system

Abstract

An object of a system according to an embodiment is to propose appropriate clothes suitable for the physique, skin color, TPO, and weather of a user.SOLUTION: A system includes a user information input unit, an analysis unit, a proposal unit, and an information registration unit. The user information input unit inputs information on the physique, skin color, TPO, and weather of the user. The analysis unit analyzes the user information input by the user information input unit. The proposal unit proposes appropriate clothes on the basis of the information analyzed by the analysis unit. The information registration unit registers information of commodities of various clothing stores.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 select appropriate clothing that suits their physique, skin color, time, occasion, and weather.

[0005] The system according to the embodiment aims to suggest appropriate clothing that suits the user's build, skin color, time, occasion, and weather. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information input unit, an analysis unit, a suggestion unit, and an information registration unit. The user information input unit inputs information about the user's physique, skin color, TPO, and weather. The analysis unit analyzes the user information input by the user information input unit. The suggestion unit suggests appropriate clothing based on the information analyzed by the analysis unit. The information registration unit allows various clothing stores to register product information. [Effects of the Invention]

[0007] The system according to the embodiment can suggest appropriate clothing that suits the user's build, skin color, time, occasion, and weather. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The clothing suggestion system according to the embodiment of the present invention is a system that suggests appropriate clothing based on the user's physique, skin color, time, occasion, and weather, thereby enabling the user to always choose comfortable and stylish clothing.

[0029] The clothing suggestion system according to the embodiment includes a user information input unit, an analysis unit, a suggestion unit, and an information registration unit. The user information input unit inputs information related to the user's physique, skin color, TPO, and weather. For example, the user can input information related to their height, weight, body type, skin color, preferred style, TPO (e.g., work, casual, formal, etc.), and weather (sunny, rainy, cold, etc.). The analysis unit analyzes the user information input by the user information input unit. For example, the generation AI analyzes information necessary to suggest appropriate clothing based on the user's physique, skin color, TPO, and weather. The suggestion unit suggests appropriate clothing based on the information analyzed by the analysis unit. For example, the generation AI suggests clothing that matches the user's physique, skin color, TPO, and weather. The information registration unit allows various clothing stores to register product information. For example, clothing stores register detailed product information and inventory information to provide users with information to select optimal clothing. This allows the clothing suggestion system to suggest appropriate clothing based on the user's physique, skin color, TPO, and weather.

[0030] The suggestion unit can suggest outfits based on user information, taking into consideration preferences for face shape, hairstyle, and accessories. For example, the user inputs their face shape, hairstyle, and accessory preferences, and the generation AI then suggests optimal outfits based on this information. For example, the suggestion unit suggests a V-neck shirt for a user with a round face, and a hat that matches the hairstyle for a user with long hair. The suggestion unit also provides a function for users to upload an image when entering their face shape, hairstyle, and accessory preferences. The generation AI analyzes this image and suggests outfits that are optimal for the user's face shape and hairstyle. The suggestion unit also provides options when the user enters their face shape, hairstyle, and accessory preferences. For example, when selecting a face shape, options such as round, oval, and square are provided, allowing the user to select the option that best suits them. This makes it possible to suggest outfits that take into consideration the user's face shape, hairstyle, and accessory preferences.

[0031] The analysis unit analyzes the user's past clothing history and purchase history based on the user information, and can suggest clothing taking into account changes in style and preferences. The analysis unit, for example, stores the user's past clothing history in a database, and the generation AI analyzes that data. For example, the analysis unit can understand changes in the user's preferences based on items purchased in the past and the styles of clothing worn, and reflect these in the next suggestion. The analysis unit also analyzes the user's purchase history and identifies preferences for specific brands and styles. For example, it can prioritize suggestions of brands and styles that have been purchased frequently in the past. The analysis unit also analyzes seasonal changes in style based on the user's past clothing history and purchase history. For example, if a user prefers a casual style in the summer, it can suggest casual summer clothing. This makes it possible to suggest clothing that takes into account the user's past clothing history and purchase history.

[0032] The user information input unit can input user information using voice recognition and image recognition technology. For example, the user information input unit allows a user to input their physique, skin color, and preferred style by voice. For example, the user can input information simply by saying, "I'm 170 cm tall, I weigh 65 kg, and I like casual styles." The user information input unit also allows a user to upload a photo of themselves, and automatically analyzes their physique and skin color using image recognition technology. For example, when a user uploads a full-body photo of themselves, the generation AI analyzes the photo and identifies their physique and skin color. The user information input unit also provides real-time feedback when a user inputs information using voice or image. For example, when a user inputs information by voice, the generation AI checks the content and provides feedback on whether the information was input accurately. This enables intuitive user information input using voice recognition and image recognition technology.

[0033] The user information input unit can work in conjunction with other health management apps and fitness apps to suggest clothing based on the user's physical condition and activity level. The user information input unit, for example, works in conjunction with a health management app to acquire physical condition data of the user. For example, the user information input unit suggests relaxing clothing based on the user's sleep data and heart rate. The user information input unit also works in conjunction with a fitness app to acquire activity level data of the user. For example, the user information input unit suggests comfortable clothing for the user to wear after exercising. The user information input unit also suggests clothing appropriate for the season and weather based on data acquired from the health management app and fitness app. For example, the user information input unit suggests clothing with high thermal insulation to a user who exercises on a cold day. This makes it possible to suggest clothing based on the user's physical condition and activity level.

[0034] The suggestion unit can reflect past feedback based on user information and suggest more personalized outfits. For example, the suggestion unit stores feedback provided by the user in a database, and the generation AI analyzes that data. For example, based on feedback such as "This shirt was a little big," the suggestion unit will suggest a shirt of a more appropriate size next time. The suggestion unit also adjusts the style of outfits suggested by the generation AI based on the user's past feedback. For example, based on feedback such as "I like casual styles," the suggestion unit will prioritize casual outfits. The suggestion unit also adjusts the color and design of outfits suggested by the generation AI based on the user's past feedback. For example, based on feedback such as "I like bright colors," the suggestion unit will suggest bright-colored outfits. This makes it possible to suggest personalized outfits that reflect the user's past feedback.

[0035] The suggestion unit incorporates seasonal trends and fashions and can suggest outfits that reflect the latest fashion. For example, the suggestion unit's generation AI collects seasonal trend information and suggests outfits based on that information. For example, it suggests floral dresses in spring and coats in winter. The suggestion unit also collects the latest trend information from fashion magazines and online fashion sites and suggests outfits based on that information. For example, it suggests items introduced in the latest fashion show. The suggestion unit also analyzes seasonal trend colors and designs and suggests outfits based on that information. For example, if blue is the trend color this summer, it will suggest blue items. This makes it possible to suggest the latest fashion that reflects seasonal trends and fashions.

[0036] The suggestion unit can add a function that incorporates the opinions of the user's friends and family, strengthening the social element. For example, the suggestion unit provides a function that allows the user to share suggested outfits with friends and family. For example, the suggestion unit can share images of suggested outfits on social media or messaging apps to collect the opinions of friends and family. The suggestion unit also provides a function that allows friends and family to comment and rate the outfits suggested by the user. For example, a friend may comment, "I think this shirt looks good on you." The suggestion unit also adjusts the outfits suggested by the generation AI based on the opinions of friends and family. For example, it may prioritize items that are highly rated by friends and family. This enables social outfit suggestions that incorporate the opinions of the user's friends and family.

[0037] The suggestion unit can suggest outfits taking into account the dress code of the user's workplace or school. For example, when the user inputs the dress code of the workplace or school, the suggestion unit allows the generation AI to suggest outfits based on that information. For example, if there is a business casual dress code, the suggestion unit suggests appropriate outfits. The suggestion unit also allows the generation AI to collect information about the dress code of the workplace or school and suggest outfits based on that information. For example, suggestions are made taking into account the dress code of a specific industry or school. The suggestion unit also allows the user to provide feedback about the dress code of the workplace or school, and the generation AI adjusts the suggestions based on that information. For example, based on feedback such as "this shirt didn't match the dress code," the suggestion unit suggests more appropriate outfits for next time. This makes it possible to suggest outfits that take into account the dress code of the user's workplace or school.

[0038] The information registration unit reflects the clothing store's inventory information in real time, allowing the user to immediately purchase the suggested outfit. For example, the information registration unit updates the clothing store's inventory information in real time, allowing the user to immediately purchase the suggested outfit. For example, it only suggests items that are in stock. The information registration unit also obtains the clothing store's inventory information via an API, and the generation AI makes suggestions based on that information. For example, it does not suggest items that are low in stock. The information registration unit also provides a function that allows the user to check inventory information in real time when purchasing the suggested outfit. For example, if an item runs out of stock during the purchase process, it suggests an alternative item. This allows the user to immediately purchase the suggested outfit.

[0039] The information registration unit can provide detailed information about products in clothing stores, allowing users to make more informed choices. For example, the information registration unit registers detailed information about products in clothing stores in a database, and the generation AI makes suggestions based on that information. For example, it provides information about materials and manufacturing methods. The information registration unit also provides information about ethical fashion, allowing users to make environmentally conscious choices. For example, it suggests products made from organic cotton or recycled materials. When providing detailed product information to users, the information registration unit displays it in a visually easy-to-understand format. For example, it uses icons and graphs about materials and manufacturing methods. This allows users to make more informed choices.

[0040] The information registration unit can link the clothing store information with other fashion-related services to provide users with a variety of options. For example, the information registration unit can link the clothing store information with a stylist service to allow users to receive suggestions from professional stylists. For example, the information registration unit can suggest items selected by the stylist. The information registration unit can also link the clothing store information with a rental service to allow users to rent suggested outfits. For example, renting outfits for specific events or occasions. The information registration unit can also link the clothing store information with other fashion-related services to provide users with a variety of options. For example, linking with online shopping sites and fashion apps. This makes it possible to provide users with a variety of options.

[0041] The information registration unit links information about clothing stores with the user's geographical location information and can suggest products that can be purchased at nearby stores. For example, the information registration unit acquires the user's geographical location information and suggests products that can be purchased at nearby stores. For example, it prioritizes suggesting products from stores that are closest to the user's current location. The information registration unit also links information about clothing stores with the geographical location information and suggests products that the user can try on at nearby stores. For example, it provides information about stores where products can be tried on. The information registration unit also provides information about sales and events held at nearby stores based on the user's location information. For example, it suggests discount information at specific stores. This makes it possible to suggest products that the user can purchase at nearby stores.

[0042] The suggestion unit enables the user to complete the purchasing procedure for the suggested outfit with one click, thereby minimizing the effort required by the user. The suggestion unit, for example, builds a system that enables the user to complete the purchasing procedure for the suggested outfit with one click. For example, the user can complete the purchasing procedure by simply clicking on the suggested outfit. The suggestion unit also provides a one-click purchasing function that enables the user to easily purchase the suggested outfit. For example, payment and delivery procedures can be completed by simply clicking on a purchase button. The suggestion unit also provides a function for the user to pre-register payment information and delivery address information to simplify the purchasing procedure for the suggested outfit. For example, the user can complete the purchasing procedure with one click based on information registered once. This minimizes the effort required by the user and simplifies the purchasing procedure for the suggested outfit.

[0043] When purchasing the suggested outfit, the suggestion unit can suggest repeat purchases and related products by taking into account the user's past purchase history and feedback. The suggestion unit, for example, analyzes the user's past purchase history and makes suggestions to encourage repeat purchases. For example, it suggests items of the same brand or style as a product purchased in the past. The suggestion unit also suggests related products based on user feedback. For example, it suggests other items from the same brand based on feedback such as "I like this shirt." The suggestion unit also builds a system that suggests repeat purchases and related products based on the user's purchase history and feedback. For example, it suggests products that are easy to coordinate with items purchased in the past. This makes it possible to suggest repeat purchases and related products by taking into account the user's past purchase history and feedback.

[0044] The suggestion unit can link the purchasing procedure for the suggested outfits with other online shopping platforms, allowing the user to select from multiple platforms. For example, the suggestion unit builds a system that links the purchasing procedure for the suggested outfits with other online shopping platforms. For example, the suggestion unit allows the user to select from multiple platforms, such as Amazon and Rakuten. The suggestion unit also links with other online shopping platforms to provide a function that allows the user to compare suggested outfits across multiple platforms. For example, it compares prices and availability. The suggestion unit also links with other online shopping platforms to simplify the purchasing procedure for the suggested outfits, allowing the user to purchase from multiple platforms with a single procedure. This allows the user to select from multiple platforms.

[0045] The suggestion unit can link the purchasing procedure for the suggested clothing with the user's payment method and delivery address information, thereby providing a smooth purchasing experience. For example, the suggestion unit registers the user's payment method and delivery address information in advance, allowing the purchasing procedure for the suggested clothing to be carried out smoothly. For example, the purchasing procedure can be completed with one click. The suggestion unit also builds a system that links with the user's payment method and delivery address information to simplify the purchasing procedure for the suggested clothing. For example, the purchasing procedure is carried out based on information registered by the user. The suggestion unit also suggests optimal delivery options based on the user's payment method and delivery address information. For example, it provides options such as the shortest delivery time or free delivery. This provides a smooth purchasing experience by linking with the user's payment method and delivery address information.

[0046] The suggestion unit accepts user feedback not only in text but also in image and audio formats, allowing for the collection of a wider variety of feedback. For example, the suggestion unit provides a function that allows the user to upload a photo of themselves wearing the suggested clothing. For example, the feedback is based on a photo of the user actually wearing the clothing. The suggestion unit also provides a function that allows the user to provide audio feedback. For example, feedback is recorded when the user simply says, "This shirt was a little big." The suggestion unit also provides an interface that allows the user to provide feedback in the form of text, image, or audio. For example, feedback is provided in the format that is most convenient for the user. This makes it possible to collect a wider variety of feedback from users.

[0047] The suggestion unit can develop an algorithm that allows the generation AI to automatically analyze user feedback and reflect it in the next suggestion. For example, the suggestion unit develops an algorithm that automatically analyzes user feedback and adjusts the next suggestion based on the results. For example, based on feedback such as "This shirt was a little big," the suggestion unit will suggest a shirt of a more appropriate size next time. The suggestion unit also analyzes user feedback with the generation AI and personalizes the suggestions based on the results. For example, based on feedback such as "I like casual styles," the suggestion unit will prioritize casual clothing suggestions. The suggestion unit also develops an algorithm that adjusts the color and design of clothing suggested by the generation AI based on user feedback. For example, based on feedback such as "I like bright colors," the suggestion unit will suggest bright-colored clothing. This makes it possible to reflect user feedback in the next suggestion.

[0048] The suggestion unit can share the user's feedback with other users and build a community-based feedback system. The suggestion unit, for example, provides a function that allows the user to share feedback provided by the user with other users. For example, the user shares feedback on suggested clothing within the community. The suggestion unit also builds a community-based feedback system that allows the user to refer to the feedback of other users. For example, the user can view the feedback of other users and use it to help make their own choices. The suggestion unit also builds a system that evaluates and ranks the feedback provided by the user within the community. For example, the user who provides the most useful feedback can be given a reward. This makes it possible to build a community-based feedback system by sharing the user's feedback with other users.

[0049] The suggestion unit allows the generation AI to automatically analyze user feedback and reflect it in suggestions to other users. For example, the suggestion unit automatically analyzes user feedback and develops an algorithm that adjusts suggestions to other users based on the results. For example, based on feedback such as "This shirt was a little big," the suggestion unit suggests shirts of an appropriate size to other users as well. The suggestion unit also analyzes user feedback with the generation AI and personalizes suggestions to other users based on the results. For example, based on feedback such as "I like casual styles," the suggestion unit preferentially suggests casual clothing to other users as well. The suggestion unit also develops an algorithm that adjusts the color and design of clothing suggested by the generation AI based on user feedback. For example, based on feedback such as "I like bright colors," the suggestion unit suggests bright-colored clothing to other users as well. This makes it possible to reflect user feedback in suggestions to other users.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The user information input unit inputs information about the user's physique, skin color, TPO, and weather. For example, the user can input information about their height, weight, body type, skin color, preferred style, TPO (e.g., work, casual, formal, etc.), and weather (sunny, rainy, cold, etc.). The analysis unit analyzes the user information input by the user information input unit. For example, the generation AI analyzes the information necessary to suggest appropriate clothing based on the user's physique, skin color, TPO, and weather. The suggestion unit suggests appropriate clothing based on the information analyzed by the analysis unit. For example, the generation AI suggests clothing that suits the user's physique, skin color, TPO, and weather. The information registration unit allows various clothing stores to register product information. For example, clothing stores register detailed product information and inventory information to provide users with information to select optimal clothing. This allows the clothing suggestion system to suggest appropriate clothing based on the user's physique, skin color, TPO, and weather.

[0052] The suggestion unit can suggest outfits based on user information, taking into consideration preferences for face shape, hairstyle, and accessories. For example, when a user inputs their face shape, hairstyle, and accessory preferences, the generation AI can suggest optimal outfits based on this information. For example, a V-neck shirt can be suggested for a user with a round face, and a hat that matches the hairstyle can be suggested for a user with long hair. The suggestion unit also provides a function for users to upload an image when inputting their face shape, hairstyle, and accessory preferences. The generation AI analyzes this image and suggests outfits that are optimal for the user's face shape and hairstyle. The suggestion unit also provides options when the user inputs their face shape, hairstyle, and accessory preferences. For example, when selecting a face shape, options such as round, oval, and square are offered, allowing the user to select the option that best suits them. This makes it possible to suggest outfits that take into consideration the user's face shape, hairstyle, and accessory preferences.

[0053] The analysis unit can analyze the user's past clothing history and purchase history based on the user information, and suggest clothing that takes into account changes in style and preferences. For example, the user's past clothing history is stored in a database, and the generation AI analyzes that data. For example, based on items purchased in the past and the styles of clothing worn, the analysis unit can understand changes in the user's preferences and reflect them in the next suggestion. The analysis unit also analyzes the user's purchase history to identify preferences for specific brands and styles. For example, it can prioritize suggestions of brands and styles that have been purchased frequently in the past. The analysis unit also analyzes seasonal changes in style based on the user's past clothing history and purchase history. For example, if a user prefers a casual style in the summer, it can suggest casual summer clothing. This makes it possible to suggest clothing that takes into account the user's past clothing history and purchase history.

[0054] The user information input unit can input user information using voice recognition and image recognition technology. For example, a user can input their physique, skin color, and preferred style by voice. For example, a user can input information by simply saying, "I'm 170 cm tall, weigh 65 kg, and like casual styles." The user information input unit can also automatically analyze physique and skin color using image recognition technology when a user uploads their own photo. For example, when a user uploads a full-body photo of themselves, the generation AI analyzes the photo and identifies their physique and skin color. The user information input unit also provides real-time feedback when a user inputs information using voice or image. For example, when a user inputs information by voice, the generation AI checks the content and provides feedback on whether the input was accurate. This enables intuitive user information input using voice recognition and image recognition technology.

[0055] The user information input unit can work with other health management apps and fitness apps to suggest clothing based on the user's physical condition and activity level. For example, it can work with a health management app to acquire the user's physical condition data. For example, it can suggest relaxing clothing based on the user's sleep data and heart rate. The user information input unit can also work with a fitness app to acquire the user's activity level data. For example, it can suggest comfortable clothing for the user to wear after exercising. The user information input unit can also suggest clothing appropriate for the season and weather based on data acquired from the health management app and fitness app. For example, it can suggest clothing with high thermal insulation to a user who exercises on a cold day. This makes it possible to suggest clothing based on the user's physical condition and activity level.

[0056] The suggestion unit can reflect past feedback based on user information and suggest more personalized outfits. For example, feedback provided by the user in the past is stored in a database, and the generation AI analyzes that data. For example, based on feedback such as "This shirt was a little big," the suggestion unit will suggest a shirt of a more appropriate size next time. The suggestion unit also adjusts the style of outfits suggested by the generation AI based on the user's past feedback. For example, based on feedback such as "I like casual styles," the suggestion unit will prioritize casual outfits. The suggestion unit also adjusts the color and design of outfits suggested by the generation AI based on the user's past feedback. For example, based on feedback such as "I like bright colors," the suggestion unit will suggest bright-colored outfits. This makes it possible to suggest personalized outfits that reflect the user's past feedback.

[0057] The suggestion unit incorporates seasonal trends and fashions and can suggest outfits that reflect the latest fashion. For example, the generation AI collects seasonal trend information and suggests outfits based on that information. For example, it suggests floral dresses in spring and coats in winter. The suggestion unit also collects the latest trend information from fashion magazines and online fashion sites and suggests outfits based on that information. For example, it suggests items introduced in the latest fashion show. The suggestion unit also analyzes seasonal trend colors and designs and suggests outfits based on that information. For example, if blue is the trend color this summer, it will suggest blue items. This makes it possible to suggest the latest fashions that reflect seasonal trends and fashions.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The user information input unit inputs information about the user's physique, skin color, TPO, and weather. For example, the user can input information about their height, weight, body type, skin color, preferred style, TPO (e.g., work, casual, formal, etc.), and weather (sunny, rainy, cold, etc.). Step 2: The analysis unit analyzes the user information entered by the user information input unit. For example, the generation AI analyzes the information necessary to suggest appropriate clothing based on the user's physique, skin color, time, place, and weather. Step 3: The suggestion unit suggests appropriate clothing based on the information analyzed by the analysis unit. For example, the generation AI suggests clothing that suits the user's physique, skin color, time, occasion, and weather. Step 4: The information registration unit allows various clothing stores to register product information. For example, clothing stores register detailed product information and inventory information, providing users with information to help them choose the best outfit.

[0060] (Example 2) The clothing suggestion system according to the embodiment of the present invention is a system that suggests appropriate clothing based on the user's physique, skin color, time, occasion, and weather, thereby enabling the user to always choose comfortable and stylish clothing.

[0061] The clothing suggestion system according to the embodiment includes a user information input unit, an analysis unit, a suggestion unit, and an information registration unit. The user information input unit inputs information related to the user's physique, skin color, TPO, and weather. For example, the user can input information related to their height, weight, body type, skin color, preferred style, TPO (e.g., work, casual, formal, etc.), and weather (sunny, rainy, cold, etc.). The analysis unit analyzes the user information input by the user information input unit. For example, the generation AI analyzes information necessary to suggest appropriate clothing based on the user's physique, skin color, TPO, and weather. The suggestion unit suggests appropriate clothing based on the information analyzed by the analysis unit. For example, the generation AI suggests clothing that matches the user's physique, skin color, TPO, and weather. The information registration unit allows various clothing stores to register product information. For example, clothing stores register detailed product information and inventory information to provide users with information to select optimal clothing. This allows the clothing suggestion system to suggest appropriate clothing based on the user's physique, skin color, TPO, and weather.

[0062] The suggestion unit can suggest outfits based on user information, taking into consideration preferences for face shape, hairstyle, and accessories. For example, the user inputs their face shape, hairstyle, and accessory preferences, and the generation AI then suggests optimal outfits based on this information. For example, the suggestion unit suggests a V-neck shirt for a user with a round face, and a hat that matches the hairstyle for a user with long hair. The suggestion unit also provides a function for users to upload an image when entering their face shape, hairstyle, and accessory preferences. The generation AI analyzes this image and suggests outfits that are optimal for the user's face shape and hairstyle. The suggestion unit also provides options when the user enters their face shape, hairstyle, and accessory preferences. For example, when selecting a face shape, options such as round, oval, and square are provided, allowing the user to select the option that best suits them. This makes it possible to suggest outfits that take into consideration the user's face shape, hairstyle, and accessory preferences.

[0063] The analysis unit analyzes the user's past clothing history and purchase history based on the user information, and can suggest clothing taking into account changes in style and preferences. The analysis unit, for example, stores the user's past clothing history in a database, and the generation AI analyzes that data. For example, the analysis unit can understand changes in the user's preferences based on items purchased in the past and the styles of clothing worn, and reflect these in the next suggestion. The analysis unit also analyzes the user's purchase history and identifies preferences for specific brands and styles. For example, it can prioritize suggestions of brands and styles that have been purchased frequently in the past. The analysis unit also analyzes seasonal changes in style based on the user's past clothing history and purchase history. For example, if a user prefers a casual style in the summer, it can suggest casual summer clothing. This makes it possible to suggest clothing that takes into account the user's past clothing history and purchase history.

[0064] The analysis unit uses the emotion estimation function to analyze the emotion felt when the user inputs information and can suggest clothing that matches that emotion. For example, when the user inputs information, the analysis unit uses the emotion estimation function to analyze facial expressions and voice to identify the user's emotion. For example, if the user is feeling stressed, the analysis unit suggests casual clothing that will help them relax. The analysis unit also uses the emotion estimation function to analyze the emotion in real time when the user inputs information and suggests clothing based on the results. For example, if the user is feeling happy, the analysis unit suggests bright-colored clothing. The analysis unit also uses the emotion estimation function to calculate an emotion score when the user inputs information and suggests clothing based on the score. For example, if the emotion score is high, the analysis unit suggests clothing that gives a positive impression. This makes it possible to suggest clothing based on the user's emotions.

[0065] The user information input unit can input user information using voice recognition and image recognition technology. For example, the user information input unit allows a user to input their physique, skin color, and preferred style by voice. For example, the user can input information simply by saying, "I'm 170 cm tall, I weigh 65 kg, and I like casual styles." The user information input unit also allows a user to upload a photo of themselves, and automatically analyzes their physique and skin color using image recognition technology. For example, when a user uploads a full-body photo of themselves, the generation AI analyzes the photo and identifies their physique and skin color. The user information input unit also provides real-time feedback when a user inputs information using voice or image. For example, when a user inputs information by voice, the generation AI checks the content and provides feedback on whether the information was input accurately. This enables intuitive user information input using voice recognition and image recognition technology.

[0066] The user information input unit can work in conjunction with other health management apps and fitness apps to suggest clothing based on the user's physical condition and activity level. The user information input unit, for example, works in conjunction with a health management app to acquire physical condition data of the user. For example, the user information input unit suggests relaxing clothing based on the user's sleep data and heart rate. The user information input unit also works in conjunction with a fitness app to acquire activity level data of the user. For example, the user information input unit suggests comfortable clothing for the user to wear after exercising. The user information input unit also suggests clothing appropriate for the season and weather based on data acquired from the health management app and fitness app. For example, the user information input unit suggests clothing with high thermal insulation to a user who exercises on a cold day. This makes it possible to suggest clothing based on the user's physical condition and activity level.

[0067] The user information input unit can use the emotion estimation function to provide an input interface that elicits positive emotions by providing real-time feedback of emotions felt when the user inputs information. For example, the user information input unit uses the emotion estimation function to analyze emotions in real time when the user inputs information and feeds back the results. For example, if the user is feeling stressed, relaxing music is played. The user information input unit also uses the emotion estimation function to provide an interface for eliciting positive emotions when the user inputs information. For example, an encouraging message is displayed according to the content of the user's input. The user information input unit also calculates an emotion score using the emotion estimation function when the user inputs information and makes suggestions to elicit positive emotions based on the score. For example, if the emotion score is low, the unit makes suggestions to the user that will help them relax. This makes it possible to provide a positive input experience that is tailored to the user's emotions.

[0068] The suggestion unit can reflect past feedback based on user information and suggest more personalized outfits. For example, the suggestion unit stores feedback provided by the user in a database, and the generation AI analyzes that data. For example, based on feedback such as "This shirt was a little big," the suggestion unit will suggest a shirt of a more appropriate size next time. The suggestion unit also adjusts the style of outfits suggested by the generation AI based on the user's past feedback. For example, based on feedback such as "I like casual styles," the suggestion unit will prioritize casual outfits. The suggestion unit also adjusts the color and design of outfits suggested by the generation AI based on the user's past feedback. For example, based on feedback such as "I like bright colors," the suggestion unit will suggest bright-colored outfits. This makes it possible to suggest personalized outfits that reflect the user's past feedback.

[0069] The suggestion unit incorporates seasonal trends and fashions and can suggest outfits that reflect the latest fashion. For example, the suggestion unit's generation AI collects seasonal trend information and suggests outfits based on that information. For example, it suggests floral dresses in spring and coats in winter. The suggestion unit also collects the latest trend information from fashion magazines and online fashion sites and suggests outfits based on that information. For example, it suggests items introduced in the latest fashion show. The suggestion unit also analyzes seasonal trend colors and designs and suggests outfits based on that information. For example, if blue is the trend color this summer, it will suggest blue items. This makes it possible to suggest the latest fashion that reflects seasonal trends and fashions.

[0070] The suggestion unit can use the emotion estimation function to analyze the emotion felt when the user inputs information and suggest clothing based on that emotion. For example, when the user inputs information, the suggestion unit uses the emotion estimation function to analyze the user's current emotion and suggest clothing that matches that emotion. For example, if the user is tired, the suggestion unit suggests casual clothing that is relaxing. The suggestion unit also analyzes the user's current emotion in real time and suggests clothing based on the results. For example, if the user is feeling happy, the suggestion unit suggests bright-colored clothing. The suggestion unit also calculates the user's emotion score and suggests clothing based on that score. For example, if the emotion score is high, the suggestion unit suggests clothing that gives a positive impression. This makes it possible to suggest clothing based on the user's emotions, which is expected to have a mood-boosting effect.

[0071] The suggestion unit can add a function that incorporates the opinions of the user's friends and family, strengthening the social element. For example, the suggestion unit provides a function that allows the user to share suggested outfits with friends and family. For example, the suggestion unit can share images of suggested outfits on social media or messaging apps to collect the opinions of friends and family. The suggestion unit also provides a function that allows friends and family to comment and rate the outfits suggested by the user. For example, a friend may comment, "I think this shirt looks good on you." The suggestion unit also adjusts the outfits suggested by the generation AI based on the opinions of friends and family. For example, it may prioritize items that are highly rated by friends and family. This enables social outfit suggestions that incorporate the opinions of the user's friends and family.

[0072] The suggestion unit can suggest outfits taking into account the dress code of the user's workplace or school. For example, when the user inputs the dress code of the workplace or school, the suggestion unit allows the generation AI to suggest outfits based on that information. For example, if there is a business casual dress code, the suggestion unit suggests appropriate outfits. The suggestion unit also allows the generation AI to collect information about the dress code of the workplace or school and suggest outfits based on that information. For example, suggestions are made taking into account the dress code of a specific industry or school. The suggestion unit also allows the user to provide feedback about the dress code of the workplace or school, and the generation AI adjusts the suggestions based on that information. For example, based on feedback such as "this shirt didn't match the dress code," the suggestion unit suggests more appropriate outfits for next time. This makes it possible to suggest outfits that take into account the dress code of the user's workplace or school.

[0073] The suggestion unit can use the emotion estimation function to suggest clothing appropriate for a specific event or occasion. For example, when a user is attending a specific event or occasion, the suggestion unit uses the emotion estimation function to analyze the user's emotions at that time and suggests appropriate clothing. For example, when attending a wedding, formal clothing is suggested. The suggestion unit also allows the user to input information about the event or occasion, and the generation AI suggests clothing based on that information. For example, when attending a party, flashy clothing is suggested. The suggestion unit also suggests clothing appropriate for a specific event or occasion based on the user's emotion score. For example, if the emotion score is high, clothing that gives a positive impression is suggested. This makes it possible to suggest clothing appropriate for a specific event or occasion.

[0074] The information registration unit reflects the clothing store's inventory information in real time, allowing the user to immediately purchase the suggested outfit. For example, the information registration unit updates the clothing store's inventory information in real time, allowing the user to immediately purchase the suggested outfit. For example, it only suggests items that are in stock. The information registration unit also obtains the clothing store's inventory information via an API, and the generation AI makes suggestions based on that information. For example, it does not suggest items that are low in stock. The information registration unit also provides a function that allows the user to check inventory information in real time when purchasing the suggested outfit. For example, if an item runs out of stock during the purchase process, it suggests an alternative item. This allows the user to immediately purchase the suggested outfit.

[0075] The information registration unit can provide detailed information about products in clothing stores, allowing users to make more informed choices. For example, the information registration unit registers detailed information about products in clothing stores in a database, and the generation AI makes suggestions based on that information. For example, it provides information about materials and manufacturing methods. The information registration unit also provides information about ethical fashion, allowing users to make environmentally conscious choices. For example, it suggests products made from organic cotton or recycled materials. When providing detailed product information to users, the information registration unit displays it in a visually easy-to-understand format. For example, it uses icons and graphs about materials and manufacturing methods. This allows users to make more informed choices.

[0076] The information registration unit uses the emotion estimation function to analyze the emotions a user feels toward a specific brand or product, and can preferentially suggest favorite brands or products. The information registration unit, for example, analyzes the emotions a user feels toward a specific brand or product, and makes suggestions based on the results. For example, it preferentially suggests products of a brand for which the user has positive emotions. The information registration unit also uses the emotion estimation function to analyze the emotions a user feels toward a specific product in real time, and adjusts suggestions based on the results. For example, it does not suggest products for which the user has negative emotions. The information registration unit also identifies favorite brands or products based on the user's emotion score, and makes suggestions based on that information. For example, it preferentially suggests products of a brand with a high emotion score. This makes it possible to suggest brands and products based on the user's emotions.

[0077] The information registration unit can link the clothing store information with other fashion-related services to provide users with a variety of options. For example, the information registration unit can link the clothing store information with a stylist service to allow users to receive suggestions from professional stylists. For example, the information registration unit can suggest items selected by the stylist. The information registration unit can also link the clothing store information with a rental service to allow users to rent suggested outfits. For example, renting outfits for specific events or occasions. The information registration unit can also link the clothing store information with other fashion-related services to provide users with a variety of options. For example, linking with online shopping sites and fashion apps. This makes it possible to provide users with a variety of options.

[0078] The information registration unit links information about clothing stores with the user's geographical location information and can suggest products that can be purchased at nearby stores. For example, the information registration unit acquires the user's geographical location information and suggests products that can be purchased at nearby stores. For example, it prioritizes suggesting products from stores that are closest to the user's current location. The information registration unit also links information about clothing stores with the geographical location information and suggests products that the user can try on at nearby stores. For example, it provides information about stores where products can be tried on. The information registration unit also provides information about sales and events held at nearby stores based on the user's location information. For example, it suggests discount information at specific stores. This makes it possible to suggest products that the user can purchase at nearby stores.

[0079] The information registration unit uses the emotion estimation function to provide feedback in real time on the emotions a user feels toward a specific brand or product, and can make suggestions that elicit positive emotions. The information registration unit, for example, analyzes in real time the emotions a user feels toward a specific brand or product, and feeds back the results. For example, it prioritizes suggesting products for which the user has positive emotions. The information registration unit also uses the emotion estimation function to analyze in real time the emotions a user feels toward a specific product, and adjusts suggestions based on the results. For example, it does not suggest products for which the user has negative emotions. The information registration unit also makes suggestions that elicit positive emotions based on the user's emotion score. For example, it prioritizes suggesting products with a high emotion score. This makes it possible to make positive suggestions based on the user's emotions.

[0080] The suggestion unit enables the user to complete the purchasing procedure for the suggested outfit with one click, thereby minimizing the effort required by the user. The suggestion unit, for example, builds a system that enables the user to complete the purchasing procedure for the suggested outfit with one click. For example, the user can complete the purchasing procedure by simply clicking on the suggested outfit. The suggestion unit also provides a one-click purchasing function that enables the user to easily purchase the suggested outfit. For example, payment and delivery procedures can be completed by simply clicking on a purchase button. The suggestion unit also provides a function for the user to pre-register payment information and delivery address information to simplify the purchasing procedure for the suggested outfit. For example, the user can complete the purchasing procedure with one click based on information registered once. This minimizes the effort required by the user and simplifies the purchasing procedure for the suggested outfit.

[0081] When purchasing the suggested outfit, the suggestion unit can suggest repeat purchases and related products by taking into account the user's past purchase history and feedback. The suggestion unit, for example, analyzes the user's past purchase history and makes suggestions to encourage repeat purchases. For example, it suggests items of the same brand or style as a product purchased in the past. The suggestion unit also suggests related products based on user feedback. For example, it suggests other items from the same brand based on feedback such as "I like this shirt." The suggestion unit also builds a system that suggests repeat purchases and related products based on the user's purchase history and feedback. For example, it suggests products that are easy to coordinate with items purchased in the past. This makes it possible to suggest repeat purchases and related products by taking into account the user's past purchase history and feedback.

[0082] The suggestion unit can link the purchasing procedure for the suggested outfits with other online shopping platforms, allowing the user to select from multiple platforms. For example, the suggestion unit builds a system that links the purchasing procedure for the suggested outfits with other online shopping platforms. For example, the suggestion unit allows the user to select from multiple platforms, such as Amazon and Rakuten. The suggestion unit also links with other online shopping platforms to provide a function that allows the user to compare suggested outfits across multiple platforms. For example, it compares prices and availability. The suggestion unit also links with other online shopping platforms to simplify the purchasing procedure for the suggested outfits, allowing the user to purchase from multiple platforms with a single procedure. This allows the user to select from multiple platforms.

[0083] The suggestion unit can link the purchasing procedure for the suggested clothing with the user's payment method and delivery address information, thereby providing a smooth purchasing experience. For example, the suggestion unit registers the user's payment method and delivery address information in advance, allowing the purchasing procedure for the suggested clothing to be carried out smoothly. For example, the purchasing procedure can be completed with one click. The suggestion unit also builds a system that links with the user's payment method and delivery address information to simplify the purchasing procedure for the suggested clothing. For example, the purchasing procedure is carried out based on information registered by the user. The suggestion unit also suggests optimal delivery options based on the user's payment method and delivery address information. For example, it provides options such as the shortest delivery time or free delivery. This provides a smooth purchasing experience by linking with the user's payment method and delivery address information.

[0084] The suggestion unit uses the emotion estimation function to provide real-time feedback on the emotions felt by the user during the purchase process, thereby providing a purchasing experience that elicits positive emotions. For example, the suggestion unit analyzes the emotions felt by the user during the purchase process in real time and provides feedback on the results. For example, if the user is feeling stressed, the suggestion unit plays relaxing music. The suggestion unit also uses the emotion estimation function to analyze the emotions felt by the user during the purchase process in real time and makes suggestions to elicit positive emotions based on the results. For example, it displays encouraging messages. The suggestion unit also provides an interface for eliciting positive emotions during the purchase process based on the user's emotion score. For example, if the emotion score is low, the suggestion unit makes suggestions to help the user relax. This provides a positive purchasing experience based on the user's emotions.

[0085] The suggestion unit accepts user feedback not only in text but also in image and audio formats, allowing for the collection of a wider variety of feedback. For example, the suggestion unit provides a function that allows the user to upload a photo of themselves wearing the suggested clothing. For example, the feedback is based on a photo of the user actually wearing the clothing. The suggestion unit also provides a function that allows the user to provide audio feedback. For example, feedback is recorded when the user simply says, "This shirt was a little big." The suggestion unit also provides an interface that allows the user to provide feedback in the form of text, image, or audio. For example, feedback is provided in the format that is most convenient for the user. This makes it possible to collect a wider variety of feedback from users.

[0086] The suggestion unit can develop an algorithm that allows the generation AI to automatically analyze user feedback and reflect it in the next suggestion. For example, the suggestion unit develops an algorithm that automatically analyzes user feedback and adjusts the next suggestion based on the results. For example, based on feedback such as "This shirt was a little big," the suggestion unit will suggest a shirt of a more appropriate size next time. The suggestion unit also analyzes user feedback with the generation AI and personalizes the suggestions based on the results. For example, based on feedback such as "I like casual styles," the suggestion unit will prioritize casual clothing suggestions. The suggestion unit also develops an algorithm that adjusts the color and design of clothing suggested by the generation AI based on user feedback. For example, based on feedback such as "I like bright colors," the suggestion unit will suggest bright-colored clothing. This makes it possible to reflect user feedback in the next suggestion.

[0087] The suggestion unit can use the emotion estimation function to analyze the user's emotion when providing feedback and provide an incentive to encourage positive feedback. For example, when a user provides feedback, the suggestion unit uses the emotion estimation function to analyze the emotion at that time and provide an incentive to encourage positive feedback. For example, the suggestion unit provides a discount coupon to a user who provides positive feedback. The suggestion unit also uses the emotion estimation function to analyze the emotion of a user when providing feedback in real time and provides an incentive based on the result. For example, a reward is added to a user who has positive emotions. The suggestion unit also provides an interface to elicit positive emotions when providing feedback based on the user's emotion score. For example, a reward is provided to the user if the emotion score is high. This makes it possible to encourage positive feedback by providing incentives based on the user's emotion.

[0088] The suggestion unit can share the user's feedback with other users and build a community-based feedback system. The suggestion unit, for example, provides a function that allows the user to share feedback provided by the user with other users. For example, the user shares feedback on suggested clothing within the community. The suggestion unit also builds a community-based feedback system that allows the user to refer to the feedback of other users. For example, the user can view the feedback of other users and use it to help make their own choices. The suggestion unit also builds a system that evaluates and ranks the feedback provided by the user within the community. For example, the user who provides the most useful feedback can be given a reward. This makes it possible to build a community-based feedback system by sharing the user's feedback with other users.

[0089] The suggestion unit allows the generation AI to automatically analyze user feedback and reflect it in suggestions to other users. For example, the suggestion unit automatically analyzes user feedback and develops an algorithm that adjusts suggestions to other users based on the results. For example, based on feedback such as "This shirt was a little big," the suggestion unit suggests shirts of an appropriate size to other users as well. The suggestion unit also analyzes user feedback with the generation AI and personalizes suggestions to other users based on the results. For example, based on feedback such as "I like casual styles," the suggestion unit preferentially suggests casual clothing to other users as well. The suggestion unit also develops an algorithm that adjusts the color and design of clothing suggested by the generation AI based on user feedback. For example, based on feedback such as "I like bright colors," the suggestion unit suggests bright-colored clothing to other users as well. This makes it possible to reflect user feedback in suggestions to other users.

[0090] The suggestion unit uses the emotion estimation function to provide feedback of the user's emotions at the time of feedback in real time, thereby providing a feedback experience that elicits positive emotions. For example, when the user provides feedback, the suggestion unit uses the emotion estimation function to analyze the user's emotions at that time in real time and feeds back the result. For example, if the user has positive emotions, the suggestion unit displays an encouraging message. The suggestion unit also uses the emotion estimation function to analyze the user's emotions when providing feedback in real time and makes suggestions to elicit positive emotions based on the results. For example, if the user has negative emotions, the suggestion unit makes suggestions to help the user relax. The suggestion unit also provides an interface for eliciting positive emotions at the time of feedback based on the user's emotion score. For example, if the emotion score is high, the suggestion unit provides the user with a reward. This makes it possible to provide a positive feedback experience based on the user's emotions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The user information input unit inputs information about the user's physique, skin color, TPO, and weather. For example, the user can input information about their height, weight, body type, skin color, preferred style, TPO (e.g., work, casual, formal, etc.), and weather (sunny, rainy, cold, etc.). The analysis unit analyzes the user information input by the user information input unit. For example, the generation AI analyzes the information necessary to suggest appropriate clothing based on the user's physique, skin color, TPO, and weather. The suggestion unit suggests appropriate clothing based on the information analyzed by the analysis unit. For example, the generation AI suggests clothing that suits the user's physique, skin color, TPO, and weather. The information registration unit allows various clothing stores to register product information. For example, clothing stores register detailed product information and inventory information to provide users with information to select optimal clothing. This allows the clothing suggestion system to suggest appropriate clothing based on the user's physique, skin color, TPO, and weather.

[0093] The suggestion unit can suggest outfits based on user information, taking into consideration preferences for face shape, hairstyle, and accessories. For example, when a user inputs their face shape, hairstyle, and accessory preferences, the generation AI can suggest optimal outfits based on this information. For example, a V-neck shirt can be suggested for a user with a round face, and a hat that matches the hairstyle can be suggested for a user with long hair. The suggestion unit also provides a function for users to upload an image when inputting their face shape, hairstyle, and accessory preferences. The generation AI analyzes this image and suggests outfits that are optimal for the user's face shape and hairstyle. The suggestion unit also provides options when the user inputs their face shape, hairstyle, and accessory preferences. For example, when selecting a face shape, options such as round, oval, and square are offered, allowing the user to select the option that best suits them. This makes it possible to suggest outfits that take into consideration the user's face shape, hairstyle, and accessory preferences.

[0094] The analysis unit can analyze the user's past clothing history and purchase history based on the user information, and suggest clothing that takes into account changes in style and preferences. For example, the user's past clothing history is stored in a database, and the generation AI analyzes that data. For example, based on items purchased in the past and the styles of clothing worn, the analysis unit can understand changes in the user's preferences and reflect them in the next suggestion. The analysis unit also analyzes the user's purchase history to identify preferences for specific brands and styles. For example, it can prioritize suggestions of brands and styles that have been purchased frequently in the past. The analysis unit also analyzes seasonal changes in style based on the user's past clothing history and purchase history. For example, if a user prefers a casual style in the summer, it can suggest casual summer clothing. This makes it possible to suggest clothing that takes into account the user's past clothing history and purchase history.

[0095] The analysis unit uses the emotion estimation function to analyze the emotion felt when the user inputs information and can suggest clothing that matches that emotion. For example, when the user inputs information, the emotion estimation function is used to analyze facial expressions and voice to identify the user's emotion. For example, if the user is feeling stressed, the analysis unit suggests casual clothing that is relaxing. The analysis unit also uses the emotion estimation function to analyze the emotion in real time when the user inputs information and suggests clothing based on the results. For example, if the user is feeling happy, the analysis unit suggests bright-colored clothing. The analysis unit also uses the emotion estimation function to calculate an emotion score when the user inputs information and suggests clothing based on the score. For example, if the emotion score is high, the analysis unit suggests clothing that gives a positive impression. This makes it possible to suggest clothing based on the user's emotions.

[0096] The user information input unit can input user information using voice recognition and image recognition technology. For example, a user can input their physique, skin color, and preferred style by voice. For example, a user can input information by simply saying, "I'm 170 cm tall, weigh 65 kg, and like casual styles." The user information input unit can also automatically analyze physique and skin color using image recognition technology when a user uploads their own photo. For example, when a user uploads a full-body photo of themselves, the generation AI analyzes the photo and identifies their physique and skin color. The user information input unit also provides real-time feedback when a user inputs information using voice or image. For example, when a user inputs information by voice, the generation AI checks the content and provides feedback on whether the input was accurate. This enables intuitive user information input using voice recognition and image recognition technology.

[0097] The user information input unit can work with other health management apps and fitness apps to suggest clothing based on the user's physical condition and activity level. For example, it can work with a health management app to acquire the user's physical condition data. For example, it can suggest relaxing clothing based on the user's sleep data and heart rate. The user information input unit can also work with a fitness app to acquire the user's activity level data. For example, it can suggest comfortable clothing for the user to wear after exercising. The user information input unit can also suggest clothing appropriate for the season and weather based on data acquired from the health management app and fitness app. For example, it can suggest clothing with high thermal insulation to a user who exercises on a cold day. This makes it possible to suggest clothing based on the user's physical condition and activity level.

[0098] The user information input unit can use the emotion estimation function to provide an input interface that elicits positive emotions by providing real-time feedback of emotions felt when the user inputs information. For example, when the user inputs information, the emotion estimation function is used to analyze the emotions in real time and the results are fed back. For example, if the user is feeling stressed, relaxing music is played. The user information input unit also provides an interface that elicits positive emotions by using the emotion estimation function when the user inputs information. For example, an encouraging message is displayed according to the content of the user's input. The user information input unit also calculates an emotion score using the emotion estimation function when the user inputs information and makes suggestions to elicit positive emotions based on the score. For example, if the emotion score is low, the unit makes suggestions to the user that will help them relax. This makes it possible to provide a positive input experience that is tailored to the user's emotions.

[0099] The suggestion unit can reflect past feedback based on user information and suggest more personalized outfits. For example, feedback provided by the user in the past is stored in a database, and the generation AI analyzes that data. For example, based on feedback such as "This shirt was a little big," the suggestion unit will suggest a shirt of a more appropriate size next time. The suggestion unit also adjusts the style of outfits suggested by the generation AI based on the user's past feedback. For example, based on feedback such as "I like casual styles," the suggestion unit will prioritize casual outfits. The suggestion unit also adjusts the color and design of outfits suggested by the generation AI based on the user's past feedback. For example, based on feedback such as "I like bright colors," the suggestion unit will suggest bright-colored outfits. This makes it possible to suggest personalized outfits that reflect the user's past feedback.

[0100] The suggestion unit incorporates seasonal trends and fashions and can suggest outfits that reflect the latest fashion. For example, the generation AI collects seasonal trend information and suggests outfits based on that information. For example, it suggests floral dresses in spring and coats in winter. The suggestion unit also collects the latest trend information from fashion magazines and online fashion sites and suggests outfits based on that information. For example, it suggests items introduced in the latest fashion show. The suggestion unit also analyzes seasonal trend colors and designs and suggests outfits based on that information. For example, if blue is the trend color this summer, it will suggest blue items. This makes it possible to suggest the latest fashions that reflect seasonal trends and fashions.

[0101] The suggestion unit can use the emotion estimation function to analyze the emotion felt when the user inputs information and suggest clothing based on that emotion. For example, when the user inputs information, the emotion estimation function can be used to analyze the current emotion and suggest clothing that matches that emotion. For example, if the user is tired, relaxing casual clothing can be suggested. The suggestion unit can also analyze the user's current emotion in real time and suggest clothing based on the results. For example, if the user is feeling happy, bright colored clothing can be suggested. The suggestion unit can also calculate the user's emotion score and suggest clothing based on that score. For example, if the emotion score is high, clothing that gives a positive impression can be suggested. This makes it possible to suggest clothing based on the user's emotions, which is expected to have the effect of lifting their mood.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The user information input unit inputs information about the user's physique, skin color, TPO, and weather. For example, the user can input information about their height, weight, body type, skin color, preferred style, TPO (e.g., work, casual, formal, etc.), and weather (sunny, rainy, cold, etc.). Step 2: The analysis unit analyzes the user information entered by the user information input unit. For example, the generation AI analyzes the information necessary to suggest appropriate clothing based on the user's physique, skin color, time, place, and weather. Step 3: The suggestion unit suggests appropriate clothing based on the information analyzed by the analysis unit. For example, the generation AI suggests clothing that suits the user's physique, skin color, time, occasion, and weather. Step 4: The information registration unit allows various clothing stores to register product information. For example, clothing stores register detailed product information and inventory information, providing users with information to help them choose the best outfit.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 user information input section for inputting information about the user's physique, skin color, time, occasion, and weather; an analysis unit that analyzes the user information input by the user information input unit; a suggestion unit that suggests appropriate clothing based on the information analyzed by the analysis unit; An information registration unit in which various clothing stores register product information. A system characterized by:

2. The proposal unit Based on the user information, the outfit is suggested taking into consideration face shape, hairstyle, and accessory preferences.

2. The system of claim 1.

3. The analysis unit Based on the user information, the system analyzes past clothing history and purchase history, and suggests clothing taking into account changes in style and preferences.

2. The system of claim 1.

4. The analysis unit Analyzing the emotions felt when the user information is entered and suggesting the clothing that matches the emotions 2. The system of claim 1.

5. The user information input unit Input the user information using voice recognition or image recognition technology 2. The system of claim 1.

6. The user information input unit Link with other health management and fitness apps to suggest clothing based on the user's physical condition and activity level.

2. The system of claim 1.

7. The user information input unit To provide an input interface that provides real-time feedback of emotions felt when inputting the user information, and draws out positive emotions.

2. The system of claim 1.

8. The proposal unit Based on the user information, past feedback is reflected and more personalized clothing is proposed.

2. The system of claim 1.

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