system

Abstract

The system according to this embodiment aims to suggest the optimal outfit based on the user's body shape data and lifestyle. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a recommendation unit, a delivery unit, and a recycling unit. The collection unit collects the user's body shape data, lifestyle, and usage scenarios. The analysis unit analyzes the information collected by the collection unit and proposes the most suitable outfit for the user. The recommendation unit receives fashion recommendations from micro-influencers and general users. The delivery unit efficiently delivers the products. The recycling unit implements clothing recycling and upcycling programs.

Description

Technical Field

[0006]

[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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, optimal coordinate proposals based on the user's body shape data and lifestyle have not been sufficiently made, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal coordinate based on the user's body shape data and lifestyle.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, a delivery unit, and a recycling unit. The collection unit collects the user's body shape data, lifestyle, and usage scenarios. The analysis unit analyzes the information collected by the collection unit and proposes the most suitable outfit for the user. The recommendation unit receives fashion recommendations from micro-influencers and general users. The delivery unit efficiently delivers the products. The recycling unit implements clothing recycling and upcycling programs. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal outfit based on the user's body shape data and lifestyle. [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 labeled 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] FIG. 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 clothing rental system according to an embodiment of the present invention is a new type of clothing rental service primarily targeting people with little interest in fashion. This clothing rental system collects the user's body shape data, lifestyle, and usage scenarios, and an AI analyzes this data to suggest the optimal outfit. It also allows users to receive fashion recommendations from micro-influencers and general users, and incorporates an efficient delivery system and a program that considers sustainability. For example, it collects the user's body shape data, lifestyle, and usage scenarios. For example, it collects information such as the user's height, weight, preferred style, and daily activities. This information is input into the AI. Next, the AI ​​analyzes the collected information and suggests the optimal outfit for the user. For example, based on the user's body shape data, the AI ​​selects clothing that suits the body shape and suggests outfits that match the lifestyle and usage scenarios. This makes it easy for users to find a style that suits them. Furthermore, it allows users to receive fashion recommendations from micro-influencers and general users. For example, users can refer to outfits suggested by influencers or friends they follow. This allows users to incorporate the opinions of people close to them into their styling. It also constructs an efficient delivery system. For example, it utilizes existing logistics systems and technologies to deliver products quickly and efficiently. This allows users to receive their ordered items quickly. Finally, the company will introduce programs that prioritize sustainability. For example, it will implement clothing recycling and upcycling programs to promote environmentally friendly fashion consumption. This will allow users to enjoy sustainable fashion. In this way, by leveraging the power of technology and community, the company aims to make fashion more accessible and enjoyable, cultivate new customer segments, and promote sustainable fashion consumption. As a result, the clothing rental system will be able to suggest optimal outfits based on the user's body shape data, lifestyle, and usage scenarios, enabling efficient delivery and sustainable fashion consumption.

[0029] The clothing rental system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, a delivery unit, and a recycling unit. The collection unit collects the user's body shape data, lifestyle, and usage scenarios. The collection unit collects information such as height, weight, preferred style, and daily activities entered by the user. The collection unit stores the information entered by the user in a database and provides it to the analysis unit. The analysis unit analyzes the information collected by the collection unit and proposes the most suitable outfit for the user. The analysis unit analyzes the collected information using AI, for example, to select clothing that suits the user's body shape and proposes outfits that match their lifestyle and usage scenarios. The analysis unit uses AI to select clothing that suits the user's body shape based on the user's body shape data and proposes outfits that match their lifestyle and usage scenarios. The recommendation unit receives fashion recommendations from micro-influencers and general users. The recommendation unit can refer to outfits suggested by influencers and friends that the user follows. The Introduction Department allows users to refer to outfits suggested by influencers and friends they follow. The Delivery Department efficiently delivers products. The Delivery Department utilizes existing logistics systems and technologies to deliver products quickly and efficiently. The Delivery Department utilizes existing logistics systems and technologies to deliver products quickly and efficiently. The Delivery Department utilizes existing logistics systems and technologies to deliver products quickly and efficiently. The Recycling Department implements clothing recycling and upcycling programs. The Recycling Department implements clothing recycling and upcycling programs to promote environmentally friendly fashion consumption. The Recycling Department implements clothing recycling and upcycling programs to promote environmentally friendly fashion consumption.As a result, the clothing rental system according to this embodiment can suggest the optimal outfit based on the user's body shape data, lifestyle, and usage scenario, enabling efficient delivery and sustainable fashion consumption.

[0030] The data collection unit collects user body shape data, lifestyle, and usage scenarios. Specifically, it collects information such as height, weight, preferred style, and daily activities entered by the user. This information can be entered by the user through a dedicated application or website. For example, a user can log in to the application and enter their height, weight, and preferred fashion style on the profile settings screen. They can also enter information about their daily activities, such as their work, hobbies, and how they spend their weekends. The data collection unit then stores this detailed user information in a database and provides it to the analysis unit. Furthermore, the data collection unit also collects the user's past rental history and ratings to understand their preferences and trends. For example, it can record the types of clothes a user has rented in the past and their ratings, and use this information to improve future recommendations. This allows the data collection unit to build a foundation for providing personalized services tailored to the user's needs.

[0031] The analysis unit analyzes the information collected by the data collection unit and proposes the most suitable outfit for the user. Specifically, it uses AI to analyze the collected information, selects clothing that suits the user's body type, and proposes outfits that match their lifestyle and usage scenarios. Based on the user's body type data, the AI ​​selects clothing of the optimal size and silhouette, taking into account factors such as height, weight, and body shape characteristics. It also suggests formal suits and dresses for business settings and relaxed styles for casual settings, depending on the user's lifestyle and usage scenarios. Furthermore, the AI ​​learns the user's preferred style and past rental history, allowing it to prioritize suggesting designs, colors, and brands that the user likes. As a result, the analysis unit can provide users with highly accurate and personalized outfits.

[0032] The recommendation section receives fashion recommendations from micro-influencers and general users. Specifically, users can refer to outfits suggested by influencers and friends they follow. For example, users can view outfit photos and videos posted by influencers they follow within the app and choose their favorite styles. General users can also post their own outfits and share them with other users. This allows the recommendation section to help users find outfits that suit them by referencing a variety of fashion styles. Furthermore, the recommendation section can provide individually customized fashion suggestions based on the user's preferences and the influencers they follow. For example, by prioritizing the display of styles suggested by specific influencers, user satisfaction can be increased.

[0033] The delivery department efficiently delivers goods. Specifically, it utilizes existing logistics systems and technologies to deliver goods quickly and efficiently. For example, the delivery department calculates the optimal delivery route for each region and works with multiple delivery companies to deliver goods to users in the shortest possible time. It can also track the delivery status in real time and notify users of the delivery status. This allows users to always know what stage their order is in and use the service with peace of mind. Furthermore, the delivery department adopts environmentally friendly delivery methods and strives to reduce its carbon footprint. For example, it promotes delivery using electric vehicles and bicycles to reduce the environmental impact. In this way, the delivery department can provide an efficient and environmentally friendly delivery service.

[0034] The Recycling Department implements clothing recycling and upcycling programs. Specifically, it inspects clothing returned by users, and those that can be reused are cleaned or repaired before being made available for rental again. Clothing that is difficult to reuse is sorted by material and recycled in cooperation with recycling companies. In addition, as part of the upcycling program, old clothing can be remade into new designs and offered as new products. In this way, the Recycling Department can minimize clothing waste and promote environmentally friendly fashion consumption. Furthermore, the Recycling Department educates users about the importance of recycling and upcycling, contributing to the realization of sustainable fashion. For example, users who participate in the recycling program can be offered points or discount coupons to encourage recycling activities. In this way, the Recycling Department can balance environmental protection with user satisfaction.

[0035] The data collection unit can analyze the user's past fashion history and select the optimal data collection method. For example, the data collection unit can identify the user's preferred style based on data of items the user has purchased in the past and customize the data collection method. For example, the data collection unit can analyze events and usage scenarios the user has participated in in the past to determine the appropriate timing for data collection. For example, the data collection unit can identify frequently purchased brands and items from the user's past purchase history and collect data based on that. By customizing the data collection method based on past fashion history, more accurate data collection becomes possible. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past fashion history data into a generating AI and have the generating AI select the optimal data collection method.

[0036] The data collection unit can filter data based on the user's current season and weather conditions during collection. For example, the data collection unit can prioritize collecting items suitable for the current season and suggest them to the user. For example, based on weather information, the data collection unit can collect waterproof items on rainy days and light clothing items on sunny days. For example, the data collection unit can pre-collect items suitable for the next season in line with the change of seasons and suggest them to the user. This enables data collection tailored to the season and weather. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current season and weather data into a generating AI and have the generating AI perform the filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during the collection process. For example, if the user lives in an urban area, the data collection unit will prioritize the collection of data suitable for urban usage scenarios. If the user lives in a suburban area, the data collection unit will prioritize the collection of data suitable for casual and relaxed styles. If the user is traveling, the data collection unit will prioritize the collection of data suitable for the climate and culture of the travel destination. This enables data collection based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0038] The data collection unit can analyze a user's social media activity and collect relevant data during the collection process. For example, the data collection unit can analyze photos and posts shared by a user on social media to identify their preferred style and collect data. For example, the data collection unit can collect relevant data by referencing the styles of influencers the user follows. For example, the data collection unit can collect appropriate data based on events and activities the user has participated in. This enables data collection based on social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0039] The analysis unit can suggest the optimal clothing size and fit based on the user's body shape data during analysis. For example, the analysis unit can suggest the optimal clothing size based on the user's height and weight. For example, the analysis unit can analyze the user's body shape data and suggest clothing with a good fit. For example, the analysis unit can suggest clothing from a specific brand or design that suits the user's body shape. This makes it possible to suggest optimal clothing based on body shape data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's body shape data into a generating AI and have the generating AI perform the task of suggesting the optimal clothing size and fit.

[0040] The analysis unit can apply different coordination algorithms depending on the user's lifestyle during analysis. For example, if the user has an active lifestyle, the analysis unit will suggest a sporty style. For example, if the user wants to use the outfit in a business setting, the analysis unit will suggest a formal style. For example, if the user has a casual lifestyle, the analysis unit will suggest a relaxed style. This makes it possible to suggest coordination that suits the user's lifestyle. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI execute the application of different coordination algorithms.

[0041] The analysis unit can determine the priority of suggestions based on the user's past purchase history during analysis. For example, the analysis unit can identify preferred styles and determine the priority of suggestions based on data of items the user has purchased in the past. For example, the analysis unit can determine the priority of suggestions by referring to brands and designs the user has purchased in the past. For example, the analysis unit can identify frequently purchased items from the user's past purchase history and determine the priority of suggestions based on that. This makes it possible to provide more appropriate suggestions by determining the priority of suggestions based on past purchase history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past purchase history data into a generating AI and have the generating AI perform the determination of the priority of suggestions.

[0042] The analysis unit can adjust the order of suggestions based on the user's areas of interest during analysis. For example, if the user is interested in a particular fashion genre, the analysis unit will prioritize suggestions related to that genre. For example, if the user is interested in a particular brand, the analysis unit will prioritize suggesting items from that brand. For example, if the user is interested in a particular event or scene, the analysis unit will prioritize suggestions suitable for that scene. By adjusting the order of suggestions based on areas of interest, more appropriate suggestions can be made. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's areas of interest data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0043] The recommendation system can improve the accuracy of recommendations by considering the influence of the influencers the user follows. For example, the recommendation system can recommend related items by referencing the style of the influencers the user follows. For example, the recommendation system can analyze the influence of influencers and prioritize recommendations from highly influential influencers. For example, the recommendation system can analyze past posts of influencers the user follows and recommend related items. This enables recommendations based on influencer influence. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input influencer influence data into a generating AI and have the generating AI perform the task of improving the accuracy of recommendations.

[0044] The recommendation unit can make recommendations while considering the fashion history of the user's friends. For example, the recommendation unit can recommend related items by referring to items purchased by the user's friends. For example, the recommendation unit can recommend related items by referring to outfits shared by the user's friends. For example, the recommendation unit can recommend related items based on events and activities attended by the user's friends. This makes it possible to make recommendations based on the fashion history of friends. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the fashion history data of the user's friends into a generating AI and have the generating AI perform the recommendations.

[0045] The recommendation unit can make recommendations while considering the user's geographical distribution. For example, if the user lives in an urban area, the recommendation unit will recommend items suitable for urban use. If the user lives in a suburban area, the recommendation unit will recommend items with a casual and relaxed style. If the user is traveling, the recommendation unit will recommend items suitable for the climate and culture of their travel destination. This enables recommendations based on geographical distribution. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical distribution data into a generating AI and have the generating AI perform the recommendations.

[0046] The recommendation section can improve the accuracy of its recommendations by referring to the user's relevant literature during the recommendation process. For example, the recommendation section can recommend relevant items based on fashion magazines and blogs the user has read. For example, the recommendation section can recommend relevant items based on information from fashion events and exhibitions the user has attended. For example, the recommendation section can recommend relevant items based on information from newsletters and mailing lists the user subscribes to. This enables recommendations based on relevant literature. Some or all of the above processing in the recommendation section may be performed using AI, for example, or without AI. For example, the recommendation section can input the user's relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the recommendations.

[0047] The delivery department can analyze a user's past delivery history to select the optimal delivery method during delivery. For example, the delivery department can suggest the optimal delivery method based on the delivery methods the user has used in the past. For example, the delivery department can identify frequently used delivery time slots from the user's past delivery history and adjust the delivery schedule accordingly. For example, the delivery department can analyze the user's past delivery history and select the most efficient delivery method. This makes it possible to select the optimal delivery method based on past delivery history. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input the user's past delivery history data into a generating AI and have the generating AI select the optimal delivery method.

[0048] The delivery department can customize the delivery method based on the user's current living situation at the time of delivery. For example, if the user is at home, the delivery department will use a regular courier service. If the user is out, the delivery department will deliver to a designated pick-up location. If the user is traveling, the delivery department will deliver to the address at their travel destination. This makes it possible to customize the delivery method based on the user's current living situation. Some or all of the above processing in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the delivery method.

[0049] The delivery unit can select the optimal delivery method by considering the user's geographical location information during delivery. For example, if the user lives in an urban area, the delivery unit will select a delivery method suitable for urban use. If the user lives in a suburban area, the delivery unit will select a casual and relaxed style of delivery. If the user is traveling, the delivery unit will select a delivery method suitable for the climate and culture of the travel destination. This makes it possible to select the optimal delivery method based on geographical location information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0050] The delivery department can analyze a user's social media activity during delivery to suggest delivery methods. For example, the delivery department can analyze photos and posts shared by the user on social media to identify their preferred style and suggest delivery methods. For example, the delivery department can suggest relevant delivery methods by referencing the styles of influencers the user follows. For example, the delivery department can suggest appropriate delivery methods based on events and activities the user has participated in. This makes it possible to suggest delivery methods based on social media activity. Some or all of the above processing in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the user's social media activity data into a generating AI and have the generating AI perform the delivery method suggestion.

[0051] The recycling unit can analyze the user's past recycling history to select the optimal recycling method during recycling. For example, the recycling unit can propose the optimal recycling method based on the recycling methods the user has used in the past. For example, the recycling unit can identify frequently used recycling methods from the user's past recycling history and select a recycling method based on that. For example, the recycling unit can analyze the user's past recycling history and select the most efficient recycling method. This makes it possible to select the optimal recycling method based on past recycling history. Some or all of the above processes in the recycling unit may be performed using AI, for example, or without AI. For example, the recycling unit can input the user's past recycling history data into a generating AI and have the generating AI perform the selection of the optimal recycling method.

[0052] The recycling unit can customize the recycling method based on the user's current living situation. For example, if the user is at home, the recycling unit will use the standard recycling method. If the user is out, the recycling unit will suggest taking the items to a designated recycling location. If the user is traveling, the recycling unit will suggest using a recycling facility at their travel destination. This makes it possible to customize the recycling method based on the user's current living situation. Some or all of the above processing in the recycling unit may be performed using AI, for example, or without AI. For example, the recycling unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the recycling method.

[0053] The recycling unit can select the optimal recycling method by considering the user's geographical location information during recycling. For example, if the user lives in an urban area, the recycling unit will select a recycling method suitable for urban usage scenarios. If the user lives in a suburban area, the recycling unit will select a casual and relaxed style of recycling method. If the user is traveling, the recycling unit will select a recycling method suitable for the climate and culture of the travel destination. This makes it possible to select the optimal recycling method based on geographical location information. Some or all of the above processing in the recycling unit may be performed using AI, for example, or without AI. For example, the recycling unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal recycling method.

[0054] The recycling unit can analyze a user's social media activity during recycling and suggest recycling methods. For example, the recycling unit can analyze photos and posts shared by the user on social media to identify their preferred style and suggest recycling methods. For example, the recycling unit can suggest relevant recycling methods by referencing the styles of influencers the user follows. For example, the recycling unit can suggest appropriate recycling methods based on events and activities the user has participated in. This makes it possible to suggest recycling methods based on social media activity. Some or all of the above processing in the recycling unit may be performed using AI, for example, or without AI. For example, the recycling unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of suggesting recycling methods.

[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 clothing rental system can also collect user health data, which can then be used in the analysis department to suggest outfits. For example, the data collection department can collect data such as the user's steps, heart rate, and sleep patterns, and provide this data to the analysis department. Based on this health data, the analysis department can suggest clothing appropriate to the user's activity level. For instance, it can suggest comfortable sportswear for active days and comfortable casual wear for relaxed days. It can also select colors and materials that have stress-reducing effects depending on the user's health condition. This makes it possible to suggest the most suitable outfits based on the user's health condition.

[0057] The data collection unit can analyze a user's past fashion history and select the optimal data collection method. For example, it can identify preferred styles based on data of items the user has purchased in the past and customize the data collection method. It can also analyze events and usage scenarios the user has participated in in the past to determine the appropriate timing for data collection. From the user's past purchase history, it can identify frequently purchased brands and items and collect data based on that. By customizing the data collection method based on past fashion history, more accurate data collection becomes possible.

[0058] The data collection unit can filter data based on the user's current season and weather conditions. For example, it can prioritize collecting items suitable for the current season and suggest them to the user. Based on weather information, it can collect waterproof items on rainy days and light clothing items on sunny days. As seasons change, it can proactively collect items suitable for the next season and suggest them to the user. This enables data collection tailored to the season and weather.

[0059] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during the data collection process. For example, if the user lives in an urban area, it will prioritize the collection of data suitable for urban usage scenarios. If the user lives in a suburban area, it will prioritize the collection of data suitable for casual and relaxed styles. If the user is traveling, it will prioritize the collection of data suitable for the climate and culture of their travel destination. This enables data collection based on geographical location information.

[0060] The data collection unit can analyze users' social media activity and collect relevant data during the collection process. For example, it can analyze photos and posts shared by users on social media to identify their preferred style and collect relevant data. It can also collect relevant data by referencing the styles of influencers that users follow. Finally, it can collect appropriate data based on events and activities that users participate in. This enables data collection based on social media activity.

[0061] The analysis unit can suggest the optimal clothing size and fit based on the user's body shape data during analysis. For example, it can suggest the optimal clothing size based on the user's height and weight. It can analyze the user's body shape data and suggest clothing with a good fit. It can also suggest clothing from specific brands or designs that suit the user's body shape. This makes it possible to suggest the optimal clothing based on body shape data.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects user body shape data, lifestyle, and usage scenarios. For example, it collects information such as height, weight, preferred style, and daily activities entered by the user and stores it in a database. Step 2: The analysis unit analyzes the information collected by the collection unit and proposes the optimal outfit for the user. For example, it uses AI to analyze the collected information, selects clothing that suits the user's body type, and proposes outfits that match their lifestyle and usage scenarios. Step 3: The introduction section receives fashion recommendations from micro-influencers and general users. For example, users can refer to outfit suggestions from influencers or friends they follow. Step 4: The delivery department efficiently delivers goods. For example, it utilizes existing logistics systems and technologies to deliver goods quickly and efficiently. Step 5: The recycling department implements clothing recycling and upcycling programs. For example, it implements clothing recycling and upcycling programs to promote environmentally friendly fashion consumption.

[0064] (Example of form 2) The clothing rental system according to an embodiment of the present invention is a new type of clothing rental service primarily targeting people with little interest in fashion. This clothing rental system collects the user's body shape data, lifestyle, and usage scenarios, and an AI analyzes this data to suggest the optimal outfit. It also allows users to receive fashion recommendations from micro-influencers and general users, and incorporates an efficient delivery system and a program that considers sustainability. For example, it collects the user's body shape data, lifestyle, and usage scenarios. For example, it collects information such as the user's height, weight, preferred style, and daily activities. This information is input into the AI. Next, the AI ​​analyzes the collected information and suggests the optimal outfit for the user. For example, based on the user's body shape data, the AI ​​selects clothing that suits the body shape and suggests outfits that match the lifestyle and usage scenarios. This makes it easy for users to find a style that suits them. Furthermore, it allows users to receive fashion recommendations from micro-influencers and general users. For example, users can refer to outfits suggested by influencers or friends they follow. This allows users to incorporate the opinions of people close to them into their styling. It also constructs an efficient delivery system. For example, it utilizes existing logistics systems and technologies to deliver products quickly and efficiently. This allows users to receive their ordered items quickly. Finally, the company will introduce programs that prioritize sustainability. For example, it will implement clothing recycling and upcycling programs to promote environmentally friendly fashion consumption. This will allow users to enjoy sustainable fashion. In this way, by leveraging the power of technology and community, the company aims to make fashion more accessible and enjoyable, cultivate new customer segments, and promote sustainable fashion consumption. As a result, the clothing rental system will be able to suggest optimal outfits based on the user's body shape data, lifestyle, and usage scenarios, enabling efficient delivery and sustainable fashion consumption.

[0065] The clothing rental system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, a delivery unit, and a recycling unit. The collection unit collects the user's body shape data, lifestyle, and usage scenarios. The collection unit collects information such as height, weight, preferred style, and daily activities entered by the user. The collection unit stores the information entered by the user in a database and provides it to the analysis unit. The analysis unit analyzes the information collected by the collection unit and proposes the most suitable outfit for the user. The analysis unit analyzes the collected information using AI, for example, to select clothing that suits the user's body shape and proposes outfits that match their lifestyle and usage scenarios. The analysis unit uses AI to select clothing that suits the user's body shape based on the user's body shape data and proposes outfits that match their lifestyle and usage scenarios. The recommendation unit receives fashion recommendations from micro-influencers and general users. The recommendation unit can refer to outfits suggested by influencers and friends that the user follows. The Introduction Department allows users to refer to outfits suggested by influencers and friends they follow. The Delivery Department efficiently delivers products. The Delivery Department utilizes existing logistics systems and technologies to deliver products quickly and efficiently. The Delivery Department utilizes existing logistics systems and technologies to deliver products quickly and efficiently. The Delivery Department utilizes existing logistics systems and technologies to deliver products quickly and efficiently. The Recycling Department implements clothing recycling and upcycling programs. The Recycling Department implements clothing recycling and upcycling programs to promote environmentally friendly fashion consumption. The Recycling Department implements clothing recycling and upcycling programs to promote environmentally friendly fashion consumption.As a result, the clothing rental system according to this embodiment can suggest the optimal outfit based on the user's body shape data, lifestyle, and usage scenario, enabling efficient delivery and sustainable fashion consumption.

[0066] The data collection unit collects user body shape data, lifestyle, and usage scenarios. Specifically, it collects information such as height, weight, preferred style, and daily activities entered by the user. This information can be entered by the user through a dedicated application or website. For example, a user can log in to the application and enter their height, weight, and preferred fashion style on the profile settings screen. They can also enter information about their daily activities, such as their work, hobbies, and how they spend their weekends. The data collection unit then stores this detailed user information in a database and provides it to the analysis unit. Furthermore, the data collection unit also collects the user's past rental history and ratings to understand their preferences and trends. For example, it can record the types of clothes a user has rented in the past and their ratings, and use this information to improve future recommendations. This allows the data collection unit to build a foundation for providing personalized services tailored to the user's needs.

[0067] The analysis unit analyzes the information collected by the data collection unit and proposes the most suitable outfit for the user. Specifically, it uses AI to analyze the collected information, selects clothing that suits the user's body type, and proposes outfits that match their lifestyle and usage scenarios. Based on the user's body type data, the AI ​​selects clothing of the optimal size and silhouette, taking into account factors such as height, weight, and body shape characteristics. It also suggests formal suits and dresses for business settings and relaxed styles for casual settings, depending on the user's lifestyle and usage scenarios. Furthermore, the AI ​​learns the user's preferred style and past rental history, allowing it to prioritize suggesting designs, colors, and brands that the user likes. As a result, the analysis unit can provide users with highly accurate and personalized outfits.

[0068] The recommendation section receives fashion recommendations from micro-influencers and general users. Specifically, users can refer to outfits suggested by influencers and friends they follow. For example, users can view outfit photos and videos posted by influencers they follow within the app and choose their favorite styles. General users can also post their own outfits and share them with other users. This allows the recommendation section to help users find outfits that suit them by referencing a variety of fashion styles. Furthermore, the recommendation section can provide individually customized fashion suggestions based on the user's preferences and the influencers they follow. For example, by prioritizing the display of styles suggested by specific influencers, user satisfaction can be increased.

[0069] The delivery department efficiently delivers goods. Specifically, it utilizes existing logistics systems and technologies to deliver goods quickly and efficiently. For example, the delivery department calculates the optimal delivery route for each region and works with multiple delivery companies to deliver goods to users in the shortest possible time. It can also track the delivery status in real time and notify users of the delivery status. This allows users to always know what stage their order is in and use the service with peace of mind. Furthermore, the delivery department adopts environmentally friendly delivery methods and strives to reduce its carbon footprint. For example, it promotes delivery using electric vehicles and bicycles to reduce the environmental impact. In this way, the delivery department can provide an efficient and environmentally friendly delivery service.

[0070] The Recycling Department implements clothing recycling and upcycling programs. Specifically, it inspects clothing returned by users, and those that can be reused are cleaned or repaired before being made available for rental again. Clothing that is difficult to reuse is sorted by material and recycled in cooperation with recycling companies. In addition, as part of the upcycling program, old clothing can be remade into new designs and offered as new products. In this way, the Recycling Department can minimize clothing waste and promote environmentally friendly fashion consumption. Furthermore, the Recycling Department educates users about the importance of recycling and upcycling, contributing to the realization of sustainable fashion. For example, users who participate in the recycling program can be offered points or discount coupons to encourage recycling activities. In this way, the Recycling Department can balance environmental protection with user satisfaction.

[0071] The data collection unit can estimate the user's emotions and adjust the timing of collecting body shape data and lifestyle data based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect data when the user is relaxed. For example, if the user is relaxed, the data collection unit can collect data immediately and quickly move to analysis. For example, if the user is in a hurry, the data collection unit can quickly collect minimal data and supplement it with more detailed data later. This allows for more appropriate data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0072] The data collection unit can analyze the user's past fashion history and select the optimal data collection method. For example, the data collection unit can identify the user's preferred style based on data of items the user has purchased in the past and customize the data collection method. For example, the data collection unit can analyze events and usage scenarios the user has participated in in the past to determine the appropriate timing for data collection. For example, the data collection unit can identify frequently purchased brands and items from the user's past purchase history and collect data based on that. By customizing the data collection method based on past fashion history, more accurate data collection becomes possible. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past fashion history data into a generating AI and have the generating AI select the optimal data collection method.

[0073] The data collection unit can filter data based on the user's current season and weather conditions during collection. For example, the data collection unit can prioritize collecting items suitable for the current season and suggest them to the user. For example, based on weather information, the data collection unit can collect waterproof items on rainy days and light clothing items on sunny days. For example, the data collection unit can pre-collect items suitable for the next season in line with the change of seasons and suggest them to the user. This enables data collection tailored to the season and weather. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current season and weather data into a generating AI and have the generating AI perform the filtering.

[0074] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data in a relaxing style. For example, if the user is having fun, the data collection unit will prioritize collecting data in a casual and fun style. For example, if the user is in a hurry, the data collection unit will prioritize collecting basic body shape data and supplement detailed data later. This allows for more appropriate data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during the collection process. For example, if the user lives in an urban area, the data collection unit will prioritize the collection of data suitable for urban usage scenarios. If the user lives in a suburban area, the data collection unit will prioritize the collection of data suitable for casual and relaxed styles. If the user is traveling, the data collection unit will prioritize the collection of data suitable for the climate and culture of the travel destination. This enables data collection based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0076] The data collection unit can analyze a user's social media activity and collect relevant data during the collection process. For example, the data collection unit can analyze photos and posts shared by a user on social media to identify their preferred style and collect data. For example, the data collection unit can collect relevant data by referencing the styles of influencers the user follows. For example, the data collection unit can collect appropriate data based on events and activities the user has participated in. This enables data collection based on social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0077] The analysis unit can estimate the user's emotions and adjust the way it presents outfit suggestions based on those emotions. For example, if the user is relaxed, the analysis unit will suggest a casual and relaxed style. If the user is stressed, the analysis unit will suggest a simple and calm style. If the user is having fun, the analysis unit will suggest a bright and cheerful style. This makes it possible to provide outfit suggestions that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The analysis unit can suggest the optimal clothing size and fit based on the user's body shape data during analysis. For example, the analysis unit can suggest the optimal clothing size based on the user's height and weight. For example, the analysis unit can analyze the user's body shape data and suggest clothing with a good fit. For example, the analysis unit can suggest clothing from a specific brand or design that suits the user's body shape. This makes it possible to suggest optimal clothing based on body shape data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's body shape data into a generating AI and have the generating AI perform the task of suggesting the optimal clothing size and fit.

[0079] The analysis unit can apply different coordination algorithms depending on the user's lifestyle during analysis. For example, if the user has an active lifestyle, the analysis unit will suggest a sporty style. For example, if the user wants to use the outfit in a business setting, the analysis unit will suggest a formal style. For example, if the user has a casual lifestyle, the analysis unit will suggest a relaxed style. This makes it possible to suggest coordination that suits the user's lifestyle. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI execute the application of different coordination algorithms.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the outfit suggestions based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide short, concise outfit suggestions. If the user is relaxed, the analysis unit will provide longer outfit suggestions that include detailed explanations. If the user is excited, the analysis unit will provide outfit suggestions that include visually stimulating effects. By adjusting the length of the outfit suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The analysis unit can determine the priority of suggestions based on the user's past purchase history during analysis. For example, the analysis unit can identify preferred styles and determine the priority of suggestions based on data of items the user has purchased in the past. For example, the analysis unit can determine the priority of suggestions by referring to brands and designs the user has purchased in the past. For example, the analysis unit can identify frequently purchased items from the user's past purchase history and determine the priority of suggestions based on that. This makes it possible to provide more appropriate suggestions by determining the priority of suggestions based on past purchase history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past purchase history data into a generating AI and have the generating AI perform the determination of the priority of suggestions.

[0082] The analysis unit can adjust the order of suggestions based on the user's areas of interest during analysis. For example, if the user is interested in a particular fashion genre, the analysis unit will prioritize suggestions related to that genre. For example, if the user is interested in a particular brand, the analysis unit will prioritize suggesting items from that brand. For example, if the user is interested in a particular event or scene, the analysis unit will prioritize suggestions suitable for that scene. By adjusting the order of suggestions based on areas of interest, more appropriate suggestions can be made. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's areas of interest data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0083] The introduction section can estimate the user's emotions and adjust how the introduction is displayed based on the estimated emotions. For example, if the user is nervous, the introduction section provides a simple and highly visible display. For example, if the user is relaxed, the introduction section provides a display that includes detailed information. For example, if the user is in a hurry, the introduction section provides a display that gets straight to the point. By adjusting how the introduction is displayed according to the user's emotions, a more appropriate introduction becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using 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 introduction section may be performed using AI, for example, or without AI. For example, the introduction section can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The recommendation system can improve the accuracy of recommendations by considering the influence of the influencers the user follows. For example, the recommendation system can recommend related items by referencing the style of the influencers the user follows. For example, the recommendation system can analyze the influence of influencers and prioritize recommendations from highly influential influencers. For example, the recommendation system can analyze past posts of influencers the user follows and recommend related items. This enables recommendations based on influencer influence. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input influencer influence data into a generating AI and have the generating AI perform the task of improving the accuracy of recommendations.

[0085] The recommendation unit can make recommendations while considering the fashion history of the user's friends. For example, the recommendation unit can recommend related items by referring to items purchased by the user's friends. For example, the recommendation unit can recommend related items by referring to outfits shared by the user's friends. For example, the recommendation unit can recommend related items based on events and activities attended by the user's friends. This makes it possible to make recommendations based on the fashion history of friends. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the fashion history data of the user's friends into a generating AI and have the generating AI perform the recommendations.

[0086] The introduction section can estimate the user's emotions and adjust the order of introductions based on the estimated emotions. For example, if the user is nervous, the introduction section will prioritize simple and easy-to-understand introductions. If the user is relaxed, the introduction section will prioritize introductions that include detailed information. If the user is in a hurry, the introduction section will prioritize introductions that get straight to the point. By adjusting the order of introductions according to the user's emotions, more appropriate introductions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using 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 introduction section may be performed using AI, for example, or not using AI. For example, the introduction section can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The recommendation unit can make recommendations while considering the user's geographical distribution. For example, if the user lives in an urban area, the recommendation unit will recommend items suitable for urban use. If the user lives in a suburban area, the recommendation unit will recommend items with a casual and relaxed style. If the user is traveling, the recommendation unit will recommend items suitable for the climate and culture of their travel destination. This enables recommendations based on geographical distribution. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical distribution data into a generating AI and have the generating AI perform the recommendations.

[0088] The recommendation section can improve the accuracy of its recommendations by referring to the user's relevant literature during the recommendation process. For example, the recommendation section can recommend relevant items based on fashion magazines and blogs the user has read. For example, the recommendation section can recommend relevant items based on information from fashion events and exhibitions the user has attended. For example, the recommendation section can recommend relevant items based on information from newsletters and mailing lists the user subscribes to. This enables recommendations based on relevant literature. Some or all of the above processing in the recommendation section may be performed using AI, for example, or without AI. For example, the recommendation section can input the user's relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the recommendations.

[0089] The delivery unit can estimate the user's emotions and adjust the delivery timing based on the estimated emotions. For example, if the user is in a hurry, the delivery unit will prioritize the shortest delivery time. For example, if the user is relaxed, the delivery unit will apply the normal delivery schedule. For example, if the user is stressed, the delivery unit will adjust the delivery timing to deliver at a time when the user can relax. This makes it possible to adjust the delivery timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The delivery department can analyze a user's past delivery history to select the optimal delivery method during delivery. For example, the delivery department can suggest the optimal delivery method based on the delivery methods the user has used in the past. For example, the delivery department can identify frequently used delivery time slots from the user's past delivery history and adjust the delivery schedule accordingly. For example, the delivery department can analyze the user's past delivery history and select the most efficient delivery method. This makes it possible to select the optimal delivery method based on past delivery history. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input the user's past delivery history data into a generating AI and have the generating AI select the optimal delivery method.

[0091] The delivery department can customize the delivery method based on the user's current living situation at the time of delivery. For example, if the user is at home, the delivery department will use a regular courier service. If the user is out, the delivery department will deliver to a designated pick-up location. If the user is traveling, the delivery department will deliver to the address at their travel destination. This makes it possible to customize the delivery method based on the user's current living situation. Some or all of the above processing in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the delivery method.

[0092] The delivery unit can estimate the user's emotions and determine delivery priorities based on those emotions. For example, if the user is in a hurry, the delivery unit will prioritize the shortest delivery time. If the user is relaxed, the delivery unit will apply the normal delivery schedule. If the user is stressed, the delivery unit will adjust the delivery timing to a time when the user can relax. This makes it possible to determine delivery priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 delivery unit may be performed using AI or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The delivery unit can select the optimal delivery method by considering the user's geographical location information during delivery. For example, if the user lives in an urban area, the delivery unit will select a delivery method suitable for urban use. If the user lives in a suburban area, the delivery unit will select a casual and relaxed style of delivery. If the user is traveling, the delivery unit will select a delivery method suitable for the climate and culture of the travel destination. This makes it possible to select the optimal delivery method based on geographical location information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0094] The delivery department can analyze a user's social media activity during delivery to suggest delivery methods. For example, the delivery department can analyze photos and posts shared by the user on social media to identify their preferred style and suggest delivery methods. For example, the delivery department can suggest relevant delivery methods by referencing the styles of influencers the user follows. For example, the delivery department can suggest appropriate delivery methods based on events and activities the user has participated in. This makes it possible to suggest delivery methods based on social media activity. Some or all of the above processing in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the user's social media activity data into a generating AI and have the generating AI perform the delivery method suggestion.

[0095] The recycling unit can estimate the user's emotions and encourage participation in the recycling program based on the estimated emotions. For example, if the user is relaxed, the recycling unit sends a message encouraging participation in the recycling program. For example, if the user is stressed, the recycling unit refrains from sending a message encouraging participation in the recycling program. For example, if the user is excited, the recycling unit emphasizes the message encouraging participation in the recycling program. This makes it possible to encourage participation in the recycling program in accordance with the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 recycling unit may be performed using AI, for example, or without AI. For example, the recycling unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The recycling unit can analyze the user's past recycling history to select the optimal recycling method during recycling. For example, the recycling unit can propose the optimal recycling method based on the recycling methods the user has used in the past. For example, the recycling unit can identify frequently used recycling methods from the user's past recycling history and select a recycling method based on that. For example, the recycling unit can analyze the user's past recycling history and select the most efficient recycling method. This makes it possible to select the optimal recycling method based on past recycling history. Some or all of the above processes in the recycling unit may be performed using AI, for example, or without AI. For example, the recycling unit can input the user's past recycling history data into a generating AI and have the generating AI perform the selection of the optimal recycling method.

[0097] The recycling unit can customize the recycling method based on the user's current living situation. For example, if the user is at home, the recycling unit will use the standard recycling method. If the user is out, the recycling unit will suggest taking the items to a designated recycling location. If the user is traveling, the recycling unit will suggest using a recycling facility at their travel destination. This makes it possible to customize the recycling method based on the user's current living situation. Some or all of the above processing in the recycling unit may be performed using AI, for example, or without AI. For example, the recycling unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the recycling method.

[0098] The recycling unit can estimate the user's emotions and determine recycling priorities based on the estimated emotions. For example, if the user is relaxed, the recycling unit will set a high recycling priority. For example, if the user is stressed, the recycling unit will set a low recycling priority. For example, if the user is excited, the recycling unit will set a high recycling priority. This makes it possible to determine recycling priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recycling unit may be performed using AI, for example, or without AI. For example, the recycling unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The recycling unit can select the optimal recycling method by considering the user's geographical location information during recycling. For example, if the user lives in an urban area, the recycling unit will select a recycling method suitable for urban usage scenarios. If the user lives in a suburban area, the recycling unit will select a casual and relaxed style of recycling method. If the user is traveling, the recycling unit will select a recycling method suitable for the climate and culture of the travel destination. This makes it possible to select the optimal recycling method based on geographical location information. Some or all of the above processing in the recycling unit may be performed using AI, for example, or without AI. For example, the recycling unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal recycling method.

[0100] The recycling unit can analyze a user's social media activity during recycling and suggest recycling methods. For example, the recycling unit can analyze photos and posts shared by the user on social media to identify their preferred style and suggest recycling methods. For example, the recycling unit can suggest relevant recycling methods by referencing the styles of influencers the user follows. For example, the recycling unit can suggest appropriate recycling methods based on events and activities the user has participated in. This makes it possible to suggest recycling methods based on social media activity. Some or all of the above processing in the recycling unit may be performed using AI, for example, or without AI. For example, the recycling unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of suggesting recycling methods.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The clothing rental system can also collect user health data, which can then be used in the analysis department to suggest outfits. For example, the data collection department can collect data such as the user's steps, heart rate, and sleep patterns, and provide this data to the analysis department. Based on this health data, the analysis department can suggest clothing appropriate to the user's activity level. For instance, it can suggest comfortable sportswear for active days and comfortable casual wear for relaxed days. It can also select colors and materials that have stress-reducing effects depending on the user's health condition. This makes it possible to suggest the most suitable outfits based on the user's health condition.

[0103] The data collection unit can estimate the user's emotions and adjust the timing of body shape data and lifestyle data collection based on the estimated emotions. For example, if the user is stressed, the data collection timing is delayed to collect data when the user is relaxed. If the user is relaxed, data is collected immediately and analysis is performed quickly. If the user is in a hurry, minimal data is collected quickly, and more detailed data is supplemented later. This allows for more appropriate data collection by adjusting the collection timing according to the user's emotions.

[0104] The analysis unit can estimate the user's emotions and adjust the way it presents outfit suggestions based on those emotions. For example, if the user is relaxed, it will suggest casual and relaxed styles. If the user is stressed, it will suggest simple and calm styles. If the user is having fun, it will suggest bright and cheerful styles. This makes it possible to offer outfit suggestions that match the user's emotions.

[0105] The introduction section can estimate the user's emotions and adjust how the introduction is displayed based on those emotions. For example, if the user is nervous, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. If the user is in a hurry, a display method that gets straight to the point is provided. By adjusting the introduction display method according to the user's emotions, a more appropriate introduction can be provided.

[0106] The delivery department can estimate the user's emotions and adjust delivery timing based on those emotions. For example, if the user is in a hurry, the shortest delivery time will be prioritized. If the user is relaxed, the normal delivery schedule will be applied. If the user is stressed, the delivery timing will be adjusted to deliver during a time when the user can relax. This makes it possible to adjust delivery timing according to the user's emotions.

[0107] The data collection unit can analyze a user's past fashion history and select the optimal data collection method. For example, it can identify preferred styles based on data of items the user has purchased in the past and customize the data collection method. It can also analyze events and usage scenarios the user has participated in in the past to determine the appropriate timing for data collection. From the user's past purchase history, it can identify frequently purchased brands and items and collect data based on that. By customizing the data collection method based on past fashion history, more accurate data collection becomes possible.

[0108] The data collection unit can filter data based on the user's current season and weather conditions. For example, it can prioritize collecting items suitable for the current season and suggest them to the user. Based on weather information, it can collect waterproof items on rainy days and light clothing items on sunny days. As seasons change, it can proactively collect items suitable for the next season and suggest them to the user. This enables data collection tailored to the season and weather.

[0109] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during the data collection process. For example, if the user lives in an urban area, it will prioritize the collection of data suitable for urban usage scenarios. If the user lives in a suburban area, it will prioritize the collection of data suitable for casual and relaxed styles. If the user is traveling, it will prioritize the collection of data suitable for the climate and culture of their travel destination. This enables data collection based on geographical location information.

[0110] The data collection unit can analyze users' social media activity and collect relevant data during the collection process. For example, it can analyze photos and posts shared by users on social media to identify their preferred style and collect relevant data. It can also collect relevant data by referencing the styles of influencers that users follow. Finally, it can collect appropriate data based on events and activities that users participate in. This enables data collection based on social media activity.

[0111] The analysis unit can suggest the optimal clothing size and fit based on the user's body shape data during analysis. For example, it can suggest the optimal clothing size based on the user's height and weight. It can analyze the user's body shape data and suggest clothing with a good fit. It can also suggest clothing from specific brands or designs that suit the user's body shape. This makes it possible to suggest the optimal clothing based on body shape data.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The data collection unit collects user body shape data, lifestyle, and usage scenarios. For example, it collects information such as height, weight, preferred style, and daily activities entered by the user and stores it in a database. Step 2: The analysis unit analyzes the information collected by the collection unit and proposes the optimal outfit for the user. For example, it uses AI to analyze the collected information, selects clothing that suits the user's body type, and proposes outfits that match their lifestyle and usage scenarios. Step 3: The introduction section receives fashion recommendations from micro-influencers and general users. For example, users can refer to outfit suggestions from influencers or friends they follow. Step 4: The delivery department efficiently delivers goods. For example, it utilizes existing logistics systems and technologies to deliver goods quickly and efficiently. Step 5: The recycling department implements clothing recycling and upcycling programs. For example, it implements clothing recycling and upcycling programs to promote environmentally friendly fashion consumption.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the collection unit, analysis unit, introduction unit, delivery unit, and recycling unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the receiving device 38 of the smart device 14 to collect the user's body shape data, lifestyle, and usage scenarios, and stores them in the database 24 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the collected information and propose the optimal coordination for the user. The introduction unit receives fashion recommendations from micro-influencers and general users using, for example, the output device 40 of the smart device 14. The delivery unit, for example, cooperates with existing logistics systems via the communication I / F 26 of the data processing unit 12 to deliver products quickly and efficiently. The recycling unit, for example, uses the specific processing unit 290 of the data processing unit 12 to implement clothing recycling and upcycling programs, promoting environmentally friendly fashion consumption. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, delivery unit, and recycling unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the microphone 238 of the smart glasses 214 to collect the user's body shape data, lifestyle, and usage scenarios, and stores them in the database 24 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the collected information and propose the optimal outfit for the user. The recommendation unit receives fashion recommendations from micro-influencers and general users, for example, using the speaker 240 of the smart glasses 214. The delivery unit, for example, cooperates with existing logistics systems via the communication I / F 26 of the data processing unit 12 to deliver products quickly and efficiently. The recycling unit, for example, uses the identification processing unit 290 of the data processing unit 12 to implement clothing recycling and upcycling programs, promoting environmentally friendly fashion consumption. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, introduction unit, delivery unit, and recycling unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the microphone 238 of the headset terminal 314 to collect the user's body shape data, lifestyle, and usage scenarios, and stores them in the database 24 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the collected information and propose the optimal outfit for the user. The introduction unit receives fashion recommendations from micro-influencers and general users using, for example, the speaker 240 of the headset terminal 314. The delivery unit, for example, cooperates with existing logistics systems via the communication I / F 26 of the data processing unit 12 to deliver goods quickly and efficiently. The recycling unit, for example, uses the specific processing unit 290 of the data processing unit 12 to implement clothing recycling and upcycling programs, promoting environmentally friendly fashion consumption. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the collection unit, analysis unit, introduction unit, delivery unit, and recycling unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the microphone 238 of the robot 414 to collect the user's body shape data, lifestyle, and usage scenarios, and stores them in the database 24 of the data processing unit 12. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the collected information and propose the optimal outfit for the user. The introduction unit receives fashion recommendations from micro-influencers and general users using, for example, the speaker 240 of the robot 414. The delivery unit, for example, cooperates with existing logistics systems via the communication I / F 26 of the data processing unit 12 to deliver goods quickly and efficiently. The recycling unit, for example, uses the identification processing unit 290 of the data processing unit 12 to implement clothing recycling and upcycling programs, promoting environmentally friendly fashion consumption. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A data collection unit that collects user body shape data, lifestyle, and usage scenarios, An analysis unit analyzes the information collected by the aforementioned collection unit and proposes the optimal coordination for the user, The introduction department receives fashion recommendations from micro-influencers and general users, The delivery department efficiently delivers goods, It includes a recycling department that implements clothing recycling and upcycling programs. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting body shape data and lifestyle information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past fashion history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is During data collection, filtering is performed based on the user's current season and weather. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the way coordination suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the system suggests the optimal clothing size and fit based on the user's body shape data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different coordination algorithms are applied depending on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the outfit suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the priority of suggestions is determined based on the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of suggestions is adjusted based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned introduction section is, It estimates the user's emotions and adjusts how recommendations are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned introduction section is, When making recommendations, we take into account the influence of the users' followed influencers to improve the accuracy of the recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned introduction section is, When making a referral, the system takes into account the fashion history of the user's friends. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned introduction section is, It estimates the user's emotions and adjusts the order of recommendations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned introduction section is, When making recommendations, we take into account the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned introduction section is, When making recommendations, we refer to relevant literature from the user to improve the accuracy of the recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned delivery department, The system estimates the user's emotions and adjusts the delivery timing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned delivery department, During delivery, the system analyzes the user's past delivery history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned delivery department, During delivery, the delivery method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned delivery department, The system estimates the user's emotions and determines delivery priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned delivery department, During delivery, the system selects the optimal delivery method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned delivery department, During delivery, we analyze the user's social media activity and suggest delivery methods. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recycling unit is It estimates the user's emotions and encourages participation in the recycling program based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recycling unit is During recycling, the system analyzes the user's past recycling history to select the optimal recycling method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned recycling unit is During recycling, the recycling method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned recycling unit is It estimates the user's emotions and determines recycling priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned recycling unit is During recycling, the system selects the optimal recycling method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned recycling unit is During recycling, we analyze users' social media activity to suggest recycling methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 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 data collection unit that collects user body shape data, lifestyle, and usage scenarios, An analysis unit analyzes the information collected by the aforementioned collection unit and proposes the optimal coordination for the user, The introduction department receives fashion recommendations from micro-influencers and general users, The delivery department efficiently delivers goods, It includes a recycling department that implements clothing recycling and upcycling programs. A system characterized by the following features.

2. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting body shape data and lifestyle information based on those estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the user's past fashion history and select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is During data collection, filtering is performed based on the user's current season and weather. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the way coordination suggestions are presented based on those estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, the system suggests the optimal clothing size and fit based on the user's body shape data. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, different coordination algorithms are applied depending on the user's lifestyle. The system according to feature 1.

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