System

Abstract

An object of a system according to an embodiment is to propose appropriate clothes to a user in accordance with temperature and weather.SOLUTION: A system includes an acquisition unit, a weather acquisition unit, a history reference unit, and a proposal unit. The acquisition unit acquires information on clothes. The weather acquisition unit acquires information on temperature and weather. The history reference unit refers to the user's preference and the past selection history based on the information acquired by the acquisition unit and the weather acquisition unit. The proposal unit proposes appropriate clothes on the basis of the information referred to by the history reference 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 technology has made it difficult for users to choose appropriate clothing according to the temperature and weather, leaving them confused.

[0005] The system according to the embodiment aims to suggest appropriate clothing to a user depending on the temperature and weather. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a weather acquisition unit, a history reference unit, and a suggestion unit. The acquisition unit acquires clothing information. The weather acquisition unit acquires temperature and weather information. The history reference unit references the user's preferences and past selection history based on the information acquired by the acquisition unit and the weather acquisition unit. The suggestion unit suggests appropriate clothing based on the information referenced by the history reference unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest appropriate clothing to the user depending on the temperature and weather. [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) The clothing suggestion system according to an embodiment of the present invention manages the clothes in a user's closet and suggests clothes that match the temperature and weather. The clothing suggestion system registers information about the clothes owned by the user in the system, acquires temperature and weather information, and suggests optimal clothing based on that information. For example, on a day when it suddenly gets warmer, the clothing suggestion system might suggest, "How about this light jacket today?" The system can also record what clothes the user has worn in the past and use that information as a reference. This allows the user to spend their days comfortably without having to worry about choosing what to wear. The clothing suggestion system thus makes it easy to choose clothes that are appropriate for sudden changes in temperature and weather, making daily life more comfortable.

[0029] The clothing suggestion system according to the embodiment includes an acquisition unit, a weather acquisition unit, a history reference unit, and a suggestion unit. The acquisition unit acquires information about clothing owned by a user. For example, the acquisition unit can acquire detailed information about the type, color, material, season, and so on of clothing. The acquisition unit can also automatically register clothing information using a two-dimensional code (e.g., a QR code (registered trademark)) or an RFID tag. The weather acquisition unit acquires weather forecast data from the Internet to ascertain the current temperature and weather. For example, the weather acquisition unit can acquire weather forecast data using an API of a specific weather service. The history reference unit references the user's preferences and past selection history based on the information acquired by the acquisition unit and the weather acquisition unit. For example, the history reference unit can record what clothing the user has worn in the past and use that as a reference. The suggestion unit suggests optimal clothing based on the information referenced by the history reference unit. For example, the suggestion unit can suggest a light jacket on a hot day. This allows the clothing suggestion system according to the embodiment to manage the clothes in the user's closet and suggest clothing appropriate for the temperature and weather.

[0030] The clothing suggestion system includes an automatic registration unit that automatically registers clothing information using a two-dimensional code or an RFID tag. The automatic registration unit automatically registers clothing information using a two-dimensional code or an RFID tag. For example, the automatic registration unit scans a two-dimensional code to obtain information such as the type, color, material, and season of the clothing. The automatic registration unit can also read an RFID tag and automatically register clothing information. This reduces the user's effort by automatically registering clothing information.

[0031] The clothing suggestion system includes a feedback unit that provides feedback on how comfortable the suggested clothing actually was. The feedback unit provides feedback on how comfortable the suggested clothing actually was. For example, the feedback unit collects impressions and evaluations of the user after wearing the suggested clothing. The feedback unit can also provide a questionnaire for the user to evaluate the comfort of the suggested clothing. In this way, feedback on the comfort of the suggested clothing can improve the system's suggestion accuracy.

[0032] The clothing suggestion system includes a data reference unit that refers to the data of other users. The data reference unit refers to the data of other users. For example, the data reference unit collects data on clothing selected by other users and uses that data as a reference. The data reference unit can also analyze the clothing selection history of other users and use that data to suggest optimal clothing. This allows for a wider variety of suggestions to be made by referring to the data of other users.

[0033] The acquisition unit can acquire detailed information on the type, color, material, and season of clothing. The acquisition unit, for example, acquires the type of clothing. For example, it acquires types such as shirt, pants, jacket, etc. The acquisition unit can also acquire the color of the clothing. For example, it acquires RGB values ​​and color names. The acquisition unit can also acquire the material of the clothing. For example, it acquires materials such as cotton, polyester, wool, etc. The acquisition unit can also acquire the season of the clothing. For example, it acquires seasons such as spring, summer, autumn, and winter. By acquiring detailed information about the clothing, more accurate suggestions can be made.

[0034] The weather acquisition unit can obtain weather forecast data from the Internet and grasp the current temperature and weather. The weather acquisition unit obtains weather forecast data, for example, using the API of a specific weather service. For example, the weather acquisition unit obtains information on the current temperature and weather. The weather acquisition unit can also periodically update the weather forecast data and obtain the latest weather information. In this way, by obtaining weather forecast data from the Internet, it becomes possible to make suggestions based on the latest weather information.

[0035] When acquiring clothing information, the acquisition unit can analyze the user's past clothing selection history and select an appropriate acquisition method. The acquisition unit, for example, analyzes the user's past clothing selection history. For example, the acquisition unit prioritizes acquiring information about clothing that the user has frequently selected in the past. The acquisition unit can also prioritize acquiring information about clothing that the user selected in a particular season. The acquisition unit can also acquire information about clothing suitable for a particular event or situation from the user's past selection history. In this way, the optimal acquisition method can be selected by analyzing the user's past clothing selection history.

[0036] When acquiring clothing information, the acquisition unit can filter the information based on the user's current fashion trends and preferences. The acquisition unit, for example, filters the information based on the user's current fashion trends and preferences. For example, the acquisition unit filters the information based on the style of clothing recently purchased by the user. The acquisition unit can also filter the information based on fashion items that the user has "liked" on social media. The acquisition unit can also filter the information based on the style of clothing that the user has given high ratings to in the past. In this way, by filtering the information based on the user's current fashion trends and preferences, more appropriate information can be acquired.

[0037] When acquiring clothing information, the acquisition unit can select the optimal acquisition means depending on the user's input method. The acquisition unit selects the optimal acquisition means depending on, for example, the user's input method. For example, when the user inputs clothing information by voice, the acquisition unit acquires the information using voice recognition technology. Furthermore, when the user inputs clothing information by text, the acquisition unit can also acquire the information using text analysis technology. Furthermore, when the user inputs clothing information by image, the acquisition unit can also acquire the information using image recognition technology. In this way, by selecting the optimal acquisition means depending on the user's input method, information acquisition is made more efficient.

[0038] When acquiring clothing information, the acquisition unit can prioritize acquiring highly relevant information in consideration of the user's geographical location information. The acquisition unit acquires information in consideration of, for example, the user's geographical location information. For example, if the user is in a cold region, the acquisition unit can prioritize acquiring information about cold weather clothing. Furthermore, if the user is in a warm region, the acquisition unit can also prioritize acquiring information about light clothing. Furthermore, if the user is in a rainy region, the acquisition unit can also prioritize acquiring information about waterproof clothing. In this way, highly relevant information can be prioritized by considering the user's geographical location information.

[0039] When acquiring clothing information, the acquisition unit can analyze the user's social media activity and acquire related information. The acquisition unit, for example, analyzes the user's social media activity. For example, the acquisition unit acquires information about fashion items that the user has "liked" on social media. The acquisition unit can also analyze posts by fashion influencers that the user follows on social media and acquire related clothing information. The acquisition unit can also acquire information about fashion items that the user has shared on social media. In this way, related information can be acquired by analyzing the user's social media activity.

[0040] When acquiring clothing information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. The acquisition unit customizes the acquisition method by reflecting the user's past feedback, for example. For example, the acquisition unit preferentially acquires information about clothing that the user has previously given a high rating. The acquisition unit can also acquire information about clothing that the user has previously given a low rating, excluding that information. The acquisition unit can also select the optimal acquisition method based on the user's past feedback. In this way, the acquisition method can be customized by reflecting the user's past feedback.

[0041] When acquiring weather information, the weather acquisition unit can improve the accuracy of current weather information by referring to past weather data. The weather acquisition unit, for example, refers to past weather data. For example, the weather acquisition unit improves the prediction accuracy of current weather information based on past weather data. The weather acquisition unit can also refer to past weather data to detect abnormal weather patterns and reflect them in current weather information. The weather acquisition unit can also analyze past weather data and evaluate the reliability of current weather information. In this way, by referring to past weather data, the accuracy of current weather information can be improved.

[0042] When acquiring weather information, the weather acquisition unit can select optimal weather information based on the user's place of residence and area of ​​activity. The weather acquisition unit acquires weather information based on, for example, the user's place of residence and area of ​​activity. For example, the weather acquisition unit prioritizes acquiring weather information for the user's place of residence. The weather acquisition unit can also prioritize acquiring weather information for the user's area of ​​activity. If the user is traveling, the weather acquisition unit can also prioritize acquiring weather information for the travel destination. This makes it possible to provide more appropriate information by selecting optimal weather information based on the user's place of residence and area of ​​activity.

[0043] When acquiring weather information, the weather acquisition unit can optimize the acquisition method by referring to the user's past weather information usage history. The weather acquisition unit, for example, refers to the user's past weather information usage history. For example, the weather acquisition unit prioritizes the use of a weather information acquisition method that the user has frequently used in the past. The weather acquisition unit can also analyze the user's past weather information usage history and select the optimal acquisition method. The weather acquisition unit can also prioritize the use of a weather information acquisition method that the user has given a high rating in the past. In this way, the acquisition method can be optimized by referring to the user's past weather information usage history.

[0044] When acquiring weather information, the weather acquisition unit can prioritize acquiring highly relevant information by taking into consideration the user's travel plans and plans to go out. The weather acquisition unit acquires weather information by taking into consideration, for example, the user's travel plans and plans to go out. For example, if the user has travel plans, the weather acquisition unit prioritizes acquiring weather information for the travel destination. Furthermore, if the user has plans to go out, the weather acquisition unit can also prioritize acquiring weather information for the destination. Furthermore, the weather acquisition unit can refer to the user's calendar information and prioritize acquiring weather information based on the plans. In this way, highly relevant information can be prioritized by taking into consideration the user's travel plans and plans to go out.

[0045] When acquiring weather information, the weather acquisition unit can analyze the user's social media activity and acquire related information. The weather acquisition unit, for example, analyzes the user's social media activity. For example, the weather acquisition unit acquires weather information about travel destinations that the user has shared on social media. The weather acquisition unit can also analyze posts from weather information accounts that the user follows on social media and acquire related information. The weather acquisition unit can also acquire weather information that the user has "liked" on social media. In this way, related information can be acquired by analyzing the user's social media activity.

[0046] When acquiring weather information, the weather acquisition unit can customize the acquisition method by reflecting the user's past feedback. The weather acquisition unit customizes the acquisition method by reflecting the user's past feedback, for example. For example, the weather acquisition unit preferentially uses an acquisition method for weather information that the user has previously given a high rating. The weather acquisition unit can also exclude an acquisition method for weather information that the user has previously given a low rating. The weather acquisition unit can also select the optimal acquisition method based on the user's past feedback. In this way, the acquisition method can be customized by reflecting the user's past feedback.

[0047] When referencing the past clothing selection history, the history reference unit can perform filtering based on the user's current fashion trends and preferences. The history reference unit filters information based on, for example, the user's current fashion trends and preferences. For example, the history reference unit filters the past clothing selection history based on the style of clothing recently purchased by the user. The history reference unit can also filter the past clothing selection history based on fashion items that the user has "liked" on social media. The history reference unit can also filter the past clothing selection history based on the style of clothing that the user has given high ratings to in the past. This allows more appropriate information to be referenced by filtering information based on the user's current fashion trends and preferences.

[0048] When referring to the past clothing selection history, the history reference unit can select optimal information by referring to the user's past weather information usage history. The history reference unit, for example, refers to the user's past weather information usage history. For example, the history reference unit preferentially refers to clothing that the user selected in the past for specific weather conditions. The history reference unit can also analyze the user's past weather information usage history and select optimal clothing selection history. The history reference unit can also preferentially refer to clothing selection history for weather conditions that the user gave high ratings to in the past. In this way, optimal information can be selected by referring to the user's past weather information usage history.

[0049] When referencing past clothing selection history, the history reference unit can optimize the reference method by reflecting the user's past feedback. The history reference unit, for example, optimizes the reference method by reflecting the user's past feedback. For example, the history reference unit preferentially references clothing selection history that the user has previously given a high rating. The history reference unit can also reference clothing selection history that the user has previously given a low rating, excluding that history. The history reference unit can also select the optimal reference method based on the user's past feedback. In this way, the reference method can be optimized by reflecting the user's past feedback.

[0050] When referencing the past clothing selection history, the history reference unit can prioritize reference to highly relevant information taking into account the user's geographical location information. The history reference unit, for example, references information taking into account the user's geographical location information. For example, if the user is in a cold region, the history reference unit can prioritize reference to the past selection history of cold weather clothing. Furthermore, if the user is in a warm region, the history reference unit can also prioritize reference to the past selection history of light clothing. Furthermore, if the user is in a rainy region, the history reference unit can also prioritize reference to the past selection history of waterproof clothing. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0051] When referencing the past clothing selection history, the history reference unit can analyze the user's social media activity and refer to related information. The history reference unit, for example, analyzes the user's social media activity. For example, the history reference unit refers to the past selection history of fashion items that the user has "liked" on social media. The history reference unit can also analyze posts by fashion influencers that the user follows on social media and refer to the related past selection history. The history reference unit can also refer to the past selection history of fashion items that the user has shared on social media. In this way, by analyzing the user's social media activity, related information can be referred to.

[0052] When referencing past clothing selection history, the history reference unit can customize the reference method by reflecting the user's past feedback. The history reference unit customizes the reference method by reflecting the user's past feedback, for example. For example, the history reference unit preferentially references clothing selection history that the user has previously given a high rating. The history reference unit can also reference clothing selection history that the user has previously given a low rating, excluding that history. The history reference unit can also select the optimal reference method based on the user's past feedback. In this way, the reference method can be customized by reflecting the user's past feedback.

[0053] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the clothing when making a suggestion. The suggestion unit adjusts the level of detail of the suggestion based on, for example, the importance of the clothing. For example, the suggestion unit makes detailed suggestions for important events. The suggestion unit can also make simple suggestions for everyday outings. The suggestion unit can also adjust the level of detail of the suggestion depending on a specific season or weather. In this way, by adjusting the level of detail of the suggestion based on the importance of the clothing, more appropriate suggestions can be made.

[0054] The suggestion unit can apply different suggestion algorithms depending on the category of clothing when making suggestions. The suggestion unit applies different suggestion algorithms depending on, for example, the category of clothing. For example, the suggestion unit applies a formal suggestion algorithm to formal clothing. The suggestion unit can also apply a casual suggestion algorithm to casual clothing. The suggestion unit can also apply a suggestion algorithm specialized for sportswear to sportswear. In this way, by applying different suggestion algorithms depending on the category of clothing, more appropriate suggestions can be made.

[0055] 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, refers to the user's past suggestion results. For example, the suggestion unit improves the accuracy of the suggestion by referring to proposals that the user has given high ratings to in the past. The suggestion unit can also improve the accuracy of the suggestion by excluding proposals that the user has given low ratings to in the past. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results.

[0056] When making suggestions, the suggestion unit can determine the priority of the suggestions based on the time of submission of the clothing. The suggestion unit determines the priority of the suggestions based on, for example, the time of submission of the clothing. For example, if an important event is approaching, the suggestion unit may prioritize suggestions of clothing appropriate for the event. The suggestion unit may also prioritize simple suggestions for everyday outings. The suggestion unit may also adjust the priority of the suggestions according to a specific season or weather. In this way, by determining the priority of the suggestions based on the time of submission of the clothing, more appropriate suggestions can be made.

[0057] The suggestion unit can adjust the order of suggestions based on the relevance of the clothing when making suggestions. The suggestion unit adjusts the order of suggestions based on, for example, the relevance of the clothing. For example, the suggestion unit preferentially suggests clothing that the user has given a high rating to in the past. The suggestion unit can also make suggestions while excluding clothing that the user has given a low rating to in the past. The suggestion unit can also adjust the order of suggestions based on the user's current fashion trends and preferences. In this way, adjusting the order of suggestions based on the relevance of the clothing can make more appropriate suggestions.

[0058] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. The suggestion unit, for example, adjusts the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user is knowledgeable about fashion, the suggestion unit makes suggestions that use a lot of technical terminology. Also, if the user is not knowledgeable about fashion, the suggestion unit can make simple and easy-to-understand suggestions. Furthermore, the suggestion unit can adjust the use of optimal technical terminology based on the user's past feedback. In this way, by adjusting the use of technical terminology in the suggestion according to the user's level of expertise, more appropriate suggestions can be made.

[0059] The automatic registration unit can automatically acquire detailed information about clothing, such as the type, color, material, and season, during automatic registration. The automatic registration unit automatically acquires detailed information about clothing using, for example, a two-dimensional code or an RFID tag. For example, the automatic registration unit scans a two-dimensional code to acquire information about clothing, such as the type, color, material, and season. The automatic registration unit can also read an RFID tag to automatically acquire clothing information. The automatic registration unit can also automatically acquire detailed information about clothing using image recognition technology. For example, the automatic registration unit analyzes an image of clothing to acquire information about clothing, such as the type, color, material, and season. The automatic registration unit can also automatically complete detailed information based on information entered by the user. This enables more accurate registration by automatically acquiring detailed information about clothing.

[0060] During automatic registration, the automatic registration unit can select the optimal registration method by referring to the user's past clothing selection history. The automatic registration unit, for example, refers to the user's past clothing selection history. For example, the automatic registration unit preferentially registers information about clothes that the user has frequently selected in the past. The automatic registration unit can also preferentially register information about clothes that the user selected in a particular season. The automatic registration unit can also register information about clothes that are suitable for a particular event or situation from the user's past selection history. In this way, the optimal registration method can be selected by referring to the user's past clothing selection history.

[0061] During automatic registration, the automatic registration unit can prioritize registering highly relevant information by taking into account the user's geographical location information. The automatic registration unit registers information by taking into account, for example, the user's geographical location information. For example, if the user is in a cold region, the automatic registration unit can prioritize registering information about cold weather clothing. Furthermore, if the user is in a warm region, the automatic registration unit can also prioritize registering information about light clothing. Furthermore, if the user is in a rainy region, the automatic registration unit can prioritize registering information about waterproof clothing. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0062] During automatic registration, the automatic registration unit can analyze the user's social media activity and automatically register related information. The automatic registration unit, for example, analyzes the user's social media activity. For example, the automatic registration unit automatically registers information about fashion items that the user has "liked" on social media. The automatic registration unit can also analyze posts by fashion influencers that the user follows on social media and automatically register information about related clothing. The automatic registration unit can also automatically register information about fashion items that the user has shared on social media. In this way, related information can be automatically registered by analyzing the user's social media activity.

[0063] 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, refers to the user's past feedback history. For example, the feedback unit preferentially uses feedback collection methods that the user has previously given high ratings to. The feedback unit can also exclude feedback collection methods that the user has previously given low ratings to. The feedback unit can also analyze the user's past feedback history and select the optimal collection method. In this way, the optimal collection method can be selected by referring to the user's past feedback history.

[0064] When collecting feedback, the feedback unit can filter the feedback based on the user's current fashion trends and preferences. The feedback unit filters information based on the user's current fashion trends and preferences, for example. For example, the feedback unit collects feedback based on the style of clothing recently purchased by the user. The feedback unit can also collect feedback based on fashion items that the user has "liked" on social media. The feedback unit can also collect feedback based on the style of clothing that the user has given high ratings to in the past. In this way, by filtering information based on the user's current fashion trends and preferences, more appropriate feedback can be collected.

[0065] When collecting feedback, the feedback unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The feedback unit collects information by taking into account, for example, the user's geographical location information. For example, if the user is in a cold region, the feedback unit can prioritize collecting feedback about cold weather clothing. Furthermore, if the user is in a warm region, the feedback unit can also prioritize collecting feedback about light clothing. Furthermore, if the user is in a rainy region, the feedback unit can also prioritize collecting feedback about waterproof clothing. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0066] When collecting feedback, the feedback unit can analyze the user's social media activity and collect related information. The feedback unit, for example, analyzes the user's social media activity. For example, the feedback unit collects feedback on fashion items that the user has "liked" on social media. The feedback unit can also analyze posts by fashion influencers that the user follows on social media and collect related feedback. The feedback unit can also collect feedback on fashion items that the user has shared on social media. In this way, by analyzing the user's social media activity, related information can be collected.

[0067] When referencing other users' data, the data reference unit can select optimal data by referring to the user's past clothing selection history. The data reference unit, for example, refers to the user's past clothing selection history. For example, the data reference unit references other users' data based on clothing selection history for which the user has previously given high ratings. The data reference unit can also reference other users' data while excluding clothing selection history for which the user has previously given low ratings. The data reference unit can also analyze the user's past clothing selection history and select optimal data of other users. In this way, optimal data can be selected by referring to the user's past clothing selection history.

[0068] When referencing data of other users, the data reference unit can perform filtering based on the user's current fashion trends and preferences. The data reference unit filters information based on, for example, the user's current fashion trends and preferences. For example, the data reference unit filters data of other users based on the style of clothing recently purchased by the user. The data reference unit can also filter data of other users based on fashion items that the user has "liked" on social media. The data reference unit can also filter data of other users based on the style of clothing that the user has given high ratings to in the past. This makes it possible to reference more appropriate information by filtering information based on the user's current fashion trends and preferences.

[0069] When referencing data of other users, the data reference unit can prioritize referencing highly relevant information by taking into account the user's geographical location information. The data reference unit, for example, references information by taking into account the user's geographical location information. For example, if the user is in a cold region, the data reference unit can prioritize referencing other users' data regarding cold weather clothing. Furthermore, if the user is in a warm region, the data reference unit can also prioritize referencing other users' data regarding light clothing. Furthermore, if the user is in a rainy region, the data reference unit can also prioritize referencing other users' data regarding waterproof clothing. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0070] When referencing data of other users, the data reference unit can analyze the user's social media activity and reference related information. The data reference unit, for example, analyzes the user's social media activity. For example, the data reference unit references data of other users regarding fashion items that the user has "liked" on social media. The data reference unit can also analyze posts of fashion influencers that the user follows on social media and reference related data of other users. The data reference unit can also reference data of other users regarding fashion items that the user has shared on social media. In this way, by analyzing the user's social media activity, related information can be referenced.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The acquisition unit can acquire clothing information taking into consideration the geographical location information of the user. For example, if the user is in a cold region, information about cold weather clothing can be acquired preferentially. Also, if the user is in a warm region, information about light clothing can be acquired preferentially. Also, if the user is in a rainy region, information about waterproof clothing can be acquired preferentially. In this way, by taking into consideration the geographical location information of the user, it is possible to acquire highly relevant information preferentially.

[0073] The suggestion unit can improve the accuracy of suggestions by reflecting the user's past feedback. For example, the accuracy of suggestions can be improved by referring to suggestions that the user has previously given a high rating. The accuracy of suggestions can also be improved by excluding suggestions that the user has previously given a low rating. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. In this way, the accuracy of suggestions can be improved by reflecting the user's past feedback.

[0074] The suggestion unit can apply different suggestion algorithms depending on the category of clothing. For example, for formal clothing, a formal suggestion algorithm can be applied. For casual clothing, a casual suggestion algorithm can be applied. For sportswear, a suggestion algorithm specialized for sportswear can be applied. In this way, by applying different suggestion algorithms depending on the category of clothing, more appropriate suggestions can be made.

[0075] The acquisition unit can analyze the user's social media activity and acquire related information. For example, it can acquire information about fashion items that the user has "liked" on social media. It can also analyze posts by fashion influencers that the user follows on social media and acquire information about related clothing. It can also acquire information about fashion items that the user has shared on social media. In this way, it is possible to acquire related information by analyzing the user's social media activity.

[0076] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the clothing. For example, detailed suggestions can be made for important events. Alternatively, simple suggestions can be made for everyday outings. The level of detail of the suggestion can also be adjusted depending on specific seasons or weather conditions. This allows for more appropriate suggestions to be made by adjusting the level of detail of the suggestion based on the importance of the clothing.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The acquisition unit acquires information about the clothes owned by the user. For example, the acquisition unit can acquire detailed information about the clothes, such as the type, color, material, and season. The acquisition unit can also automatically register the clothing information using a two-dimensional code or RFID tag. Step 2: The weather acquisition unit acquires weather forecast data from the Internet and determines the current temperature and weather. For example, the weather acquisition unit can acquire weather forecast data using the API of a specific weather service. Step 3: The history reference unit references the user's preferences and past selection history based on the information acquired by the acquisition unit and the weather acquisition unit. For example, the history reference unit can record what clothes the user has worn in the past and use that as a reference. Step 4: The suggestion unit suggests optimal clothing based on the information referenced by the history reference unit. For example, the suggestion unit can suggest a light jacket on a hot day.

[0079] (Example 2) The clothing suggestion system according to an embodiment of the present invention manages the clothes in a user's closet and suggests clothes that match the temperature and weather. The clothing suggestion system registers information about the clothes owned by the user in the system, acquires temperature and weather information, and suggests optimal clothing based on that information. For example, on a day when it suddenly gets warmer, the clothing suggestion system might suggest, "How about this light jacket today?" The system can also record what clothes the user has worn in the past and use that information as a reference. This allows the user to spend their days comfortably without having to worry about choosing what to wear. The clothing suggestion system thus makes it easy to choose clothes that are appropriate for sudden changes in temperature and weather, making daily life more comfortable.

[0080] An outfit suggestion system according to an embodiment includes an acquisition unit, a weather acquisition unit, a history reference unit, and a suggestion unit. The acquisition unit acquires information about clothes owned by a user. For example, the acquisition unit can acquire detailed information about the type, color, material, season, and so on of the clothes. The acquisition unit can also automatically register outfit information using a two-dimensional code (e.g., a QR code) or an RFID tag. The weather acquisition unit acquires weather forecast data from the Internet to ascertain the current temperature and weather. For example, the weather acquisition unit can acquire weather forecast data using an API of a specific weather service. The history reference unit references the user's preferences and past selection history based on the information acquired by the acquisition unit and the weather acquisition unit. For example, the history reference unit can record what clothes the user has worn in the past and refer to that information. The suggestion unit suggests optimal outfits based on the information referenced by the history reference unit. For example, the suggestion unit can suggest a light jacket on a hot day. This allows the outfit suggestion system according to an embodiment to manage the clothes in the user's closet and suggest outfits appropriate for the temperature and weather.

[0081] The clothing suggestion system includes an automatic registration unit that automatically registers clothing information using a two-dimensional code or an RFID tag. The automatic registration unit automatically registers clothing information using a two-dimensional code or an RFID tag. For example, the automatic registration unit scans a two-dimensional RF code to obtain information such as the type of clothing, color, material, and season. The automatic registration unit can also read an RFID tag and automatically register clothing information. This reduces the user's effort by automatically registering clothing information.

[0082] The clothing suggestion system includes a feedback unit that provides feedback on how comfortable the suggested clothing actually was. The feedback unit provides feedback on how comfortable the suggested clothing actually was. For example, the feedback unit collects impressions and evaluations of the user after wearing the suggested clothing. The feedback unit can also provide a questionnaire for the user to evaluate the comfort of the suggested clothing. In this way, feedback on the comfort of the suggested clothing can improve the system's suggestion accuracy.

[0083] The clothing suggestion system includes a data reference unit that refers to the data of other users. The data reference unit refers to the data of other users. For example, the data reference unit collects data on clothing selected by other users and uses that data as a reference. The data reference unit can also analyze the clothing selection history of other users and use that data to suggest optimal clothing. This allows for a wider variety of suggestions to be made by referring to the data of other users.

[0084] The acquisition unit can acquire detailed information on the type, color, material, and season of clothing. The acquisition unit, for example, acquires the type of clothing. For example, it acquires types such as shirt, pants, jacket, etc. The acquisition unit can also acquire the color of the clothing. For example, it acquires RGB values ​​and color names. The acquisition unit can also acquire the material of the clothing. For example, it acquires materials such as cotton, polyester, wool, etc. The acquisition unit can also acquire the season of the clothing. For example, it acquires seasons such as spring, summer, autumn, and winter. By acquiring detailed information about the clothing, more accurate suggestions can be made.

[0085] The weather acquisition unit can obtain weather forecast data from the Internet and grasp the current temperature and weather. The weather acquisition unit obtains weather forecast data, for example, using the API of a specific weather service. For example, the weather acquisition unit obtains information on the current temperature and weather. The weather acquisition unit can also periodically update the weather forecast data and obtain the latest weather information. In this way, by obtaining weather forecast data from the Internet, it becomes possible to make suggestions based on the latest weather information.

[0086] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring clothing information based on the estimated user's emotions. The acquisition unit, for example, estimates the user's emotions. For example, the acquisition unit can estimate the user's emotions using facial expression recognition technology. The acquisition unit can also estimate the user's emotions using voice analysis technology. The acquisition unit can also estimate the user's emotions using text analysis technology. The acquisition unit adjusts the timing of acquiring clothing information based on the estimated user's emotions. For example, if the user is feeling stressed, the acquisition unit can automatically acquire clothing information to avoid burdening the user. If the user is relaxed, the acquisition unit can provide the user with an option to manually enter clothing information. If the user is in a hurry, the acquisition unit can select the fastest method of acquiring clothing information. This reduces the burden on the user by adjusting the timing of acquiring clothing information according to the user's emotions.

[0087] When acquiring clothing information, the acquisition unit can analyze the user's past clothing selection history and select an appropriate acquisition method. The acquisition unit, for example, analyzes the user's past clothing selection history. For example, the acquisition unit prioritizes acquiring information about clothing that the user has frequently selected in the past. The acquisition unit can also prioritize acquiring information about clothing that the user selected in a particular season. The acquisition unit can also acquire information about clothing suitable for a particular event or situation from the user's past selection history. In this way, the optimal acquisition method can be selected by analyzing the user's past clothing selection history.

[0088] When acquiring clothing information, the acquisition unit can filter the information based on the user's current fashion trends and preferences. The acquisition unit, for example, filters the information based on the user's current fashion trends and preferences. For example, the acquisition unit filters the information based on the style of clothing recently purchased by the user. The acquisition unit can also filter the information based on fashion items that the user has "liked" on social media. The acquisition unit can also filter the information based on the style of clothing that the user has given high ratings to in the past. In this way, by filtering the information based on the user's current fashion trends and preferences, more appropriate information can be acquired.

[0089] When acquiring clothing information, the acquisition unit can select the optimal acquisition means depending on the user's input method. The acquisition unit selects the optimal acquisition means depending on, for example, the user's input method. For example, when the user inputs clothing information by voice, the acquisition unit acquires the information using voice recognition technology. Furthermore, when the user inputs clothing information by text, the acquisition unit can also acquire the information using text analysis technology. Furthermore, when the user inputs clothing information by image, the acquisition unit can also acquire the information using image recognition technology. In this way, by selecting the optimal acquisition means depending on the user's input method, information acquisition is made more efficient.

[0090] The acquisition unit can estimate the user's emotions and determine the priority of clothing information to be acquired based on the estimated user's emotions. The acquisition unit, for example, estimates the user's emotions. For example, the acquisition unit can estimate the user's emotions using facial expression recognition technology. The acquisition unit can also estimate the user's emotions using voice analysis technology. The acquisition unit can also estimate the user's emotions using text analysis technology. The acquisition unit determines the priority of clothing information to be acquired based on the estimated user's emotions. For example, when the user is feeling stressed, the acquisition unit can prioritize acquiring clothing information that emphasizes comfort. When the user is relaxed, the acquisition unit can prioritize acquiring clothing information that emphasizes style. When the user is in a hurry, the acquisition unit can prioritize acquiring clothing information that can be acquired quickly. In this way, by determining the priority of clothing information according to the user's emotions, more appropriate information can be acquired.

[0091] When acquiring clothing information, the acquisition unit can prioritize acquiring highly relevant information in consideration of the user's geographical location information. The acquisition unit acquires information in consideration of, for example, the user's geographical location information. For example, if the user is in a cold region, the acquisition unit can prioritize acquiring information about cold weather clothing. Furthermore, if the user is in a warm region, the acquisition unit can also prioritize acquiring information about light clothing. Furthermore, if the user is in a rainy region, the acquisition unit can also prioritize acquiring information about waterproof clothing. In this way, highly relevant information can be prioritized by considering the user's geographical location information.

[0092] When acquiring clothing information, the acquisition unit can analyze the user's social media activity and acquire related information. The acquisition unit, for example, analyzes the user's social media activity. For example, the acquisition unit acquires information about fashion items that the user has "liked" on social media. The acquisition unit can also analyze posts by fashion influencers that the user follows on social media and acquire related clothing information. The acquisition unit can also acquire information about fashion items that the user has shared on social media. In this way, related information can be acquired by analyzing the user's social media activity.

[0093] When acquiring clothing information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. The acquisition unit customizes the acquisition method by reflecting the user's past feedback, for example. For example, the acquisition unit preferentially acquires information about clothing that the user has previously given a high rating. The acquisition unit can also acquire information about clothing that the user has previously given a low rating, excluding that information. The acquisition unit can also select the optimal acquisition method based on the user's past feedback. In this way, the acquisition method can be customized by reflecting the user's past feedback.

[0094] The weather acquisition unit can estimate the user's emotions and adjust the timing of acquiring weather information based on the estimated user's emotions. The weather acquisition unit, for example, estimates the user's emotions. For example, the weather acquisition unit can estimate the user's emotions using facial expression recognition technology. The weather acquisition unit can also estimate the user's emotions using voice analysis technology. The weather acquisition unit can also estimate the user's emotions using text analysis technology. The weather acquisition unit adjusts the timing of acquiring weather information based on the estimated user's emotions. For example, if the user is feeling stressed, the weather acquisition unit can automatically acquire weather information to avoid burdening the user. If the user is relaxed, the weather acquisition unit can provide the user with an option to manually check weather information. If the user is in a hurry, the weather acquisition unit can select the fastest way to acquire weather information. This reduces the burden on the user by adjusting the timing of acquiring weather information according to the user's emotions.

[0095] When acquiring weather information, the weather acquisition unit can improve the accuracy of current weather information by referring to past weather data. The weather acquisition unit, for example, refers to past weather data. For example, the weather acquisition unit improves the prediction accuracy of current weather information based on past weather data. The weather acquisition unit can also refer to past weather data to detect abnormal weather patterns and reflect them in current weather information. The weather acquisition unit can also analyze past weather data and evaluate the reliability of current weather information. In this way, by referring to past weather data, the accuracy of current weather information can be improved.

[0096] When acquiring weather information, the weather acquisition unit can select optimal weather information based on the user's place of residence and area of ​​activity. The weather acquisition unit acquires weather information based on, for example, the user's place of residence and area of ​​activity. For example, the weather acquisition unit prioritizes acquiring weather information for the user's place of residence. The weather acquisition unit can also prioritize acquiring weather information for the user's area of ​​activity. If the user is traveling, the weather acquisition unit can also prioritize acquiring weather information for the travel destination. This makes it possible to provide more appropriate information by selecting optimal weather information based on the user's place of residence and area of ​​activity.

[0097] When acquiring weather information, the weather acquisition unit can optimize the acquisition method by referring to the user's past weather information usage history. The weather acquisition unit, for example, refers to the user's past weather information usage history. For example, the weather acquisition unit prioritizes the use of a weather information acquisition method that the user has frequently used in the past. The weather acquisition unit can also analyze the user's past weather information usage history and select the optimal acquisition method. The weather acquisition unit can also prioritize the use of a weather information acquisition method that the user has given a high rating in the past. In this way, the acquisition method can be optimized by referring to the user's past weather information usage history.

[0098] The weather acquisition unit can estimate the user's emotions and determine the priority of weather information to be acquired based on the estimated user's emotions. The weather acquisition unit, for example, estimates the user's emotions. For example, the weather acquisition unit can estimate the user's emotions using facial expression recognition technology. The weather acquisition unit can also estimate the user's emotions using voice analysis technology. The weather acquisition unit can also estimate the user's emotions using text analysis technology. The weather acquisition unit determines the priority of weather information to be acquired based on the estimated user's emotions. For example, if the user is feeling stressed, the weather acquisition unit can prioritize acquiring important weather information. If the user is relaxed, the weather acquisition unit can prioritize acquiring detailed weather information. If the user is in a hurry, the weather acquisition unit can prioritize acquiring weather information that can be acquired quickly. In this way, by determining the priority of weather information according to the user's emotions, more appropriate information can be acquired.

[0099] When acquiring weather information, the weather acquisition unit can prioritize acquiring highly relevant information by taking into consideration the user's travel plans and plans to go out. The weather acquisition unit acquires weather information by taking into consideration, for example, the user's travel plans and plans to go out. For example, if the user has travel plans, the weather acquisition unit prioritizes acquiring weather information for the travel destination. Furthermore, if the user has plans to go out, the weather acquisition unit can also prioritize acquiring weather information for the destination. Furthermore, the weather acquisition unit can refer to the user's calendar information and prioritize acquiring weather information based on the plans. In this way, highly relevant information can be prioritized by taking into consideration the user's travel plans and plans to go out.

[0100] When acquiring weather information, the weather acquisition unit can analyze the user's social media activity and acquire related information. The weather acquisition unit, for example, analyzes the user's social media activity. For example, the weather acquisition unit acquires weather information about travel destinations that the user has shared on social media. The weather acquisition unit can also analyze posts from weather information accounts that the user follows on social media and acquire related information. The weather acquisition unit can also acquire weather information that the user has "liked" on social media. In this way, related information can be acquired by analyzing the user's social media activity.

[0101] When acquiring weather information, the weather acquisition unit can customize the acquisition method by reflecting the user's past feedback. The weather acquisition unit customizes the acquisition method by reflecting the user's past feedback, for example. For example, the weather acquisition unit preferentially uses an acquisition method for weather information that the user has previously given a high rating. The weather acquisition unit can also exclude an acquisition method for weather information that the user has previously given a low rating. The weather acquisition unit can also select the optimal acquisition method based on the user's past feedback. In this way, the acquisition method can be customized by reflecting the user's past feedback.

[0102] The history reference unit can estimate the user's emotions and adjust the method of referencing the past clothing selection history based on the estimated user's emotions. The history reference unit, for example, estimates the user's emotions. For example, the history reference unit can estimate the user's emotions using facial expression recognition technology. The history reference unit can also estimate the user's emotions using voice analysis technology. The history reference unit can also estimate the user's emotions using text analysis technology. The history reference unit adjusts the method of referencing the past clothing selection history based on the estimated user's emotions. For example, if the user is feeling stressed, the history reference unit automatically references the past clothing selection history to reduce the burden on the user. If the user is relaxed, the history reference unit can provide the user with an option to manually check the past clothing selection history. If the user is in a hurry, the history reference unit can select the fastest method of referencing the past clothing selection history. This reduces the burden on the user by adjusting the method of referencing the past clothing selection history according to the user's emotions.

[0103] When referencing the past clothing selection history, the history reference unit can perform filtering based on the user's current fashion trends and preferences. The history reference unit filters information based on, for example, the user's current fashion trends and preferences. For example, the history reference unit filters the past clothing selection history based on the style of clothing recently purchased by the user. The history reference unit can also filter the past clothing selection history based on fashion items that the user has "liked" on social media. The history reference unit can also filter the past clothing selection history based on the style of clothing that the user has given high ratings to in the past. This allows more appropriate information to be referenced by filtering information based on the user's current fashion trends and preferences.

[0104] When referring to the past clothing selection history, the history reference unit can select optimal information by referring to the user's past weather information usage history. The history reference unit, for example, refers to the user's past weather information usage history. For example, the history reference unit preferentially refers to clothing that the user selected in the past for specific weather conditions. The history reference unit can also analyze the user's past weather information usage history and select optimal clothing selection history. The history reference unit can also preferentially refer to clothing selection history for weather conditions that the user gave high ratings to in the past. In this way, optimal information can be selected by referring to the user's past weather information usage history.

[0105] When referencing past clothing selection history, the history reference unit can optimize the reference method by reflecting the user's past feedback. The history reference unit, for example, optimizes the reference method by reflecting the user's past feedback. For example, the history reference unit preferentially references clothing selection history that the user has previously given a high rating. The history reference unit can also reference clothing selection history that the user has previously given a low rating, excluding that history. The history reference unit can also select the optimal reference method based on the user's past feedback. In this way, the reference method can be optimized by reflecting the user's past feedback.

[0106] The history reference unit can estimate the user's emotion and determine the priority of the history to be referenced based on the estimated user's emotion. The history reference unit, for example, estimates the user's emotion. For example, the history reference unit can estimate the user's emotion using facial expression recognition technology. The history reference unit can also estimate the user's emotion using voice analysis technology. The history reference unit can also estimate the user's emotion using text analysis technology. The history reference unit determines the priority of the history to be referenced based on the estimated user's emotion. For example, if the user is feeling stressed, the history reference unit can prioritize referencing past clothing selection history that emphasizes comfort. Also, if the user is relaxed, the history reference unit can prioritize referencing past clothing selection history that emphasizes style. Also, if the user is in a hurry, the history reference unit can prioritize referencing past clothing selection history that can be referenced quickly. In this way, by determining the priority of the history to be referenced based on the user's emotion, more appropriate information can be referenced.

[0107] When referencing the past clothing selection history, the history reference unit can prioritize reference to highly relevant information taking into account the user's geographical location information. The history reference unit, for example, references information taking into account the user's geographical location information. For example, if the user is in a cold region, the history reference unit can prioritize reference to the past selection history of cold weather clothing. Furthermore, if the user is in a warm region, the history reference unit can also prioritize reference to the past selection history of light clothing. Furthermore, if the user is in a rainy region, the history reference unit can also prioritize reference to the past selection history of waterproof clothing. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0108] When referencing the past clothing selection history, the history reference unit can analyze the user's social media activity and refer to related information. The history reference unit, for example, analyzes the user's social media activity. For example, the history reference unit refers to the past selection history of fashion items that the user has "liked" on social media. The history reference unit can also analyze posts by fashion influencers that the user follows on social media and refer to the related past selection history. The history reference unit can also refer to the past selection history of fashion items that the user has shared on social media. In this way, by analyzing the user's social media activity, related information can be referred to.

[0109] When referencing past clothing selection history, the history reference unit can customize the reference method by reflecting the user's past feedback. The history reference unit customizes the reference method by reflecting the user's past feedback, for example. For example, the history reference unit preferentially references clothing selection history that the user has previously given a high rating. The history reference unit can also reference clothing selection history that the user has previously given a low rating, excluding that history. The history reference unit can also select the optimal reference method based on the user's past feedback. In this way, the reference method can be customized by reflecting the user's past feedback.

[0110] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. For example, the suggestion unit can estimate the user's emotion using facial expression recognition technology. The suggestion unit can also estimate the user's emotion using voice analysis technology. The suggestion unit can also estimate the user's emotion using text analysis technology. The suggestion unit adjusts the way the suggestion is expressed based on the estimated user's emotion. For example, if the user is feeling stressed, the suggestion unit can make a simple and intuitive suggestion. If the user is relaxed, the suggestion unit can make a detailed suggestion. If the user is in a hurry, the suggestion unit can make a suggestion that is easy to understand. In this way, by adjusting the way the suggestion is expressed based on the user's emotion, more appropriate suggestions can be made.

[0111] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the clothing when making a suggestion. The suggestion unit adjusts the level of detail of the suggestion based on, for example, the importance of the clothing. For example, the suggestion unit makes detailed suggestions for important events. The suggestion unit can also make simple suggestions for everyday outings. The suggestion unit can also adjust the level of detail of the suggestion depending on a specific season or weather. In this way, by adjusting the level of detail of the suggestion based on the importance of the clothing, more appropriate suggestions can be made.

[0112] The suggestion unit can apply different suggestion algorithms depending on the category of clothing when making suggestions. The suggestion unit applies different suggestion algorithms depending on, for example, the category of clothing. For example, the suggestion unit applies a formal suggestion algorithm to formal clothing. The suggestion unit can also apply a casual suggestion algorithm to casual clothing. The suggestion unit can also apply a suggestion algorithm specialized for sportswear to sportswear. In this way, by applying different suggestion algorithms depending on the category of clothing, more appropriate suggestions can be made.

[0113] 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, refers to the user's past suggestion results. For example, the suggestion unit improves the accuracy of the suggestion by referring to proposals that the user has given high ratings to in the past. The suggestion unit can also improve the accuracy of the suggestion by excluding proposals that the user has given low ratings to in the past. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results.

[0114] 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, for example, estimates the user's emotion. For example, the suggestion unit can estimate the user's emotion using facial expression recognition technology. The suggestion unit can also estimate the user's emotion using voice analysis technology. The suggestion unit can also estimate the user's emotion using text analysis technology. The suggestion unit adjusts the length of the suggestion based on the estimated user's emotion. For example, if the user is feeling stressed, the suggestion unit can make a short and to-the-point suggestion. If the user is relaxed, the suggestion unit can make a detailed suggestion. If the user is in a hurry, the suggestion unit can make a suggestion that is quick to understand. In this way, by adjusting the length of the suggestion according to the user's emotion, more appropriate suggestions can be made.

[0115] When making suggestions, the suggestion unit can determine the priority of the suggestions based on the time of submission of the clothing. The suggestion unit determines the priority of the suggestions based on, for example, the time of submission of the clothing. For example, if an important event is approaching, the suggestion unit may prioritize suggestions of clothing appropriate for the event. The suggestion unit may also prioritize simple suggestions for everyday outings. The suggestion unit may also adjust the priority of the suggestions according to a specific season or weather. In this way, by determining the priority of the suggestions based on the time of submission of the clothing, more appropriate suggestions can be made.

[0116] The suggestion unit can adjust the order of suggestions based on the relevance of the clothing when making suggestions. The suggestion unit adjusts the order of suggestions based on, for example, the relevance of the clothing. For example, the suggestion unit preferentially suggests clothing that the user has given a high rating to in the past. The suggestion unit can also make suggestions while excluding clothing that the user has given a low rating to in the past. The suggestion unit can also adjust the order of suggestions based on the user's current fashion trends and preferences. In this way, adjusting the order of suggestions based on the relevance of the clothing can make more appropriate suggestions.

[0117] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. The suggestion unit, for example, adjusts the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user is knowledgeable about fashion, the suggestion unit makes suggestions that use a lot of technical terminology. Also, if the user is not knowledgeable about fashion, the suggestion unit can make simple and easy-to-understand suggestions. Furthermore, the suggestion unit can adjust the use of optimal technical terminology based on the user's past feedback. In this way, by adjusting the use of technical terminology in the suggestion according to the user's level of expertise, more appropriate suggestions can be made.

[0118] The automatic registration unit can estimate the user's emotions and adjust the timing of automatic registration of clothing information based on the estimated user's emotions. The automatic registration unit, for example, estimates the user's emotions. For example, the automatic registration unit can estimate the user's emotions using facial expression recognition technology. The automatic registration unit can also estimate the user's emotions using voice analysis technology. The automatic registration unit can also estimate the user's emotions using text analysis technology. The automatic registration unit adjusts the timing of automatic registration of clothing information based on the estimated user's emotions. For example, if the user is feeling stressed, the automatic registration unit can automatically register clothing information to avoid burdening the user. If the user is relaxed, the automatic registration unit can provide the user with an option to manually register clothing information. If the user is in a hurry, the automatic registration unit can select the fastest method to register clothing information. In this way, the burden on the user can be reduced by adjusting the timing of automatic registration of clothing information according to the user's emotions.

[0119] The automatic registration unit can automatically acquire detailed information about clothing, such as the type, color, material, and season, during automatic registration. The automatic registration unit automatically acquires detailed information about clothing using, for example, a two-dimensional code or an RFID tag. For example, the automatic registration unit scans a two-dimensional code to acquire information about clothing, such as the type, color, material, and season. The automatic registration unit can also read an RFID tag to automatically acquire clothing information. The automatic registration unit can also automatically acquire detailed information about clothing using image recognition technology. For example, the automatic registration unit analyzes an image of clothing to acquire information about clothing, such as the type, color, material, and season. The automatic registration unit can also automatically complete detailed information based on information entered by the user. This enables more accurate registration by automatically acquiring detailed information about clothing.

[0120] During automatic registration, the automatic registration unit can select the optimal registration method by referring to the user's past clothing selection history. The automatic registration unit, for example, refers to the user's past clothing selection history. For example, the automatic registration unit preferentially registers information about clothes that the user has frequently selected in the past. The automatic registration unit can also preferentially register information about clothes that the user selected in a particular season. The automatic registration unit can also register information about clothes that are suitable for a particular event or situation from the user's past selection history. In this way, the optimal registration method can be selected by referring to the user's past clothing selection history.

[0121] The automatic registration unit can estimate the user's emotions and determine the priority of clothing information to be automatically registered based on the estimated user's emotions. The automatic registration unit, for example, estimates the user's emotions. For example, the automatic registration unit can estimate the user's emotions using facial expression recognition technology. The automatic registration unit can also estimate the user's emotions using voice analysis technology. The automatic registration unit can also estimate the user's emotions using text analysis technology. The automatic registration unit determines the priority of clothing information to be automatically registered based on the estimated user's emotions. For example, if the user is feeling stressed, the automatic registration unit can prioritize registering clothing information that emphasizes comfort. Also, if the user is relaxed, the automatic registration unit can prioritize registering clothing information that emphasizes style. Also, if the user is in a hurry, the automatic registration unit can prioritize registering clothing information that can be registered quickly. In this way, by determining the priority of clothing information according to the user's emotions, more appropriate information can be registered.

[0122] During automatic registration, the automatic registration unit can prioritize registering highly relevant information by taking into account the user's geographical location information. The automatic registration unit registers information by taking into account, for example, the user's geographical location information. For example, if the user is in a cold region, the automatic registration unit can prioritize registering information about cold weather clothing. Furthermore, if the user is in a warm region, the automatic registration unit can also prioritize registering information about light clothing. Furthermore, if the user is in a rainy region, the automatic registration unit can prioritize registering information about waterproof clothing. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0123] During automatic registration, the automatic registration unit can analyze the user's social media activity and automatically register related information. The automatic registration unit, for example, analyzes the user's social media activity. For example, the automatic registration unit automatically registers information about fashion items that the user has "liked" on social media. The automatic registration unit can also analyze posts by fashion influencers that the user follows on social media and automatically register information about related clothing. The automatic registration unit can also automatically register information about fashion items that the user has shared on social media. In this way, related information can be automatically registered by analyzing the user's social media activity.

[0124] 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, for example, estimates the user's emotion. For example, the feedback unit can estimate the user's emotion using facial expression recognition technology. The feedback unit can also estimate the user's emotion using voice analysis technology. The feedback unit can also estimate the user's emotion using text analysis technology. The feedback unit adjusts the feedback collection method based on the estimated user's emotion. For example, if the user is feeling stressed, the feedback unit can provide a simple feedback method. If the user is relaxed, the feedback unit can provide a detailed feedback method. If the user is in a hurry, the feedback unit can provide a method that allows quick feedback collection. In this way, the burden on the user can be reduced by adjusting the feedback collection method according to the user's emotion.

[0125] 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, refers to the user's past feedback history. For example, the feedback unit preferentially uses feedback collection methods that the user has previously given high ratings to. The feedback unit can also exclude feedback collection methods that the user has previously given low ratings to. The feedback unit can also analyze the user's past feedback history and select the optimal collection method. In this way, the optimal collection method can be selected by referring to the user's past feedback history.

[0126] When collecting feedback, the feedback unit can filter the feedback based on the user's current fashion trends and preferences. The feedback unit filters information based on the user's current fashion trends and preferences, for example. For example, the feedback unit collects feedback based on the style of clothing recently purchased by the user. The feedback unit can also collect feedback based on fashion items that the user has "liked" on social media. The feedback unit can also collect feedback based on the style of clothing that the user has given high ratings to in the past. In this way, by filtering information based on the user's current fashion trends and preferences, more appropriate feedback can be collected.

[0127] The feedback unit can estimate the user's emotion and determine the priority of feedback based on the estimated user's emotion. The feedback unit, for example, estimates the user's emotion. For example, the feedback unit can estimate the user's emotion using facial expression recognition technology. The feedback unit can also estimate the user's emotion using voice analysis technology. The feedback unit can also estimate the user's emotion using text analysis technology. The feedback unit determines the priority of feedback based on the estimated user's emotion. For example, if the user is feeling stressed, the feedback unit can prioritize collecting important feedback. If the user is relaxed, the feedback unit can prioritize collecting detailed feedback. If the user is in a hurry, the feedback unit can prioritize collecting feedback that can be collected quickly. In this way, by determining the priority of feedback according to the user's emotion, more appropriate information can be collected.

[0128] When collecting feedback, the feedback unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The feedback unit collects information by taking into account, for example, the user's geographical location information. For example, if the user is in a cold region, the feedback unit can prioritize collecting feedback about cold weather clothing. Furthermore, if the user is in a warm region, the feedback unit can also prioritize collecting feedback about light clothing. Furthermore, if the user is in a rainy region, the feedback unit can also prioritize collecting feedback about waterproof clothing. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0129] When collecting feedback, the feedback unit can analyze the user's social media activity and collect related information. The feedback unit, for example, analyzes the user's social media activity. For example, the feedback unit collects feedback on fashion items that the user has "liked" on social media. The feedback unit can also analyze posts by fashion influencers that the user follows on social media and collect related feedback. The feedback unit can also collect feedback on fashion items that the user has shared on social media. In this way, by analyzing the user's social media activity, related information can be collected.

[0130] The data reference unit can estimate the user's emotions and adjust the method of referencing other users' data based on the estimated user's emotions. The data reference unit, for example, estimates the user's emotions. For example, the data reference unit can estimate the user's emotions using facial expression recognition technology. The data reference unit can also estimate the user's emotions using voice analysis technology. The data reference unit can also estimate the user's emotions using text analysis technology. The data reference unit adjusts the method of referencing other users' data based on the estimated user's emotions. For example, if the user is feeling stressed, the data reference unit can provide a simple and intuitive data reference method. If the user is relaxed, the data reference unit can provide a detailed data reference method. If the user is in a hurry, the data reference unit can provide a data reference method that allows quick reference. This reduces the burden on the user by adjusting the method of referencing other users' data based on the user's emotions.

[0131] When referencing other users' data, the data reference unit can select optimal data by referring to the user's past clothing selection history. The data reference unit, for example, refers to the user's past clothing selection history. For example, the data reference unit references other users' data based on clothing selection history for which the user has previously given high ratings. The data reference unit can also reference other users' data while excluding clothing selection history for which the user has previously given low ratings. The data reference unit can also analyze the user's past clothing selection history and select optimal data of other users. In this way, optimal data can be selected by referring to the user's past clothing selection history.

[0132] When referencing data of other users, the data reference unit can perform filtering based on the user's current fashion trends and preferences. The data reference unit filters information based on, for example, the user's current fashion trends and preferences. For example, the data reference unit filters data of other users based on the style of clothing recently purchased by the user. The data reference unit can also filter data of other users based on fashion items that the user has "liked" on social media. The data reference unit can also filter data of other users based on the style of clothing that the user has given high ratings to in the past. This makes it possible to reference more appropriate information by filtering information based on the user's current fashion trends and preferences.

[0133] The data reference unit can estimate the user's emotion and determine the priority of data to be referenced based on the estimated user's emotion. The data reference unit, for example, estimates the user's emotion. For example, the data reference unit can estimate the user's emotion using facial expression recognition technology. The data reference unit can also estimate the user's emotion using voice analysis technology. The data reference unit can also estimate the user's emotion using text analysis technology. The data reference unit determines the priority of data to be referenced based on the estimated user's emotion. For example, if the user is feeling stressed, the data reference unit can prioritize reference to important data. Also, if the user is relaxed, the data reference unit can prioritize reference to detailed data. Also, if the user is in a hurry, the data reference unit can prioritize reference to data that can be referenced quickly. In this way, by determining the priority of data to be referenced according to the user's emotion, more appropriate information can be referenced.

[0134] When referencing data of other users, the data reference unit can prioritize referencing highly relevant information by taking into account the user's geographical location information. The data reference unit, for example, references information by taking into account the user's geographical location information. For example, if the user is in a cold region, the data reference unit can prioritize referencing other users' data regarding cold weather clothing. Furthermore, if the user is in a warm region, the data reference unit can also prioritize referencing other users' data regarding light clothing. Furthermore, if the user is in a rainy region, the data reference unit can also prioritize referencing other users' data regarding waterproof clothing. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0135] When referencing data of other users, the data reference unit can analyze the user's social media activity and reference related information. The data reference unit, for example, analyzes the user's social media activity. For example, the data reference unit references data of other users regarding fashion items that the user has "liked" on social media. The data reference unit can also analyze posts of fashion influencers that the user follows on social media and reference related data of other users. The data reference unit can also reference data of other users regarding fashion items that the user has shared on social media. In this way, by analyzing the user's social media activity, related information can be referenced. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, weather acquisition unit, history reference unit, and suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the weather acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires weather forecast data from the Internet. For example, the history reference unit is realized by the specific processing unit 290 of the data processing device 12 and refers to the user's past clothing selection history. For example, the suggestion unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and suggests optimal clothing based on the temperature and weather. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, weather acquisition unit, history reference unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the weather acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires weather forecast data from the Internet. For example, the history reference unit is realized by the specific processing unit 290 of the data processing device 12 and refers to the user's past clothing selection history. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and suggests optimal clothing based on the temperature and weather. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, weather acquisition unit, history reference unit, and suggestion unit 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 acquisition unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the weather acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires weather forecast data from the Internet. For example, the history reference unit is realized by the specific processing unit 290 of the data processing device 12 and refers to the user's past clothing selection history. For example, the suggestion unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12 and suggests optimal clothing based on the temperature and weather. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, weather acquisition unit, history reference unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the weather acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires weather forecast data from the Internet. For example, the history reference unit is realized by the specific processing unit 290 of the data processing device 12 and refers to the user's past clothing selection history. For example, the suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and suggests optimal clothing based on the temperature and weather.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can make simple and intuitive suggestions. If the user is relaxed, the suggestion unit can also make detailed suggestions. If the user is in a hurry, the suggestion unit can also make suggestions that are easy to understand quickly. In this way, by adjusting the way suggestions are expressed according to the user's emotions, more appropriate suggestions can be made.

[0138] The acquisition unit can acquire clothing information taking into consideration the geographical location information of the user. For example, if the user is in a cold region, information about cold weather clothing can be acquired preferentially. Also, if the user is in a warm region, information about light clothing can be acquired preferentially. Also, if the user is in a rainy region, information about waterproof clothing can be acquired preferentially. In this way, by taking into consideration the geographical location information of the user, it is possible to acquire highly relevant information preferentially.

[0139] The suggestion unit can improve the accuracy of suggestions by reflecting the user's past feedback. For example, the accuracy of suggestions can be improved by referring to suggestions that the user has previously given a high rating. The accuracy of suggestions can also be improved by excluding suggestions that the user has previously given a low rating. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. In this way, the accuracy of suggestions can be improved by reflecting the user's past feedback.

[0140] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring clothing information based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can automatically acquire clothing information to avoid burdening the user. Alternatively, if the user is relaxed, the acquisition unit can provide the user with an option to manually input clothing information. Alternatively, if the user is in a hurry, the acquisition unit can select the fastest method of acquiring clothing information. This reduces the burden on the user by adjusting the timing of acquiring clothing information according to the user's emotions.

[0141] The suggestion unit can apply different suggestion algorithms depending on the category of clothing. For example, for formal clothing, a formal suggestion algorithm can be applied. For casual clothing, a casual suggestion algorithm can be applied. For sportswear, a suggestion algorithm specialized for sportswear can be applied. In this way, by applying different suggestion algorithms depending on the category of clothing, more appropriate suggestions can be made.

[0142] The history reference unit can estimate the user's emotions and adjust the method of referring to the past clothing selection history based on the estimated user emotions. For example, if the user is feeling stressed, the history reference unit can automatically refer to the past clothing selection history to reduce the burden on the user. Also, if the user is relaxed, the history reference unit can provide the user with an option to manually check the past clothing selection history. Also, if the user is in a hurry, the history reference unit can select the quickest method of referring to the past clothing selection history. In this way, the burden on the user can be reduced by adjusting the method of referring to the past clothing selection history according to the user's emotions.

[0143] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can make short, to-the-point suggestions. If the user is relaxed, the suggestion unit can make detailed suggestions. If the user is in a hurry, the suggestion unit can make quick, understandable suggestions. In this way, by adjusting the length of the suggestions according to the user's emotions, more appropriate suggestions can be made.

[0144] The acquisition unit can analyze the user's social media activity and acquire related information. For example, it can acquire information about fashion items that the user has "liked" on social media. It can also analyze posts by fashion influencers that the user follows on social media and acquire information about related clothing. It can also acquire information about fashion items that the user has shared on social media. In this way, it is possible to acquire related information by analyzing the user's social media activity.

[0145] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the clothing. For example, detailed suggestions can be made for important events. Alternatively, simple suggestions can be made for everyday outings. The level of detail of the suggestion can also be adjusted depending on specific seasons or weather conditions. This allows for more appropriate suggestions to be made by adjusting the level of detail of the suggestion based on the importance of the clothing.

[0146] The feedback unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user's emotions. For example, if the user is feeling stressed, the feedback unit can provide a simple feedback method. If the user is relaxed, the feedback unit can provide a detailed feedback method. If the user is in a hurry, the feedback unit can provide a method for quickly collecting feedback. In this way, the burden on the user can be reduced by adjusting the feedback collection method according to the user's emotions.

[0147] The processing flow of the second embodiment will be briefly explained below.

[0148] Step 1: The acquisition unit acquires information about the clothes owned by the user. For example, the acquisition unit can acquire detailed information about the clothes, such as the type, color, material, and season. The acquisition unit can also automatically register the clothing information using a two-dimensional code or RFID tag. Step 2: The weather acquisition unit acquires weather forecast data from the Internet and determines the current temperature and weather. For example, the weather acquisition unit can acquire weather forecast data using the API of a specific weather service. Step 3: The history reference unit references the user's preferences and past selection history based on the information acquired by the acquisition unit and the weather acquisition unit. For example, the history reference unit can record what clothes the user has worn in the past and use that as a reference. Step 4: The suggestion unit suggests optimal clothing based on the information referenced by the history reference unit. For example, the suggestion unit can suggest a light jacket on a hot day.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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 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.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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 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.

[0200] 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.

[0201] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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."

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0219] 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.

[0220] [Explanation of symbols]

[0221] 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. an acquisition unit that acquires clothing information; a weather acquisition unit that acquires temperature and weather information; a history reference unit that references a user's preferences and past selection history based on the information acquired by the acquisition unit and the weather acquisition unit; a suggestion unit that suggests appropriate clothing based on the information referenced by the history reference unit; Equipped with A system characterized by:

2. Equipped with an automatic registration unit that automatically registers clothing information using a 2D code or RFID tag 2. The system of claim 1.

3. Equipped with a feedback section that provides feedback on how comfortable the suggested clothing was in reality 2. The system of claim 1.

4. Equipped with a data reference section that allows users to refer to other users' data 2. The system of claim 1.

5. The acquisition unit Get detailed information about clothing type, color, material, and season 2. The system of claim 1.

6. The weather acquisition unit Obtain weather forecast data from the Internet and check the current temperature and weather 2. The system of claim 1.

7. The acquisition unit Estimate the user's emotions and adjust the timing of acquiring clothing information based on the estimated user emotions.

2. The system of claim 1.

8. The acquisition unit When acquiring clothing information, the system analyzes the user's past clothing selection history and selects the appropriate acquisition method.

2. The system of claim 1.

9. The acquisition unit When obtaining clothing information, filtering is performed based on the user's current fashion trends and preferences.

2. The system of claim 1.

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