable about fashion, the generation unit can provide an explanation using a lot of technical terminology. Furthermore, if the user is not knowledgeable about fashion, the generation unit can also provide an explanation in simple terms. Furthermore, the generation unit can provide an explanation using appropriate terminology according to the user's level of expertise. This allows the use of technical terminology in the generation according to the user's level of expertise. Technical terminology includes, but is not limited to, fashion terms and technical terms. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0047] When providing images, the providing unit can select the optimal display method by referring to the user's past browsing history. For example, the providing unit can prioritize providing display methods that the user has previously preferred. The providing unit can also suggest the optimal display method based on the user's past browsing history. Furthermore, the providing unit can analyze the user's past browsing history and prioritize displaying highly relevant images. This makes it possible to select the optimal display method by referring to the user's past browsing history. The browsing history includes, for example, viewed pages, viewing time, etc., but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's browsing history data to the generation AI and cause the generation AI to select the optimal display method.

[0048] When providing images, the providing unit can customize the display content according to the user's current task. For example, if the user is performing a specific task, the providing unit can prioritize displaying images related to that task. The providing unit can also provide optimal display content based on the user's current task. Furthermore, the providing unit can also display highly relevant images by referring to the user's past task history. This allows the display content to be customized according to the user's current task. Tasks include, but are not limited to, shopping, research, entertainment, etc. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's task data into a generating AI and have the generating AI customize the display content.

[0049] The providing unit can improve the display method by reflecting user feedback when providing an image. The providing unit can improve the display method based on, for example, past user feedback. The providing unit can also suggest an optimal display method by referring to the user feedback. Furthermore, the providing unit can customize the display content by reflecting user feedback. This allows the display method to be improved by reflecting user feedback. Examples of improvements to the display method include, but are not limited to, usability testing and A / B testing. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the display method.

[0050] When providing an image, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This allows the optimal display method to be selected by taking into account the user's device information. Device information includes, but is not limited to, the device type, OS version, etc. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0051] When providing an image, the providing unit can make the display content multilingual according to the user's language setting. The providing unit, for example, automatically sets the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the display content in that language. This makes it possible to make the display content multilingual according to the user's language setting. Language settings include, for example, the user's device settings and app language settings, but are not limited to these examples. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into the generation AI and cause the generation AI to set the display content to be multilingual.

[0052] When providing images, the providing unit can analyze the user's social media activity and provide related images. For example, the providing unit can provide images related to images shared by the user on social media. The providing unit can also analyze the user's social media activity and provide related images. Furthermore, the providing unit can provide related images by referring to the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related images can be provided. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to such examples. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to provide related images.

[0053] During the purchase process, the purchasing unit can select the optimal procedure by referring to the user's past purchase history. The purchasing unit, for example, suggests related products based on products the user has previously purchased. The purchasing unit can also suggest the optimal purchase procedure based on the user's past purchase history. Furthermore, the purchasing unit can analyze the user's past purchase history and preferentially suggest highly relevant products. This allows the optimal procedure to be selected by referring to the user's past purchase history. The purchase history includes, for example, previously purchased products and purchase dates and times, etc., but is not limited to such examples. Some or all of the above-described processing in the purchasing unit may be performed using, or without, AI. For example, the purchasing unit can input the user's purchase history data into a generation AI and have the generation AI select the optimal procedure.

[0054] The purchasing unit can customize the purchasing process according to the user's current purchasing intent during the purchasing process. For example, if the user has a high purchasing intent, the purchasing unit can provide a quick purchasing process. Furthermore, if the user has a low purchasing intent, the purchasing unit can provide detailed information to increase the user's purchasing intent. Furthermore, the purchasing unit can suggest an optimal purchasing process according to the user's purchasing intent. This allows the purchasing process to be customized according to the user's current purchasing intent. Purchasing intent includes, for example, survey results, behavioral data, etc., but is not limited to such examples. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input the user's purchasing intent data into a generation AI and have the generation AI customize the purchasing process.

[0055] The purchasing unit can improve the purchasing procedure by reflecting user feedback during the purchasing process. The purchasing unit can improve the purchasing procedure based on, for example, the user's past feedback. The purchasing unit can also suggest an optimal purchasing procedure by referring to the user's feedback. Furthermore, the purchasing unit can customize the purchasing procedure content by reflecting the user's feedback. This allows the purchasing procedure to be improved by reflecting the user's feedback. Feedback includes, for example, survey results, user reviews, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input user feedback data into a generation AI and have the generation AI improve the purchasing procedure.

[0056] During the purchase process, the purchasing unit can select the optimal procedure by taking into account the user's geographical location information. For example, if the user is in a specific area, the purchasing unit can prioritize products related to that area. Furthermore, if the user is traveling, the purchasing unit can prioritize products related to the user's travel destination. Furthermore, if the user is at home, the purchasing unit can prioritize products related to the area around the user's home. This allows the optimal procedure to be selected by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. Some or all of the above-described processing in the purchasing unit may be performed using, or without, AI. For example, the purchasing unit can input the user's geographical location data into a generation AI and have the generation AI select the optimal procedure.

[0057] The purchasing unit can analyze the user's social media activity during the purchase process and suggest related procedures. For example, the purchasing unit can suggest procedures related to products the user has shared on social media. The purchasing unit can also analyze the user's social media activity and suggest related procedures. Furthermore, the purchasing unit can suggest related procedures based on the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related procedures can be suggested. Social media activity includes, for example, the content of posts, the number of likes, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the purchasing unit may be performed using, or without, AI. For example, the purchasing unit can input the user's social media data into a generation AI and have the generation AI suggest related procedures.

[0058] During the purchase process, the purchasing unit can customize the purchase process by reflecting the user's past feedback. For example, the purchasing unit can improve the purchase process based on the user's past feedback. The purchasing unit can also suggest an optimal purchase process by referring to the user's feedback. Furthermore, the purchasing unit can customize the purchase process content by reflecting the user's feedback. This allows the purchase process to be customized by reflecting the user's past feedback. Feedback includes, for example, survey results, user reviews, etc., but is not limited to such examples. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input user feedback data into a generation AI and have the generation AI customize the purchase process.

[0059] When ordering, the customization unit can select the optimal method by referring to the user's past customization history. For example, the customization unit can suggest related products based on products that the user has previously ordered. The customization unit can also suggest the optimal customization procedure based on the user's past customization history. Furthermore, the customization unit can analyze the user's past customization history and prioritize suggesting highly relevant products. This allows the optimal method to be selected by referring to the user's past customization history. The customization history includes, for example, past customization products, order dates, etc., but is not limited to such examples. Some or all of the above-described processing in the customization unit may be performed using, or without, AI. For example, the customization unit can input the user's customization history data into a generation AI and have the generation AI select the optimal method.

[0060] The customization unit can customize the means according to the user's current body shape and preferences when customizing the garment. For example, the customization unit can suggest clothing that fits the user's current body shape. The customization unit can also customize the design and color according to the user's preferences. Furthermore, the customization unit can suggest the optimal customization procedure based on the user's body shape and preferences. This allows the means to be customized according to the user's current body shape and preferences. Body shape and preferences include, but are not limited to, height, weight, and design preferences. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input data on the user's body shape and preferences into a generation AI and have the generation AI customize the means.

[0061] The customization unit can improve the customization method by reflecting user feedback when customizing. For example, the customization unit improves the customization procedure based on the user's past feedback. The customization unit can also suggest the optimal customization procedure by referring to the user's feedback. Furthermore, the customization unit can customize the content of the customization procedure by reflecting the user's feedback. This allows the customization method to be improved by reflecting the user's feedback. Method improvements include, but are not limited to, usability testing and A / B testing. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input user feedback data into a generation AI and have the generation AI improve the method.

[0062] When customizing, the customization unit can select the optimal method by taking into account the user's geographical location information. For example, if the user is in a specific area, the customization unit can suggest a customization procedure related to that area. Furthermore, if the user is traveling, the customization unit can suggest a customization procedure related to the travel destination. Furthermore, if the user is at home, the customization unit can suggest a customization procedure related to the area around the user's home. This allows the optimal customization method to be selected by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. Some or all of the above-described processing in the customization unit may be performed using, or without, AI. For example, the customization unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal method.

[0063] The customization unit can analyze the user's social media activity and suggest relevant methods when customizing. For example, the customization unit can suggest customization procedures related to products shared by the user on social media. The customization unit can also analyze the user's social media activity and suggest relevant customization procedures. Furthermore, the customization unit can suggest relevant customization procedures based on the activity of the user's friends on social media. This makes it possible to analyze the user's social media activity and suggest relevant customization methods. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to such examples. Some or all of the above-described processing in the customization unit may be performed using, or without, AI. For example, the customization unit can input the user's social media data into a generation AI and have the generation AI suggest relevant methods.

[0064] The customization unit can customize the customization method by reflecting the user's past feedback when customizing. For example, the customization unit can improve the customization procedure based on the user's past feedback. The customization unit can also suggest the optimal customization procedure by referring to the user's feedback. Furthermore, the customization unit can customize the content of the customization procedure by reflecting the user's feedback. This allows the customization method to be customized by reflecting the user's past feedback. Feedback includes, for example, survey results, user reviews, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input user feedback data into a generation AI and have the generation AI customize the method.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] When accepting a user's image, the reception unit can refer to the user's past fashion history and suggest the most suitable clothing style. For example, the reception unit can analyze the style and color of clothing the user has previously chosen and suggest clothing with a similar style. The reception unit can also suggest new related clothing based on the user's past purchase history. Furthermore, the reception unit can refer to the user's past feedback and preferentially suggest clothing styles that the user prefers. This makes it possible to make more personalized suggestions by utilizing the user's past fashion history.

[0067] The generator can generate clothing that takes into account the user's health data when analyzing the user's body type and facial features. For example, clothing that emphasizes a healthy appearance can be generated based on the user's weight and BMI. The generator can also suggest appropriate clothing based on the user's exercise habits and diet. Furthermore, the generator can also generate clothing that motivates the user in accordance with their health goals (e.g., dieting or muscle building). This makes it possible to suggest clothing that takes the user's health condition into consideration.

[0068] When making the generated images viewable on the user's device, the providing unit can customize the display method based on the user's visual preferences. For example, the providing unit can provide an interface that reflects the user's preferred color tone and font style. The providing unit can also automatically switch between night mode and dark mode to reduce the user's visual fatigue. Furthermore, the providing unit can adjust the display order and layout of the images according to the user's visual preferences. This provides a display method that takes into account the user's visual comfort.

[0069] If the selected outfit is an existing product, the purchasing unit can suggest related items based on the user's purchasing history. For example, it can suggest items that are easy to coordinate with items the user has previously purchased. The purchasing unit can also analyze the user's purchasing history and prioritize the display of frequently purchased brands and styles. Furthermore, the purchasing unit can provide specific sales and discount information based on the user's purchasing history. This makes it possible to provide a more personalized purchasing experience by utilizing the user's purchasing history.

[0070] If the selected clothing is not an existing product, the customization unit can suggest customized clothing based on the user's lifestyle. For example, if the user likes outdoor activities, the customization unit can suggest durable materials and designs. The customization unit can also suggest functional designs based on the user's occupation and daily activities. Furthermore, the customization unit can also suggest clothing suitable for the season and weather based on the user's lifestyle. This allows customization to suit the user's lifestyle.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit receives an image of a user. The image of the user includes, for example, a face photo, a full-body photo, a specific pose, etc. The reception unit receives an image taken by the user with a device. Step 2: The generation unit uses a generation AI to generate multiple different types of clothing based on the image received by the reception unit. The generated clothing includes, for example, casual, formal, and sportswear. The generation unit analyzes the user's body type and facial features and generates appropriate clothing. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate appropriate clothing based on the user's body type and facial features. Step 3: The providing unit provides the image generated by the generating unit to the user. The providing unit makes the generated image viewable on the user's device. The providing unit provides the image via a website, a mobile app, email, etc. Step 4: If the clothing selected from the image provided by the provider is an existing product, the purchase unit performs a purchase procedure at the online shop, including online payment and delivery procedures. Step 5: If the clothing selected from the image provided by the provision unit is not an existing product, the customization unit creates clothing adjusted based on the user's body type and preferences. Customization includes size adjustment, design changes, etc. The customization unit creates clothing customized to fit the user's body type and preferences.

[0073] (Example 2) A system according to an embodiment of the present invention uses an image generation AI to generate images of various outfits featuring the user as a model, allowing the user to purchase their favorite outfits as existing products or have them custom-made. The system allows users to upload their own images, and the image generation AI generates images of the user wearing various outfits based on the user's image. The user then browses the generated images and selects an outfit they like. If the selected outfit is an existing product, it can be purchased as is. If it is not available, it can be custom-made. For example, a user uploads an image of themselves. It is recommended that the user upload an image taken from the front. For example, an image taken with a device can be uploaded. This image is then input into the image generation AI. The image generation AI then analyzes the input image of the user and generates images of the user wearing various outfits. The image generation AI analyzes the user's body type and facial features and generates outfits that match the user's appearance. For example, various styles of outfits, such as casual, formal, and seasonal, can be generated. The generated images can be viewed on the user's device. The user can then select an outfit they like by viewing the generated images. For example, they can select a specific dress or suit from the generated images. If the selected outfit is an existing product, it can be purchased as is. For example, if the selected dress is an existing product, the purchase procedure can be carried out at an online shop. If there is no existing product, it can be made to order. In the case of made to order, the outfit is created to be customized according to the user's body type and preferences. In this way, the system allows the user to try on outfits using themselves as models, and to easily purchase or have made to order outfits that they like. In this way, the system improves user satisfaction and allows users to enjoy fashion that meets their individual needs.

[0074] A fashion generation system according to an embodiment includes a reception unit, a generation unit, a provision unit, a purchase unit, and a custom-made unit. The reception unit receives a user's image. The user's image may include, but is not limited to, a face photo, a full-body photo, or a specific pose. The reception unit receives, for example, an image taken by the user on a device. The generation unit uses a generation AI to generate multiple different types of outfits based on the image received by the reception unit. The generated outfits may include, but are not limited to, casual, formal, and sportswear. The generation unit analyzes, for example, the user's body type and facial features and generates outfits that match the outfits. The generation AI generates appropriate outfits based on the user's body type and facial features using a text generation AI (e.g., LLM) or a multimodal generation AI. The provision unit provides the image generated by the generation unit to the user. The provision unit, for example, makes the generated image viewable on the user's device. The provision unit provides the image via, for example, a website, a mobile app, or email. The purchasing unit performs a purchasing procedure at an online shop if the outfit selected from the image provided by the providing unit is an existing product. The purchasing procedure includes, but is not limited to, online payment and delivery procedures, for example. For example, if the selected outfit is an existing product, the purchasing unit performs a purchasing procedure at an online shop. For example, if the outfit selected from the image provided by the providing unit is not an existing product, the customization unit creates outfits adjusted based on the user's body type and preferences. Customization includes, but is not limited to, size adjustments and design changes, for example. The customization unit creates outfits customized to suit the user's body type and preferences, for example. In this way, the fashion generation system according to the embodiment generates various outfits based on the user's images, allowing the user to purchase or have custom-made outfits that they like.

[0075] The reception unit can receive images taken by a user with a device. Examples of devices include, but are not limited to, smartphones, tablets, and digital cameras. For example, the reception unit can receive images taken by a user with a smartphone. The reception unit can also receive images taken by a user with a tablet. Furthermore, the reception unit can also receive images taken by a user with a digital camera. This allows the reception unit to receive images taken by a user with a device.

[0076] The generation unit can analyze the user's body shape and facial features and generate appropriate clothing based on the user's body shape and facial features. Body shape and facial features include, but are not limited to, height, weight, and facial contours. For example, the generation unit can analyze the user's height and generate clothing that matches the height. The generation unit can also analyze the user's weight and generate clothing that matches the weight. Furthermore, the generation unit can analyze the user's facial contours and generate clothing that matches the weight. This makes it possible to generate clothing that matches the user's body shape and facial features. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's body shape and facial features into the generation AI and cause the generation AI to generate appropriate clothing.

[0077] The providing unit can make the generated image viewable on the user's device. Examples of viewable devices include, but are not limited to, a web browser, a dedicated app, and the like. For example, the providing unit can make the generated image viewable on a web browser. The providing unit can also make the generated image viewable on a dedicated app. Furthermore, the providing unit can send the generated image by email, allowing the user to view the image by email. This allows the user to view the generated image on the device.

[0078] If the selected outfit is an existing product, the purchasing unit can complete the purchase process at an online shop. Examples of online shops include, but are not limited to, Yahoo! Shopping and ZOZOTOWN. For example, if the selected outfit is available for purchase at Yahoo! Shopping, the purchasing unit can complete the purchase process at Yahoo! Shopping. Also, if the selected outfit is available for purchase at ZOZOTOWN, the purchasing unit can complete the purchase process at ZOZOTOWN. Furthermore, if the selected outfit is available at another online shop, the purchasing unit can complete the purchase process at that online shop. This allows the purchasing process to be completed at an online shop if the selected outfit is an existing product.

[0079] If the selected clothing is not an existing product, the customization unit can create clothing that is adjusted based on the user's body type and preferences. Examples of adjustments include, but are not limited to, size adjustments and design changes. For example, the customization unit can adjust the size to fit the user's body type. The customization unit can also change the design to fit the user's preferences. Furthermore, the customization unit can create clothing that is balanced overall based on the user's body type and preferences. In this way, if the selected clothing is not an existing product, clothing customized to fit the user's body type and preferences can be created. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input data on the user's body type and preferences into a generation AI and cause the generation AI to generate customized clothing.

[0080] The reception unit can estimate the user's emotions and adjust the timing of image reception based on the estimated user emotions. For example, if the user is relaxed, the reception unit can send a notification prompting the user to upload an image. Furthermore, if the user is feeling stressed, the reception unit can also suggest postponing image uploading. Furthermore, if the user is excited, the reception unit can display an interface prompting the user to immediately upload an image. This allows the timing of image reception to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0081] The reception unit can analyze the user's past image upload history and select the optimal reception method. For example, the reception unit can analyze the time periods during which the user frequently uploaded in the past and send notifications during those time periods. The reception unit can also preferentially suggest devices that the user has used in the past. Furthermore, the reception unit can also encourage uploading on specific days of the week based on the user's past upload history. This makes it possible to analyze the user's past image upload history and select the optimal reception method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past upload history data into the generation AI and have the generation AI select the optimal reception method.

[0082] When receiving images, the reception unit may perform filtering based on the user's current project or area of ​​interest. For example, the reception unit may only accept images related to the user's ongoing project. The reception unit may also preferentially accept related images based on the user's area of ​​interest. Furthermore, the reception unit may refer to the user's past project history to accept highly relevant images. This allows filtering based on the user's current project or area of ​​interest. Examples of filtering include, but are not limited to, keyword filtering and category filtering. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit may input data on the user's project or area of ​​interest into a generation AI and have the generation AI perform filtering.

[0083] When accepting an image, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user selects voice input, the acceptance unit can accept the image using voice recognition technology. Furthermore, if the user selects text input, the acceptance unit can also accept the image using text analysis technology. Furthermore, if the user selects image input, the acceptance unit can also accept the image using image analysis technology. This allows the optimal acceptance means to be selected depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's input data to a generation AI and cause the generation AI to select the optimal acceptance means.

[0084] The reception unit can estimate the user's emotions and determine the priority of images to be accepted based on the estimated user emotions. For example, when the user is relaxed, the reception unit accepts images with normal priority. Furthermore, when the user is stressed, the reception unit can preferentially accept images with high importance. Furthermore, when the user is excited, the reception unit can immediately accept images. This allows the priority of images to be accepted to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] When accepting images, the reception unit can prioritize accepting highly relevant images in consideration of the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize accepting images related to that area. Furthermore, if the user is traveling, the reception unit can prioritize accepting images related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize accepting images related to the area around the user's home. This allows highly relevant images to be prioritized in consideration of the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to select highly relevant images.

[0086] When receiving an image, the reception unit can analyze the user's social media activity and receive related images. For example, the reception unit can preferentially receive images shared by the user on social media. The reception unit can also analyze the user's social media activity and receive related images. Furthermore, the reception unit can also receive related images by referring to the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related images can be received. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to such examples. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related images.

[0087] When accepting an image, the reception unit can customize the reception method by reflecting the user's past feedback. For example, the reception unit preferentially suggests a reception method that the user has previously preferred. The reception unit can also improve the reception method based on the user's past feedback. Furthermore, the reception unit can also suggest an optimal reception method by referring to the user's past feedback. This allows the reception method to be customized by reflecting the user's past feedback. Feedback includes, for example, survey results, user reviews, etc., but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data into the generation AI and cause the generation AI to customize the reception method.

[0088] The generation unit can estimate the user's emotions and adjust the clothing generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate casual clothing. Furthermore, if the user is stressed, the generation unit can generate relaxing clothing. Furthermore, if the user is excited, the generation unit can generate flashy clothing. This allows the clothing generation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the clothing generation method.

[0089] When generating clothing, the generation unit can adjust the level of detail of the generation based on the user's body type and facial features. For example, the generation unit generates clothing that fits well to the user's body type. The generation unit can also adjust the design around the face to match the user's facial features. Furthermore, the generation unit can generate clothing that achieves overall balance by taking the user's body type and facial features into consideration. This allows the level of detail of the generation to be adjusted based on the user's body type and facial features. The level of detail includes, but is not limited to, resolution and level of detail. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the user's body type and facial features into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0090] When generating clothing, the generation unit can apply different generation algorithms depending on the season or event. For example, the generation unit applies an algorithm that generates cool clothing in summer. The generation unit can also apply an algorithm that generates warm clothing in winter. Furthermore, the generation unit can apply an algorithm that generates clothing suited to a specific event (such as a wedding or a party). This makes it possible to apply different generation algorithms depending on the season or event. Examples of generation algorithms include, but are not limited to, deep learning and rule-based generation. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input data on the season or event into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0091] When generating outfits, the generation unit can improve the accuracy of the generation by referring to the user's past outfit selections. For example, the generation unit generates outfits of a similar style by referring to the style of outfits selected by the user in the past. The generation unit can also reflect the user's preferred colors and designs based on the user's past selections. Furthermore, the generation unit can analyze the user's past selections and generate optimal outfits. This can improve the accuracy of the generation by referring to the user's past outfit selections. Accuracy includes, but is not limited to, accuracy and reproducibility. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's past selection data into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0092] The generation unit can estimate the user's emotions and adjust the style of the generated clothing based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate casual-style clothing. Furthermore, if the user is stressed, the generation unit can generate relaxing-style clothing. Furthermore, if the user is excited, the generation unit can generate flashy-style clothing. This allows the style of the generated clothing to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the clothing style.

[0093] When generating outfits, the generation unit can determine the generation priority based on the user's submission time. For example, if the user is in a hurry, the generation unit can generate outfits immediately. Furthermore, if the user has set a specific deadline, the generation unit can also generate outfits to meet that deadline. Furthermore, if the user has not specified a submission time, the generation unit can generate outfits with normal priority. This allows the generation priority to be determined based on the user's submission time. The submission time includes, but is not limited to, the submission date and time, submission frequency, etc. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's submission time data into the generation AI and have the generation AI determine the generation priority.

[0094] When generating outfits, the generation unit can adjust the order of generation based on the user's relevance. For example, the generation unit prioritizes generating outfits related to a specific event. The generation unit can also prioritize generating highly relevant outfits based on the user's field of interest. Furthermore, the generation unit can prioritize generating highly relevant outfits by referring to the user's past selection results. This allows the order of generation to be adjusted based on the user's relevance. Relevance includes, for example, a user profile, a past selection history, etc., but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0095] When generating an outfit, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user is knowledgeable about fashion, the generation unit can provide an explanation using a lot of technical terminology. Furthermore, if the user is not knowledgeable about fashion, the generation unit can also provide an explanation in simple terms. Furthermore, the generation unit can provide an explanation using appropriate terminology according to the user's level of expertise. This allows the use of technical terminology in the generation according to the user's level of expertise. Technical terminology includes, but is not limited to, fashion terms and technical terms. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0096] The providing unit can estimate the user's emotions and adjust the display method of the images to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can display images at a leisurely pace. Furthermore, if the user is stressed, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is excited, the providing unit can provide a visually stimulating display method. This allows the display method of the images to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0097] When providing images, the providing unit can select the optimal display method by referring to the user's past browsing history. For example, the providing unit can prioritize providing display methods that the user has previously preferred. The providing unit can also suggest the optimal display method based on the user's past browsing history. Furthermore, the providing unit can analyze the user's past browsing history and prioritize displaying highly relevant images. This makes it possible to select the optimal display method by referring to the user's past browsing history. The browsing history includes, for example, viewed pages, viewing time, etc., but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's browsing history data to the generation AI and cause the generation AI to select the optimal display method.

[0098] When providing images, the providing unit can customize the display content according to the user's current task. For example, if the user is performing a specific task, the providing unit can prioritize displaying images related to that task. The providing unit can also provide optimal display content based on the user's current task. Furthermore, the providing unit can also display highly relevant images by referring to the user's past task history. This allows the display content to be customized according to the user's current task. Tasks include, but are not limited to, shopping, research, entertainment, etc. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's task data into a generating AI and have the generating AI customize the display content.

[0099] The providing unit can improve the display method by reflecting user feedback when providing an image. The providing unit can improve the display method based on, for example, past user feedback. The providing unit can also suggest an optimal display method by referring to the user feedback. Furthermore, the providing unit can customize the display content by reflecting user feedback. This allows the display method to be improved by reflecting user feedback. Examples of improvements to the display method include, but are not limited to, usability testing and A / B testing. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the display method.

[0100] The providing unit can estimate the user's emotions and determine the priority of images to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit provides images in a normal priority order. Furthermore, when the user is stressed, the providing unit can also provide images with a higher priority order. Furthermore, when the user is excited, the providing unit can also provide images immediately. This allows the priority of images to be provided to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority order of images.

[0101] When providing an image, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This allows the optimal display method to be selected by taking into account the user's device information. Device information includes, but is not limited to, the device type, OS version, etc. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0102] When providing an image, the providing unit can make the display content multilingual according to the user's language setting. The providing unit, for example, automatically sets the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the display content in that language. This makes it possible to make the display content multilingual according to the user's language setting. Language settings include, for example, the user's device settings and app language settings, but are not limited to these examples. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into the generation AI and cause the generation AI to set the display content to be multilingual.

[0103] When providing images, the providing unit can analyze the user's social media activity and provide related images. For example, the providing unit can provide images related to images shared by the user on social media. The providing unit can also analyze the user's social media activity and provide related images. Furthermore, the providing unit can provide related images by referring to the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related images can be provided. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to such examples. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to provide related images.

[0104] The purchasing unit can estimate the user's emotions and adjust the purchasing procedure based on the estimated user emotions. For example, the purchasing unit can provide a normal purchasing procedure when the user is relaxed. The purchasing unit can also provide a simplified purchasing procedure when the user is stressed. Furthermore, the purchasing unit can also provide a quick purchasing procedure when the user is excited. This allows the purchasing procedure to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the purchasing unit can be performed using, for example, AI, or without AI. For example, the purchasing unit can input the user's emotion data into the generation AI and have the generation AI adjust the purchasing procedure.

[0105] During the purchase process, the purchasing unit can select the optimal procedure by referring to the user's past purchase history. The purchasing unit, for example, suggests related products based on products the user has previously purchased. The purchasing unit can also suggest the optimal purchase procedure based on the user's past purchase history. Furthermore, the purchasing unit can analyze the user's past purchase history and preferentially suggest highly relevant products. This allows the optimal procedure to be selected by referring to the user's past purchase history. The purchase history includes, for example, previously purchased products and purchase dates and times, etc., but is not limited to such examples. Some or all of the above-described processing in the purchasing unit may be performed using, or without, AI. For example, the purchasing unit can input the user's purchase history data into a generation AI and have the generation AI select the optimal procedure.

[0106] The purchasing unit can customize the purchasing process according to the user's current purchasing intent during the purchasing process. For example, if the user has a high purchasing intent, the purchasing unit can provide a quick purchasing process. Furthermore, if the user has a low purchasing intent, the purchasing unit can provide detailed information to increase the user's purchasing intent. Furthermore, the purchasing unit can suggest an optimal purchasing process according to the user's purchasing intent. This allows the purchasing process to be customized according to the user's current purchasing intent. Purchasing intent includes, for example, survey results, behavioral data, etc., but is not limited to such examples. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input the user's purchasing intent data into a generation AI and have the generation AI customize the purchasing process.

[0107] The purchasing unit can improve the purchasing procedure by reflecting user feedback during the purchasing process. The purchasing unit can improve the purchasing procedure based on, for example, the user's past feedback. The purchasing unit can also suggest an optimal purchasing procedure by referring to the user's feedback. Furthermore, the purchasing unit can customize the purchasing procedure content by reflecting the user's feedback. This allows the purchasing procedure to be improved by reflecting the user's feedback. Feedback includes, for example, survey results, user reviews, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input user feedback data into a generation AI and have the generation AI improve the purchasing procedure.

[0108] The purchasing unit can estimate the user's emotions and determine the priority of the purchase process based on the estimated user emotions. For example, if the user is relaxed, the purchasing unit can perform the purchase process with normal priority. Furthermore, if the user is feeling stressed, the purchasing unit can prioritize purchases of high importance. Furthermore, if the user is excited, the purchasing unit can immediately perform the purchase process. This allows the priority of the purchase process to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the purchasing unit can be performed using, for example, AI, or without AI. For example, the purchasing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the purchase process.

[0109] During the purchase process, the purchasing unit can select the optimal procedure by taking into account the user's geographical location information. For example, if the user is in a specific area, the purchasing unit can prioritize products related to that area. Furthermore, if the user is traveling, the purchasing unit can prioritize products related to the user's travel destination. Furthermore, if the user is at home, the purchasing unit can prioritize products related to the area around the user's home. This allows the optimal procedure to be selected by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. Some or all of the above-described processing in the purchasing unit may be performed using, or without, AI. For example, the purchasing unit can input the user's geographical location data into a generation AI and have the generation AI select the optimal procedure.

[0110] The purchasing unit can analyze the user's social media activity during the purchase process and suggest related procedures. For example, the purchasing unit can suggest procedures related to products the user has shared on social media. The purchasing unit can also analyze the user's social media activity and suggest related procedures. Furthermore, the purchasing unit can suggest related procedures based on the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related procedures can be suggested. Social media activity includes, for example, the content of posts, the number of likes, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the purchasing unit may be performed using, or without, AI. For example, the purchasing unit can input the user's social media data into a generation AI and have the generation AI suggest related procedures.

[0111] During the purchase process, the purchasing unit can customize the purchase process by reflecting the user's past feedback. For example, the purchasing unit can improve the purchase process based on the user's past feedback. The purchasing unit can also suggest an optimal purchase process by referring to the user's feedback. Furthermore, the purchasing unit can customize the purchase process content by reflecting the user's feedback. This allows the purchase process to be customized by reflecting the user's past feedback. Feedback includes, for example, survey results, user reviews, etc., but is not limited to such examples. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input user feedback data into a generation AI and have the generation AI customize the purchase process.

[0112] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated user emotions. For example, if the user is relaxed, the customization unit can provide a normal customization procedure. Furthermore, if the user is stressed, the customization unit can provide a simplified customization procedure. Furthermore, if the user is excited, the customization unit can provide a quick customization procedure. This allows the customization method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the customization unit can be performed using AI, or can be performed without AI. For example, the customization unit can input the user's emotion data into the generation AI and have the generation AI adjust the customization method.

[0113] When ordering, the customization unit can select the optimal method by referring to the user's past customization history. For example, the customization unit can suggest related products based on products that the user has previously ordered. The customization unit can also suggest the optimal customization procedure based on the user's past customization history. Furthermore, the customization unit can analyze the user's past customization history and prioritize suggesting highly relevant products. This allows the optimal method to be selected by referring to the user's past customization history. The customization history includes, for example, past customization products, order dates, etc., but is not limited to such examples. Some or all of the above-described processing in the customization unit may be performed using, or without, AI. For example, the customization unit can input the user's customization history data into a generation AI and have the generation AI select the optimal method.

[0114] The customization unit can customize the means according to the user's current body shape and preferences when customizing the garment. For example, the customization unit can suggest clothing that fits the user's current body shape. The customization unit can also customize the design and color according to the user's preferences. Furthermore, the customization unit can suggest the optimal customization procedure based on the user's body shape and preferences. This allows the means to be customized according to the user's current body shape and preferences. Body shape and preferences include, but are not limited to, height, weight, and design preferences. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input data on the user's body shape and preferences into a generation AI and have the generation AI customize the means.

[0115] The customization unit can improve the customization method by reflecting user feedback when customizing. For example, the customization unit improves the customization procedure based on the user's past feedback. The customization unit can also suggest the optimal customization procedure by referring to the user's feedback. Furthermore, the customization unit can customize the content of the customization procedure by reflecting the user's feedback. This allows the customization method to be improved by reflecting the user's feedback. Method improvements include, but are not limited to, usability testing and A / B testing. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input user feedback data into a generation AI and have the generation AI improve the method.

[0116] The customization unit can estimate the user's emotions and determine the priority of customization based on the estimated user's emotions. For example, if the user is relaxed, the customization unit can perform customization procedures with normal priority. Furthermore, if the user is stressed, the customization unit can also prioritize customization procedures with high importance. Furthermore, if the user is excited, the customization unit can also perform customization procedures immediately. This makes it possible to determine the priority of customization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the customization unit can be performed using AI, for example, or without AI. For example, the customization unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of customization.

[0117] When customizing, the customization unit can select the optimal method by taking into account the user's geographical location information. For example, if the user is in a specific area, the customization unit can suggest a customization procedure related to that area. Furthermore, if the user is traveling, the customization unit can suggest a customization procedure related to the travel destination. Furthermore, if the user is at home, the customization unit can suggest a customization procedure related to the area around the user's home. This allows the optimal customization method to be selected by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. Some or all of the above-described processing in the customization unit may be performed using, or without, AI. For example, the customization unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal method.

[0118] The customization unit can analyze the user's social media activity and suggest relevant methods when customizing. For example, the customization unit can suggest customization procedures related to products shared by the user on social media. The customization unit can also analyze the user's social media activity and suggest relevant customization procedures. Furthermore, the customization unit can suggest relevant customization procedures based on the activity of the user's friends on social media. This makes it possible to analyze the user's social media activity and suggest relevant customization methods. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to such examples. Some or all of the above-described processing in the customization unit may be performed using, or without, AI. For example, the customization unit can input the user's social media data into a generation AI and have the generation AI suggest relevant methods.

[0119] The customization unit can customize the customization method by reflecting the user's past feedback when customizing. For example, the customization unit can improve the customization procedure based on the user's past feedback. The customization unit can also suggest the optimal customization procedure by referring to the user's feedback. Furthermore, the customization unit can customize the content of the customization procedure by reflecting the user's feedback. This allows the customization method to be customized by reflecting the user's past feedback. Feedback includes, for example, survey results, user reviews, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input user feedback data into a generation AI and have the generation AI customize the method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, purchase unit, and custom-made unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives an image of the user using the camera 42 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates outfits based on the user's image using image generation AI. The provision unit provides the generated image to the user using the output device 40 of the smart device 14. The purchase unit is realized by the specific processing unit 290 of the data processing device 12 and performs a purchase procedure at an online shop. The custom-made unit is realized by the specific processing unit 290 of the data processing device 12 and customizes outfits based on the user's body type and preferences. === Hard Collateral 1-2 === Each of the multiple elements including the reception unit, generation unit, provision unit, purchase unit, and custom-made unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives an image of the user using the camera 42 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates outfits based on the image of the user using image generation AI. The provision unit provides the generated image to the user using the speaker 240 of the smart glasses 214. The purchase unit is realized by the specific processing unit 290 of the data processing device 12 and performs a purchase procedure at an online shop. The custom-made unit is realized by the specific processing unit 290 of the data processing device 12 and customizes outfits based on the user's body type and preferences. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, purchase unit, and custom-made unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives an image of the user using the camera 42 of the headset-type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates outfits based on the user's image using image generation AI. The provision unit provides the generated image to the user using the display 343 of the headset-type terminal 314. The purchase unit is realized by the specific processing unit 290 of the data processing device 12 and performs a purchase procedure at the online shop. The custom-made unit is realized by the specific processing unit 290 of the data processing device 12 and customizes outfits based on the user's body type and preferences. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, purchase unit, and custom-made unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives an image of the user using the camera 42 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates outfits based on the image of the user using image generation AI. The provision unit provides the generated image to the user using the speaker 240 of the robot 414. The purchase unit is realized by the specific processing unit 290 of the data processing device 12 and performs a purchase procedure at an online shop. The custom-made unit is realized by the specific processing unit 290 of the data processing device 12 and customizes the outfits based on the user's body type and preferences.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] When accepting a user's image, the reception unit can refer to the user's past fashion history and suggest the most suitable clothing style. For example, the reception unit can analyze the style and color of clothing the user has previously chosen and suggest clothing with a similar style. The reception unit can also suggest new related clothing based on the user's past purchase history. Furthermore, the reception unit can refer to the user's past feedback and preferentially suggest clothing styles that the user prefers. This makes it possible to make more personalized suggestions by utilizing the user's past fashion history.

[0122] The generator can generate clothing that takes into account the user's health data when analyzing the user's body type and facial features. For example, clothing that emphasizes a healthy appearance can be generated based on the user's weight and BMI. The generator can also suggest appropriate clothing based on the user's exercise habits and diet. Furthermore, the generator can also generate clothing that motivates the user in accordance with their health goals (e.g., dieting or muscle building). This makes it possible to suggest clothing that takes the user's health condition into consideration.

[0123] When making the generated images viewable on the user's device, the providing unit can customize the display method based on the user's visual preferences. For example, the providing unit can provide an interface that reflects the user's preferred color tone and font style. The providing unit can also automatically switch between night mode and dark mode to reduce the user's visual fatigue. Furthermore, the providing unit can adjust the display order and layout of the images according to the user's visual preferences. This provides a display method that takes into account the user's visual comfort.

[0124] If the selected outfit is an existing product, the purchasing unit can suggest related items based on the user's purchasing history. For example, it can suggest items that are easy to coordinate with items the user has previously purchased. The purchasing unit can also analyze the user's purchasing history and prioritize the display of frequently purchased brands and styles. Furthermore, the purchasing unit can provide specific sales and discount information based on the user's purchasing history. This makes it possible to provide a more personalized purchasing experience by utilizing the user's purchasing history.

[0125] If the selected clothing is not an existing product, the customization unit can suggest customized clothing based on the user's lifestyle. For example, if the user likes outdoor activities, the customization unit can suggest durable materials and designs. The customization unit can also suggest functional designs based on the user's occupation and daily activities. Furthermore, the customization unit can also suggest clothing suitable for the season and weather based on the user's lifestyle. This allows customization to suit the user's lifestyle.

[0126] The reception unit can estimate the user's emotions and adjust the image reception method based on the estimated user's emotions. For example, if the user is relaxed, the reception unit can send a notification prompting the user to upload an image. Furthermore, if the user is feeling stressed, the reception unit can suggest that the user postpone uploading an image. Furthermore, if the user is excited, the reception unit can display an interface prompting the user to immediately upload an image. In this way, the image reception method can be adjusted according to the user's emotions.

[0127] The generation unit can estimate the user's emotions and adjust the clothing generation method based on the estimated user's emotions. For example, if the user is relaxed, casual clothing is generated. Furthermore, if the user is stressed, the generation unit can also generate relaxing clothing. Furthermore, if the user is excited, the generation unit can also generate flashy clothing. In this way, the clothing generation method can be adjusted according to the user's emotions.

[0128] The providing unit can estimate the user's emotions and adjust the display method of the images to be provided based on the estimated user's emotions. For example, if the user is relaxed, the images are displayed at a leisurely pace. Furthermore, if the user is feeling stressed, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is excited, the providing unit can provide a visually stimulating display method. In this way, the display method of the images to be provided can be adjusted according to the user's emotions.

[0129] The purchasing unit can estimate the user's emotions and adjust the purchasing procedure based on the estimated user's emotions. For example, if the user is relaxed, the purchasing unit can provide a normal purchasing procedure. If the user is stressed, the purchasing unit can also provide a simplified purchasing procedure. Furthermore, if the user is excited, the purchasing unit can also provide a quick purchasing procedure. In this way, the purchasing procedure can be adjusted according to the user's emotions.

[0130] The customization unit can estimate the user's emotion and adjust the customization method based on the estimated user's emotion. For example, if the user is relaxed, a normal customization procedure is provided. Also, if the user is stressed, the customization unit can provide a simplified customization procedure. Furthermore, if the user is excited, the customization unit can provide a quick customization procedure. In this way, the customization method can be adjusted according to the user's emotion.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit receives an image of a user. The image of the user includes, for example, a face photo, a full-body photo, a specific pose, etc. The reception unit receives an image taken by the user with a device. Step 2: The generation unit uses a generation AI to generate multiple different types of clothing based on the image received by the reception unit. The generated clothing includes, for example, casual, formal, and sportswear. The generation unit analyzes the user's body type and facial features and generates appropriate clothing. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate appropriate clothing based on the user's body type and facial features. Step 3: The providing unit provides the image generated by the generating unit to the user. The providing unit makes the generated image viewable on the user's device. The providing unit provides the image via a website, a mobile app, email, etc. Step 4: If the clothing selected from the image provided by the provider is an existing product, the purchase unit performs a purchase procedure at the online shop, including online payment and delivery procedures. Step 5: If the clothing selected from the image provided by the provision unit is not an existing product, the customization unit creates clothing adjusted based on the user's body type and preferences. Customization includes size adjustment, design changes, etc. The customization unit creates clothing customized to fit the user's body type and preferences.

[0133] 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.

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

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

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

[0140] The 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.

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

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0144] 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.

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

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives an image of a user; a generator for generating a plurality of different types of clothing based on the image received by the receiver; a providing unit that provides the image generated by the generating unit to a user; a purchasing unit that performs a purchase procedure if the clothing selected from the image provided by the providing unit is an existing product; and a custom-made unit that creates a custom-made garment if the garment selected from the image provided by the providing unit is not an existing product. A system characterized by:

2. The reception unit Accepts images taken by the user on the device 2. The system of claim 1.

3. The generation unit Analyzes the user's body shape and facial features and generates appropriate clothing based on the user's body shape and facial features 2. The system of claim 1.

4. The providing unit The generated image is made viewable on the user's device 2. The system of claim 1.

5. The purchasing department If the selected outfit is an existing item, complete the purchase process in the online shop.

2. The system of claim 1.

6. The custom-made department If the selected outfit is not available, create a tailored outfit based on the user's body type and preferences.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of image acceptance based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past image upload history and select the optimal reception method 2. The system of claim 1.

Images