System

Abstract

An object of a system according to an embodiment is to propose coordination in accordance with the latest fashion trend by utilizing clothes possessed by a user.SOLUTION: A system includes a clothing database creation part, a trend analysis part, and a coordination proposal part. The clothing database creation unit creates a database of the user's own clothing. A trend analysis part analyzes the latest fashion trend and weather information on the basis of the clothing information made into the database by the clothing database making part. The coordination proposal unit proposes coordination based on the information analyzed by the trend analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for users to coordinate outfits that match the latest fashion trends using the clothes they already own.

[0005] The system according to the embodiment aims to propose outfits that match the latest fashion trends by utilizing the clothes that the user already owns. [Means for solving the problem]

[0006] The system according to the embodiment includes a clothing database unit, a trend analysis unit, and a coordination suggestion unit. The clothing database unit creates a database of the user's clothing. The trend analysis unit analyzes the latest fashion trends and weather information based on the clothing information created in the database by the clothing database unit. The coordination suggestion unit suggests coordination based on the information analyzed by the trend analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest outfits that match the latest fashion trends by utilizing the clothes that the user already owns. [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 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 coordination suggestion system according to an embodiment of the present invention is a system that makes the most of the clothes a user owns, and uses a generation AI to suggest coordinations that take into account the latest fashion trends and weather. As a result, the coordination suggestion system allows users to enjoy the latest fashions with the clothes they own, and also contributes to curbing mass production, mass consumption, and mass waste.

[0029] The outfit suggestion system according to the embodiment includes a clothing database creation unit, a trend analysis unit, and an outfit suggestion unit. The clothing database creation unit creates a database of a user's clothing. For example, the user takes photos of the clothing and uploads them to the app. The generation AI analyzes these photos and registers information such as the type, color, material, and design of the clothing in the database. For example, information such as a white shirt, black pants, and a red skirt owned by the user is registered. The trend analysis unit analyzes the latest fashion trends and weather information based on the clothing information databased by the clothing database creation unit. For example, the generation AI collects and analyzes information from fashion magazines, websites, and weather forecast sites. The generation AI receives inputs from prompts containing information from fashion magazines, websites, and weather forecast sites, and performs analysis based on the prompts. The outfit suggestion unit proposes outfits based on the information analyzed by the trend analysis unit. For example, the generation AI proposes optimal outfits for the user based on the clothing database, the latest trends, and weather information. The generation AI receives inputs from prompts containing information such as the user's destination and time of outing, and generates outfits based on the prompts. As a result, the coordination suggestion system according to the embodiment allows the user to make the most of the clothes they already own and enjoy the latest fashion trends.

[0030] The clothing database creation unit can analyze clothing from a 360-degree perspective using not only photos of the clothing but also videos, allowing for the creation of a more detailed database. For example, the clothing database creation unit allows users to not only take photos of clothing, but also shoot and upload videos. The generation AI analyzes these videos and grasps the overall image of the clothing from a 360-degree perspective. This allows the details and shapes of the clothing to be registered in the database more accurately. This allows the details and shapes of the clothing to be registered in the database more accurately.

[0031] The clothing database section can record how often clothes are used and how many times they are washed, and evaluate their degree of deterioration. For example, the clothing database section can add a function to the app that records each time a user wears clothes. Based on this data, the generation AI can analyze how often clothes are used and evaluate their degree of deterioration. This allows it to evaluate the degree of deterioration of the clothes and warn the user.

[0032] The clothing database unit can register accessories, shoes, and other small items in the clothing database, and suggest total outfits. For example, the clothing database unit takes photos of the accessories and shoes the user owns and uploads them to the app. The generation AI analyzes these photos and registers them in the database in the same way as the clothing. This makes it possible to create total outfits that combine all of the items the user owns.

[0033] The clothing database creation unit can add a function that allows users to share the clothing database with family and friends and enjoy coordinating outfits together. For example, the clothing database creation unit can add a function that allows users to share the clothing database with family and friends. For example, a sharing link can be generated within the app to share the database with other users. This allows users to enjoy coordinating outfits together with family and friends.

[0034] The trend analysis unit analyzes fashion shows and celebrity social media posts in real time, allowing it to instantly reflect the latest trends. For example, the trend analysis unit analyzes videos and images from fashion shows in real time and registers the latest trend information in a database. The generation AI uses this data to instantly reflect the latest trends. This allows it to instantly reflect the latest fashion trends.

[0035] The trend analysis unit analyzes not only weather information, but also pollen and UV information, and can suggest outfits that take health into consideration. For example, the trend analysis unit collects pollen and UV information in addition to weather information, and the generation AI analyzes it. For example, on days with high pollen counts, it will suggest outfits that take pollen prevention measures into consideration. This makes it possible to suggest outfits that take health into consideration.

[0036] The trend analysis unit can suggest outfits for travel destinations based on the latest trends and weather information. For example, the generation AI of the trend analysis unit suggests outfits based on weather information and the latest trends at the travel destination. For example, it suggests clothing that matches the temperature and weather at the travel destination. This makes it possible to suggest the best outfits for travel destinations.

[0037] The trend analysis unit reflects the user's past purchase history and preferences in the trend analysis, enabling more personalized suggestions. For example, the trend analysis unit analyzes the user's past purchase history, and the generation AI reflects this in the trend analysis. For example, it may suggest outfits that combine previously purchased items with the latest trends. This makes it possible to make personalized suggestions based on the user's past purchase history and preferences.

[0038] The outfit suggestion unit can suggest the most suitable outfits for the user, taking into consideration their body type and skin color. For example, the outfit suggestion unit collects the user's body type data, and the generation AI suggests the most suitable outfits based on that data. For example, it suggests silhouettes and designs that match the user's body type. This makes it possible to suggest the most suitable outfits based on the user's body type and skin color.

[0039] The coordination suggestion unit can propose evolved styles by reflecting the user's past fashion history. For example, the coordination suggestion unit analyzes the user's past fashion history, and the generation AI proposes evolved styles based on that data. For example, it proposes new styles based on past coordinations. This makes it possible to propose evolved styles based on the user's past fashion history.

[0040] The coordination suggestion unit can add a function that incorporates the opinions of the user's friends and family. For example, the coordination suggestion unit adds a function that allows the user to share coordination suggestions with friends and family and collect their opinions. For example, the app can generate a shared link and receive feedback from other users. This makes it possible to make coordination suggestions that reflect the opinions of the user's friends and family.

[0041] The seasonal change suggestion unit can suggest fashion items specialized for seasonal changes. The seasonal change suggestion unit adds, for example, a function to suggest fashion items specialized for seasonal changes. For example, in early spring, it suggests light jackets and layered items. This makes it possible to suggest fashion items specialized for seasonal changes.

[0042] The seasonal change suggestion unit can suggest items that are easy to put on and take off to cope with sudden changes in temperature at the change of seasons. The seasonal change suggestion unit adds a function to suggest items that are easy to put on and take off to cope with sudden changes in temperature at the change of seasons, for example. For example, cardigans and hoodies are suggested. This makes it possible to suggest items that are easy to put on and take off to cope with sudden changes in temperature.

[0043] The seasonal change suggestion unit can suggest coordination that matches a specific event at the change of seasons. The seasonal change suggestion unit adds a function to suggest coordination that matches a specific event at the change of seasons, for example. For example, it suggests outfits that are suitable for cherry blossom viewing or autumn leaf viewing. This makes it possible to suggest coordination that matches a specific event at the change of seasons.

[0044] The seasonal change suggestion unit can suggest coordination that takes into consideration health aspects at the time of the change of seasons. The seasonal change suggestion unit adds, for example, a function to suggest coordination that takes into consideration health aspects at the time of the change of seasons. For example, it suggests warm clothes to prevent colds and masks to prevent hay fever. This makes it possible to suggest coordination that takes into consideration health aspects at the time of the change of seasons.

[0045] The destination and time of going out consideration section analyzes the geographic information of the destination and can suggest optimal clothing based on the terrain and mode of transportation. For example, the destination and time of going out consideration section collects geographic information of the destination, and the generation AI suggests optimal clothing based on that data. For example, in mountainous areas, it suggests cold weather gear and hiking boots. This makes it possible to suggest optimal clothing based on the geographic information of the destination and mode of transportation.

[0046] The destination and time of going out consideration unit can propose an outfit that takes into account the temperature difference between day and night depending on the time of going out. The destination and time of going out consideration unit adds a function to propose an outfit that takes into account the temperature difference between day and night depending on, for example, the time of going out. For example, it suggests light clothing during the day and warm clothing at night. This makes it possible to propose an outfit that takes into account the temperature difference between day and night.

[0047] The destination and time of going out consideration section can take into account the culture and customs of the destination and suggest outfits that are appropriate for that area. For example, the destination and time of going out consideration section collects the culture and customs of the destination, and the generation AI suggests optimal outfits based on that data. For example, it suggests outfits that match the dress code of a specific country or region. This makes it possible to suggest outfits based on the culture and customs of the destination.

[0048] The destination and time spent outside the home can suggest outfits that match the activity at the home. The destination and time spent outside the home consideration unit adds a function to suggest outfits that match the activity at the home. For example, it suggests easy-to-move-in clothes for hiking and stylish clothes for shopping. This makes it possible to suggest outfits that match the activity at the home.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The outfit suggestion system can also make suggestions that take into account the user's health condition. For example, if the user enters their health information into the app, the generating AI can use that information to suggest outfits that suit their physical condition. For example, if you have a cold, it can suggest warm clothes and a mask. Also, if you have allergies, it can suggest clothes that avoid allergens. This makes it possible to suggest optimal outfits based on the user's health condition.

[0051] The outfit suggestion system can also make suggestions based on the user's lifestyle. For example, if the user inputs their daily activities and hobbies into the app, the generation AI can use that information to suggest outfits that fit their lifestyle. For example, it can suggest easy-to-move-in clothes for a user who plays sports, and business casual clothes for a user who does a lot of office work. It can also suggest waterproof clothes for a user who likes the outdoors. This makes it possible to suggest optimal outfits based on the user's lifestyle.

[0052] The outfit suggestion system can also suggest evolved styles based on the user's past outfit history. For example, if the user records past outfits in the app, the generation AI can analyze that data and make new suggestions based on past styles. For example, it can suggest new styles that incorporate the latest trends based on outfits that were popular in the past. It can also suggest outfits that avoid outfits that were unpopular in the past. This makes it possible to suggest evolved styles based on the user's past outfit history.

[0053] The outfit suggestion system can also add a function that incorporates the opinions of the user's friends and family. For example, a function could be added that allows the user to share outfit suggestions with friends and family and collect their opinions. For example, a shared link could be generated within the app to receive feedback from other users. This would enable outfit suggestions that reflect the opinions of the user's friends and family. For example, based on a friend's opinion that "this color looks good on you," the generation AI could suggest an outfit that incorporates that color. Also, based on a family member's opinion that "this outfit should be avoided," the generation AI could suggest an outfit that avoids that outfit. This would enable optimal outfit suggestions that reflect the opinions of the user's friends and family.

[0054] The outfit suggestion system can also consider the user's body type and skin color to suggest the most suitable outfit. For example, the generation AI can collect data on the user's body type and suggest the most suitable outfit based on that data. For example, it can suggest silhouettes and designs that suit the body type. It can also suggest colors that match the skin color. For example, it can suggest brightly colored outfits to users with light skin, and suggest subdued colors to users with dark skin. This makes it possible to suggest the most suitable outfit based on the user's body type and skin color.

[0055] The outfit suggestion system can also make suggestions based on the user's location and time of day. For example, the generation AI can collect geographical information about the location and use that data to suggest optimal outfits. For example, it can suggest cold weather gear and hiking boots in mountainous areas, and casual clothing in urban areas. It can also suggest outfits that take into account the temperature difference between day and night depending on the time of day. For example, it can suggest light clothing during the day and cold weather gear at night. This makes it possible to suggest optimal outfits based on the geographical information of the location and the time of day.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The clothing database creation unit creates a database of the user's clothing. For example, the user takes photos of the clothing and uploads them to the app. The generation AI analyzes these photos and registers information such as the type of clothing, color, material, and design in the database. For example, it registers information such as the white shirt, black pants, and red skirt that the user owns. Step 2: The trend analysis unit analyzes the latest fashion trends and weather information based on the clothing information databased by the clothing database unit. For example, the generation AI collects and analyzes information from fashion magazines, websites, and weather forecast sites. The input to the generation AI is a prompt containing information from fashion magazines, websites, and weather forecast sites, and the generation AI performs analysis based on the prompt. Step 3: The outfit suggestion unit proposes outfits based on the information analyzed by the trend analysis unit. For example, the generation AI proposes the optimal outfit for the user based on its clothing database, the latest trends, and weather information. The input to the generation AI is a prompt that includes information such as the user's destination and time of going out, and the generation AI generates outfits based on the prompt.

[0058] (Example 2) The coordination suggestion system according to an embodiment of the present invention is a system that makes the most of the clothes a user owns, and uses a generation AI to suggest coordinations that take into account the latest fashion trends and weather. As a result, the coordination suggestion system allows users to enjoy the latest fashions with the clothes they own, and also contributes to curbing mass production, mass consumption, and mass waste.

[0059] The outfit suggestion system according to the embodiment includes a clothing database creation unit, a trend analysis unit, and an outfit suggestion unit. The clothing database creation unit creates a database of a user's clothing. For example, the user takes photos of the clothing and uploads them to the app. The generation AI analyzes these photos and registers information such as the type, color, material, and design of the clothing in the database. For example, information such as a white shirt, black pants, and a red skirt owned by the user is registered. The trend analysis unit analyzes the latest fashion trends and weather information based on the clothing information databased by the clothing database creation unit. For example, the generation AI collects and analyzes information from fashion magazines, websites, and weather forecast sites. The generation AI receives inputs from prompts containing information from fashion magazines, websites, and weather forecast sites, and performs analysis based on the prompts. The outfit suggestion unit proposes outfits based on the information analyzed by the trend analysis unit. For example, the generation AI proposes optimal outfits for the user based on the clothing database, the latest trends, and weather information. The generation AI receives inputs from prompts containing information such as the user's destination and time of outing, and generates outfits based on the prompts. As a result, the coordination suggestion system according to the embodiment allows the user to make the most of the clothes they already own and enjoy the latest fashion trends.

[0060] The clothing database creation unit can analyze clothing from a 360-degree perspective using not only photos of the clothing but also videos, allowing for the creation of a more detailed database. For example, the clothing database creation unit allows users to not only take photos of clothing, but also shoot and upload videos. The generation AI analyzes these videos and grasps the overall image of the clothing from a 360-degree perspective. This allows the details and shapes of the clothing to be registered in the database more accurately. This allows the details and shapes of the clothing to be registered in the database more accurately.

[0061] The clothing database section can record how often clothes are used and how many times they are washed, and evaluate their degree of deterioration. For example, the clothing database section can add a function to the app that records each time a user wears clothes. Based on this data, the generation AI can analyze how often clothes are used and evaluate their degree of deterioration. This allows it to evaluate the degree of deterioration of the clothes and warn the user.

[0062] The clothing database unit uses the emotion estimation function to analyze the emotions a user has toward specific clothing items and can recommend clothing items based on those emotions. For example, the clothing database unit adds a function to record the emotions a user feels when wearing clothing items. Based on this data, the generation AI analyzes the emotions a user has toward specific clothing items and registers them in the database. This makes it possible to recommend clothing items based on the user's emotions.

[0063] The clothing database unit can register accessories, shoes, and other small items in the clothing database, and suggest total outfits. For example, the clothing database unit takes photos of the accessories and shoes the user owns and uploads them to the app. The generation AI analyzes these photos and registers them in the database in the same way as the clothing. This makes it possible to create total outfits that combine all of the items the user owns.

[0064] The clothing database creation unit can add a function that allows users to share the clothing database with family and friends and enjoy coordinating outfits together. For example, the clothing database creation unit can add a function that allows users to share the clothing database with family and friends. For example, a sharing link can be generated within the app to share the database with other users. This allows users to enjoy coordinating outfits together with family and friends.

[0065] The clothing database unit uses the emotion estimation function to record the emotions felt when the user wears specific clothing, and can suggest the next outfit based on those emotions. For example, the clothing database unit adds a function to record the emotions felt when the user wears clothing. Based on this data, the generation AI analyzes the emotions felt when the user wears specific clothing and registers them in the database. This makes it possible to suggest the next outfit based on the user's emotions.

[0066] The trend analysis unit analyzes fashion shows and celebrity social media posts in real time, allowing it to instantly reflect the latest trends. For example, the trend analysis unit analyzes videos and images from fashion shows in real time and registers the latest trend information in a database. The generation AI uses this data to instantly reflect the latest trends. This allows it to instantly reflect the latest fashion trends.

[0067] The trend analysis unit analyzes not only weather information, but also pollen and UV information, and can suggest outfits that take health into consideration. For example, the trend analysis unit collects pollen and UV information in addition to weather information, and the generation AI analyzes it. For example, on days with high pollen counts, it will suggest outfits that take pollen prevention measures into consideration. This makes it possible to suggest outfits that take health into consideration.

[0068] The trend analysis unit uses the emotion estimation function to analyze the emotions that users have toward specific weather conditions or trends, and can perform trend analysis based on those emotions. For example, the trend analysis unit adds a function to record the emotions that users have toward specific weather conditions or trends. Based on this data, the generation AI analyzes the user's emotions and reflects them in the trend analysis. This makes it possible to perform trend analysis based on the user's emotions.

[0069] The trend analysis unit can suggest outfits for travel destinations based on the latest trends and weather information. For example, the generation AI of the trend analysis unit suggests outfits based on weather information and the latest trends at the travel destination. For example, it suggests clothing that matches the temperature and weather at the travel destination. This makes it possible to suggest the best outfits for travel destinations.

[0070] The trend analysis unit reflects the user's past purchase history and preferences in the trend analysis, enabling more personalized suggestions. For example, the trend analysis unit analyzes the user's past purchase history, and the generation AI reflects this in the trend analysis. For example, it may suggest outfits that combine previously purchased items with the latest trends. This makes it possible to make personalized suggestions based on the user's past purchase history and preferences.

[0071] The trend analysis unit uses the emotion estimation function to analyze the emotions that users have toward specific trends or weather, and can make trend suggestions based on those emotions. For example, the trend analysis unit adds a function to record the emotions that users have toward specific trends or weather. Based on this data, the generation AI analyzes the user's emotions and reflects them in trend suggestions. This makes it possible to make trend suggestions based on the user's emotions.

[0072] The outfit suggestion unit can suggest the most suitable outfits for the user, taking into consideration their body type and skin color. For example, the outfit suggestion unit collects the user's body type data, and the generation AI suggests the most suitable outfits based on that data. For example, it suggests silhouettes and designs that match the user's body type. This makes it possible to suggest the most suitable outfits based on the user's body type and skin color.

[0073] The coordination suggestion unit can propose evolved styles by reflecting the user's past fashion history. For example, the coordination suggestion unit analyzes the user's past fashion history, and the generation AI proposes evolved styles based on that data. For example, it proposes new styles based on past coordinations. This makes it possible to propose evolved styles based on the user's past fashion history.

[0074] The coordination suggestion unit can use the emotion estimation function to analyze the emotions a user has toward a specific coordination and suggest coordinations based on those emotions. For example, the coordination suggestion unit adds a function to record the emotions a user has toward a specific coordination. Based on this data, the generation AI analyzes the user's emotions and reflects them in the coordination suggestions. This makes it possible to suggest coordinations based on the user's emotions.

[0075] The coordination suggestion unit can add a function that incorporates the opinions of the user's friends and family. For example, the coordination suggestion unit adds a function that allows the user to share coordination suggestions with friends and family and collect their opinions. For example, the app can generate a shared link and receive feedback from other users. This makes it possible to make coordination suggestions that reflect the opinions of the user's friends and family.

[0076] The coordination suggestion unit can use the emotion estimation function to analyze the emotions a user has toward a specific coordination and suggest coordinations based on those emotions. For example, the coordination suggestion unit adds a function to record the emotions a user has toward a specific coordination. Based on this data, the generation AI analyzes the user's emotions and reflects them in the coordination suggestions. This makes it possible to suggest coordinations based on the user's emotions.

[0077] The seasonal change suggestion unit can suggest fashion items specialized for seasonal changes. The seasonal change suggestion unit adds, for example, a function to suggest fashion items specialized for seasonal changes. For example, in early spring, it suggests light jackets and layered items. This makes it possible to suggest fashion items specialized for seasonal changes.

[0078] The seasonal change suggestion unit can suggest items that are easy to put on and take off to cope with sudden changes in temperature at the change of seasons. The seasonal change suggestion unit adds a function to suggest items that are easy to put on and take off to cope with sudden changes in temperature at the change of seasons, for example. For example, cardigans and hoodies are suggested. This makes it possible to suggest items that are easy to put on and take off to cope with sudden changes in temperature.

[0079] The seasonal change suggestion unit uses an emotion estimation function to analyze the user's emotions regarding seasonal changes and can make suggestions based on those emotions. For example, the seasonal change suggestion unit adds a function to record the user's emotions regarding seasonal changes. Based on this data, the generation AI analyzes the user's emotions and reflects them in the suggestions. This makes it possible to make suggestions regarding seasonal changes based on the user's emotions.

[0080] The seasonal change suggestion unit can suggest coordination that matches a specific event at the change of seasons. The seasonal change suggestion unit adds a function to suggest coordination that matches a specific event at the change of seasons, for example. For example, it suggests outfits that are suitable for cherry blossom viewing or autumn leaf viewing. This makes it possible to suggest coordination that matches a specific event at the change of seasons.

[0081] The seasonal change suggestion unit can suggest coordination that takes into consideration health aspects at the time of the change of seasons. The seasonal change suggestion unit adds, for example, a function to suggest coordination that takes into consideration health aspects at the time of the change of seasons. For example, it suggests warm clothes to prevent colds and masks to prevent hay fever. This makes it possible to suggest coordination that takes into consideration health aspects at the time of the change of seasons.

[0082] The seasonal change suggestion unit uses an emotion estimation function to analyze the user's emotions regarding seasonal changes and can make suggestions based on those emotions. For example, the seasonal change suggestion unit adds a function to record the user's emotions regarding seasonal changes. Based on this data, the generation AI analyzes the user's emotions and reflects them in the suggestions. This makes it possible to make suggestions regarding seasonal changes based on the user's emotions.

[0083] The destination and time of going out consideration section analyzes the geographic information of the destination and can suggest optimal clothing based on the terrain and mode of transportation. For example, the destination and time of going out consideration section collects geographic information of the destination, and the generation AI suggests optimal clothing based on that data. For example, in mountainous areas, it suggests cold weather gear and hiking boots. This makes it possible to suggest optimal clothing based on the geographic information of the destination and mode of transportation.

[0084] The destination and time of going out consideration unit can propose an outfit that takes into account the temperature difference between day and night depending on the time of going out. The destination and time of going out consideration unit adds a function to propose an outfit that takes into account the temperature difference between day and night depending on, for example, the time of going out. For example, it suggests light clothing during the day and warm clothing at night. This makes it possible to propose an outfit that takes into account the temperature difference between day and night.

[0085] The destination and time of going out consideration unit uses the emotion estimation function to analyze the emotions the user has regarding a specific destination or time of going out, and can make suggestions based on those emotions. For example, the destination and time of going out consideration unit adds a function to record the emotions the user has regarding a specific destination or time of going out. The generation AI uses this data to analyze the user's emotions and reflects them in the suggestions. This makes it possible to suggest destinations and times of going out based on the user's emotions.

[0086] The destination and time of going out consideration section can take into account the culture and customs of the destination and suggest outfits that are appropriate for that area. For example, the destination and time of going out consideration section collects the culture and customs of the destination, and the generation AI suggests optimal outfits based on that data. For example, it suggests outfits that match the dress code of a specific country or region. This makes it possible to suggest outfits based on the culture and customs of the destination.

[0087] The destination and time spent outside the home can suggest outfits that match the activity at the home. The destination and time spent outside the home consideration unit adds a function to suggest outfits that match the activity at the home. For example, it suggests easy-to-move-in clothes for hiking and stylish clothes for shopping. This makes it possible to suggest outfits that match the activity at the home.

[0088] The destination and time of going out consideration unit uses the emotion estimation function to analyze the emotions the user has regarding a specific destination or time of going out, and can make suggestions based on those emotions. For example, the destination and time of going out consideration unit adds a function to record the emotions the user has regarding a specific destination or time of going out. The generation AI uses this data to analyze the user's emotions and reflects them in the suggestions. This makes it possible to suggest destinations and times of going out based on the user's emotions.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The outfit suggestion system can also make suggestions that take into account the user's health condition. For example, if the user enters their health information into the app, the generating AI can use that information to suggest outfits that suit their physical condition. For example, if you have a cold, it can suggest warm clothes and a mask. Also, if you have allergies, it can suggest clothes that avoid allergens. This makes it possible to suggest optimal outfits based on the user's health condition.

[0091] The outfit suggestion system can also make suggestions based on the user's lifestyle. For example, if the user inputs their daily activities and hobbies into the app, the generation AI can use that information to suggest outfits that fit their lifestyle. For example, it can suggest easy-to-move-in clothes for a user who plays sports, and business casual clothes for a user who does a lot of office work. It can also suggest waterproof clothes for a user who likes the outdoors. This makes it possible to suggest optimal outfits based on the user's lifestyle.

[0092] The outfit suggestion system can also suggest evolved styles based on the user's past outfit history. For example, if the user records past outfits in the app, the generation AI can analyze that data and make new suggestions based on past styles. For example, it can suggest new styles that incorporate the latest trends based on outfits that were popular in the past. It can also suggest outfits that avoid outfits that were unpopular in the past. This makes it possible to suggest evolved styles based on the user's past outfit history.

[0093] The outfit suggestion system can also estimate the user's emotions and suggest outfits based on those emotions. For example, when a user inputs their emotions into the app, the generation AI analyzes the data and makes suggestions based on the user's emotions. For example, if a user is feeling stressed, it can suggest relaxing clothing, and if they are feeling down, it can suggest bright-colored clothing. Also, if a user is nervous about a particular event, it can suggest clothing that will make them feel confident. This makes it possible to suggest optimal outfits based on the user's emotions.

[0094] The outfit suggestion system can also add a function that incorporates the opinions of the user's friends and family. For example, a function could be added that allows the user to share outfit suggestions with friends and family and collect their opinions. For example, a shared link could be generated within the app to receive feedback from other users. This would enable outfit suggestions that reflect the opinions of the user's friends and family. For example, based on a friend's opinion that "this color looks good on you," the generation AI could suggest an outfit that incorporates that color. Also, based on a family member's opinion that "this outfit should be avoided," the generation AI could suggest an outfit that avoids that outfit. This would enable optimal outfit suggestions that reflect the opinions of the user's friends and family.

[0095] The outfit suggestion system can also estimate the user's emotions and perform trend analysis based on those emotions. For example, a function could be added that allows users to record their feelings toward specific trends or weather. The generation AI would use this data to analyze the user's emotions and reflect them in trend analysis. For example, if a user feels depressed on a rainy day, the system could suggest trends that will lift their spirits even on rainy days. Also, if a user has positive feelings toward a specific fashion item, the system could suggest trends that incorporate that item. This makes it possible to analyze trends based on the user's emotions.

[0096] The outfit suggestion system can also consider the user's body type and skin color to suggest the most suitable outfit. For example, the generation AI can collect data on the user's body type and suggest the most suitable outfit based on that data. For example, it can suggest silhouettes and designs that suit the body type. It can also suggest colors that match the skin color. For example, it can suggest brightly colored outfits to users with light skin, and suggest subdued colors to users with dark skin. This makes it possible to suggest the most suitable outfit based on the user's body type and skin color.

[0097] The outfit suggestion system can further estimate the user's emotions and make suggestions for seasonal changes based on those emotions. For example, a function could be added that records the user's emotions regarding the change of seasons. The generation AI would use this data to analyze the user's emotions and reflect them in the suggestions. For example, if the user is excited about the arrival of spring, it could suggest bright spring-like clothing. On the other hand, if the user feels sad about the arrival of autumn, it could suggest warm-colored clothing. This makes it possible to make suggestions for seasonal changes based on the user's emotions.

[0098] The outfit suggestion system can also make suggestions based on the user's location and time of day. For example, the generation AI can collect geographical information about the location and use that data to suggest optimal outfits. For example, it can suggest cold weather gear and hiking boots in mountainous areas, and casual clothing in urban areas. It can also suggest outfits that take into account the temperature difference between day and night depending on the time of day. For example, it can suggest light clothing during the day and cold weather gear at night. This makes it possible to suggest optimal outfits based on the geographical information of the location and the time of day.

[0099] The outfit suggestion system can further estimate the user's emotions and suggest destinations and times to go out based on those emotions. For example, a function can be added that records the emotions the user feels toward specific destinations and times to go out. The generation AI can then use this data to analyze the user's emotions and reflect them in the suggestions. For example, if the user feels anxious about going out at night, the system can suggest clothing that gives a sense of security. Also, if the user has positive feelings toward a specific destination, the system can suggest clothing that suits that destination. This makes it possible to suggest destinations and times to go out based on the user's emotions.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The clothing database creation unit creates a database of the user's clothing. For example, the user takes photos of the clothing and uploads them to the app. The generation AI analyzes these photos and registers information such as the type of clothing, color, material, and design in the database. For example, it registers information such as the white shirt, black pants, and red skirt that the user owns. Step 2: The trend analysis unit analyzes the latest fashion trends and weather information based on the clothing information databased by the clothing database unit. For example, the generation AI collects and analyzes information from fashion magazines, websites, and weather forecast sites. The input to the generation AI is a prompt containing information from fashion magazines, websites, and weather forecast sites, and the generation AI performs analysis based on the prompt. Step 3: The outfit suggestion unit proposes outfits based on the information analyzed by the trend analysis unit. For example, the generation AI proposes the optimal outfit for the user based on its clothing database, the latest trends, and weather information. The input to the generation AI is a prompt that includes information such as the user's destination and time of going out, and the generation AI generates outfits based on the prompt.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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. [Explanation of symbols]

[0169] 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 clothes database creation unit that creates a database of the user's clothes; a trend analysis unit that analyzes the latest fashion trends and weather information based on the clothing information databased by the clothing database unit; a coordinate suggestion unit that suggests a coordinate based on the information analyzed by the trend analysis unit. A system characterized by:

2. The clothing database creation unit In addition to photographs of the clothing, we will also use videos to analyze the clothing from a 360-degree perspective and build a more detailed database.

2. The system of claim 1.

3. The trend analysis unit Analyzes fashion shows and celebrity social media posts in real time to instantly reflect the latest trends 2. The system of claim 1.

4. The coordination suggestion unit Taking into account the user's body type and skin color, the system suggests the most suitable outfits.

2. The system of claim 1.

5. The proposal section for the change of seasons is, Propose fashion items that are specific to the change of seasons 2. The system of claim 1.

6. The clothing database creation unit Using emotion estimation, the system analyzes the user's feelings toward specific clothing items and recommends clothing based on those feelings.

2. The system of claim 1.

7. The trend analysis unit Using emotion estimation, we analyze the emotions users have toward specific weather conditions or trends, and then perform trend analysis based on those emotions.

2. The system of claim 1.

8. The coordination suggestion unit Using emotion estimation, the app analyzes the emotions users have toward specific outfits and suggests outfits based on those emotions.

2. The system of claim 1.

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