system

Abstract

The system according to this embodiment aims to allow users to select clothes and accessories that suit them and to obtain objective evaluations. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and an evaluation unit. The reception unit inputs the user's body type, hairstyle, and clothing preferences. The analysis unit analyzes the information input by the reception unit and selects clothing and accessories that suit the user. The generation unit generates an image of the user wearing the clothing and accessories selected by the analysis unit. The evaluation unit performs an objective evaluation of the image generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for a user to obtain an objective evaluation when choosing clothes and accessories suitable for themselves.

[0005] The system according to the embodiment aims to enable a user to choose clothes and accessories suitable for themselves and obtain an objective evaluation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an evaluation unit. The reception unit receives input from the user regarding their body type, hairstyle, and clothing preferences. The analysis unit analyzes the information input by the reception unit and selects clothing and accessories that suit the user. The generation unit generates images of the user wearing the clothing and accessories selected by the analysis unit. The evaluation unit performs an objective evaluation of the images generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to select clothes and accessories that suit them and obtain objective evaluations. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] 0Figure 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The fashion check system according to an embodiment of the present invention is a system that provides a mechanism for users to select clothes and accessories that suit them and to check them before purchasing. The fashion check system allows the user to input their body type, hairstyle, and clothing preferences, and the AI ​​selects clothes and accessories that suit the user and generates an image of the user wearing them. The AI ​​also selects clothes and accessories that suit the clothing of the person the user is playing with and generates an image of the user wearing them. Furthermore, it generates an image using the clothes, accessories, and hairstyle selected by the user. The AI ​​performs an objective evaluation of these images, assessing the good points, bad points, and whether they are in line with current trends. This mechanism allows the user to check whether items suit them before purchasing and to obtain an objective fashion evaluation. For example, the user inputs their body type, hairstyle, and clothing preferences. For example, they input information such as height, weight, hair length, hair color, and preferred fashion style. This information is input to the AI. Next, the AI ​​analyzes the input information and selects clothes and accessories that suit the user. For example, the AI ​​selects clothes that suit the user's body type and considers combinations with accessories. This selects the optimal fashion items for the user. The AI ​​generates an image of the user wearing the selected clothes and accessories. For example, the AI ​​generates images of the user wearing selected clothes and accessories tailored to their body type and hairstyle. This allows the user to see what they would look like in those clothes. Furthermore, the AI ​​also selects clothes and accessories that match the outfits of people the user is playing with and generates images of the user wearing them. For example, the AI ​​selects clothes and accessories that match the user's friend's outfit and generates images of the user wearing them. This allows the user to see how their outfits would look when playing with friends. The AI ​​also generates images using clothes, accessories, and hairstyles chosen by the user. For example, the user inputs their chosen clothes, accessories, and hairstyle into the AI ​​and generates images of themselves wearing them. This allows the user to see how their chosen fashion items would look. Finally, the AI ​​provides an objective evaluation of the generated images. For example, the AI ​​analyzes the images and evaluates their strengths, weaknesses, and whether they are in line with current trends.This allows users to obtain an objective evaluation of their own fashion choices. This system enables users to check whether clothes and accessories suit them before purchasing them, and whether hairstyles and hair colors suit them before changing them. Furthermore, because they can receive an objective evaluation of their own fashion choices, they can enjoy fashion with greater confidence. The fashion check system can select the most suitable fashion items, generate images, and provide evaluations based on the user's body type, hairstyle, and clothing preferences.

[0029] The fashion check system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an evaluation unit. The reception unit receives input of the user's body type, hairstyle, and clothing preferences. The user's body type includes, but is not limited to, height, weight, and body fat percentage. The hairstyle includes, but is not limited to, hair length, hair color, and style. Clothing preferences include, but are not limited to, casual, formal, and sporty styles. The reception unit provides, for example, an interface for the user to input their body type, hairstyle, and clothing preferences. The analysis unit analyzes the information entered by the reception unit and selects clothes and accessories that suit the user. The analysis unit uses, for example, AI to analyze the user's body type, hairstyle, and clothing preferences and selects the optimal fashion items. The analysis unit selects clothes that fit the user's body type and considers combinations with accessories. The analysis unit can also select accessories that match the user's hairstyle. The generation unit generates images of the user wearing the clothes and accessories selected by the analysis unit. The generation unit generates images of the user wearing selected clothing and accessories, for example, using AI to match the user's body type and hairstyle. The generation unit generates images of the user wearing selected clothing and accessories based on the user's body type and hairstyle. The generation unit can also generate images of the user wearing selected accessories, for example, to match the user's hairstyle. The evaluation unit performs an objective evaluation of the images generated by the generation unit. The evaluation unit analyzes the generated images using AI and evaluates their strengths, weaknesses, and whether they are in line with current trends. The evaluation unit evaluates the design, color, and fit of the generated images, for example. The evaluation unit can also evaluate whether the generated images are in line with the latest fashion trends. As a result, the fashion check system according to this embodiment can select the optimal fashion items and generate and evaluate images based on the user's body type, hairstyle, and clothing preferences.

[0030] The reception desk allows users to input their body type, hairstyle, and clothing preferences. Body type includes, but is not limited to, height, weight, and body fat percentage. Hair style includes, but is not limited to, hair length, color, and style. Clothing preferences include, but are not limited to, casual, formal, and sporty. The reception desk provides an interface for users to input their body type, hairstyle, and clothing preferences. Specifically, the reception desk features an intuitive user interface to allow users to easily input information. For example, height and weight can be entered using sliders or dropdown menus. For hairstyle and clothing preferences, the system employs image or icon selection to provide a visually clear input method. Furthermore, the reception desk can save and reuse previously entered information, reducing the effort required for each input. For example, it can automatically retrieve previously entered body type and hairstyle information and modify it as needed. The reception desk also includes a function to encrypt and securely store entered information to protect user privacy. This allows users to confidently enter their information. Furthermore, the reception unit transmits the information entered by the user to the analysis unit in real time, enabling rapid analysis and the provision of results. This allows the reception unit to efficiently collect information on the user's body type, hairstyle, and clothing preferences, improving the overall performance of the system.

[0031] The analysis department analyzes the information entered by the reception department and selects clothing and accessories that suit the user. For example, the analysis department uses AI to analyze the user's body type, hairstyle, and clothing preferences to select the most suitable fashion items. Specifically, the AI ​​identifies the size and style of clothing that fits the user's body type based on the user's body type data. For example, it recommends longer pants and jackets for tall users and selects tight-fitting clothing for users with a low body fat percentage. Regarding hairstyles, it selects the most suitable accessories according to the length and color of the hair. For example, it recommends headbands and hair clips for users with long hair and selects accessories in colors that match the hair color. Furthermore, regarding clothing preferences, it selects appropriate items based on styles such as casual, formal, and sporty. For example, it recommends denim and T-shirts for users who prefer a casual style and selects suits and dresses for users who prefer a formal style. The analysis department comprehensively analyzes this information and proposes the most suitable combination of fashion items for the user. Furthermore, the analysis unit can utilize past data and trend information to provide suggestions based on the latest fashion trends. This allows the analysis unit to select the optimal fashion items tailored to each user's individual needs, thereby increasing user satisfaction.

[0032] The generation unit generates images of the user wearing clothes and accessories selected by the analysis unit. For example, the generation unit uses AI to create images of the user wearing the selected clothes and accessories, tailored to their body shape and hairstyle. Specifically, the generation AI creates a 3D model based on the user's body shape data and applies the selected clothes and accessories to that model. For instance, it recreates a realistic body shape based on the user's height and weight, and then dresses them in the selected clothes. Similarly, it recreates a realistic hairstyle that reflects hair length and color, and then applies the accessories. Because the generated images realistically reproduce how the user would look wearing the clothes and accessories, the user can visually confirm which fashion items suit them. Furthermore, the generation unit can generate images from different angles and poses. For example, it can generate images from the front, side, and back, allowing the user to see themselves from all angles. The generation unit can also generate images with different backgrounds and lighting conditions, making it easier for the user to visualize actual usage scenarios. In this way, the generation unit provides users with visually easy-to-understand information and supports them in selecting fashion items.

[0033] The evaluation unit performs an objective evaluation of the images generated by the generation unit. For example, the evaluation unit analyzes the generated images using AI and evaluates their strengths, weaknesses, and suitability for current trends. Specifically, the AI ​​uses image recognition technology to evaluate the design, color, and fit of the generated images. For example, it evaluates whether the clothing design suits the user's body type, whether the color matches the user's skin tone, and whether the fit is appropriate. The AI ​​also evaluates whether the generated images are in line with current fashion trends based on the latest trends. For example, it checks whether they match the trend colors and styles of the current season. Furthermore, the evaluation unit can also perform customized evaluations based on the user's preferences. For example, if the user prefers a particular style or color, the evaluation can be adjusted based on that preference. The evaluation unit comprehensively analyzes these evaluation results and provides specific feedback to the user. For example, it specifically indicates which parts of the selected clothing are particularly good and which parts could be improved. In addition, the evaluation unit can collect user feedback and continuously improve the accuracy of the evaluation algorithm. In this way, the evaluation unit can provide users with objective and specific evaluations and support them in selecting fashion items.

[0034] The analysis unit can analyze the clothing data of people playing with the user and select clothing and accessories that suit the user. For example, the analysis unit can collect clothing data of people playing with the user and analyze it using AI. For example, the analysis unit can select clothing and accessories that suit the user based on the clothing data of the user's friends. For example, the analysis unit can also analyze the clothing data of the user's friends and select fashion items that suit the user. This allows for the selection of fashion items that match the clothing of people playing with the user. Clothing data includes, but is not limited to, color, style, and brand. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the clothing data of people playing with the user into the AI ​​and have the AI ​​select clothing and accessories that suit the user.

[0035] The generation unit can generate images using clothes, accessories, and hairstyles selected by the user. For example, the generation unit can input the clothes, accessories, and hairstyles selected by the user into the AI ​​and generate images of the user wearing them. For example, the generation unit can generate images using the AI ​​based on the clothes, accessories, and hairstyles selected by the user. The generation unit can also generate images using the AI ​​based on the fashion items selected by the user. This allows the generation of images using the fashion items selected by the user. The selected clothes, accessories, and hairstyles include, but are not limited to, the user's selection criteria and selection procedure. Some or all of the above-described processes in the generation unit may be performed using the AI ​​or not. For example, the generation unit can input data on the clothes, accessories, and hairstyles selected by the user into the AI ​​and have the AI ​​generate images.

[0036] The evaluation unit can assess the generated images for their good points, bad points, and whether they are in line with current trends. For example, the evaluation unit can analyze the generated images using AI and assess their good points, bad points, and whether they are in line with current trends. The evaluation unit can assess the design, color, fit, etc., of the generated images. The evaluation unit can also assess whether the generated images are in line with the latest fashion trends. This allows for an objective evaluation of the generated images. Good points and bad points include, but are not limited to, design, color, and fit. Whether they are in line with current trends includes, but are not limited to, the latest fashion trends and seasonal trends. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the data of the generated images into AI and have the AI ​​perform the evaluation.

[0037] The evaluation unit may include a providing unit that provides evaluation results. The evaluation unit may, for example, analyze images generated using AI and provide evaluation results. The evaluation unit may, for example, evaluate the design, color, fit, etc., of the generated images and provide the results. The evaluation unit may also, for example, evaluate whether the generated images are in line with the latest fashion trends and provide the results. This allows evaluation results to be provided to the user. Evaluation results may include, but are not limited to, scores, comments, and recommended items. Some or all of the processing described above in the providing unit may be performed using, for example, AI, or not using AI. For example, the providing unit may input evaluation result data into AI and have AI perform the provision of evaluation results.

[0038] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can use AI to analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the user's frequently entered preferences for body type, hairstyle, and clothing. For example, the reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest the user's preferred body type, hairstyle, and clothing for a specific time period based on their past input history. This improves input efficiency by suggesting the optimal input method based on past input history. Input history includes, but is not limited to, past input data, frequency, and patterns. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's past input history data into AI and have the AI ​​suggest the optimal input method.

[0039] The reception desk can suggest region-specific fashion styles, taking into account the user's current location. For example, the reception desk can use AI to obtain the user's current location and suggest region-specific fashion styles based on that information. For example, the reception desk can suggest appropriate fashion styles based on the climate and culture of the region the user is currently in. For example, if the user is traveling, the reception desk can also suggest region-specific fashion styles for the destination. For example, if the user is attending a specific event, the reception desk can suggest fashion styles suitable for the event in that region. In this way, by suggesting region-specific fashion styles, the reception desk can provide the user with suitable fashion items. Current location information includes, but is not limited to, GPS data and location services. Region-specific fashion styles include, but are not limited to, local culture, climate, and trends. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's current location data into AI and have the AI ​​suggest region-specific fashion styles.

[0040] The reception desk can analyze a user's social media activity and suggest relevant fashion items. For example, the reception desk can use AI to analyze a user's social media activity and suggest relevant fashion items. For example, the reception desk can suggest relevant items based on fashion items that the user has liked or commented on on social media. For example, the reception desk can analyze the fashion styles of influencers that the user follows and suggest items of a similar style. For example, the reception desk can analyze photos and videos posted by the user and suggest new fashion items based on items that the user has worn in the past. In this way, by suggesting relevant fashion items based on social media activity, items that match the user's preferences can be provided. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Relevant fashion items include, but are not limited to, brands, styles, and colors. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​suggest relevant fashion items.

[0041] The analysis unit can analyze a user's past fashion selection history and select the optimal analysis method. For example, the analysis unit can use AI to analyze a user's past fashion selection history and select the optimal analysis method. For example, the analysis unit can suggest similar style items based on fashion items the user has previously selected. For example, the analysis unit can also prioritize suggesting items from specific brands or designers based on the user's past selection history. For example, the analysis unit can analyze a user's past selection history and suggest items suitable for the season or event. By selecting the optimal analysis method based on past selection history, it is possible to suggest more appropriate fashion items. Fashion selection history includes, but is not limited to, past purchase history, try-on history, and evaluation history. The optimal analysis method includes, but is not limited to, data type, purpose of analysis, and accuracy. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's past fashion selection history data into AI and have the AI ​​select the optimal analysis method.

[0042] The analysis unit can customize the analysis results based on the user's current lifestyle and events. For example, the analysis unit uses AI to acquire the user's current lifestyle and events, and customizes the analysis results based on that information. For example, if the user is participating in a specific event, the analysis unit can suggest fashion items suitable for that event. For example, if the user is traveling, the analysis unit can also suggest fashion items based on the climate and culture of the destination. For example, if the user is going to work or school, the analysis unit can suggest fashion items suitable for that environment. By providing analysis results tailored to the user's lifestyle and events, it is possible to suggest more appropriate fashion items. Lifestyle and events include, but are not limited to, work, school, and special events. Customization of the analysis results includes, but are not limited to, the user's needs, circumstances, and preferences. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's lifestyle and event data into AI and have the AI ​​perform the customization of the analysis results.

[0043] The analysis unit can take into account the user's geographical location information and reflect region-specific fashion trends in its analysis. For example, the analysis unit can use AI to acquire the user's geographical location information and reflect region-specific fashion trends in its analysis based on that information. For example, the analysis unit can suggest appropriate fashion items based on the climate and culture of the region where the user is currently located. For example, if the user is traveling, the analysis unit can also reflect region-specific fashion trends in its analysis. For example, if the user is participating in a specific event, the analysis unit can suggest fashion items suitable for the event in that region. By reflecting region-specific fashion trends in the analysis, it is possible to suggest more appropriate fashion items. Geographical location information includes, but is not limited to, GPS data and location services. Region-specific fashion trends include, but are not limited to, local culture, climate, and trends. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's geographical location information data into AI and have AI perform an analysis of region-specific fashion trends.

[0044] The analysis unit can analyze a user's social media activity and reflect relevant fashion items in the analysis. For example, the analysis unit can use AI to analyze a user's social media activity and reflect relevant fashion items in the analysis. For example, the analysis unit can suggest relevant items based on fashion items that a user has "liked" or commented on on social media. For example, the analysis unit can analyze the fashion styles of influencers that a user follows and suggest items of a similar style. For example, the analysis unit can analyze photos and videos posted by a user and suggest new fashion items based on items that the user has worn in the past. In this way, by suggesting relevant fashion items based on social media activity, items that match the user's preferences can be provided. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Relevant fashion items include, but are not limited to, the brand, style, and color. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's social media activity data into AI and have the AI ​​perform the analysis of relevant fashion items.

[0045] The generation unit can analyze the user's past image generation history and select the optimal generation method. For example, the generation unit can use AI to analyze the user's past image generation history and select the optimal generation method. For example, the generation unit can generate images of a similar style based on images previously generated by the user. For example, the generation unit can also prioritize reflecting specific styles or themes from the user's past image generation history. For example, the generation unit can analyze the user's past image generation history and select the most efficient generation method. This allows for the generation of more appropriate images by selecting the optimal generation method based on past image generation history. Image generation history includes, but is not limited to, past generated images, generation conditions, and evaluation results. Optimal generation methods include, but are not limited to, the type of algorithm, generation purpose, and accuracy. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's past image generation history data into AI and have the AI ​​select the optimal generation method.

[0046] The generation unit can customize the images generated based on the user's current life circumstances and events. For example, the generation unit can use AI to acquire the user's current life circumstances and events and customize the images generated based on that information. For example, if the user is participating in a specific event, the generation unit can generate an image suitable for that event. For example, if the user is traveling, the generation unit can also generate an image based on the climate and culture of the destination. For example, if the user is going to work or school, the generation unit can generate an image suitable for that environment. This allows for the provision of more appropriate images by generating images that match the user's life circumstances and events. Life circumstances and events include, but are not limited to, work, school, and special events. Image customization includes, but is not limited to, the user's needs, circumstances, and preferences. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the user's life circumstances and event data into AI and have the AI ​​perform image customization.

[0047] The generation unit can reflect region-specific backgrounds and objects in images, taking into account the user's geographical location information. For example, the generation unit can use AI to acquire the user's geographical location information and reflect region-specific backgrounds and objects in images based on that information. For example, the generation unit can reflect appropriate backgrounds and objects in images based on the climate and culture of the region where the user is currently located. For example, if the user is traveling, the generation unit can also reflect region-specific backgrounds and objects in images. For example, if the user is participating in a specific event, the generation unit can reflect backgrounds and objects appropriate for the event in that region in images. By reflecting region-specific backgrounds and objects in images, more appropriate images can be provided. Geographical location information includes, but is not limited to, GPS data and location services. Region-specific backgrounds and objects include, but are not limited to, local culture, scenery, and decorations. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the user's geographical location data into the AI ​​and have the AI ​​perform tasks such as reflecting region-specific backgrounds and objects.

[0048] The generation unit can analyze a user's social media activity and reflect relevant fashion items in the image. For example, the generation unit can use AI to analyze a user's social media activity and reflect relevant fashion items in the image. For example, the generation unit can reflect relevant items in the image based on fashion items that the user has "liked" or commented on on social media. For example, the generation unit can analyze the fashion style of influencers that the user follows and reflect similar style items in the image. For example, the generation unit can analyze photos and videos posted by the user and reflect new fashion items in the image based on items worn in the past. In this way, by reflecting relevant fashion items in the image based on social media activity, images that match the user's preferences can be provided. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Relevant fashion items include, but are not limited to, the brand, style, and color. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the user's social media activity data into the AI ​​and have the AI ​​perform the task of reflecting related fashion items.

[0049] The evaluation unit can analyze a user's past evaluation history and select the optimal evaluation method. For example, the evaluation unit can use AI to analyze a user's past evaluation history and select the optimal evaluation method. For example, the evaluation unit can provide evaluation methods of a similar style based on evaluations the user has received in the past. For example, the evaluation unit can also prioritize reflecting specific evaluation criteria from a user's past evaluation history. For example, the evaluation unit can analyze a user's past evaluation history and select the most efficient evaluation method. This allows for the provision of more appropriate evaluations by selecting the optimal evaluation method based on past evaluation history. Evaluation history includes, but is not limited to, past evaluation data, evaluation criteria, and evaluation results. Optimal evaluation methods include, but are not limited to, evaluation objectives, accuracy, and usability. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or without AI. For example, the evaluation unit can input the user's past evaluation history data into AI and have the AI ​​select the optimal evaluation method.

[0050] The evaluation unit can customize evaluation results based on the user's current living situation and events. For example, the evaluation unit can use AI to acquire the user's current living situation and events and customize the evaluation results based on that information. For example, if the user is participating in a specific event, the evaluation unit can provide evaluation criteria appropriate for that event. For example, if the user is traveling, the evaluation unit can also customize the evaluation results based on the climate and culture of the destination. For example, if the user is going to work or school, the evaluation unit can provide evaluation criteria appropriate for that environment. This allows for more appropriate evaluations by providing evaluation results that are tailored to the user's living situation and events. Living situations and events include, but are not limited to, work, school, and special events. Customization of evaluation results includes, but are not limited to, the user's needs, circumstances, and preferences. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the user's living situation and event data into AI and have the AI ​​perform the customization of evaluation results.

[0051] The evaluation unit can take into account the user's geographical location information and reflect region-specific fashion trends in its evaluation. For example, the evaluation unit can use AI to acquire the user's geographical location information and reflect region-specific fashion trends in its evaluation based on that information. For example, the evaluation unit can evaluate appropriate fashion items based on the climate and culture of the region where the user is currently located. For example, if the user is traveling, the evaluation unit can also reflect region-specific fashion trends in its evaluation. For example, if the user is participating in a specific event, the evaluation unit can evaluate fashion items suitable for the event in that region. By reflecting region-specific fashion trends in the evaluation, a more appropriate evaluation can be provided. Geographical location information includes, but is not limited to, GPS data and location services. Region-specific fashion trends include, but are not limited to, local culture, climate, and trends. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the user's geographical location information data into AI and have the AI ​​perform an evaluation of region-specific fashion trends.

[0052] The evaluation unit can analyze a user's social media activity and reflect relevant fashion items in its evaluation. For example, the evaluation unit can use AI to analyze a user's social media activity and reflect relevant fashion items in its evaluation. For example, the evaluation unit can evaluate relevant items based on fashion items that a user has "liked" or commented on on social media. For example, the evaluation unit can analyze the fashion styles of influencers that a user follows and evaluate items of similar styles. For example, the evaluation unit can analyze photos and videos posted by a user and evaluate new fashion items based on items that the user has worn in the past. By reflecting relevant fashion items in the evaluation based on social media activity, the evaluation unit can provide evaluations that match the user's preferences. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Relevant fashion items include, but are not limited to, brands, styles, and colors. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the user's social media activity data into AI and have the AI ​​perform the evaluation of relevant fashion items.

[0053] The service provider can analyze a user's past evaluation result delivery history and select the optimal delivery method. For example, the service provider can use AI to analyze a user's past evaluation result delivery history and select the optimal delivery method. For example, the service provider can select a similar style of delivery method based on evaluation results the user has received in the past. For example, the service provider can prioritize reflecting a specific delivery method based on a user's past evaluation result delivery history. For example, the service provider can analyze a user's past evaluation result delivery history and select the most efficient delivery method. This allows for the provision of more appropriate feedback by selecting the optimal delivery method based on past evaluation result delivery history. Evaluation result delivery history includes, but is not limited to, past evaluation results, delivery methods, and reactions to evaluation results. Optimal delivery methods include, but are not limited to, the purpose of the evaluation, accuracy, and usability. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's past evaluation result delivery history data into AI and have the AI ​​select the optimal delivery method.

[0054] The service provider can take into account the user's geographical location information and reflect region-specific fashion trends in the evaluation results. For example, the service provider can use AI to acquire the user's geographical location information and reflect region-specific fashion trends in the evaluation results based on that information. For example, the service provider can evaluate appropriate fashion items based on the climate and culture of the region where the user is currently located. For example, if the user is traveling, the service provider can also reflect region-specific fashion trends in the evaluation. For example, if the user is participating in a specific event, the service provider can evaluate fashion items suitable for the event in that region. By reflecting region-specific fashion trends in the evaluation results, more appropriate feedback can be provided. Geographical location information includes, but is not limited to, GPS data and location services. Region-specific fashion trends include, but are not limited to, local culture, climate, and trends. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's geographical location information data into AI and have the AI ​​perform an evaluation of region-specific fashion trends.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can analyze the user's past fashion selection history and suggest the optimal input method. For example, it can automatically display fashion items and styles that the user has frequently selected in the past as suggestions. Furthermore, it can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. This improves input efficiency by suggesting the optimal input method based on past selection history. Input history includes, but is not limited to, past input data, frequency, and patterns. Some or all of the processing described above in the reception desk may be performed using AI or not.

[0057] The analysis unit can customize the analysis results based on the user's current lifestyle and events. For example, if the user is participating in a specific event, it can suggest fashion items suitable for that event. If the user is traveling, it can also suggest fashion items based on the climate and culture of the destination. This allows for the suggestion of more appropriate fashion items by providing analysis results tailored to the user's lifestyle and events. Lifestyle and events include, but are not limited to, work, school, and special events. Some or all of the processing described above in the analysis unit may be performed using AI or not.

[0058] The reception desk can suggest region-specific fashion styles, taking into account the user's current location. For example, it can suggest appropriate fashion styles based on the climate and culture of the area the user is currently in. If the user is traveling, it can also suggest region-specific fashion styles of the destination. This allows the system to provide the user with fashion items that are suitable for them by suggesting region-specific fashion styles. Current location information includes, but is not limited to, GPS data and location services. Region-specific fashion styles include, but are not limited to, local culture, climate, and trends. Some or all of the processing described above in the reception desk may be performed using AI or not.

[0059] The analysis unit can analyze a user's past fashion selection history and select the optimal analysis method. For example, it can suggest similar style items based on the fashion items the user has previously selected. It can also prioritize suggesting items from specific brands or designers based on the user's past selection history. By selecting the optimal analysis method based on past selection history, it is possible to suggest more appropriate fashion items. Fashion selection history includes, but is not limited to, past purchase history, try-on history, and evaluation history. The optimal analysis method includes, but is not limited to, data type, purpose of analysis, and accuracy. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0060] The generation unit can analyze the user's past image generation history and select the optimal generation method. For example, it can generate images in a similar style based on images the user has generated in the past. It can also prioritize reflecting specific styles or themes from the user's past image generation history. This allows for the generation of more appropriate images by selecting the optimal generation method based on the past image generation history. The image generation history includes, but is not limited to, past generated images, generation conditions, and evaluation results. The optimal generation method includes, but is not limited to, the type of algorithm, generation purpose, and accuracy. Some or all of the above processing in the generation unit may be performed using AI or not.

[0061] The evaluation unit can take into account the user's geographical location information and reflect region-specific fashion trends in its evaluation. For example, it can evaluate appropriate fashion items based on the climate and culture of the area where the user is currently located. If the user is traveling, it can also reflect the region-specific fashion trends of the destination in its evaluation. This allows for the provision of more appropriate evaluations by reflecting region-specific fashion trends. Geographical location information includes, but is not limited to, GPS data and location services. Region-specific fashion trends include, but are not limited to, local culture, climate, and trends. Some or all of the processing described above in the evaluation unit may be performed using AI or not.

[0062] The delivery unit can analyze a user's past evaluation result delivery history and select the optimal delivery method. For example, it can select a similar style of delivery method based on evaluation results the user has received in the past. It can also prioritize reflecting a specific delivery method based on the user's past evaluation result delivery history. This allows for more appropriate feedback to be provided by selecting the optimal delivery method based on past evaluation result delivery history. The evaluation result delivery history includes, but is not limited to, past evaluation results, delivery methods, and reactions to the evaluation results. The optimal delivery method includes, but is not limited to, the purpose of the evaluation, accuracy, and usability. Some or all of the above processing in the delivery unit may be performed using AI or not.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk inputs the user's body type, hairstyle, and clothing preferences. The user's body type includes, for example, height, weight, and body fat percentage. Hair style includes hair length, hair color, and style, and clothing preferences include casual, formal, and sporty. The reception desk provides an interface for the user to input their body type, hairstyle, and clothing preferences. Step 2: The analysis unit analyzes the information entered by the reception unit and selects clothing and accessories that suit the user. The analysis unit uses AI to analyze the user's body type, hairstyle, and clothing preferences and selects the most suitable fashion items. For example, it selects clothing that suits the user's body type and considers how to combine it with accessories. It can also select accessories that suit the user's hairstyle. Step 3: The generation unit generates images of the user wearing the clothes and accessories selected by the analysis unit. The generation unit uses AI to generate images of the user wearing the selected clothes and accessories, tailored to the user's body type and hairstyle. For example, it can generate images of the user wearing the selected clothes and accessories based on the user's body type and hairstyle. Step 4: The evaluation unit performs an objective evaluation of the images generated by the generation unit. The evaluation unit analyzes the generated images using AI and evaluates their strengths, weaknesses, and whether they are in line with current trends. For example, it can evaluate the design, color scheme, and fit of the generated images to determine if they are in line with the latest fashion trends.

[0065] (Example of form 2) The fashion check system according to an embodiment of the present invention is a system that provides a mechanism for users to select clothes and accessories that suit them and to check them before purchasing. The fashion check system allows the user to input their body type, hairstyle, and clothing preferences, and the AI ​​selects clothes and accessories that suit the user and generates an image of the user wearing them. The AI ​​also selects clothes and accessories that suit the clothing of the person the user is playing with and generates an image of the user wearing them. Furthermore, it generates an image using the clothes, accessories, and hairstyle selected by the user. The AI ​​performs an objective evaluation of these images, assessing the good points, bad points, and whether they are in line with current trends. This mechanism allows the user to check whether items suit them before purchasing and to obtain an objective fashion evaluation. For example, the user inputs their body type, hairstyle, and clothing preferences. For example, they input information such as height, weight, hair length, hair color, and preferred fashion style. This information is input to the AI. Next, the AI ​​analyzes the input information and selects clothes and accessories that suit the user. For example, the AI ​​selects clothes that suit the user's body type and considers combinations with accessories. This selects the optimal fashion items for the user. The AI ​​generates an image of the user wearing the selected clothes and accessories. For example, the AI ​​generates images of the user wearing selected clothes and accessories tailored to their body type and hairstyle. This allows the user to see what they would look like in those clothes. Furthermore, the AI ​​also selects clothes and accessories that match the outfits of people the user is playing with and generates images of the user wearing them. For example, the AI ​​selects clothes and accessories that match the user's friend's outfit and generates images of the user wearing them. This allows the user to see how their outfits would look when playing with friends. The AI ​​also generates images using clothes, accessories, and hairstyles chosen by the user. For example, the user inputs their chosen clothes, accessories, and hairstyle into the AI ​​and generates images of themselves wearing them. This allows the user to see how their chosen fashion items would look. Finally, the AI ​​provides an objective evaluation of the generated images. For example, the AI ​​analyzes the images and evaluates their strengths, weaknesses, and whether they are in line with current trends.This allows users to obtain an objective evaluation of their own fashion choices. This system enables users to check whether clothes and accessories suit them before purchasing them, and whether hairstyles and hair colors suit them before changing them. Furthermore, because they can receive an objective evaluation of their own fashion choices, they can enjoy fashion with greater confidence. The fashion check system can select the most suitable fashion items, generate images, and provide evaluations based on the user's body type, hairstyle, and clothing preferences.

[0066] The fashion check system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an evaluation unit. The reception unit receives input of the user's body type, hairstyle, and clothing preferences. The user's body type includes, but is not limited to, height, weight, and body fat percentage. The hairstyle includes, but is not limited to, hair length, hair color, and style. Clothing preferences include, but are not limited to, casual, formal, and sporty styles. The reception unit provides, for example, an interface for the user to input their body type, hairstyle, and clothing preferences. The analysis unit analyzes the information entered by the reception unit and selects clothes and accessories that suit the user. The analysis unit uses, for example, AI to analyze the user's body type, hairstyle, and clothing preferences and selects the optimal fashion items. The analysis unit selects clothes that fit the user's body type and considers combinations with accessories. The analysis unit can also select accessories that match the user's hairstyle. The generation unit generates images of the user wearing the clothes and accessories selected by the analysis unit. The generation unit generates images of the user wearing selected clothing and accessories, for example, using AI to match the user's body type and hairstyle. The generation unit generates images of the user wearing selected clothing and accessories based on the user's body type and hairstyle. The generation unit can also generate images of the user wearing selected accessories, for example, to match the user's hairstyle. The evaluation unit performs an objective evaluation of the images generated by the generation unit. The evaluation unit analyzes the generated images using AI and evaluates their strengths, weaknesses, and whether they are in line with current trends. The evaluation unit evaluates the design, color, and fit of the generated images, for example. The evaluation unit can also evaluate whether the generated images are in line with the latest fashion trends. As a result, the fashion check system according to this embodiment can select the optimal fashion items and generate and evaluate images based on the user's body type, hairstyle, and clothing preferences.

[0067] The reception desk allows users to input their body type, hairstyle, and clothing preferences. Body type includes, but is not limited to, height, weight, and body fat percentage. Hair style includes, but is not limited to, hair length, color, and style. Clothing preferences include, but are not limited to, casual, formal, and sporty. The reception desk provides an interface for users to input their body type, hairstyle, and clothing preferences. Specifically, the reception desk features an intuitive user interface to allow users to easily input information. For example, height and weight can be entered using sliders or dropdown menus. For hairstyle and clothing preferences, the system employs image or icon selection to provide a visually clear input method. Furthermore, the reception desk can save and reuse previously entered information, reducing the effort required for each input. For example, it can automatically retrieve previously entered body type and hairstyle information and modify it as needed. The reception desk also includes a function to encrypt and securely store entered information to protect user privacy. This allows users to confidently enter their information. Furthermore, the reception unit transmits the information entered by the user to the analysis unit in real time, enabling rapid analysis and the provision of results. This allows the reception unit to efficiently collect information on the user's body type, hairstyle, and clothing preferences, improving the overall performance of the system.

[0068] The analysis department analyzes the information entered by the reception department and selects clothing and accessories that suit the user. For example, the analysis department uses AI to analyze the user's body type, hairstyle, and clothing preferences to select the most suitable fashion items. Specifically, the AI ​​identifies the size and style of clothing that fits the user's body type based on the user's body type data. For example, it recommends longer pants and jackets for tall users and selects tight-fitting clothing for users with a low body fat percentage. Regarding hairstyles, it selects the most suitable accessories according to the length and color of the hair. For example, it recommends headbands and hair clips for users with long hair and selects accessories in colors that match the hair color. Furthermore, regarding clothing preferences, it selects appropriate items based on styles such as casual, formal, and sporty. For example, it recommends denim and T-shirts for users who prefer a casual style and selects suits and dresses for users who prefer a formal style. The analysis department comprehensively analyzes this information and proposes the most suitable combination of fashion items for the user. Furthermore, the analysis unit can utilize past data and trend information to provide suggestions based on the latest fashion trends. This allows the analysis unit to select the optimal fashion items tailored to each user's individual needs, thereby increasing user satisfaction.

[0069] The generation unit generates images of the user wearing clothes and accessories selected by the analysis unit. For example, the generation unit uses AI to create images of the user wearing the selected clothes and accessories, tailored to their body shape and hairstyle. Specifically, the generation AI creates a 3D model based on the user's body shape data and applies the selected clothes and accessories to that model. For instance, it recreates a realistic body shape based on the user's height and weight, and then dresses them in the selected clothes. Similarly, it recreates a realistic hairstyle that reflects hair length and color, and then applies the accessories. Because the generated images realistically reproduce how the user would look wearing the clothes and accessories, the user can visually confirm which fashion items suit them. Furthermore, the generation unit can generate images from different angles and poses. For example, it can generate images from the front, side, and back, allowing the user to see themselves from all angles. The generation unit can also generate images with different backgrounds and lighting conditions, making it easier for the user to visualize actual usage scenarios. In this way, the generation unit provides users with visually easy-to-understand information and supports them in selecting fashion items.

[0070] The evaluation unit performs an objective evaluation of the images generated by the generation unit. For example, the evaluation unit analyzes the generated images using AI and evaluates their strengths, weaknesses, and suitability for current trends. Specifically, the AI ​​uses image recognition technology to evaluate the design, color, and fit of the generated images. For example, it evaluates whether the clothing design suits the user's body type, whether the color matches the user's skin tone, and whether the fit is appropriate. The AI ​​also evaluates whether the generated images are in line with current fashion trends based on the latest trends. For example, it checks whether they match the trend colors and styles of the current season. Furthermore, the evaluation unit can also perform customized evaluations based on the user's preferences. For example, if the user prefers a particular style or color, the evaluation can be adjusted based on that preference. The evaluation unit comprehensively analyzes these evaluation results and provides specific feedback to the user. For example, it specifically indicates which parts of the selected clothing are particularly good and which parts could be improved. In addition, the evaluation unit can collect user feedback and continuously improve the accuracy of the evaluation algorithm. In this way, the evaluation unit can provide users with objective and specific evaluations and support them in selecting fashion items.

[0071] The analysis unit can analyze the clothing data of people playing with the user and select clothing and accessories that suit the user. For example, the analysis unit can collect clothing data of people playing with the user and analyze it using AI. For example, the analysis unit can select clothing and accessories that suit the user based on the clothing data of the user's friends. For example, the analysis unit can also analyze the clothing data of the user's friends and select fashion items that suit the user. This allows for the selection of fashion items that match the clothing of people playing with the user. Clothing data includes, but is not limited to, color, style, and brand. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the clothing data of people playing with the user into the AI ​​and have the AI ​​select clothing and accessories that suit the user.

[0072] The generation unit can generate images using clothes, accessories, and hairstyles selected by the user. For example, the generation unit can input the clothes, accessories, and hairstyles selected by the user into the AI ​​and generate images of the user wearing them. For example, the generation unit can generate images using the AI ​​based on the clothes, accessories, and hairstyles selected by the user. The generation unit can also generate images using the AI ​​based on the fashion items selected by the user. This allows the generation of images using the fashion items selected by the user. The selected clothes, accessories, and hairstyles include, but are not limited to, the user's selection criteria and selection procedure. Some or all of the above-described processes in the generation unit may be performed using the AI ​​or not. For example, the generation unit can input data on the clothes, accessories, and hairstyles selected by the user into the AI ​​and have the AI ​​generate images.

[0073] The evaluation unit can assess the generated images for their good points, bad points, and whether they are in line with current trends. For example, the evaluation unit can analyze the generated images using AI and assess their good points, bad points, and whether they are in line with current trends. The evaluation unit can assess the design, color, fit, etc., of the generated images. The evaluation unit can also assess whether the generated images are in line with the latest fashion trends. This allows for an objective evaluation of the generated images. Good points and bad points include, but are not limited to, design, color, and fit. Whether they are in line with current trends includes, but are not limited to, the latest fashion trends and seasonal trends. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the data of the generated images into AI and have the AI ​​perform the evaluation.

[0074] The evaluation unit may include a providing unit that provides evaluation results. The evaluation unit may, for example, analyze images generated using AI and provide evaluation results. The evaluation unit may, for example, evaluate the design, color, fit, etc., of the generated images and provide the results. The evaluation unit may also, for example, evaluate whether the generated images are in line with the latest fashion trends and provide the results. This allows evaluation results to be provided to the user. Evaluation results may include, but are not limited to, scores, comments, and recommended items. Some or all of the processing described above in the providing unit may be performed using, for example, AI, or not using AI. For example, the providing unit may input evaluation result data into AI and have AI perform the provision of evaluation results.

[0075] The reception desk can estimate the user's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, the reception desk can use AI to estimate the user's emotions and change the design of the input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface and minimize the input steps. For example, if the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of body type, hairstyle, and clothing preferences. This provides a more comfortable input experience by changing the design of the input interface according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into the AI ​​and have the AI ​​implement changes to the design of the input interface.

[0076] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can use AI to analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the user's frequently entered preferences for body type, hairstyle, and clothing. For example, the reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest the user's preferred body type, hairstyle, and clothing for a specific time period based on their past input history. This improves input efficiency by suggesting the optimal input method based on past input history. Input history includes, but is not limited to, past input data, frequency, and patterns. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's past input history data into AI and have the AI ​​suggest the optimal input method.

[0077] The reception desk can suggest region-specific fashion styles, taking into account the user's current location. For example, the reception desk can use AI to obtain the user's current location and suggest region-specific fashion styles based on that information. For example, the reception desk can suggest appropriate fashion styles based on the climate and culture of the region the user is currently in. For example, if the user is traveling, the reception desk can also suggest region-specific fashion styles for the destination. For example, if the user is attending a specific event, the reception desk can suggest fashion styles suitable for the event in that region. In this way, by suggesting region-specific fashion styles, the reception desk can provide the user with suitable fashion items. Current location information includes, but is not limited to, GPS data and location services. Region-specific fashion styles include, but are not limited to, local culture, climate, and trends. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's current location data into AI and have the AI ​​suggest region-specific fashion styles.

[0078] The reception desk can estimate the user's emotions and dynamically change the order of inputs based on the estimated emotions. For example, the reception desk can use AI to estimate the user's emotions and change the order of inputs based on the estimated emotions. For example, if the user is nervous, the reception desk may prompt the user to enter the simplest input items first. For example, if the user is relaxed, the reception desk may present detailed input items first and provide a customizable input method. For example, if the user is in a hurry, the reception desk may prioritize presenting the most important input items to allow for quick completion of the input. This provides a more comfortable input experience by changing the order of inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into AI and have the AI ​​perform the reordering of inputs.

[0079] The reception desk can analyze a user's social media activity and suggest relevant fashion items. For example, the reception desk can use AI to analyze a user's social media activity and suggest relevant fashion items. For example, the reception desk can suggest relevant items based on fashion items that the user has liked or commented on on social media. For example, the reception desk can analyze the fashion styles of influencers that the user follows and suggest items of a similar style. For example, the reception desk can analyze photos and videos posted by the user and suggest new fashion items based on items that the user has worn in the past. In this way, by suggesting relevant fashion items based on social media activity, items that match the user's preferences can be provided. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Relevant fashion items include, but are not limited to, brands, styles, and colors. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​suggest relevant fashion items.

[0080] The reception desk can adjust the priority of input fields based on the user's current mood and physical condition. For example, the reception desk may use AI to estimate the user's current mood and physical condition and adjust the priority of input fields based on that information. For example, if the user is tired, the reception desk may prompt the user to fill in the simplest input fields first. For example, if the user is energetic, the reception desk may present detailed input fields first and provide a customizable input method. For example, if the user is in a hurry, the reception desk may prioritize the most important input fields to allow for quick completion. This provides a more comfortable input experience by adjusting the priority of input fields according to the user's mood and physical condition. Mood and physical condition include, but are not limited to, stress levels, energy levels, and emotional states. The priority of input fields includes, but are not limited to, importance, urgency, and usability. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk may input the user's mood and physical condition data into AI and have the AI ​​perform the adjustment of input field priorities.

[0081] The analysis unit can estimate the user's emotions and dynamically adjust the analysis algorithm based on the estimated emotions. For example, the analysis unit can use AI to estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and suggest more fashion items. For example, if the user is in a hurry, the analysis unit can perform a rapid analysis and suggest the most suitable fashion items. For example, if the user is excited, the analysis unit will prioritize suggesting visually stimulating fashion items. In this way, by adjusting the analysis algorithm according to the user's emotions, more appropriate fashion items can be suggested. Emotions include, but are not limited to, joy, sadness, and surprise. The analysis algorithm includes, but is not limited to, machine learning algorithms and data mining techniques. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​perform the adjustment of the analysis algorithm.

[0082] The analysis unit can analyze a user's past fashion selection history and select the optimal analysis method. For example, the analysis unit can use AI to analyze a user's past fashion selection history and select the optimal analysis method. For example, the analysis unit can suggest similar style items based on fashion items the user has previously selected. For example, the analysis unit can also prioritize suggesting items from specific brands or designers based on the user's past selection history. For example, the analysis unit can analyze a user's past selection history and suggest items suitable for the season or event. By selecting the optimal analysis method based on past selection history, it is possible to suggest more appropriate fashion items. Fashion selection history includes, but is not limited to, past purchase history, try-on history, and evaluation history. The optimal analysis method includes, but is not limited to, data type, purpose of analysis, and accuracy. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's past fashion selection history data into AI and have the AI ​​select the optimal analysis method.

[0083] The analysis unit can customize the analysis results based on the user's current lifestyle and events. For example, the analysis unit uses AI to acquire the user's current lifestyle and events, and customizes the analysis results based on that information. For example, if the user is participating in a specific event, the analysis unit can suggest fashion items suitable for that event. For example, if the user is traveling, the analysis unit can also suggest fashion items based on the climate and culture of the destination. For example, if the user is going to work or school, the analysis unit can suggest fashion items suitable for that environment. By providing analysis results tailored to the user's lifestyle and events, it is possible to suggest more appropriate fashion items. Lifestyle and events include, but are not limited to, work, school, and special events. Customization of the analysis results includes, but are not limited to, the user's needs, circumstances, and preferences. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's lifestyle and event data into AI and have the AI ​​perform the customization of the analysis results.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can use AI to estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, a more visually appealing display becomes possible. Emotions include, but are not limited to, joy, sadness, and surprise. Display methods of the analysis results include, but are not limited to, graphs, text, and interactive displays. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into AI and have AI perform the adjustment of the display method of the analysis results.

[0085] The analysis unit can take into account the user's geographical location information and reflect region-specific fashion trends in its analysis. For example, the analysis unit can use AI to acquire the user's geographical location information and reflect region-specific fashion trends in its analysis based on that information. For example, the analysis unit can suggest appropriate fashion items based on the climate and culture of the region where the user is currently located. For example, if the user is traveling, the analysis unit can also reflect region-specific fashion trends in its analysis. For example, if the user is participating in a specific event, the analysis unit can suggest fashion items suitable for the event in that region. By reflecting region-specific fashion trends in the analysis, it is possible to suggest more appropriate fashion items. Geographical location information includes, but is not limited to, GPS data and location services. Region-specific fashion trends include, but are not limited to, local culture, climate, and trends. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's geographical location information data into AI and have AI perform an analysis of region-specific fashion trends.

[0086] The analysis unit can analyze a user's social media activity and reflect relevant fashion items in the analysis. For example, the analysis unit can use AI to analyze a user's social media activity and reflect relevant fashion items in the analysis. For example, the analysis unit can suggest relevant items based on fashion items that a user has "liked" or commented on on social media. For example, the analysis unit can analyze the fashion styles of influencers that a user follows and suggest items of a similar style. For example, the analysis unit can analyze photos and videos posted by a user and suggest new fashion items based on items that the user has worn in the past. In this way, by suggesting relevant fashion items based on social media activity, items that match the user's preferences can be provided. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Relevant fashion items include, but are not limited to, the brand, style, and color. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's social media activity data into AI and have the AI ​​perform the analysis of relevant fashion items.

[0087] The generation unit can estimate the user's emotions and adjust the style of the generated images based on the estimated emotions. For example, the generation unit can use AI to estimate the user's emotions and adjust the style of the generated images based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate images that progress at a leisurely pace. For example, if the user is in a hurry, the generation unit can also generate images that emphasize the shortest route. For example, if the user is excited, the generation unit can generate images with visually stimulating effects. This allows for the generation of more appropriate images by adjusting the style of the images according to the user's emotions. Emotions include, but are not limited to, joy, sadness, and surprise. Image style includes, but is not limited to, color tone, layout, and filters. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user emotion data into AI and have the AI ​​perform image style adjustments.

[0088] The generation unit can analyze the user's past image generation history and select the optimal generation method. For example, the generation unit can use AI to analyze the user's past image generation history and select the optimal generation method. For example, the generation unit can generate images of a similar style based on images previously generated by the user. For example, the generation unit can also prioritize reflecting specific styles or themes from the user's past image generation history. For example, the generation unit can analyze the user's past image generation history and select the most efficient generation method. This allows for the generation of more appropriate images by selecting the optimal generation method based on past image generation history. Image generation history includes, but is not limited to, past generated images, generation conditions, and evaluation results. Optimal generation methods include, but are not limited to, the type of algorithm, generation purpose, and accuracy. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's past image generation history data into AI and have the AI ​​select the optimal generation method.

[0089] The generation unit can customize the images generated based on the user's current life circumstances and events. For example, the generation unit can use AI to acquire the user's current life circumstances and events and customize the images generated based on that information. For example, if the user is participating in a specific event, the generation unit can generate an image suitable for that event. For example, if the user is traveling, the generation unit can also generate an image based on the climate and culture of the destination. For example, if the user is going to work or school, the generation unit can generate an image suitable for that environment. This allows for the provision of more appropriate images by generating images that match the user's life circumstances and events. Life circumstances and events include, but are not limited to, work, school, and special events. Image customization includes, but is not limited to, the user's needs, circumstances, and preferences. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the user's life circumstances and event data into AI and have the AI ​​perform image customization.

[0090] The generation unit can estimate the user's emotions and adjust the order of generated images based on the estimated emotions. For example, the generation unit can use AI to estimate the user's emotions and adjust the order of generated images based on the estimated emotions. For example, if the user is nervous, the generation unit may display a simple, highly visible image first. For example, if the user is relaxed, the generation unit may also display an image containing detailed information first. For example, if the user is in a hurry, the generation unit may display an image that gets straight to the point first. This allows for the provision of more appropriate images by adjusting the order of images according to the user's emotions. Emotions include, but are not limited to, joy, sadness, and surprise. The order of images includes, but are not limited to, importance, urgency, and usability. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user emotion data into AI and have the AI ​​perform the image order adjustment.

[0091] The generation unit can reflect region-specific backgrounds and objects in images, taking into account the user's geographical location information. For example, the generation unit can use AI to acquire the user's geographical location information and reflect region-specific backgrounds and objects in images based on that information. For example, the generation unit can reflect appropriate backgrounds and objects in images based on the climate and culture of the region where the user is currently located. For example, if the user is traveling, the generation unit can also reflect region-specific backgrounds and objects in images. For example, if the user is participating in a specific event, the generation unit can reflect backgrounds and objects appropriate for the event in that region in images. By reflecting region-specific backgrounds and objects in images, more appropriate images can be provided. Geographical location information includes, but is not limited to, GPS data and location services. Region-specific backgrounds and objects include, but are not limited to, local culture, scenery, and decorations. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the user's geographical location data into the AI ​​and have the AI ​​perform tasks such as reflecting region-specific backgrounds and objects.

[0092] The generation unit can analyze a user's social media activity and reflect relevant fashion items in the image. For example, the generation unit can use AI to analyze a user's social media activity and reflect relevant fashion items in the image. For example, the generation unit can reflect relevant items in the image based on fashion items that the user has "liked" or commented on on social media. For example, the generation unit can analyze the fashion style of influencers that the user follows and reflect similar style items in the image. For example, the generation unit can analyze photos and videos posted by the user and reflect new fashion items in the image based on items worn in the past. In this way, by reflecting relevant fashion items in the image based on social media activity, images that match the user's preferences can be provided. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Relevant fashion items include, but are not limited to, the brand, style, and color. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the user's social media activity data into the AI ​​and have the AI ​​perform the task of reflecting related fashion items.

[0093] The evaluation unit can estimate the user's emotions and dynamically adjust the evaluation criteria based on the estimated emotions. For example, the evaluation unit can use AI to estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is relaxed, the evaluation unit can provide detailed evaluation criteria and more feedback. For example, if the user is in a hurry, the evaluation unit can also provide a quick evaluation and the most important feedback. For example, if the user is excited, the evaluation unit can prioritize providing visually stimulating feedback. This allows for a more appropriate evaluation by adjusting the evaluation criteria according to the user's emotions. Emotions include, but are not limited to, joy, sadness, and surprise. Evaluation criteria include, but are not limited to, design, color, and fit. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input user emotion data into AI and have the AI ​​perform the adjustment of the evaluation criteria.

[0094] The evaluation unit can analyze a user's past evaluation history and select the optimal evaluation method. For example, the evaluation unit can use AI to analyze a user's past evaluation history and select the optimal evaluation method. For example, the evaluation unit can provide evaluation methods of a similar style based on evaluations the user has received in the past. For example, the evaluation unit can also prioritize reflecting specific evaluation criteria from a user's past evaluation history. For example, the evaluation unit can analyze a user's past evaluation history and select the most efficient evaluation method. This allows for the provision of more appropriate evaluations by selecting the optimal evaluation method based on past evaluation history. Evaluation history includes, but is not limited to, past evaluation data, evaluation criteria, and evaluation results. Optimal evaluation methods include, but are not limited to, evaluation objectives, accuracy, and usability. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or without AI. For example, the evaluation unit can input the user's past evaluation history data into AI and have the AI ​​select the optimal evaluation method.

[0095] The evaluation unit can customize evaluation results based on the user's current living situation and events. For example, the evaluation unit can use AI to acquire the user's current living situation and events and customize the evaluation results based on that information. For example, if the user is participating in a specific event, the evaluation unit can provide evaluation criteria appropriate for that event. For example, if the user is traveling, the evaluation unit can also customize the evaluation results based on the climate and culture of the destination. For example, if the user is going to work or school, the evaluation unit can provide evaluation criteria appropriate for that environment. This allows for more appropriate evaluations by providing evaluation results that are tailored to the user's living situation and events. Living situations and events include, but are not limited to, work, school, and special events. Customization of evaluation results includes, but are not limited to, the user's needs, circumstances, and preferences. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the user's living situation and event data into AI and have the AI ​​perform the customization of evaluation results.

[0096] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, the evaluation unit can use AI to estimate the user's emotions and adjust the display method of the evaluation results based on the estimated emotions. For example, if the user is nervous, the evaluation unit can provide a simple and highly visible display method. For example, if the user is relaxed, the evaluation unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the evaluation unit can provide a concise display method. By adjusting the display method of the evaluation results according to the user's emotions, a more visually appealing display becomes possible. Emotions include, but are not limited to, joy, sadness, and surprise. Display methods of evaluation results include, but are not limited to, graphs, text, and interactive displays. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input user emotion data into AI and have AI perform the adjustment of the display method of the evaluation results.

[0097] The evaluation unit can take into account the user's geographical location information and reflect region-specific fashion trends in its evaluation. For example, the evaluation unit can use AI to acquire the user's geographical location information and reflect region-specific fashion trends in its evaluation based on that information. For example, the evaluation unit can evaluate appropriate fashion items based on the climate and culture of the region where the user is currently located. For example, if the user is traveling, the evaluation unit can also reflect region-specific fashion trends in its evaluation. For example, if the user is participating in a specific event, the evaluation unit can evaluate fashion items suitable for the event in that region. By reflecting region-specific fashion trends in the evaluation, a more appropriate evaluation can be provided. Geographical location information includes, but is not limited to, GPS data and location services. Region-specific fashion trends include, but are not limited to, local culture, climate, and trends. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the user's geographical location information data into AI and have the AI ​​perform an evaluation of region-specific fashion trends.

[0098] The evaluation unit can analyze a user's social media activity and reflect relevant fashion items in its evaluation. For example, the evaluation unit can use AI to analyze a user's social media activity and reflect relevant fashion items in its evaluation. For example, the evaluation unit can evaluate relevant items based on fashion items that a user has "liked" or commented on on social media. For example, the evaluation unit can analyze the fashion styles of influencers that a user follows and evaluate items of similar styles. For example, the evaluation unit can analyze photos and videos posted by a user and evaluate new fashion items based on items that the user has worn in the past. By reflecting relevant fashion items in the evaluation based on social media activity, the evaluation unit can provide evaluations that match the user's preferences. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Relevant fashion items include, but are not limited to, brands, styles, and colors. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the user's social media activity data into AI and have the AI ​​perform the evaluation of relevant fashion items.

[0099] The service provider can estimate the user's emotions and adjust the method of providing evaluation results based on the estimated emotions. For example, the service provider can use AI to estimate the user's emotions and adjust the method of providing evaluation results based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed evaluation results and more feedback. For example, if the user is in a hurry, the service provider can also provide evaluation results quickly and provide the most important feedback. For example, if the user is excited, the service provider can prioritize providing visually stimulating feedback. This allows for more appropriate feedback to be provided by adjusting the method of providing evaluation results according to the user's emotions. Emotions include, but are not limited to, joy, sadness, and surprise. Methods of providing evaluation results include, but are not limited to, detailed evaluation results, quick evaluation results, and visually stimulating feedback. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​adjust the method of providing evaluation results.

[0100] The service provider can analyze a user's past evaluation result delivery history and select the optimal delivery method. For example, the service provider can use AI to analyze a user's past evaluation result delivery history and select the optimal delivery method. For example, the service provider can select a similar style of delivery method based on evaluation results the user has received in the past. For example, the service provider can prioritize reflecting a specific delivery method based on a user's past evaluation result delivery history. For example, the service provider can analyze a user's past evaluation result delivery history and select the most efficient delivery method. This allows for the provision of more appropriate feedback by selecting the optimal delivery method based on past evaluation result delivery history. Evaluation result delivery history includes, but is not limited to, past evaluation results, delivery methods, and reactions to evaluation results. Optimal delivery methods include, but are not limited to, the purpose of the evaluation, accuracy, and usability. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's past evaluation result delivery history data into AI and have the AI ​​select the optimal delivery method.

[0101] The service provider can estimate the user's emotions and adjust the timing of providing evaluation results based on the estimated emotions. For example, the service provider can use AI to estimate the user's emotions and adjust the timing of providing evaluation results based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed evaluation results and more feedback. For example, if the user is in a hurry, the service provider can also provide evaluation results quickly and provide the most important feedback. For example, if the user is excited, the service provider can prioritize providing visually stimulating feedback. This allows for more appropriate feedback to be provided by adjusting the timing of providing evaluation results according to the user's emotions. Emotions include, but are not limited to, joy, sadness, and surprise. The timing of providing evaluation results includes, but is not limited to, detailed evaluation results, quick evaluation results, and visually stimulating feedback. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​adjust the timing of providing evaluation results.

[0102] The service provider can take into account the user's geographical location information and reflect region-specific fashion trends in the evaluation results. For example, the service provider can use AI to acquire the user's geographical location information and reflect region-specific fashion trends in the evaluation results based on that information. For example, the service provider can evaluate appropriate fashion items based on the climate and culture of the region where the user is currently located. For example, if the user is traveling, the service provider can also reflect region-specific fashion trends in the evaluation. For example, if the user is participating in a specific event, the service provider can evaluate fashion items suitable for the event in that region. By reflecting region-specific fashion trends in the evaluation results, more appropriate feedback can be provided. Geographical location information includes, but is not limited to, GPS data and location services. Region-specific fashion trends include, but are not limited to, local culture, climate, and trends. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's geographical location information data into AI and have the AI ​​perform an evaluation of region-specific fashion trends.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The reception desk can analyze the user's past fashion selection history and suggest the optimal input method. For example, it can automatically display fashion items and styles that the user has frequently selected in the past as suggestions. Furthermore, it can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. This improves input efficiency by suggesting the optimal input method based on past selection history. Input history includes, but is not limited to, past input data, frequency, and patterns. Some or all of the processing described above in the reception desk may be performed using AI or not.

[0105] The analysis unit can customize the analysis results based on the user's current lifestyle and events. For example, if the user is participating in a specific event, it can suggest fashion items suitable for that event. If the user is traveling, it can also suggest fashion items based on the climate and culture of the destination. This allows for the suggestion of more appropriate fashion items by providing analysis results tailored to the user's lifestyle and events. Lifestyle and events include, but are not limited to, work, school, and special events. Some or all of the processing described above in the analysis unit may be performed using AI or not.

[0106] The generation unit can estimate the user's emotions and adjust the style of the generated images based on the estimated emotions. For example, if the user is relaxed, it can generate images that progress at a leisurely pace. If the user is in a hurry, it can also generate images that emphasize the shortest route. This allows for the generation of more appropriate images by adjusting the style of the images according to the user's emotions. Emotions include, but are not limited to, joy, sadness, and surprise. Image style includes, but is not limited to, color tone, layout, and filters. Some or all of the above processing in the generation unit may be performed using AI or not.

[0107] The evaluation unit can estimate the user's emotions and dynamically adjust the evaluation criteria based on the estimated user emotions. For example, if the user is relaxed, it can provide detailed evaluation criteria and more feedback. If the user is in a hurry, it can also provide a quick evaluation and the most important feedback. This allows for more appropriate evaluations by adjusting the evaluation criteria according to the user's emotions. Emotions include, but are not limited to, joy, sadness, and surprise. Evaluation criteria include, but are not limited to, design, color, and fit. Some or all of the processing described above in the evaluation unit may be performed using AI or not.

[0108] The service provider can estimate the user's emotions and adjust the method of providing evaluation results based on the estimated emotions. For example, if the user is relaxed, it can provide detailed evaluation results and more feedback. If the user is in a hurry, it can provide evaluation results quickly and provide the most important feedback. This allows for more appropriate feedback to be provided by adjusting the method of providing evaluation results according to the user's emotions. Emotions include, but are not limited to, joy, sadness, and surprise. Methods of providing evaluation results include, but are not limited to, detailed evaluation results, quick evaluation results, and visually stimulating feedback. Some or all of the processing described above in the service provider may be performed using AI or not.

[0109] The reception desk can suggest region-specific fashion styles, taking into account the user's current location. For example, it can suggest appropriate fashion styles based on the climate and culture of the area the user is currently in. If the user is traveling, it can also suggest region-specific fashion styles of the destination. This allows the system to provide the user with fashion items that are suitable for them by suggesting region-specific fashion styles. Current location information includes, but is not limited to, GPS data and location services. Region-specific fashion styles include, but are not limited to, local culture, climate, and trends. Some or all of the processing described above in the reception desk may be performed using AI or not.

[0110] The analysis unit can analyze a user's past fashion selection history and select the optimal analysis method. For example, it can suggest similar style items based on the fashion items the user has previously selected. It can also prioritize suggesting items from specific brands or designers based on the user's past selection history. By selecting the optimal analysis method based on past selection history, it is possible to suggest more appropriate fashion items. Fashion selection history includes, but is not limited to, past purchase history, try-on history, and evaluation history. The optimal analysis method includes, but is not limited to, data type, purpose of analysis, and accuracy. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0111] The generation unit can analyze the user's past image generation history and select the optimal generation method. For example, it can generate images in a similar style based on images the user has generated in the past. It can also prioritize reflecting specific styles or themes from the user's past image generation history. This allows for the generation of more appropriate images by selecting the optimal generation method based on the past image generation history. The image generation history includes, but is not limited to, past generated images, generation conditions, and evaluation results. The optimal generation method includes, but is not limited to, the type of algorithm, generation purpose, and accuracy. Some or all of the above processing in the generation unit may be performed using AI or not.

[0112] The evaluation unit can take into account the user's geographical location information and reflect region-specific fashion trends in its evaluation. For example, it can evaluate appropriate fashion items based on the climate and culture of the area where the user is currently located. If the user is traveling, it can also reflect the region-specific fashion trends of the destination in its evaluation. This allows for the provision of more appropriate evaluations by reflecting region-specific fashion trends. Geographical location information includes, but is not limited to, GPS data and location services. Region-specific fashion trends include, but are not limited to, local culture, climate, and trends. Some or all of the processing described above in the evaluation unit may be performed using AI or not.

[0113] The delivery unit can analyze a user's past evaluation result delivery history and select the optimal delivery method. For example, it can select a similar style of delivery method based on evaluation results the user has received in the past. It can also prioritize reflecting a specific delivery method based on the user's past evaluation result delivery history. This allows for more appropriate feedback to be provided by selecting the optimal delivery method based on past evaluation result delivery history. The evaluation result delivery history includes, but is not limited to, past evaluation results, delivery methods, and reactions to the evaluation results. The optimal delivery method includes, but is not limited to, the purpose of the evaluation, accuracy, and usability. Some or all of the above processing in the delivery unit may be performed using AI or not.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The reception desk inputs the user's body type, hairstyle, and clothing preferences. The user's body type includes, for example, height, weight, and body fat percentage. Hair style includes hair length, hair color, and style, and clothing preferences include casual, formal, and sporty. The reception desk provides an interface for the user to input their body type, hairstyle, and clothing preferences. Step 2: The analysis unit analyzes the information entered by the reception unit and selects clothing and accessories that suit the user. The analysis unit uses AI to analyze the user's body type, hairstyle, and clothing preferences and selects the most suitable fashion items. For example, it selects clothing that suits the user's body type and considers how to combine it with accessories. It can also select accessories that suit the user's hairstyle. Step 3: The generation unit generates images of the user wearing the clothes and accessories selected by the analysis unit. The generation unit uses AI to generate images of the user wearing the selected clothes and accessories, tailored to the user's body type and hairstyle. For example, it can generate images of the user wearing the selected clothes and accessories based on the user's body type and hairstyle. Step 4: The evaluation unit performs an objective evaluation of the images generated by the generation unit. The evaluation unit analyzes the generated images using AI and evaluates their strengths, weaknesses, and whether they are in line with current trends. For example, it can evaluate the design, color scheme, and fit of the generated images to determine if they are in line with the latest fashion trends.

[0116] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0117] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0118] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0119] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and evaluation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for inputting the user's body type, hairstyle, and clothing preferences. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses AI to analyze the user's body type, hairstyle, and clothing preferences and select the most suitable fashion items. The generation unit is implemented by the control unit 46A of the smart device 14 and generates an image of the user wearing the selected clothes and accessories. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the generated image to evaluate its good points, bad points, and whether it is in line with current trends. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and evaluation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for inputting the user's body type, hairstyle, and clothing preferences. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses AI to analyze the user's body type, hairstyle, and clothing preferences and select the most suitable fashion items. The generation unit is implemented by the control unit 46A of the smart glasses 214 and generates an image of the user wearing the selected clothes and accessories. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the generated image to evaluate its good points, bad points, and whether it is in line with current trends. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0139] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and evaluation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and provides an interface for inputting the user's body type, hairstyle, and clothing preferences. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses AI to analyze the user's body type, hairstyle, and clothing preferences and select the most suitable fashion items. The generation unit is implemented by the control unit 46A of the headset terminal 314 and generates an image of the user wearing the selected clothes and accessories. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the generated image to evaluate its good points, bad points, and whether it is in line with current trends. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] As shown in Figure 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.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0156] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0159] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0160] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0163] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and evaluation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and provides an interface for inputting the user's body type, hairstyle, and clothing preferences. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses AI to analyze the user's body type, hairstyle, and clothing preferences and select the most suitable fashion items. The generation unit is implemented by, for example, the control unit 46A of the robot 414 and generates an image of the user wearing the selected clothes and accessories. The evaluation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the generated image to evaluate its good points, bad points, and whether it is in line with current trends. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0169] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0171] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0172] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0173] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0177] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0178] 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.

[0179] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0180] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0181] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0182] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0184] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0185] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0186] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0187] (Note 1) A reception area where users input their body type, hairstyle, and clothing preferences, The analysis unit analyzes the information entered by the reception unit and selects clothing and accessories that suit the user, A generation unit generates images of a user wearing clothes and accessories selected by the analysis unit, The system includes an evaluation unit that performs an objective evaluation of the image generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The system analyzes the clothing data of people playing with the user and selects clothes and accessories that suit the user. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generates images using the clothes, accessories, and hairstyle selected by the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit, Evaluate the generated images for their good points, bad points, and whether they are in line with current trends. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, It includes a unit that provides evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We suggest regionally specific fashion styles, taking into account the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and dynamically changes the order of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is We analyze users' social media activity and suggest relevant fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Prioritize input fields based on the user's current mood and physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and dynamically adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We analyze the user's past fashion choices and select the optimal analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Customize analysis results based on the user's current life circumstances and events. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, By considering the user's geographical location, region-specific fashion trends are reflected in the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Analyze users' social media activity and incorporate related fashion items into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the style of the generated images based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system analyzes the user's past image generation history and selects the optimal generation method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is Customize the images generated based on the user's current life circumstances and events. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the order of generated images based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is The system takes the user's geographical location into account and incorporates region-specific backgrounds and objects into the images. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is Analyze users' social media activity and reflect relevant fashion items in images. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, It estimates the user's emotions and dynamically adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, Analyze the user's past rating history and select the optimal rating method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit, Customize evaluation results based on the user's current life circumstances and events. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The evaluation unit, The evaluation incorporates region-specific fashion trends, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The evaluation unit, Analyze users' social media activity and reflect relevant fashion items in the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, We estimate the user's emotions and adjust the way evaluation results are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, We analyze the user's past evaluation result submission history and select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the timing of providing evaluation results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, The evaluation results will reflect region-specific fashion trends, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input their body type, hairstyle, and clothing preferences, The analysis unit analyzes the information entered by the reception unit and selects clothing and accessories that suit the user, A generation unit generates images of a user wearing clothes and accessories selected by the analysis unit, The system includes an evaluation unit that performs an objective evaluation of the image generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, The system analyzes the clothing data of people playing with the user and selects clothes and accessories that suit the user. The system according to feature 1.

3. The generating unit is Generates images using the clothes, accessories, and hairstyle selected by the user. The system according to feature 1.

4. The evaluation unit, Evaluate the generated images for their good points, bad points, and whether they are in line with current trends. The system according to feature 1.

5. The evaluation unit, It includes a unit that provides evaluation results. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is We suggest regionally specific fashion styles, taking into account the user's current location. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and dynamically changes the order of inputs based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is We analyze users' social media activity and suggest relevant fashion items. The system according to feature 1.

11. The aforementioned reception unit is Prioritize input fields based on the user's current mood and physical condition. The system according to feature 1.

Images