system

Abstract

The system according to this embodiment aims to understand how often a user wears the clothes they own and to provide appropriate recommendations based on that understanding. [Solution] The system according to the embodiment comprises a listing unit, a visualization unit, and a recommendation unit. The listing unit lists the clothes owned by the user. The visualization unit visualizes the frequency of use of the clothes listed by the listing unit. The recommendation unit makes recommendations to the user based on the frequency of use visualized by the visualization unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, the usage frequency of clothes owned by a user has not been grasped, and appropriate recommendations have not been made based on it, so there is room for improvement.

[0005] The system according to the embodiment aims to grasp the usage frequency of clothes owned by a user and make appropriate recommendations based on it.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a listing unit, a visualization unit, and a recommendation unit. The listing unit lists the clothes owned by the user. The visualization unit visualizes the frequency of use of the clothes listed by the listing unit. The recommendation unit makes recommendations to the user based on the frequency of use visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to this embodiment can understand how often a user uses the clothes they own and make appropriate recommendations based on that. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

[0016] [First Embodiment] 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 management system according to an embodiment of the present invention is a system that efficiently manages the clothes owned by a user and makes recommendations based on the frequency of use. This clothing management system lists the clothes owned by the user, visualizes the frequency of use of the listed clothes, and makes recommendations based on the visualized frequency of use. The clothing management system also proposes optimal coordination based on the clothes owned by the user and generates clothing designs that the user prefers. For example, the clothing management system stores the clothes owned by the user in a database by taking pictures of them and uploading them. Next, the clothing management system visualizes the frequency of use of the listed clothes, allowing the user to grasp the frequency of use of their clothes at a glance. Furthermore, based on the visualized frequency of use, the clothing management system recommends actions to the user, such as "You're wearing this again" or "You haven't worn this in a while." The clothing management system also proposes optimal coordination based on the clothes owned by the user and generates clothing designs that the user prefers. As a result, the user can efficiently manage the clothes they own and receive recommendations based on the frequency of use, thus solving the problems of having too many similar clothes or having storage space overflowing due to having many similar clothes. Furthermore, since users can generate their own preferred clothing designs, ambiguous and potential issues are also resolved. This allows the clothing management system to streamline users' clothing management and provide recommendations based on usage frequency.

[0029] The clothing management system according to this embodiment comprises a listing unit, a visualization unit, and a recommendation unit. The listing unit lists the clothing owned by the user. The clothing owned by the user includes, but is not limited to, shirts, pants, and jackets. The listing unit registers the information of the clothing in the database, for example, when the user takes a picture of the clothing and uploads the image. The listing unit also allows the user to manually input information about the clothing. For example, the listing unit provides an interface for inputting information such as the brand, size, color, and material of the clothing. The visualization unit visualizes the frequency of use of the clothing listed by the listing unit. The frequency of use is calculated, for example, based on the number of times the clothing is worn or the duration of wear, but is not limited to this example. The visualization unit visually displays the frequency of use of the clothing, for example, using graphs or charts. The visualization unit can also display the frequency of use of the clothing in a calendar format. For example, the visualization unit displays the frequency of use of each piece of clothing by date, allowing the user to see at a glance which clothes they wear and how often. The recommendation unit makes recommendations to the user based on the frequency of use visualized by the visualization unit. Recommendations include, but are not limited to, suggested actions such as "You're wearing this again" or "You haven't worn this in a while." For example, the recommendation unit might recommend "You're wearing this again" for frequently used clothing and "You haven't worn this in a while" for infrequently used clothing. The recommendation unit can also make recommendations based on the condition of the clothing or the season. For example, if winter clothing is not being worn in the summer, the recommendation unit might recommend "Let's organize your winter clothes." As a result, the clothing management system according to this embodiment can streamline the user's clothing management and provide recommendations based on frequency of use.

[0030] The listing unit lists the clothes owned by the user. These clothes include, but are not limited to, shirts, pants, and jackets. The listing unit registers clothing information in its database, for example, when the user takes a picture of their clothes and uploads the image. Specifically, the user takes a picture of their clothes with a smartphone or digital camera and uploads the image through a dedicated application. The uploaded image is analyzed by AI, and information such as the type of clothing, brand, size, color, and material is automatically extracted. The AI ​​uses image recognition technology to identify the characteristics of the clothing and register them in the database. The listing unit also allows the user to manually input clothing information. For example, the listing unit provides an interface for inputting information such as the brand, size, color, and material of the clothing. The user can manually enter each item using the application's input form and register it in the database. Furthermore, the listing unit also has a function to quickly register clothing information using barcodes and QR codes (registered trademarks). By scanning the barcode or QR code on the tag attached to the clothing, the relevant information is automatically added to the database. This allows the listing unit to efficiently and accurately list information about the clothes owned by the user and register it in the database.

[0031] The visualization unit visualizes the frequency of use of clothing listed by the listing unit. Frequency of use is calculated based on, for example, the number of times or the duration of wear, but is not limited to such examples. The visualization unit visually displays the frequency of use of clothing using, for example, graphs and charts. Specifically, it can visually compare the frequency of use of each piece of clothing using bar graphs, pie charts, line graphs, etc. The visualization unit can also display the frequency of use of clothing in a calendar format. For example, the visualization unit can display the frequency of use of each piece of clothing by day, allowing the user to see at a glance which clothes they wear and how often. Furthermore, the visualization unit also has a function to provide visual feedback to the user based on the frequency of use of clothing. For example, it can display frequently used clothing in a darker color and less frequently used clothing in a lighter color, allowing the user to intuitively understand their usage status. The visualization unit can also provide statistical information regarding the frequency of use of clothing. For example, it can display the number of uses per month or the annual usage trend in a graph, allowing the user to understand their long-term usage patterns. This allows the visualization unit to gain a detailed understanding of how the user's clothes are being used, supporting efficient clothing management.

[0032] The recommendation unit makes recommendations to the user based on the frequency of use visualized by the visualization unit. Recommendations include, but are not limited to, suggested actions such as "You're wearing this again" or "You haven't worn this in a while." For example, the recommendation unit might recommend "You're wearing this again" for frequently used clothing and "You haven't worn this in a while" for infrequently used clothing. Specifically, the recommendation unit uses AI to analyze the user's clothing usage patterns and generate optimal recommendations. The AI ​​considers past usage data and external factors such as season and weather to suggest the best clothing choices for the user. For example, if winter clothes are not being worn in the summer, the recommendation unit might recommend "Let's organize your winter clothes." The recommendation unit can also make recommendations based on the condition of the clothing and the season. For example, if the clothing is deteriorated, it might recommend "This clothing needs repair," and depending on the season, it might suggest "This clothing is perfect for this season." Furthermore, the recommendation system can also suggest outfits based on the user's preferences and style. For example, if a user prefers a particular color or style, it can suggest the most suitable outfit based on that information. This allows the recommendation system to streamline the user's clothing management and provide recommendations based on usage frequency.

[0033] The clothing management system includes a suggestion unit that proposes outfits based on the clothes owned by the user. The suggestion unit proposes the optimal outfit based on the clothes owned by the user. For example, the suggestion unit analyzes the colors and styles of the clothes owned by the user and proposes the best combinations. For example, the suggestion unit proposes a color combination of shirts and pants owned by the user. The suggestion unit can also propose formal or casual outfits based on the style of the clothes owned by the user. For example, the suggestion unit proposes a combination of jackets and pants owned by the user, providing an outfit suitable for formal occasions. The suggestion unit can also propose the optimal outfit according to the season of the clothes owned by the user. For example, the suggestion unit proposes a combination of winter coats and sweaters, providing an outfit suitable for cold seasons. This allows the user to be offered the best outfit using the clothes they own. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data on the clothes owned by the user into a generating AI and have the generating AI produce an optimal outfit suggestion.

[0034] The clothing management system includes a generation unit that generates clothing designs preferred by the user. The generation unit generates clothing designs preferred by the user. For example, the generation unit generates clothing designs based on the user's preferred colors and styles. For example, the generation unit generates designs for shirts and pants in the user's preferred colors. The generation unit can also generate designs for formal or casual clothing based on the user's preferred style. For example, the generation unit generates designs for formal jackets and casual T-shirts preferred by the user. The generation unit can also generate optimal clothing designs according to the user's preferred season. For example, the generation unit generates designs for winter coats and summer shirts. This allows the user to generate clothing designs to their liking. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input elements of clothing designs preferred by the user into the generation AI and have the generation AI generate the optimal design.

[0035] The recommendation unit can recommend actions to the user, such as "wearing it again" or "haven't worn it in a while," based on the frequency of use visualized by the visualization unit. For example, the recommendation unit can recommend "wearing it again" for clothing that is frequently worn. It can also recommend "haven't worn it in a while" for clothing that is rarely worn. For example, the recommendation unit can recommend "wearing it again" for a shirt that the user frequently wears, and "haven't worn it in a while" for a jacket that the user rarely wears. The recommendation unit can also make recommendations based on the condition of the clothing or the season. For example, if winter clothes are not being worn in the summer, the recommendation unit can recommend "organize your winter clothes." This allows the user to receive specific recommendations based on their frequency of use. 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 usage frequency data visualized by the visualization unit into the generating AI, and have the generating AI perform the generation of recommendations.

[0036] The suggestion unit can propose outfits based on the clothing listed by the listing unit. For example, the suggestion unit can analyze the colors and styles of the clothes owned by the user and propose the best combinations. For example, the suggestion unit can propose color combinations of shirts and pants owned by the user. The suggestion unit can also propose formal and casual outfits based on the styles of the clothes owned by the user. For example, the suggestion unit can propose a combination of jackets and pants owned by the user, providing an outfit suitable for formal occasions. The suggestion unit can also propose the best outfits according to the season of the clothes owned by the user. For example, the suggestion unit can propose a combination of winter coats and sweaters, providing an outfit suitable for cold seasons. In this way, the user can be offered the best outfits based on the listed clothes. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the clothing data listed by the listing unit into a generating AI and have the generating AI produce suggestions for the best outfits.

[0037] The generation unit can generate clothing designs that reflect the user's preferences. For example, the generation unit can generate clothing designs based on the user's preferred colors and styles. For instance, it can generate designs for shirts and pants in the user's preferred colors. The generation unit can also generate designs for formal or casual clothing based on the user's preferred style. For example, it can generate designs for formal jackets and casual T-shirts in the user's preferred style. Furthermore, the generation unit can generate optimal clothing designs according to the user's preferred season. For example, it can generate designs for winter coats and summer shirts. This allows the user to generate clothing designs based on their own preferences. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input elements of clothing designs preferred by the user into a generation AI and have the generation AI generate the optimal design.

[0038] The listing unit can analyze the user's past listing history and select a listing method. For example, the listing unit can automatically suggest a listing method that the user has frequently used in the past. The listing unit can also predict and suggest the optimal listing method for a specific time period based on the user's past listing history. For example, the listing unit can analyze the types and frequency of clothing items that the user has listed in the past and select the optimal listing method. In this way, the optimal listing method can be provided by analyzing the past listing history. Some or all of the above processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can input the user's past listing history data into a generating AI and have the generating AI select the optimal listing method.

[0039] The listing unit can filter clothing items based on the user's current fashion trends and the season. For example, the listing unit analyzes the user's current fashion trends and filters the clothing items to be listed. The listing unit can also prioritize listing appropriate clothing items according to the season. For example, the listing unit selects clothing items to list by referring to the user's past fashion history. This allows for the listing of appropriate clothing items based on the current fashion trends and the season. Some or all of the above processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can input the user's fashion trend data into a generating AI and have the generating AI perform the filtering.

[0040] The listing unit can prioritize listing clothing items that are highly relevant based on the user's geographical location information when creating a clothing list. For example, the listing unit can list appropriate clothing items based on the climate of the area where the user is currently located. Furthermore, if the user is traveling, the listing unit can list clothing items suitable for the climate and culture of their travel destination. For example, the listing unit can list clothing items that match the local fashion trends based on the user's geographical location information. This allows for the prioritization of highly relevant clothing items by considering geographical location information. Some or all of the above processing in the listing unit may be performed using AI, or not. For example, the listing unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of creating a list of highly relevant clothing items.

[0041] The listing unit can analyze a user's social media activity and list relevant clothing items when creating a clothing list. For example, the listing unit can list clothing styles that the user has "liked" on social media. The listing unit can also list clothing items based on the styles of fashion influencers that the user follows. For example, the listing unit can analyze recent fashion trends from the user's social media posts and select clothing items to list. In this way, relevant clothing items can be listed by analyzing social media activity. Some or all of the above processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of creating a list of relevant clothing items.

[0042] The visualization unit can adjust the level of detail displayed based on the importance of the clothing when visualizing usage frequency. For example, the visualization unit can display detailed information for frequently worn clothing and simplified information for less frequently worn clothing. The visualization unit can also adjust the level of detail displayed based on the price and brand of the clothing. For example, the visualization unit can customize the level of detail displayed according to the user's preference. This allows for more appropriate visualization by adjusting the level of detail displayed based on the importance of the clothing. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input clothing importance data into a generating AI and have the generating AI perform the adjustment of the level of detail displayed.

[0043] The visualization unit can apply different visualization algorithms depending on the clothing category when visualizing usage frequency. For example, the visualization unit can apply different visualization algorithms for casual wear and formal wear. Furthermore, the visualization unit can apply different visualization algorithms depending on the season. For example, the visualization unit can apply different visualization algorithms based on the material and color of the clothing. This allows for more appropriate visualization by applying different visualization algorithms according to the clothing category. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input clothing category data into a generating AI and have the generating AI execute the application of the visualization algorithm.

[0044] The visualization unit can determine the display priority based on the purchase date of the clothing when visualizing usage frequency. For example, the visualization unit may prioritize displaying recently purchased clothing. It can also prioritize displaying clothing that has been purchased a certain period of time ago. For example, the visualization unit may analyze the user's purchase history to determine the optimal display priority. This allows for more appropriate visualization by determining the display priority based on the purchase date of the clothing. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input clothing purchase date data into a generating AI and have the generating AI perform the determination of the display priority.

[0045] The visualization unit can adjust the display order based on the relevance of clothing items when visualizing usage frequency. For example, the visualization unit can group together clothing items from the same brand. It can also group together clothing items of the same color or material. For example, the visualization unit can group together highly relevant clothing items by referring to the user's coordination history. This allows for more appropriate visualization by adjusting the display order based on the relevance of clothing items. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input clothing relevance data into a generating AI and have the generating AI perform the adjustment of the display order.

[0046] The recommendation unit can adjust the level of detail in its recommendations based on the importance of the clothing items. For example, it can provide detailed recommendations for frequently worn clothing and simplified recommendations for less frequently worn clothing. The recommendation unit can also adjust the level of detail based on the price and brand of the clothing. For instance, it can customize the level of detail according to the user's preferences. This allows for more appropriate recommendations by adjusting the level of detail based on the importance of the clothing items. Some or all of the above processing in the recommendation unit may be performed using AI, or not. For example, the recommendation unit can input clothing importance data into a generating AI and have the generating AI adjust the level of detail in the recommendations.

[0047] The recommendation unit can apply different recommendation algorithms depending on the clothing category when making recommendations. For example, the recommendation unit can apply different recommendation algorithms for casual wear and formal wear. Furthermore, the recommendation unit can also apply different recommendation algorithms depending on the season. For example, the recommendation unit can apply different recommendation algorithms based on the material and color of the clothing. This allows for more appropriate recommendations by applying different recommendation algorithms depending on the clothing category. 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 clothing category data into a generating AI and have the generating AI execute the application of recommendation algorithms.

[0048] The recommendation unit can determine the recommendation priority based on the frequency of clothing use. For example, the recommendation unit can prioritize recommending clothes that are worn frequently. Alternatively, the recommendation unit can prioritize recommending clothes that are worn infrequently. For example, the recommendation unit can analyze the user's usage frequency and determine the optimal recommendation priority. This allows for more appropriate recommendations by determining the recommendation priority based on the frequency of clothing use. 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 clothing usage frequency data into a generating AI and have the generating AI perform the determination of recommendation priority.

[0049] The recommendation unit can adjust the order of recommendations based on the relevance of the clothing items. For example, the recommendation unit can recommend clothing items from the same brand together. It can also recommend clothing items of the same color or material together. For example, the recommendation unit can recommend highly relevant clothing items together by referring to the user's coordination history. By adjusting the order of recommendations based on the relevance of the clothing items, more appropriate recommendations become possible. 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 clothing relevance data into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0050] The suggestion unit can analyze the user's past outfit history to provide optimal suggestions when suggesting outfits. For example, the suggestion unit can make optimal suggestions based on outfits the user has previously enjoyed wearing. Furthermore, the suggestion unit can also provide suggestions suitable for specific events based on the user's past outfit history. For example, the suggestion unit can analyze the user's past outfit history and provide optimal suggestions based on the season and weather. This allows for optimal suggestions by analyzing past outfit history. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the user's past outfit history data into a generating AI and have the generating AI execute optimal suggestions.

[0051] The suggestion unit can customize its suggestions based on the user's current fashion trends when providing outfit suggestions. For example, the suggestion unit can analyze the user's current fashion trends and suggest the most suitable outfit. It can also suggest outfits based on clothes the user has recently purchased. For example, the suggestion unit can analyze the user's social media activity and make suggestions based on current fashion trends. By customizing suggestions based on current fashion trends, more appropriate suggestions can be made. Some or all of the above processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's fashion trend data into a generating AI and have the generating AI perform the customization of suggestions.

[0052] The suggestion unit can make optimal suggestions by considering the user's geographical location when proposing outfits. For example, the suggestion unit can suggest an appropriate outfit based on the climate of the area where the user is currently located. Furthermore, if the user is traveling, the suggestion unit can suggest an outfit suitable for the climate and culture of their destination. For example, the suggestion unit can suggest an outfit that matches the local fashion trends based on the user's geographical location. This allows for optimal outfit suggestions by considering geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI execute the optimal suggestion.

[0053] The suggestion unit can analyze the user's social media activity when suggesting outfits. For example, the suggestion unit can suggest outfit styles that the user has "liked" on social media. The suggestion unit can also suggest outfits based on the styles of fashion influencers that the user follows. For example, the suggestion unit can analyze recent fashion trends from the user's social media posts and make suggestions. By analyzing social media activity, it becomes possible to make more appropriate outfit suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI execute the suggestions.

[0054] The generation unit can generate the optimal design by analyzing the user's past design history when generating clothing designs. For example, the generation unit can generate the optimal design based on designs the user has previously preferred to wear. The generation unit can also generate designs suitable for specific events based on the user's past design history. For example, the generation unit can analyze the user's past design history and generate the optimal design according to the season and weather. This makes it possible to generate the optimal design by analyzing past design history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's past design history data into a generation AI and have the generation AI execute the generation of the optimal design.

[0055] The generation unit can customize clothing designs based on the user's current fashion trends. For example, the generation unit can analyze the user's current fashion trends and generate the optimal design. The generation unit can also customize designs based on clothes the user has recently purchased. For example, the generation unit can analyze the user's social media activity and generate designs based on current fashion trends. This allows for more appropriate designs by customizing them based on current fashion trends. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's fashion trend data into a generation AI and have the generation AI perform the design customization.

[0056] The generation unit can generate optimal clothing designs by considering the user's geographical location information. For example, the generation unit can generate appropriate designs based on the climate of the area where the user is currently located. Furthermore, if the user is traveling, the generation unit can generate designs suitable for the climate and culture of their travel destination. For example, the generation unit can generate designs that match the local fashion trends based on the user's geographical location information. This allows for optimal designs by considering geographical location information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI generate the optimal design.

[0057] The generation unit can generate clothing designs by analyzing the user's social media activity. For example, the generation unit can generate designs based on the styles of designs the user has "liked" on social media. The generation unit can also generate designs by referencing the styles of fashion influencers the user follows. For example, the generation unit can analyze recent fashion trends from the user's social media posts and generate designs. This allows for more appropriate designs by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the design generation.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The listing unit can analyze the user's past purchase history and determine the priority of clothing items to list. For example, the listing unit prioritizes listing brands and styles that the user frequently purchases. The listing unit can also adjust the priority of clothing items to list by considering how often the user has worn the clothes they have purchased in the past. For example, the listing unit prioritizes listing clothes that the user frequently wears and postpones listing clothes that are rarely worn. This allows for more appropriate listing by considering the user's past purchase history. Some or all of the above processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can input the user's purchase history data into a generating AI and have the generating AI perform the determination of listing priorities.

[0060] The listing unit can determine the priority of clothing items to list based on the user's geographical location. For example, the listing unit prioritizes listing appropriate clothing items based on the climate of the user's current location. Furthermore, if the user is traveling, the listing unit can list clothing items suitable for the climate and culture of their destination. For example, the listing unit lists clothing items that match the local fashion trends based on the user's geographical location. This allows for the prioritization of highly relevant clothing items by considering geographical location. Some or all of the above processing in the listing unit may be performed using AI, or without AI. For example, the listing unit can input the user's geographical location data into a generating AI and have the generating AI create a list of highly relevant clothing items.

[0061] The visualization unit can determine the display priority based on the purchase date of the clothing when visualizing usage frequency. For example, the visualization unit can prioritize displaying recently purchased clothing. It can also prioritize displaying clothing that has been purchased a certain period of time ago. For example, the visualization unit can analyze the user's purchase history to determine the optimal display priority. This allows for more appropriate visualization by determining the display priority based on the purchase date of the clothing. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input clothing purchase date data into a generating AI and have the generating AI perform the determination of the display priority.

[0062] The recommendation unit can determine the recommendation priority based on the frequency of clothing use. For example, the recommendation unit can prioritize recommending clothes that are worn frequently. It can also prioritize recommending clothes that are worn infrequently. For example, the recommendation unit can analyze the user's usage frequency and determine the optimal recommendation priority. By determining the recommendation priority based on the frequency of clothing use, more appropriate recommendations become possible. 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 clothing usage frequency data into a generating AI and have the generating AI perform the determination of recommendation priority.

[0063] The suggestion unit can analyze the user's past outfit history to provide optimal suggestions when suggesting outfits. For example, the suggestion unit can provide optimal suggestions based on outfits the user has previously enjoyed wearing. Furthermore, the suggestion unit can also provide suggestions suitable for specific events based on the user's past outfit history. For example, the suggestion unit can analyze the user's past outfit history and provide optimal suggestions based on the season and weather. This allows for optimal suggestions by analyzing past outfit history. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the user's past outfit history data into a generating AI and have the generating AI execute optimal suggestions.

[0064] The generation unit can generate clothing designs by analyzing the user's social media activity. For example, the generation unit can generate designs based on the styles of designs the user has "liked" on social media. The generation unit can also generate designs by referencing the styles of fashion influencers the user follows. For example, the generation unit can analyze recent fashion trends from the user's social media posts and generate designs. This allows for more appropriate designs by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the design generation.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The listing section lists the clothes owned by the user. Users can upload images of their clothes by taking photos of them, or manually enter information about their clothes. The listing section provides an interface for entering information such as the brand, size, color, and material of the clothes. Step 2: The visualization unit visualizes the frequency of use of the clothing items listed by the listing unit. The frequency of use is calculated based on the number of times the clothing is worn and the duration of wear, and is visually displayed in graph, chart, or calendar format. Step 3: The recommendation unit makes recommendations to the user based on the usage frequency visualized by the visualization unit. Recommendations include suggestion actions such as "You're wearing this again" for frequently used clothes and "You haven't worn this in a while" for less frequently used clothes. Recommendations are also made based on the condition of the clothes and the season.

[0067] (Example of form 2) The clothing management system according to an embodiment of the present invention is a system that efficiently manages the clothes owned by a user and makes recommendations based on the frequency of use. This clothing management system lists the clothes owned by the user, visualizes the frequency of use of the listed clothes, and makes recommendations based on the visualized frequency of use. The clothing management system also proposes optimal coordination based on the clothes owned by the user and generates clothing designs that the user prefers. For example, the clothing management system stores the clothes owned by the user in a database by taking pictures of them and uploading them. Next, the clothing management system visualizes the frequency of use of the listed clothes, allowing the user to grasp the frequency of use of their clothes at a glance. Furthermore, based on the visualized frequency of use, the clothing management system recommends actions to the user, such as "You're wearing this again" or "You haven't worn this in a while." The clothing management system also proposes optimal coordination based on the clothes owned by the user and generates clothing designs that the user prefers. As a result, the user can efficiently manage the clothes they own and receive recommendations based on the frequency of use, thus solving the problems of having too many similar clothes or having storage space overflowing due to having many similar clothes. Furthermore, since users can generate their own preferred clothing designs, ambiguous and potential issues are also resolved. This allows the clothing management system to streamline users' clothing management and provide recommendations based on usage frequency.

[0068] The clothing management system according to this embodiment comprises a listing unit, a visualization unit, and a recommendation unit. The listing unit lists the clothing owned by the user. The clothing owned by the user includes, but is not limited to, shirts, pants, and jackets. The listing unit registers the information of the clothing in the database, for example, when the user takes a picture of the clothing and uploads the image. The listing unit also allows the user to manually input information about the clothing. For example, the listing unit provides an interface for inputting information such as the brand, size, color, and material of the clothing. The visualization unit visualizes the frequency of use of the clothing listed by the listing unit. The frequency of use is calculated, for example, based on the number of times the clothing is worn or the duration of wear, but is not limited to this example. The visualization unit visually displays the frequency of use of the clothing, for example, using graphs or charts. The visualization unit can also display the frequency of use of the clothing in a calendar format. For example, the visualization unit displays the frequency of use of each piece of clothing by date, allowing the user to see at a glance which clothes they wear and how often. The recommendation unit makes recommendations to the user based on the frequency of use visualized by the visualization unit. Recommendations include, but are not limited to, suggested actions such as "You're wearing this again" or "You haven't worn this in a while." For example, the recommendation unit might recommend "You're wearing this again" for frequently used clothing and "You haven't worn this in a while" for infrequently used clothing. The recommendation unit can also make recommendations based on the condition of the clothing or the season. For example, if winter clothing is not being worn in the summer, the recommendation unit might recommend "Let's organize your winter clothes." As a result, the clothing management system according to this embodiment can streamline the user's clothing management and provide recommendations based on frequency of use.

[0069] The listing unit lists the clothes owned by the user. These clothes include, but are not limited to, shirts, pants, and jackets. The listing unit registers clothing information in its database, for example, when the user takes a picture of their clothes and uploads the image. Specifically, the user takes a picture of their clothes with a smartphone or digital camera and uploads the image through a dedicated application. The uploaded image is analyzed by AI, and information such as the type of clothing, brand, size, color, and material is automatically extracted. The AI ​​uses image recognition technology to identify the characteristics of the clothing and register them in the database. The listing unit also allows the user to manually input clothing information. For example, the listing unit provides an interface for inputting information such as the brand, size, color, and material of the clothing. The user can manually enter each item using the application's input form and register it in the database. Furthermore, the listing unit also has a function to quickly register clothing information using barcodes and QR codes. By scanning the barcode or QR code on the tag attached to the clothing, the relevant information is automatically added to the database. This allows the listing unit to efficiently and accurately list information about the clothes owned by the user and register it in the database.

[0070] The visualization unit visualizes the frequency of use of clothing listed by the listing unit. Frequency of use is calculated based on, for example, the number of times or the duration of wear, but is not limited to such examples. The visualization unit visually displays the frequency of use of clothing using, for example, graphs and charts. Specifically, it can visually compare the frequency of use of each piece of clothing using bar graphs, pie charts, line graphs, etc. The visualization unit can also display the frequency of use of clothing in a calendar format. For example, the visualization unit can display the frequency of use of each piece of clothing by day, allowing the user to see at a glance which clothes they wear and how often. Furthermore, the visualization unit also has a function to provide visual feedback to the user based on the frequency of use of clothing. For example, it can display frequently used clothing in a darker color and less frequently used clothing in a lighter color, allowing the user to intuitively understand their usage status. The visualization unit can also provide statistical information regarding the frequency of use of clothing. For example, it can display the number of uses per month or the annual usage trend in a graph, allowing the user to understand their long-term usage patterns. This allows the visualization unit to gain a detailed understanding of how the user's clothes are being used, supporting efficient clothing management.

[0071] The recommendation unit makes recommendations to the user based on the frequency of use visualized by the visualization unit. Recommendations include, but are not limited to, suggested actions such as "You're wearing this again" or "You haven't worn this in a while." For example, the recommendation unit might recommend "You're wearing this again" for frequently used clothing and "You haven't worn this in a while" for infrequently used clothing. Specifically, the recommendation unit uses AI to analyze the user's clothing usage patterns and generate optimal recommendations. The AI ​​considers past usage data and external factors such as season and weather to suggest the best clothing choices for the user. For example, if winter clothes are not being worn in the summer, the recommendation unit might recommend "Let's organize your winter clothes." The recommendation unit can also make recommendations based on the condition of the clothing and the season. For example, if the clothing is deteriorated, it might recommend "This clothing needs repair," and depending on the season, it might suggest "This clothing is perfect for this season." Furthermore, the recommendation system can also suggest outfits based on the user's preferences and style. For example, if a user prefers a particular color or style, it can suggest the most suitable outfit based on that information. This allows the recommendation system to streamline the user's clothing management and provide recommendations based on usage frequency.

[0072] The clothing management system includes a suggestion unit that proposes outfits based on the clothes owned by the user. The suggestion unit proposes the optimal outfit based on the clothes owned by the user. For example, the suggestion unit analyzes the colors and styles of the clothes owned by the user and proposes the best combinations. For example, the suggestion unit proposes a color combination of shirts and pants owned by the user. The suggestion unit can also propose formal or casual outfits based on the style of the clothes owned by the user. For example, the suggestion unit proposes a combination of jackets and pants owned by the user, providing an outfit suitable for formal occasions. The suggestion unit can also propose the optimal outfit according to the season of the clothes owned by the user. For example, the suggestion unit proposes a combination of winter coats and sweaters, providing an outfit suitable for cold seasons. This allows the user to be offered the best outfit using the clothes they own. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data on the clothes owned by the user into a generating AI and have the generating AI produce an optimal outfit suggestion.

[0073] The clothing management system includes a generation unit that generates clothing designs preferred by the user. The generation unit generates clothing designs preferred by the user. For example, the generation unit generates clothing designs based on the user's preferred colors and styles. For example, the generation unit generates designs for shirts and pants in the user's preferred colors. The generation unit can also generate designs for formal or casual clothing based on the user's preferred style. For example, the generation unit generates designs for formal jackets and casual T-shirts preferred by the user. The generation unit can also generate optimal clothing designs according to the user's preferred season. For example, the generation unit generates designs for winter coats and summer shirts. This allows the user to generate clothing designs to their liking. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input elements of clothing designs preferred by the user into the generation AI and have the generation AI generate the optimal design.

[0074] The recommendation unit can recommend actions to the user, such as "wearing it again" or "haven't worn it in a while," based on the frequency of use visualized by the visualization unit. For example, the recommendation unit can recommend "wearing it again" for clothing that is frequently worn. It can also recommend "haven't worn it in a while" for clothing that is rarely worn. For example, the recommendation unit can recommend "wearing it again" for a shirt that the user frequently wears, and "haven't worn it in a while" for a jacket that the user rarely wears. The recommendation unit can also make recommendations based on the condition of the clothing or the season. For example, if winter clothes are not being worn in the summer, the recommendation unit can recommend "organize your winter clothes." This allows the user to receive specific recommendations based on their frequency of use. 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 usage frequency data visualized by the visualization unit into the generating AI, and have the generating AI perform the generation of recommendations.

[0075] The suggestion unit can propose outfits based on the clothing listed by the listing unit. For example, the suggestion unit can analyze the colors and styles of the clothes owned by the user and propose the best combinations. For example, the suggestion unit can propose color combinations of shirts and pants owned by the user. The suggestion unit can also propose formal and casual outfits based on the styles of the clothes owned by the user. For example, the suggestion unit can propose a combination of jackets and pants owned by the user, providing an outfit suitable for formal occasions. The suggestion unit can also propose the best outfits according to the season of the clothes owned by the user. For example, the suggestion unit can propose a combination of winter coats and sweaters, providing an outfit suitable for cold seasons. In this way, the user can be offered the best outfits based on the listed clothes. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the clothing data listed by the listing unit into a generating AI and have the generating AI produce suggestions for the best outfits.

[0076] The generation unit can generate clothing designs that reflect the user's preferences. For example, the generation unit can generate clothing designs based on the user's preferred colors and styles. For instance, it can generate designs for shirts and pants in the user's preferred colors. The generation unit can also generate designs for formal or casual clothing based on the user's preferred style. For example, it can generate designs for formal jackets and casual T-shirts in the user's preferred style. Furthermore, the generation unit can generate optimal clothing designs according to the user's preferred season. For example, it can generate designs for winter coats and summer shirts. This allows the user to generate clothing designs based on their own preferences. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input elements of clothing designs preferred by the user into a generation AI and have the generation AI generate the optimal design.

[0077] The listing unit can estimate the user's emotions and adjust the timing of listing clothes based on the estimated emotions. For example, if the user is stressed, the listing unit may simplify the listing process to complete it quickly. If the user is relaxed, the listing unit may also provide detailed listing options and suggest a customizable listing method. For example, if the user is in a hurry, the listing unit may prioritize voice input to allow for quick listing of clothes. This allows for more appropriate listing by adjusting the timing of listing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the listing unit may be performed using AI or not. For example, the listing unit can input user emotion data into the generative AI and have the generative AI adjust the timing of listing.

[0078] The listing unit can analyze the user's past listing history and select a listing method. For example, the listing unit can automatically suggest a listing method that the user has frequently used in the past. The listing unit can also predict and suggest the optimal listing method for a specific time period based on the user's past listing history. For example, the listing unit can analyze the types and frequency of clothing items that the user has listed in the past and select the optimal listing method. In this way, the optimal listing method can be provided by analyzing the past listing history. Some or all of the above processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can input the user's past listing history data into a generating AI and have the generating AI select the optimal listing method.

[0079] The listing unit can filter clothing items based on the user's current fashion trends and the season. For example, the listing unit analyzes the user's current fashion trends and filters the clothing items to be listed. The listing unit can also prioritize listing appropriate clothing items according to the season. For example, the listing unit selects clothing items to list by referring to the user's past fashion history. This allows for the listing of appropriate clothing items based on the current fashion trends and the season. Some or all of the above processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can input the user's fashion trend data into a generating AI and have the generating AI perform the filtering.

[0080] The listing unit can estimate the user's emotions and determine the priority of the clothes to list based on the estimated emotions. For example, if the user is stressed, the listing unit may simplify the listing process to complete it quickly. If the user is relaxed, the listing unit may also provide detailed listing options and suggest a customizable listing method. For example, if the user is in a hurry, the listing unit may prioritize voice input to allow for quick listing of clothes. This enables more appropriate listing by determining the priority of the clothes to list according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the listing unit may be performed using AI or not. For example, the listing unit can input user emotion data into a generative AI and have the generative AI determine the listing priority.

[0081] The listing unit can prioritize listing clothing items that are highly relevant based on the user's geographical location information when creating a clothing list. For example, the listing unit can list appropriate clothing items based on the climate of the area where the user is currently located. Furthermore, if the user is traveling, the listing unit can list clothing items suitable for the climate and culture of their travel destination. For example, the listing unit can list clothing items that match the local fashion trends based on the user's geographical location information. This allows for the prioritization of highly relevant clothing items by considering geographical location information. Some or all of the above processing in the listing unit may be performed using AI, or not. For example, the listing unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of creating a list of highly relevant clothing items.

[0082] The listing unit can analyze a user's social media activity and list relevant clothing items when creating a clothing list. For example, the listing unit can list clothing styles that the user has "liked" on social media. The listing unit can also list clothing items based on the styles of fashion influencers that the user follows. For example, the listing unit can analyze recent fashion trends from the user's social media posts and select clothing items to list. In this way, relevant clothing items can be listed by analyzing social media activity. Some or all of the above processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of creating a list of relevant clothing items.

[0083] The visualization unit can estimate the user's emotions and adjust the display method for usage frequency based on the estimated emotions. For example, if the user is stressed, the visualization unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is in a hurry, the visualization unit can provide a concise display method. This allows for more appropriate display by adjusting the usage frequency display method according to 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-described processing in the visualization unit may be performed using AI, or not. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI adjust the display method.

[0084] The visualization unit can adjust the level of detail displayed based on the importance of the clothing when visualizing usage frequency. For example, the visualization unit can display detailed information for frequently worn clothing and simplified information for less frequently worn clothing. The visualization unit can also adjust the level of detail displayed based on the price and brand of the clothing. For example, the visualization unit can customize the level of detail displayed according to the user's preference. This allows for more appropriate visualization by adjusting the level of detail displayed based on the importance of the clothing. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input clothing importance data into a generating AI and have the generating AI perform the adjustment of the level of detail displayed.

[0085] The visualization unit can apply different visualization algorithms depending on the clothing category when visualizing usage frequency. For example, the visualization unit can apply different visualization algorithms for casual wear and formal wear. Furthermore, the visualization unit can apply different visualization algorithms depending on the season. For example, the visualization unit can apply different visualization algorithms based on the material and color of the clothing. This allows for more appropriate visualization by applying different visualization algorithms according to the clothing category. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input clothing category data into a generating AI and have the generating AI execute the application of the visualization algorithm.

[0086] The visualization unit can estimate the user's emotions and adjust the display order of frequently used items based on the estimated emotions. For example, if the user is stressed, the visualization unit may prioritize displaying frequently worn clothing. Furthermore, if the user is relaxed, the visualization unit can provide a display order that includes detailed information. For example, if the user is in a hurry, the visualization unit may provide a concise display order. This allows for a more appropriate display by adjusting the display order of frequently used items according to 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the visualization unit may be performed using AI, or not. For example, the visualization unit can input user emotion data into a generative AI and have the generative AI adjust the display order.

[0087] The visualization unit can determine the display priority based on the purchase date of the clothing when visualizing usage frequency. For example, the visualization unit may prioritize displaying recently purchased clothing. It can also prioritize displaying clothing that has been purchased a certain period of time ago. For example, the visualization unit may analyze the user's purchase history to determine the optimal display priority. This allows for more appropriate visualization by determining the display priority based on the purchase date of the clothing. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input clothing purchase date data into a generating AI and have the generating AI perform the determination of the display priority.

[0088] The visualization unit can adjust the display order based on the relevance of clothing items when visualizing usage frequency. For example, the visualization unit can group together clothing items from the same brand. It can also group together clothing items of the same color or material. For example, the visualization unit can group together highly relevant clothing items by referring to the user's coordination history. This allows for more appropriate visualization by adjusting the display order based on the relevance of clothing items. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input clothing relevance data into a generating AI and have the generating AI perform the adjustment of the display order.

[0089] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is stressed, the recommendation unit can provide simple and easy-to-understand recommendations. If the user is relaxed, the recommendation unit can also provide recommendations that include more detailed information. For example, if the user is in a hurry, the recommendation unit can provide concise recommendations. By adjusting the way recommendations are presented according to the user's emotions, more appropriate recommendations can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user emotion data into the generative AI and have the generative AI adjust the way recommendations are presented.

[0090] The recommendation unit can adjust the level of detail in its recommendations based on the importance of the clothing items. For example, it can provide detailed recommendations for frequently worn clothing and simplified recommendations for less frequently worn clothing. The recommendation unit can also adjust the level of detail based on the price and brand of the clothing. For instance, it can customize the level of detail according to the user's preferences. This allows for more appropriate recommendations by adjusting the level of detail based on the importance of the clothing items. Some or all of the above processing in the recommendation unit may be performed using AI, or not. For example, the recommendation unit can input clothing importance data into a generating AI and have the generating AI adjust the level of detail in the recommendations.

[0091] The recommendation unit can apply different recommendation algorithms depending on the clothing category when making recommendations. For example, the recommendation unit can apply different recommendation algorithms for casual wear and formal wear. Furthermore, the recommendation unit can also apply different recommendation algorithms depending on the season. For example, the recommendation unit can apply different recommendation algorithms based on the material and color of the clothing. This allows for more appropriate recommendations by applying different recommendation algorithms depending on the clothing category. 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 clothing category data into a generating AI and have the generating AI execute the application of recommendation algorithms.

[0092] The recommendation unit can estimate the user's emotions and adjust the length of recommendations based on the estimated emotions. For example, if the user is stressed, the recommendation unit will provide short, concise recommendations. Conversely, if the user is relaxed, the recommendation unit can provide longer recommendations with more detailed explanations. For example, if the user is in a hurry, the recommendation unit will provide quick and concise recommendations. By adjusting the length of recommendations according to the user's emotions, more appropriate recommendations can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user emotion data into the generative AI and have the generative AI adjust the length of the recommendations.

[0093] The recommendation unit can determine the recommendation priority based on the frequency of clothing use. For example, the recommendation unit can prioritize recommending clothes that are worn frequently. Alternatively, the recommendation unit can prioritize recommending clothes that are worn infrequently. For example, the recommendation unit can analyze the user's usage frequency and determine the optimal recommendation priority. This allows for more appropriate recommendations by determining the recommendation priority based on the frequency of clothing use. 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 clothing usage frequency data into a generating AI and have the generating AI perform the determination of recommendation priority.

[0094] The recommendation unit can adjust the order of recommendations based on the relevance of the clothing items. For example, the recommendation unit can recommend clothing items from the same brand together. It can also recommend clothing items of the same color or material together. For example, the recommendation unit can recommend highly relevant clothing items together by referring to the user's coordination history. By adjusting the order of recommendations based on the relevance of the clothing items, more appropriate recommendations become possible. 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 clothing relevance data into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0095] The suggestion unit can estimate the user's emotions and adjust its coordination suggestion method based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide simple and highly visible coordination suggestions. If the user is relaxed, the suggestion unit can also provide coordination suggestions that include detailed information. For example, if the user is in a hurry, the suggestion unit can provide concise coordination suggestions. By adjusting the coordination suggestion method 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 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the coordination suggestion method.

[0096] The suggestion unit can analyze the user's past outfit history to provide optimal suggestions when suggesting outfits. For example, the suggestion unit can make optimal suggestions based on outfits the user has previously enjoyed wearing. Furthermore, the suggestion unit can also provide suggestions suitable for specific events based on the user's past outfit history. For example, the suggestion unit can analyze the user's past outfit history and provide optimal suggestions based on the season and weather. This allows for optimal suggestions by analyzing past outfit history. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the user's past outfit history data into a generating AI and have the generating AI execute optimal suggestions.

[0097] The suggestion unit can customize its suggestions based on the user's current fashion trends when providing outfit suggestions. For example, the suggestion unit can analyze the user's current fashion trends and suggest the most suitable outfit. It can also suggest outfits based on clothes the user has recently purchased. For example, the suggestion unit can analyze the user's social media activity and make suggestions based on current fashion trends. By customizing suggestions based on current fashion trends, more appropriate suggestions can be made. Some or all of the above processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's fashion trend data into a generating AI and have the generating AI perform the customization of suggestions.

[0098] The suggestion unit can estimate the user's emotions and determine the priority of outfits based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and highly visible outfit suggestions. If the user is relaxed, the suggestion unit can also provide outfit suggestions that include more detailed information. For example, if the user is in a hurry, the suggestion unit can provide concise outfit suggestions. This allows for more appropriate suggestions by determining the priority of outfits according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of outfits.

[0099] The suggestion unit can make optimal suggestions by considering the user's geographical location when proposing outfits. For example, the suggestion unit can suggest an appropriate outfit based on the climate of the area where the user is currently located. Furthermore, if the user is traveling, the suggestion unit can suggest an outfit suitable for the climate and culture of their destination. For example, the suggestion unit can suggest an outfit that matches the local fashion trends based on the user's geographical location. This allows for optimal outfit suggestions by considering geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI execute the optimal suggestion.

[0100] The suggestion unit can analyze the user's social media activity when suggesting outfits. For example, the suggestion unit can suggest outfit styles that the user has "liked" on social media. The suggestion unit can also suggest outfits based on the styles of fashion influencers that the user follows. For example, the suggestion unit can analyze recent fashion trends from the user's social media posts and make suggestions. By analyzing social media activity, it becomes possible to make more appropriate outfit suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI execute the suggestions.

[0101] The generation unit can estimate the user's emotions and adjust the method of generating clothing designs based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate loosely designed clothing. If the user is in a hurry, the generation unit can also provide a simple and quickly generated design. For example, if the user is excited, the generation unit will generate a visually stimulating design. This allows for more appropriate designs by adjusting the clothing design generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the design generation method.

[0102] The generation unit can generate the optimal design by analyzing the user's past design history when generating clothing designs. For example, the generation unit can generate the optimal design based on designs the user has previously preferred to wear. The generation unit can also generate designs suitable for specific events based on the user's past design history. For example, the generation unit can analyze the user's past design history and generate the optimal design according to the season and weather. This makes it possible to generate the optimal design by analyzing past design history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's past design history data into a generation AI and have the generation AI execute the generation of the optimal design.

[0103] The generation unit can customize clothing designs based on the user's current fashion trends. For example, the generation unit can analyze the user's current fashion trends and generate the optimal design. The generation unit can also customize designs based on clothes the user has recently purchased. For example, the generation unit can analyze the user's social media activity and generate designs based on current fashion trends. This allows for more appropriate designs by customizing them based on current fashion trends. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's fashion trend data into a generation AI and have the generation AI perform the design customization.

[0104] The generation unit can estimate the user's emotions and determine design priorities based on those emotions. For example, if the user is relaxed, the generation unit will prioritize generating loose-fitting clothing designs. If the user is in a hurry, the generation unit can prioritize providing simple and quickly generated designs. For example, if the user is excited, the generation unit will prioritize generating visually stimulating designs. This allows for more appropriate designs by prioritizing designs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI determine design priorities.

[0105] The generation unit can generate optimal clothing designs by considering the user's geographical location information. For example, the generation unit can generate appropriate designs based on the climate of the area where the user is currently located. Furthermore, if the user is traveling, the generation unit can generate designs suitable for the climate and culture of their travel destination. For example, the generation unit can generate designs that match the local fashion trends based on the user's geographical location information. This allows for optimal designs by considering geographical location information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI generate the optimal design.

[0106] The generation unit can generate clothing designs by analyzing the user's social media activity. For example, the generation unit can generate designs based on the styles of designs the user has "liked" on social media. The generation unit can also generate designs by referencing the styles of fashion influencers the user follows. For example, the generation unit can analyze recent fashion trends from the user's social media posts and generate designs. This allows for more appropriate designs by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the design generation.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The listing unit can analyze the user's past purchase history and determine the priority of clothing items to list. For example, the listing unit prioritizes listing brands and styles that the user frequently purchases. The listing unit can also adjust the priority of clothing items to list by considering how often the user has worn the clothes they have purchased in the past. For example, the listing unit prioritizes listing clothes that the user frequently wears and postpones listing clothes that are rarely worn. This allows for more appropriate listing by considering the user's past purchase history. Some or all of the above processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can input the user's purchase history data into a generating AI and have the generating AI perform the determination of listing priorities.

[0109] The suggestion unit can estimate the user's emotions and adjust its coordination suggestion method based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide simple and highly visible coordination suggestions. If the user is relaxed, the suggestion unit can also provide coordination suggestions that include detailed information. For example, if the user is in a hurry, the suggestion unit can provide concise coordination suggestions. By adjusting the coordination suggestion method 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 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the coordination suggestion method.

[0110] The generation unit can estimate the user's emotions and adjust the method of generating clothing designs based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate loosely designed clothing. If the user is in a hurry, the generation unit can also provide a simple and quickly generated design. For example, if the user is excited, the generation unit will generate a visually stimulating design. By adjusting the method of generating clothing designs according to the user's emotions, more appropriate designs can be created. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the design generation method.

[0111] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is stressed, the recommendation unit can provide simple and easy-to-understand recommendations. If the user is relaxed, the recommendation unit can also provide recommendations that include more detailed information. For example, if the user is in a hurry, the recommendation unit can provide concise recommendations. By adjusting the way recommendations are presented according to the user's emotions, more appropriate recommendations can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, or not. For example, the recommendation unit can input user emotion data into the generative AI and have the generative AI adjust the way recommendations are presented.

[0112] The listing unit can determine the priority of clothing items to list based on the user's geographical location. For example, the listing unit prioritizes listing appropriate clothing items based on the climate of the user's current location. Furthermore, if the user is traveling, the listing unit can list clothing items suitable for the climate and culture of their destination. For example, the listing unit lists clothing items that match the local fashion trends based on the user's geographical location. This allows for the prioritization of highly relevant clothing items by considering geographical location. Some or all of the above processing in the listing unit may be performed using AI, or without AI. For example, the listing unit can input the user's geographical location data into a generating AI and have the generating AI create a list of highly relevant clothing items.

[0113] The visualization unit can determine the display priority based on the purchase date of the clothing when visualizing usage frequency. For example, the visualization unit can prioritize displaying recently purchased clothing. It can also prioritize displaying clothing that has been purchased a certain period of time ago. For example, the visualization unit can analyze the user's purchase history to determine the optimal display priority. This allows for more appropriate visualization by determining the display priority based on the purchase date of the clothing. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input clothing purchase date data into a generating AI and have the generating AI perform the determination of the display priority.

[0114] The recommendation unit can determine the recommendation priority based on the frequency of clothing use. For example, the recommendation unit can prioritize recommending clothes that are worn frequently. It can also prioritize recommending clothes that are worn infrequently. For example, the recommendation unit can analyze the user's usage frequency and determine the optimal recommendation priority. By determining the recommendation priority based on the frequency of clothing use, more appropriate recommendations become possible. 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 clothing usage frequency data into a generating AI and have the generating AI perform the determination of recommendation priority.

[0115] The suggestion unit can analyze the user's past outfit history to provide optimal suggestions when suggesting outfits. For example, the suggestion unit can provide optimal suggestions based on outfits the user has previously enjoyed wearing. Furthermore, the suggestion unit can also provide suggestions suitable for specific events based on the user's past outfit history. For example, the suggestion unit can analyze the user's past outfit history and provide optimal suggestions based on the season and weather. This allows for optimal suggestions by analyzing past outfit history. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the user's past outfit history data into a generating AI and have the generating AI execute optimal suggestions.

[0116] The generation unit can generate clothing designs by analyzing the user's social media activity. For example, the generation unit can generate designs based on the styles of designs the user has "liked" on social media. The generation unit can also generate designs by referencing the styles of fashion influencers the user follows. For example, the generation unit can analyze recent fashion trends from the user's social media posts and generate designs. This allows for more appropriate designs by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the design generation.

[0117] The visualization unit can estimate the user's emotions and adjust the display order of frequently used items based on the estimated emotions. For example, if the user is feeling stressed, the visualization unit will prioritize displaying frequently worn clothing. Furthermore, if the user is relaxed, the visualization unit can provide a display order that includes detailed information. For example, if the user is in a hurry, the visualization unit will provide a concise display order. This allows for a more appropriate display by adjusting the display order of frequently used items according to 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the visualization unit may be performed using AI, or not. For example, the visualization unit can input user emotion data into a generative AI and have the generative AI adjust the display order.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The listing section lists the clothes owned by the user. Users can upload images of their clothes by taking photos of them, or manually enter information about their clothes. The listing section provides an interface for entering information such as the brand, size, color, and material of the clothes. Step 2: The visualization unit visualizes the frequency of use of the clothing items listed by the listing unit. The frequency of use is calculated based on the number of times the clothing is worn and the duration of wear, and is visually displayed in graph, chart, or calendar format. Step 3: The recommendation unit makes recommendations to the user based on the usage frequency visualized by the visualization unit. Recommendations include suggestion actions such as "You're wearing this again" for frequently used clothes and "You haven't worn this in a while" for less frequently used clothes. Recommendations are also made based on the condition of the clothes and the season.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] For example, the listing unit is implemented by either the data processing unit 12 or the smart device 14. For instance, the listing unit uses the camera 42 of the smart device 14 to photograph the clothes owned by the user and uploads the images to the database 24 of the data processing unit 12 to register the information about the clothes. The visualization unit is implemented by the specific processing unit 290 of the data processing unit 12 and displays the frequency of use of the listed clothes in graphs and charts. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes recommendations to the user based on the frequency of use. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] For example, the listing unit is implemented by either the data processing unit 12 or the smart glasses 214. For instance, the listing unit uses the camera 42 of the smart glasses 214 to photograph the clothes owned by the user and uploads the images to the database 24 of the data processing unit 12 to register the information about the clothes. The visualization unit is implemented by the identification processing unit 290 of the data processing unit 12 and displays the frequency of use of the listed clothes in graphs and charts. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and makes recommendations to the user based on the frequency of use. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] For example, the listing unit is implemented in either the data processing unit 12 or the headset terminal 314. For instance, the listing unit uses the camera 42 of the headset terminal 314 to photograph the clothes owned by the user and uploads the images to the database 24 of the data processing unit 12 to register the information about the clothes. The visualization unit is implemented by the specific processing unit 290 of the data processing unit 12 and displays the frequency of use of the listed clothes in graphs and charts. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes recommendations to the user based on the frequency of use. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] For example, the listing unit is implemented by either the data processing unit 12 or the robot 414. For instance, the listing unit uses the camera 42 of the robot 414 to photograph the clothes owned by the user and uploads the images to the database 24 of the data processing unit 12 to register the information about the clothes. The visualization unit is implemented by the specific processing unit 290 of the data processing unit 12 and displays the usage frequency of the listed clothes in graphs and charts. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes recommendations to the user based on usage frequency. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Note 1) A listing unit that lists the clothes owned by the user, A visualization unit visualizes the frequency of use of the clothing listed by the listing unit, The system includes a recommendation unit that makes recommendations to the user based on the usage frequency visualized by the visualization unit. A system characterized by the following features. (Note 2) The system includes a suggestion section that proposes outfit combinations based on the clothes the user owns. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a generation unit that generates clothing designs preferred by the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The recommendation unit is, Based on the usage frequency visualized by the visualization unit, the system recommends actions to the user, such as "I'm wearing it again" or "I haven't worn it in a while." The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the clothing items listed by the listing unit, we propose outfit combinations. The system described in Appendix 2, characterized by the features described herein. (Note 6) The generating unit is Generate clothing designs that reflect the user's preferences. The system described in Appendix 3, characterized by the features described herein. (Note 7) The listing unit, The system estimates the user's emotions and adjusts the timing of clothing listings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The listing unit, Analyze the user's past listing history and select a listing method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The listing unit, When listing clothing items, the system filters them based on the user's current fashion trends and the season. The system described in Appendix 1, characterized by the features described herein. (Note 10) The listing unit, It estimates the user's emotions and determines the priority of the clothing items to list based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The listing unit, When listing clothing items, the system prioritizes listing items that are more relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The listing unit, When creating a clothing list, the system analyzes the user's social media activity and lists related clothing items. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned visualization unit, It estimates the user's sentiment and adjusts how usage frequency is displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned visualization unit, When visualizing usage frequency, adjust the level of detail displayed based on the importance of each piece of clothing. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned visualization unit, When visualizing usage frequency, different visualization algorithms are applied depending on the clothing category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned visualization unit, It estimates the user's sentiment and adjusts the display order of usage frequency based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned visualization unit, When visualizing usage frequency, prioritize display based on when the clothing was purchased. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned visualization unit, When visualizing usage frequency, adjust the display order based on the relevance of the clothing items. The system described in Appendix 1, characterized by the features described herein. (Note 19) The recommendation unit is, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The recommendation unit is, When making recommendations, adjust the level of detail based on the importance of the clothing items. The system described in Appendix 1, characterized by the features described herein. (Note 21) The recommendation unit is, When making recommendations, different recommendation algorithms are applied depending on the clothing category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The recommendation unit is, It estimates the user's emotions and adjusts the length of recommendations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The recommendation unit is, When making recommendations, the priority of recommendations is determined based on how often the clothes are worn. The system described in Appendix 1, characterized by the features described herein. (Note 24) The recommendation unit is, When making recommendations, the order of recommendations is adjusted based on the relevance of the clothing items. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the styling suggestions based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When suggesting outfit combinations, the system analyzes the user's past outfit history to provide the most suitable suggestions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When suggesting outfit combinations, the suggestions are customized based on the user's current fashion trends. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of the coordination based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When suggesting outfit combinations, the system takes the user's geographical location into consideration to provide the most suitable suggestions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When suggesting outfit combinations, we analyze the user's social media activity to make recommendations. The system described in Appendix 2, characterized by the features described herein. (Note 31) The generating unit is The system estimates the user's emotions and adjusts the clothing design generation method based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The generating unit is When generating clothing designs, the system analyzes the user's past design history to generate the optimal design. The system described in Appendix 3, characterized by the features described herein. (Note 33) The generating unit is When generating clothing designs, the designs are customized based on the user's current fashion trends. The system described in Appendix 3, characterized by the features described herein. (Note 34) The generating unit is We estimate user emotions and determine design priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The generating unit is When generating clothing designs, the system takes the user's geographical location into consideration to create the optimal design. The system described in Appendix 3, characterized by the features described herein. (Note 36) The generating unit is When generating clothing designs, the system analyzes the user's social media activity to create the designs. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0192] 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 listing unit that lists the clothes owned by the user, A visualization unit visualizes the frequency of use of the clothing listed by the listing unit, The system includes a recommendation unit that makes recommendations to the user based on the usage frequency visualized by the visualization unit. A system characterized by the following features.

2. The system includes a suggestion unit that proposes outfit combinations based on the clothes owned by the user. The system according to feature 1.

3. The system includes a generation unit that generates clothing designs preferred by the user. The system according to feature 1.

4. The aforementioned proposal section is, Based on the clothing items listed by the aforementioned listing unit, the system proposes outfit combinations. The system according to feature 2.

5. The generating unit is The system generates clothing designs that reflect the user's preferences. The system according to claim 3.

6. The listing unit, The system estimates the user's emotions and adjusts the timing of listing clothing items based on the estimated emotions of the user. The system according to feature 1.

7. The listing unit, The user's past listing history is analyzed, and a listing method is selected. The system according to feature 1.

8. The listing unit, When listing clothing items, the system filters them based on the user's current fashion trends and the season. The system according to feature 1.

9. The listing unit, The system estimates the user's emotions and determines the priority of the clothing items to be listed based on the estimated emotions of the user. The system according to feature 1.

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