system

Abstract

The system according to the embodiment aims to efficiently manage clothing and propose outfits. [Solution] A system according to an embodiment includes a storage unit, a classification unit, and a suggestion unit. The storage unit stores clothing images captured by a camera. The classification unit processes the clothing data stored by the storage unit as 3D data and classifies it by color and shape. The suggestion unit suggests outfits based on the data classified by the classification unit, taking into account the season, weather, temperature, and trends.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, managing clothing and suggesting outfits was a time-consuming process, making it difficult to do so efficiently.

[0005] The system according to the embodiment aims to efficiently manage clothing and propose outfits. [Means for solving the problem]

[0006] The system according to the embodiment includes a storage unit, a classification unit, and a suggestion unit. The storage unit stores clothing photographed by a camera. The classification unit processes the clothing data stored by the storage unit as 3D data and classifies it by color and shape. The suggestion unit suggests outfits based on the data classified by the classification unit, according to the season, weather, temperature, and trends. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage clothing and suggest coordination ideas. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A clothing management system according to an embodiment of the present invention stores clothing photographed with a camera and processes it as 3D data to classify it by color, shape, etc. Based on the stored clothing data, the system proposes daily outfits based on the season, weather, temperature, and trends. The system also tracks clothing usage and suggests listing rarely used clothing on a flea market site. The system also features a one-stop shopping service that connects with multiple shopping sites. It is designed to be used as a platform for matching users with coordinators. For example, a key feature of the system is that it stores clothing photographed by users with a camera and processes it as 3D data to classify it by color, shape, etc. Another important feature is the ability to suggest outfits based on the stored data based on the season, weather, temperature, and trends. Another unique feature is that it tracks clothing usage and suggests listing rarely used clothing on a flea market site. This allows users to efficiently dispose of unwanted clothing. The system also conveniently connects with multiple shopping sites to enable one-stop shopping. Its use as a platform for matching users with coordinators is also noteworthy. This allows the clothing management system to efficiently manage the user's clothing and suggest optimal outfits.

[0029] A clothing management system according to an embodiment includes a storage unit, a classification unit, and a suggestion unit. The storage unit stores clothing images captured with a camera. The storage unit can store clothing data in, for example, a digital format. The storage unit supports various data formats, such as JPEG, PNG, and 3D data. The storage unit can store, for example, images of clothing captured by a user with a camera and manage the data as digital data. The storage unit can also acquire and store 3D data of clothing using a 3D scanner. The classification unit processes the clothing data stored by the storage unit as 3D data and classifies it by color and shape. The classification unit can analyze the color of clothing using, for example, image processing technology and classify it based on a color range. The classification unit can also analyze the shape of clothing using shape analysis technology and classify it based on shape characteristics. For example, the classification unit can classify the color of clothing based on RGB values, dividing it into categories such as red, blue, and green. The classification unit can also analyze the shape of clothing using contour extraction technology and classify it into categories such as shirts, pants, and skirts. The suggestion unit suggests outfits according to the season, weather, temperature, and trends based on the data classified by the classification unit. The suggestion unit, for example, acquires weather data and suggests optimal outfits based on the current weather and temperature. The suggestion unit can also refer to fashion trend data and suggest outfits based on the latest trends. For example, the suggestion unit suggests clothes made of warm materials when the current temperature is low, and suggests clothes made of cool materials when the temperature is high. The suggestion unit can also suggest clothes with popular colors and designs based on the latest fashion trends. This allows the clothing management system according to the embodiment to efficiently manage the user's clothing and suggest optimal outfits.

[0030] The clothing management system includes a storage unit that stores the usage status of clothing. The storage unit stores the usage status of clothing. For example, the storage unit can record the frequency and duration of use of clothing. The storage unit manages the usage status by saving the number of times and duration of wear of clothing by a user in a database. For example, the storage unit counts the number of times a user wears a particular piece of clothing and stores the data. The storage unit can also record the duration of use of clothing and manage which pieces of clothing were used for how long. This allows the storage unit to grasp and manage the usage status of a user's clothing in detail. Furthermore, the storage unit can identify infrequently used clothing based on the usage status data. For example, the storage unit can identify clothing that has been used infrequently within a certain period of time and provide the data to a suggestion unit. This allows the suggestion unit to suggest selling infrequently used clothing on a flea market site. This allows the clothing management system to efficiently manage the usage status of a user's clothing and efficiently dispose of unnecessary clothing.

[0031] The clothing management system includes a suggestion unit that identifies infrequently used clothing and suggests listing the clothing on a flea market site. The suggestion unit identifies infrequently used clothing and suggests listing the clothing on a flea market site. The suggestion unit, for example, identifies infrequently used clothing based on usage data provided by the storage unit. The suggestion unit identifies clothing whose usage frequency is below a certain threshold and suggests listing the clothing on a flea market site. For example, the suggestion unit identifies clothing that has been used less than three times in the past six months and suggests listing the clothing on a flea market site. The suggestion unit also has a function that simplifies the listing process for infrequently used clothing. For example, the suggestion unit automatically inputs information required for listing a clothing on a flea market site, allowing users to easily complete the listing process. This allows the suggestion unit to assist users in efficiently disposing of unwanted clothing. Furthermore, the suggestion unit can also suggest the timing and pricing of the listing. For example, the suggestion unit suggests the optimal timing for listing the clothing based on the season and trends and sets appropriate prices. This allows the suggestion unit to support the user in efficiently disposing of unwanted clothes and earning profits.

[0032] The clothing management system includes a linking unit that links with multiple shopping sites and enables bulk purchases. The linking unit links with multiple shopping sites and enables bulk purchases. For example, the linking unit obtains product information using APIs of the multiple shopping sites and provides it to the user. The linking unit enables the user to search, compare, and purchase products from multiple shopping sites on a single platform. For example, when a user searches for a specific garment, the linking unit obtains information on the corresponding product from multiple shopping sites and displays it in a list. The linking unit can also compare prices and inventory status on each shopping site and suggest optimal purchase options. For example, the linking unit can suggest the shopping site offering the lowest price, allowing the user to save costs. The linking unit also enables the user to complete purchase procedures in bulk. For example, the linking unit provides a function for adding products from multiple shopping sites to a cart and completing payment procedures in bulk. This reduces the hassle of using multiple shopping sites and helps the user complete purchase procedures efficiently. The linking unit also manages purchase history and allows the user to refer to information on products purchased in the past. For example, the linking unit may store a history of products that the user has previously purchased, allowing the user to easily process repurchases or returns, thereby improving the user's shopping experience.

[0033] The clothing management system includes a matching unit that serves as a platform for matching users with coordinators. The matching unit serves as a platform for matching users with coordinators. For example, the matching unit has a function for introducing appropriate coordinators when a user requests coordination advice. The matching unit selects an optimal coordinator based on the user's fashion style and preferences. For example, the matching unit displays coordinator profiles based on the user's input fashion preferences and goals, allowing the user to select a coordinator. The matching unit can also display ratings and reviews of coordinators to help the user select a reliable coordinator. For example, the matching unit evaluates the reliability of coordinators based on past user ratings and reviews and provides the evaluation to the user. Furthermore, the matching unit has a function for supporting communication between users and coordinators. For example, the matching unit provides a chat function and a video call function, allowing the user to communicate directly with a coordinator. This allows the matching unit to improve the convenience of users when receiving coordination advice. Furthermore, the matching unit manages the user's coordination history, allowing the user to refer to past coordination information. For example, the matching unit can save the history of outfits the user has received in the past and use this information when requesting the same coordinator again. This allows the matching unit to support the ongoing relationship between the user and the coordinator and improve the experience of receiving fashion advice.

[0034] The storage unit analyzes the user's past storage history and selects an appropriate storage method when saving clothing. The storage unit analyzes the user's past storage history and selects an appropriate storage method when saving clothing. For example, the storage unit refers to data on clothing previously saved by the user and suggests an optimal storage method. The storage unit automatically suggests a storage method for clothing that the user has frequently saved in the past. For example, the storage unit analyzes data on clothing previously saved by the user and suggests an optimal storage method when saving similar clothing. The storage unit can also suggest a storage method tailored to a specific season or event based on the user's past storage history. For example, the storage unit refers to data on clothing previously saved by the user for a specific season or event and suggests an optimal storage method when saving under similar conditions. Furthermore, the storage unit can analyze the user's past storage patterns and suggest the most efficient storage method. For example, the storage unit suggests the most efficient storage method based on data on clothing previously saved by the user. In this way, the storage unit can suggest an optimal storage method by analyzing the user's past storage history. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's past saved data into the generation AI and have the generation AI select the optimal storage method.

[0035] The storage unit selects clothes to save based on the user's current fashion trends. The storage unit selects clothes to save based on the user's current fashion trends. For example, the storage unit analyzes the user's current fashion style and preferences to select clothes to save. The storage unit filters the clothes to save based on the user's currently preferred fashion style. For example, the storage unit prioritizes saving clothes in the user's currently preferred colors and designs. The storage unit can also select clothes to save based on the user's recent purchase history. For example, the storage unit references data on clothes recently purchased by the user and prioritizes saving clothes with similar styles. The storage unit can also analyze the user's social media activity to save clothes that match current fashion trends. For example, the storage unit selects clothes to save based on clothes the user has shared on social media and the styles of fashion influencers the user follows. In this way, the storage unit can optimize the clothes to save by filtering based on the user's current fashion trends. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's fashion data into the generation AI and have the generation AI select the clothes to be saved.

[0036] When saving clothing, the storage unit prioritizes saving highly relevant clothing based on the user's geographical location information. When saving clothing, the storage unit prioritizes saving highly relevant clothing based on the user's geographical location information. For example, the storage unit prioritizes saving clothing that matches the climate of the user's current location. The storage unit acquires climate data for the user's current location and prioritizes saving clothing that is suitable for the location. For example, if the user is in a cold region, the storage unit prioritizes saving clothing made of warm materials. Furthermore, when the user is traveling, the storage unit can also prioritize saving clothing that matches the climate of the travel destination. For example, the storage unit acquires climate data for the user's travel destination and prioritizes saving clothing that is suitable for the region. Furthermore, the storage unit can also prioritize saving clothing that matches local fashion trends based on the user's geographical location information. For example, the storage unit analyzes fashion trends in the user's current location and prioritizes saving clothing styles that are popular in the region. In this way, the storage unit can prioritize saving highly relevant clothing in consideration of the user's geographical location information. Some or all of the above-described processing in the storage unit may be performed using AI or without AI. For example, the storage unit may input the user's geographical location data to the generation AI and cause the generation AI to select highly relevant clothing.

[0037] The storage unit analyzes the user's social media activity when saving clothing and stores related clothing. The storage unit analyzes the user's social media activity when saving clothing and stores related clothing. For example, the storage unit prioritizes saving clothing shared by the user on social media. The storage unit analyzes images and posts shared by the user on social media and stores the clothing. For example, the storage unit analyzes images of clothing shared by the user on social media and stores the clothing. The storage unit can also store clothing based on the style of a fashion influencer the user follows on social media. For example, the storage unit analyzes posts by the fashion influencers the user follows and stores clothing that matches the style. Furthermore, the storage unit can also store clothing that matches current trends based on the user's social media activity. For example, the storage unit analyzes the user's social media activity and stores clothing that matches current fashion trends. In this way, the storage unit can store related clothing by analyzing the user's social media activity. Some or all of the above-mentioned processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input the user's social media data into the generation AI and have the generation AI select related clothing.

[0038] The classification unit improves the accuracy of classification based on the interrelationships between clothes during classification. The classification unit improves the accuracy of classification based on the interrelationships between clothes during classification. The classification unit proposes an optimal classification method based on, for example, the interrelationships between colors and shapes of clothes. The classification unit analyzes the interrelationships between colors and shapes of clothes and groups highly related clothes. For example, the classification unit groups clothes with the same color tone or design to help suggest outfits. The classification unit can also improve the accuracy of classification by taking into account the interrelationships between materials and designs of clothes. For example, the classification unit groups clothes with the same material or design to make it easier for the user to select. Furthermore, the classification unit can propose a classification method that takes into account the interrelationships between clothes based on the frequency of use and seasonality of clothes. For example, the classification unit groups clothes used in the same season to suggest outfits for each season. In this way, the classification unit can improve the accuracy of classification by taking into account the interrelationships between clothes. Some or all of the above-mentioned processing in the classification unit may be performed, for example, using AI or without AI. For example, the classification unit can input clothing interrelationship data into the generation AI and cause the generation AI to improve the accuracy of classification.

[0039] The classification unit performs classification based on material information of the clothing. The classification unit performs classification based on material information of the clothing. For example, the classification unit acquires material information of the clothing and performs classification based on that information. The classification unit analyzes material information of the clothing and groups clothing made of the same material. For example, the classification unit performs classification based on material information such as cotton, polyester, and wool, and groups clothing made of the same material. The classification unit can also improve classification accuracy by taking into account material characteristics of the clothing. For example, the classification unit performs classification based on material characteristics (breathability, heat retention, etc.) to enable a user to select optimal clothing depending on the season and purpose. Furthermore, the classification unit can suggest a classification method suited to the season and weather based on material information of the clothing. For example, the classification unit prioritizes classification of clothing made of breathable materials in summer and clothing made of heat-retaining materials in winter. In this way, the classification unit can improve classification accuracy by taking into account material information of the clothing. Some or all of the above-described processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input information about the material of the clothing into the generation AI and have the generation AI improve the accuracy of the classification.

[0040] The classification unit performs classification based on the geographical distribution of clothing. The classification unit performs classification based on the geographical distribution of clothing. For example, the classification unit acquires geographical distribution data of clothing and performs classification based on that information. The classification unit analyzes the geographical distribution of clothing and groups clothing used in the same region. For example, the classification unit groups clothing popular in a specific region and classifies the clothing based on the fashion trends of the region. The classification unit can also improve the accuracy of classification by taking the geographical distribution of clothing into account. For example, the classification unit prioritizes classifying clothing used in the same region, allowing the user to select clothing that matches the fashion trends of the region. Furthermore, the classification unit can suggest a classification method that matches the climate and culture of the region based on the geographical distribution of clothing. For example, the classification unit prioritizes classifying clothing made of warm materials in cold regions and clothing made of breathable materials in warm regions. In this way, the classification unit can improve the accuracy of classification by taking the geographical distribution of clothing into account. Some or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input geographical distribution data of clothing into the generation AI and cause the generation AI to improve the accuracy of classification.

[0041] The classification unit improves the accuracy of classification based on literature related to clothing during classification. The classification unit improves the accuracy of classification based on literature related to clothing during classification. For example, the classification unit acquires literature related to clothing and performs classification based on the information. The classification unit analyzes literature related to clothing and performs classification based on the latest fashion trends and technical information. For example, the classification unit refers to literature related to the latest fashion trends and performs classification based on the information. The classification unit can also improve the accuracy of classification by referring to literature related to clothing. For example, the classification unit refers to technical literature related to clothing materials and designs and performs classification based on the information. Furthermore, the classification unit can propose a classification method tailored to the latest fashion trends based on literature related to clothing. For example, the classification unit refers to literature related to the latest fashion trends and performs classification based on the information, allowing the user to select clothing that matches the latest trends. In this way, the classification unit can improve the accuracy of classification by referring to literature related to clothing. Some or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input related literature data about clothing into the generation AI and have the generation AI improve the accuracy of classification.

[0042] The suggestion unit adjusts the level of detail of the suggestion based on the importance of the clothing when making a suggestion. The suggestion unit adjusts the level of detail of the suggestion based on the importance of the clothing when making a suggestion. The suggestion unit adjusts the level of detail of the suggestion based on, for example, the frequency of use of the clothing or the importance of the event. The suggestion unit proposes detailed outfits for clothing to be worn for an important event. For example, the suggestion unit provides detailed outfit options when the user is attending an important event, allowing the user to make an optimal selection. The suggestion unit also proposes simple outfits for clothing worn daily. For example, the suggestion unit proposes basic outfits for clothing worn daily by the user, allowing the user to quickly select an outfit. Furthermore, the suggestion unit can also propose detailed outfits for clothing suited to a specific season or weather. For example, the suggestion unit provides detailed outfit options when the user selects clothing suited to a specific season or weather. As a result, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the clothing, thereby improving user convenience. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input clothing importance data to the generation AI and cause the generation AI to adjust the level of detail of the suggestions.

[0043] The suggestion unit applies different suggestion algorithms depending on the clothing category when making a suggestion. The suggestion unit applies different suggestion algorithms depending on the clothing category when making a suggestion. For example, the suggestion unit applies an optimal suggestion algorithm depending on the clothing category (formal, casual, sportswear, etc.). For formal clothing, the suggestion unit applies a formal coordination algorithm. For example, when a user is attending a formal event, the suggestion unit provides formal coordination options, allowing the user to make an optimal selection. Furthermore, for casual clothing, the suggestion unit applies a casual coordination algorithm. For example, for casual clothing that the user wears daily, the suggestion unit provides casual coordination options, allowing the user to quickly make a selection. Furthermore, for sportswear, the suggestion unit can also apply a coordination algorithm suitable for sports. For example, when a user plays sports, the suggestion unit provides coordination options suitable for sports. In this way, the suggestion unit can improve user convenience by applying an optimal suggestion algorithm depending on the clothing category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input clothing category data into the generation AI and cause the generation AI to apply the proposed algorithm.

[0044] The suggestion unit, when making suggestions, determines a priority of the suggestions based on the frequency of use of the clothing. The suggestion unit, when making suggestions, determines a priority of the suggestions based on the frequency of use of the clothing. For example, the suggestion unit acquires clothing use frequency data and determines a priority of the suggestions based on the information. The suggestion unit prioritizes suggesting frequently used clothing. For example, the suggestion unit prioritizes suggesting clothing that the user uses frequently, allowing the user to quickly select clothing. The suggestion unit can also prioritize suggesting less frequently used clothing. For example, the suggestion unit can suggest clothing that the user does not use often, providing the user with an opportunity to try new outfits. Furthermore, the suggestion unit can prioritize suggesting clothing that matches a specific season or event. For example, when the user selects clothing that matches a specific season or event, the suggestion unit prioritizes suggesting frequently used clothing. As a result, the suggestion unit can improve user convenience by determining a priority of the suggestions based on the frequency of use of the clothing. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input clothing usage frequency data into the generation AI and have the generation AI determine the priority of suggestions.

[0045] The suggestion unit adjusts the order of suggestions based on the relevance of the clothing when suggesting the clothes. The suggestion unit adjusts the order of suggestions based on the relevance of the clothing when suggesting the clothes. The suggestion unit determines an optimal suggestion order based on, for example, the relevance of the color or shape of the clothing. The suggestion unit analyzes the relevance of the color or shape of the clothing and prioritizes suggesting highly relevant clothing. For example, the suggestion unit prioritizes suggesting clothing with the same color tone or design, allowing the user to easily select. The suggestion unit can also adjust the order of suggestions taking into account the relevance of the material or design of the clothing. For example, the suggestion unit prioritizes suggesting clothing with the same material or design, allowing the user to efficiently select. Furthermore, the suggestion unit can also suggest clothing in order of relevance based on the frequency of use or seasonality of the clothing. For example, the suggestion unit prioritizes suggesting clothing worn in the same season and suggests coordination for each season. In this way, the suggestion unit can improve user convenience by adjusting the order of suggestions based on the relevance of the clothing. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input clothing association data into the generation AI and cause the generation AI to adjust the order of suggestions.

[0046] The storage unit selects an appropriate storage method based on past usage data when storing usage information. The storage unit selects an appropriate storage method based on past usage data when storing usage information. For example, the storage unit refers to data on clothes the user has used in the past and suggests an optimal storage method. The storage unit automatically suggests a storage method for clothes the user has frequently used in the past. For example, the storage unit analyzes data on clothes the user has used in the past and suggests an optimal storage method when storing similar clothes. The storage unit can also suggest a storage method tailored to a specific season or event based on the user's past usage data. For example, the storage unit refers to data on clothes the user has used in the past for a specific season or event and suggests an optimal storage method when storing under similar conditions. Furthermore, the storage unit can analyze the user's past usage patterns and suggest the most efficient storage method. For example, the storage unit suggests the most efficient storage method based on data on clothes the user has used in the past. As a result, the storage unit can suggest an optimal storage method by referring to the user's past usage data. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's past usage data into the generation AI and have the generation AI select the optimal storage method.

[0047] The storage unit weights the stored data based on when the clothes were used when storing the usage status. The storage unit weights the stored data based on when the clothes were used when storing the usage status. For example, the storage unit acquires data on when the clothes were used and weights the stored data based on that information. The storage unit analyzes when the clothes were used and weights important data. For example, the storage unit weights data on clothes used in a specific season or event and preferentially stores that data. The storage unit can also adjust the weighting of the stored data taking into account the frequency of clothing use. For example, the storage unit weights data on clothes used frequently and preferentially stores that data. Furthermore, the storage unit can weight data according to seasons or events based on the clothing usage status. For example, the storage unit weights data on clothes used in a specific season or event and preferentially stores that data. In this way, the storage unit can improve the accuracy of the data by weighting the stored data based on when the clothes were used. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input data on when clothes were used into the generation AI and have the generation AI weight the stored data.

[0048] The linking unit selects an appropriate linking method based on the user's past purchase history at the time of linking. The linking unit selects an appropriate linking method based on the user's past purchase history at the time of linking. For example, the linking unit refers to data on products purchased by the user in the past and proposes an optimal linking method. The linking unit analyzes data on products purchased by the user in the past and proposes an optimal linking method when purchasing similar products. For example, the linking unit suggests an optimal shopping site based on data on products purchased by the user in the past. The linking unit can also suggest a linking method tailored to a specific season or event based on the user's past purchase history. For example, the linking unit refers to data on products purchased by the user in the past in accordance with a specific season or event and proposes an optimal linking method when purchasing under similar conditions. Furthermore, the linking unit can analyze the user's past purchasing patterns and propose the most efficient linking method. For example, the linking unit suggests the most efficient linking method based on data on products purchased by the user in the past. As a result, the linking unit can propose an optimal linking method by referring to the user's past purchase history. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's past purchase data into the generation AI and have the generation AI select the optimal collaboration method.

[0049] The linking unit selects an appropriate linking method based on the user's geographical location information at the time of linking. The linking unit selects an appropriate linking method based on the user's geographical location information at the time of linking. For example, the linking unit acquires geographical location information of the user's current location and proposes an optimal linking method based on that information. The linking unit prioritizes linking with shopping sites in the user's current location. For example, the linking unit suggests shopping sites in the user's current location so that the user can receive products quickly. Furthermore, if the user is traveling, the linking unit can prioritize linking with shopping sites in the user's travel destination. For example, the linking unit acquires geographical location information of the user's travel destination and suggests shopping sites in that area. Furthermore, the linking unit can prioritize linking with shopping sites that sell local specialties based on the user's geographical location information. For example, the linking unit suggests shopping sites that sell local specialties so that the user can purchase local specialties. In this way, the linking unit can propose an optimal linking method taking the user's geographical location information into consideration. Some or all of the above-described processing in the linking unit may be performed, for example, using AI or without AI. For example, the collaboration unit can input the user's geographical location data into the generation AI and have the generation AI select the optimal collaboration method.

[0050] The matching unit selects an appropriate matching method based on the user's past coordination history during matching. The matching unit selects an appropriate matching method based on the user's past coordination history during matching. For example, the matching unit refers to data on coordinators used by the user in the past and proposes an optimal matching method. The matching unit analyzes data on coordinators used by the user in the past and proposes an optimal matching method when a similar coordination is desired. For example, the matching unit proposes an optimal coordinator based on data on coordinators used by the user in the past. The matching unit can also propose a matching method tailored to a specific season or event based on the user's past coordination history. For example, the matching unit refers to data on coordinators used by the user in the past for a specific season or event and proposes an optimal method when matching under similar conditions. Furthermore, the matching unit can analyze the user's past coordination patterns and propose the most efficient matching method. For example, the matching unit proposes the most efficient matching method based on data on coordinators used by the user in the past. In this way, the matching unit can propose an optimal matching method by referring to the user's past coordination history. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit may input the user's past coordination data into the generation AI and have the generation AI select the optimal matching method.

[0051] The matching unit selects an appropriate matching method based on the user's geographical location information during matching. The matching unit selects an appropriate matching method based on the user's geographical location information during matching. For example, the matching unit acquires geographical location information of the user's current location and proposes an optimal matching method based on that information. The matching unit prioritizes matching with a coordinator in the user's current location. For example, the matching unit may propose a coordinator in the user's current location, allowing the user to quickly request coordination. Furthermore, if the user is traveling, the matching unit may prioritize matching with a coordinator in the user's travel destination. For example, the matching unit acquires geographical location information of the user's travel destination and proposes a coordinator in that area. Furthermore, the matching unit may prioritize matching with a coordinator who is familiar with local fashion trends based on the user's geographical location information. For example, the matching unit may propose a coordinator who is familiar with fashion trends in the user's current location, allowing the user to request coordination that matches local fashion. In this way, the matching unit can propose an optimal matching method taking the user's geographical location information into consideration. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit may input the user's geographical location data into the generation AI and cause the generation AI to select the optimal matching method.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The storage unit can also suggest storage methods based on the material and design of the clothing when storing the user's clothes. For example, the storage unit can suggest special storage methods for clothing made of delicate materials and provide advice to help the user preserve the clothing for a longer period of time. The storage unit can also suggest points to be careful about when storing clothing of a specific design or brand. Furthermore, the storage unit can check the condition of the clothing when the user stores it and suggest necessary maintenance. In this way, the storage unit can help the user properly store their clothing and use it for a long period of time.

[0054] When storing the user's clothing usage status, the storage unit can also classify data based on the purpose and scene of the clothing. For example, the storage unit can classify the user's clothing used in different scenes, such as work, casual, and sports, and manage the usage status for each. The storage unit can also record the usage status of clothing for specific events or seasons, allowing the user to refer to past usage history. Furthermore, the storage unit can record how the user combined specific clothing items to use as a reference for coordinating outfits. This allows the storage unit to grasp and manage the user's clothing usage status in more detail.

[0055] The suggestion unit can also suggest the timing of clothing maintenance and cleaning based on the usage status of the user's clothing. For example, the suggestion unit can notify the user that cleaning and maintenance are necessary when a specific piece of clothing has been used a certain number of times. The suggestion unit can also provide advice on how to store and maintain clothing in accordance with the change of seasons. Furthermore, the suggestion unit can also suggest a maintenance schedule for the user to use the clothing for a long period of time, and help the user to properly manage the clothing. In this way, the suggestion unit can provide support for the user to prolong the life of their clothing.

[0056] The linking unit can also provide information on post-purchase maintenance and care when a user purchases clothes. For example, when a specific piece of clothing is purchased, the linking unit can display maintenance methods and cleaning precautions for that clothing. The linking unit can also suggest a maintenance schedule for the clothing purchased by the user, encouraging regular care. Furthermore, the linking unit can suggest accessories and maintenance products related to the clothing purchased by the user, allowing them to be purchased in one stop. In this way, the linking unit can support the user in properly caring for the clothing they purchased and using it for a long period of time.

[0057] The matching unit can also suggest an appropriate coordinator when a user requests coordination tailored to a specific event or scene. For example, when a user is attending a specific event such as a wedding or business meeting, the matching unit can suggest a coordinator suitable for the event. In addition, if a user is interested in a specific fashion style or brand, the matching unit can also suggest a coordinator who is knowledgeable about that style or brand. Furthermore, if a user requests coordination tailored to a specific region or culture, the matching unit can also suggest a coordinator who is knowledgeable about that region or culture. In this way, the matching unit can support the user in receiving coordination tailored to their specific needs.

[0058] The storage unit can also analyze the user's past storage history and determine the priority of the clothes to be stored. For example, the storage unit can prioritize storing clothes that the user has frequently saved in the past. The storage unit can also prioritize storing clothes that the user has saved for a specific season or event. Furthermore, the storage unit can analyze the user's past storage patterns and suggest the most efficient storage method. In this way, the storage unit can suggest the optimal storage method by analyzing the user's past storage history.

[0059] The storage unit may adjust the criteria for selecting clothes to be saved based on the user's current fashion trends. For example, the storage unit may preferentially save clothes of the user's currently preferred color or design. The storage unit may also select clothes to save based on the user's recent purchase history. Furthermore, the storage unit may analyze the user's social media activity and save clothes that match the user's current fashion trends. This allows the storage unit to optimize the clothes to be saved based on the user's current fashion trends.

[0060] The storage unit may also preferentially store highly relevant clothing based on the user's geographical location information. For example, if the user is in a cold region, the storage unit may preferentially store clothing made of warm materials. Also, if the user is traveling, the storage unit may preferentially store clothing suited to the climate of the travel destination. Furthermore, the storage unit may also store clothing that matches local fashion trends based on the user's geographical location information. In this way, the storage unit can preferentially store highly relevant clothing taking into account the user's geographical location information.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The storage unit stores the clothing image captured by the camera. The storage unit can store clothing data in digital format, and supports various data formats such as JPEG, PNG, and 3D data. Images of clothing captured by the user with the camera are saved and managed as digital data. 3D data of clothing can also be acquired and saved using a 3D scanner. Step 2: The classification unit processes the clothing data stored by the storage unit as 3D data and classifies it by color and shape. The classification unit uses image processing technology to analyze the color of the clothing and classify it based on the color range. It also uses shape analysis technology to analyze the shape of the clothing and classify it based on the shape characteristics. For example, clothing color can be classified based on RGB values ​​and divided into categories such as red, blue, and green. Furthermore, clothing shape can be analyzed using contour extraction technology and divided into categories such as shirts, pants, and skirts. Step 3: The suggestion unit suggests outfits according to the season, weather, temperature, and trends based on the data classified by the classification unit. The suggestion unit acquires weather data and suggests optimal outfits based on the current weather and temperature. It also refers to fashion trend data and suggests outfits based on the latest trends. For example, if the current temperature is low, it suggests clothes made of warm materials, and if the temperature is high, it suggests clothes made of cool materials. It can also suggest clothes with popular colors and designs based on the latest fashion trends.

[0063] (Example 2) A clothing management system according to an embodiment of the present invention stores clothing photographed with a camera and processes it as 3D data to classify it by color, shape, etc. Based on the stored clothing data, the system proposes daily outfits based on the season, weather, temperature, and trends. The system also tracks clothing usage and suggests listing rarely used clothing on a flea market site. The system also features a one-stop shopping service that connects with multiple shopping sites. It is designed to be used as a platform for matching users with coordinators. For example, a key feature of the system is that it stores clothing photographed by users with a camera and processes it as 3D data to classify it by color, shape, etc. Another important feature is the ability to suggest outfits based on the stored data based on the season, weather, temperature, and trends. Another unique feature is that it tracks clothing usage and suggests listing rarely used clothing on a flea market site. This allows users to efficiently dispose of unwanted clothing. The system also conveniently connects with multiple shopping sites to enable one-stop shopping. Its use as a platform for matching users with coordinators is also noteworthy. This allows the clothing management system to efficiently manage the user's clothing and suggest optimal outfits.

[0064] A clothing management system according to an embodiment includes a storage unit, a classification unit, and a suggestion unit. The storage unit stores clothing images captured with a camera. The storage unit can store clothing data in, for example, a digital format. The storage unit supports various data formats, such as JPEG, PNG, and 3D data. The storage unit can store, for example, images of clothing captured by a user with a camera and manage the data as digital data. The storage unit can also acquire and store 3D data of clothing using a 3D scanner. The classification unit processes the clothing data stored by the storage unit as 3D data and classifies it by color and shape. The classification unit can analyze the color of clothing using, for example, image processing technology and classify it based on a color range. The classification unit can also analyze the shape of clothing using shape analysis technology and classify it based on shape characteristics. For example, the classification unit can classify the color of clothing based on RGB values, dividing it into categories such as red, blue, and green. The classification unit can also analyze the shape of clothing using contour extraction technology and classify it into categories such as shirts, pants, and skirts. The suggestion unit suggests outfits according to the season, weather, temperature, and trends based on the data classified by the classification unit. The suggestion unit, for example, acquires weather data and suggests optimal outfits based on the current weather and temperature. The suggestion unit can also refer to fashion trend data and suggest outfits based on the latest trends. For example, the suggestion unit suggests clothes made of warm materials when the current temperature is low, and suggests clothes made of cool materials when the temperature is high. The suggestion unit can also suggest clothes with popular colors and designs based on the latest fashion trends. This allows the clothing management system according to the embodiment to efficiently manage the user's clothing and suggest optimal outfits.

[0065] The clothing management system includes a storage unit that stores the usage status of clothing. The storage unit stores the usage status of clothing. For example, the storage unit can record the frequency and duration of use of clothing. The storage unit manages the usage status by saving the number of times and duration of wear of clothing by a user in a database. For example, the storage unit counts the number of times a user wears a particular piece of clothing and stores the data. The storage unit can also record the duration of use of clothing and manage which pieces of clothing were used for how long. This allows the storage unit to grasp and manage the usage status of a user's clothing in detail. Furthermore, the storage unit can identify infrequently used clothing based on the usage status data. For example, the storage unit can identify clothing that has been used infrequently within a certain period of time and provide the data to a suggestion unit. This allows the suggestion unit to suggest selling infrequently used clothing on a flea market site. This allows the clothing management system to efficiently manage the usage status of a user's clothing and efficiently dispose of unnecessary clothing.

[0066] The clothing management system includes a suggestion unit that identifies infrequently used clothing and suggests listing the clothing on a flea market site. The suggestion unit identifies infrequently used clothing and suggests listing the clothing on a flea market site. The suggestion unit, for example, identifies infrequently used clothing based on usage data provided by the storage unit. The suggestion unit identifies clothing whose usage frequency is below a certain threshold and suggests listing the clothing on a flea market site. For example, the suggestion unit identifies clothing that has been used less than three times in the past six months and suggests listing the clothing on a flea market site. The suggestion unit also has a function that simplifies the listing process for infrequently used clothing. For example, the suggestion unit automatically inputs information required for listing a clothing on a flea market site, allowing users to easily complete the listing process. This allows the suggestion unit to assist users in efficiently disposing of unwanted clothing. Furthermore, the suggestion unit can also suggest the timing and pricing of the listing. For example, the suggestion unit suggests the optimal timing for listing the clothing based on the season and trends and sets appropriate prices. This allows the suggestion unit to support the user in efficiently disposing of unwanted clothes and earning profits.

[0067] The clothing management system includes a linking unit that links with multiple shopping sites and enables bulk purchases. The linking unit links with multiple shopping sites and enables bulk purchases. For example, the linking unit obtains product information using APIs of the multiple shopping sites and provides it to the user. The linking unit enables the user to search, compare, and purchase products from multiple shopping sites on a single platform. For example, when a user searches for a specific garment, the linking unit obtains information on the corresponding product from multiple shopping sites and displays it in a list. The linking unit can also compare prices and inventory status on each shopping site and suggest optimal purchase options. For example, the linking unit can suggest the shopping site offering the lowest price, allowing the user to save costs. The linking unit also enables the user to complete purchase procedures in bulk. For example, the linking unit provides a function for adding products from multiple shopping sites to a cart and completing payment procedures in bulk. This reduces the hassle of using multiple shopping sites and helps the user complete purchase procedures efficiently. The linking unit also manages purchase history and allows the user to refer to information on products purchased in the past. For example, the linking unit may store a history of products that the user has previously purchased, allowing the user to easily process repurchases or returns, thereby improving the user's shopping experience.

[0068] The clothing management system includes a matching unit that serves as a platform for matching users with coordinators. The matching unit serves as a platform for matching users with coordinators. For example, the matching unit has a function for introducing appropriate coordinators when a user requests coordination advice. The matching unit selects an optimal coordinator based on the user's fashion style and preferences. For example, the matching unit displays coordinator profiles based on the user's input fashion preferences and goals, allowing the user to select a coordinator. The matching unit can also display ratings and reviews of coordinators to help the user select a reliable coordinator. For example, the matching unit evaluates the reliability of coordinators based on past user ratings and reviews and provides the evaluation to the user. Furthermore, the matching unit has a function for supporting communication between users and coordinators. For example, the matching unit provides a chat function and a video call function, allowing the user to communicate directly with a coordinator. This allows the matching unit to improve the convenience of users when receiving coordination advice. Furthermore, the matching unit manages the user's coordination history, allowing the user to refer to past coordination information. For example, the matching unit can save the history of outfits the user has received in the past and use this information when requesting the same coordinator again. This allows the matching unit to support the ongoing relationship between the user and the coordinator and improve the experience of receiving fashion advice.

[0069] The storage unit estimates the user's emotions and adjusts the timing of saving the clothes based on the estimated user emotions. The storage unit estimates the user's emotions and adjusts the timing of saving the clothes based on the estimated user emotions. The storage unit uses, for example, technology that analyzes the user's facial expressions and voice to estimate emotions. If the user is feeling stressed, the storage unit simplifies the saving operation, allowing the user to save with one click. For example, if the storage unit determines that the user is feeling stressed, it displays a larger save button and allows the user to complete the save operation with one click. Furthermore, if the user is relaxed, the storage unit provides detailed saving options and suggests a customizable saving method. For example, if the storage unit determines that the user is relaxed, it displays options that allow the user to select the format and location of the data to be saved. Furthermore, if the user is in a hurry, the storage unit prioritizes voice input, allowing the user to quickly save the clothes. For example, if the storage unit determines that the user is in a hurry, it enables a voice input function and allows the user to issue a save command by voice. This improves user convenience by adjusting the timing of saving the clothes according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, 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 storage unit may be performed using, or without, an AI. For example, the storage unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0070] The storage unit analyzes the user's past storage history and selects an appropriate storage method when saving clothing. The storage unit analyzes the user's past storage history and selects an appropriate storage method when saving clothing. For example, the storage unit refers to data on clothing previously saved by the user and suggests an optimal storage method. The storage unit automatically suggests a storage method for clothing that the user has frequently saved in the past. For example, the storage unit analyzes data on clothing previously saved by the user and suggests an optimal storage method when saving similar clothing. The storage unit can also suggest a storage method tailored to a specific season or event based on the user's past storage history. For example, the storage unit refers to data on clothing previously saved by the user for a specific season or event and suggests an optimal storage method when saving under similar conditions. Furthermore, the storage unit can analyze the user's past storage patterns and suggest the most efficient storage method. For example, the storage unit suggests the most efficient storage method based on data on clothing previously saved by the user. In this way, the storage unit can suggest an optimal storage method by analyzing the user's past storage history. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's past saved data into the generation AI and have the generation AI select the optimal storage method.

[0071] The storage unit selects clothes to save based on the user's current fashion trends. The storage unit selects clothes to save based on the user's current fashion trends. For example, the storage unit analyzes the user's current fashion style and preferences to select clothes to save. The storage unit filters the clothes to save based on the user's currently preferred fashion style. For example, the storage unit prioritizes saving clothes in the user's currently preferred colors and designs. The storage unit can also select clothes to save based on the user's recent purchase history. For example, the storage unit references data on clothes recently purchased by the user and prioritizes saving clothes with similar styles. The storage unit can also analyze the user's social media activity to save clothes that match current fashion trends. For example, the storage unit selects clothes to save based on clothes the user has shared on social media and the styles of fashion influencers the user follows. In this way, the storage unit can optimize the clothes to save by filtering based on the user's current fashion trends. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's fashion data into the generation AI and have the generation AI select the clothes to be saved.

[0072] The storage unit estimates the user's emotions and determines the priority of clothes to be saved based on the estimated user emotions. The storage unit estimates the user's emotions and determines the priority of clothes to be saved based on the estimated user emotions. The storage unit uses, for example, technology that analyzes the user's facial expressions and voice to estimate emotions. The storage unit prioritizes saving frequently used clothes when the user is feeling stressed. For example, when the storage unit determines that the user is feeling stressed, it prioritizes saving clothes that are used daily. Furthermore, when the user is relaxed, it prioritizes saving clothes that are appropriate for the season or an event. For example, when the storage unit determines that the user is relaxed, it prioritizes saving clothes that are appropriate for a specific season or event. Furthermore, when the user is in a hurry, it prioritizes saving recently used clothes. For example, when the storage unit determines that the user is in a hurry, it prioritizes saving recently used clothes. This improves user convenience by determining the priority of clothes to be saved based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed using AI, or may be performed without using AI. For example, the storage unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0073] When saving clothing, the storage unit prioritizes saving highly relevant clothing based on the user's geographical location information. When saving clothing, the storage unit prioritizes saving highly relevant clothing based on the user's geographical location information. For example, the storage unit prioritizes saving clothing that matches the climate of the user's current location. The storage unit acquires climate data for the user's current location and prioritizes saving clothing that is suitable for the location. For example, if the user is in a cold region, the storage unit prioritizes saving clothing made of warm materials. Furthermore, when the user is traveling, the storage unit can also prioritize saving clothing that matches the climate of the travel destination. For example, the storage unit acquires climate data for the user's travel destination and prioritizes saving clothing that is suitable for the region. Furthermore, the storage unit can also prioritize saving clothing that matches local fashion trends based on the user's geographical location information. For example, the storage unit analyzes fashion trends in the user's current location and prioritizes saving clothing styles that are popular in the region. In this way, the storage unit can prioritize saving highly relevant clothing in consideration of the user's geographical location information. Some or all of the above-described processing in the storage unit may be performed using AI or without AI. For example, the storage unit may input the user's geographical location data to the generation AI and cause the generation AI to select highly relevant clothing.

[0074] The storage unit analyzes the user's social media activity when saving clothing and stores related clothing. The storage unit analyzes the user's social media activity when saving clothing and stores related clothing. For example, the storage unit prioritizes saving clothing shared by the user on social media. The storage unit analyzes images and posts shared by the user on social media and stores the clothing. For example, the storage unit analyzes images of clothing shared by the user on social media and stores the clothing. The storage unit can also store clothing based on the style of a fashion influencer the user follows on social media. For example, the storage unit analyzes posts by the fashion influencers the user follows and stores clothing that matches the style. Furthermore, the storage unit can also store clothing that matches current trends based on the user's social media activity. For example, the storage unit analyzes the user's social media activity and stores clothing that matches current fashion trends. In this way, the storage unit can store related clothing by analyzing the user's social media activity. Some or all of the above-mentioned processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input the user's social media data into the generation AI and have the generation AI select related clothing.

[0075] The classification unit estimates the user's emotions and adjusts classification criteria based on the estimated user emotions. The classification unit estimates the user's emotions and adjusts classification criteria based on the estimated user emotions. The classification unit uses, for example, technology that analyzes the user's facial expressions and voice to estimate emotions. If the user is feeling stressed, the classification unit provides simple classification criteria to simplify operation. For example, if the classification unit determines that the user is feeling stressed, the classification unit provides basic classification criteria based on color and shape to simplify operation. Furthermore, if the user is relaxed, the classification unit provides detailed classification criteria and suggests a customizable classification method. For example, if the classification unit determines that the user is relaxed, the classification unit provides an option to classify based on subtle differences in color or detailed characteristics of shape. Furthermore, if the user is in a hurry, the classification unit prioritizes voice input to enable quick classification. For example, if the classification unit determines that the user is in a hurry, the classification unit enables a voice input function to allow the user to issue classification instructions by voice. This improves user convenience by adjusting classification criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, 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 classification unit may be performed using, or without, an AI. For example, the classification unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0076] The classification unit improves the accuracy of classification based on the interrelationships between clothes during classification. The classification unit improves the accuracy of classification based on the interrelationships between clothes during classification. The classification unit proposes an optimal classification method based on, for example, the interrelationships between colors and shapes of clothes. The classification unit analyzes the interrelationships between colors and shapes of clothes and groups highly related clothes. For example, the classification unit groups clothes with the same color tone or design to help suggest outfits. The classification unit can also improve the accuracy of classification by taking into account the interrelationships between materials and designs of clothes. For example, the classification unit groups clothes with the same material or design to make it easier for the user to select. Furthermore, the classification unit can propose a classification method that takes into account the interrelationships between clothes based on the frequency of use and seasonality of clothes. For example, the classification unit groups clothes used in the same season to suggest outfits for each season. In this way, the classification unit can improve the accuracy of classification by taking into account the interrelationships between clothes. Some or all of the above-mentioned processing in the classification unit may be performed, for example, using AI or without AI. For example, the classification unit can input clothing interrelationship data into the generation AI and cause the generation AI to improve the accuracy of classification.

[0077] The classification unit performs classification based on material information of the clothing. The classification unit performs classification based on material information of the clothing. For example, the classification unit acquires material information of the clothing and performs classification based on that information. The classification unit analyzes material information of the clothing and groups clothing made of the same material. For example, the classification unit performs classification based on material information such as cotton, polyester, and wool, and groups clothing made of the same material. The classification unit can also improve classification accuracy by taking into account material characteristics of the clothing. For example, the classification unit performs classification based on material characteristics (breathability, heat retention, etc.) to enable a user to select optimal clothing depending on the season and purpose. Furthermore, the classification unit can suggest a classification method suited to the season and weather based on material information of the clothing. For example, the classification unit prioritizes classification of clothing made of breathable materials in summer and clothing made of heat-retaining materials in winter. In this way, the classification unit can improve classification accuracy by taking into account material information of the clothing. Some or all of the above-described processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input information about the material of the clothing into the generation AI and have the generation AI improve the accuracy of the classification.

[0078] The classification unit estimates the user's emotions and adjusts the order in which the classification results are displayed based on the estimated user emotions. The classification unit estimates the user's emotions and adjusts the order in which the classification results are displayed based on the estimated user emotions. The classification unit uses, for example, technology that analyzes the user's facial expressions and voice to estimate emotions. The classification unit prioritizes displaying important information when the user is feeling stressed. For example, if the classification unit determines that the user is feeling stressed, it displays important clothing information first, allowing the user to make a quick selection. Furthermore, if the user is relaxed, the classification unit displays the results in an order that includes detailed information. For example, if the classification unit determines that the user is relaxed, it sequentially displays detailed clothing information (e.g., material, design, frequency of use, etc.). Furthermore, if the user is in a hurry, the classification unit displays the results in an order that highlights the main points. For example, if the classification unit determines that the user is in a hurry, it displays basic clothing information (e.g., color, shape, size, etc.) first, allowing the user to make a quick selection. This allows the order in which the classification results are displayed to be adjusted according to the user's emotions, thereby improving user convenience. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the classification unit may be performed using, or without, an AI. For example, the classification unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0079] The classification unit performs classification based on the geographical distribution of clothing. The classification unit performs classification based on the geographical distribution of clothing. For example, the classification unit acquires geographical distribution data of clothing and performs classification based on that information. The classification unit analyzes the geographical distribution of clothing and groups clothing used in the same region. For example, the classification unit groups clothing popular in a specific region and classifies the clothing based on the fashion trends of the region. The classification unit can also improve the accuracy of classification by taking the geographical distribution of clothing into account. For example, the classification unit prioritizes classifying clothing used in the same region, allowing the user to select clothing that matches the fashion trends of the region. Furthermore, the classification unit can suggest a classification method that matches the climate and culture of the region based on the geographical distribution of clothing. For example, the classification unit prioritizes classifying clothing made of warm materials in cold regions and clothing made of breathable materials in warm regions. In this way, the classification unit can improve the accuracy of classification by taking the geographical distribution of clothing into account. Some or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input geographical distribution data of clothing into the generation AI and cause the generation AI to improve the accuracy of classification.

[0080] The classification unit improves the accuracy of classification based on literature related to clothing during classification. The classification unit improves the accuracy of classification based on literature related to clothing during classification. For example, the classification unit acquires literature related to clothing and performs classification based on the information. The classification unit analyzes literature related to clothing and performs classification based on the latest fashion trends and technical information. For example, the classification unit refers to literature related to the latest fashion trends and performs classification based on the information. The classification unit can also improve the accuracy of classification by referring to literature related to clothing. For example, the classification unit refers to technical literature related to clothing materials and designs and performs classification based on the information. Furthermore, the classification unit can propose a classification method tailored to the latest fashion trends based on literature related to clothing. For example, the classification unit refers to literature related to the latest fashion trends and performs classification based on the information, allowing the user to select clothing that matches the latest trends. In this way, the classification unit can improve the accuracy of classification by referring to literature related to clothing. Some or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input related literature data about clothing into the generation AI and have the generation AI improve the accuracy of classification.

[0081] The suggestion unit estimates the user's emotions and adjusts the method of suggesting outfits based on the estimated user emotions. The suggestion unit estimates the user's emotions and adjusts the method of suggesting outfits based on the estimated user emotions. The suggestion unit uses, for example, technology that analyzes the user's facial expressions and voice to estimate emotions. The suggestion unit suggests simple and intuitive outfits when the user is feeling stressed. For example, if the suggestion unit determines that the user is feeling stressed, it suggests basic outfits, allowing the user to quickly select them. Furthermore, if the user is relaxed, it provides detailed outfit options and makes customizable suggestions. For example, if the suggestion unit determines that the user is relaxed, it provides detailed color and design options, allowing the user to freely customize them. Furthermore, if the user is in a hurry, it quickly suggests outfits. For example, if the suggestion unit determines that the user is in a hurry, it suggests simple outfits, allowing the user to quickly select them. This improves user convenience by adjusting the method of suggesting outfits based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit may input user facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0082] The suggestion unit adjusts the level of detail of the suggestion based on the importance of the clothing when making a suggestion. The suggestion unit adjusts the level of detail of the suggestion based on the importance of the clothing when making a suggestion. The suggestion unit adjusts the level of detail of the suggestion based on, for example, the frequency of use of the clothing or the importance of the event. The suggestion unit proposes detailed outfits for clothing to be worn for an important event. For example, the suggestion unit provides detailed outfit options when the user is attending an important event, allowing the user to make an optimal selection. The suggestion unit also proposes simple outfits for clothing worn daily. For example, the suggestion unit proposes basic outfits for clothing worn daily by the user, allowing the user to quickly select an outfit. Furthermore, the suggestion unit can also propose detailed outfits for clothing suited to a specific season or weather. For example, the suggestion unit provides detailed outfit options when the user selects clothing suited to a specific season or weather. As a result, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the clothing, thereby improving user convenience. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input clothing importance data to the generation AI and cause the generation AI to adjust the level of detail of the suggestions.

[0083] The suggestion unit applies different suggestion algorithms depending on the clothing category when making a suggestion. The suggestion unit applies different suggestion algorithms depending on the clothing category when making a suggestion. For example, the suggestion unit applies an optimal suggestion algorithm depending on the clothing category (formal, casual, sportswear, etc.). For formal clothing, the suggestion unit applies a formal coordination algorithm. For example, when a user is attending a formal event, the suggestion unit provides formal coordination options, allowing the user to make an optimal selection. Furthermore, for casual clothing, the suggestion unit applies a casual coordination algorithm. For example, for casual clothing that the user wears daily, the suggestion unit provides casual coordination options, allowing the user to quickly make a selection. Furthermore, for sportswear, the suggestion unit can also apply a coordination algorithm suitable for sports. For example, when a user plays sports, the suggestion unit provides coordination options suitable for sports. In this way, the suggestion unit can improve user convenience by applying an optimal suggestion algorithm depending on the clothing category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input clothing category data into the generation AI and cause the generation AI to apply the proposed algorithm.

[0084] The suggestion unit estimates the user's emotions and adjusts the length of the suggestions based on the estimated user emotions. The suggestion unit estimates the user's emotions and adjusts the length of the suggestions based on the estimated user emotions. The suggestion unit uses, for example, technology that analyzes the user's facial expressions and voice to estimate emotions. If the user is feeling stressed, the suggestion unit makes short, concise suggestions. For example, if the suggestion unit determines that the user is feeling stressed, it suggests basic outfits with short descriptions, allowing the user to quickly select them. Furthermore, if the user is relaxed, the suggestion unit makes longer suggestions with detailed descriptions. For example, if the suggestion unit determines that the user is relaxed, it makes suggestions with detailed descriptions of colors and designs, allowing the user to freely customize them. Furthermore, if the user is in a hurry, the suggestion unit makes quick, concise suggestions. For example, if the suggestion unit determines that the user is in a hurry, it suggests simple outfits with short descriptions, allowing the user to quickly select them. This improves user convenience by adjusting the length of the suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit may input user facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0085] The suggestion unit, when making suggestions, determines a priority of the suggestions based on the frequency of use of the clothing. The suggestion unit, when making suggestions, determines a priority of the suggestions based on the frequency of use of the clothing. For example, the suggestion unit acquires clothing use frequency data and determines a priority of the suggestions based on the information. The suggestion unit prioritizes suggesting frequently used clothing. For example, the suggestion unit prioritizes suggesting clothing that the user uses frequently, allowing the user to quickly select clothing. The suggestion unit can also prioritize suggesting less frequently used clothing. For example, the suggestion unit can suggest clothing that the user does not use often, providing the user with an opportunity to try new outfits. Furthermore, the suggestion unit can prioritize suggesting clothing that matches a specific season or event. For example, when the user selects clothing that matches a specific season or event, the suggestion unit prioritizes suggesting frequently used clothing. As a result, the suggestion unit can improve user convenience by determining a priority of the suggestions based on the frequency of use of the clothing. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input clothing usage frequency data into the generation AI and have the generation AI determine the priority of suggestions.

[0086] The suggestion unit adjusts the order of suggestions based on the relevance of the clothing when suggesting the clothes. The suggestion unit adjusts the order of suggestions based on the relevance of the clothing when suggesting the clothes. The suggestion unit determines an optimal suggestion order based on, for example, the relevance of the color or shape of the clothing. The suggestion unit analyzes the relevance of the color or shape of the clothing and prioritizes suggesting highly relevant clothing. For example, the suggestion unit prioritizes suggesting clothing with the same color tone or design, allowing the user to easily select. The suggestion unit can also adjust the order of suggestions taking into account the relevance of the material or design of the clothing. For example, the suggestion unit prioritizes suggesting clothing with the same material or design, allowing the user to efficiently select. Furthermore, the suggestion unit can also suggest clothing in order of relevance based on the frequency of use or seasonality of the clothing. For example, the suggestion unit prioritizes suggesting clothing worn in the same season and suggests coordination for each season. In this way, the suggestion unit can improve user convenience by adjusting the order of suggestions based on the relevance of the clothing. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input clothing association data into the generation AI and cause the generation AI to adjust the order of suggestions.

[0087] The storage unit estimates the user's emotions and adjusts the storage method of the usage status based on the estimated user emotions. The storage unit estimates the user's emotions and adjusts the storage method of the usage status based on the estimated user emotions. The storage unit uses, for example, technology that analyzes the user's facial expressions and voice to estimate emotions. The storage unit provides a simple storage method and simplifies operation when the user is feeling stressed. For example, when the storage unit determines that the user is feeling stressed, it provides a basic storage method, allowing the user to operate it quickly. Furthermore, when the user is relaxed, it provides detailed storage options and suggests a customizable storage method. For example, when the storage unit determines that the user is relaxed, it provides detailed storage options, allowing the user to freely customize them. Furthermore, when the user is in a hurry, the storage unit prioritizes voice input to quickly store the usage status. For example, when the storage unit determines that the user is in a hurry, it enables a voice input function, allowing the user to issue storage instructions by voice. This allows the storage method of the usage status to be adjusted according to the user's emotions, thereby improving user convenience. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0088] The storage unit selects an appropriate storage method based on past usage data when storing usage information. The storage unit selects an appropriate storage method based on past usage data when storing usage information. For example, the storage unit refers to data on clothes the user has used in the past and suggests an optimal storage method. The storage unit automatically suggests a storage method for clothes the user has frequently used in the past. For example, the storage unit analyzes data on clothes the user has used in the past and suggests an optimal storage method when storing similar clothes. The storage unit can also suggest a storage method tailored to a specific season or event based on the user's past usage data. For example, the storage unit refers to data on clothes the user has used in the past for a specific season or event and suggests an optimal storage method when storing under similar conditions. Furthermore, the storage unit can analyze the user's past usage patterns and suggest the most efficient storage method. For example, the storage unit suggests the most efficient storage method based on data on clothes the user has used in the past. As a result, the storage unit can suggest an optimal storage method by referring to the user's past usage data. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's past usage data into the generation AI and have the generation AI select the optimal storage method.

[0089] The accumulation unit estimates the user's emotions and adjusts the accumulation frequency of the usage status based on the estimated user emotions. The accumulation unit estimates the user's emotions and adjusts the accumulation frequency of the usage status based on the estimated user emotions. The accumulation unit uses, for example, technology that analyzes the user's facial expressions and voice to estimate emotions. The accumulation unit sets a low accumulation frequency and simplifies operation when the user is feeling stressed. For example, when the accumulation unit determines that the user is feeling stressed, it sets a low accumulation frequency to allow the user to operate quickly. Furthermore, when the user is relaxed, the accumulation unit provides detailed accumulation options and accumulates frequently. For example, when the accumulation unit determines that the user is relaxed, it provides detailed accumulation options to allow the user to freely customize. Furthermore, when the user is in a hurry, the accumulation unit prioritizes voice input to allow the usage status to be accumulated quickly. For example, when the accumulation unit determines that the user is in a hurry, it enables a voice input function to allow the user to issue accumulation instructions by voice. This allows the user's convenience to be improved by adjusting the accumulation frequency of the usage status according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0090] The storage unit weights the stored data based on when the clothes were used when storing the usage status. The storage unit weights the stored data based on when the clothes were used when storing the usage status. For example, the storage unit acquires data on when the clothes were used and weights the stored data based on that information. The storage unit analyzes when the clothes were used and weights important data. For example, the storage unit weights data on clothes used in a specific season or event and preferentially stores that data. The storage unit can also adjust the weighting of the stored data taking into account the frequency of clothing use. For example, the storage unit weights data on clothes used frequently and preferentially stores that data. Furthermore, the storage unit can weight data according to seasons or events based on the clothing usage status. For example, the storage unit weights data on clothes used in a specific season or event and preferentially stores that data. In this way, the storage unit can improve the accuracy of the data by weighting the stored data based on when the clothes were used. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input data on when clothes were used into the generation AI and have the generation AI weight the stored data.

[0091] The linking unit estimates the user's emotions and adjusts a linking method with the shopping site based on the estimated user emotions. The linking unit estimates the user's emotions and adjusts a linking method with the shopping site based on the estimated user emotions. The linking unit uses, for example, technology that analyzes the user's facial expressions and voice to estimate emotions. The linking unit provides a simple linking method and simplifies operation when the user is feeling stressed. For example, when the linking unit determines that the user is feeling stressed, it provides a basic linking method, allowing the user to operate quickly. Furthermore, when the user is relaxed, it provides detailed linking options and suggests a customizable linking method. For example, when the linking unit determines that the user is relaxed, it provides detailed linking options, allowing the user to freely customize them. Furthermore, when the user is in a hurry, the linking unit prioritizes voice input to enable quick linking. For example, when the linking unit determines that the user is in a hurry, it enables a voice input function, allowing the user to issue linking instructions by voice. This allows the user's convenience to be improved by adjusting the linking method with the shopping site according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0092] The linking unit selects an appropriate linking method based on the user's past purchase history at the time of linking. The linking unit selects an appropriate linking method based on the user's past purchase history at the time of linking. For example, the linking unit refers to data on products purchased by the user in the past and proposes an optimal linking method. The linking unit analyzes data on products purchased by the user in the past and proposes an optimal linking method when purchasing similar products. For example, the linking unit suggests an optimal shopping site based on data on products purchased by the user in the past. The linking unit can also suggest a linking method tailored to a specific season or event based on the user's past purchase history. For example, the linking unit refers to data on products purchased by the user in the past in accordance with a specific season or event and proposes an optimal linking method when purchasing under similar conditions. Furthermore, the linking unit can analyze the user's past purchasing patterns and propose the most efficient linking method. For example, the linking unit suggests the most efficient linking method based on data on products purchased by the user in the past. As a result, the linking unit can propose an optimal linking method by referring to the user's past purchase history. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's past purchase data into the generation AI and have the generation AI select the optimal collaboration method.

[0093] The linking unit estimates a user's emotions and determines a priority order of linked shopping sites based on the estimated user emotions. The linking unit estimates a user's emotions and determines a priority order of linked shopping sites based on the estimated user emotions. The linking unit uses, for example, technology that analyzes a user's facial expressions and voice to estimate emotions. The linking unit prioritizes linking with highly reliable shopping sites when the user is feeling stressed. For example, when the linking unit determines that the user is feeling stressed, it prioritizes suggesting highly reliable shopping sites, allowing the user to make purchases with peace of mind. Furthermore, when the user is relaxed, the linking unit prioritizes linking with shopping sites that offer detailed options. For example, when the linking unit determines that the user is relaxed, it prioritizes suggesting shopping sites that offer detailed product information and customization options. Furthermore, when the user is in a hurry, the linking unit prioritizes linking with shopping sites that allow quick purchases. For example, when the linking unit determines that the user is in a hurry, it prioritizes suggesting shopping sites that offer fast delivery and easy purchase procedures. In this way, by determining the priority order of linked shopping sites based on the user's emotions, user convenience can be improved. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0094] The linking unit selects an appropriate linking method based on the user's geographical location information at the time of linking. The linking unit selects an appropriate linking method based on the user's geographical location information at the time of linking. For example, the linking unit acquires geographical location information of the user's current location and proposes an optimal linking method based on that information. The linking unit prioritizes linking with shopping sites in the user's current location. For example, the linking unit suggests shopping sites in the user's current location so that the user can receive products quickly. Furthermore, if the user is traveling, the linking unit can prioritize linking with shopping sites in the user's travel destination. For example, the linking unit acquires geographical location information of the user's travel destination and suggests shopping sites in that area. Furthermore, the linking unit can prioritize linking with shopping sites that sell local specialties based on the user's geographical location information. For example, the linking unit suggests shopping sites that sell local specialties so that the user can purchase local specialties. In this way, the linking unit can propose an optimal linking method taking the user's geographical location information into consideration. Some or all of the above-described processing in the linking unit may be performed, for example, using AI or without AI. For example, the collaboration unit can input the user's geographical location data into the generation AI and have the generation AI select the optimal collaboration method.

[0095] The matching unit estimates the user's emotions and adjusts the matching method with a coordinator based on the estimated user emotions. The matching unit estimates the user's emotions and adjusts the matching method with a coordinator based on the estimated user emotions. The matching unit uses, for example, technology that analyzes the user's facial expressions and voice to estimate emotions. The matching unit provides a simple matching method and simplifies operation when the user is feeling stressed. For example, when the matching unit determines that the user is feeling stressed, it provides a basic matching method, allowing the user to operate quickly. Furthermore, when the user is relaxed, the matching unit provides detailed matching options and suggests a customizable matching method. For example, when the matching unit determines that the user is relaxed, it provides detailed matching options, allowing the user to freely customize them. Furthermore, when the user is in a hurry, the matching unit prioritizes voice input to enable quick matching. For example, when the matching unit determines that the user is in a hurry, it enables a voice input function, allowing the user to issue matching instructions by voice. This improves user convenience by adjusting the matching method with a coordinator based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0096] The matching unit selects an appropriate matching method based on the user's past coordination history during matching. The matching unit selects an appropriate matching method based on the user's past coordination history during matching. For example, the matching unit refers to data on coordinators used by the user in the past and proposes an optimal matching method. The matching unit analyzes data on coordinators used by the user in the past and proposes an optimal matching method when a similar coordination is desired. For example, the matching unit proposes an optimal coordinator based on data on coordinators used by the user in the past. The matching unit can also propose a matching method tailored to a specific season or event based on the user's past coordination history. For example, the matching unit refers to data on coordinators used by the user in the past for a specific season or event and proposes an optimal method when matching under similar conditions. Furthermore, the matching unit can analyze the user's past coordination patterns and propose the most efficient matching method. For example, the matching unit proposes the most efficient matching method based on data on coordinators used by the user in the past. In this way, the matching unit can propose an optimal matching method by referring to the user's past coordination history. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit may input the user's past coordination data into the generation AI and have the generation AI select the optimal matching method.

[0097] The matching unit estimates the user's emotions and determines a matching priority based on the estimated user emotions. The matching unit estimates the user's emotions and determines a matching priority based on the estimated user emotions. The matching unit uses, for example, technology that analyzes the user's facial expressions and voice to estimate emotions. The matching unit prioritizes matching with a highly reliable coordinator when the user is feeling stressed. For example, when the matching unit determines that the user is feeling stressed, it preferentially suggests a highly reliable coordinator, allowing the user to request coordination with peace of mind. Furthermore, when the user is relaxed, the matching unit prioritizes matching with a coordinator that offers detailed options. For example, when the matching unit determines that the user is relaxed, it preferentially suggests a coordinator that offers detailed coordination options. Furthermore, when the user is in a hurry, the matching unit prioritizes matching with a coordinator that can respond quickly. For example, when the matching unit determines that the user is in a hurry, it preferentially suggests a coordinator that can respond quickly. In this way, by determining the matching priority based on the user's emotions, user convenience can be improved. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0098] The matching unit selects an appropriate matching method based on the user's geographical location information during matching. The matching unit selects an appropriate matching method based on the user's geographical location information during matching. For example, the matching unit acquires geographical location information of the user's current location and proposes an optimal matching method based on that information. The matching unit prioritizes matching with a coordinator in the user's current location. For example, the matching unit may propose a coordinator in the user's current location, allowing the user to quickly request coordination. Furthermore, if the user is traveling, the matching unit may prioritize matching with a coordinator in the user's travel destination. For example, the matching unit acquires geographical location information of the user's travel destination and proposes a coordinator in that area. Furthermore, the matching unit may prioritize matching with a coordinator who is familiar with local fashion trends based on the user's geographical location information. For example, the matching unit may propose a coordinator who is familiar with fashion trends in the user's current location, allowing the user to request coordination that matches local fashion. In this way, the matching unit can propose an optimal matching method taking the user's geographical location information into consideration. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit may input the user's geographical location data into the generation AI and cause the generation AI to select the optimal matching method. === Hard Collateral 1-1 === Each of the multiple elements, including the storage unit, classification unit, suggestion unit, accumulation unit, linking unit, and matching unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the storage unit photographs clothing using the camera 42 of the smart device 14 and stores the photograph as digital data by the specific processing unit 290 of the data processing device 12. The classification unit processes the clothing data as 3D data by the specific processing unit 290 of the data processing device 12 and classifies the data by color and shape. The suggestion unit suggests outfits based on the season, weather, temperature, and trends by the specific processing unit 290 of the data processing device 12. The storage unit accumulates clothing usage status by the specific processing unit 290 of the data processing device 12 and identifies rarely used clothing. The linking unit links with multiple shopping sites by the specific processing unit 290 of the data processing device 12 to enable bulk purchases. The matching unit matches users with coordinators by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the storage unit, classification unit, suggestion unit, accumulation unit, linking unit, and matching unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the storage unit photographs clothing using the camera 42 of the smart glasses 214 and stores the photographed clothing as digital data by the specific processing unit 290 of the data processing device 12. The classification unit processes the clothing data as 3D data by the specific processing unit 290 of the data processing device 12 and classifies the data by color and shape. The suggestion unit suggests outfits based on the season, weather, temperature, and trends by the specific processing unit 290 of the data processing device 12. The storage unit accumulates clothing usage status by the specific processing unit 290 of the data processing device 12 and identifies rarely used clothing. The linking unit links with multiple shopping sites by the specific processing unit 290 of the data processing device 12 to enable bulk purchases. The matching unit matches users with coordinators by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the storage unit, classification unit, suggestion unit, accumulation unit, linking unit, and matching unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the storage unit photographs clothing using the camera 42 of the headset-type terminal 314 and stores the photograph as digital data by the specific processing unit 290 of the data processing device 12. The classification unit processes the clothing data as 3D data by the specific processing unit 290 of the data processing device 12 and classifies it by color and shape. The suggestion unit suggests outfits based on the season, weather, temperature, and trends by the specific processing unit 290 of the data processing device 12. The storage unit accumulates clothing usage status by the specific processing unit 290 of the data processing device 12 and identifies rarely used clothing. The linking unit links with multiple shopping sites by the specific processing unit 290 of the data processing device 12 to enable bulk purchases. The matching unit matches users with coordinators by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the storage unit, classification unit, suggestion unit, accumulation unit, linking unit, and matching unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the storage unit photographs clothing using the camera 42 of the robot 414 and stores the photographs as digital data using the specific processing unit 290 of the data processing device 12. The classification unit processes the clothing data as 3D data using the specific processing unit 290 of the data processing device 12 and classifies the data by color and shape. The suggestion unit suggests outfits based on the season, weather, temperature, and trends using the specific processing unit 290 of the data processing device 12. The storage unit accumulates clothing usage status using the specific processing unit 290 of the data processing device 12 and identifies rarely used clothing. The linking unit links with multiple shopping sites using the specific processing unit 290 of the data processing device 12 to enable bulk purchases. The matching unit matches users with coordinators using the specific processing unit 290 of the data processing device 12.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The storage unit can also suggest storage methods based on the material and design of the clothing when storing the user's clothes. For example, the storage unit can suggest special storage methods for clothing made of delicate materials and provide advice to help the user preserve the clothing for a longer period of time. The storage unit can also suggest points to be careful about when storing clothing of a specific design or brand. Furthermore, the storage unit can check the condition of the clothing when the user stores it and suggest necessary maintenance. In this way, the storage unit can help the user properly store their clothing and use it for a long period of time.

[0101] When storing the user's clothing usage status, the storage unit can also classify data based on the purpose and scene of the clothing. For example, the storage unit can classify the user's clothing used in different scenes, such as work, casual, and sports, and manage the usage status for each. The storage unit can also record the usage status of clothing for specific events or seasons, allowing the user to refer to past usage history. Furthermore, the storage unit can record how the user combined specific clothing items to use as a reference for coordinating outfits. This allows the storage unit to grasp and manage the user's clothing usage status in more detail.

[0102] The suggestion unit can also suggest the timing of clothing maintenance and cleaning based on the usage status of the user's clothing. For example, the suggestion unit can notify the user that cleaning and maintenance are necessary when a specific piece of clothing has been used a certain number of times. The suggestion unit can also provide advice on how to store and maintain clothing in accordance with the change of seasons. Furthermore, the suggestion unit can also suggest a maintenance schedule for the user to use the clothing for a long period of time, and help the user to properly manage the clothing. In this way, the suggestion unit can provide support for the user to prolong the life of their clothing.

[0103] The linking unit can also provide information on post-purchase maintenance and care when a user purchases clothes. For example, when a specific piece of clothing is purchased, the linking unit can display maintenance methods and cleaning precautions for that clothing. The linking unit can also suggest a maintenance schedule for the clothing purchased by the user, encouraging regular care. Furthermore, the linking unit can suggest accessories and maintenance products related to the clothing purchased by the user, allowing them to be purchased in one stop. In this way, the linking unit can support the user in properly caring for the clothing they purchased and using it for a long period of time.

[0104] The matching unit can also suggest an appropriate coordinator when a user requests coordination tailored to a specific event or scene. For example, when a user is attending a specific event such as a wedding or business meeting, the matching unit can suggest a coordinator suitable for the event. In addition, if a user is interested in a specific fashion style or brand, the matching unit can also suggest a coordinator who is knowledgeable about that style or brand. Furthermore, if a user requests coordination tailored to a specific region or culture, the matching unit can also suggest a coordinator who is knowledgeable about that region or culture. In this way, the matching unit can support the user in receiving coordination tailored to their specific needs.

[0105] The storage unit may estimate the user's emotions and adjust criteria for selecting clothes to be saved based on the estimated user's emotions. For example, if the user is feeling stressed, the storage unit may prioritize saving clothes that are relaxing. Also, if the user is feeling relaxed, the storage unit may prioritize saving clothes for special events. Furthermore, if the user is in a hurry, the storage unit may prioritize saving clothes that are worn daily. In this way, the storage unit can improve user convenience by adjusting criteria for selecting clothes to be saved based on the user's emotions.

[0106] The storage unit can also analyze the user's past storage history and determine the priority of the clothes to be stored. For example, the storage unit can prioritize storing clothes that the user has frequently saved in the past. The storage unit can also prioritize storing clothes that the user has saved for a specific season or event. Furthermore, the storage unit can analyze the user's past storage patterns and suggest the most efficient storage method. In this way, the storage unit can suggest the optimal storage method by analyzing the user's past storage history.

[0107] The storage unit may adjust the criteria for selecting clothes to be saved based on the user's current fashion trends. For example, the storage unit may preferentially save clothes of the user's currently preferred color or design. The storage unit may also select clothes to save based on the user's recent purchase history. Furthermore, the storage unit may analyze the user's social media activity and save clothes that match the user's current fashion trends. This allows the storage unit to optimize the clothes to be saved based on the user's current fashion trends.

[0108] The storage unit may estimate the user's emotions and determine the priority of clothes to be saved based on the estimated user's emotions. For example, when the user is feeling stressed, the storage unit may prioritize saving frequently used clothes. When the user is feeling relaxed, the storage unit may prioritize saving clothes that are appropriate for the season or an event. Furthermore, when the user is in a hurry, the storage unit may prioritize saving recently used clothes. In this way, the storage unit can improve user convenience by prioritizing clothes to be saved according to the user's emotions.

[0109] The storage unit may also preferentially store highly relevant clothing based on the user's geographical location information. For example, if the user is in a cold region, the storage unit may preferentially store clothing made of warm materials. Also, if the user is traveling, the storage unit may preferentially store clothing suited to the climate of the travel destination. Furthermore, the storage unit may also store clothing that matches local fashion trends based on the user's geographical location information. In this way, the storage unit can preferentially store highly relevant clothing taking into account the user's geographical location information.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The storage unit stores the clothing image captured by the camera. The storage unit can store clothing data in digital format, and supports various data formats such as JPEG, PNG, and 3D data. Images of clothing captured by the user with the camera are saved and managed as digital data. 3D data of clothing can also be acquired and saved using a 3D scanner. Step 2: The classification unit processes the clothing data stored by the storage unit as 3D data and classifies it by color and shape. The classification unit uses image processing technology to analyze the color of the clothing and classify it based on the color range. It also uses shape analysis technology to analyze the shape of the clothing and classify it based on the shape characteristics. For example, clothing color can be classified based on RGB values ​​and divided into categories such as red, blue, and green. Furthermore, clothing shape can be analyzed using contour extraction technology and divided into categories such as shirts, pants, and skirts. Step 3: The suggestion unit suggests outfits according to the season, weather, temperature, and trends based on the data classified by the classification unit. The suggestion unit acquires weather data and suggests optimal outfits based on the current weather and temperature. It also refers to fashion trend data and suggests outfits based on the latest trends. For example, if the current temperature is low, it suggests clothes made of warm materials, and if the temperature is high, it suggests clothes made of cool materials. It can also suggest clothes with popular colors and designs based on the latest fashion trends.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0114] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a 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.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0168] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0169] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0173] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0174] 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.

[0175] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0176] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0177] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0178] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0181] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0183] [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a storage unit that stores the clothes photographed by the camera; a classification unit that processes the clothing data stored by the storage unit as 3D data and classifies the data by color and shape; a suggestion unit that suggests outfits according to season, weather, temperature, and trends based on the data classified by the classification unit. A system characterized by:

2. Equipped with a storage unit that records the usage status of clothing 2. The system of claim 1.

3. We have a proposal department that identifies underused clothing and proposes selling it on flea market sites.

2. The system of claim 1.

4. Equipped with a linking function that allows you to connect with multiple shopping sites and make bulk purchases 2. The system of claim 1.

5. Equipped with a matching section that serves as a place to match users with coordinators 2. The system of claim 1.

6. The storage unit Estimate the user's emotions and adjust the timing of saving clothes based on the estimated user emotions.

2. The system of claim 1.

7. The storage unit When saving clothes, the system analyzes the user's past saving history and selects the appropriate saving method.

2. The system of claim 1.

8. The storage unit When saving clothes, filter based on the user's current fashion trends 2. The system of claim 1.

9. The storage unit Estimate the user's emotions and prioritize the clothes to be saved based on the estimated user emotions.

2. The system of claim 1.

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