System

Abstract

An object of a system according to an embodiment is to analyze existing clothing data of a user and propose optimal coordination.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, an advice unit, and a presentation unit. The collection unit collects clothing data of a user. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes coordination on the basis of an analysis result obtained by the analysis unit. The advice unit provides advice for the coordination proposed by the proposal unit. The presentation unit presents an item to be newly purchased based on the advice provided by the advice unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately utilize a user's existing clothing data to suggest optimal outfits, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's existing clothing data and propose optimal outfits. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, an advice unit, and a presentation unit. The collection unit collects clothing data of a user. The analysis unit analyzes the data collected by the collection unit. The suggestion unit suggests outfits based on the analysis results obtained by the analysis unit. The advice unit provides advice on the outfits suggested by the suggestion unit. The presentation unit suggests new items to purchase based on the advice provided by the advice unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's existing clothing data and suggest optimal outfits. [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) An outfit suggestion system according to an embodiment of the present invention collects a user's clothing data and uses a generation AI to analyze, suggest, advise, and present outfits. The outfit suggestion system collects the user's clothing data and uses a generation AI to analyze, suggest, advise, and present outfits to assist the user in choosing outfits. For example, the user inputs their own clothing data into the outfit suggestion system, such as photos of the clothing, brand, color, size, material, and seasonal information. This information is analyzed by the generation AI. The outfit suggestion system then uses the generation AI to suggest optimal outfits based on the user's clothing data. The generation AI also considers the user's preferences and past outfit history when making suggestions. For example, it may suggest outfits tailored to specific events or seasons. Furthermore, the generation AI also provides advice on outfits the user has created themselves. For example, it may provide advice on color combinations and style balance. Finally, the outfit suggestion system uses the generation AI to suggest new items to purchase. For example, it may suggest accessories or shoes that go well with a specific outfit. This allows the outfit suggestion system to assist the user in choosing outfits, allowing them to gain confidence in their outfits when going out. It also allows users to efficiently purchase new items, broadening their fashion options. This allows the coordination suggestion system to assist users in choosing outfits, giving them confidence in coordinating outfits when going out. It also allows users to efficiently purchase new items, broadening their fashion options.

[0029] The coordination suggestion system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, an advice unit, and a presentation unit. The collection unit collects clothing data from a user. The user's clothing data includes, for example, but is not limited to, photos of the clothing, brand, color, size, material, and seasonal information. The collection unit, for example, allows a user to take photos of clothing using a smartphone camera and collects the data through a dedicated app. The collection unit can also allow a user to manually input clothing brand, color, size, material, and seasonal information. The collection unit can also automatically detect clothing material and size using a sensor. For example, the collection unit can analyze photos of clothing taken with a smartphone camera using image recognition technology to identify the clothing brand and color. The dedicated app collects the data manually entered by the user and stores it in a database. The sensor detects the clothing material and size and collects it as data. The analysis unit uses a generative AI to analyze the data collected by the collection unit. The analysis can be performed using, for example, but is not limited to, image recognition technology or data mining technology. For example, the generation AI analyzes photos of clothing using deep learning to extract clothing features. The analysis unit can also analyze the user's clothing data using data mining technology. The analysis unit can also use the generation AI to analyze trend information based on the user's clothing data. For example, the generation AI analyzes photos of clothing using deep learning to extract clothing features. Data mining technology is a technique for extracting useful information from large amounts of data and is used to analyze the user's clothing data. The generation AI analyzes trend information and compares it with the user's clothing data to provide analysis results. The suggestion unit uses the generation AI to suggest outfits based on the analysis results obtained by the analysis unit. Suggestions are made based on, for example, the user's preferences and past outfit history, but are not limited to such examples. For example, the generation AI takes the user's preferences into consideration and suggests outfits tailored to specific events or seasons. The suggestion unit can also use the generation AI to make suggestions based on the user's past outfit history.The suggestion unit can also use the generation AI to make suggestions taking into consideration the user's preferences and trend information. For example, the generation AI can suggest outfits tailored to specific events or seasons taking into consideration the user's preferences. The generation AI can suggest styles and color combinations preferred by the user based on the user's past coordination history. The generation AI can suggest outfits based on the latest fashions taking into consideration trend information. The advice unit can use the generation AI to provide advice on the outfits suggested by the suggestion unit. The advice can be, for example, about color combinations and style balance, but is not limited to such examples. For example, the generation AI can use a color wheel to determine whether a color combination is appropriate. The advice unit can also use the generation AI to provide advice on style balance. The advice unit can also use the generation AI to provide advice taking into consideration the user's preferences and trend information. For example, the generation AI can use a color wheel to determine whether a color combination is appropriate. The generation AI can consider style balance to determine whether the outfit is balanced. The generation AI can provide advice on outfits taking into consideration the user's preferences and trend information. The presentation unit uses the generation AI to present new items to be purchased based on the advice provided by the advice unit. Presentations are made based on, for example, the user's preferences, past purchase history, and current trend information, but are not limited to these examples. For example, the generation AI may suggest accessories, shoes, etc. that go well with a specific outfit, taking into account the user's preferences. The presentation unit can also use the generation AI to make suggestions based on the user's past purchase history. The presentation unit can also use the generation AI to make suggestions, taking into account current trend information. For example, the generation AI may suggest accessories, shoes, etc. that go well with a specific outfit, taking into account the user's preferences. The generation AI may suggest items that the user likes, based on the user's past purchase history. The generation AI may suggest items based on the latest fashions, taking into account current trend information.As a result, the coordination suggestion system according to the embodiment supports the user in choosing clothes, allowing the user to gain confidence in coordinating outfits when going out. In addition, the system also allows the user to efficiently purchase new items, broadening the scope of fashion.

[0030] The collection unit can collect data including photos of the user's clothing, brand, color, size, material, and season information. For example, the collection unit allows the user to take photos of clothing using a smartphone camera and collect the data through a dedicated app. The collection unit can also allow the user to manually input clothing brand, color, size, material, and season information. For example, the collection unit can analyze photos of clothing taken by the user with a smartphone camera using image recognition technology to identify the clothing brand and color. The dedicated app collects the data manually entered by the user and stores it in a database. This allows for the collection of detailed data about the user's clothing, enabling more accurate coordination suggestions. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input photos of clothing taken with a smartphone camera into a generation AI and have the generation AI generate text data from the image data.

[0031] The analysis unit can analyze photos of clothing using image recognition technology. The analysis unit can use, for example, a generative AI to analyze photos of clothing using deep learning technology. For example, the generative AI can extract clothing features and identify information such as brand, color, and material. The analysis unit can also use the generative AI to analyze photos of clothing using image recognition technology. For example, the generative AI can extract detailed information from photos of clothing using computer vision technology. The analysis unit can also use the generative AI to analyze trend information based on user clothing data. For example, the generative AI can analyze photos of clothing and extract trend information using deep learning technology. This allows detailed information to be analyzed from photos of clothing using image recognition technology. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input photo data of clothing to the generative AI and cause the generative AI to extract detailed information from the image data.

[0032] The suggestion unit can suggest outfits based on the user's preferences and past coordination history. The suggestion unit, for example, uses a generation AI to suggest outfits taking into account the user's preferences. For example, the generation AI suggests outfits tailored to specific events or seasons based on the user's preferences. The suggestion unit can also use the generation AI to make suggestions based on the user's past coordination history. For example, the generation AI analyzes the user's past coordination history to suggest styles and color combinations preferred by the user. The suggestion unit can also use the generation AI to make suggestions taking into account the user's preferences and trend information. For example, the generation AI suggests outfits tailored to specific events or seasons taking into account the user's preferences. The generation AI suggests styles and color combinations preferred by the user based on the user's past coordination history. The generation AI suggests outfits based on the latest fashions taking into account trend information. This enables more appropriate outfit suggestions by taking into account the user's preferences and past history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's preferences and past coordination history into the generation AI and have the generation AI execute outfit suggestions.

[0033] The advice unit can provide advice on color combinations and style balance. The advice unit can provide advice on color combinations using, for example, a generation AI. For example, the generation AI can use a color wheel to determine whether a color combination is appropriate. The advice unit can also provide advice on style balance using the generation AI. For example, the generation AI can determine whether an outfit is balanced by taking style balance into consideration. The advice unit can also provide advice by taking user preferences and trend information into consideration. For example, the generation AI can provide advice tailored to a specific event or season by taking user preferences into consideration. The generation AI can provide advice on styles and color combinations preferred by the user based on the user's past coordination history. The generation AI can provide advice based on the latest fashions by taking trend information into consideration. This improves the user's coordination skills by providing advice on color combinations and style balance. Some or all of the above-described processing by the advice unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the advice unit can input the user's coordination ideas into the generation AI and have the generation AI execute advice on color combinations and style balance.

[0034] The presentation unit can present new items to purchase based on the user's preferences, past purchase history, and current trend information. The presentation unit, for example, uses a generation AI to present new items to purchase taking into account the user's preferences. For example, the generation AI suggests accessories or shoes that match a specific outfit based on the user's preferences. The presentation unit can also use the generation AI to make suggestions based on the user's past purchase history. For example, the generation AI analyzes the user's past purchase history and suggests items the user likes. The presentation unit can also use the generation AI to make suggestions taking into account current trend information. For example, the generation AI suggests items based on the latest fashions based on current trend information. This makes it possible to present appropriate items by taking into account the user's preferences, past purchase history, and current trend information. Some or all of the above-described processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the user's preferences, past purchase history, and current trend information into the generation AI and cause the generation AI to present new items to purchase.

[0035] The outfit suggestion system includes a feedback unit that collects user feedback. The feedback unit collects feedback, for example, through a user survey. For example, the feedback unit conducts a survey on outfit suggestions to users to collect their opinions and impressions. The feedback unit can also collect feedback through review comments. For example, the feedback unit provides a platform where users can post comments on outfit suggestions and collects user feedback. In this way, collecting user feedback can be used to improve the system. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input user survey results and review comments into a generation AI and have the generation AI analyze the feedback.

[0036] The coordination suggestion system includes a trend analysis unit that analyzes current trend information. The trend analysis unit, for example, collects data from fashion magazines and online shops and analyzes the trend information. For example, the trend analysis unit collects articles from fashion magazines and sales data from online shops and analyzes current trends. The trend analysis unit can also analyze trend information using a generation AI. For example, the generation AI analyzes current trends based on data from fashion magazines and online shops. In this way, the latest fashion information can be provided by analyzing the current trend information. Some or all of the above-described processing in the trend analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the trend analysis unit can input data from fashion magazines and online shops into the generation AI and have the generation AI analyze the trend information.

[0037] The collection unit can analyze the user's past clothing data collection history and select the optimal collection method. The collection unit, for example, analyzes the user's past clothing data collection history. For example, the collection unit preferentially suggests collection methods (photos, text, etc.) that the user has frequently used in the past. The collection unit can also perform collection during a specific time period based on the user's past collection history. For example, the collection unit analyzes the user's past collection history and suggests the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the past collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past collection history data into a generation AI and cause the generation AI to select the optimal collection method.

[0038] When collecting clothing data, the collection unit can filter the data based on the user's current fashion trends and areas of interest. The collection unit, for example, analyzes the user's current fashion trends and areas of interest. For example, the collection unit collects data based on the fashion styles in which the user is currently interested. The collection unit can also filter the data based on the user's areas of interest (casual, formal, etc.). For example, the collection unit analyzes the user's current fashion trends and prioritizes collecting related data. This makes it possible to collect highly relevant data by filtering the data based on the user's current fashion trends and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's fashion trends and areas of interest to the generation AI and have the generation AI filter the data.

[0039] When collecting clothing data, the collection unit can select the optimal collection means depending on the user's input method. The collection unit selects the optimal collection means depending on, for example, the user's input method (voice, text, image, etc.). For example, if the user prefers voice input, the collection unit collects clothing data by voice. Also, if the user prefers text input, the collection unit can collect clothing data by text. For example, if the user prefers image input, the collection unit collects clothing data by image. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data depending on the user's input method to a generation AI and cause the generation AI to select the optimal collection means.

[0040] When collecting clothing data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, acquires the user's geographical location information. For example, the collection unit identifies the user's current location using GPS data. The collection unit can also acquire the user's geographical location information using a location information service. For example, the collection unit prioritizes collecting clothing data suitable for the climate of the area where the user is currently located. The collection unit can also collect clothing data related to places the user plans to visit. For example, the collection unit collects data that matches local fashion trends based on the user's geographical location information. This makes it possible to prioritize collecting highly relevant data by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0041] When collecting clothing data, the collection unit can analyze the user's social media activity and collect related data. The collection unit, for example, analyzes the user's social media activity. For example, the collection unit collects photos of clothing shared by the user on social media. The collection unit can also analyze the content of the user's social media posts to collect related clothing data. For example, the collection unit collects related clothing data by referring to the activities of the user's friends on social media. This allows for efficient collection of related data by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related data.

[0042] When collecting clothing data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, analyzes the user's past feedback. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit can also select the optimal collection means from the user's past feedback. For example, the collection unit customizes the collection method by reflecting the user's feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0043] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the clothing. The analysis unit, for example, evaluates the importance of the clothing. For example, the analysis unit evaluates the importance based on the frequency of use, price, brand value, etc. of the clothing. The analysis unit can also adjust the level of detail of the analysis based on the importance of the clothing. For example, the analysis unit performs a detailed analysis on important clothing and a brief analysis on less important clothing. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the clothing. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input clothing importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] During analysis, the analysis unit can apply different analysis algorithms depending on the clothing category. The analysis unit, for example, classifies clothing categories. For example, the analysis unit classifies clothing based on categories such as tops, bottoms, and outerwear. The analysis unit can also apply different analysis algorithms depending on the clothing category. For example, the analysis unit applies a casual analysis algorithm to casual clothing and a formal analysis algorithm to formal clothing. This improves the accuracy of analysis by applying the optimal analysis algorithm depending on the clothing category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input clothing category data to a generation AI and cause the generation AI to apply an analysis algorithm.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, analyzes the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and improves the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0046] During analysis, the analysis unit can determine the priority of analysis based on the time when the clothing was submitted. The analysis unit, for example, evaluates the time when the clothing was submitted. For example, the analysis unit evaluates the time when the clothing was submitted based on the submission date and submission time. The analysis unit can also determine the priority of analysis based on the time when the clothing was submitted. For example, the analysis unit prioritizes analysis of recently submitted clothing and postpones analysis of older submitted clothing. In this way, determining the priority of analysis based on the time when the clothing was submitted enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the clothing was submitted to the generation AI and have the generation AI determine the priority of analysis.

[0047] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the clothing. The analysis unit, for example, evaluates the relevance of the clothing. For example, the analysis unit evaluates the relevance of the clothing based on common attributes or related topics. The analysis unit can also adjust the order of analysis based on the relevance of the clothing. For example, the analysis unit prioritizes analysis of highly relevant clothing and postpones analysis of less relevant clothing. By adjusting the order of analysis based on the relevance of the clothing, efficient analysis is possible. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input clothing relevance data to a generation AI and cause the generation AI to adjust the order of analysis.

[0048] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. For example, the analysis unit evaluates whether the user is a beginner, intermediate, or advanced user. The analysis unit can also adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses detailed technical terminology if the user has technical expertise, and uses concise and easy-to-understand terminology if the user does not have technical expertise. By adjusting the technical terminology in the analysis according to the user's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI execute the use of technical terminology.

[0049] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the outfit. The suggestion unit, for example, evaluates the importance of the outfit. For example, the suggestion unit evaluates the importance of the outfit based on the importance of the event or the user's preferences. The suggestion unit can also adjust the level of detail of the proposal based on the importance of the outfit. For example, the suggestion unit makes detailed suggestions for important outfits and brief suggestions for outfits with low importance. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the outfit. 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 importance data of the outfit into a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0050] When making a proposal, the proposal unit can apply different proposal algorithms depending on the event or season. The proposal unit, for example, classifies events and seasons. For example, the proposal unit classifies events and seasons such as wedding, party, spring, and summer. The proposal unit can also apply different proposal algorithms depending on the event or season. For example, the proposal unit applies a proposal algorithm tailored to the event and a proposal algorithm tailored to the season. This improves the accuracy of the proposal by applying the optimal proposal algorithm depending on the event or season. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input event and season data into a generation AI and cause the generation AI to apply a proposal algorithm.

[0051] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, analyzes the user's past suggestion results. For example, the suggestion unit improves the accuracy of the suggestion based on the user's past suggestion results. The suggestion unit can also adjust the suggestion algorithm by referring to the user's past suggestion results. For example, the suggestion unit analyzes the user's past suggestion results and improves the accuracy of the suggestion. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0052] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time when the outfit was submitted. The suggestion unit, for example, evaluates the time when the outfit was submitted. For example, the suggestion unit evaluates the time when the outfit was submitted based on the submission date and submission time. The suggestion unit can also determine the priority of the proposal based on the time when the outfit was submitted. For example, the suggestion unit prioritizes the most recently submitted outfit and postpones the most recently submitted outfit. This enables efficient proposals by determining the priority of the proposal based on the time when the outfit was submitted. 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 the submission time data of the outfit into the generation AI and have the generation AI determine the priority of the proposals.

[0053] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the outfits. The suggestion unit, for example, evaluates the relevance of the outfits. For example, the suggestion unit evaluates the relevance of the outfits based on common attributes or related topics. The suggestion unit can also adjust the order of suggestions based on the relevance of the outfits. For example, the suggestion unit prioritizes suggesting highly relevant outfits and postpones less relevant outfits. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the outfits. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input relevance data of the outfits to a generation AI and cause the generation AI to adjust the order of suggestions.

[0054] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. The suggestion unit, for example, evaluates the user's level of expertise. For example, the suggestion unit evaluates whether the user is a beginner, intermediate, or advanced user. The suggestion unit can also adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit uses detailed technical terminology if the user has technical expertise, and uses concise and easy-to-understand terminology if the user does not have technical expertise. By adjusting the technical terminology in the proposal according to the user's level of expertise, it is possible to provide a more understandable proposal. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input the user's level of expertise data into a generation AI and cause the generation AI to execute the use of technical terminology.

[0055] When giving advice, the advice unit can adjust the level of detail of the advice based on the importance of the outfit. The advice unit, for example, evaluates the importance of the outfit. For example, the advice unit evaluates the importance of the outfit based on the importance of the event or the user's preferences. The advice unit can also adjust the level of detail of the advice based on the importance of the outfit. For example, the advice unit provides detailed advice for important outfits and brief advice for outfits with low importance. By adjusting the level of detail of the advice based on the importance of the outfit, efficient advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input importance data of the outfit into a generation AI and cause the generation AI to adjust the level of detail of the advice.

[0056] When providing advice, the advice unit can apply different advice algorithms depending on the color combination and style balance. The advice unit, for example, evaluates the color combination and style balance. For example, the advice unit evaluates color combinations based on a color wheel or complementary color relationships. The advice unit can also evaluate the balance of styles such as casual, formal, and mixed styles. For example, the advice unit applies an advice algorithm related to color combinations and an advice algorithm related to style balance. This improves the accuracy of advice by applying the optimal advice algorithm depending on the color combination and style balance. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input color combination and style balance data to a generation AI and cause the generation AI to apply the advice algorithm.

[0057] When providing advice, the advice unit can improve the accuracy of the advice by referring to the user's past advice results. The advice unit, for example, analyzes the user's past advice results. For example, the advice unit improves the accuracy of the advice based on the user's past advice results. The advice unit can also adjust the advice algorithm by referring to the user's past advice results. For example, the advice unit analyzes the user's past advice results and improves the accuracy of the advice. In this way, the accuracy of the advice is improved by referring to the user's past advice results. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0058] When giving advice, the advice unit can determine the priority of advice based on the time when the outfit was submitted. The advice unit, for example, evaluates the time when the outfit was submitted. For example, the advice unit evaluates the time when the outfit was submitted based on the submission date and submission time. The advice unit can also determine the priority of advice based on the time when the outfit was submitted. For example, the advice unit prioritizes advice on the most recently submitted outfit and postpones the most recently submitted outfit. This enables efficient advice by determining the priority of advice based on the time when the outfit was submitted. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input data on the time when the outfit was submitted to the generation AI and have the generation AI determine the priority of advice.

[0059] When giving advice, the advice unit can adjust the order of advice based on the relevance of the outfits. The advice unit, for example, evaluates the relevance of the outfits. For example, the advice unit evaluates the relevance of the outfits based on common attributes or related topics. The advice unit can also adjust the order of advice based on the relevance of the outfits. For example, the advice unit prioritizes advice on highly relevant outfits and postpones less relevant outfits. This allows for efficient advice by adjusting the order of advice based on the relevance of the outfits. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input outfit relevance data into a generation AI and have the generation AI adjust the order of advice.

[0060] When providing advice, the advice unit can adjust the use of technical terms in the advice depending on the user's level of expertise. The advice unit, for example, evaluates the user's level of expertise. For example, the advice unit evaluates whether the user is a beginner, intermediate, or advanced user. The advice unit can also adjust the use of technical terms in the advice depending on the user's level of expertise. For example, the advice unit uses detailed technical terms if the user has technical expertise, and uses concise and easy-to-understand terms if the user does not have technical expertise. By adjusting the technical terms in the advice depending on the user's level of expertise, more understandable advice can be provided. Some or all of the above-described processing in the advice unit may be performed using AI, for example, or may be performed without AI. For example, the advice unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terms.

[0061] The presentation unit can adjust the level of detail of the presentation based on the importance of the item when presenting the item. The presentation unit, for example, evaluates the importance of the item. For example, the presentation unit evaluates the importance of the item based on frequency of use, price, brand value, etc. The presentation unit can also adjust the level of detail of the presentation based on the importance of the item. For example, the presentation unit provides detailed information for important items and brief information for less important items. This enables efficient presentation by adjusting the level of detail of the presentation based on the importance of the item. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI, for example. For example, the presentation unit can input item importance data to a generation AI and cause the generation AI to adjust the level of detail of the presentation.

[0062] The presentation unit can apply different presentation algorithms depending on the category of the item when presenting the items. The presentation unit, for example, classifies the category of the items. For example, the presentation unit classifies items based on categories such as tops, bottoms, and outerwear. The presentation unit can also apply different presentation algorithms depending on the category of the item. For example, the presentation unit applies a presentation algorithm for accessories to accessories and a presentation algorithm for shoes to shoes. This improves the accuracy of presentation by applying the optimal presentation algorithm depending on the category of the item. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input item category data to a generation AI and cause the generation AI to apply a presentation algorithm.

[0063] The presentation unit can improve the accuracy of presentation by referring to the user's past presentation results when presenting. The presentation unit, for example, analyzes the user's past presentation results. For example, the presentation unit improves the accuracy of presentation based on the user's past presentation results. The presentation unit can also adjust the presentation algorithm by referring to the user's past presentation results. For example, the presentation unit analyzes the user's past presentation results and improves the accuracy of presentation. In this way, the accuracy of presentation is improved by referring to the user's past presentation results. Some or all of the above-described processing in the presentation unit may be performed using AI, for example, or may be performed without using AI. For example, the presentation unit can input the user's past presentation result data into the generation AI and cause the generation AI to improve the accuracy of presentation.

[0064] The presentation unit can determine the presentation priority based on the submission time of the item at the time of presentation. The presentation unit, for example, evaluates the submission time of the item. For example, the presentation unit evaluates the submission time of the item based on the submission date or submission time. The presentation unit can also determine the presentation priority based on the submission time of the item. For example, the presentation unit prioritizes the presentation of recently submitted items and postpones the presentation of older submitted items. This enables efficient presentation by determining the presentation priority based on the submission time of the item. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input item submission time data to the generation AI and cause the generation AI to determine the presentation priority.

[0065] The presentation unit can adjust the order of presentation based on the relevance of the items when presenting them. The presentation unit, for example, evaluates the relevance of the items. For example, the presentation unit evaluates the relevance of the items based on common attributes or related topics. The presentation unit can also adjust the order of presentation based on the relevance of the items. For example, the presentation unit prioritizes presenting highly relevant items and postpones less relevant items. This enables efficient presentation by adjusting the order of presentation based on the relevance of the items. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input item relevance data to a generation AI and cause the generation AI to adjust the order of presentation.

[0066] The presentation unit can adjust the use of technical terminology in the presentation according to the user's level of expertise. The presentation unit, for example, evaluates the user's level of expertise. For example, the presentation unit evaluates whether the user is a beginner, intermediate, or advanced user. The presentation unit can also adjust the use of technical terminology in the presentation according to the user's level of expertise. For example, the presentation unit uses detailed technical terminology if the user has technical expertise, and uses concise and easy-to-understand terminology if the user does not have technical expertise. This allows for adjusting the technical terminology in the presentation according to the user's level of expertise, thereby providing a more understandable presentation. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology.

[0067] When collecting feedback, the feedback unit can select the optimal collection method by referring to the user's past feedback history. The feedback unit, for example, analyzes the user's past feedback history. For example, the feedback unit adjusts the collection method based on feedback provided by the user in the past. The feedback unit can also select the optimal collection means from the user's past feedback history. For example, the feedback unit analyzes the user's feedback history and customizes the collection method. In this way, the optimal collection method can be selected by referring to the user's past feedback history. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback history data into a generation AI and cause the generation AI to select a collection method.

[0068] The feedback unit can perform filtering based on the user's current areas of interest when collecting feedback. The feedback unit, for example, analyzes the user's current areas of interest. For example, the feedback unit collects feedback based on the fashion style in which the user is currently interested. The feedback unit can also filter the feedback based on the user's areas of interest (casual, formal, etc.). For example, the feedback unit analyzes the user's current areas of interest and preferentially collects related feedback. In this way, by filtering based on the user's current areas of interest, highly relevant feedback can be collected. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's area of ​​interest data to a generation AI and have the generation AI perform the filtering.

[0069] When collecting feedback, the feedback unit can prioritize collecting highly relevant feedback by taking into account the user's geographical location information. The feedback unit, for example, acquires the user's geographical location information. For example, the feedback unit identifies the user's current location using GPS data. The feedback unit can also acquire the user's geographical location information using a location information service. For example, the feedback unit prioritizes collecting feedback that is suitable for the climate of the area where the user is currently located. The feedback unit can also collect feedback related to places the user plans to visit. For example, the feedback unit collects feedback that matches local fashion trends based on the user's geographical location information. In this way, highly relevant feedback can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's geographical location information to a generation AI and cause the generation AI to collect highly relevant feedback.

[0070] When collecting feedback, the feedback unit can analyze the user's social media activity and collect relevant feedback. The feedback unit, for example, analyzes the user's social media activity. For example, the feedback unit collects feedback regarding photos of clothing shared by the user on social media. The feedback unit can also analyze the content of the user's social media posts and collect relevant feedback. For example, the feedback unit collects relevant feedback by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, relevant feedback can be efficiently collected. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's social media activity data into a generation AI and cause the generation AI to collect relevant feedback.

[0071] During trend analysis, the trend analysis unit can predict a current trend by referring to past trend data. The trend analysis unit, for example, analyzes past trend data. For example, the trend analysis unit predicts a current trend based on past trend data. The trend analysis unit can also adjust a trend analysis algorithm by referring to past trend data. For example, the trend analysis unit analyzes past trend data and predicts a current trend with high accuracy. In this way, by referring to past trend data, a current trend can be predicted with high accuracy. Some or all of the above-described processing in the trend analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the trend analysis unit can input past trend data to a generation AI and have the generation AI execute a current trend prediction.

[0072] The trend analysis unit can apply different trend analysis methods to each clothing category during trend analysis. The trend analysis unit, for example, classifies clothing categories. For example, the trend analysis unit classifies clothing based on categories such as tops, bottoms, and outerwear. The trend analysis unit can also apply different trend analysis methods to each clothing category. For example, the trend analysis unit applies a casual trend analysis method to casual clothing and a formal trend analysis method to formal clothing. This improves the accuracy of trend analysis by applying the optimal trend analysis method to each clothing category. Some or all of the above-described processing in the trend analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the trend analysis unit can input clothing category data to a generation AI and cause the generation AI to apply a trend analysis method.

[0073] During trend analysis, the trend analysis unit can improve the accuracy of the analysis by referring to the user's past trend analysis results. The trend analysis unit, for example, analyzes the user's past trend analysis results. For example, the trend analysis unit improves the accuracy of the analysis based on the user's past trend analysis results. The trend analysis unit can also adjust the analysis algorithm by referring to the user's past trend analysis results. For example, the trend analysis unit analyzes the user's past trend analysis results to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past trend analysis results. Some or all of the above-described processing in the trend analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the trend analysis unit can input the user's past trend analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0074] During trend analysis, the trend analysis unit can analyze changes in trends based on the time when the clothing was submitted. The trend analysis unit, for example, evaluates the time when the clothing was submitted. For example, the trend analysis unit evaluates the time when the clothing was submitted based on the submission date and submission time. The trend analysis unit can also analyze changes in trends based on the time when the clothing was submitted. For example, the trend analysis unit analyzes changes in trends based on recently submitted clothing and postpones clothing that was submitted earlier. In this way, by analyzing changes in trends based on the time when the clothing was submitted, it is possible to provide the latest trend information. Some or all of the above-mentioned processing in the trend analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the trend analysis unit can input data on the time when the clothing was submitted to the generation AI and cause the generation AI to analyze changes in trends.

[0075] During trend analysis, the trend analysis unit can analyze trends by referring to clothing-related market data. The trend analysis unit, for example, analyzes clothing-related market data. For example, the trend analysis unit analyzes trends based on market research data and sales data. The trend analysis unit can also adjust the trend analysis algorithm by referring to clothing-related market data. For example, the trend analysis unit analyzes clothing-related market data and predicts trends with high accuracy. As a result, trends can be predicted with high accuracy by referring to clothing-related market data. Some or all of the above-mentioned processing in the trend analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the trend analysis unit can input clothing-related market data to a generation AI and cause the generation AI to perform trend analysis.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The outfit suggestion system includes a feedback unit that collects user feedback. The feedback unit collects feedback, for example, through a user survey. For example, the feedback unit conducts a survey on outfit suggestions to users to collect their opinions and impressions. The feedback unit can also collect feedback through review comments. For example, the feedback unit provides a platform where users can post comments on outfit suggestions and collects user feedback. In this way, collecting user feedback can be used to improve the system. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input user survey results and review comments into a generation AI and have the generation AI analyze the feedback.

[0078] The coordination suggestion system includes a trend analysis unit that analyzes current trend information. The trend analysis unit, for example, collects data from fashion magazines and online shops and analyzes the trend information. For example, the trend analysis unit collects articles from fashion magazines and sales data from online shops and analyzes current trends. The trend analysis unit can also analyze trend information using a generation AI. For example, the generation AI analyzes current trends based on data from fashion magazines and online shops. In this way, the latest fashion information can be provided by analyzing the current trend information. Some or all of the above-described processing in the trend analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the trend analysis unit can input data from fashion magazines and online shops into the generation AI and have the generation AI analyze the trend information.

[0079] The collection unit can analyze the user's past clothing data collection history and select the optimal collection method. The collection unit, for example, analyzes the user's past clothing data collection history. For example, the collection unit preferentially suggests collection methods (photos, text, etc.) that the user has frequently used in the past. The collection unit can also perform collection during a specific time period based on the user's past collection history. For example, the collection unit analyzes the user's past collection history and suggests the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the past collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past collection history data into a generation AI and cause the generation AI to select the optimal collection method.

[0080] When collecting clothing data, the collection unit can filter the data based on the user's current fashion trends and areas of interest. The collection unit, for example, analyzes the user's current fashion trends and areas of interest. For example, the collection unit collects data based on the fashion styles in which the user is currently interested. The collection unit can also filter the data based on the user's areas of interest (casual, formal, etc.). For example, the collection unit analyzes the user's current fashion trends and prioritizes collecting related data. This makes it possible to collect highly relevant data by filtering the data based on the user's current fashion trends and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's fashion trends and areas of interest to the generation AI and have the generation AI filter the data.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the clothing. The analysis unit, for example, evaluates the importance of the clothing. For example, the analysis unit evaluates the importance based on the frequency of use, price, brand value, etc. of the clothing. The analysis unit can also adjust the level of detail of the analysis based on the importance of the clothing. For example, the analysis unit performs a detailed analysis on important clothing and a brief analysis on less important clothing. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the clothing. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input clothing importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the outfit. The suggestion unit, for example, evaluates the importance of the outfit. For example, the suggestion unit evaluates the importance of the outfit based on the importance of the event or the user's preferences. The suggestion unit can also adjust the level of detail of the proposal based on the importance of the outfit. For example, the suggestion unit makes detailed suggestions for important outfits and brief suggestions for outfits with low importance. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the outfit. 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 importance data of the outfit into a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0083] When providing advice, the advice unit can apply different advice algorithms depending on the color combination and style balance. The advice unit, for example, evaluates the color combination and style balance. For example, the advice unit evaluates color combinations based on a color wheel or complementary color relationships. The advice unit can also evaluate the balance of styles such as casual, formal, and mixed styles. For example, the advice unit applies an advice algorithm related to color combinations and an advice algorithm related to style balance. This improves the accuracy of advice by applying the optimal advice algorithm depending on the color combination and style balance. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input color combination and style balance data to a generation AI and cause the generation AI to apply the advice algorithm.

[0084] The presentation unit can adjust the level of detail of the presentation based on the importance of the item when presenting the item. The presentation unit, for example, evaluates the importance of the item. For example, the presentation unit evaluates the importance of the item based on frequency of use, price, brand value, etc. The presentation unit can also adjust the level of detail of the presentation based on the importance of the item. For example, the presentation unit provides detailed information for important items and brief information for less important items. This enables efficient presentation by adjusting the level of detail of the presentation based on the importance of the item. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI, for example. For example, the presentation unit can input item importance data to a generation AI and cause the generation AI to adjust the level of detail of the presentation.

[0085] When collecting feedback, the feedback unit can select the optimal collection method by referring to the user's past feedback history. The feedback unit, for example, analyzes the user's past feedback history. For example, the feedback unit adjusts the collection method based on feedback provided by the user in the past. The feedback unit can also select the optimal collection means from the user's past feedback history. For example, the feedback unit analyzes the user's feedback history and customizes the collection method. In this way, the optimal collection method can be selected by referring to the user's past feedback history. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback history data into a generation AI and cause the generation AI to select a collection method.

[0086] The processing flow of the first embodiment will be briefly explained below.

[0087] Step 1: The collection unit collects the user's clothing data. The user's clothing data includes photos of the clothing, brand, color, size, material, and season information. The collection unit collects data by having the user take photos of the clothing using a smartphone camera and using a dedicated app. The user can also manually enter the clothing brand, color, size, material, and season information. Furthermore, the material and size of the clothing can be automatically detected using sensors. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. The analysis is performed using image recognition and data mining technologies. The generation AI uses deep learning to analyze photos of clothing and extract clothing features. It can also use data mining technology to analyze user clothing data and analyze trend information. Step 3: The suggestion unit uses the generation AI to suggest outfits based on the analysis results obtained by the analysis unit. Suggestions are made taking into consideration the user's preferences, past outfit history, and trend information. The generation AI suggests outfits tailored to specific events or seasons, as well as style and color combinations that the user prefers. Step 4: The advice unit uses the generation AI to provide advice on the outfits proposed by the suggestion unit. The advice is given on color combinations and style balance. The generation AI uses a color wheel to determine whether the color combination is appropriate, and considers style balance to determine whether the outfit is balanced. Step 5: The suggestion unit uses the generation AI to suggest new items to purchase based on the advice provided by the advice unit. Recommendations are based on the user's preferences, past purchase history, and current trend information. The generation AI suggests accessories, shoes, etc. that go well with a specific outfit, and suggests items based on the latest fashions, taking into account the user's preferences and trend information.

[0088] (Example 2) An outfit suggestion system according to an embodiment of the present invention collects a user's clothing data and uses a generation AI to analyze, suggest, advise, and present outfits. The outfit suggestion system collects the user's clothing data and uses a generation AI to analyze, suggest, advise, and present outfits to assist the user in choosing outfits. For example, the user inputs their own clothing data into the outfit suggestion system, such as photos of the clothing, brand, color, size, material, and seasonal information. This information is analyzed by the generation AI. The outfit suggestion system then uses the generation AI to suggest optimal outfits based on the user's clothing data. The generation AI also considers the user's preferences and past outfit history when making suggestions. For example, it may suggest outfits tailored to specific events or seasons. Furthermore, the generation AI also provides advice on outfits the user has created themselves. For example, it may provide advice on color combinations and style balance. Finally, the outfit suggestion system uses the generation AI to suggest new items to purchase. For example, it may suggest accessories or shoes that go well with a specific outfit. This allows the outfit suggestion system to assist the user in choosing outfits, allowing them to gain confidence in their outfits when going out. It also allows users to efficiently purchase new items, broadening their fashion options. This allows the coordination suggestion system to assist users in choosing outfits, giving them confidence in coordinating outfits when going out. It also allows users to efficiently purchase new items, broadening their fashion options.

[0089] The coordination suggestion system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, an advice unit, and a presentation unit. The collection unit collects clothing data from a user. The user's clothing data includes, for example, but is not limited to, photos of the clothing, brand, color, size, material, and seasonal information. The collection unit, for example, allows a user to take photos of clothing using a smartphone camera and collects the data through a dedicated app. The collection unit can also allow a user to manually input clothing brand, color, size, material, and seasonal information. The collection unit can also automatically detect clothing material and size using a sensor. For example, the collection unit can analyze photos of clothing taken with a smartphone camera using image recognition technology to identify the clothing brand and color. The dedicated app collects the data manually entered by the user and stores it in a database. The sensor detects the clothing material and size and collects it as data. The analysis unit uses a generative AI to analyze the data collected by the collection unit. The analysis can be performed using, for example, but is not limited to, image recognition technology or data mining technology. For example, the generation AI analyzes photos of clothing using deep learning to extract clothing features. The analysis unit can also analyze the user's clothing data using data mining technology. The analysis unit can also use the generation AI to analyze trend information based on the user's clothing data. For example, the generation AI analyzes photos of clothing using deep learning to extract clothing features. Data mining technology is a technique for extracting useful information from large amounts of data and is used to analyze the user's clothing data. The generation AI analyzes trend information and compares it with the user's clothing data to provide analysis results. The suggestion unit uses the generation AI to suggest outfits based on the analysis results obtained by the analysis unit. Suggestions are made based on, for example, the user's preferences and past outfit history, but are not limited to such examples. For example, the generation AI takes the user's preferences into consideration and suggests outfits tailored to specific events or seasons. The suggestion unit can also use the generation AI to make suggestions based on the user's past outfit history.The suggestion unit can also use the generation AI to make suggestions taking into consideration the user's preferences and trend information. For example, the generation AI can suggest outfits tailored to specific events or seasons taking into consideration the user's preferences. The generation AI can suggest styles and color combinations preferred by the user based on the user's past coordination history. The generation AI can suggest outfits based on the latest fashions taking into consideration trend information. The advice unit can use the generation AI to provide advice on the outfits suggested by the suggestion unit. The advice can be, for example, about color combinations and style balance, but is not limited to such examples. For example, the generation AI can use a color wheel to determine whether a color combination is appropriate. The advice unit can also use the generation AI to provide advice on style balance. The advice unit can also use the generation AI to provide advice taking into consideration the user's preferences and trend information. For example, the generation AI can use a color wheel to determine whether a color combination is appropriate. The generation AI can consider style balance to determine whether the outfit is balanced. The generation AI can provide advice on outfits taking into consideration the user's preferences and trend information. The presentation unit uses the generation AI to present new items to be purchased based on the advice provided by the advice unit. Presentations are made based on, for example, the user's preferences, past purchase history, and current trend information, but are not limited to these examples. For example, the generation AI may suggest accessories, shoes, etc. that go well with a specific outfit, taking into account the user's preferences. The presentation unit can also use the generation AI to make suggestions based on the user's past purchase history. The presentation unit can also use the generation AI to make suggestions, taking into account current trend information. For example, the generation AI may suggest accessories, shoes, etc. that go well with a specific outfit, taking into account the user's preferences. The generation AI may suggest items that the user likes, based on the user's past purchase history. The generation AI may suggest items based on the latest fashions, taking into account current trend information.As a result, the coordination suggestion system according to the embodiment supports the user in choosing clothes, allowing the user to gain confidence in coordinating outfits when going out. In addition, the system also allows the user to efficiently purchase new items, broadening the scope of fashion.

[0090] The collection unit can collect data including photos of the user's clothing, brand, color, size, material, and season information. For example, the collection unit allows the user to take photos of clothing using a smartphone camera and collect the data through a dedicated app. The collection unit can also allow the user to manually input clothing brand, color, size, material, and season information. For example, the collection unit can analyze photos of clothing taken by the user with a smartphone camera using image recognition technology to identify the clothing brand and color. The dedicated app collects the data manually entered by the user and stores it in a database. This allows for the collection of detailed data about the user's clothing, enabling more accurate coordination suggestions. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input photos of clothing taken with a smartphone camera into a generation AI and have the generation AI generate text data from the image data.

[0091] The analysis unit can analyze photos of clothing using image recognition technology. The analysis unit can use, for example, a generative AI to analyze photos of clothing using deep learning technology. For example, the generative AI can extract clothing features and identify information such as brand, color, and material. The analysis unit can also use the generative AI to analyze photos of clothing using image recognition technology. For example, the generative AI can extract detailed information from photos of clothing using computer vision technology. The analysis unit can also use the generative AI to analyze trend information based on user clothing data. For example, the generative AI can analyze photos of clothing and extract trend information using deep learning technology. This allows detailed information to be analyzed from photos of clothing using image recognition technology. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input photo data of clothing to the generative AI and cause the generative AI to extract detailed information from the image data.

[0092] The suggestion unit can suggest outfits based on the user's preferences and past coordination history. The suggestion unit, for example, uses a generation AI to suggest outfits taking into account the user's preferences. For example, the generation AI suggests outfits tailored to specific events or seasons based on the user's preferences. The suggestion unit can also use the generation AI to make suggestions based on the user's past coordination history. For example, the generation AI analyzes the user's past coordination history to suggest styles and color combinations preferred by the user. The suggestion unit can also use the generation AI to make suggestions taking into account the user's preferences and trend information. For example, the generation AI suggests outfits tailored to specific events or seasons taking into account the user's preferences. The generation AI suggests styles and color combinations preferred by the user based on the user's past coordination history. The generation AI suggests outfits based on the latest fashions taking into account trend information. This enables more appropriate outfit suggestions by taking into account the user's preferences and past history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's preferences and past coordination history into the generation AI and have the generation AI execute outfit suggestions.

[0093] The advice unit can provide advice on color combinations and style balance. The advice unit can provide advice on color combinations using, for example, a generation AI. For example, the generation AI can use a color wheel to determine whether a color combination is appropriate. The advice unit can also provide advice on style balance using the generation AI. For example, the generation AI can determine whether an outfit is balanced by taking style balance into consideration. The advice unit can also provide advice by taking user preferences and trend information into consideration. For example, the generation AI can provide advice tailored to a specific event or season by taking user preferences into consideration. The generation AI can provide advice on styles and color combinations preferred by the user based on the user's past coordination history. The generation AI can provide advice based on the latest fashions by taking trend information into consideration. This improves the user's coordination skills by providing advice on color combinations and style balance. Some or all of the above-described processing by the advice unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the advice unit can input the user's coordination ideas into the generation AI and have the generation AI execute advice on color combinations and style balance.

[0094] The presentation unit can present new items to purchase based on the user's preferences, past purchase history, and current trend information. The presentation unit, for example, uses a generation AI to present new items to purchase taking into account the user's preferences. For example, the generation AI suggests accessories or shoes that match a specific outfit based on the user's preferences. The presentation unit can also use the generation AI to make suggestions based on the user's past purchase history. For example, the generation AI analyzes the user's past purchase history and suggests items the user likes. The presentation unit can also use the generation AI to make suggestions taking into account current trend information. For example, the generation AI suggests items based on the latest fashions based on current trend information. This makes it possible to present appropriate items by taking into account the user's preferences, past purchase history, and current trend information. Some or all of the above-described processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the user's preferences, past purchase history, and current trend information into the generation AI and cause the generation AI to present new items to purchase.

[0095] The outfit suggestion system includes a feedback unit that collects user feedback. The feedback unit collects feedback, for example, through a user survey. For example, the feedback unit conducts a survey on outfit suggestions to users to collect their opinions and impressions. The feedback unit can also collect feedback through review comments. For example, the feedback unit provides a platform where users can post comments on outfit suggestions and collects user feedback. In this way, collecting user feedback can be used to improve the system. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input user survey results and review comments into a generation AI and have the generation AI analyze the feedback.

[0096] The coordination suggestion system includes a trend analysis unit that analyzes current trend information. The trend analysis unit, for example, collects data from fashion magazines and online shops and analyzes the trend information. For example, the trend analysis unit collects articles from fashion magazines and sales data from online shops and analyzes current trends. The trend analysis unit can also analyze trend information using a generation AI. For example, the generation AI analyzes current trends based on data from fashion magazines and online shops. In this way, the latest fashion information can be provided by analyzing the current trend information. Some or all of the above-described processing in the trend analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the trend analysis unit can input data from fashion magazines and online shops into the generation AI and have the generation AI analyze the trend information.

[0097] The collection unit can estimate the user's emotions and adjust the timing of collecting clothing data based on the estimated user's emotions. The collection unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the collection unit analyzes the user's facial expression data captured by a camera to estimate the user's emotions. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit analyzes the user's voice data and estimates the emotion based on the tone and speed of the voice. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit analyzes text data entered by the user and estimates the emotion. This allows data to be collected at a more appropriate time by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0098] The collection unit can analyze the user's past clothing data collection history and select the optimal collection method. The collection unit, for example, analyzes the user's past clothing data collection history. For example, the collection unit preferentially suggests collection methods (photos, text, etc.) that the user has frequently used in the past. The collection unit can also perform collection during a specific time period based on the user's past collection history. For example, the collection unit analyzes the user's past collection history and suggests the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the past collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past collection history data into a generation AI and cause the generation AI to select the optimal collection method.

[0099] When collecting clothing data, the collection unit can filter the data based on the user's current fashion trends and areas of interest. The collection unit, for example, analyzes the user's current fashion trends and areas of interest. For example, the collection unit collects data based on the fashion styles in which the user is currently interested. The collection unit can also filter the data based on the user's areas of interest (casual, formal, etc.). For example, the collection unit analyzes the user's current fashion trends and prioritizes collecting related data. This makes it possible to collect highly relevant data by filtering the data based on the user's current fashion trends and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's fashion trends and areas of interest to the generation AI and have the generation AI filter the data.

[0100] When collecting clothing data, the collection unit can select the optimal collection means depending on the user's input method. The collection unit selects the optimal collection means depending on, for example, the user's input method (voice, text, image, etc.). For example, if the user prefers voice input, the collection unit collects clothing data by voice. Also, if the user prefers text input, the collection unit can collect clothing data by text. For example, if the user prefers image input, the collection unit collects clothing data by image. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data depending on the user's input method to a generation AI and cause the generation AI to select the optimal collection means.

[0101] The collection unit can estimate the user's emotions and determine the priority of clothing data to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the collection unit analyzes the user's facial expression data captured by a camera to estimate the user's emotions. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit analyzes the user's voice data and estimates the emotion based on the tone and speed of the voice. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit analyzes text data entered by the user and estimates the emotion. This allows important data to be collected preferentially by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0102] When collecting clothing data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, acquires the user's geographical location information. For example, the collection unit identifies the user's current location using GPS data. The collection unit can also acquire the user's geographical location information using a location information service. For example, the collection unit prioritizes collecting clothing data suitable for the climate of the area where the user is currently located. The collection unit can also collect clothing data related to places the user plans to visit. For example, the collection unit collects data that matches local fashion trends based on the user's geographical location information. This makes it possible to prioritize collecting highly relevant data by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0103] When collecting clothing data, the collection unit can analyze the user's social media activity and collect related data. The collection unit, for example, analyzes the user's social media activity. For example, the collection unit collects photos of clothing shared by the user on social media. The collection unit can also analyze the content of the user's social media posts to collect related clothing data. For example, the collection unit collects related clothing data by referring to the activities of the user's friends on social media. This allows for efficient collection of related data by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related data.

[0104] When collecting clothing data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, analyzes the user's past feedback. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit can also select the optimal collection means from the user's past feedback. For example, the collection unit customizes the collection method by reflecting the user's feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0105] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the analysis unit analyzes facial expression data of the user captured by a camera to estimate the user's emotion. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit analyzes the user's voice data and estimates the emotion based on the tone and speed of the voice. The analysis unit can also estimate the user's emotion using text analysis technology. For example, the analysis unit analyzes text data entered by the user and estimates the emotion. This allows the analysis to be adjusted according to the user's emotion, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0106] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the clothing. The analysis unit, for example, evaluates the importance of the clothing. For example, the analysis unit evaluates the importance based on the frequency of use, price, brand value, etc. of the clothing. The analysis unit can also adjust the level of detail of the analysis based on the importance of the clothing. For example, the analysis unit performs a detailed analysis on important clothing and a brief analysis on less important clothing. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the clothing. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input clothing importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0107] During analysis, the analysis unit can apply different analysis algorithms depending on the clothing category. The analysis unit, for example, classifies clothing categories. For example, the analysis unit classifies clothing based on categories such as tops, bottoms, and outerwear. The analysis unit can also apply different analysis algorithms depending on the clothing category. For example, the analysis unit applies a casual analysis algorithm to casual clothing and a formal analysis algorithm to formal clothing. This improves the accuracy of analysis by applying the optimal analysis algorithm depending on the clothing category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input clothing category data to a generation AI and cause the generation AI to apply an analysis algorithm.

[0108] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, analyzes the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and improves the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0109] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the analysis unit analyzes the user's facial expression data captured by a camera to estimate the user's emotion. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit analyzes the user's voice data and estimates the emotion based on the tone and speed of the voice. The analysis unit can also estimate the user's emotion using text analysis technology. For example, the analysis unit analyzes text data entered by the user and estimates the emotion. This allows the length of the analysis to be adjusted according to the user's emotion, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0110] During analysis, the analysis unit can determine the priority of analysis based on the time when the clothing was submitted. The analysis unit, for example, evaluates the time when the clothing was submitted. For example, the analysis unit evaluates the time when the clothing was submitted based on the submission date and submission time. The analysis unit can also determine the priority of analysis based on the time when the clothing was submitted. For example, the analysis unit prioritizes analysis of recently submitted clothing and postpones analysis of older submitted clothing. In this way, determining the priority of analysis based on the time when the clothing was submitted enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the clothing was submitted to the generation AI and have the generation AI determine the priority of analysis.

[0111] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the clothing. The analysis unit, for example, evaluates the relevance of the clothing. For example, the analysis unit evaluates the relevance of the clothing based on common attributes or related topics. The analysis unit can also adjust the order of analysis based on the relevance of the clothing. For example, the analysis unit prioritizes analysis of highly relevant clothing and postpones analysis of less relevant clothing. By adjusting the order of analysis based on the relevance of the clothing, efficient analysis is possible. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input clothing relevance data to a generation AI and cause the generation AI to adjust the order of analysis.

[0112] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. For example, the analysis unit evaluates whether the user is a beginner, intermediate, or advanced user. The analysis unit can also adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses detailed technical terminology if the user has technical expertise, and uses concise and easy-to-understand terminology if the user does not have technical expertise. By adjusting the technical terminology in the analysis according to the user's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI execute the use of technical terminology.

[0113] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. The suggestion unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the suggestion unit analyzes facial expression data of the user captured by a camera to estimate the user's emotion. The suggestion unit can also estimate the user's emotion using voice analysis technology. For example, the suggestion unit analyzes the user's voice data and estimates the emotion based on the tone and speed of the voice. The suggestion unit can also estimate the user's emotion using text analysis technology. For example, the suggestion unit analyzes text data entered by the user to estimate the emotion. This allows the suggestion unit to adjust the way in which suggestions are expressed based on the user's emotion, thereby providing more appropriate suggestions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0114] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the outfit. The suggestion unit, for example, evaluates the importance of the outfit. For example, the suggestion unit evaluates the importance of the outfit based on the importance of the event or the user's preferences. The suggestion unit can also adjust the level of detail of the proposal based on the importance of the outfit. For example, the suggestion unit makes detailed suggestions for important outfits and brief suggestions for outfits with low importance. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the outfit. 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 importance data of the outfit into a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0115] When making a proposal, the proposal unit can apply different proposal algorithms depending on the event or season. The proposal unit, for example, classifies events and seasons. For example, the proposal unit classifies events and seasons such as wedding, party, spring, and summer. The proposal unit can also apply different proposal algorithms depending on the event or season. For example, the proposal unit applies a proposal algorithm tailored to the event and a proposal algorithm tailored to the season. This improves the accuracy of the proposal by applying the optimal proposal algorithm depending on the event or season. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input event and season data into a generation AI and cause the generation AI to apply a proposal algorithm.

[0116] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, analyzes the user's past suggestion results. For example, the suggestion unit improves the accuracy of the suggestion based on the user's past suggestion results. The suggestion unit can also adjust the suggestion algorithm by referring to the user's past suggestion results. For example, the suggestion unit analyzes the user's past suggestion results and improves the accuracy of the suggestion. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0117] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the suggestion unit analyzes the user's facial expression data captured by a camera to estimate the user's emotion. The suggestion unit can also estimate the user's emotion using voice analysis technology. For example, the suggestion unit analyzes the user's voice data and estimates the emotion based on the tone and speed of the voice. The suggestion unit can also estimate the user's emotion using text analysis technology. For example, the suggestion unit analyzes text data entered by the user to estimate the emotion. This allows the length of the suggestion to be adjusted according to the user's emotion, thereby providing more appropriate suggestions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0118] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time when the outfit was submitted. The suggestion unit, for example, evaluates the time when the outfit was submitted. For example, the suggestion unit evaluates the time when the outfit was submitted based on the submission date and submission time. The suggestion unit can also determine the priority of the proposal based on the time when the outfit was submitted. For example, the suggestion unit prioritizes the most recently submitted outfit and postpones the most recently submitted outfit. This enables efficient proposals by determining the priority of the proposal based on the time when the outfit was submitted. 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 the submission time data of the outfit into the generation AI and have the generation AI determine the priority of the proposals.

[0119] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the outfits. The suggestion unit, for example, evaluates the relevance of the outfits. For example, the suggestion unit evaluates the relevance of the outfits based on common attributes or related topics. The suggestion unit can also adjust the order of suggestions based on the relevance of the outfits. For example, the suggestion unit prioritizes suggesting highly relevant outfits and postpones less relevant outfits. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the outfits. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input relevance data of the outfits to a generation AI and cause the generation AI to adjust the order of suggestions.

[0120] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. The suggestion unit, for example, evaluates the user's level of expertise. For example, the suggestion unit evaluates whether the user is a beginner, intermediate, or advanced user. The suggestion unit can also adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit uses detailed technical terminology if the user has technical expertise, and uses concise and easy-to-understand terminology if the user does not have technical expertise. By adjusting the technical terminology in the proposal according to the user's level of expertise, it is possible to provide a more understandable proposal. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input the user's level of expertise data into a generation AI and cause the generation AI to execute the use of technical terminology.

[0121] The advice unit can estimate the user's emotion and adjust the way in which advice is presented based on the estimated user's emotion. The advice unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the advice unit analyzes facial expression data of the user captured by a camera to estimate the user's emotion. The advice unit can also estimate the user's emotion using voice analysis technology. For example, the advice unit analyzes the user's voice data and estimates the emotion based on the tone and speed of the voice. The advice unit can also estimate the user's emotion using text analysis technology. For example, the advice unit analyzes text data entered by the user to estimate the emotion. This allows the user to provide more appropriate advice by adjusting the way in which advice is presented based on the user's emotion. The emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, 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 advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0122] When giving advice, the advice unit can adjust the level of detail of the advice based on the importance of the outfit. The advice unit, for example, evaluates the importance of the outfit. For example, the advice unit evaluates the importance of the outfit based on the importance of the event or the user's preferences. The advice unit can also adjust the level of detail of the advice based on the importance of the outfit. For example, the advice unit provides detailed advice for important outfits and brief advice for outfits with low importance. By adjusting the level of detail of the advice based on the importance of the outfit, efficient advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input importance data of the outfit into a generation AI and cause the generation AI to adjust the level of detail of the advice.

[0123] When providing advice, the advice unit can apply different advice algorithms depending on the color combination and style balance. The advice unit, for example, evaluates the color combination and style balance. For example, the advice unit evaluates color combinations based on a color wheel or complementary color relationships. The advice unit can also evaluate the balance of styles such as casual, formal, and mixed styles. For example, the advice unit applies an advice algorithm related to color combinations and an advice algorithm related to style balance. This improves the accuracy of advice by applying the optimal advice algorithm depending on the color combination and style balance. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input color combination and style balance data to a generation AI and cause the generation AI to apply the advice algorithm.

[0124] When providing advice, the advice unit can improve the accuracy of the advice by referring to the user's past advice results. The advice unit, for example, analyzes the user's past advice results. For example, the advice unit improves the accuracy of the advice based on the user's past advice results. The advice unit can also adjust the advice algorithm by referring to the user's past advice results. For example, the advice unit analyzes the user's past advice results and improves the accuracy of the advice. In this way, the accuracy of the advice is improved by referring to the user's past advice results. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0125] The advice unit can estimate the user's emotion and adjust the length of advice based on the estimated user's emotion. The advice unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the advice unit analyzes facial expression data of the user captured by a camera to estimate the user's emotion. The advice unit can also estimate the user's emotion using voice analysis technology. For example, the advice unit analyzes the user's voice data and estimates the emotion based on the tone and speed of the voice. The advice unit can also estimate the user's emotion using text analysis technology. For example, the advice unit analyzes text data entered by the user to estimate the emotion. This allows the length of advice to be adjusted according to the user's emotion, thereby providing more appropriate advice. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or without AI. For example, the advice unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0126] When giving advice, the advice unit can determine the priority of advice based on the time when the outfit was submitted. The advice unit, for example, evaluates the time when the outfit was submitted. For example, the advice unit evaluates the time when the outfit was submitted based on the submission date and submission time. The advice unit can also determine the priority of advice based on the time when the outfit was submitted. For example, the advice unit prioritizes advice on the most recently submitted outfit and postpones the most recently submitted outfit. This enables efficient advice by determining the priority of advice based on the time when the outfit was submitted. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input data on the time when the outfit was submitted to the generation AI and have the generation AI determine the priority of advice.

[0127] When giving advice, the advice unit can adjust the order of advice based on the relevance of the outfits. The advice unit, for example, evaluates the relevance of the outfits. For example, the advice unit evaluates the relevance of the outfits based on common attributes or related topics. The advice unit can also adjust the order of advice based on the relevance of the outfits. For example, the advice unit prioritizes advice on highly relevant outfits and postpones less relevant outfits. This allows for efficient advice by adjusting the order of advice based on the relevance of the outfits. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input outfit relevance data into a generation AI and have the generation AI adjust the order of advice.

[0128] When providing advice, the advice unit can adjust the use of technical terms in the advice depending on the user's level of expertise. The advice unit, for example, evaluates the user's level of expertise. For example, the advice unit evaluates whether the user is a beginner, intermediate, or advanced user. The advice unit can also adjust the use of technical terms in the advice depending on the user's level of expertise. For example, the advice unit uses detailed technical terms if the user has technical expertise, and uses concise and easy-to-understand terms if the user does not have technical expertise. By adjusting the technical terms in the advice depending on the user's level of expertise, more understandable advice can be provided. Some or all of the above-described processing in the advice unit may be performed using AI, for example, or may be performed without AI. For example, the advice unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terms.

[0129] The presentation unit can estimate the user's emotion and determine the priority of items to be presented based on the estimated user's emotion. The presentation unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the presentation unit can analyze the user's facial expression data captured by a camera to estimate the user's emotion. The presentation unit can also estimate the user's emotion using voice analysis technology. For example, the presentation unit can analyze the user's voice data and estimate the emotion based on the tone and speed of the voice. The presentation unit can also estimate the user's emotion using text analysis technology. For example, the presentation unit can analyze text data entered by the user to estimate the emotion. This allows the priority of items to be determined according to the user's emotion, thereby preferentially presenting important items. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit can be performed using, for example, AI, or without AI. For example, the presentation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0130] The presentation unit can adjust the level of detail of the presentation based on the importance of the item when presenting the item. The presentation unit, for example, evaluates the importance of the item. For example, the presentation unit evaluates the importance of the item based on frequency of use, price, brand value, etc. The presentation unit can also adjust the level of detail of the presentation based on the importance of the item. For example, the presentation unit provides detailed information for important items and brief information for less important items. This enables efficient presentation by adjusting the level of detail of the presentation based on the importance of the item. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI, for example. For example, the presentation unit can input item importance data to a generation AI and cause the generation AI to adjust the level of detail of the presentation.

[0131] The presentation unit can apply different presentation algorithms depending on the category of the item when presenting the items. The presentation unit, for example, classifies the category of the items. For example, the presentation unit classifies items based on categories such as tops, bottoms, and outerwear. The presentation unit can also apply different presentation algorithms depending on the category of the item. For example, the presentation unit applies a presentation algorithm for accessories to accessories and a presentation algorithm for shoes to shoes. This improves the accuracy of presentation by applying the optimal presentation algorithm depending on the category of the item. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input item category data to a generation AI and cause the generation AI to apply a presentation algorithm.

[0132] The presentation unit can improve the accuracy of presentation by referring to the user's past presentation results when presenting. The presentation unit, for example, analyzes the user's past presentation results. For example, the presentation unit improves the accuracy of presentation based on the user's past presentation results. The presentation unit can also adjust the presentation algorithm by referring to the user's past presentation results. For example, the presentation unit analyzes the user's past presentation results and improves the accuracy of presentation. In this way, the accuracy of presentation is improved by referring to the user's past presentation results. Some or all of the above-described processing in the presentation unit may be performed using AI, for example, or may be performed without using AI. For example, the presentation unit can input the user's past presentation result data into the generation AI and cause the generation AI to improve the accuracy of presentation.

[0133] The presentation unit can estimate the user's emotion and adjust the presentation length based on the estimated user's emotion. The presentation unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the presentation unit can analyze the user's facial expression data captured by a camera to estimate the user's emotion. The presentation unit can also estimate the user's emotion using voice analysis technology. For example, the presentation unit can analyze the user's voice data and estimate the emotion based on the tone and speed of the voice. The presentation unit can also estimate the user's emotion using text analysis technology. For example, the presentation unit can analyze text data entered by the user to estimate the emotion. This allows the presentation length to be adjusted according to the user's emotion, thereby providing a more appropriate presentation. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the presentation unit can be performed using, for example, AI, or without AI. For example, the presentation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0134] The presentation unit can determine the presentation priority based on the submission time of the item at the time of presentation. The presentation unit, for example, evaluates the submission time of the item. For example, the presentation unit evaluates the submission time of the item based on the submission date or submission time. The presentation unit can also determine the presentation priority based on the submission time of the item. For example, the presentation unit prioritizes the presentation of recently submitted items and postpones the presentation of older submitted items. This enables efficient presentation by determining the presentation priority based on the submission time of the item. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input item submission time data to the generation AI and cause the generation AI to determine the presentation priority.

[0135] The presentation unit can adjust the order of presentation based on the relevance of the items when presenting them. The presentation unit, for example, evaluates the relevance of the items. For example, the presentation unit evaluates the relevance of the items based on common attributes or related topics. The presentation unit can also adjust the order of presentation based on the relevance of the items. For example, the presentation unit prioritizes presenting highly relevant items and postpones less relevant items. This enables efficient presentation by adjusting the order of presentation based on the relevance of the items. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input item relevance data to a generation AI and cause the generation AI to adjust the order of presentation.

[0136] The presentation unit can adjust the use of technical terminology in the presentation according to the user's level of expertise. The presentation unit, for example, evaluates the user's level of expertise. For example, the presentation unit evaluates whether the user is a beginner, intermediate, or advanced user. The presentation unit can also adjust the use of technical terminology in the presentation according to the user's level of expertise. For example, the presentation unit uses detailed technical terminology if the user has technical expertise, and uses concise and easy-to-understand terminology if the user does not have technical expertise. This allows for adjusting the technical terminology in the presentation according to the user's level of expertise, thereby providing a more understandable presentation. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology.

[0137] The feedback unit can estimate the user's emotion and adjust the feedback collection method based on the estimated user's emotion. The feedback unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the feedback unit analyzes the user's facial expression data captured by a camera to estimate the user's emotion. The feedback unit can also estimate the user's emotion using voice analysis technology. For example, the feedback unit analyzes the user's voice data and estimates the emotion based on the tone and speed of the voice. The feedback unit can also estimate the user's emotion using text analysis technology. For example, the feedback unit analyzes text data entered by the user to estimate the emotion. This allows for adjusting the feedback collection method according to the user's emotion, thereby collecting more appropriate feedback. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions.

[0138] When collecting feedback, the feedback unit can select the optimal collection method by referring to the user's past feedback history. The feedback unit, for example, analyzes the user's past feedback history. For example, the feedback unit adjusts the collection method based on feedback provided by the user in the past. The feedback unit can also select the optimal collection means from the user's past feedback history. For example, the feedback unit analyzes the user's feedback history and customizes the collection method. In this way, the optimal collection method can be selected by referring to the user's past feedback history. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback history data into a generation AI and cause the generation AI to select a collection method.

[0139] The feedback unit can perform filtering based on the user's current areas of interest when collecting feedback. The feedback unit, for example, analyzes the user's current areas of interest. For example, the feedback unit collects feedback based on the fashion style in which the user is currently interested. The feedback unit can also filter the feedback based on the user's areas of interest (casual, formal, etc.). For example, the feedback unit analyzes the user's current areas of interest and preferentially collects related feedback. In this way, by filtering based on the user's current areas of interest, highly relevant feedback can be collected. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's area of ​​interest data to a generation AI and have the generation AI perform the filtering.

[0140] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. The feedback unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the feedback unit can analyze the user's facial expression data captured by a camera to estimate the user's emotions. The feedback unit can also estimate the user's emotions using voice analysis technology. For example, the feedback unit can analyze the user's voice data and estimate the emotion based on the tone and speed of the voice. The feedback unit can also estimate the user's emotions using text analysis technology. For example, the feedback unit can analyze text data entered by the user to estimate the emotion. This allows important feedback to be collected preferentially by determining the priority of feedback according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions.

[0141] When collecting feedback, the feedback unit can prioritize collecting highly relevant feedback by taking into account the user's geographical location information. The feedback unit, for example, acquires the user's geographical location information. For example, the feedback unit identifies the user's current location using GPS data. The feedback unit can also acquire the user's geographical location information using a location information service. For example, the feedback unit prioritizes collecting feedback that is suitable for the climate of the area where the user is currently located. The feedback unit can also collect feedback related to places the user plans to visit. For example, the feedback unit collects feedback that matches local fashion trends based on the user's geographical location information. In this way, highly relevant feedback can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's geographical location information to a generation AI and cause the generation AI to collect highly relevant feedback.

[0142] When collecting feedback, the feedback unit can analyze the user's social media activity and collect relevant feedback. The feedback unit, for example, analyzes the user's social media activity. For example, the feedback unit collects feedback regarding photos of clothing shared by the user on social media. The feedback unit can also analyze the content of the user's social media posts and collect relevant feedback. For example, the feedback unit collects relevant feedback by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, relevant feedback can be efficiently collected. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's social media activity data into a generation AI and cause the generation AI to collect relevant feedback.

[0143] The trend analysis unit can estimate the user's emotion and adjust the presentation method of the trend analysis based on the estimated user's emotion. The trend analysis unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the trend analysis unit analyzes the user's facial expression data captured by a camera to estimate the user's emotion. The trend analysis unit can also estimate the user's emotion using voice analysis technology. For example, the trend analysis unit analyzes the user's voice data and estimates the emotion based on the tone and speed of the voice. The trend analysis unit can also estimate the user's emotion using text analysis technology. For example, the trend analysis unit analyzes text data entered by the user to estimate the emotion. This allows the presentation method of the trend analysis to be adjusted according to the user's emotion, thereby providing more appropriate trend information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the trend analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the trend analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0144] During trend analysis, the trend analysis unit can predict a current trend by referring to past trend data. The trend analysis unit, for example, analyzes past trend data. For example, the trend analysis unit predicts a current trend based on past trend data. The trend analysis unit can also adjust a trend analysis algorithm by referring to past trend data. For example, the trend analysis unit analyzes past trend data and predicts a current trend with high accuracy. In this way, by referring to past trend data, a current trend can be predicted with high accuracy. Some or all of the above-described processing in the trend analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the trend analysis unit can input past trend data to a generation AI and have the generation AI execute a current trend prediction.

[0145] The trend analysis unit can apply different trend analysis methods to each clothing category during trend analysis. The trend analysis unit, for example, classifies clothing categories. For example, the trend analysis unit classifies clothing based on categories such as tops, bottoms, and outerwear. The trend analysis unit can also apply different trend analysis methods to each clothing category. For example, the trend analysis unit applies a casual trend analysis method to casual clothing and a formal trend analysis method to formal clothing. This improves the accuracy of trend analysis by applying the optimal trend analysis method to each clothing category. Some or all of the above-described processing in the trend analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the trend analysis unit can input clothing category data to a generation AI and cause the generation AI to apply a trend analysis method.

[0146] During trend analysis, the trend analysis unit can improve the accuracy of the analysis by referring to the user's past trend analysis results. The trend analysis unit, for example, analyzes the user's past trend analysis results. For example, the trend analysis unit improves the accuracy of the analysis based on the user's past trend analysis results. The trend analysis unit can also adjust the analysis algorithm by referring to the user's past trend analysis results. For example, the trend analysis unit analyzes the user's past trend analysis results to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past trend analysis results. Some or all of the above-described processing in the trend analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the trend analysis unit can input the user's past trend analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0147] The trend analysis unit can estimate a user's emotion and adjust the importance of a trend based on the estimated user's emotion. The trend analysis unit estimates a user's emotion using, for example, facial expression recognition technology. For example, the trend analysis unit analyzes facial expression data of a user captured by a camera to estimate the user's emotion. The trend analysis unit can also estimate a user's emotion using voice analysis technology. For example, the trend analysis unit analyzes the user's voice data and estimates the emotion based on the tone and speed of the voice. The trend analysis unit can also estimate a user's emotion using text analysis technology. For example, the trend analysis unit analyzes text data entered by a user to estimate the emotion. This allows the importance of a trend to be adjusted according to the user's emotion, thereby providing more appropriate trend information. Emotion estimation is achieved using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the trend analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the trend analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0148] During trend analysis, the trend analysis unit can analyze changes in trends based on the time when the clothing was submitted. The trend analysis unit, for example, evaluates the time when the clothing was submitted. For example, the trend analysis unit evaluates the time when the clothing was submitted based on the submission date and submission time. The trend analysis unit can also analyze changes in trends based on the time when the clothing was submitted. For example, the trend analysis unit analyzes changes in trends based on recently submitted clothing and postpones clothing that was submitted earlier. In this way, by analyzing changes in trends based on the time when the clothing was submitted, it is possible to provide the latest trend information. Some or all of the above-mentioned processing in the trend analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the trend analysis unit can input data on the time when the clothing was submitted to the generation AI and cause the generation AI to analyze changes in trends.

[0149] During trend analysis, the trend analysis unit can analyze trends by referring to clothing-related market data. The trend analysis unit, for example, analyzes clothing-related market data. For example, the trend analysis unit analyzes trends based on market research data and sales data. The trend analysis unit can also adjust the trend analysis algorithm by referring to clothing-related market data. For example, the trend analysis unit analyzes clothing-related market data and predicts trends with high accuracy. As a result, trends can be predicted with high accuracy by referring to clothing-related market data. Some or all of the above-mentioned processing in the trend analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the trend analysis unit can input clothing-related market data to a generation AI and cause the generation AI to perform trend analysis. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, advice unit, presentation unit, feedback unit, trend analysis unit, and emotion estimation function, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects clothing data using the camera 42 or sensors of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes outfits based on the analysis results. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice on the proposed outfits. The presentation unit is realized by the control unit 46A of the smart device 14 and proposes new items to purchase. The feedback unit is realized by the control unit 46A of the smart device 14 and collects user feedback. The trend analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes current trend information. The emotion estimation function estimates the user's emotion using the camera 42 and microphone 38B of the smart device 14, and the emotion is analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, advice unit, presentation unit, feedback unit, trend analysis unit, and emotion estimation function, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects clothing data using the camera 42 or sensors of the smart glasses 214, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes outfits based on the analysis results. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice on the proposed outfits. The presentation unit is realized by the control unit 46A of the smart glasses 214 and proposes new items to purchase. The feedback unit is realized by the control unit 46A of the smart glasses 214 and collects user feedback. The trend analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes current trend information. The emotion estimation function estimates the user's emotion using the camera 42 and microphone 238 of the smart glasses 214, and the emotion is analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, advice unit, presentation unit, feedback unit, trend analysis unit, and emotion estimation function described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects clothing data using the camera 42 or sensors of the headset-type terminal 314, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes outfits based on the analysis results. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice on the proposed outfits. The presentation unit is realized by the control unit 46A of the headset-type terminal 314 and proposes new items to purchase. The feedback unit is realized by the control unit 46A of the headset-type terminal 314 and collects user feedback. The trend analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes current trend information. The emotion estimation function estimates the user's emotion using the camera 42 and microphone 238 of the headset type terminal 314, and the emotion is analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, advice unit, presentation unit, feedback unit, trend analysis unit, and emotion estimation function, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects clothing data using the camera 42 and sensors of the robot 414, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes outfits based on the analysis results. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice on the proposed outfits. The presentation unit is realized by the control unit 46A of the robot 414 and proposes new items to purchase. The feedback unit is realized by the control unit 46A of the robot 414 and collects user feedback. The trend analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes current trend information. The emotion estimation function estimates the user's emotion using the camera 42 and microphone 238 of the robot 414, and the emotion is analyzed by the specific processing unit 290 of the data processing device 12.

[0150] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0151] The outfit suggestion system includes a feedback unit that collects user feedback. The feedback unit collects feedback, for example, through a user survey. For example, the feedback unit conducts a survey on outfit suggestions to users to collect their opinions and impressions. The feedback unit can also collect feedback through review comments. For example, the feedback unit provides a platform where users can post comments on outfit suggestions and collects user feedback. In this way, collecting user feedback can be used to improve the system. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input user survey results and review comments into a generation AI and have the generation AI analyze the feedback.

[0152] The coordination suggestion system includes a trend analysis unit that analyzes current trend information. The trend analysis unit, for example, collects data from fashion magazines and online shops and analyzes the trend information. For example, the trend analysis unit collects articles from fashion magazines and sales data from online shops and analyzes current trends. The trend analysis unit can also analyze trend information using a generation AI. For example, the generation AI analyzes current trends based on data from fashion magazines and online shops. In this way, the latest fashion information can be provided by analyzing the current trend information. Some or all of the above-described processing in the trend analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the trend analysis unit can input data from fashion magazines and online shops into the generation AI and have the generation AI analyze the trend information.

[0153] The collection unit can estimate the user's emotions and adjust the timing of collecting clothing data based on the estimated user's emotions. The collection unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the collection unit analyzes the user's facial expression data captured by a camera to estimate the user's emotions. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit analyzes the user's voice data and estimates the emotion based on the tone and speed of the voice. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit analyzes text data entered by the user and estimates the emotion. This allows data to be collected at a more appropriate time by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0154] The collection unit can analyze the user's past clothing data collection history and select the optimal collection method. The collection unit, for example, analyzes the user's past clothing data collection history. For example, the collection unit preferentially suggests collection methods (photos, text, etc.) that the user has frequently used in the past. The collection unit can also perform collection during a specific time period based on the user's past collection history. For example, the collection unit analyzes the user's past collection history and suggests the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the past collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past collection history data into a generation AI and cause the generation AI to select the optimal collection method.

[0155] When collecting clothing data, the collection unit can filter the data based on the user's current fashion trends and areas of interest. The collection unit, for example, analyzes the user's current fashion trends and areas of interest. For example, the collection unit collects data based on the fashion styles in which the user is currently interested. The collection unit can also filter the data based on the user's areas of interest (casual, formal, etc.). For example, the collection unit analyzes the user's current fashion trends and prioritizes collecting related data. This makes it possible to collect highly relevant data by filtering the data based on the user's current fashion trends and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's fashion trends and areas of interest to the generation AI and have the generation AI filter the data.

[0156] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the analysis unit analyzes facial expression data of the user captured by a camera to estimate the user's emotion. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit analyzes the user's voice data and estimates the emotion based on the tone and speed of the voice. The analysis unit can also estimate the user's emotion using text analysis technology. For example, the analysis unit analyzes text data entered by the user and estimates the emotion. This allows the analysis to be adjusted according to the user's emotion, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0157] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the clothing. The analysis unit, for example, evaluates the importance of the clothing. For example, the analysis unit evaluates the importance based on the frequency of use, price, brand value, etc. of the clothing. The analysis unit can also adjust the level of detail of the analysis based on the importance of the clothing. For example, the analysis unit performs a detailed analysis on important clothing and a brief analysis on less important clothing. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the clothing. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input clothing importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0158] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. The suggestion unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the suggestion unit analyzes facial expression data of the user captured by a camera to estimate the user's emotion. The suggestion unit can also estimate the user's emotion using voice analysis technology. For example, the suggestion unit analyzes the user's voice data and estimates the emotion based on the tone and speed of the voice. The suggestion unit can also estimate the user's emotion using text analysis technology. For example, the suggestion unit analyzes text data entered by the user to estimate the emotion. This allows the suggestion unit to adjust the way in which suggestions are expressed based on the user's emotion, thereby providing more appropriate suggestions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0159] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the outfit. The suggestion unit, for example, evaluates the importance of the outfit. For example, the suggestion unit evaluates the importance of the outfit based on the importance of the event or the user's preferences. The suggestion unit can also adjust the level of detail of the proposal based on the importance of the outfit. For example, the suggestion unit makes detailed suggestions for important outfits and brief suggestions for outfits with low importance. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the outfit. 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 importance data of the outfit into a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0160] The advice unit can estimate the user's emotion and adjust the way in which advice is presented based on the estimated user's emotion. The advice unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the advice unit analyzes facial expression data of the user captured by a camera to estimate the user's emotion. The advice unit can also estimate the user's emotion using voice analysis technology. For example, the advice unit analyzes the user's voice data and estimates the emotion based on the tone and speed of the voice. The advice unit can also estimate the user's emotion using text analysis technology. For example, the advice unit analyzes text data entered by the user to estimate the emotion. This allows the user to provide more appropriate advice by adjusting the way in which advice is presented based on the user's emotion. The emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, 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 advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0161] When providing advice, the advice unit can apply different advice algorithms depending on the color combination and style balance. The advice unit, for example, evaluates the color combination and style balance. For example, the advice unit evaluates color combinations based on a color wheel or complementary color relationships. The advice unit can also evaluate the balance of styles such as casual, formal, and mixed styles. For example, the advice unit applies an advice algorithm related to color combinations and an advice algorithm related to style balance. This improves the accuracy of advice by applying the optimal advice algorithm depending on the color combination and style balance. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input color combination and style balance data to a generation AI and cause the generation AI to apply the advice algorithm.

[0162] The presentation unit can estimate the user's emotion and determine the priority of items to be presented based on the estimated user's emotion. The presentation unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the presentation unit can analyze the user's facial expression data captured by a camera to estimate the user's emotion. The presentation unit can also estimate the user's emotion using voice analysis technology. For example, the presentation unit can analyze the user's voice data and estimate the emotion based on the tone and speed of the voice. The presentation unit can also estimate the user's emotion using text analysis technology. For example, the presentation unit can analyze text data entered by the user to estimate the emotion. This allows the priority of items to be determined according to the user's emotion, thereby preferentially presenting important items. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit can be performed using, for example, AI, or without AI. For example, the presentation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0163] The presentation unit can adjust the level of detail of the presentation based on the importance of the item when presenting the item. The presentation unit, for example, evaluates the importance of the item. For example, the presentation unit evaluates the importance of the item based on frequency of use, price, brand value, etc. The presentation unit can also adjust the level of detail of the presentation based on the importance of the item. For example, the presentation unit provides detailed information for important items and brief information for less important items. This enables efficient presentation by adjusting the level of detail of the presentation based on the importance of the item. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI, for example. For example, the presentation unit can input item importance data to a generation AI and cause the generation AI to adjust the level of detail of the presentation.

[0164] The feedback unit can estimate the user's emotion and adjust the feedback collection method based on the estimated user's emotion. The feedback unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the feedback unit analyzes the user's facial expression data captured by a camera to estimate the user's emotion. The feedback unit can also estimate the user's emotion using voice analysis technology. For example, the feedback unit analyzes the user's voice data and estimates the emotion based on the tone and speed of the voice. The feedback unit can also estimate the user's emotion using text analysis technology. For example, the feedback unit analyzes text data entered by the user to estimate the emotion. This allows for adjusting the feedback collection method according to the user's emotion, thereby collecting more appropriate feedback. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions.

[0165] When collecting feedback, the feedback unit can select the optimal collection method by referring to the user's past feedback history. The feedback unit, for example, analyzes the user's past feedback history. For example, the feedback unit adjusts the collection method based on feedback provided by the user in the past. The feedback unit can also select the optimal collection means from the user's past feedback history. For example, the feedback unit analyzes the user's feedback history and customizes the collection method. In this way, the optimal collection method can be selected by referring to the user's past feedback history. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback history data into a generation AI and cause the generation AI to select a collection method.

[0166] The processing flow of the second embodiment will be briefly explained below.

[0167] Step 1: The collection unit collects the user's clothing data. The user's clothing data includes photos of the clothing, brand, color, size, material, and season information. The collection unit collects data by having the user take photos of the clothing using a smartphone camera and using a dedicated app. The user can also manually enter the clothing brand, color, size, material, and season information. Furthermore, the material and size of the clothing can be automatically detected using sensors. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. The analysis is performed using image recognition and data mining technologies. The generation AI uses deep learning to analyze photos of clothing and extract clothing features. It can also use data mining technology to analyze user clothing data and analyze trend information. Step 3: The suggestion unit uses the generation AI to suggest outfits based on the analysis results obtained by the analysis unit. Suggestions are made taking into consideration the user's preferences, past outfit history, and trend information. The generation AI suggests outfits tailored to specific events or seasons, as well as style and color combinations that the user prefers. Step 4: The advice unit uses the generation AI to provide advice on the outfits proposed by the suggestion unit. The advice is given on color combinations and style balance. The generation AI uses a color wheel to determine whether the color combination is appropriate, and considers style balance to determine whether the outfit is balanced. Step 5: The suggestion unit uses the generation AI to suggest new items to purchase based on the advice provided by the advice unit. Recommendations are based on the user's preferences, past purchase history, and current trend information. The generation AI suggests accessories, shoes, etc. that go well with a specific outfit, and suggests items based on the latest fashions, taking into account the user's preferences and trend information.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0188] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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.

[0195] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0202] 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.

[0203] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0204] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0219] 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.

[0220] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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."

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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, in order to avoid confusion and to 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.

[0238] 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.

[0239] [Explanation of symbols]

[0240] 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 collection unit that collects clothing data of a user; an analysis unit that analyzes the data collected by the collection unit; a suggestion unit that suggests outfits based on the analysis results obtained by the analysis unit; an advice unit that provides advice on the coordination suggested by the suggestion unit; a suggestion unit that suggests new items to be purchased based on the advice provided by the advice unit; Equipped with A system characterized by:

2. The collecting unit Collect data including photos of users' clothing, brand, color, size, material, and season information The system of claim 1 .

3. The analysis unit Analyzing photos of clothing using image recognition technology The system of claim 1 .

4. The proposal unit Suggests outfits based on the user's preferences and past outfit history The system of claim 1 .

5. The advice unit Providing advice on color combinations and style balance The system of claim 1 .

6. The presentation unit Recommend new items to purchase based on user preferences, past purchase history, and current trends The system of claim 1 .

7. A feedback section is provided to collect user feedback. The system of claim 1 .

8. Equipped with a trend analysis unit that analyzes current trend information The system of claim 1 .

Images