System

Abstract

An object of a system according to an embodiment is to make it possible to efficiently select and visually confirm clothing that suits a user.SOLUTION: A system includes a photograph registering part, a clothing information uploading part, a recommending part, a 3D model displaying part, and a purchasing part. The photograph registration unit registers a photograph showing the size of the user. A clothing information upload part uploads several pieces of clothing usually used. The recommendation unit recommends clothing determined to suit the user on the basis of the information acquired by the photograph registration unit and the clothing information upload unit. A 3D model display part displays the clothes recommended by the recommendation part on a demonstration screen in which the clothes are modeled into 3D. The purchase part purchases the clothes displayed by the 3D model display part.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 had the problem that it is difficult to efficiently select and visually confirm clothes that suit a user.

[0005] The system according to the embodiment aims to enable a user to efficiently select clothes that suit the user and visually check the clothes. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo registration unit, a clothing information upload unit, a recommendation unit, a 3D model display unit, and a purchase unit. The photo registration unit registers a photo showing the user's height. The clothing information upload unit uploads several pieces of clothing that the user normally wears. The recommendation unit recommends clothing that it determines will suit the user based on the information acquired by the photo registration unit and the clothing information upload unit. The 3D model display unit displays the clothing recommended by the recommendation unit as a 3D model on a demo screen. The purchase unit purchases the clothing displayed by the 3D model display unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently select clothes that suit the user and enable the user to visually check the clothes. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The recommendation system according to an embodiment of the present invention is a system in which a user registers a photo showing their height and shape and uploads a few pieces of clothing that they normally wear.The system then recommends clothes that AI determines would suit the user, and the user can check the clothes on a 3D model demo screen and actually purchase the clothes if they like them.This allows the recommendation system to easily find clothes that suit them and then actually purchase those clothes.

[0029] A recommendation system according to an embodiment includes a photo registration unit, a clothing information upload unit, a recommendation unit, a 3D model display unit, and a purchase unit. The photo registration unit registers a photo showing the user's height and shape. For example, a full-body photo taken from the front or a photo taken from the side is registered. The photo registration unit is used by AI to analyze the user's body shape and characteristics. For example, the AI ​​acquires information such as the user's height, weight, and body shape. The clothing information upload unit uploads several pieces of clothing that the user wears on a daily basis. For example, photos of shirts, pants, dresses, etc. that the user often wears are uploaded. The clothing information upload unit is used by AI to understand the user's preferences and style. For example, the AI ​​understands the user's fashion style and color preferences. The recommendation unit recommends clothes that it determines will suit the user based on the information acquired by the photo registration unit and the clothing information upload unit. For example, the AI ​​considers the user's body shape and preferences and suggests clothes of a specific brand or design. The generation AI makes recommendations based on prompts containing the user's photo and clothing information. The 3D model display unit displays a 3D model of the clothing recommended by the recommendation unit on a demo screen. For example, the user can see their 3D model rotating and walking while wearing the selected clothing. The purchase unit purchases the clothing displayed by the 3D model display unit. For example, by clicking a "Purchase" button on the demo screen, the user can be directed to an online store and complete the purchase process. This allows the recommendation system according to the embodiment to easily find clothing that suits them and then actually purchase the clothing. For example, this system is very convenient when a user wants to try a new style or is looking for new clothes for a special event. It also solves the problem of not being able to try on clothes when shopping online.

[0030] When analyzing a user's photo, the AI ​​in the photo registration unit can perform a detailed analysis of the skeletal and muscle structure, enabling more precise body shape information to be obtained. For example, when analyzing a user's photo, the AI ​​in the photo registration unit can perform a detailed analysis of the skeletal shape and muscle placement to accurately grasp the characteristics of the body shape. For example, it analyzes shoulder width, waist position, muscle development, etc., and recreates the user's body shape as a 3D model. This allows for more precise acquisition of the user's body shape information, making it possible to recommend more appropriate clothing.

[0031] The photo registration unit uses AI to analyze the user's posture and walking style based on a photo of the user, and can provide advice on correcting posture and improving walking. For example, the photo registration unit uses AI to analyze posture distortions and walking habits based on a photo of the user, and provide advice on correcting posture. For example, it analyzes shoulder height and spinal curvature and suggests exercises to maintain correct posture. This allows for health management by analyzing the user's posture and walking style and providing advice on correcting posture and improving walking.

[0032] The photo registration unit can capture a user's photo as 3D scan data and generate a more detailed body model. The photo registration unit, for example, captures a user's photo as 3D scan data and generates a detailed body model. For example, the photo registration unit accurately reproduces the user's body shape based on photos taken from multiple angles. This allows for a more detailed reproduction of the user's body shape, making it possible to recommend more appropriate clothing.

[0033] When analyzing a user's photos, the photo registration unit uses AI to analyze skin tone and texture, allowing it to recommend skin care products. For example, the photo registration unit analyzes a user's photos and performs a detailed analysis of skin tone and texture. For example, it can recommend optimal skin care products based on skin color and texture. This makes it possible to recommend skin care products that suit the user's skin condition.

[0034] The clothing information uploading unit uses AI to analyze the materials and textures of uploaded clothing and can suggest clothing made from new materials that suit the user's preferences. For example, the clothing information uploading unit uses AI to analyze the materials and textures of uploaded clothing and can suggest clothing made from new materials that suit the user's preferences. For example, it can suggest comfortable clothing based on materials such as cotton and linen. This allows for recommendations that provide a higher level of satisfaction by suggesting clothing made from new materials that suit the user's preferences.

[0035] The clothing information uploading unit uses AI to analyze the colors and patterns of uploaded clothing and suggest new designs that match the season and trends. For example, the clothing information uploading unit uses AI to analyze the colors and patterns of uploaded clothing and suggest new designs that match the season and trends. For example, it suggests bright colors and floral patterns in spring, and muted colors and checkered patterns in autumn. This allows it to suggest new clothing designs that match the season and trends, improving the user's fashion style.

[0036] The clothing information uploading unit allows AI to suggest outfits based on clothing information uploaded by the user, and provide a total coordinated look. For example, the clothing information uploading unit allows AI to suggest a total coordinated look based on clothing information uploaded by the user. For example, it may suggest an outfit that combines a shirt, pants, and accessories. This allows AI to suggest a total coordinated look to the user, and provide a more unified fashion style.

[0037] The clothing information uploading unit allows AI to suggest clothing that matches the user's lifestyle based on the uploaded clothing information. For example, the clothing information uploading unit allows AI to suggest clothing that matches the user's lifestyle based on the uploaded clothing information. For example, it may suggest easy-to-move-in clothes for an active lifestyle and formal clothes for office work. This allows for more practical recommendations by suggesting clothing that matches the user's lifestyle.

[0038] The recommendation section uses AI to make recommendations that take into account not only the user's body type and preferences, but also past purchase history and reviews. For example, the recommendation section uses AI to make recommendations that take into account not only the user's body type and preferences, but also past purchase history and reviews. For example, it may suggest clothes of a similar style based on the design and size of clothes previously purchased. This allows for more accurate recommendations by taking into account the user's past purchase history and reviews.

[0039] The recommendation unit uses AI to take into account the fashion styles of the user's friends and family and make group coordination suggestions. For example, the recommendation unit uses AI to take into account the fashion styles of the user's friends and family and make group coordination suggestions. For example, it can analyze photos of all family members and suggest coordinated outfits. This makes it possible to consider the fashion styles of the user's friends and family and make group coordination suggestions.

[0040] The recommendation unit uses AI to consider the dress code of the user's workplace or event and suggest appropriate clothing. For example, the recommendation unit uses AI to consider the dress code of the user's workplace or event and suggest appropriate clothing. For example, it can suggest clothes suitable for business casual or formal events. This makes it possible to suggest appropriate clothing by considering the dress code of the user's workplace or event.

[0041] The 3D model display unit allows the 3D model to reflect the user's movements and facial expressions in real time, providing a more realistic fitting experience.The 3D model display unit allows the 3D model to reflect the user's movements and facial expressions in real time, providing a more realistic fitting experience.For example, the 3D model reflects the user's movements such as walking and turning.This allows the user's movements and facial expressions to be reflected in real time, providing a more realistic fitting experience.

[0042] The 3D model display unit can simulate how the 3D model looks under different lighting conditions and backgrounds, recreating actual usage scenes. For example, the 3D model display unit can simulate how the 3D model looks under different lighting conditions and backgrounds, recreating actual usage scenes. For example, it can simulate indoor and outdoor lighting conditions, and daytime and nighttime lighting conditions. This allows the actual usage scenes to be reproduced by simulating how the 3D model looks under different lighting conditions and backgrounds.

[0043] The 3D model display unit can generate versions of the 3D model with different body shapes and ages, and simulate future changes in body shape. For example, the 3D model display unit can generate versions of the 3D model with different body shapes and ages, and simulate future changes in body shape. For example, the 3D model display unit can predict the body shape several years from now based on the current body shape and check how clothes will fit. This allows future changes in body shape to be simulated by generating versions with different body shapes and ages.

[0044] The 3D model display unit can simulate how the 3D model will look in different seasons and weather conditions, and suggest coordination for each season. For example, the 3D model display unit can simulate how the 3D model will look in different seasons and weather conditions, and suggest coordination for each season. For example, it can simulate how the 3D model will look on a summer beach or in a winter snowy landscape. This makes it possible to suggest coordination for each season by simulating how the 3D model will look in different seasons and weather conditions.

[0045] During the purchasing process, the AI ​​can make additional recommendations based on the user's past purchase history and reviews. For example, during the purchasing process, the AI ​​can suggest items that go well with clothes previously purchased. This allows for additional recommendations based on the user's past purchase history and reviews, enabling a more satisfying purchasing experience.

[0046] During the purchasing process, AI can suggest sizes and customization options that match the user's body type and preferences. For example, during the purchasing process, AI can suggest sizes and customization options that match the user's body type and preferences. For example, it can suggest sizes that fit the user's body type and customization of designs that match their preferences. This allows for a more satisfying purchasing experience by suggesting sizes and customization options that match the user's body type and preferences.

[0047] The purchasing department can use AI to suggest gifts for the user's friends and family during the purchase process. For example, the purchasing department can use AI to suggest gifts for the user's friends and family during the purchase process. For example, it can suggest the most suitable gift based on the user's purchase history and the preferences of friends and family. This allows for a more satisfying purchasing experience by suggesting gifts for the user's friends and family.

[0048] During the purchase process, the purchasing department can use AI to suggest accessories and shoes that match the user's lifestyle. For example, during the purchase process, the purchasing department can use AI to suggest accessories and shoes that match the user's lifestyle. For example, it can suggest accessories and shoes that match the user's usual activities and preferences. This allows for a more satisfying purchasing experience by suggesting accessories and shoes that match the user's lifestyle.

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

[0050] A recommendation system can also be equipped with a health management unit that monitors the user's health status. For example, it can acquire heart rate, step count, and sleep data from a wearable device that the user uses daily and analyze the user's health status. This makes it possible to provide fashion advice tailored to the user's health status and suggest lifestyle changes to support health. For example, a user who exercises less can be suggested sportswear that encourages an active lifestyle. Furthermore, a user who experiences poor quality sleep can be suggested clothing made of materials and with a relaxing design. This makes it possible to make recommendations that take the user's health status into consideration, enabling more personalized services to be provided.

[0051] The recommendation system can also be equipped with a hobby analysis unit that analyzes a user's hobbies and interests. For example, the system can analyze content shared by the user on social media or blogs to understand the user's hobbies and interests. This makes it possible to suggest fashion items that match the user's hobbies and interests. For example, a user who enjoys the outdoors can be suggested highly functional outdoor wear. Or, a user who frequently attends music festivals can be suggested a casual style suitable for festivals. This makes it possible to make recommendations that match the user's hobbies and interests, resulting in a more satisfying service.

[0052] The recommendation system can also be equipped with an eco-awareness analysis unit that takes the user's environmental awareness into account. For example, it can analyze the environmental impact and recyclability of products purchased by the user and suggest eco-friendly products. This makes it possible to suggest environmentally conscious fashion items. For example, it can suggest clothes made from recycled materials or items from brands that use environmentally friendly manufacturing processes. Furthermore, if the user is interested in sustainable fashion, it can suggest eco-friendly styles. This makes it possible to make recommendations based on the user's environmental awareness and promote more sustainable fashion.

[0053] The recommendation system can further include a travel plan analysis unit that takes into account the user's travel plans. For example, if the user inputs their travel destination and travel period, the system can suggest fashion items suited to the climate and culture of the destination. This makes it possible to suggest appropriate clothing for the travel destination. For example, a user traveling to a beach resort can be suggested light and comfortable resort wear. Furthermore, a user traveling to a cold region can be suggested outerwear and accessories that provide good cold protection. This makes it possible to make recommendations based on the user's travel plans, allowing them to enjoy their trip more comfortably.

[0054] The recommendation system can further include an occupational analysis unit that takes into account the user's occupation and work content. For example, based on the occupational information entered by the user, it can suggest fashion items that are appropriate for the work content. This makes it possible to suggest appropriate attire for the workplace. For example, formal suits and business casual items can be suggested for business people. Furthermore, it can suggest unique and free styles for users engaged in creative occupations. This makes it possible to make recommendations based on the user's occupation and work content, thereby improving their fashion style at work.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The photo registration unit registers a photo that shows the user's height. For example, a full-body photo taken from the front or a photo taken from the side can be registered. The photo registration unit is also used by the AI ​​to analyze the user's body shape and characteristics. For example, the AI ​​obtains information such as the user's height, weight, and body shape. Step 2: The clothing information uploading unit uploads a few pieces of clothing that the user normally wears. For example, the user uploads photos of their favorite shirts, pants, dresses, etc. The clothing information uploading unit also helps the AI ​​understand the user's preferences and style. For example, the AI ​​can understand the user's fashion style and color preferences. Step 3: The recommendation unit recommends clothes that it determines will suit the user based on the information obtained by the photo registration unit and clothing information upload unit. For example, the AI ​​may suggest clothes of a specific brand or design, taking into account the user's body type and preferences. The generation AI makes recommendations based on prompts containing the user's photo and clothing information. Step 4: The 3D model display unit displays the clothes recommended by the recommendation unit as a 3D model on a demo screen. For example, the user can see how a 3D model of themselves wearing the clothes they selected rotates and walks. Step 5: The purchasing unit purchases the clothes displayed by the 3D model display unit. For example, by clicking the "Purchase" button on the demo screen, the user is taken to an online store and can complete the purchase procedure.

[0057] (Example 2) The recommendation system according to an embodiment of the present invention is a system in which a user registers a photo showing their height and shape and uploads a few pieces of clothing that they normally wear.The system then recommends clothes that AI determines would suit the user, and the user can check the clothes on a 3D model demo screen and actually purchase the clothes if they like them.This allows the recommendation system to easily find clothes that suit them and then actually purchase those clothes.

[0058] A recommendation system according to an embodiment includes a photo registration unit, a clothing information upload unit, a recommendation unit, a 3D model display unit, and a purchase unit. The photo registration unit registers a photo showing the user's height and shape. For example, a full-body photo taken from the front or a photo taken from the side is registered. The photo registration unit is used by AI to analyze the user's body shape and characteristics. For example, the AI ​​acquires information such as the user's height, weight, and body shape. The clothing information upload unit uploads several pieces of clothing that the user wears on a daily basis. For example, photos of shirts, pants, dresses, etc. that the user often wears are uploaded. The clothing information upload unit is used by AI to understand the user's preferences and style. For example, the AI ​​understands the user's fashion style and color preferences. The recommendation unit recommends clothes that it determines will suit the user based on the information acquired by the photo registration unit and the clothing information upload unit. For example, the AI ​​considers the user's body shape and preferences and suggests clothes of a specific brand or design. The generation AI makes recommendations based on prompts containing the user's photo and clothing information. The 3D model display unit displays a 3D model of the clothing recommended by the recommendation unit on a demo screen. For example, the user can see their 3D model rotating and walking while wearing the selected clothing. The purchase unit purchases the clothing displayed by the 3D model display unit. For example, by clicking a "Purchase" button on the demo screen, the user can be directed to an online store and complete the purchase process. This allows the recommendation system according to the embodiment to easily find clothing that suits them and then actually purchase the clothing. For example, this system is very convenient when a user wants to try a new style or is looking for new clothes for a special event. It also solves the problem of not being able to try on clothes when shopping online.

[0059] When analyzing a user's photo, the AI ​​in the photo registration unit can perform a detailed analysis of the skeletal and muscle structure, enabling more precise body shape information to be obtained. For example, when analyzing a user's photo, the AI ​​in the photo registration unit can perform a detailed analysis of the skeletal shape and muscle placement to accurately grasp the characteristics of the body shape. For example, it analyzes shoulder width, waist position, muscle development, etc., and recreates the user's body shape as a 3D model. This allows for more precise acquisition of the user's body shape information, making it possible to recommend more appropriate clothing.

[0060] The photo registration unit uses AI to analyze the user's posture and walking style based on a photo of the user, and can provide advice on correcting posture and improving walking. For example, the photo registration unit uses AI to analyze posture distortions and walking habits based on a photo of the user, and provide advice on correcting posture. For example, it analyzes shoulder height and spinal curvature and suggests exercises to maintain correct posture. This allows for health management by analyzing the user's posture and walking style and providing advice on correcting posture and improving walking.

[0061] The photo registration unit can use the emotion estimation function to analyze the user's emotion when taking a photo and recommend taking a photo in a relaxed state. For example, the photo registration unit uses the emotion estimation function to analyze the user's facial expression and posture when taking a photo and recommend taking a photo in a relaxed state. For example, if the user is nervous, advice to relax is displayed. This allows the user to take a photo in a relaxed state and obtain more natural body shape information.

[0062] The photo registration unit can capture a user's photo as 3D scan data and generate a more detailed body model. The photo registration unit, for example, captures a user's photo as 3D scan data and generates a detailed body model. For example, the photo registration unit accurately reproduces the user's body shape based on photos taken from multiple angles. This allows for a more detailed reproduction of the user's body shape, making it possible to recommend more appropriate clothing.

[0063] When analyzing a user's photos, the photo registration unit uses AI to analyze skin tone and texture, allowing it to recommend skin care products. For example, the photo registration unit analyzes a user's photos and performs a detailed analysis of skin tone and texture. For example, it can recommend optimal skin care products based on skin color and texture. This makes it possible to recommend skin care products that suit the user's skin condition.

[0064] The photo registration unit can use the emotion estimation function to analyze the emotion of the user when taking a photo in real time and provide advice to bring out positive emotions. For example, the photo registration unit can use the emotion estimation function to analyze the emotion of the user when taking a photo in real time and provide advice to bring out positive emotions. For example, the photo registration unit can suggest a joke to bring out a smile or a breathing technique to relax. This allows the user to take a photo with positive emotions, thereby obtaining more natural body shape information.

[0065] The clothing information uploading unit uses AI to analyze the materials and textures of uploaded clothing and can suggest clothing made from new materials that suit the user's preferences. For example, the clothing information uploading unit uses AI to analyze the materials and textures of uploaded clothing and can suggest clothing made from new materials that suit the user's preferences. For example, it can suggest comfortable clothing based on materials such as cotton and linen. This allows for recommendations that provide a higher level of satisfaction by suggesting clothing made from new materials that suit the user's preferences.

[0066] The clothing information uploading unit uses AI to analyze the colors and patterns of uploaded clothing and suggest new designs that match the season and trends. For example, the clothing information uploading unit uses AI to analyze the colors and patterns of uploaded clothing and suggest new designs that match the season and trends. For example, it suggests bright colors and floral patterns in spring, and muted colors and checkered patterns in autumn. This allows it to suggest new clothing designs that match the season and trends, improving the user's fashion style.

[0067] The clothing information uploading unit can use the emotion estimation function to analyze the emotions felt when the user wears specific clothing and suggest clothing that elicits positive emotions. For example, the clothing information uploading unit can use the emotion estimation function to analyze the emotions felt when the user wears specific clothing and suggest clothing that elicits positive emotions. For example, it can suggest clothing that makes the user smile or gives the user confidence. This allows for recommendations that will give the user positive emotions, thereby enabling more satisfying recommendations.

[0068] The clothing information uploading unit allows AI to suggest outfits based on clothing information uploaded by the user, and provide a total coordinated look. For example, the clothing information uploading unit allows AI to suggest a total coordinated look based on clothing information uploaded by the user. For example, it may suggest an outfit that combines a shirt, pants, and accessories. This allows AI to suggest a total coordinated look to the user, and provide a more unified fashion style.

[0069] The clothing information uploading unit allows AI to suggest clothing that matches the user's lifestyle based on the uploaded clothing information. For example, the clothing information uploading unit allows AI to suggest clothing that matches the user's lifestyle based on the uploaded clothing information. For example, it may suggest easy-to-move-in clothes for an active lifestyle and formal clothes for office work. This allows for more practical recommendations by suggesting clothing that matches the user's lifestyle.

[0070] The clothing information uploading unit can use the emotion estimation function to analyze in real time the emotions felt by the user when wearing specific clothing and suggest outfits that will elicit positive emotions. For example, the clothing information uploading unit can use the emotion estimation function to analyze in real time the emotions felt by the user when wearing specific clothing and suggest outfits that will elicit positive emotions. For example, it can suggest outfits that will give the user confidence. This allows for recommendations that will give the user positive emotions, thereby enabling more satisfying recommendations.

[0071] The recommendation section uses AI to make recommendations that take into account not only the user's body type and preferences, but also past purchase history and reviews. For example, the recommendation section uses AI to make recommendations that take into account not only the user's body type and preferences, but also past purchase history and reviews. For example, it may suggest clothes of a similar style based on the design and size of clothes previously purchased. This allows for more accurate recommendations by taking into account the user's past purchase history and reviews.

[0072] The recommendation unit uses the emotion estimation function to analyze the emotions a user has toward a specific recommendation and can make recommendations that elicit positive emotions. For example, the recommendation unit uses the emotion estimation function to analyze the emotions a user has toward a specific recommendation and can make recommendations that elicit positive emotions. For example, the recommendation unit can suggest clothes that make the user smile or clothes that give the user confidence. This makes it possible to make recommendations that elicit positive emotions in the user, thereby making it possible to make recommendations that result in higher satisfaction.

[0073] The recommendation unit uses AI to take into account the fashion styles of the user's friends and family and make group coordination suggestions. For example, the recommendation unit uses AI to take into account the fashion styles of the user's friends and family and make group coordination suggestions. For example, it can analyze photos of all family members and suggest coordinated outfits. This makes it possible to consider the fashion styles of the user's friends and family and make group coordination suggestions.

[0074] The recommendation unit uses AI to consider the dress code of the user's workplace or event and suggest appropriate clothing. For example, the recommendation unit uses AI to consider the dress code of the user's workplace or event and suggest appropriate clothing. For example, it can suggest clothes suitable for business casual or formal events. This makes it possible to suggest appropriate clothing by considering the dress code of the user's workplace or event.

[0075] The recommendation unit can use the emotion estimation function to analyze in real time the emotions a user has toward a specific recommendation and make recommendations that elicit positive emotions. For example, the recommendation unit can use the emotion estimation function to analyze in real time the emotions a user has toward a specific recommendation and make recommendations that elicit positive emotions. For example, the recommendation unit can suggest clothes that make the user smile or clothes that give the user confidence. This makes it possible to make recommendations that elicit positive emotions from the user, thereby making it possible to make recommendations that result in higher satisfaction.

[0076] The 3D model display unit allows the 3D model to reflect the user's movements and facial expressions in real time, providing a more realistic fitting experience.The 3D model display unit allows the 3D model to reflect the user's movements and facial expressions in real time, providing a more realistic fitting experience.For example, the 3D model reflects the user's movements such as walking and turning.This allows the user's movements and facial expressions to be reflected in real time, providing a more realistic fitting experience.

[0077] The 3D model display unit can simulate how the 3D model looks under different lighting conditions and backgrounds, recreating actual usage scenes. For example, the 3D model display unit can simulate how the 3D model looks under different lighting conditions and backgrounds, recreating actual usage scenes. For example, it can simulate indoor and outdoor lighting conditions, and daytime and nighttime lighting conditions. This allows the actual usage scenes to be reproduced by simulating how the 3D model looks under different lighting conditions and backgrounds.

[0078] The 3D model display unit can use the emotion estimation function to analyze the emotions felt by the user when viewing the 3D model and make adjustments to elicit positive emotions. For example, the 3D model display unit can use the emotion estimation function to analyze the emotions felt by the user when viewing the 3D model and make adjustments to elicit positive emotions. For example, the 3D model can be adapted to reflect poses and expressions that make the user smile. This allows the 3D model to be adjusted to elicit positive emotions, enabling a more satisfying fitting experience.

[0079] The 3D model display unit can generate versions of the 3D model with different body shapes and ages, and simulate future changes in body shape. For example, the 3D model display unit can generate versions of the 3D model with different body shapes and ages, and simulate future changes in body shape. For example, the 3D model display unit can predict the body shape several years from now based on the current body shape and check how clothes will fit. This allows future changes in body shape to be simulated by generating versions with different body shapes and ages.

[0080] The 3D model display unit can simulate how the 3D model will look in different seasons and weather conditions, and suggest coordination for each season. For example, the 3D model display unit can simulate how the 3D model will look in different seasons and weather conditions, and suggest coordination for each season. For example, it can simulate how the 3D model will look on a summer beach or in a winter snowy landscape. This makes it possible to suggest coordination for each season by simulating how the 3D model will look in different seasons and weather conditions.

[0081] The 3D model display unit can use the emotion estimation function to analyze the emotions felt by the user when viewing the 3D model in real time and make adjustments to elicit positive emotions. For example, the 3D model display unit can use the emotion estimation function to analyze the emotions felt by the user when viewing the 3D model in real time and make adjustments to elicit positive emotions. For example, the 3D model can be adapted to reflect poses and expressions that make the user smile. This allows the 3D model to be adjusted to elicit positive emotions, enabling a more satisfying fitting experience.

[0082] During the purchasing process, the AI ​​can make additional recommendations based on the user's past purchase history and reviews. For example, during the purchasing process, the AI ​​can suggest items that go well with clothes previously purchased. This allows for additional recommendations based on the user's past purchase history and reviews, enabling a more satisfying purchasing experience.

[0083] During the purchasing process, AI can suggest sizes and customization options that match the user's body type and preferences. For example, during the purchasing process, AI can suggest sizes and customization options that match the user's body type and preferences. For example, it can suggest sizes that fit the user's body type and customization of designs that match their preferences. This allows for a more satisfying purchasing experience by suggesting sizes and customization options that match the user's body type and preferences.

[0084] The purchasing unit can use the emotion estimation function to analyze the emotions felt by the user during the purchasing process and provide an interface for eliciting positive emotions. For example, the purchasing unit can use the emotion estimation function to analyze the emotions felt by the user during the purchasing process and provide an interface for eliciting positive emotions. For example, the purchasing unit can provide an interface with a design and color usage that helps the user relax. This allows the user to have a more satisfying purchasing experience by providing an interface that helps them feel positive emotions.

[0085] The purchasing department can use AI to suggest gifts for the user's friends and family during the purchase process. For example, the purchasing department can use AI to suggest gifts for the user's friends and family during the purchase process. For example, it can suggest the most suitable gift based on the user's purchase history and the preferences of friends and family. This allows for a more satisfying purchasing experience by suggesting gifts for the user's friends and family.

[0086] During the purchase process, the purchasing department can use AI to suggest accessories and shoes that match the user's lifestyle. For example, during the purchase process, the purchasing department can use AI to suggest accessories and shoes that match the user's lifestyle. For example, it can suggest accessories and shoes that match the user's usual activities and preferences. This allows for a more satisfying purchasing experience by suggesting accessories and shoes that match the user's lifestyle.

[0087] The purchasing unit can use the emotion estimation function to analyze the emotions felt by the user during the purchasing process in real time and provide an interface for eliciting positive emotions. For example, the purchasing unit can use the emotion estimation function to analyze the emotions felt by the user during the purchasing process in real time and provide an interface for eliciting positive emotions. For example, the purchasing unit can provide an interface with a design and color usage that helps the user relax. This allows the user to have a more satisfying purchasing experience by providing an interface that helps them feel positive emotions.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] A recommendation system can also be equipped with a health management unit that monitors the user's health status. For example, it can acquire heart rate, step count, and sleep data from a wearable device that the user uses daily and analyze the user's health status. This makes it possible to provide fashion advice tailored to the user's health status and suggest lifestyle changes to support health. For example, a user who exercises less can be suggested sportswear that encourages an active lifestyle. Furthermore, a user who experiences poor quality sleep can be suggested clothing made of materials and with a relaxing design. This makes it possible to make recommendations that take the user's health status into consideration, enabling more personalized services to be provided.

[0090] The recommendation system can also be equipped with a hobby analysis unit that analyzes a user's hobbies and interests. For example, the system can analyze content shared by the user on social media or blogs to understand the user's hobbies and interests. This makes it possible to suggest fashion items that match the user's hobbies and interests. For example, a user who enjoys the outdoors can be suggested highly functional outdoor wear. Or, a user who frequently attends music festivals can be suggested a casual style suitable for festivals. This makes it possible to make recommendations that match the user's hobbies and interests, resulting in a more satisfying service.

[0091] The recommendation system can also be equipped with an eco-awareness analysis unit that takes the user's environmental awareness into account. For example, it can analyze the environmental impact and recyclability of products purchased by the user and suggest eco-friendly products. This makes it possible to suggest environmentally conscious fashion items. For example, it can suggest clothes made from recycled materials or items from brands that use environmentally friendly manufacturing processes. Furthermore, if the user is interested in sustainable fashion, it can suggest eco-friendly styles. This makes it possible to make recommendations based on the user's environmental awareness and promote more sustainable fashion.

[0092] The recommendation system can further include a travel plan analysis unit that takes into account the user's travel plans. For example, if the user inputs their travel destination and travel period, the system can suggest fashion items suited to the climate and culture of the destination. This makes it possible to suggest appropriate clothing for the travel destination. For example, a user traveling to a beach resort can be suggested light and comfortable resort wear. Furthermore, a user traveling to a cold region can be suggested outerwear and accessories that provide good cold protection. This makes it possible to make recommendations based on the user's travel plans, allowing them to enjoy their trip more comfortably.

[0093] The recommendation system can further include an occupational analysis unit that takes into account the user's occupation and work content. For example, based on the occupational information entered by the user, it can suggest fashion items that are appropriate for the work content. This makes it possible to suggest appropriate attire for the workplace. For example, formal suits and business casual items can be suggested for business people. Furthermore, it can suggest unique and free styles for users engaged in creative occupations. This makes it possible to make recommendations based on the user's occupation and work content, thereby improving their fashion style at work.

[0094] The recommendation system can also analyze the user's emotions and suggest fashion items that correspond to the emotion. For example, if the user is feeling stressed, it can suggest clothes made of materials and with designs that have a relaxing effect. Also, if the user wants to feel more confident, it can suggest elegant and stylish items. This allows for recommendations that are more satisfying by suggesting fashion items that correspond to the user's emotions. For example, if the user is nervous about attending a particular event, it can suggest clothes that have a relaxing effect, which can help relieve the user's tension. Also, when the user goes on a date, it can suggest stylish clothes that will make them feel confident, which can elicit positive emotions.

[0095] The recommendation system can also analyze the user's emotions in real time and suggest outfits that correspond to the user's emotions. For example, if the user is tired, it can suggest outfits that have a relaxing effect. Also, if the user feels like cheering up, it can suggest outfits with bright colors and uplifting designs. This makes it possible to make recommendations that are more satisfying by suggesting outfits that correspond to the user's emotions. For example, if the user is tired from work, it can suggest casual outfits that have a relaxing effect, helping to soothe the user's fatigue. Also, when the user is going to a party with friends, it can suggest outfits with bright colors that will cheer them up, drawing out positive emotions.

[0096] The recommendation system can also analyze the user's emotions and suggest accessories and shoes that correspond to the user's emotions. For example, if the user is feeling down, it can suggest glamorous accessories to lift their spirits. Also, if the user feels like relaxing, it can suggest comfortable shoes. This allows for recommendations that match the user's emotions, resulting in more satisfying recommendations. For example, if a user is nervous about attending a particular event, it can relieve the user's tension by suggesting shoes that have a relaxing effect. Also, when a user goes on a date, it can elicit positive emotions by suggesting glamorous accessories to lift their spirits.

[0097] Recommendation systems can also analyze users' emotions in real time and provide a purchasing interface that corresponds to their emotions. For example, if a user is feeling stressed, an interface with a design and color scheme that has a relaxing effect can be provided. Alternatively, if the user is excited, a simple and calming interface can be provided. This allows for a more satisfying purchasing experience by providing a purchasing interface that corresponds to the user's emotions. For example, if a user is nervous about attending a particular event, an interface with a relaxing effect can be provided to ease the user's tension. Similarly, when a user goes on a date, a simple and calming interface can be provided to elicit positive emotions.

[0098] The recommendation system can further analyze the user's emotions and provide feedback according to the emotions. For example, if the user is dissatisfied with a particular recommendation, feedback suggesting improvements can be provided. Also, if the user is satisfied, positive feedback can be provided. By providing feedback according to the user's emotions, it is possible to make recommendations that result in higher satisfaction. For example, if the user is dissatisfied with a particular recommendation, feedback suggesting improvements can be provided, thereby resolving the user's dissatisfaction. Also, if the user is satisfied, positive feedback can be provided, thereby further increasing the user's satisfaction.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The photo registration unit registers a photo that shows the user's height. For example, a full-body photo taken from the front or a photo taken from the side can be registered. The photo registration unit is also used by the AI ​​to analyze the user's body shape and characteristics. For example, the AI ​​obtains information such as the user's height, weight, and body shape. Step 2: The clothing information uploading unit uploads a few pieces of clothing that the user normally wears. For example, the user uploads photos of their favorite shirts, pants, dresses, etc. The clothing information uploading unit also helps the AI ​​understand the user's preferences and style. For example, the AI ​​can understand the user's fashion style and color preferences. Step 3: The recommendation unit recommends clothes that it determines will suit the user based on the information obtained by the photo registration unit and clothing information upload unit. For example, the AI ​​may suggest clothes of a specific brand or design, taking into account the user's body type and preferences. The generation AI makes recommendations based on prompts containing the user's photo and clothing information. Step 4: The 3D model display unit displays the clothes recommended by the recommendation unit as a 3D model on a demo screen. For example, the user can see how a 3D model of themselves wearing the clothes they selected rotates and walks. Step 5: The purchasing unit purchases the clothes displayed by the 3D model display unit. For example, by clicking the "Purchase" button on the demo screen, the user is taken to an online store and can complete the purchase procedure.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] 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]

[0168] 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 photo registration unit for registering a photo showing the user's height; A clothing information upload section where you can upload a few pieces of clothing you normally wear, a recommendation unit that recommends clothes that are judged to look good on the user based on the information acquired by the photo registration unit and the clothing information upload unit; a 3D model display unit that displays a demo screen of the clothes recommended by the recommendation unit as a 3D model; a purchasing unit for purchasing the clothes displayed by the 3D model display unit. A system characterized by:

2. The photo registration unit The user's photo is taken as 3D scan data to generate a more detailed body model.

2. The system of claim 1.

3. The clothing information upload unit The AI ​​analyzes the materials and textures of the uploaded clothing and suggests new clothing made from materials that suit the user's preferences.

2. The system of claim 1.

4. The recommendation unit Recommendations are made taking into consideration not only the user's body type and preferences, but also past purchase history and reviews.

2. The system of claim 1.

5. The 3D model display unit The 3D model reflects the user's movements and facial expressions in real time, providing a more realistic try-on experience.

2. The system of claim 1.

6. The purchasing department Using the emotion estimation function, the emotions felt by the user during the purchase process are analyzed, and an interface is provided to elicit positive emotions.

2. The system of claim 1.

7. The photo registration unit Using an emotion estimation function, the user's emotions at the time of taking a photo are analyzed, and photos taken in a relaxed state are recommended.

2. The system of claim 1.

8. The clothing information upload unit Using an emotion estimation function, the emotions felt by the user when wearing the specific clothing are analyzed, and the clothing that elicits positive emotions is suggested.

2. The system of claim 1.

Images