System

Abstract

An object of a system according to an embodiment is to diagnose coordination of a user, create ranking based on evaluation, and give a privilege.SOLUTION: A system according to an embodiment includes a coordination diagnosis unit, an evaluation unit, a ranking unit, and a privilege granting unit. The coordination diagnosis section diagnoses coordination of the user. The evaluation section evaluates the coordination diagnosed by the coordination diagnosis section. The ranking unit creates a ranking based on a result of the evaluation by the evaluation unit. The privilege granting unit grants a privilege to a higher-ranking person in the ranking created by the ranking unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have limited means for users to objectively know how their outfits are evaluated, and there is room for improvement.

[0005] The system according to the embodiment aims to evaluate a user's coordination, create a ranking based on the evaluation, and award benefits. [Means for solving the problem]

[0006] The system according to the embodiment includes a coordination diagnosis unit, an evaluation unit, a ranking unit, and a reward granting unit. The coordination diagnosis unit diagnoses a user's coordination. The evaluation unit evaluates the coordination diagnosed by the coordination diagnosis unit. The ranking unit creates a ranking based on the results of the evaluation by the evaluation unit. The reward granting unit grants rewards to users ranked high in the ranking created by the ranking unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate a user's coordination, create a ranking based on the evaluation, and award benefits. [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 coordination diagnosis system according to an embodiment of the present invention is a system in which a generation AI diagnoses coordinations suited to various occasions when a user is unsure of what clothes to wear or is unsure whether the outfit suits them, and shares the evaluation of each outfit. The coordination diagnosis system then evaluates the user's coordination and ranks their sense of fashion based on the evaluation. Top performers can be awarded special rewards, such as collaborations with famous brands. Furthermore, profits can be generated through social media advertising and expansion of sales channels to group company e-commerce sites.

[0029] The coordination diagnosis system according to the embodiment includes a coordination diagnosis unit, an evaluation unit, a ranking unit, and a reward granting unit. The coordination diagnosis unit diagnoses a user's coordination. For example, the generation AI proposes an appropriate coordination based on a scene input by the user. The generation AI uses a text generation AI (e.g., LLM) to analyze information about the user's clothing and the scene and propose an appropriate coordination. The generation AI can also use a multimodal generation AI to analyze the user's image and text information and propose coordination. The evaluation unit evaluates the coordination diagnosed by the coordination diagnosis unit. For example, the generation AI evaluates the coordination's color combination and suitability for the scene. The generation AI can also evaluate the coordination based on the user's past evaluation history. The ranking unit creates a ranking based on the results evaluated by the evaluation unit. For example, the generation AI ranks users' sense of style based on the evaluation scores. The generation AI can also set a ranking update frequency and update the ranking in real time. The reward granting unit grants rewards to users ranked high in the ranking created by the ranking unit. For example, top-ranked users may be granted collaboration rewards with famous brands. The reward granting unit can also provide top-ranked users with opportunities to build a career as a stylist. As a result, the coordination diagnosis system according to the embodiment can improve the user's fashion sense by diagnosing, evaluating, and ranking the user's coordination and granting rewards.

[0030] The coordination diagnosis unit learns the user's past coordination history and can make personalized suggestions that reflect individual style trends. For example, the coordination diagnosis unit uses a generation AI to analyze the user's past coordination history and learn frequently chosen colors and styles. For example, it can suggest similar new coordinations based on the casual styles that the user has often chosen in the past. The coordination diagnosis unit also suggests styles suitable for specific events or seasons based on the user's past coordination history. For example, it can suggest a new style suitable for this summer based on past summer coordinations. The coordination diagnosis unit also uses a generation AI to learn the user's past coordination history and understand tendencies to prefer items from specific brands or designers. For example, it can suggest coordinations that incorporate new items from the user's favorite brand. This makes it possible to learn the user's past coordination history and make suggestions that reflect individual style trends.

[0031] The outfit diagnosis unit can analyze the individual characteristics of a user, such as their body type, skin color, and hairstyle, and suggest the optimal outfit based on that. For example, the outfit diagnosis unit uses a generative AI to analyze the user's body type and suggest clothes with a silhouette and fit that suits that body type. For example, it might suggest a long coat and wide pants for a tall user. The outfit diagnosis unit can also analyze the user's skin color and suggest color combinations that suit that skin color. For example, it might suggest olive green or burgundy for warm skin tones. The outfit diagnosis unit can also analyze the user's hairstyle and suggest styles and accessories that go well with that hairstyle. For example, it might suggest a simple and modern style for a user with short hair. This makes it possible to suggest the optimal outfit based on the individual characteristics of the user, such as their body type, skin color, and hairstyle.

[0032] The coordination diagnosis unit can take into account the user's lifestyle, hobbies, and preferences and suggest the best coordination for a specific event or activity. For example, the generation AI in the coordination diagnosis unit analyzes the user's lifestyle and suggests coordination suitable for everyday activities. For example, a functional and casual style is suggested for a user who likes the outdoors. The coordination diagnosis unit also suggests coordination suitable for a specific event based on the user's hobbies and preferences. For example, a user attending a music festival is suggested an easy-to-move-in and stylish style. The coordination diagnosis unit also takes into account the user's lifestyle, hobbies, and preferences and suggests coordination suitable for a travel destination. For example, a relaxed resort style is suggested for a user going to a beach resort. In this way, the generation AI can take into account the user's lifestyle, hobbies, and preferences and suggest the best coordination for a specific event or activity.

[0033] The outfit diagnosis unit incorporates seasonal and weather information and can suggest optimal outfits in real time. For example, the generative AI in the outfit diagnosis unit uses seasonal information to suggest outfits that are appropriate for the season. For example, in winter it would suggest coats and sweaters made of warm materials. The outfit diagnosis unit also obtains weather information in real time and suggests outfits that are appropriate for the weather of the day. For example, it would suggest waterproof jackets and boots on rainy days. The outfit diagnosis unit also proposes outfits that are appropriate for specific events based on seasonal and weather information. For example, it would suggest light linen clothing for a summer beach party. This allows the system to incorporate seasonal and weather information and suggest optimal outfits in real time.

[0034] The evaluation unit can analyze feedback from other users on a user's coordination and reflect the evaluation in the ranking. For example, the generation AI of the evaluation unit analyzes feedback from other users and reflects coordinations that receive many positive evaluations in the ranking. For example, the evaluation is based on the number of "likes" and comments. The evaluation unit also scores feedback from other users and creates rankings based on the scores. For example, the content of the feedback is analyzed and coordinations that receive many positive comments are given high ratings. The evaluation unit also analyzes feedback from other users in real time using the generation AI and dynamically updates the rankings. For example, the rankings are adjusted each time new feedback is added. This makes it possible to analyze feedback from other users and reflect the evaluations in the rankings.

[0035] The evaluation unit can analyze trends and fashions in outfits and provide evaluations that reflect the latest fashion trends. For example, the evaluation unit uses a generation AI to analyze the latest fashion trends and give high ratings to outfits that match those trends. For example, it evaluates outfits that incorporate this season's popular colors and styles. The evaluation unit also collects trend information from fashion magazines and social media and evaluates outfits based on that information. For example, it gives high ratings to styles recommended by popular influencers. The evaluation unit also uses a generation AI to learn past trend data and analyze changes in trends. For example, it compares past trends with current trends and provides an evaluation that reflects the latest trends. This makes it possible to analyze trends and fashions in outfits and provide an evaluation that reflects the latest fashion trends.

[0036] The evaluation unit can analyze fashion styles from different cultural spheres and regions and evaluate them from a global perspective. For example, the generation AI analyzes fashion styles from different cultural spheres and evaluates them based on those styles. For example, it compares fashion styles from Asia, Europe, and America and reflects this in the evaluation. The evaluation unit also collects style information from fashion magazines and social media from different regions and evaluates based on that information. For example, it performs evaluations that reflect regional trends. The generation AI also learns the fashion history of different cultural spheres and performs evaluations that take that historical background into account. For example, it evaluates coordination that combines traditional and modern styles. This makes it possible to analyze fashion styles from different cultural spheres and evaluate them from a global perspective.

[0037] The evaluation unit can create a ranking for each attribute, taking into account attributes such as the user's age, gender, and occupation. For example, the generation AI in the evaluation unit takes into account the user's age and evaluates outfits appropriate for that age. For example, it evaluates trendy styles for young people and elegant styles for middle-aged and older people. The evaluation unit also evaluates outfits appropriate for the gender of the user based on the user's gender. For example, it evaluates simple and functional styles for men and feminine and glamorous styles for women. The evaluation unit also considers the user's occupation and evaluates outfits appropriate for that occupation. For example, it evaluates formal styles for business people and unique styles for creative workers. This makes it possible to create a ranking for each attribute, taking into account attributes such as the user's age, gender, and occupation.

[0038] The generation AI can analyze the content of users' posts and automatically select and display the most appropriate advertisement. For example, the generation AI analyzes the content of users' posts and selects the most appropriate advertisement based on the theme and keywords of the post. For example, clothing advertisements are displayed for fashion-related posts. The generation AI also analyzes the content of users' posts in real time and automatically displays advertisements related to that content. For example, travel-related advertisements are displayed for travel posts. The generation AI also learns the user's posting history and selects the most appropriate advertisement based on the content of past posts. For example, advertisements related to themes that have been posted frequently in the past are displayed. This makes it possible to analyze the content of users' posts and automatically select and display the most appropriate advertisement.

[0039] The generation AI can analyze the interests of a user's followers and display targeted advertisements. For example, the generation AI can analyze the interests of a user's followers and display advertisements that are most suitable for them. For example, it can display advertisements related to topics that many followers are interested in. The generation AI can also analyze the content posted by a user's followers and display targeted advertisements based on that content. For example, it can display advertisements related to topics that many followers post about. The generation AI can also analyze the interests of a user's followers in real time and dynamically adjust advertisements based on the results. For example, it can update the advertisement content if the followers' interests change. This makes it possible to analyze the interests of a user's followers and display targeted advertisements.

[0040] The generation AI can analyze engagement with users' posts and automatically adjust the timing to maximize advertising effectiveness. For example, the generation AI can analyze engagement with users' posts and display ads at times when engagement is high. For example, it can display ads immediately after a post receives many likes and comments. The generation AI can also automatically adjust the timing of ad display based on engagement data for users' posts. For example, it can display ads when engagement reaches its peak. The generation AI can also learn users' posting history and identify the optimal timing to display ads based on past engagement data. For example, if engagement is high during a particular time of day or day of the week, it can display ads at that time. This makes it possible to analyze engagement with users' posts and automatically adjust the timing to maximize advertising effectiveness.

[0041] The generation AI can suggest products and services related to the content posted by the user and display them as advertisements. For example, the generation AI analyzes the content posted by the user and suggests products and services related to that content. For example, for fashion-related posts, advertisements for clothing and accessories are displayed. The generation AI can also identify related products and services based on the content posted by the user and display them as advertisements. For example, for travel posts, it can suggest travel-related services. The generation AI can also learn the user's posting history and suggest related products and services based on past posts. For example, it can display advertisements for products related to themes that have been posted frequently in the past. This makes it possible to suggest products and services related to the content posted by the user and display them as advertisements.

[0042] Generative AI can analyze a user's purchasing history and suggest products that match their individual tastes. For example, generative AI can analyze a user's purchasing history and suggest products that match their individual tastes based on products purchased in the past. For example, it can suggest new products based on brands and styles purchased in the past. Generative AI can also suggest related products based on a user's purchasing history. For example, it can suggest products that go well with items purchased in the past. Generative AI can also learn a user's purchasing history and suggest products that are suitable for specific seasons or events. For example, it can suggest products that are suitable for this summer based on past summer purchasing history. This makes it possible to analyze a user's purchasing history and suggest products that match their individual tastes.

[0043] The generation AI can check the stock status of items used in a user's outfit in real time and suggest products that can be purchased immediately. For example, the generation AI can check the stock status of items used in a user's outfit in real time and suggest products that can be purchased immediately. For example, it can prioritize suggesting items that are in stock. The generation AI can also suggest alternative products based on the stock status of items used in a user's outfit. For example, it can suggest items with similar designs if an item is out of stock. The generation AI can also monitor the stock status of items used in a user's outfit in real time and notify the user when stock is running low. For example, it can suggest that an item that is running low be purchased as soon as possible. This makes it possible to check the stock status of items used in a user's outfit in real time and suggest products that can be purchased immediately.

[0044] The generation AI can suggest related products and accessories based on the user's purchasing history, promoting cross-selling. For example, the generation AI can analyze the user's purchasing history and suggest related products and accessories based on previously purchased products. For example, it can suggest accessories that go well with a purchased dress. The generation AI can also identify related products to promote cross-selling based on the user's purchasing history. For example, it can suggest products that go well with purchased items. The generation AI can also learn the user's purchasing history and suggest related products that are suitable for specific seasons or events. For example, it can suggest accessories that are suitable for this summer based on past summer purchasing history. This makes it possible to suggest related products and accessories based on the user's purchasing history, promoting cross-selling.

[0045] Generative AI can make periodic recommendations based on a user's purchasing history and encourage repeat purchases. For example, generative AI can analyze a user's purchasing history and periodically recommend new products. For example, it can propose new collections each season. Generative AI can also make recommendations to encourage repeat purchases based on a user's purchasing history. For example, it can propose new products from the same brand as a product previously purchased. Generative AI can also learn a user's purchasing history and make recommendations tailored to specific events or anniversaries. For example, it can propose products suitable for birthdays and anniversaries. This makes it possible to make periodic recommendations based on a user's purchasing history and encourage repeat purchases.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The coordination diagnosis unit takes into account the user's lifestyle, hobbies, and preferences and can suggest the best coordination for a specific event or activity. For example, the generation AI analyzes the user's lifestyle and suggests coordination suitable for everyday activities. For example, a functional and casual style is suggested for a user who likes the outdoors. The coordination diagnosis unit also suggests coordination suitable for a specific event based on the user's hobbies and preferences. For example, a user attending a music festival is suggested an easy-to-move-in and stylish style. The coordination diagnosis unit also takes into account the user's lifestyle, hobbies, and preferences and suggests coordination suitable for a travel destination. For example, a relaxed resort style is suggested for a user going to a beach resort. In this way, the generation AI can suggest the best coordination for a specific event or activity, taking into account the user's lifestyle, hobbies, and preferences.

[0048] The outfit diagnosis unit incorporates seasonal and weather information and can suggest optimal outfits in real time. For example, the generative AI will propose outfits that are appropriate for the season based on seasonal information. For example, it will propose coats and sweaters made of warm materials in winter. The outfit diagnosis unit also obtains weather information in real time and suggests outfits that are appropriate for the weather of the day. For example, it will propose waterproof jackets and boots on rainy days. The outfit diagnosis unit will also propose outfits that are appropriate for specific events based on seasonal and weather information. For example, it will propose light linen clothing for a summer beach party. This allows the system to incorporate seasonal and weather information and suggest optimal outfits in real time.

[0049] The evaluation unit can analyze feedback from other users on a user's outfits and reflect that evaluation in the rankings. For example, the generation AI analyzes feedback from other users and reflects outfits that receive a lot of positive evaluations in the rankings. For example, the evaluation is based on the number of "likes" and comments. The evaluation unit also scores feedback from other users and creates rankings based on the scores. For example, it analyzes the content of the feedback and gives high ratings to outfits that receive a lot of positive comments. The evaluation unit also has the generation AI analyze feedback from other users in real time and dynamically update the rankings. For example, it adjusts the rankings every time new feedback is added. This makes it possible to analyze feedback from other users and reflect that evaluation in the rankings.

[0050] The evaluation unit can analyze trends and fashions in outfits and provide evaluations that reflect the latest fashion trends. For example, the generation AI analyzes the latest fashion trends and gives high ratings to outfits that match those trends. For example, it evaluates outfits that incorporate this season's popular colors and styles. The evaluation unit also collects trend information from fashion magazines and social media and evaluates outfits based on that information. For example, it gives high ratings to styles recommended by popular influencers. The evaluation unit also has the generation AI learn past trend data and analyze changes in trends. For example, it compares past trends with current trends and provides an evaluation that reflects the latest trends. This makes it possible to analyze trends and fashions in outfits and provide an evaluation that reflects the latest fashion trends.

[0051] The evaluation unit can analyze fashion styles from different cultural spheres and regions and evaluate them from a global perspective. For example, the generation AI analyzes fashion styles from different cultural spheres and evaluates them based on those styles. For example, it compares fashion styles from Asia, Europe, and America and reflects this in the evaluation. The evaluation unit also collects style information from fashion magazines and social media from different regions and evaluates based on that information. For example, it evaluates styles that reflect regional trends. The generation AI also learns the fashion history of different cultural spheres and evaluates styles that take that historical background into account. For example, it evaluates outfits that combine traditional and modern styles. This makes it possible to analyze fashion styles from different cultural spheres and evaluate them from a global perspective.

[0052] The evaluation unit can create a ranking for each attribute, taking into account attributes such as the user's age, gender, and occupation. For example, the generation AI can take the user's age into account and evaluate outfits that are appropriate for that age. For example, it can evaluate trendy styles for young people and elegant styles for middle-aged and older people. The evaluation unit can also evaluate outfits that are appropriate for the user's gender, taking into account the user's gender. For example, it can evaluate simple and functional styles for men and feminine and glamorous styles for women. The evaluation unit can also consider the user's occupation and evaluate outfits that are appropriate for that occupation. For example, it can evaluate formal styles for business people and unique styles for creative professionals. This makes it possible to create a ranking for each attribute, taking into account attributes such as the user's age, gender, and occupation.

[0053] The generation AI can analyze the content of users' posts and automatically select and display the most appropriate advertisements. For example, it can analyze the content of users' posts and select the most appropriate advertisements based on the theme and keywords of the post. For example, it can display clothing advertisements for fashion-related posts. The generation AI can also analyze the content of users' posts in real time and automatically display advertisements related to that content. For example, it can display travel-related advertisements for travel posts. The generation AI can also learn the user's posting history and select the most appropriate advertisements based on the content of past posts. For example, it can display advertisements related to themes that have been posted frequently in the past. This makes it possible to analyze the content of users' posts and automatically select and display the most appropriate advertisements.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The outfit diagnosis unit diagnoses the user's outfit. For example, the generation AI suggests an appropriate outfit based on the scene entered by the user. The generation AI uses a text generation AI (e.g., LLM) to analyze information about the user's clothing and the scene and suggest an appropriate outfit. The generation AI can also use a multimodal generation AI to analyze the user's image and text information and suggest outfits. Step 2: The evaluation unit evaluates the outfit diagnosed by the outfit diagnosis unit. For example, the generation AI evaluates the outfit's color combination and its suitability for the scene. The generation AI can also evaluate the outfit based on the user's past evaluation history. Step 3: The ranking unit creates a ranking based on the results evaluated by the evaluation unit. For example, the generation AI ranks the user's sense based on the evaluation score. The generation AI can also set the update frequency of the ranking and update the ranking in real time. Step 4: The reward granting unit grants rewards to those ranked highly by the ranking unit. For example, the rewards may include collaborations with famous brands. The reward granting unit may also provide those ranked highly with opportunities to build careers as stylists.

[0056] (Example 2) The coordination diagnosis system according to an embodiment of the present invention is a system in which a generation AI diagnoses coordinations suited to various occasions when a user is unsure of what clothes to wear or is unsure whether the outfit suits them, and shares the evaluation of each outfit. The coordination diagnosis system then evaluates the user's coordination and ranks their sense of fashion based on the evaluation. Top performers can be awarded special rewards, such as collaborations with famous brands. Furthermore, profits can be generated through social media advertising and expansion of sales channels to group company e-commerce sites.

[0057] The coordination diagnosis system according to the embodiment includes a coordination diagnosis unit, an evaluation unit, a ranking unit, and a reward granting unit. The coordination diagnosis unit diagnoses a user's coordination. For example, the generation AI proposes an appropriate coordination based on a scene input by the user. The generation AI uses a text generation AI (e.g., LLM) to analyze information about the user's clothing and the scene and propose an appropriate coordination. The generation AI can also use a multimodal generation AI to analyze the user's image and text information and propose coordination. The evaluation unit evaluates the coordination diagnosed by the coordination diagnosis unit. For example, the generation AI evaluates the coordination's color combination and suitability for the scene. The generation AI can also evaluate the coordination based on the user's past evaluation history. The ranking unit creates a ranking based on the results evaluated by the evaluation unit. For example, the generation AI ranks users' sense of style based on the evaluation scores. The generation AI can also set a ranking update frequency and update the ranking in real time. The reward granting unit grants rewards to users ranked high in the ranking created by the ranking unit. For example, top-ranked users may be granted collaboration rewards with famous brands. The reward granting unit can also provide top-ranked users with opportunities to build a career as a stylist. As a result, the coordination diagnosis system according to the embodiment can improve the user's fashion sense by diagnosing, evaluating, and ranking the user's coordination and granting rewards.

[0058] The coordination diagnosis unit learns the user's past coordination history and can make personalized suggestions that reflect individual style trends. For example, the coordination diagnosis unit uses a generation AI to analyze the user's past coordination history and learn frequently chosen colors and styles. For example, it can suggest similar new coordinations based on the casual styles that the user has often chosen in the past. The coordination diagnosis unit also suggests styles suitable for specific events or seasons based on the user's past coordination history. For example, it can suggest a new style suitable for this summer based on past summer coordinations. The coordination diagnosis unit also uses a generation AI to learn the user's past coordination history and understand tendencies to prefer items from specific brands or designers. For example, it can suggest coordinations that incorporate new items from the user's favorite brand. This makes it possible to learn the user's past coordination history and make suggestions that reflect individual style trends.

[0059] The outfit diagnosis unit can analyze the individual characteristics of a user, such as their body type, skin color, and hairstyle, and suggest the optimal outfit based on that. For example, the outfit diagnosis unit uses a generative AI to analyze the user's body type and suggest clothes with a silhouette and fit that suits that body type. For example, it might suggest a long coat and wide pants for a tall user. The outfit diagnosis unit can also analyze the user's skin color and suggest color combinations that suit that skin color. For example, it might suggest olive green or burgundy for warm skin tones. The outfit diagnosis unit can also analyze the user's hairstyle and suggest styles and accessories that go well with that hairstyle. For example, it might suggest a simple and modern style for a user with short hair. This makes it possible to suggest the optimal outfit based on the individual characteristics of the user, such as their body type, skin color, and hairstyle.

[0060] The coordination diagnosis unit uses the emotion estimation function to analyze the emotions of the user when trying on an outfit in real time, and can suggest coordinations that elicit positive emotions. For example, the coordination diagnosis unit uses the emotion estimation function to analyze the facial expressions and voice of the user when trying on clothes, and suggests coordinations that elicit positive emotions. For example, it prioritizes suggesting coordinations that elicit a lot of smiles. The coordination diagnosis unit also identifies colors and styles that elicit positive emotions based on emotional data from the user when trying on clothes. For example, it suggests color combinations that make the user feel happy. The coordination diagnosis unit also uses the emotion estimation function to monitor emotional changes in real time when the user tries on clothes, and suggests coordinations that enhance positive emotions. For example, it incorporates items that cause a positive change in emotions while trying on clothes. In this way, it is possible to analyze the emotions of the user when trying on outfits in real time, and suggest coordinations that elicit positive emotions.

[0061] The coordination diagnosis unit can take into account the user's lifestyle, hobbies, and preferences and suggest the best coordination for a specific event or activity. For example, the generation AI in the coordination diagnosis unit analyzes the user's lifestyle and suggests coordination suitable for everyday activities. For example, a functional and casual style is suggested for a user who likes the outdoors. The coordination diagnosis unit also suggests coordination suitable for a specific event based on the user's hobbies and preferences. For example, a user attending a music festival is suggested an easy-to-move-in and stylish style. The coordination diagnosis unit also takes into account the user's lifestyle, hobbies, and preferences and suggests coordination suitable for a travel destination. For example, a relaxed resort style is suggested for a user going to a beach resort. In this way, the generation AI can take into account the user's lifestyle, hobbies, and preferences and suggest the best coordination for a specific event or activity.

[0062] The outfit diagnosis unit incorporates seasonal and weather information and can suggest optimal outfits in real time. For example, the generative AI in the outfit diagnosis unit uses seasonal information to suggest outfits that are appropriate for the season. For example, in winter it would suggest coats and sweaters made of warm materials. The outfit diagnosis unit also obtains weather information in real time and suggests outfits that are appropriate for the weather of the day. For example, it would suggest waterproof jackets and boots on rainy days. The outfit diagnosis unit also proposes outfits that are appropriate for specific events based on seasonal and weather information. For example, it would suggest light linen clothing for a summer beach party. This allows the system to incorporate seasonal and weather information and suggest optimal outfits in real time.

[0063] The coordination diagnosis unit can use the emotion estimation function to analyze the emotions of the user when choosing an outfit and make suggestions to reduce stress. For example, the coordination diagnosis unit can use the emotion estimation function to analyze the stress level of the user when choosing an outfit and make suggestions to reduce stress. For example, it can suggest a relaxing casual style. The coordination diagnosis unit can also identify colors and materials that reduce stress based on emotional data from the user when choosing an outfit. For example, it can suggest clothes in muted colors and soft materials. The coordination diagnosis unit can also use the emotion estimation function to monitor emotional changes in real time when the user is choosing an outfit and provide advice to reduce stress. For example, it can narrow down the options and make suggestions. This makes it possible to analyze the emotions of the user when choosing an outfit and make suggestions to reduce stress.

[0064] The evaluation unit can analyze feedback from other users on a user's coordination and reflect the evaluation in the ranking. For example, the generation AI of the evaluation unit analyzes feedback from other users and reflects coordinations that receive many positive evaluations in the ranking. For example, the evaluation is based on the number of "likes" and comments. The evaluation unit also scores feedback from other users and creates rankings based on the scores. For example, the content of the feedback is analyzed and coordinations that receive many positive comments are given high ratings. The evaluation unit also analyzes feedback from other users in real time using the generation AI and dynamically updates the rankings. For example, the rankings are adjusted each time new feedback is added. This makes it possible to analyze feedback from other users and reflect the evaluations in the rankings.

[0065] The evaluation unit can analyze trends and fashions in outfits and provide evaluations that reflect the latest fashion trends. For example, the evaluation unit uses a generation AI to analyze the latest fashion trends and give high ratings to outfits that match those trends. For example, it evaluates outfits that incorporate this season's popular colors and styles. The evaluation unit also collects trend information from fashion magazines and social media and evaluates outfits based on that information. For example, it gives high ratings to styles recommended by popular influencers. The evaluation unit also uses a generation AI to learn past trend data and analyze changes in trends. For example, it compares past trends with current trends and provides an evaluation that reflects the latest trends. This makes it possible to analyze trends and fashions in outfits and provide an evaluation that reflects the latest fashion trends.

[0066] The evaluation unit uses the emotion estimation function to analyze the emotions of the user when evaluating the coordination, and can prioritize emotionally positive evaluations in the ranking. The evaluation unit, for example, uses the emotion estimation function to analyze the emotions of the user when evaluating the coordination, and prioritizes evaluations with stronger positive emotions in the ranking. For example, the evaluation is made based on smiling or happy expressions. The evaluation unit also prioritizes positive evaluations in the ranking based on emotional data when the user evaluates the coordination. For example, evaluations with higher emotion scores are reflected in higher rankings. The evaluation unit also uses the emotion estimation function to monitor emotional changes in real time when the user evaluates the coordination, and prioritizes evaluations with stronger positive emotions in the ranking. For example, if the emotion changes to positive during the evaluation, the evaluation is reflected in a higher ranking. This makes it possible to analyze the emotions of the user when evaluating the coordination, and prioritize emotionally positive evaluations in the ranking.

[0067] The evaluation unit can analyze fashion styles from different cultural spheres and regions and evaluate them from a global perspective. For example, the generation AI analyzes fashion styles from different cultural spheres and evaluates them based on those styles. For example, it compares fashion styles from Asia, Europe, and America and reflects this in the evaluation. The evaluation unit also collects style information from fashion magazines and social media from different regions and evaluates based on that information. For example, it performs evaluations that reflect regional trends. The generation AI also learns the fashion history of different cultural spheres and performs evaluations that take that historical background into account. For example, it evaluates coordination that combines traditional and modern styles. This makes it possible to analyze fashion styles from different cultural spheres and evaluate them from a global perspective.

[0068] The evaluation unit can create a ranking for each attribute, taking into account attributes such as the user's age, gender, and occupation. For example, the generation AI in the evaluation unit takes into account the user's age and evaluates outfits appropriate for that age. For example, it evaluates trendy styles for young people and elegant styles for middle-aged and older people. The evaluation unit also evaluates outfits appropriate for the gender of the user based on the user's gender. For example, it evaluates simple and functional styles for men and feminine and glamorous styles for women. The evaluation unit also considers the user's occupation and evaluates outfits appropriate for that occupation. For example, it evaluates formal styles for business people and unique styles for creative workers. This makes it possible to create a ranking for each attribute, taking into account attributes such as the user's age, gender, and occupation.

[0069] The evaluation unit can use the emotion estimation function to analyze the emotions the user felt when receiving an evaluation and provide feedback to elicit positive emotions. The evaluation unit, for example, uses the emotion estimation function to analyze the emotions the user felt when receiving an evaluation and provide feedback to elicit positive emotions. For example, it displays an encouraging message in response to the evaluation result. The evaluation unit also provides specific advice to elicit positive emotions based on the emotional data of the user when receiving an evaluation. For example, it presents tips that will be useful for the next outfit. The evaluation unit also uses the emotion estimation function to monitor changes in the user's emotions when receiving an evaluation in real time and provide feedback that strengthens positive emotions. For example, if the emotions change to positive during the evaluation, the change is emphasized. In this way, it is possible to analyze the emotions the user felt when receiving an evaluation and provide feedback to elicit positive emotions.

[0070] The generation AI can analyze the content of users' posts and automatically select and display the most appropriate advertisement. For example, the generation AI analyzes the content of users' posts and selects the most appropriate advertisement based on the theme and keywords of the post. For example, clothing advertisements are displayed for fashion-related posts. The generation AI also analyzes the content of users' posts in real time and automatically displays advertisements related to that content. For example, travel-related advertisements are displayed for travel posts. The generation AI also learns the user's posting history and selects the most appropriate advertisement based on the content of past posts. For example, advertisements related to themes that have been posted frequently in the past are displayed. This makes it possible to analyze the content of users' posts and automatically select and display the most appropriate advertisement.

[0071] The generation AI can analyze the interests of a user's followers and display targeted advertisements. For example, the generation AI can analyze the interests of a user's followers and display advertisements that are most suitable for them. For example, it can display advertisements related to topics that many followers are interested in. The generation AI can also analyze the content posted by a user's followers and display targeted advertisements based on that content. For example, it can display advertisements related to topics that many followers post about. The generation AI can also analyze the interests of a user's followers in real time and dynamically adjust advertisements based on the results. For example, it can update the advertisement content if the followers' interests change. This makes it possible to analyze the interests of a user's followers and display targeted advertisements.

[0072] The emotion estimation function can analyze the emotions a user feels when viewing an advertisement, and prioritize displaying advertisements that elicit positive emotions. The emotion estimation function, for example, analyzes the emotions a user feels when viewing an advertisement, and prioritizes displaying advertisements that elicit positive emotions. For example, it displays advertisements that make the user smile. The emotion estimation function also identifies advertisements that elicit positive emotions based on emotional data when the user views an advertisement. For example, it prioritizes displaying advertisements with high emotion scores. The emotion estimation function also monitors changes in emotions when the user views an advertisement in real time, and prioritizes displaying advertisements that intensify positive emotions. For example, if emotions change to positive after viewing an advertisement, it prioritizes displaying that advertisement. This makes it possible to analyze the emotions a user feels when viewing an advertisement, and prioritize displaying advertisements that elicit positive emotions.

[0073] The generation AI can analyze engagement with users' posts and automatically adjust the timing to maximize advertising effectiveness. For example, the generation AI can analyze engagement with users' posts and display ads at times when engagement is high. For example, it can display ads immediately after a post receives many likes and comments. The generation AI can also automatically adjust the timing of ad display based on engagement data for users' posts. For example, it can display ads when engagement reaches its peak. The generation AI can also learn users' posting history and identify the optimal timing to display ads based on past engagement data. For example, if engagement is high during a particular time of day or day of the week, it can display ads at that time. This makes it possible to analyze engagement with users' posts and automatically adjust the timing to maximize advertising effectiveness.

[0074] The generation AI can suggest products and services related to the content posted by the user and display them as advertisements. For example, the generation AI analyzes the content posted by the user and suggests products and services related to that content. For example, for fashion-related posts, advertisements for clothing and accessories are displayed. The generation AI can also identify related products and services based on the content posted by the user and display them as advertisements. For example, for travel posts, it can suggest travel-related services. The generation AI can also learn the user's posting history and suggest related products and services based on past posts. For example, it can display advertisements for products related to themes that have been posted frequently in the past. This makes it possible to suggest products and services related to the content posted by the user and display them as advertisements.

[0075] The emotion estimation function can analyze the emotions felt when a user clicks on an ad and formulate an advertising strategy that elicits positive emotions. For example, the emotion estimation function analyzes the emotions felt when a user clicks on an ad and formulates an advertising strategy that elicits positive emotions. For example, it prioritizes displaying ads that make the user smile after clicking. The emotion estimation function also identifies ads that elicit positive emotions based on emotional data from the user when they click on an ad. For example, it prioritizes displaying ads with high emotional scores. The emotion estimation function also monitors changes in emotions felt when a user clicks on an ad in real time and formulates an advertising strategy that strengthens positive emotions. For example, if emotions change to positive after clicking, it prioritizes adopting that advertising strategy. This makes it possible to analyze the emotions felt when a user clicks on an ad and formulate an advertising strategy that elicits positive emotions.

[0076] Generative AI can analyze a user's purchasing history and suggest products that match their individual tastes. For example, generative AI can analyze a user's purchasing history and suggest products that match their individual tastes based on products purchased in the past. For example, it can suggest new products based on brands and styles purchased in the past. Generative AI can also suggest related products based on a user's purchasing history. For example, it can suggest products that go well with items purchased in the past. Generative AI can also learn a user's purchasing history and suggest products that are suitable for specific seasons or events. For example, it can suggest products that are suitable for this summer based on past summer purchasing history. This makes it possible to analyze a user's purchasing history and suggest products that match their individual tastes.

[0077] The generation AI can check the stock status of items used in a user's outfit in real time and suggest products that can be purchased immediately. For example, the generation AI can check the stock status of items used in a user's outfit in real time and suggest products that can be purchased immediately. For example, it can prioritize suggesting items that are in stock. The generation AI can also suggest alternative products based on the stock status of items used in a user's outfit. For example, it can suggest items with similar designs if an item is out of stock. The generation AI can also monitor the stock status of items used in a user's outfit in real time and notify the user when stock is running low. For example, it can suggest that an item that is running low be purchased as soon as possible. This makes it possible to check the stock status of items used in a user's outfit in real time and suggest products that can be purchased immediately.

[0078] The emotion estimation function can analyze the emotions a user feels when purchasing a product and provide a purchasing experience that elicits positive emotions. For example, the emotion estimation function can analyze the emotions a user feels when purchasing a product and provide a purchasing experience that elicits positive emotions. For example, it can display a message that makes the user feel joy or satisfaction at the time of purchase. The emotion estimation function can also provide specific advice to elicit positive emotions based on the emotional data a user receives when purchasing a product. For example, it can present tips on post-purchase care and styling. The emotion estimation function can also monitor changes in emotions a user experiences when purchasing a product in real time and provide a purchasing experience that strengthens positive emotions. For example, if emotions change to a positive one during a purchase, it can highlight that change. This makes it possible to analyze the emotions a user feels when purchasing a product and provide a purchasing experience that elicits positive emotions.

[0079] The generation AI can suggest related products and accessories based on the user's purchasing history, promoting cross-selling. For example, the generation AI can analyze the user's purchasing history and suggest related products and accessories based on previously purchased products. For example, it can suggest accessories that go well with a purchased dress. The generation AI can also identify related products to promote cross-selling based on the user's purchasing history. For example, it can suggest products that go well with purchased items. The generation AI can also learn the user's purchasing history and suggest related products that are suitable for specific seasons or events. For example, it can suggest accessories that are suitable for this summer based on past summer purchasing history. This makes it possible to suggest related products and accessories based on the user's purchasing history, promoting cross-selling.

[0080] Generative AI can make periodic recommendations based on a user's purchasing history and encourage repeat purchases. For example, generative AI can analyze a user's purchasing history and periodically recommend new products. For example, it can propose new collections each season. Generative AI can also make recommendations to encourage repeat purchases based on a user's purchasing history. For example, it can propose new products from the same brand as a product previously purchased. Generative AI can also learn a user's purchasing history and make recommendations tailored to specific events or anniversaries. For example, it can propose products suitable for birthdays and anniversaries. This makes it possible to make periodic recommendations based on a user's purchasing history and encourage repeat purchases.

[0081] The emotion estimation function can analyze the emotions a user feels after purchasing a product and provide after-sales service to elicit positive emotions. For example, the emotion estimation function can analyze the emotions a user feels after purchasing a product and provide after-sales service to elicit positive emotions. For example, it can send a thank you message after a purchase. The emotion estimation function can also provide specific advice to elicit positive emotions based on emotional data from a user after purchasing a product. For example, it can suggest post-purchase care methods and styling tips. The emotion estimation function can also monitor changes in emotions after a user purchases a product in real time and provide after-sales service to strengthen positive emotions. For example, if emotions change to a positive one after a purchase, it can highlight that change. This makes it possible to analyze the emotions a user feels after purchasing a product and provide after-sales service to elicit positive emotions.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The coordination diagnosis unit takes into account the user's lifestyle, hobbies, and preferences and can suggest the best coordination for a specific event or activity. For example, the generation AI analyzes the user's lifestyle and suggests coordination suitable for everyday activities. For example, a functional and casual style is suggested for a user who likes the outdoors. The coordination diagnosis unit also suggests coordination suitable for a specific event based on the user's hobbies and preferences. For example, a user attending a music festival is suggested an easy-to-move-in and stylish style. The coordination diagnosis unit also takes into account the user's lifestyle, hobbies, and preferences and suggests coordination suitable for a travel destination. For example, a relaxed resort style is suggested for a user going to a beach resort. In this way, the generation AI can suggest the best coordination for a specific event or activity, taking into account the user's lifestyle, hobbies, and preferences.

[0084] The outfit diagnosis unit incorporates seasonal and weather information and can suggest optimal outfits in real time. For example, the generative AI will propose outfits that are appropriate for the season based on seasonal information. For example, it will propose coats and sweaters made of warm materials in winter. The outfit diagnosis unit also obtains weather information in real time and suggests outfits that are appropriate for the weather of the day. For example, it will propose waterproof jackets and boots on rainy days. The outfit diagnosis unit will also propose outfits that are appropriate for specific events based on seasonal and weather information. For example, it will propose light linen clothing for a summer beach party. This allows the system to incorporate seasonal and weather information and suggest optimal outfits in real time.

[0085] The coordination diagnosis unit can use the emotion estimation function to analyze the emotions of the user when trying on an outfit in real time, and suggest coordinations that elicit positive emotions. For example, the emotion estimation function can be used to analyze the facial expressions and voice of the user when trying on clothes, and suggest coordinations that elicit positive emotions. For example, coordinations that elicit a lot of smiles can be prioritized. The coordination diagnosis unit can also identify colors and styles that elicit positive emotions based on emotional data from the user when trying on clothes. For example, it can suggest color combinations that make the user feel happy. The coordination diagnosis unit can also use the emotion estimation function to monitor emotional changes in real time when the user tries on clothes, and suggest coordinations that enhance positive emotions. For example, it can incorporate items that elicit a positive change in emotions while trying on clothes. In this way, the coordination diagnosis unit can analyze the emotions of the user when trying on outfits in real time, and suggest coordinations that elicit positive emotions.

[0086] The evaluation unit can analyze feedback from other users on a user's outfits and reflect that evaluation in the rankings. For example, the generation AI analyzes feedback from other users and reflects outfits that receive a lot of positive evaluations in the rankings. For example, the evaluation is based on the number of "likes" and comments. The evaluation unit also scores feedback from other users and creates rankings based on the scores. For example, it analyzes the content of the feedback and gives high ratings to outfits that receive a lot of positive comments. The evaluation unit also has the generation AI analyze feedback from other users in real time and dynamically update the rankings. For example, it adjusts the rankings every time new feedback is added. This makes it possible to analyze feedback from other users and reflect that evaluation in the rankings.

[0087] The evaluation unit can analyze trends and fashions in outfits and provide evaluations that reflect the latest fashion trends. For example, the generation AI analyzes the latest fashion trends and gives high ratings to outfits that match those trends. For example, it evaluates outfits that incorporate this season's popular colors and styles. The evaluation unit also collects trend information from fashion magazines and social media and evaluates outfits based on that information. For example, it gives high ratings to styles recommended by popular influencers. The evaluation unit also has the generation AI learn past trend data and analyze changes in trends. For example, it compares past trends with current trends and provides an evaluation that reflects the latest trends. This makes it possible to analyze trends and fashions in outfits and provide an evaluation that reflects the latest fashion trends.

[0088] The evaluation unit uses the emotion estimation function to analyze the emotions of the user when evaluating an outfit, and can prioritize emotionally positive evaluations in the ranking. For example, the emotion estimation function can be used to analyze the emotions of the user when evaluating an outfit, and prioritize evaluations with stronger positive emotions in the ranking. For example, the evaluation can be based on smiling or happy facial expressions. The evaluation unit also prioritizes positive evaluations in the ranking based on emotional data from the user when evaluating the outfit. For example, evaluations with higher emotion scores are prioritized. The evaluation unit also uses the emotion estimation function to monitor emotional changes in real time when the user evaluates the outfit, and prioritizes evaluations with stronger positive emotions in the ranking. For example, if the emotion changes to positive during the evaluation, the evaluation is prioritized. This makes it possible to analyze the emotions of the user when evaluating an outfit, and prioritize emotionally positive evaluations in the ranking.

[0089] The evaluation unit can analyze fashion styles from different cultural spheres and regions and evaluate them from a global perspective. For example, the generation AI analyzes fashion styles from different cultural spheres and evaluates them based on those styles. For example, it compares fashion styles from Asia, Europe, and America and reflects this in the evaluation. The evaluation unit also collects style information from fashion magazines and social media from different regions and evaluates based on that information. For example, it evaluates styles that reflect regional trends. The generation AI also learns the fashion history of different cultural spheres and evaluates styles that take that historical background into account. For example, it evaluates outfits that combine traditional and modern styles. This makes it possible to analyze fashion styles from different cultural spheres and evaluate them from a global perspective.

[0090] The evaluation unit can create a ranking for each attribute, taking into account attributes such as the user's age, gender, and occupation. For example, the generation AI can take the user's age into account and evaluate outfits that are appropriate for that age. For example, it can evaluate trendy styles for young people and elegant styles for middle-aged and older people. The evaluation unit can also evaluate outfits that are appropriate for the user's gender, taking into account the user's gender. For example, it can evaluate simple and functional styles for men and feminine and glamorous styles for women. The evaluation unit can also consider the user's occupation and evaluate outfits that are appropriate for that occupation. For example, it can evaluate formal styles for business people and unique styles for creative professionals. This makes it possible to create a ranking for each attribute, taking into account attributes such as the user's age, gender, and occupation.

[0091] The evaluation unit can use the emotion estimation function to analyze the emotions the user felt when receiving an evaluation and provide feedback to elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions the user felt when receiving an evaluation and provide feedback to elicit positive emotions. For example, an encouraging message can be displayed in response to the evaluation result. The evaluation unit can also provide specific advice to elicit positive emotions based on the emotional data of the user when receiving an evaluation. For example, it can present tips that will be useful for the next outfit. The evaluation unit can also use the emotion estimation function to monitor changes in the user's emotions when receiving an evaluation in real time and provide feedback that strengthens positive emotions. For example, if the emotions change to positive during the evaluation, the change can be emphasized. This makes it possible to analyze the emotions the user felt when receiving an evaluation and provide feedback to elicit positive emotions.

[0092] The generation AI can analyze the content of users' posts and automatically select and display the most appropriate advertisements. For example, it can analyze the content of users' posts and select the most appropriate advertisements based on the theme and keywords of the post. For example, it can display clothing advertisements for fashion-related posts. The generation AI can also analyze the content of users' posts in real time and automatically display advertisements related to that content. For example, it can display travel-related advertisements for travel posts. The generation AI can also learn the user's posting history and select the most appropriate advertisements based on the content of past posts. For example, it can display advertisements related to themes that have been posted frequently in the past. This makes it possible to analyze the content of users' posts and automatically select and display the most appropriate advertisements.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The outfit diagnosis unit diagnoses the user's outfit. For example, the generation AI suggests an appropriate outfit based on the scene entered by the user. The generation AI uses a text generation AI (e.g., LLM) to analyze information about the user's clothing and the scene and suggest an appropriate outfit. The generation AI can also use a multimodal generation AI to analyze the user's image and text information and suggest outfits. Step 2: The evaluation unit evaluates the outfit diagnosed by the outfit diagnosis unit. For example, the generation AI evaluates the outfit's color combination and its suitability for the scene. The generation AI can also evaluate the outfit based on the user's past evaluation history. Step 3: The ranking unit creates a ranking based on the results evaluated by the evaluation unit. For example, the generation AI ranks the user's sense based on the evaluation score. The generation AI can also set the update frequency of the ranking and update the ranking in real time. Step 4: The reward granting unit grants rewards to those ranked highly by the ranking unit. For example, the rewards may include collaborations with famous brands. The reward granting unit may also provide those ranked highly with opportunities to build careers as stylists.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] 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]

[0162] 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 coordination diagnosis unit that diagnoses a user's coordination; an evaluation unit that evaluates the coordination diagnosed by the coordination diagnosis unit; a ranking unit that creates a ranking based on the results of the evaluation by the evaluation unit; a reward granting unit that grants rewards to players ranked high by the ranking unit. A system characterized by:

2. The coordinate diagnosis unit The system learns the user's past coordination history and makes personalized suggestions that reflect the user's individual style trends.

2. The system of claim 1.

3. The coordinate diagnosis unit Analyze the user's individual characteristics, such as body type, skin color, and hairstyle, and suggest optimal outfits based on those characteristics.

2. The system of claim 1.

4. The coordinate diagnosis unit Analyze the emotions of the user when trying on outfits in real time and suggest outfits that evoke positive emotions 2. The system of claim 1.

5. The coordinate diagnosis unit Taking into consideration the user's lifestyle and hobbies, the system suggests the best outfits for specific events and activities.

2. The system of claim 1.

Images