System

Abstract

An object of a system according to an embodiment is to make it easier for a customer to select clothing that suits the customer in online shopping.SOLUTION: A system includes a collection unit, a diagnosis unit, a generation unit, and a proposal unit. The collection unit performs a 3D scan of the user. The diagnosis unit performs face type, personal color, or skeleton diagnosis on the basis of the data collected by the collection unit. The generation unit simulates the movement of the clothes based on the diagnosis result obtained by the diagnosis unit. The proposal unit allows the stylist to make a proposal based on the simulation result generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for customers to choose clothes that suit them when shopping online, resulting in low satisfaction.

[0005] The system according to the embodiment aims to make it easier for customers to select clothes that suit them when shopping online. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a diagnosis unit, a generation unit, and a suggestion unit. The collection unit performs a 3D scan of the user. The diagnosis unit diagnoses the face type, personal color, and bone structure based on the data collected by the collection unit. The generation unit simulates the movement of clothing based on the diagnosis results obtained by the diagnosis unit. The suggestion unit allows a stylist to make suggestions based on the simulation results generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can facilitate customers to select clothes that suit them when shopping online. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A virtual fitting system according to an embodiment of the present invention allows a user to 3D scan themselves using a smartphone, and a generative AI simulates the movement of clothing, providing feedback similar to that of a real person trying on the garment. The virtual fitting system uses a smartphone to 3D scan themselves and diagnose their face type, personal color, and skeletal structure. Generative AI then simulates the movement of clothing, providing feedback similar to that of a real person trying on the garment. For example, in a virtual fitting system, a user 3D scans themselves using a smartphone. Recent smartphones are equipped with 3D scanning capabilities, which can be used to create a 3D representation of the user's body. The virtual fitting system can also create a 3D representation from photographs using telemetry stitching technology. Next, the virtual fitting system allows the user to diagnose their face type, personal color, and skeletal structure, and input measurement data such as height and body shape. This allows for a more realistic fitting experience. The virtual fitting system then uses generative AI to move the user's 3D model and simulate the movement of the clothing. The generative AI receives input from the user's 3D model and clothing data, which the generative AI uses to generate a simulation. For example, the generative AI receives a prompt such as "Please simulate the movement of this clothing" and simulates the movement of the clothing. Next, the virtual fitting system allows a stylist to make suggestions based on the simulation results created by the generative AI. This allows users to receive styling suggestions that suit them. The virtual fitting system also allows users who find it difficult to visit a physical store, such as those looking for children's or maternity clothing, to visit an online store from the comfort of their own home and undergo a virtual fitting. This allows the virtual fitting system to improve customer satisfaction in fashion e-commerce and reduce post-purchase gaps. The virtual fitting system can thus improve customer satisfaction by consistently performing everything from 3D scanning of users to diagnosis, simulation, and suggestions.For example, users can use their smartphones to perform a 3D scan of themselves, and the generative AI can simulate the movement of the clothes, providing feedback as if they were actually trying them on. In addition, a stylist can provide suggestions, allowing users to receive styling suggestions that suit them best. This can reduce any gaps after purchase.

[0029] A virtual fitting system according to an embodiment includes a collection unit, a diagnosis unit, a generation unit, and a suggestion unit. The collection unit performs a 3D scan of a user. 3D scanning of a user can be performed using, but is not limited to, a scanning technique using a smartphone camera or a telemetry stitching technique using multiple photographs. The collection unit, for example, uses a smartphone camera to create a 3D representation of the user's body shape. The collection unit can also create a 3D representation from photographs using a telemetry stitching technique that stitches together photographs. For example, the collection unit may take multiple photographs using a smartphone camera and stitch them together to generate a 3D model. The diagnosis unit performs a face type, personal color, and bone structure diagnosis based on the data collected by the collection unit. The diagnosis unit may perform a face type, personal color, and bone structure diagnosis using, for example, AI. For example, the diagnosis unit may diagnose a face type based on the shape of the face, the arrangement of facial features, and their proportions. The diagnosis unit may also diagnose a personal color based on skin color, hair color, eye color, and other factors. The diagnosis unit can also perform skeletal diagnosis based on bone shape, body proportions, muscle distribution, etc. The generation unit simulates the movement of clothes based on the diagnosis results obtained by the diagnosis unit. The generation unit simulates the movement of clothes using, for example, a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit simulates the movement of clothes using, for example, a physical simulation or machine learning. For example, the generation unit simulates the movement of clothes using a physical model. The generation unit can also simulate the movement of clothes using a machine learning algorithm. For example, the generation unit simulates the movement based on the material and design of the clothes. The suggestion unit allows a stylist to make suggestions based on the simulation results generated by the generation unit. For example, the suggestion unit allows a stylist to look at items virtually tried on by a customer and make styling suggestions and advice. The suggestion unit can also make automatic suggestions using an algorithm. For example, the suggestion unit makes more personalized suggestions based on items the user has tried on in the past.As a result, the virtual fitting system according to the embodiment can improve customer satisfaction by consistently performing everything from 3D scanning of the user to diagnosis, simulation, and suggestions. For example, a user can 3D scan themselves using a smartphone, and the generation AI can simulate the movement of the clothes, providing feedback as if they were actually trying them on. In addition, a stylist can make suggestions, allowing the user to receive styling suggestions that suit them. This can reduce any gaps after purchase.

[0030] The collection unit can generate a 3D representation of the user's body shape using scanning technology using a smartphone camera or telemetry stitching technology using multiple photographs. The collection unit can generate a 3D representation of the user's body shape using, for example, a smartphone camera. For example, the collection unit can take multiple photographs using a smartphone camera and stitch them together to generate a 3D model. The collection unit can also generate a 3D representation from photographs using telemetry stitching technology. For example, the collection unit can take multiple photographs and stitch them together to generate a 3D model. This allows for easy virtual fitting by generating a 3D representation of the user's body shape using a smartphone or photographs. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input photo data acquired by a smartphone camera into a generation AI and have the generation AI generate a 3D model from the photo data.

[0031] The diagnosis unit can use AI to diagnose face type, personal color, and bone structure. The diagnosis unit, for example, uses AI to diagnose face type, personal color, and bone structure. For example, the diagnosis unit diagnoses face type based on face shape, feature arrangement, and proportions. The diagnosis unit can also diagnose personal color based on skin color, hair color, eye color, and the like. The diagnosis unit can also diagnose bone structure based on bone shape, body proportions, muscle structure, and the like. In this way, the use of AI improves the accuracy of face type, personal color, and bone structure diagnoses. Some or all of the above-described processing in the diagnosis unit may be performed using AI, for example, or may be performed without using AI. For example, the diagnosis unit can input face shape data to a generation AI and have the generation AI perform a face type diagnosis.

[0032] The generation unit can simulate the movement of the clothes using physical simulation or machine learning. The generation unit simulates the movement of the clothes using, for example, a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit simulates the movement of the clothes using, for example, physical simulation or machine learning. For example, the generation unit simulates the movement of the clothes using a physical model. The generation unit can also simulate the movement of the clothes using a machine learning algorithm. For example, the generation unit simulates the movement based on the material and design of the clothes. This improves the accuracy of the simulation of the movement of the clothes by using physical simulation or machine learning. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input design data of the clothes into the generation AI and cause the generation AI to simulate the movement of the clothes.

[0033] The suggestion unit allows a stylist to view the customer's virtual try-on items and make styling suggestions and advice. For example, the suggestion unit allows a stylist to view the customer's virtual try-on items and make styling suggestions and advice. For example, the suggestion unit allows a stylist to view the customer's virtual try-on items and make styling suggestions and advice. The suggestion unit can also make automatic suggestions using an algorithm. For example, the suggestion unit makes more personalized suggestions based on items the user has tried on in the past. This allows the stylist to view the virtual try-on items and make suggestions, making more personalized suggestions possible. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input data of the customer's virtual try-on items into a generation AI and have the generation AI execute styling suggestions.

[0034] The suggestion unit can make automatic suggestions using an algorithm. The suggestion unit makes automatic suggestions using, for example, an algorithm. For example, the suggestion unit makes more personalized suggestions based on items the user has tried on in the past. This enables automatic suggestions by using an algorithm, improving the efficiency of suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's try-on data into the generation AI and cause the generation AI to make automatic suggestions.

[0035] The collection unit can analyze the user's past scan data and select the optimal scan method. For example, the collection unit can automatically set the optimal scan angle and distance based on the user's past scan data. The collection unit can also suggest the optimal scan method for specific environmental conditions (lighting, background, etc.) based on the user's past scan data. The collection unit can also analyze the user's past scan data and make adjustments to improve scan accuracy. In this way, by analyzing the past scan data, the optimal scan method is selected and scan accuracy is improved. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past scan data into a generation AI and have the generation AI select the optimal scan method.

[0036] The collection unit can improve scan accuracy based on the user's current environment during scanning. For example, the collection unit can detect the user's current lighting conditions and automatically adjust optimal scan settings. The collection unit can also analyze the user's background and remove background noise to improve scan accuracy. The collection unit can also detect the user's environmental sounds and make adjustments to minimize noise during scanning. This improves scan accuracy based on the user's current environment, enabling more accurate 3D scans. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input current environmental data into a generation AI and have the generation AI improve scan accuracy.

[0037] The collection unit can select the optimal scanning means depending on the user's input method when scanning. For example, if the user selects voice input, the collection unit starts scanning based on a voice command. Furthermore, if the user selects text input, the collection unit can also adjust the scanning procedure based on text instructions. Furthermore, if the user selects image input, the collection unit can also set the optimal angle and distance for scanning based on image analysis. This improves user convenience by selecting the optimal scanning means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data to a generation AI and cause the generation AI to start scanning based on a voice command.

[0038] When scanning, the collection unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can adjust the scan data based on the environmental conditions of the area. Furthermore, if the user is traveling, the collection unit can optimize the scan data based on the environmental conditions of the travel destination. Furthermore, if the user is at home, the collection unit can acquire scan data based on the environmental conditions of the user's home. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to acquire highly relevant data.

[0039] The collection unit can analyze the user's social media activity at the time of scanning and acquire related data. The collection unit can supplement the scan data based on, for example, photos shared by the user on social media. The collection unit can also analyze the user's social media activity and acquire related scan data. The collection unit can also acquire related scan data by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related data can be acquired and the accuracy of the scan can be improved. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social media data into a generation AI and cause the generation AI to acquire related data.

[0040] The collection unit can customize the scan method by reflecting the user's past feedback during scanning. For example, the collection unit adjusts the scan method based on feedback provided by the user in the past. The collection unit can also preferentially apply specific scan settings based on the user's past feedback. The collection unit can also analyze the user's past feedback and make adjustments to improve scan accuracy. In this way, the scan method is customized by reflecting the user's past feedback, thereby improving scan accuracy. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input past feedback data into a generation AI and cause the generation AI to customize the scan method.

[0041] The diagnostic unit can adjust the level of detail of the diagnosis based on the user's level of importance during the diagnosis. For example, if the user is attending an important event, the diagnostic unit provides detailed diagnostic results and suggests optimal styling. Furthermore, if the user is aiming for daily use, the diagnostic unit can provide concise diagnostic results for easy use. Furthermore, if the user has a specific purpose, the diagnostic unit can also provide diagnostic results tailored to that purpose. By adjusting the level of detail of the diagnosis based on the user's level of importance, optimal diagnostic results can be provided for the user. Some or all of the above-described processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input the user's level of importance data into a generation AI and cause the generation AI to adjust the level of detail of the diagnosis.

[0042] The diagnostic unit can apply different diagnostic algorithms depending on the user's category during diagnosis. For example, if the user is a child, the diagnostic unit can apply a diagnostic algorithm for kids to suggest appropriate styling. Furthermore, if the user is pregnant, the diagnostic unit can apply a diagnostic algorithm for maternity to suggest optimal styling. Furthermore, if the user is senior, the diagnostic unit can apply a diagnostic algorithm for seniors to suggest appropriate styling. In this way, by applying different diagnostic algorithms depending on the user's category, more appropriate diagnostic results can be provided. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input user category data into a generation AI and cause the generation AI to apply a diagnostic algorithm.

[0043] During diagnosis, the diagnostic unit can improve the accuracy of the diagnosis by referring to the user's past diagnostic results. For example, the diagnostic unit can adjust the current diagnostic result based on the user's past diagnostic results to improve accuracy. The diagnostic unit can also extract specific patterns from the user's past diagnostic results and optimize the diagnostic algorithm. The diagnostic unit can also analyze the user's past diagnostic results and make adjustments to improve the accuracy of the diagnosis. In this way, the accuracy of the diagnosis is improved by referring to the user's past diagnostic results. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input past diagnostic result data into the generation AI and cause the generation AI to improve the accuracy of the diagnosis.

[0044] During diagnosis, the diagnosis unit can determine the priority of the diagnosis based on the user's submission time. For example, if the user is in a hurry, the diagnosis unit can increase the priority of the diagnosis and provide results quickly. Furthermore, if the user is participating in a specific event, the diagnosis unit can adjust the priority of the diagnosis to match the event. Furthermore, if the user is intending to use the device for daily use, the diagnosis unit can set the priority of the diagnosis to the normal level. This allows for quick diagnosis results to be provided by determining the priority of the diagnosis based on the user's submission time. Some or all of the above-described processing in the diagnosis unit may be performed using, for example, AI, or may be performed without AI. For example, the diagnosis unit can input submission time data into a generation AI and have the generation AI determine the priority of the diagnosis.

[0045] During diagnosis, the diagnostic unit can adjust the order of diagnoses based on the user's relevance. For example, if the user belongs to a specific category, the diagnostic unit can prioritize diagnoses related to that category. Furthermore, if the user has a specific purpose, the diagnostic unit can also prioritize diagnoses related to that purpose. Furthermore, the diagnostic unit can prioritize highly relevant diagnoses based on the user's past diagnostic results. By adjusting the order of diagnoses based on the user's relevance, more relevant diagnostic results can be provided. Some or all of the above-described processing in the diagnostic unit may be performed using, for example, AI, or may be performed without AI. For example, the diagnostic unit can input relevance data to a generation AI and cause the generation AI to adjust the order of diagnoses.

[0046] During diagnosis, the diagnostic unit can adjust the use of technical terminology in the diagnosis according to the user's level of expertise. For example, if the user has technical expertise, the diagnostic unit can provide diagnostic results using detailed technical terminology. Furthermore, if the user is a beginner, the diagnostic unit can provide diagnostic results in simple language to make them easier to understand. The diagnostic unit can also analyze the user's level of expertise and adjust the use of appropriate technical terminology. By adjusting the use of technical terminology according to the user's level of expertise, diagnostic results that are easy for the user to understand can be provided. Some or all of the above-described processing in the diagnostic unit may be performed using, for example, AI, or may be performed without AI. For example, the diagnostic unit can input technical expertise level data into a generation AI and have the generation AI use technical terminology.

[0047] The generation unit can adjust the level of detail of the simulation based on the importance of the clothes during the simulation. For example, in the case of clothes for an important event, the generation unit causes the generation AI to generate a detailed simulation. In addition, in the case of clothes intended for everyday use, the generation unit can also cause the generation AI to generate a concise simulation. In addition, in the case of clothes with a specific purpose, the generation unit can also cause the generation AI to generate a simulation according to that purpose. In this way, by adjusting the level of detail of the simulation based on the importance of the clothes, it is possible to provide an optimal simulation result for the user. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit may input clothing importance data to the generation AI and cause the generation AI to adjust the level of detail of the simulation.

[0048] The generation unit can apply different simulation algorithms depending on the clothing category during simulation. For example, in the case of formal wear, the generation AI applies a simulation algorithm that corresponds to formal movements and postures. In addition, in the case of sportswear, the generation unit can apply a simulation algorithm that takes ease of movement and stretchability into consideration. In addition, in the case of casual wear, the generation AI can apply a simulation algorithm that corresponds to everyday movements. In this way, by applying different simulation algorithms depending on the clothing category, more appropriate simulation results can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input clothing category data into the generation AI and cause the generation AI to apply a simulation algorithm.

[0049] During a simulation, the generation unit can improve the accuracy of the simulation by referring to the user's past simulation results. For example, the generation unit adjusts the current simulation based on the user's past simulation results to improve accuracy. The generation unit can also extract specific patterns from the user's past simulation results and optimize the simulation algorithm. The generation unit can also analyze the user's past simulation results and make adjustments to improve the accuracy of the simulation. In this way, the accuracy of the simulation is improved by referring to the user's past simulation results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past simulation result data into the generation AI and cause the generation AI to improve the accuracy of the simulation.

[0050] During a simulation, the generation unit can determine the priority of the simulation based on the time when the clothes were submitted. For example, in the case of clothes for an important event, the generation unit can increase the priority of the simulation and provide results quickly. The generation unit can also set the priority of the simulation to normal for clothes intended for everyday use. In addition, in the case of clothes with a specific purpose, the generation unit can adjust the priority of the simulation according to the purpose. In this way, by determining the priority of the simulation based on the time when the clothes were submitted, it is possible to provide quick simulation results. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input submission time data into the generation AI and have the generation AI determine the priority of the simulation.

[0051] During the simulation, the generation unit can adjust the order of the simulation based on the relevance of the clothes. For example, in the case of formal wear, the generation unit can prioritize a simulation that corresponds to formal movements and postures. Furthermore, in the case of sportswear, the generation unit can prioritize a simulation that takes into account ease of movement and stretchability. Furthermore, in the case of casual wear, the generation unit can prioritize a simulation that corresponds to everyday movements. In this way, by adjusting the order of the simulation based on the relevance of the clothes, it is possible to provide a simulation result that is more relevant. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input clothing relevance data into the generation AI and cause the generation AI to adjust the order of the simulation.

[0052] During the simulation, the generation unit can adjust the use of technical terminology in the simulation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can provide simulation results using detailed technical terminology. Furthermore, if the user is a beginner, the generation unit can provide simulation results in simple language to make them easier to understand. The generation unit can also analyze the user's level of expertise and adjust the use of appropriate technical terminology. By adjusting the use of technical terminology according to the user's level of expertise, simulation results that are easy for the user to understand can be provided. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input technical expertise level data into the generation AI and have the generation AI execute the use of technical terminology.

[0053] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the product. For example, in the case of a product for an important event, the suggestion unit makes detailed suggestions and suggests optimal styling. In addition, in the case of a product intended for everyday use, the suggestion unit makes concise suggestions to enable easy use. In addition, in the case of a product with a specific purpose, the suggestion unit can also make suggestions according to that purpose. In this way, by adjusting the level of detail of the suggestion based on the importance of the product, it is possible to provide an optimal suggestion for the user. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input product importance data to a generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0054] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the product category. For example, in the case of formal wear, the suggestion unit can apply an algorithm that suggests formal styling. In addition, in the case of sportswear, the suggestion unit can apply an algorithm that suggests styling that emphasizes ease of movement and functionality. In addition, in the case of casual wear, the suggestion unit can apply an algorithm that suggests everyday styling. In this way, by applying different suggestion algorithms depending on the product category, more appropriate suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input product category data into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0055] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit adjusts the current proposal based on the user's past proposal results to improve accuracy. The suggestion unit can also extract specific patterns from the user's past proposal results and optimize the proposal algorithm. The suggestion unit can also analyze the user's past proposal results and make adjustments to improve the accuracy of the proposal. In this way, the accuracy of the proposal is improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0056] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the product. For example, in the case of a product for an important event, the proposal unit can increase the priority of the proposal and provide results quickly. The proposal unit can also set the priority of the proposal to the normal level for a product intended for everyday use. In addition, in the case of a product with a specific purpose, the proposal unit can also adjust the priority of the proposal according to the purpose. In this way, by determining the priority of the proposal based on the submission time of the product, it is possible to provide a proposal result quickly. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input submission time data into the generation AI and have the generation AI determine the priority of the proposal.

[0057] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of the products. For example, in the case of formal wear, the suggestion unit can prioritize proposing formal styling. In addition, in the case of sportswear, the suggestion unit can prioritize proposing styling that emphasizes ease of movement and functionality. In addition, in the case of casual wear, the suggestion unit can prioritize proposing everyday styling. In this way, by adjusting the order of suggestions based on the relevance of the products, more relevant suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input product relevance data into the generation AI and cause the generation AI to adjust the order of suggestions.

[0058] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make the proposal using detailed technical terminology. Furthermore, if the user is a beginner, the suggestion unit can make the proposal in simple language to make it easier to understand. The suggestion unit can also analyze the user's level of expertise and adjust the use of appropriate technical terminology. By adjusting the use of technical terminology according to the user's level of expertise, it is possible to provide a proposal that is easy for the user to understand. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input technical expertise level data into a generation AI and cause the generation AI to use technical terminology.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When acquiring 3D scan data of a user, the collection unit can monitor the user's health condition and adjust the timing and method of scanning according to the user's health condition. For example, if the user is tired, the collection unit can delay the timing of the scan to encourage the user to rest. Also, if the user is healthy, the collection unit can quickly perform the scan and collect data efficiently. Also, if the user is ill, the collection unit can temporarily suspend the scan and wait until the user's health condition improves. This allows for more appropriate data collection by adjusting the timing and method of the scan according to the user's health condition.

[0061] The generation unit can analyze the user's past purchase history and improve the accuracy of the simulation based on the purchase history. For example, the generation unit can adjust the current simulation based on data on clothes the user previously purchased to improve accuracy. The generation unit can also extract specific patterns from the user's past purchase history and optimize the simulation algorithm. The generation unit can also analyze the user's past purchase history and make adjustments to improve the accuracy of the simulation. In this way, the accuracy of the simulation can be improved by referring to the user's past purchase history.

[0062] The collection unit can analyze the user's lifestyle data and suggest the optimal scan timing based on the lifestyle. For example, if the user is a morning person, the collection unit can suggest scanning in the morning. Alternatively, if the user is a night owl, the collection unit can suggest scanning in the evening. Furthermore, by analyzing the user's lifestyle data and suggesting the optimal scan timing, it becomes possible to perform a scan that matches the user's lifestyle rhythm. In this way, by suggesting the optimal scan timing based on the user's lifestyle, the accuracy of the scan is improved.

[0063] The suggestion unit can analyze the user's past feedback and improve the accuracy of the suggestions based on the feedback. For example, the suggestion unit can adjust the current suggestions based on feedback provided by the user in the past to improve accuracy. The suggestion unit can also extract specific patterns from the user's past feedback and optimize the suggestion algorithm. The suggestion unit can also analyze the user's past feedback and make adjustments to improve the accuracy of the suggestions. In this way, the accuracy of the suggestions can be improved by referring to the user's past feedback.

[0064] The generation unit can analyze the user's past simulation results and improve the accuracy of a new simulation based on the simulation results. For example, the generation unit can adjust the current simulation based on the user's past simulation results to improve accuracy. The generation unit can also extract specific patterns from the user's past simulation results and optimize the simulation algorithm. The generation unit can also analyze the user's past simulation results and make adjustments to improve the accuracy of the simulation. In this way, the accuracy of the simulation can be improved by referring to the user's past simulation results.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit performs a 3D scan of the user. 3D scanning of the user can be done using scanning technology using a smartphone camera or telemetry stitching technology using multiple photos. For example, the collection unit can use a smartphone camera to create a 3D image of the user's body and then use telemetry stitching technology to stitch together photos to create a 3D model. Step 2: The diagnosis unit diagnoses face type, personal color, and bone structure based on the data collected by the collection unit. The diagnosis unit uses AI to diagnose face type based on facial shape, feature placement, and proportions, and diagnoses personal color based on skin color, hair color, eye color, etc. It also diagnoses bone structure based on bone shape, body proportions, muscle structure, etc. Step 3: The generator simulates the movement of the clothes based on the diagnosis results obtained by the diagnosis unit. The generator uses a generative AI to simulate the movement of the clothes using physics simulation and machine learning algorithms, and simulates the movement based on the material and design of the clothes. Step 4: In the suggestion section, a stylist makes suggestions based on the simulation results generated by the generation section. The suggestion section allows the stylist to view the virtual items being tried on by the customer and provide styling suggestions and advice. It is also possible to make automatic suggestions using algorithms, providing more personalized suggestions based on items the user has tried on in the past.

[0067] (Example 2) A virtual fitting system according to an embodiment of the present invention allows a user to 3D scan themselves using a smartphone, and a generative AI simulates the movement of clothing, providing feedback similar to that of a real person trying on the garment. The virtual fitting system uses a smartphone to 3D scan themselves and diagnose their face type, personal color, and skeletal structure. Generative AI then simulates the movement of clothing, providing feedback similar to that of a real person trying on the garment. For example, in a virtual fitting system, a user 3D scans themselves using a smartphone. Recent smartphones are equipped with 3D scanning capabilities, which can be used to create a 3D representation of the user's body. The virtual fitting system can also create a 3D representation from photographs using telemetry stitching technology. Next, the virtual fitting system allows the user to diagnose their face type, personal color, and skeletal structure, and input measurement data such as height and body shape. This allows for a more realistic fitting experience. The virtual fitting system then uses generative AI to move the user's 3D model and simulate the movement of the clothing. The generative AI receives input from the user's 3D model and clothing data, which the generative AI uses to generate a simulation. For example, the generative AI receives a prompt such as "Please simulate the movement of this clothing" and simulates the movement of the clothing. Next, the virtual fitting system allows a stylist to make suggestions based on the simulation results created by the generative AI. This allows users to receive styling suggestions that suit them. The virtual fitting system also allows users who find it difficult to visit a physical store, such as those looking for children's or maternity clothing, to visit an online store from the comfort of their own home and undergo a virtual fitting. This allows the virtual fitting system to improve customer satisfaction in fashion e-commerce and reduce post-purchase gaps. The virtual fitting system can thus improve customer satisfaction by consistently performing everything from 3D scanning of users to diagnosis, simulation, and suggestions.For example, users can use their smartphones to perform a 3D scan of themselves, and the generative AI can simulate the movement of the clothes, providing feedback as if they were actually trying them on. In addition, a stylist can provide suggestions, allowing users to receive styling suggestions that suit them best. This can reduce any gaps after purchase.

[0068] A virtual fitting system according to an embodiment includes a collection unit, a diagnosis unit, a generation unit, and a suggestion unit. The collection unit performs a 3D scan of a user. 3D scanning of a user can be performed using, but is not limited to, a scanning technique using a smartphone camera or a telemetry stitching technique using multiple photographs. The collection unit, for example, uses a smartphone camera to create a 3D representation of the user's body shape. The collection unit can also create a 3D representation from photographs using a telemetry stitching technique that stitches together photographs. For example, the collection unit may take multiple photographs using a smartphone camera and stitch them together to generate a 3D model. The diagnosis unit performs a face type, personal color, and bone structure diagnosis based on the data collected by the collection unit. The diagnosis unit may perform a face type, personal color, and bone structure diagnosis using, for example, AI. For example, the diagnosis unit may diagnose a face type based on the shape of the face, the arrangement of facial features, and their proportions. The diagnosis unit may also diagnose a personal color based on skin color, hair color, eye color, and other factors. The diagnosis unit can also perform skeletal diagnosis based on bone shape, body proportions, muscle distribution, etc. The generation unit simulates the movement of clothes based on the diagnosis results obtained by the diagnosis unit. The generation unit simulates the movement of clothes using, for example, a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit simulates the movement of clothes using, for example, a physical simulation or machine learning. For example, the generation unit simulates the movement of clothes using a physical model. The generation unit can also simulate the movement of clothes using a machine learning algorithm. For example, the generation unit simulates the movement based on the material and design of the clothes. The suggestion unit allows a stylist to make suggestions based on the simulation results generated by the generation unit. For example, the suggestion unit allows a stylist to look at items virtually tried on by a customer and make styling suggestions and advice. The suggestion unit can also make automatic suggestions using an algorithm. For example, the suggestion unit makes more personalized suggestions based on items the user has tried on in the past.As a result, the virtual fitting system according to the embodiment can improve customer satisfaction by consistently performing everything from 3D scanning of the user to diagnosis, simulation, and suggestions. For example, a user can 3D scan themselves using a smartphone, and the generation AI can simulate the movement of the clothes, providing feedback as if they were actually trying them on. In addition, a stylist can make suggestions, allowing the user to receive styling suggestions that suit them. This can reduce any gaps after purchase.

[0069] The collection unit can generate a 3D representation of the user's body shape using scanning technology using a smartphone camera or telemetry stitching technology using multiple photographs. The collection unit can generate a 3D representation of the user's body shape using, for example, a smartphone camera. For example, the collection unit can take multiple photographs using a smartphone camera and stitch them together to generate a 3D model. The collection unit can also generate a 3D representation from photographs using telemetry stitching technology. For example, the collection unit can take multiple photographs and stitch them together to generate a 3D model. This allows for easy virtual fitting by generating a 3D representation of the user's body shape using a smartphone or photographs. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input photo data acquired by a smartphone camera into a generation AI and have the generation AI generate a 3D model from the photo data.

[0070] The diagnosis unit can use AI to diagnose face type, personal color, and bone structure. The diagnosis unit, for example, uses AI to diagnose face type, personal color, and bone structure. For example, the diagnosis unit diagnoses face type based on face shape, feature arrangement, and proportions. The diagnosis unit can also diagnose personal color based on skin color, hair color, eye color, and the like. The diagnosis unit can also diagnose bone structure based on bone shape, body proportions, muscle structure, and the like. In this way, the use of AI improves the accuracy of face type, personal color, and bone structure diagnoses. Some or all of the above-described processing in the diagnosis unit may be performed using AI, for example, or may be performed without using AI. For example, the diagnosis unit can input face shape data to a generation AI and have the generation AI perform a face type diagnosis.

[0071] The generation unit can simulate the movement of the clothes using physical simulation or machine learning. The generation unit simulates the movement of the clothes using, for example, a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit simulates the movement of the clothes using, for example, physical simulation or machine learning. For example, the generation unit simulates the movement of the clothes using a physical model. The generation unit can also simulate the movement of the clothes using a machine learning algorithm. For example, the generation unit simulates the movement based on the material and design of the clothes. This improves the accuracy of the simulation of the movement of the clothes by using physical simulation or machine learning. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input design data of the clothes into the generation AI and cause the generation AI to simulate the movement of the clothes.

[0072] The suggestion unit allows a stylist to view the customer's virtual try-on items and make styling suggestions and advice. For example, the suggestion unit allows a stylist to view the customer's virtual try-on items and make styling suggestions and advice. For example, the suggestion unit allows a stylist to view the customer's virtual try-on items and make styling suggestions and advice. The suggestion unit can also make automatic suggestions using an algorithm. For example, the suggestion unit makes more personalized suggestions based on items the user has tried on in the past. This allows the stylist to view the virtual try-on items and make suggestions, making more personalized suggestions possible. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input data of the customer's virtual try-on items into a generation AI and have the generation AI execute styling suggestions.

[0073] The suggestion unit can make automatic suggestions using an algorithm. The suggestion unit makes automatic suggestions using, for example, an algorithm. For example, the suggestion unit makes more personalized suggestions based on items the user has tried on in the past. This enables automatic suggestions by using an algorithm, improving the efficiency of suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's try-on data into the generation AI and cause the generation AI to make automatic suggestions.

[0074] The collection unit can estimate the user's emotions and adjust the timing of the 3D scan based on the estimated user's emotions. For example, if the user is relaxed, the collection unit can adjust the timing of the scan slowly to allow the user to maintain a natural posture. Furthermore, if the user is nervous, the collection unit can also speed up the timing of the scan to reduce the user's burden. Furthermore, if the user is excited, the collection unit can adjust the timing of the scan and wait until the user calms down. This allows for a more natural scan by adjusting the timing of the 3D scan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0075] The collection unit can analyze the user's past scan data and select the optimal scan method. For example, the collection unit can automatically set the optimal scan angle and distance based on the user's past scan data. The collection unit can also suggest the optimal scan method for specific environmental conditions (lighting, background, etc.) based on the user's past scan data. The collection unit can also analyze the user's past scan data and make adjustments to improve scan accuracy. In this way, by analyzing the past scan data, the optimal scan method is selected and scan accuracy is improved. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past scan data into a generation AI and have the generation AI select the optimal scan method.

[0076] The collection unit can improve scan accuracy based on the user's current environment during scanning. For example, the collection unit can detect the user's current lighting conditions and automatically adjust optimal scan settings. The collection unit can also analyze the user's background and remove background noise to improve scan accuracy. The collection unit can also detect the user's environmental sounds and make adjustments to minimize noise during scanning. This improves scan accuracy based on the user's current environment, enabling more accurate 3D scans. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input current environmental data into a generation AI and have the generation AI improve scan accuracy.

[0077] The collection unit can select the optimal scanning means depending on the user's input method when scanning. For example, if the user selects voice input, the collection unit starts scanning based on a voice command. Furthermore, if the user selects text input, the collection unit can also adjust the scanning procedure based on text instructions. Furthermore, if the user selects image input, the collection unit can also set the optimal angle and distance for scanning based on image analysis. This improves user convenience by selecting the optimal scanning means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data to a generation AI and cause the generation AI to start scanning based on a voice command.

[0078] The collection unit can estimate the user's emotions and prioritize scan data based on the estimated user emotions. For example, if the user is relaxed, the collection unit can increase the level of detail in the scan data and prioritize acquiring more precise data. Furthermore, if the user is nervous, the collection unit can quickly acquire scan data to reduce the user's burden. Furthermore, if the user is excited, the collection unit can temporarily suspend acquisition of scan data and wait until the user calms down. This allows for more appropriate data to be acquired by prioritizing scan data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0079] When scanning, the collection unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can adjust the scan data based on the environmental conditions of the area. Furthermore, if the user is traveling, the collection unit can optimize the scan data based on the environmental conditions of the travel destination. Furthermore, if the user is at home, the collection unit can acquire scan data based on the environmental conditions of the user's home. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to acquire highly relevant data.

[0080] The collection unit can analyze the user's social media activity at the time of scanning and acquire related data. The collection unit can supplement the scan data based on, for example, photos shared by the user on social media. The collection unit can also analyze the user's social media activity and acquire related scan data. The collection unit can also acquire related scan data by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related data can be acquired and the accuracy of the scan can be improved. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social media data into a generation AI and cause the generation AI to acquire related data.

[0081] The collection unit can customize the scan method by reflecting the user's past feedback during scanning. For example, the collection unit adjusts the scan method based on feedback provided by the user in the past. The collection unit can also preferentially apply specific scan settings based on the user's past feedback. The collection unit can also analyze the user's past feedback and make adjustments to improve scan accuracy. In this way, the scan method is customized by reflecting the user's past feedback, thereby improving scan accuracy. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input past feedback data into a generation AI and cause the generation AI to customize the scan method.

[0082] The diagnosis unit can estimate the user's emotions and adjust the way the diagnosis is presented based on the estimated user emotions. For example, if the user is relaxed, the diagnosis unit can provide a detailed diagnosis result to make it easier for the user to understand. Furthermore, if the user is nervous, the diagnosis unit can provide a concise and to-the-point diagnosis result to reduce the user's burden. Furthermore, if the user is excited, the diagnosis unit can provide a visually appealing diagnosis result to attract the user's interest. By adjusting the way the diagnosis is presented based on the user's emotions, it is possible to provide a diagnosis result that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the diagnosis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the diagnosis unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0083] The diagnostic unit can adjust the level of detail of the diagnosis based on the user's level of importance during the diagnosis. For example, if the user is attending an important event, the diagnostic unit provides detailed diagnostic results and suggests optimal styling. Furthermore, if the user is aiming for daily use, the diagnostic unit can provide concise diagnostic results for easy use. Furthermore, if the user has a specific purpose, the diagnostic unit can also provide diagnostic results tailored to that purpose. By adjusting the level of detail of the diagnosis based on the user's level of importance, optimal diagnostic results can be provided for the user. Some or all of the above-described processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input the user's level of importance data into a generation AI and cause the generation AI to adjust the level of detail of the diagnosis.

[0084] The diagnostic unit can apply different diagnostic algorithms depending on the user's category during diagnosis. For example, if the user is a child, the diagnostic unit can apply a diagnostic algorithm for kids to suggest appropriate styling. Furthermore, if the user is pregnant, the diagnostic unit can apply a diagnostic algorithm for maternity to suggest optimal styling. Furthermore, if the user is senior, the diagnostic unit can apply a diagnostic algorithm for seniors to suggest appropriate styling. In this way, by applying different diagnostic algorithms depending on the user's category, more appropriate diagnostic results can be provided. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input user category data into a generation AI and cause the generation AI to apply a diagnostic algorithm.

[0085] During diagnosis, the diagnostic unit can improve the accuracy of the diagnosis by referring to the user's past diagnostic results. For example, the diagnostic unit can adjust the current diagnostic result based on the user's past diagnostic results to improve accuracy. The diagnostic unit can also extract specific patterns from the user's past diagnostic results and optimize the diagnostic algorithm. The diagnostic unit can also analyze the user's past diagnostic results and make adjustments to improve the accuracy of the diagnosis. In this way, the accuracy of the diagnosis is improved by referring to the user's past diagnostic results. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input past diagnostic result data into the generation AI and cause the generation AI to improve the accuracy of the diagnosis.

[0086] The diagnosis unit can estimate the user's emotions and adjust the length of the diagnosis based on the estimated user emotions. For example, if the user is relaxed, the diagnosis unit can provide a detailed diagnosis to make it easier for the user to understand. Furthermore, if the user is nervous, the diagnosis unit can provide a concise and to-the-point diagnosis to reduce the user's burden. Furthermore, if the user is excited, the diagnosis unit can provide a visually appealing diagnosis to attract the user's interest. By adjusting the length of the diagnosis based on the user's emotions, an optimal diagnostic experience can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the diagnosis unit can be performed using, for example, AI, or without AI. For example, the diagnosis unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0087] During diagnosis, the diagnosis unit can determine the priority of the diagnosis based on the user's submission time. For example, if the user is in a hurry, the diagnosis unit can increase the priority of the diagnosis and provide results quickly. Furthermore, if the user is participating in a specific event, the diagnosis unit can adjust the priority of the diagnosis to match the event. Furthermore, if the user is intending to use the device for daily use, the diagnosis unit can set the priority of the diagnosis to the normal level. This allows for quick diagnosis results to be provided by determining the priority of the diagnosis based on the user's submission time. Some or all of the above-described processing in the diagnosis unit may be performed using, for example, AI, or may be performed without AI. For example, the diagnosis unit can input submission time data into a generation AI and have the generation AI determine the priority of the diagnosis.

[0088] During diagnosis, the diagnostic unit can adjust the order of diagnoses based on the user's relevance. For example, if the user belongs to a specific category, the diagnostic unit can prioritize diagnoses related to that category. Furthermore, if the user has a specific purpose, the diagnostic unit can also prioritize diagnoses related to that purpose. Furthermore, the diagnostic unit can prioritize highly relevant diagnoses based on the user's past diagnostic results. By adjusting the order of diagnoses based on the user's relevance, more relevant diagnostic results can be provided. Some or all of the above-described processing in the diagnostic unit may be performed using, for example, AI, or may be performed without AI. For example, the diagnostic unit can input relevance data to a generation AI and cause the generation AI to adjust the order of diagnoses.

[0089] During diagnosis, the diagnostic unit can adjust the use of technical terminology in the diagnosis according to the user's level of expertise. For example, if the user has technical expertise, the diagnostic unit can provide diagnostic results using detailed technical terminology. Furthermore, if the user is a beginner, the diagnostic unit can provide diagnostic results in simple language to make them easier to understand. The diagnostic unit can also analyze the user's level of expertise and adjust the use of appropriate technical terminology. By adjusting the use of technical terminology according to the user's level of expertise, diagnostic results that are easy for the user to understand can be provided. Some or all of the above-described processing in the diagnostic unit may be performed using, for example, AI, or may be performed without AI. For example, the diagnostic unit can input technical expertise level data into a generation AI and have the generation AI use technical terminology.

[0090] The generation unit can estimate the user's emotions and adjust the way the simulation is presented based on the estimated user's emotions. For example, if the user is relaxed, the generation AI generates a simulation that proceeds at a leisurely pace. If the user is in a hurry, the generation AI can generate a simulation that emphasizes the shortest route. If the user is excited, the generation AI can generate a simulation that adds visually stimulating effects. By adjusting the way the simulation is presented based on the user's emotions, an optimal simulation experience can be provided for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0091] The generation unit can adjust the level of detail of the simulation based on the importance of the clothes during the simulation. For example, in the case of clothes for an important event, the generation unit causes the generation AI to generate a detailed simulation. In addition, in the case of clothes intended for everyday use, the generation unit can also cause the generation AI to generate a concise simulation. In addition, in the case of clothes with a specific purpose, the generation unit can also cause the generation AI to generate a simulation according to that purpose. In this way, by adjusting the level of detail of the simulation based on the importance of the clothes, it is possible to provide an optimal simulation result for the user. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit may input clothing importance data to the generation AI and cause the generation AI to adjust the level of detail of the simulation.

[0092] The generation unit can apply different simulation algorithms depending on the clothing category during simulation. For example, in the case of formal wear, the generation AI applies a simulation algorithm that corresponds to formal movements and postures. In addition, in the case of sportswear, the generation unit can apply a simulation algorithm that takes ease of movement and stretchability into consideration. In addition, in the case of casual wear, the generation AI can apply a simulation algorithm that corresponds to everyday movements. In this way, by applying different simulation algorithms depending on the clothing category, more appropriate simulation results can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input clothing category data into the generation AI and cause the generation AI to apply a simulation algorithm.

[0093] During a simulation, the generation unit can improve the accuracy of the simulation by referring to the user's past simulation results. For example, the generation unit adjusts the current simulation based on the user's past simulation results to improve accuracy. The generation unit can also extract specific patterns from the user's past simulation results and optimize the simulation algorithm. The generation unit can also analyze the user's past simulation results and make adjustments to improve the accuracy of the simulation. In this way, the accuracy of the simulation is improved by referring to the user's past simulation results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past simulation result data into the generation AI and cause the generation AI to improve the accuracy of the simulation.

[0094] The generation unit can estimate the user's emotions and adjust the length of the simulation based on the estimated user emotions. For example, when the user is relaxed, the generation AI generates a detailed simulation to make it easier for the user to understand. Furthermore, when the user is in a hurry, the generation AI can generate a concise and to-the-point simulation to reduce the user's burden. Furthermore, when the user is excited, the generation AI can generate a visually appealing simulation to attract the user's interest. By adjusting the length of the simulation based on the user's emotions, an optimal simulation experience can be provided for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0095] During a simulation, the generation unit can determine the priority of the simulation based on the time when the clothes were submitted. For example, in the case of clothes for an important event, the generation unit can increase the priority of the simulation and provide results quickly. The generation unit can also set the priority of the simulation to normal for clothes intended for everyday use. In addition, in the case of clothes with a specific purpose, the generation unit can adjust the priority of the simulation according to the purpose. In this way, by determining the priority of the simulation based on the time when the clothes were submitted, it is possible to provide quick simulation results. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input submission time data into the generation AI and have the generation AI determine the priority of the simulation.

[0096] During the simulation, the generation unit can adjust the order of the simulation based on the relevance of the clothes. For example, in the case of formal wear, the generation unit can prioritize a simulation that corresponds to formal movements and postures. Furthermore, in the case of sportswear, the generation unit can prioritize a simulation that takes into account ease of movement and stretchability. Furthermore, in the case of casual wear, the generation unit can prioritize a simulation that corresponds to everyday movements. In this way, by adjusting the order of the simulation based on the relevance of the clothes, it is possible to provide a simulation result that is more relevant. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input clothing relevance data into the generation AI and cause the generation AI to adjust the order of the simulation.

[0097] During the simulation, the generation unit can adjust the use of technical terminology in the simulation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can provide simulation results using detailed technical terminology. Furthermore, if the user is a beginner, the generation unit can provide simulation results in simple language to make them easier to understand. The generation unit can also analyze the user's level of expertise and adjust the use of appropriate technical terminology. By adjusting the use of technical terminology according to the user's level of expertise, simulation results that are easy for the user to understand can be provided. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input technical expertise level data into the generation AI and have the generation AI execute the use of technical terminology.

[0098] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on the estimated user emotions. For example, when the user is relaxed, the suggestion unit can provide detailed suggestions to make them easier for the user to understand. Furthermore, when the user is nervous, the suggestion unit can provide concise and to-the-point suggestions to reduce the user's burden. Furthermore, when the user is excited, the suggestion unit can provide visually appealing suggestions to attract the user's interest. By adjusting the way the suggestions are presented based on the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0099] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the product. For example, in the case of a product for an important event, the suggestion unit makes detailed suggestions and suggests optimal styling. In addition, in the case of a product intended for everyday use, the suggestion unit makes concise suggestions to enable easy use. In addition, in the case of a product with a specific purpose, the suggestion unit can also make suggestions according to that purpose. In this way, by adjusting the level of detail of the suggestion based on the importance of the product, it is possible to provide an optimal suggestion for the user. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input product importance data to a generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0100] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the product category. For example, in the case of formal wear, the suggestion unit can apply an algorithm that suggests formal styling. In addition, in the case of sportswear, the suggestion unit can apply an algorithm that suggests styling that emphasizes ease of movement and functionality. In addition, in the case of casual wear, the suggestion unit can apply an algorithm that suggests everyday styling. In this way, by applying different suggestion algorithms depending on the product category, more appropriate suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input product category data into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0101] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit adjusts the current proposal based on the user's past proposal results to improve accuracy. The suggestion unit can also extract specific patterns from the user's past proposal results and optimize the proposal algorithm. The suggestion unit can also analyze the user's past proposal results and make adjustments to improve the accuracy of the proposal. In this way, the accuracy of the proposal is improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0102] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, when the user is relaxed, the suggestion unit can provide detailed suggestions to make them easier for the user to understand. Furthermore, when the user is nervous, the suggestion unit can provide concise and to-the-point suggestions to reduce the user's burden. Furthermore, when the user is excited, the suggestion unit can provide visually appealing suggestions to attract the user's interest. By adjusting the length of the suggestions based on the user's emotions, the optimal suggestions can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0103] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the product. For example, in the case of a product for an important event, the proposal unit can increase the priority of the proposal and provide results quickly. The proposal unit can also set the priority of the proposal to the normal level for a product intended for everyday use. In addition, in the case of a product with a specific purpose, the proposal unit can also adjust the priority of the proposal according to the purpose. In this way, by determining the priority of the proposal based on the submission time of the product, it is possible to provide a proposal result quickly. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input submission time data into the generation AI and have the generation AI determine the priority of the proposal.

[0104] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of the products. For example, in the case of formal wear, the suggestion unit can prioritize proposing formal styling. In addition, in the case of sportswear, the suggestion unit can prioritize proposing styling that emphasizes ease of movement and functionality. In addition, in the case of casual wear, the suggestion unit can prioritize proposing everyday styling. In this way, by adjusting the order of suggestions based on the relevance of the products, more relevant suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input product relevance data into the generation AI and cause the generation AI to adjust the order of suggestions.

[0105] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make the proposal using detailed technical terminology. Furthermore, if the user is a beginner, the suggestion unit can make the proposal in simple language to make it easier to understand. The suggestion unit can also analyze the user's level of expertise and adjust the use of appropriate technical terminology. By adjusting the use of technical terminology according to the user's level of expertise, it is possible to provide a proposal that is easy for the user to understand. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input technical expertise level data into a generation AI and cause the generation AI to use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, diagnosis unit, generation unit, and suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can generate a 3D representation of the user's body shape using the camera 42 of the smart device 14. For example, the diagnosis unit is realized by the specific processing unit 290 of the data processing device 12 and performs face type, personal color, and bone structure diagnosis. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates the movement of clothes using a generation AI. For example, the suggestion unit is realized by the control unit 46A of the smart device 14 and a stylist makes suggestions. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, diagnosis unit, generation unit, and suggestion unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can generate a 3D representation of the user's body shape using the camera 42 of the smart glasses 214. For example, the diagnosis unit is realized by the specific processing unit 290 of the data processing device 12 and performs face type, personal color, and bone structure diagnosis. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates the movement of clothes using a generation AI. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214 and a stylist makes suggestions. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, diagnosis unit, generation unit, and suggestion unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can generate a 3D representation of the user's body shape using the camera 42 of the headset-type terminal 314. For example, the diagnosis unit is realized by the specific processing unit 290 of the data processing device 12 and performs face type, personal color, and bone structure diagnosis. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates the movement of clothes using a generation AI. For example, the suggestion unit is realized by the control unit 46A of the headset-type terminal 314, and a stylist makes suggestions. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, diagnosis unit, generation unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can generate a 3D representation of the user's body shape using the camera 42 of the robot 414. For example, the diagnosis unit is realized by the specific processing unit 290 of the data processing device 12 and performs face type, personal color, and bone structure diagnosis. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates the movement of clothes using a generation AI. For example, the suggestion unit is realized by the control unit 46A of the robot 414, and a stylist makes suggestions.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When acquiring 3D scan data of a user, the collection unit can monitor the user's health condition and adjust the timing and method of scanning according to the user's health condition. For example, if the user is tired, the collection unit can delay the timing of the scan to encourage the user to rest. Also, if the user is healthy, the collection unit can quickly perform the scan and collect data efficiently. Also, if the user is ill, the collection unit can temporarily suspend the scan and wait until the user's health condition improves. This allows for more appropriate data collection by adjusting the timing and method of the scan according to the user's health condition.

[0108] The diagnosis unit can estimate the user's emotions and adjust the method of feedback of the diagnosis result based on the estimated user's emotions. For example, if the user is relaxed, the diagnosis unit can provide detailed feedback that is easy for the user to understand. If the user is nervous, the diagnosis unit can provide concise and to-the-point feedback to reduce the user's burden. If the user is excited, the diagnosis unit can provide visually attractive feedback to attract the user's interest. In this way, by adjusting the method of feedback of the diagnosis result based on the user's emotions, it is possible to provide feedback that is easy for the user to understand.

[0109] The generation unit can analyze the user's past purchase history and improve the accuracy of the simulation based on the purchase history. For example, the generation unit can adjust the current simulation based on data on clothes the user previously purchased to improve accuracy. The generation unit can also extract specific patterns from the user's past purchase history and optimize the simulation algorithm. The generation unit can also analyze the user's past purchase history and make adjustments to improve the accuracy of the simulation. In this way, the accuracy of the simulation can be improved by referring to the user's past purchase history.

[0110] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, when the user is relaxed, the suggestion unit can provide detailed suggestions that are easy for the user to understand. When the user is nervous, the suggestion unit can provide concise and to-the-point suggestions to reduce the user's burden. When the user is excited, the suggestion unit can provide visually appealing suggestions to attract the user's interest. In this way, by adjusting the timing of suggestions based on the user's emotions, it is possible to provide optimal suggestions for the user.

[0111] The collection unit can analyze the user's lifestyle data and suggest the optimal scan timing based on the lifestyle. For example, if the user is a morning person, the collection unit can suggest scanning in the morning. Alternatively, if the user is a night owl, the collection unit can suggest scanning in the evening. Furthermore, by analyzing the user's lifestyle data and suggesting the optimal scan timing, it becomes possible to perform a scan that matches the user's lifestyle rhythm. In this way, by suggesting the optimal scan timing based on the user's lifestyle, the accuracy of the scan is improved.

[0112] The diagnosis unit can estimate the user's emotions and adjust the order of diagnoses based on the estimated user emotions. For example, if the user is relaxed, the diagnosis unit can perform a detailed diagnosis to make it easier for the user to understand. If the user is nervous, the diagnosis unit can perform a concise and to-the-point diagnosis to reduce the user's burden. If the user is excited, the diagnosis unit can perform a visually appealing diagnosis to attract the user's interest. In this way, by adjusting the order of diagnoses based on the user's emotions, it is possible to provide the user with an optimal diagnostic experience.

[0113] The generation unit can estimate the user's emotions and adjust the speed of the simulation based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a simulation that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate a simulation that emphasizes the shortest route. If the user is excited, the generation unit can generate a simulation that adds visually stimulating effects. In this way, by adjusting the speed of the simulation based on the user's emotions, it is possible to provide the user with an optimal simulation experience.

[0114] The suggestion unit can analyze the user's past feedback and improve the accuracy of the suggestions based on the feedback. For example, the suggestion unit can adjust the current suggestions based on feedback provided by the user in the past to improve accuracy. The suggestion unit can also extract specific patterns from the user's past feedback and optimize the suggestion algorithm. The suggestion unit can also analyze the user's past feedback and make adjustments to improve the accuracy of the suggestions. In this way, the accuracy of the suggestions can be improved by referring to the user's past feedback.

[0115] The collection unit can estimate the user's emotions and adjust the level of scan detail based on the estimated user's emotions. For example, if the user is relaxed, the collection unit can perform a detailed scan to obtain more precise data. If the user is nervous, the collection unit can perform a brief scan to reduce the user's burden. If the user is excited, the collection unit can perform a visually appealing scan to attract the user's interest. In this way, by adjusting the level of scan detail based on the user's emotions, it is possible to provide an optimal scanning experience for the user.

[0116] The generation unit can analyze the user's past simulation results and improve the accuracy of a new simulation based on the simulation results. For example, the generation unit can adjust the current simulation based on the user's past simulation results to improve accuracy. The generation unit can also extract specific patterns from the user's past simulation results and optimize the simulation algorithm. The generation unit can also analyze the user's past simulation results and make adjustments to improve the accuracy of the simulation. In this way, the accuracy of the simulation can be improved by referring to the user's past simulation results.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit performs a 3D scan of the user. 3D scanning of the user can be done using scanning technology using a smartphone camera or telemetry stitching technology using multiple photos. For example, the collection unit can use a smartphone camera to create a 3D image of the user's body and then use telemetry stitching technology to stitch together photos to create a 3D model. Step 2: The diagnosis unit diagnoses face type, personal color, and bone structure based on the data collected by the collection unit. The diagnosis unit uses AI to diagnose face type based on facial shape, feature placement, and proportions, and diagnoses personal color based on skin color, hair color, eye color, etc. It also diagnoses bone structure based on bone shape, body proportions, muscle structure, etc. Step 3: The generator simulates the movement of the clothes based on the diagnosis results obtained by the diagnosis unit. The generator uses a generative AI to simulate the movement of the clothes using physics simulation and machine learning algorithms, and simulates the movement based on the material and design of the clothes. Step 4: In the suggestion section, a stylist makes suggestions based on the simulation results generated by the generation section. The suggestion section allows the stylist to view the virtual items being tried on by the customer and provide styling suggestions and advice. It is also possible to make automatic suggestions using algorithms, providing more personalized suggestions based on items the user has tried on in the past.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 collection unit that performs a 3D scan of the user; a diagnosis unit that diagnoses a face type, a personal color, or a bone structure based on the data collected by the collection unit; a generation unit that simulates the movement of clothes based on the diagnosis result obtained by the diagnosis unit; a suggestion unit that allows a stylist to make suggestions based on the simulation results generated by the generation unit; Equipped with A system characterized by:

2. The collecting unit The user's body shape is created in 3D using scanning technology using a smartphone camera or telemetry stitching technology using multiple photos.

2. The system of claim 1.

3. The diagnostic unit Uses AI to diagnose face type, personal color, and bone structure 2. The system of claim 1.

4. The generation unit Simulating clothing movement using physics simulation or machine learning 2. The system of claim 1.

5. The proposal unit A stylist virtually tries on items for the customer and provides styling suggestions and advice 2. The system of claim 1.

6. The proposal unit Use algorithms to make automatic suggestions 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of 3D scanning based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past scan data and select the optimal scan method 2. The system of claim 1.

9. The collecting unit During scanning, improve scan accuracy based on the user's current environment 2. The system of claim 1.

10. The collecting unit When scanning, the optimal scanning method is selected according to the user's input method.

2. The system of claim 1.

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