system

Abstract

The system according to the embodiment aims to enable a user to easily find the best outfit to suit the situation of the day. [Solution] A system according to an embodiment includes a selection unit, a registration unit, a suggestion unit, a fashion unit, and a hairstyle unit. The selection unit accepts input of a situation. The registration unit registers the user's wardrobe based on the situation accepted by the selection unit. The suggestion unit suggests outfits based on the wardrobe registered by the registration unit. The fashion unit incorporates the latest fashions into the outfits suggested by the suggestion unit. The hairstyle unit suggests hairstyles based on the outfits suggested by the fashion unit.

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 techniques have had the problem that it is difficult for users to find the best outfit to suit the situation of the day.

[0005] The system according to the embodiment aims to enable a user to easily find the best outfit to suit the situation of the day. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, a registration unit, a suggestion unit, a fashion unit, and a hairstyle unit. The selection unit accepts input of a situation. The registration unit registers the user's wardrobe based on the situation accepted by the selection unit. The suggestion unit suggests outfits based on the wardrobe registered by the registration unit. The fashion unit incorporates the latest fashions into the outfits suggested by the suggestion unit. The hairstyle unit suggests hairstyles based on the outfits suggested by the fashion unit. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to easily find the best outfit to suit the situation of the day. [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 outfit suggestion system according to an embodiment of the present invention is a system that suggests the optimal outfit to a user based on the situation of the day. The system allows the user to select a situation, register their current wardrobe, and suggest the optimal outfit. The system also suggests outfits incorporating the latest fashions and hairstyles. For example, the user selects a situation such as work, a date, or outdoors, and registers their current wardrobe using photos and text. The system then suggests the optimal outfit based on the situation selected by the user. Furthermore, the system also suggests outfits incorporating the latest fashions and hairstyles that match the situation. In this way, the outfit suggestion system can understand the user's wardrobe, make suggestions incorporating the latest fashions, and suggest hairstyles, thereby providing total support for the user's fashion.

[0029] A coordination suggestion system according to an embodiment includes a selection unit, a registration unit, a suggestion unit, a fashion unit, and a hairstyle unit. The selection unit accepts input of a situation from a user. For example, the user can select a situation such as work, a date, or outdoors. The selection unit can also estimate the user's emotions and present situation options based on the estimated emotions. The registration unit registers the user's wardrobe using photos and text. For example, the user can register their own clothes, shoes, bags, accessories, etc. using photos and text. The registration unit can also estimate the user's emotions and adjust the wardrobe registration method based on the estimated emotions. The suggestion unit suggests optimal coordination based on the situation and wardrobe. For example, it can suggest business casual attire for work and a slightly more glamorous outfit for a date. The suggestion unit can also estimate the user's emotions and adjust the coordination suggestion method based on the estimated emotions. The fashion unit suggests coordinations incorporating the latest fashions. For example, it can suggest coordinations incorporating this season's trend colors and designs. The fashion unit can also estimate the user's emotions and adjust how the user incorporates the latest fashions based on the estimated emotions. The hairstyle unit can suggest hairstyles that suit the situation. For example, it can suggest a neat hairstyle for work and a slightly more glamorous hairstyle for a date. The hairstyle unit can also estimate the user's emotions and adjust the hairstyle suggestion method based on the estimated emotions. As a result, the coordination suggestion system according to the embodiment can provide total support for the user's fashion by suggesting optimal coordination that suits the user's situation and also suggesting the latest fashions and hairstyles.

[0030] The selection unit can analyze the past situation selection history and predict and present the optimal situation for the user. For example, the selection unit can prioritize and present situations that the user has frequently selected in the past. The selection unit can also predict and present situations that the user will select on a specific day of the week or time period. The selection unit can also predict and present situations related to a specific event based on the user's past selection history. This makes it possible to predict and present the optimal situation based on the past selection history, thereby making it possible to make suggestions that match the user's preferences. Some or all of the above-mentioned processing in the selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the selection unit can input the user's past selection history data into a generation AI and cause the generation AI to predict the optimal situation.

[0031] When selecting a situation, the selection unit can suggest an appropriate situation by referring to the user's calendar information. The selection unit can, for example, refer to the schedule registered in the user's calendar and suggest related situations. The selection unit can also suggest a situation related to a specific event from the user's calendar information. The selection unit can also suggest an optimal situation that matches the schedule based on the user's calendar information. In this way, by referring to the user's calendar information, it is possible to suggest an optimal situation that matches the schedule. Some or all of the above-mentioned processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's calendar information to a generation AI and cause the generation AI to suggest an optimal situation.

[0032] When selecting a situation, the selection unit can present options taking into account the user's current weather information. For example, the selection unit can prioritize presenting situations that can be enjoyed indoors when it is raining. The selection unit can also prioritize presenting situations that can be enjoyed outdoors when it is sunny. The selection unit can also prioritize presenting situations that can be enjoyed in warm places on snowy days. In this way, more appropriate situations can be suggested by taking weather information into consideration. Some or all of the above-mentioned processing in the selection unit may be performed using, or without, AI, for example. For example, the selection unit can input the user's current weather information into the generation AI and cause the generation AI to suggest the optimal situation.

[0033] When selecting a situation, the selection unit can prioritize presenting highly relevant situations by taking into account the user's geographical location information. For example, the selection unit can prioritize presenting situations that can be enjoyed near the user's current location. Furthermore, if the user is in a specific area, the selection unit can prioritize presenting situations related to that area. Furthermore, if the user is traveling, the selection unit can prioritize presenting situations that can be enjoyed at the user's travel destination. In this way, by taking the geographical location information into consideration, situations suitable for the user's current location can be suggested. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's geographical location information to a generation AI and cause the generation AI to suggest optimal situations.

[0034] When selecting a situation, the selection unit can analyze the user's social media activity and present related situations. For example, the selection unit presents situations related to places where the user has checked in on social media. The selection unit can also analyze the content of the user's social media posts and present related situations. The selection unit can also present related situations by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, situations based on the user's interests and concerns can be suggested. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest optimal situations.

[0035] When selecting a situation, the selection unit can customize options by reflecting the user's past feedback. For example, the selection unit preferentially presents situations that the user has previously rated highly. The selection unit can also present situations that the user has previously rated poorly, excluding these. The selection unit can also analyze the user's past feedback and customize and present an optimal situation. This makes it possible to suggest situations that match the user's preferences by reflecting the past feedback. Some or all of the above-described processing in the selection unit may be performed using, or without, AI, for example. For example, the selection unit can input the user's past feedback data into a generation AI and cause the generation AI to customize the optimal situation.

[0036] When registering a wardrobe, the registration unit can analyze the user's past registration history and suggest a registration method. For example, the registration unit can automatically display items that the user has frequently registered in the past as candidates. The registration unit can also prioritize suggesting registration methods (voice, text, etc.) that the user has used in the past. The registration unit can also predict and suggest items that will be used in a specific time period based on the user's past registration history. In this way, by analyzing the past registration history, it is possible to suggest the optimal registration method for the user. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's past registration history data into a generation AI and have the generation AI suggest the optimal registration method.

[0037] When registering a wardrobe, the registration unit can suggest registration content taking into account the user's current fashion trends. The registration unit can suggest registration content based on, for example, the user's currently preferred fashion style. The registration unit can also suggest related items based on items recently purchased by the user. The registration unit can also analyze the user's current fashion trends and suggest optimal registration content. This makes it possible to suggest optimal registration content for the user by taking current fashion trends into consideration. Some or all of the above-mentioned processing in the registration unit can be performed, for example, using AI or without AI. For example, the registration unit can input the user's current fashion trend data into the generation AI and have the generation AI suggest optimal registration content.

[0038] When registering a wardrobe, the registration unit can select the optimal registration means depending on the user's input method. For example, if the user uses voice input, the registration unit registers items using voice recognition technology. Furthermore, if the user uses text input, the registration unit can also register items using text analysis technology. Furthermore, if the user uses image input, the registration unit can also register items using image recognition technology. This allows for more efficient wardrobe registration by providing the optimal registration means depending on the user's input method. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's voice data into a generation AI and have the generation AI perform voice recognition.

[0039] When registering a wardrobe, the registration unit can prioritize registering highly relevant items by taking into account the user's geographical location information. For example, the registration unit prioritizes registering items suitable for the climate of the user's current location. Furthermore, if the user is in a specific region, the registration unit can prioritize registering items related to that region. Furthermore, if the user is traveling, the registration unit can prioritize registering items to be used at the travel destination. In this way, by taking the geographical location information into consideration, items suitable for the user's current location can be prioritized to be registered. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input the user's geographical location information to the generation AI and cause the generation AI to register optimal items.

[0040] The registration unit can analyze the user's social media activity and register related items when registering a wardrobe. For example, the registration unit registers items related to places where the user has checked in on social media. The registration unit can also analyze the content of the user's social media posts and register related items. The registration unit can also register related items with reference to the activities of the user's friends on social media. In this way, by analyzing social media activity, items based on the user's interests can be registered. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's social media activity data into a generation AI and cause the generation AI to register optimal items.

[0041] The registration unit can customize the registration method by reflecting the user's past feedback when registering a wardrobe. For example, the registration unit preferentially suggests registration methods that the user has previously rated highly. The registration unit can also exclude registration methods that the user has previously rated poorly when suggesting new registration methods. The registration unit can also analyze the user's past feedback and customize and suggest the optimal registration method. This makes it possible to provide the user with the optimal registration method by reflecting the past feedback. Some or all of the above-described processing in the registration unit can be performed using, for example, AI, or can be performed without using AI. For example, the registration unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the optimal registration method.

[0042] When proposing a coordination, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the items. For example, the suggestion unit may make suggestions with an emphasis on important items (e.g., jackets and dresses). The suggestion unit may also make concise suggestions for auxiliary items (e.g., accessories and shoes). The suggestion unit can also adjust the level of detail of the suggestion based on the importance of the items. This allows suggestions that focus on items that are important to the user by providing a level of detail of the suggestion based on the importance of the items. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit may input item importance data into a generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0043] When proposing a coordination, the suggestion unit can apply different suggestion algorithms depending on the category of the item. For example, for the clothing category, the suggestion unit can apply a suggestion algorithm that emphasizes harmony in color and design. For the shoe category, the suggestion unit can also apply a suggestion algorithm that emphasizes comfort and functionality. For the accessory category, the suggestion unit can also apply a suggestion algorithm that adds accents to the overall coordination. In this way, by applying a suggestion algorithm depending on the item category, more appropriate coordination can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input item category data into a generation AI and cause the generation AI to apply a suggestion algorithm.

[0044] When proposing an outfit, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit makes suggestions by referring to outfits that the user has previously given high ratings. The suggestion unit can also make suggestions by excluding outfits that the user has previously given low ratings. The suggestion unit can also analyze the user's past suggestion results and suggest optimal outfits. In this way, by referring to the past suggestion results, the optimal outfit for the user can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0045] When proposing a coordination, the suggestion unit can determine the priority of the suggestions based on the submission date of the items. For example, the suggestion unit can prioritize newly registered items. The suggestion unit can also prioritize seasonal items. The suggestion unit can also prioritize items recently used by the user. This allows for more appropriate coordination to be suggested by providing a priority of suggestions based on the submission date of the items. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input item submission date data into the generation AI and have the generation AI determine the priority of the suggestions.

[0046] When proposing a coordination, the suggestion unit can adjust the order of suggestions based on the relevance of items. For example, the suggestion unit can prioritize suggesting combinations of clothes and shoes. The suggestion unit can also prioritize suggesting combinations of accessories and bags. The suggestion unit can also adjust the order of suggestions based on the relevance of items. This makes it possible to suggest more appropriate coordination by providing an order of suggestions based on the relevance of items. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input item relevance data into a generation AI and cause the generation AI to adjust the order of suggestions.

[0047] When proposing an outfit, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, the suggestion unit may make suggestions that use a lot of technical terminology for a user who is knowledgeable about fashion. The suggestion unit may also make suggestions that use simpler language for a user who is not knowledgeable about fashion. The suggestion unit may also adjust the use of technical terminology in the suggestion according to the user's level of expertise. This allows for more appropriate outfits to be suggested by providing suggested technical terminology that matches the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology.

[0048] When incorporating the latest fashion, the fashion department can analyze the user's past fashion history and make optimal suggestions. For example, the fashion department makes suggestions based on fashion styles that the user has previously rated highly. The fashion department can also make suggestions by excluding fashion styles that the user has previously rated poorly. The fashion department can also analyze the user's past fashion history and suggest optimal latest fashions. In this way, by analyzing past fashion history, it is possible to suggest optimal latest fashions to the user. Some or all of the above-mentioned processing in the fashion department may be performed using AI, for example, or may be performed without using AI. For example, the fashion department can input the user's past fashion history data into a generation AI and have the generation AI suggest optimal latest fashions.

[0049] When incorporating the latest fashions, the fashion department can customize the suggestions taking into account the user's current fashion trends. For example, the fashion department can suggest the latest fashions based on the user's currently preferred fashion style. The fashion department can also suggest related latest fashions based on items recently purchased by the user. The fashion department can also analyze the user's current fashion trends and suggest the most suitable latest fashions. This allows the most suitable latest fashions to be suggested to the user by taking current fashion trends into consideration. Some or all of the above-mentioned processing in the fashion department may be performed using AI, for example, or may be performed without using AI. For example, the fashion department can input the user's current fashion trend data into the generation AI and have the generation AI suggest the most suitable latest fashions.

[0050] When incorporating the latest fashion, the fashion department can improve the suggestion method by reflecting user feedback. For example, the fashion department prioritizes the use of suggestion methods that users have previously rated highly. The fashion department can also exclude suggestion methods that users have previously rated poorly. The fashion department can also analyze the user's past feedback and customize and use the optimal suggestion method. This allows the optimal suggestion method to be provided to the user by reflecting past feedback. Some or all of the above-mentioned processing in the fashion department may be performed using AI, for example, or may be performed without using AI. For example, the fashion department can input the user's past feedback data into the generation AI and have the generation AI improve the suggestion method.

[0051] The fashion department can make optimal suggestions when incorporating the latest fashions, taking into account the user's geographical location information. For example, the fashion department can suggest the latest fashions that are suitable for the climate of the user's current location. Furthermore, if the user is in a specific area, the fashion department can suggest the latest fashions related to that area. Furthermore, if the user is traveling, the fashion department can suggest the latest fashions to wear at the travel destination. In this way, by taking geographical location information into consideration, the latest fashions suitable for the user's current location can be suggested. Some or all of the above-described processing in the fashion department may be performed using AI, for example, or may be performed without using AI. For example, the fashion department can input the user's geographical location information into the generation AI and have the generation AI suggest the latest fashions that are optimal for the user.

[0052] When incorporating the latest fashion, the fashion department can customize the suggestions by analyzing the user's social media activity. For example, the fashion department can suggest the latest fashion related to the location where the user checked in on social media. The fashion department can also analyze the content of the user's social media posts to suggest related latest fashion. The fashion department can also suggest related latest fashion based on the activity of the user's friends on social media. In this way, by analyzing social media activity, the latest fashion can be suggested based on the user's interests. Some or all of the above-mentioned processing in the fashion department may be performed using, for example, AI, or may be performed without using AI. For example, the fashion department can input the user's social media activity data into a generation AI and have the generation AI suggest optimal latest fashion.

[0053] When incorporating the latest fashion, the fashion department can customize the suggestion method by reflecting the user's past feedback. For example, the fashion department prioritizes the use of suggestion methods that the user has previously rated highly. The fashion department can also exclude and use suggestion methods that the user has previously rated poorly. The fashion department can also analyze the user's past feedback and customize and use the optimal suggestion method. In this way, by reflecting past feedback, the optimal suggestion method can be provided to the user. Some or all of the above-described processing in the fashion department may be performed using, for example, AI, or may be performed without using AI. For example, the fashion department can input the user's past feedback data into a generation AI and have the generation AI customize the suggestion method.

[0054] When suggesting a hairstyle, the hairstyle unit can analyze the user's past hairstyle history and make the optimal suggestion. For example, the hairstyle unit makes the suggestion by referring to hairstyles that the user has previously given a high rating. The hairstyle unit can also make suggestions by excluding hairstyles that the user has previously given a low rating. The hairstyle unit can also analyze the user's past hairstyle history and suggest the optimal hairstyle. In this way, by analyzing the past hairstyle history, the optimal hairstyle can be suggested to the user. Some or all of the above-mentioned processing in the hairstyle unit may be performed using, for example, AI, or may be performed without using AI. For example, the hairstyle unit can input the user's past hairstyle history data into the generation AI and cause the generation AI to suggest the optimal hairstyle.

[0055] When suggesting a hairstyle, the hairstyle unit can customize the suggestion content by taking into account the user's current hairstyle trends. For example, the hairstyle unit can suggest an optimal hairstyle based on the user's currently preferred hairstyle. The hairstyle unit can also suggest a related hairstyle based on a hairstyle the user has recently tried. The hairstyle unit can also analyze the user's current hairstyle trends and suggest an optimal hairstyle. This makes it possible to suggest an optimal hairstyle for the user by taking the current hairstyle trends into consideration. Some or all of the above-described processing in the hairstyle unit may be performed using AI, for example, or may be performed without using AI. For example, the hairstyle unit can input the user's current hairstyle trend data into the generation AI and cause the generation AI to suggest an optimal hairstyle.

[0056] The hairstyle unit can improve the suggestion method by reflecting user feedback when suggesting a hairstyle. For example, the hairstyle unit preferentially uses suggestion methods that the user has previously rated highly. The hairstyle unit can also exclude suggestion methods that the user has previously rated poorly. The hairstyle unit can also analyze the user's past feedback and customize and use the optimal suggestion method. In this way, the optimal suggestion method can be provided to the user by reflecting the past feedback. Some or all of the above-described processing in the hairstyle unit may be performed using AI, for example, or may be performed without using AI. For example, the hairstyle unit can input the user's past feedback data into the generation AI and cause the generation AI to improve the suggestion method.

[0057] When suggesting a hairstyle, the hairstyle unit can make the optimal suggestion by taking into account the user's geographical location information. For example, the hairstyle unit can suggest a hairstyle that is suitable for the climate of the user's current location. Furthermore, if the user is in a specific region, the hairstyle unit can also suggest a hairstyle related to that region. Furthermore, if the user is traveling, the hairstyle unit can also suggest a hairstyle to be worn at the travel destination. In this way, by taking the geographical location information into consideration, a hairstyle that is suitable for the user's current location can be suggested. Some or all of the above-described processing in the hairstyle unit may be performed using, for example, AI, or may be performed without using AI. For example, the hairstyle unit can input the user's geographical location information to the generation AI and cause the generation AI to suggest the optimal hairstyle.

[0058] When suggesting a hairstyle, the hairstyle unit can analyze the user's social media activity to customize the suggestion. For example, the hairstyle unit can suggest hairstyles related to places where the user has checked in on social media. The hairstyle unit can also analyze the content of the user's social media posts to suggest related hairstyles. The hairstyle unit can also suggest related hairstyles based on the activity of the user's friends on social media. In this way, by analyzing social media activity, hairstyles can be suggested based on the user's interests. Some or all of the above-mentioned processing in the hairstyle unit may be performed using, for example, AI, or may be performed without using AI. For example, the hairstyle unit can input the user's social media activity data into the generation AI and have the generation AI suggest the optimal hairstyle.

[0059] When suggesting a hairstyle, the hairstyle unit can customize the suggestion method by reflecting the user's past feedback. For example, the hairstyle unit preferentially uses suggestion methods that the user has previously rated highly. The hairstyle unit can also exclude suggestion methods that the user has previously rated poorly. The hairstyle unit can also analyze the user's past feedback and customize and use the optimal suggestion method. In this way, the optimal suggestion method can be provided to the user by reflecting the past feedback. Some or all of the above-described processing in the hairstyle unit may be performed using, for example, AI, or may be performed without using AI. For example, the hairstyle unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the suggestion method.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The outfit suggestion system can further include a health management unit that acquires the user's health data and reflects it in the suggestions. For example, it can suggest outfits that support a healthy lifestyle based on data such as the user's number of steps and heart rate. If the user is not getting enough exercise, it can suggest outfits suitable for active situations, and conversely, if the user appears tired, it can suggest outfits that are relaxing. The health management unit can also acquire the user's dietary data and make suggestions that take nutritional balance into consideration. This makes it possible to provide the optimal outfits according to the user's health condition.

[0062] The outfit suggestion system can further include a hobby reflection unit that makes suggestions that reflect the user's hobbies and interests. For example, if the user likes music, the system can suggest outfits suitable for music events. If the user likes sports, the system can suggest outfits suitable for watching sports or other activities. Furthermore, the hobby reflection unit can also suggest outfits that match the user's indoor hobbies, such as reading or watching movies. This makes it possible to provide optimal outfits that match the user's hobbies and interests.

[0063] The outfit suggestion system can further include a purchase history analysis unit that analyzes the user's past purchase history and reflects the results in the suggestions. For example, it can suggest related outfits based on items the user has purchased in the past. If the user has a preference for a particular brand or style, the purchase history analysis unit can also suggest outfits that match that brand or style. Furthermore, the purchase history analysis unit can also suggest optimal outfits by taking into account the frequency with which the user has used items purchased in the past. This makes it possible to provide optimal outfits based on the user's purchase history.

[0064] The outfit suggestion system can further include a lifestyle reflection unit that acquires lifestyle data of the user and reflects it in the suggestions. For example, if the user is an office worker, a business casual outfit can be suggested. If the user is a freelancer, a casual outfit can be suggested. Furthermore, if the user is an outdoorsy person, the lifestyle reflection unit can suggest an outfit suitable for outdoor activities. This makes it possible to provide the optimal outfit that suits the user's lifestyle.

[0065] The outfit suggestion system may further include a social media analysis unit that analyzes the user's social media activity and reflects the results in the suggestions. For example, the system may suggest outfits related to places where the user has checked in on social media. The social media analysis unit may also analyze the content of the user's social media posts and suggest related outfits. The social media analysis unit may also suggest related outfits based on the activities of the user's friends on social media. In this way, by analyzing social media activity, the system may provide optimal outfits based on the user's interests.

[0066] The coordination suggestion system can further include a feedback reflection unit that reflects user feedback to improve the suggestion content. For example, it can preferentially use suggestion methods that users have previously rated highly. The feedback reflection unit can also exclude suggestion methods that users have previously rated poorly. Furthermore, the feedback reflection unit can analyze the user's past feedback and customize and use the optimal suggestion method. In this way, it is possible to provide the user with the optimal suggestion method by reflecting past feedback.

[0067] The outfit suggestion system can further include a specialized knowledge reflection unit that adjusts the content of suggestions according to the user's level of specialized knowledge. For example, suggestions that use a lot of specialized terminology are made to a user who is knowledgeable about fashion. The specialized knowledge reflection unit can also make suggestions that are explained in simple language to a user who is not knowledgeable about fashion. Furthermore, the specialized knowledge reflection unit can also adjust the use of specialized terminology in the suggestions according to the user's level of specialized knowledge. This makes it possible to provide optimal suggestions according to the user's level of specialized knowledge.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The selection unit accepts a situation input from the user. For example, the user can select a situation such as work, a date, or outdoors. The selection unit can also estimate the user's emotions and present situation options based on the estimated emotions. Step 2: The registration unit registers the user's wardrobe using photos and text. For example, the user can register their clothes, shoes, bags, accessories, etc. using photos and text. The registration unit can also estimate the user's emotions and adjust the wardrobe registration method based on the estimated emotions. Step 3: The suggestion module suggests the best outfit based on the situation and wardrobe. For example, it might suggest business casual attire for work, and something a little more glamorous for a date. The suggestion module can also estimate the user's emotions and adjust the way it suggests outfits based on the estimated emotions. Step 4: The fashion department suggests outfits incorporating the latest fashions. For example, they suggest outfits incorporating this season's trend colors and designs. The fashion department can also estimate the user's emotions and adjust how the latest fashions are incorporated based on the estimated emotions. Step 5: The hairstyle section suggests hairstyles that suit the situation. For example, it might suggest a formal hairstyle for work, or a more glamorous hairstyle for a date. The hairstyle section can also estimate the user's emotions and adjust the hairstyle suggestion method based on the estimated emotions.

[0070] (Example 2) The outfit suggestion system according to an embodiment of the present invention is a system that suggests the optimal outfit to a user based on the situation of the day. The system allows the user to select a situation, register their current wardrobe, and suggest the optimal outfit. The system also suggests outfits incorporating the latest fashions and hairstyles. For example, the user selects a situation such as work, a date, or outdoors, and registers their current wardrobe using photos and text. The system then suggests the optimal outfit based on the situation selected by the user. Furthermore, the system also suggests outfits incorporating the latest fashions and hairstyles that match the situation. In this way, the outfit suggestion system can understand the user's wardrobe, make suggestions incorporating the latest fashions, and suggest hairstyles, thereby providing total support for the user's fashion.

[0071] A coordination suggestion system according to an embodiment includes a selection unit, a registration unit, a suggestion unit, a fashion unit, and a hairstyle unit. The selection unit accepts input of a situation from a user. For example, the user can select a situation such as work, a date, or outdoors. The selection unit can also estimate the user's emotions and present situation options based on the estimated emotions. The registration unit registers the user's wardrobe using photos and text. For example, the user can register their own clothes, shoes, bags, accessories, etc. using photos and text. The registration unit can also estimate the user's emotions and adjust the wardrobe registration method based on the estimated emotions. The suggestion unit suggests optimal coordination based on the situation and wardrobe. For example, it can suggest business casual attire for work and a slightly more glamorous outfit for a date. The suggestion unit can also estimate the user's emotions and adjust the coordination suggestion method based on the estimated emotions. The fashion unit suggests coordinations incorporating the latest fashions. For example, it can suggest coordinations incorporating this season's trend colors and designs. The fashion unit can also estimate the user's emotions and adjust how the user incorporates the latest fashions based on the estimated emotions. The hairstyle unit can suggest hairstyles that suit the situation. For example, it can suggest a neat hairstyle for work and a slightly more glamorous hairstyle for a date. The hairstyle unit can also estimate the user's emotions and adjust the hairstyle suggestion method based on the estimated emotions. As a result, the coordination suggestion system according to the embodiment can provide total support for the user's fashion by suggesting optimal coordination that suits the user's situation and also suggesting the latest fashions and hairstyles.

[0072] The selection unit can estimate the user's emotions and present situation options based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can prioritize presenting relaxing situations (e.g., relaxing at a cafe). Furthermore, if the user is having fun, the selection unit can prioritize presenting active situations (e.g., outdoor activities). Furthermore, if the user is tired, the selection unit can prioritize presenting relaxing situations (e.g., relaxing at home). By presenting situation options according to the user's emotions, more appropriate situations can be suggested. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit can be performed using, for example, an AI. For example, the selection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0073] The selection unit can analyze the past situation selection history and predict and present the optimal situation for the user. For example, the selection unit can prioritize and present situations that the user has frequently selected in the past. The selection unit can also predict and present situations that the user will select on a specific day of the week or time period. The selection unit can also predict and present situations related to a specific event based on the user's past selection history. This makes it possible to predict and present the optimal situation based on the past selection history, thereby making it possible to make suggestions that match the user's preferences. Some or all of the above-mentioned processing in the selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the selection unit can input the user's past selection history data into a generation AI and cause the generation AI to predict the optimal situation.

[0074] When selecting a situation, the selection unit can suggest an appropriate situation by referring to the user's calendar information. The selection unit can, for example, refer to the schedule registered in the user's calendar and suggest related situations. The selection unit can also suggest a situation related to a specific event from the user's calendar information. The selection unit can also suggest an optimal situation that matches the schedule based on the user's calendar information. In this way, by referring to the user's calendar information, it is possible to suggest an optimal situation that matches the schedule. Some or all of the above-mentioned processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's calendar information to a generation AI and cause the generation AI to suggest an optimal situation.

[0075] When selecting a situation, the selection unit can present options taking into account the user's current weather information. For example, the selection unit can prioritize presenting situations that can be enjoyed indoors when it is raining. The selection unit can also prioritize presenting situations that can be enjoyed outdoors when it is sunny. The selection unit can also prioritize presenting situations that can be enjoyed in warm places on snowy days. In this way, more appropriate situations can be suggested by taking weather information into consideration. Some or all of the above-mentioned processing in the selection unit may be performed using, or without, AI, for example. For example, the selection unit can input the user's current weather information into the generation AI and cause the generation AI to suggest the optimal situation.

[0076] The selection unit can estimate the user's emotions and prioritize situations based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can prioritize presenting relaxing situations. Furthermore, if the user is having fun, the selection unit can prioritize active situations. Furthermore, if the user is tired, the selection unit can prioritize presenting relaxing situations. This allows more appropriate situations to be proposed by prioritizing situations according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit can be performed using, for example, an AI, or without an AI. For example, the selection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0077] When selecting a situation, the selection unit can prioritize presenting highly relevant situations by taking into account the user's geographical location information. For example, the selection unit can prioritize presenting situations that can be enjoyed near the user's current location. Furthermore, if the user is in a specific area, the selection unit can prioritize presenting situations related to that area. Furthermore, if the user is traveling, the selection unit can prioritize presenting situations that can be enjoyed at the user's travel destination. In this way, by taking the geographical location information into consideration, situations suitable for the user's current location can be suggested. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's geographical location information to a generation AI and cause the generation AI to suggest optimal situations.

[0078] When selecting a situation, the selection unit can analyze the user's social media activity and present related situations. For example, the selection unit presents situations related to places where the user has checked in on social media. The selection unit can also analyze the content of the user's social media posts and present related situations. The selection unit can also present related situations by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, situations based on the user's interests and concerns can be suggested. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest optimal situations.

[0079] When selecting a situation, the selection unit can customize options by reflecting the user's past feedback. For example, the selection unit preferentially presents situations that the user has previously rated highly. The selection unit can also present situations that the user has previously rated poorly, excluding these. The selection unit can also analyze the user's past feedback and customize and present an optimal situation. This makes it possible to suggest situations that match the user's preferences by reflecting the past feedback. Some or all of the above-described processing in the selection unit may be performed using, or without, AI, for example. For example, the selection unit can input the user's past feedback data into a generation AI and cause the generation AI to customize the optimal situation.

[0080] The registration unit can estimate the user's emotions and adjust the wardrobe registration method based on the estimated user emotions. For example, if the user is feeling stressed, the registration unit can provide a simple interface and minimize the registration procedure. Furthermore, if the user is relaxed, the registration unit can provide detailed registration options and suggest a customizable registration method. Furthermore, if the user is in a hurry, the registration unit can prioritize voice input and enable quick wardrobe registration. This allows for a more comfortable wardrobe registration by providing a registration method tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit can be performed using AI, for example, or without AI. For example, the registration unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0081] When registering a wardrobe, the registration unit can analyze the user's past registration history and suggest a registration method. For example, the registration unit can automatically display items that the user has frequently registered in the past as candidates. The registration unit can also prioritize suggesting registration methods (voice, text, etc.) that the user has used in the past. The registration unit can also predict and suggest items that will be used in a specific time period based on the user's past registration history. In this way, by analyzing the past registration history, it is possible to suggest the optimal registration method for the user. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's past registration history data into a generation AI and have the generation AI suggest the optimal registration method.

[0082] When registering a wardrobe, the registration unit can suggest registration content taking into account the user's current fashion trends. The registration unit can suggest registration content based on, for example, the user's currently preferred fashion style. The registration unit can also suggest related items based on items recently purchased by the user. The registration unit can also analyze the user's current fashion trends and suggest optimal registration content. This makes it possible to suggest optimal registration content for the user by taking current fashion trends into consideration. Some or all of the above-mentioned processing in the registration unit can be performed, for example, using AI or without AI. For example, the registration unit can input the user's current fashion trend data into the generation AI and have the generation AI suggest optimal registration content.

[0083] When registering a wardrobe, the registration unit can select the optimal registration means depending on the user's input method. For example, if the user uses voice input, the registration unit registers items using voice recognition technology. Furthermore, if the user uses text input, the registration unit can also register items using text analysis technology. Furthermore, if the user uses image input, the registration unit can also register items using image recognition technology. This allows for more efficient wardrobe registration by providing the optimal registration means depending on the user's input method. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's voice data into a generation AI and have the generation AI perform voice recognition.

[0084] The registration unit can estimate the user's emotions and determine the priority of wardrobe items to be registered based on the estimated user emotions. For example, if the user is feeling stressed, the registration unit can prioritize registering relaxing items. Furthermore, if the user is having fun, the registration unit can prioritize registering active items. Furthermore, if the user is tired, the registration unit can prioritize registering relaxing items. By prioritizing wardrobe items according to the user's emotions, more appropriate items can be prioritized and registered. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit can be performed using, for example, an AI. For example, the registration unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] When registering a wardrobe, the registration unit can prioritize registering highly relevant items by taking into account the user's geographical location information. For example, the registration unit prioritizes registering items suitable for the climate of the user's current location. Furthermore, if the user is in a specific region, the registration unit can prioritize registering items related to that region. Furthermore, if the user is traveling, the registration unit can prioritize registering items to be used at the travel destination. In this way, by taking the geographical location information into consideration, items suitable for the user's current location can be prioritized to be registered. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input the user's geographical location information to the generation AI and cause the generation AI to register optimal items.

[0086] The registration unit can analyze the user's social media activity and register related items when registering a wardrobe. For example, the registration unit registers items related to places where the user has checked in on social media. The registration unit can also analyze the content of the user's social media posts and register related items. The registration unit can also register related items with reference to the activities of the user's friends on social media. In this way, by analyzing social media activity, items based on the user's interests can be registered. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's social media activity data into a generation AI and cause the generation AI to register optimal items.

[0087] The registration unit can customize the registration method by reflecting the user's past feedback when registering a wardrobe. For example, the registration unit preferentially suggests registration methods that the user has previously rated highly. The registration unit can also exclude registration methods that the user has previously rated poorly when suggesting new registration methods. The registration unit can also analyze the user's past feedback and customize and suggest the optimal registration method. This makes it possible to provide the user with the optimal registration method by reflecting the past feedback. Some or all of the above-described processing in the registration unit can be performed using, for example, AI, or can be performed without using AI. For example, the registration unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the optimal registration method.

[0088] The suggestion unit can estimate the user's emotions and adjust the coordination suggestion method based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a relaxing coordination. Furthermore, if the user is having fun, the suggestion unit can suggest an active coordination. Furthermore, if the user is tired, the suggestion unit can suggest a relaxing coordination. This allows for a more appropriate coordination suggestion method to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0089] When proposing a coordination, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the items. For example, the suggestion unit may make suggestions with an emphasis on important items (e.g., jackets and dresses). The suggestion unit may also make concise suggestions for auxiliary items (e.g., accessories and shoes). The suggestion unit can also adjust the level of detail of the suggestion based on the importance of the items. This allows suggestions that focus on items that are important to the user by providing a level of detail of the suggestion based on the importance of the items. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit may input item importance data into a generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0090] When proposing a coordination, the suggestion unit can apply different suggestion algorithms depending on the category of the item. For example, for the clothing category, the suggestion unit can apply a suggestion algorithm that emphasizes harmony in color and design. For the shoe category, the suggestion unit can also apply a suggestion algorithm that emphasizes comfort and functionality. For the accessory category, the suggestion unit can also apply a suggestion algorithm that adds accents to the overall coordination. In this way, by applying a suggestion algorithm depending on the item category, more appropriate coordination can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input item category data into a generation AI and cause the generation AI to apply a suggestion algorithm.

[0091] When proposing an outfit, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit makes suggestions by referring to outfits that the user has previously given high ratings. The suggestion unit can also make suggestions by excluding outfits that the user has previously given low ratings. The suggestion unit can also analyze the user's past suggestion results and suggest optimal outfits. In this way, by referring to the past suggestion results, the optimal outfit for the user can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0092] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows for the length of suggestions to be tailored to the user's emotions, resulting in more appropriate outfit suggestions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0093] When proposing a coordination, the suggestion unit can determine the priority of the suggestions based on the submission date of the items. For example, the suggestion unit can prioritize newly registered items. The suggestion unit can also prioritize seasonal items. The suggestion unit can also prioritize items recently used by the user. This allows for more appropriate coordination to be suggested by providing a priority of suggestions based on the submission date of the items. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input item submission date data into the generation AI and have the generation AI determine the priority of the suggestions.

[0094] When proposing a coordination, the suggestion unit can adjust the order of suggestions based on the relevance of items. For example, the suggestion unit can prioritize suggesting combinations of clothes and shoes. The suggestion unit can also prioritize suggesting combinations of accessories and bags. The suggestion unit can also adjust the order of suggestions based on the relevance of items. This makes it possible to suggest more appropriate coordination by providing an order of suggestions based on the relevance of items. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input item relevance data into a generation AI and cause the generation AI to adjust the order of suggestions.

[0095] When proposing an outfit, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, the suggestion unit may make suggestions that use a lot of technical terminology for a user who is knowledgeable about fashion. The suggestion unit may also make suggestions that use simpler language for a user who is not knowledgeable about fashion. The suggestion unit may also adjust the use of technical terminology in the suggestion according to the user's level of expertise. This allows for more appropriate outfits to be suggested by providing suggested technical terminology that matches the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology.

[0096] The fashion unit can estimate the user's emotions and adjust how the user incorporates the latest fashion based on the estimated user emotions. For example, if the user is feeling stressed, the fashion unit can suggest the latest relaxing fashion. Furthermore, if the user is having fun, the fashion unit can suggest the latest active fashion. Furthermore, if the user is tired, the fashion unit can suggest the latest relaxing fashion. This allows for more appropriate outfit suggestions by providing how to incorporate the latest fashion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the fashion unit may be performed using, for example, AI, or without AI. For example, the fashion unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0097] When incorporating the latest fashion, the fashion department can analyze the user's past fashion history and make optimal suggestions. For example, the fashion department makes suggestions based on fashion styles that the user has previously rated highly. The fashion department can also make suggestions by excluding fashion styles that the user has previously rated poorly. The fashion department can also analyze the user's past fashion history and suggest optimal latest fashions. In this way, by analyzing past fashion history, it is possible to suggest optimal latest fashions to the user. Some or all of the above-mentioned processing in the fashion department may be performed using AI, for example, or may be performed without using AI. For example, the fashion department can input the user's past fashion history data into a generation AI and have the generation AI suggest optimal latest fashions.

[0098] When incorporating the latest fashions, the fashion department can customize the suggestions taking into account the user's current fashion trends. For example, the fashion department can suggest the latest fashions based on the user's currently preferred fashion style. The fashion department can also suggest related latest fashions based on items recently purchased by the user. The fashion department can also analyze the user's current fashion trends and suggest the most suitable latest fashions. This allows the most suitable latest fashions to be suggested to the user by taking current fashion trends into consideration. Some or all of the above-mentioned processing in the fashion department may be performed using AI, for example, or may be performed without using AI. For example, the fashion department can input the user's current fashion trend data into the generation AI and have the generation AI suggest the most suitable latest fashions.

[0099] When incorporating the latest fashion, the fashion department can improve the suggestion method by reflecting user feedback. For example, the fashion department prioritizes the use of suggestion methods that users have previously rated highly. The fashion department can also exclude suggestion methods that users have previously rated poorly. The fashion department can also analyze the user's past feedback and customize and use the optimal suggestion method. This allows the optimal suggestion method to be provided to the user by reflecting past feedback. Some or all of the above-mentioned processing in the fashion department may be performed using AI, for example, or may be performed without using AI. For example, the fashion department can input the user's past feedback data into the generation AI and have the generation AI improve the suggestion method.

[0100] The fashion unit can estimate the user's emotions and prioritize the latest fashions based on the estimated user emotions. For example, if the user is feeling stressed, the fashion unit can prioritize suggesting the latest relaxing fashions. Furthermore, if the user is having fun, the fashion unit can prioritize suggesting the latest active fashions. Furthermore, if the user is tired, the fashion unit can prioritize suggesting the latest relaxing fashions. This allows for more appropriate outfit suggestions by providing the latest fashions prioritized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the fashion unit can be performed using, for example, AI, or without AI. For example, the fashion unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0101] The fashion department can make optimal suggestions when incorporating the latest fashions, taking into account the user's geographical location information. For example, the fashion department can suggest the latest fashions that are suitable for the climate of the user's current location. Furthermore, if the user is in a specific area, the fashion department can suggest the latest fashions related to that area. Furthermore, if the user is traveling, the fashion department can suggest the latest fashions to wear at the travel destination. In this way, by taking geographical location information into consideration, the latest fashions suitable for the user's current location can be suggested. Some or all of the above-described processing in the fashion department may be performed using AI, for example, or may be performed without using AI. For example, the fashion department can input the user's geographical location information into the generation AI and have the generation AI suggest the latest fashions that are optimal for the user.

[0102] When incorporating the latest fashion, the fashion department can customize the suggestions by analyzing the user's social media activity. For example, the fashion department can suggest the latest fashion related to the location where the user checked in on social media. The fashion department can also analyze the content of the user's social media posts to suggest related latest fashion. The fashion department can also suggest related latest fashion based on the activity of the user's friends on social media. In this way, by analyzing social media activity, the latest fashion can be suggested based on the user's interests. Some or all of the above-mentioned processing in the fashion department may be performed using, for example, AI, or may be performed without using AI. For example, the fashion department can input the user's social media activity data into a generation AI and have the generation AI suggest optimal latest fashion.

[0103] When incorporating the latest fashion, the fashion department can customize the suggestion method by reflecting the user's past feedback. For example, the fashion department prioritizes the use of suggestion methods that the user has previously rated highly. The fashion department can also exclude and use suggestion methods that the user has previously rated poorly. The fashion department can also analyze the user's past feedback and customize and use the optimal suggestion method. In this way, by reflecting past feedback, the optimal suggestion method can be provided to the user. Some or all of the above-described processing in the fashion department may be performed using, for example, AI, or may be performed without using AI. For example, the fashion department can input the user's past feedback data into a generation AI and have the generation AI customize the suggestion method.

[0104] The hairstyle unit can estimate the user's emotions and adjust the hairstyle suggestion method based on the estimated user's emotions. For example, if the user is feeling stressed, the hairstyle unit can suggest a relaxing hairstyle. Furthermore, if the user is having fun, the hairstyle unit can also suggest an active hairstyle. Furthermore, if the user is tired, the hairstyle unit can also suggest a relaxing hairstyle. This provides a hairstyle suggestion method according to the user's emotions, thereby suggesting a more appropriate hairstyle. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the hairstyle unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the hairstyle unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0105] When suggesting a hairstyle, the hairstyle unit can analyze the user's past hairstyle history and make the optimal suggestion. For example, the hairstyle unit makes the suggestion by referring to hairstyles that the user has previously given a high rating. The hairstyle unit can also make suggestions by excluding hairstyles that the user has previously given a low rating. The hairstyle unit can also analyze the user's past hairstyle history and suggest the optimal hairstyle. In this way, by analyzing the past hairstyle history, the optimal hairstyle can be suggested to the user. Some or all of the above-mentioned processing in the hairstyle unit may be performed using, for example, AI, or may be performed without using AI. For example, the hairstyle unit can input the user's past hairstyle history data into the generation AI and cause the generation AI to suggest the optimal hairstyle.

[0106] When suggesting a hairstyle, the hairstyle unit can customize the suggestion content by taking into account the user's current hairstyle trends. For example, the hairstyle unit can suggest an optimal hairstyle based on the user's currently preferred hairstyle. The hairstyle unit can also suggest a related hairstyle based on a hairstyle the user has recently tried. The hairstyle unit can also analyze the user's current hairstyle trends and suggest an optimal hairstyle. This makes it possible to suggest an optimal hairstyle for the user by taking the current hairstyle trends into consideration. Some or all of the above-described processing in the hairstyle unit may be performed using AI, for example, or may be performed without using AI. For example, the hairstyle unit can input the user's current hairstyle trend data into the generation AI and cause the generation AI to suggest an optimal hairstyle.

[0107] The hairstyle unit can improve the suggestion method by reflecting user feedback when suggesting a hairstyle. For example, the hairstyle unit preferentially uses suggestion methods that the user has previously rated highly. The hairstyle unit can also exclude suggestion methods that the user has previously rated poorly. The hairstyle unit can also analyze the user's past feedback and customize and use the optimal suggestion method. In this way, the optimal suggestion method can be provided to the user by reflecting the past feedback. Some or all of the above-described processing in the hairstyle unit may be performed using AI, for example, or may be performed without using AI. For example, the hairstyle unit can input the user's past feedback data into the generation AI and cause the generation AI to improve the suggestion method.

[0108] The hairstyle unit can estimate the user's emotions and prioritize hairstyles based on the estimated user emotions. For example, if the user is feeling stressed, the hairstyle unit can prioritize suggesting hairstyles that are relaxing. Furthermore, if the user is having fun, the hairstyle unit can prioritize suggesting hairstyles that are active. Furthermore, if the user is tired, the hairstyle unit can prioritize suggesting hairstyles that are relaxing. This allows for a more appropriate hairstyle to be suggested by providing a hairstyle priority order according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the hairstyle unit can be performed using, for example, AI, or without AI. For example, the hairstyle unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0109] When suggesting a hairstyle, the hairstyle unit can make the optimal suggestion by taking into account the user's geographical location information. For example, the hairstyle unit can suggest a hairstyle that is suitable for the climate of the user's current location. Furthermore, if the user is in a specific region, the hairstyle unit can also suggest a hairstyle related to that region. Furthermore, if the user is traveling, the hairstyle unit can also suggest a hairstyle to be worn at the travel destination. In this way, by taking the geographical location information into consideration, a hairstyle that is suitable for the user's current location can be suggested. Some or all of the above-described processing in the hairstyle unit may be performed using, for example, AI, or may be performed without using AI. For example, the hairstyle unit can input the user's geographical location information to the generation AI and cause the generation AI to suggest the optimal hairstyle.

[0110] When suggesting a hairstyle, the hairstyle unit can analyze the user's social media activity to customize the suggestion. For example, the hairstyle unit can suggest hairstyles related to places where the user has checked in on social media. The hairstyle unit can also analyze the content of the user's social media posts to suggest related hairstyles. The hairstyle unit can also suggest related hairstyles based on the activity of the user's friends on social media. In this way, by analyzing social media activity, hairstyles can be suggested based on the user's interests. Some or all of the above-mentioned processing in the hairstyle unit may be performed using, for example, AI, or may be performed without using AI. For example, the hairstyle unit can input the user's social media activity data into the generation AI and have the generation AI suggest the optimal hairstyle.

[0111] When suggesting a hairstyle, the hairstyle unit can customize the suggestion method by reflecting the user's past feedback. For example, the hairstyle unit preferentially uses suggestion methods that the user has previously rated highly. The hairstyle unit can also exclude suggestion methods that the user has previously rated poorly. The hairstyle unit can also analyze the user's past feedback and customize and use the optimal suggestion method. In this way, the optimal suggestion method can be provided to the user by reflecting the past feedback. Some or all of the above-described processing in the hairstyle unit may be performed using, for example, AI, or may be performed without using AI. For example, the hairstyle unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the suggestion method. === Hard Collateral 1-1 === Each of the multiple elements including the selection unit, registration unit, suggestion unit, fashion unit, and hairstyle 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 selection unit is realized by the control unit 46A of the smart device 14 and accepts input of the user's situation. The registration unit is realized by the control unit 46A of the smart device 14 and registers the user's wardrobe using photos and text. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal outfits based on the situation and wardrobe. The fashion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests outfits incorporating the latest fashions. The hairstyle unit is realized by the specific processing unit 290 of the data processing device 12 and suggests hairstyles suited to the situation. === Hard Collateral 1-2 === Each of the multiple elements including the selection unit, registration unit, suggestion unit, fashion unit, and hairstyle unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart glasses 214 and accepts input of the user's situation. The registration unit is realized by the control unit 46A of the smart glasses 214 and registers the user's wardrobe using photos and text. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal outfits based on the situation and wardrobe. The fashion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests outfits incorporating the latest fashions. The hairstyle unit is realized by the specific processing unit 290 of the data processing device 12 and suggests hairstyles suited to the situation. === Hard Collateral 1-3 === Each of the multiple elements including the selection unit, registration unit, suggestion unit, fashion unit, and hairstyle 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 selection unit is realized by the control unit 46A of the headset type terminal 314 and accepts input of the user's situation. The registration unit is realized by the control unit 46A of the headset type terminal 314 and registers the user's wardrobe using photos and text. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal outfits based on the situation and wardrobe. The fashion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests outfits incorporating the latest fashions. The hairstyle unit is realized by the specific processing unit 290 of the data processing device 12 and suggests hairstyles suited to the situation. === Hard Collateral 1-4 === Each of the multiple elements including the selection unit, registration unit, suggestion unit, fashion unit, and hairstyle unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414 and accepts input of the user's situation. The registration unit is realized by the control unit 46A of the robot 414 and registers the user's wardrobe using photos and text. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal outfits based on the situation and wardrobe. The fashion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests outfits incorporating the latest fashions. The hairstyle unit is realized by the specific processing unit 290 of the data processing device 12 and suggests hairstyles suited to the situation.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The outfit suggestion system can further include a health management unit that acquires the user's health data and reflects it in the suggestions. For example, it can suggest outfits that support a healthy lifestyle based on data such as the user's number of steps and heart rate. If the user is not getting enough exercise, it can suggest outfits suitable for active situations, and conversely, if the user appears tired, it can suggest outfits that are relaxing. The health management unit can also acquire the user's dietary data and make suggestions that take nutritional balance into consideration. This makes it possible to provide the optimal outfits according to the user's health condition.

[0114] The selection unit can estimate the user's emotions and present situation options based on the estimated emotions. For example, if the user is feeling stressed, the selection unit can prioritize presenting relaxing situations (e.g., relaxing at a cafe). Furthermore, if the user is having fun, the selection unit can prioritize presenting active situations (e.g., outdoor activities). Furthermore, if the user is tired, the selection unit can prioritize presenting relaxing situations (e.g., relaxing at home). By presenting situation options according to the user's emotions, more appropriate situations can be suggested. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the selection unit may be performed using AI, or may be performed without AI. For example, the selection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0115] The outfit suggestion system can further include a hobby reflection unit that makes suggestions that reflect the user's hobbies and interests. For example, if the user likes music, the system can suggest outfits suitable for music events. If the user likes sports, the system can suggest outfits suitable for watching sports or other activities. Furthermore, the hobby reflection unit can also suggest outfits that match the user's indoor hobbies, such as reading or watching movies. This makes it possible to provide optimal outfits that match the user's hobbies and interests.

[0116] The outfit suggestion system can further include a purchase history analysis unit that analyzes the user's past purchase history and reflects the results in the suggestions. For example, it can suggest related outfits based on items the user has purchased in the past. If the user has a preference for a particular brand or style, the purchase history analysis unit can also suggest outfits that match that brand or style. Furthermore, the purchase history analysis unit can also suggest optimal outfits by taking into account the frequency with which the user has used items purchased in the past. This makes it possible to provide optimal outfits based on the user's purchase history.

[0117] The outfit suggestion system can further include a lifestyle reflection unit that acquires lifestyle data of the user and reflects it in the suggestions. For example, if the user is an office worker, a business casual outfit can be suggested. If the user is a freelancer, a casual outfit can be suggested. Furthermore, if the user is an outdoorsy person, the lifestyle reflection unit can suggest an outfit suitable for outdoor activities. This makes it possible to provide the optimal outfit that suits the user's lifestyle.

[0118] The selection unit can estimate the user's emotions and prioritize situations based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can prioritize presenting relaxing situations. Furthermore, if the user is having fun, the selection unit can prioritize active situations. Furthermore, if the user is tired, the selection unit can prioritize presenting relaxing situations. This allows more appropriate situations to be proposed by prioritizing situations according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit can be performed using, for example, an AI, or without an AI. For example, the selection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0119] The outfit suggestion system may further include a social media analysis unit that analyzes the user's social media activity and reflects the results in the suggestions. For example, the system may suggest outfits related to places where the user has checked in on social media. The social media analysis unit may also analyze the content of the user's social media posts and suggest related outfits. The social media analysis unit may also suggest related outfits based on the activities of the user's friends on social media. In this way, by analyzing social media activity, the system may provide optimal outfits based on the user's interests.

[0120] The coordination suggestion system can further include a feedback reflection unit that reflects user feedback to improve the suggestion content. For example, it can preferentially use suggestion methods that users have previously rated highly. The feedback reflection unit can also exclude suggestion methods that users have previously rated poorly. Furthermore, the feedback reflection unit can analyze the user's past feedback and customize and use the optimal suggestion method. In this way, it is possible to provide the user with the optimal suggestion method by reflecting past feedback.

[0121] The outfit suggestion system can further include a specialized knowledge reflection unit that adjusts the content of suggestions according to the user's level of specialized knowledge. For example, suggestions that use a lot of specialized terminology are made to a user who is knowledgeable about fashion. The specialized knowledge reflection unit can also make suggestions that are explained in simple language to a user who is not knowledgeable about fashion. Furthermore, the specialized knowledge reflection unit can also adjust the use of specialized terminology in the suggestions according to the user's level of specialized knowledge. This makes it possible to provide optimal suggestions according to the user's level of specialized knowledge.

[0122] The registration unit can estimate the user's emotions and adjust the wardrobe registration method based on the estimated user emotions. For example, if the user is feeling stressed, the registration unit can provide a simple interface and minimize the registration procedure. Furthermore, if the user is relaxed, the registration unit can provide detailed registration options and suggest a customizable registration method. Furthermore, if the user is in a hurry, the registration unit can prioritize voice input and enable quick wardrobe registration. This allows for a more comfortable wardrobe registration by providing a registration method tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit can be performed using AI, for example, or without AI. For example, the registration unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The selection unit accepts a situation input from the user. For example, the user can select a situation such as work, a date, or outdoors. The selection unit can also estimate the user's emotions and present situation options based on the estimated emotions. Step 2: The registration unit registers the user's wardrobe using photos and text. For example, the user can register their clothes, shoes, bags, accessories, etc. using photos and text. The registration unit can also estimate the user's emotions and adjust the wardrobe registration method based on the estimated emotions. Step 3: The suggestion module suggests the best outfit based on the situation and wardrobe. For example, it might suggest business casual attire for work, and something a little more glamorous for a date. The suggestion module can also estimate the user's emotions and adjust the way it suggests outfits based on the estimated emotions. Step 4: The fashion department suggests outfits incorporating the latest fashions. For example, they suggest outfits incorporating this season's trend colors and designs. The fashion department can also estimate the user's emotions and adjust how the latest fashions are incorporated based on the estimated emotions. Step 5: The hairstyle section suggests hairstyles that suit the situation. For example, it might suggest a formal hairstyle for work, or a more glamorous hairstyle for a date. The hairstyle section can also estimate the user's emotions and adjust the hairstyle suggestion method based on the estimated emotions.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a selection unit that accepts input of a situation; a registration unit that registers a wardrobe of a user based on the situation accepted by the selection unit; a suggestion unit that suggests coordination based on the wardrobe registered by the registration unit; a fashion department that incorporates the latest fashions into the coordination suggested by the suggestion department; a hairstyle section that suggests a hairstyle based on the coordination suggested by the fashion section. A system characterized by:

2. The selection unit Estimate the user's emotions and present situation options based on the estimated user emotions.

2. The system of claim 1.

3. The selection unit Analyzes the user's past situation selection history and predicts and presents the situation to the user.

2. The system of claim 1.

4. The selection unit When selecting a situation, the system refers to the user's calendar information and suggests appropriate situations.

2. The system of claim 1.

5. The selection unit When selecting a situation, the system presents options taking into account the user's current weather information.

2. The system of claim 1.

6. The selection unit Estimate the user's emotions and prioritize situations based on the estimated user emotions.

2. The system of claim 1.

7. The selection unit When selecting a situation, the system takes into account the user's geographical location information and prioritizes the presentation of highly relevant situations.

2. The system of claim 1.

8. The selection unit When selecting a situation, the system analyzes the user's social media activity and presents relevant situations.

2. The system of claim 1.

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