system

Abstract

The system according to this embodiment aims to provide users with a realistic try-on experience when shopping online. [Solution] The system according to the embodiment comprises an acquisition unit, a generation unit, a fitting unit, a provision unit, and a simulation unit. The acquisition unit acquires the user's photo and profile information. The generation unit generates an avatar based on the information acquired by the acquisition unit. The fitting unit makes the avatar generated by the generation unit try on clothes and accessories from an online shop. The provision unit provides the fitting video generated by the fitting unit. The simulation unit simulates changes in body shape and aging.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for a user to obtain a real experience as if actually trying on a product in online shopping.

[0005] The system according to the embodiment aims to enable a user to obtain a real try-on experience in online shopping.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a generation unit, a fitting unit, a provision unit, and a simulation unit. The acquisition unit acquires the user's photo and profile information. The generation unit generates an avatar based on the information acquired by the acquisition unit. The fitting unit makes the avatar generated by the generation unit try on clothes and accessories from an online shop. The provision unit provides the fitting video generated by the fitting unit. The simulation unit simulates changes in body shape and aging. [Effects of the Invention]

[0007] The system according to this embodiment allows users to have a realistic try-on experience when shopping online. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that creates a sophisticated AI avatar based on a user's personal photo and profile information. This system creates a sophisticated AI avatar based on the user's personal photo and profile information. The system then has the AI ​​avatar try on clothes and accessories from an online shop, generating a realistic virtual try-on video. Through the generated try-on video, the user can have a realistic experience as if they were actually trying on the products. Furthermore, the system creates avatars that simulate changes in body shape and aging, providing try-on images that anticipate future appearances. First, the user provides a personal photo and profile information. This information is input into the generating AI. The generating AI analyzes the provided information and generates a sophisticated avatar of the user. For example, an avatar that accurately reproduces the user's facial features and body shape is generated. Next, the generated avatar is made to try on clothes and accessories from an online shop. The generating AI has the avatar try on the items selected by the user and generates a realistic virtual try-on video of the process. For example, the avatar tries on a dress or accessory selected by the user, and the process is displayed as a video. Furthermore, the generating AI also creates avatars that simulate changes in body shape and aging. For example, if a user specifies their future body shape and age, the generating AI updates the avatar based on that information to recreate their future appearance. It then generates a try-on video for this avatar as well and provides it to the user. This system allows users to have a realistic try-on experience from the comfort of their homes. For instance, users can view a video that simulates them actually trying on clothes before purchasing them from an online shop. In addition, since try-on images simulating future body shape and age are also provided, users can make purchase decisions from a long-term perspective. In this way, the system can generate sophisticated avatars based on the user's photos and profile information and provide realistic virtual try-on videos.

[0029] The system according to this embodiment comprises an acquisition unit, a generation unit, a fitting unit, a provision unit, and a simulation unit. The acquisition unit acquires the user's photograph and profile information. The acquisition unit acquires information that reproduces the user's facial features and body shape in detail, for example. The acquisition unit extracts the user's facial features from high-resolution photographs and reproduces the body shape in detail using photographs from multiple angles. The generation unit generates an avatar based on the information acquired by the acquisition unit. The generation unit generates an elaborate avatar based on the user's facial features and body shape, for example, using a generation AI. The generation unit reproduces the user's facial features in detail, for example, using a text generation AI (e.g., LLM). The generation unit can also reproduce the user's body shape in detail using a multimodal generation AI. The fitting unit has the avatar generated by the generation unit try on clothes and accessories from an online shop. The fitting unit has the avatar try on items selected by the user and generates a realistic virtual fitting video of the process. The fitting unit, for example, uses a generation AI to have an avatar try on dresses and accessories selected by the user, and displays the process as a video. The provision unit provides the fitting video generated by the fitting unit. The provision unit provides the generated fitting video to the user. The provision unit displays the fitting video through, for example, a web application or mobile application. The simulation unit simulates changes in body shape and aging. For example, if the user specifies their future body shape and age, the simulation unit updates the avatar using a generation AI to recreate that future appearance. For example, the simulation unit simulates changes in the user's body shape and aging, and updates the avatar based on that information. As a result, the system can generate a sophisticated avatar based on the user's photos and profile information and provide realistic virtual fitting videos.

[0030] The acquisition unit retrieves the user's photos and profile information. For example, it acquires information that precisely reproduces the user's facial features and body shape. Specifically, it uses high-resolution photos provided by the user to extract detailed facial features. These features include eye shape and position, nose height, mouth shape, skin color, and texture. To reproduce body shape, users are required to provide full-body photos taken from multiple angles. This allows the acquisition unit to accurately understand the user's body shape and obtain detailed measurements such as height, weight, shoulder width, waist, and hips. Furthermore, the acquisition unit also collects the user's profile information. This profile information includes age, gender, preferred fashion style, and past purchase history. This information plays a crucial role in subsequent processing in the generation and fitting units. The acquisition unit centrally manages this data and stores it using encryption technology to ensure security. This allows for efficient collection of necessary information while protecting user privacy.

[0031] The generation unit generates avatars based on information acquired by the acquisition unit. For example, the generation unit uses a generation AI to create sophisticated avatars based on the user's facial features and body shape. Specifically, it uses a text generation AI (e.g., LLM) to reproduce the user's facial features in detail. The text generation AI generates text data describing the user's facial features and creates a 3D model of the face based on that data. It can also reproduce the user's body shape in detail using a multimodal generation AI. The multimodal generation AI integrates and processes image data and text data to generate a 3D model that accurately reproduces the user's body shape. The generation unit combines these AI technologies to generate sophisticated avatars that integrate the user's face and body shape. Furthermore, the generation unit can also customize the avatar's clothing and accessory style, taking into account the user's profile information. This allows the generation unit to provide avatars tailored to the user's personality and preferences.

[0032] The fitting section allows users to try on clothes and accessories from online shops using avatars generated by the generation section. For example, the fitting section can have the avatar try on items selected by the user and generate a realistic virtual try-on video. Specifically, it uses generation AI to have the avatar try on dresses and accessories selected by the user and displays the result as a video. The generation AI accurately reproduces the shape, color, and texture of the clothes and accessories, and makes them fit naturally to the avatar's body shape. The fitting section can also generate 360-degree rotating videos so that users can view the avatar from different angles. This allows users to check in detail how the selected items look on them. Furthermore, the fitting section also provides a function that allows users to try on multiple items simultaneously. This allows users to try on different combinations of clothes and accessories to find the best outfit.

[0033] The service provider provides the try-on videos generated by the try-on service provider. The service provider provides the generated try-on videos to users, for example, by displaying them through web or mobile applications. The service provider provides an easily accessible interface for users and can play the try-on videos in high resolution. The service provider also provides functions for users to save and share the try-on videos. This allows users to share try-on videos with friends and family and get their opinions. Furthermore, the service provider can collect user feedback and continuously improve the quality of the try-on videos and the user experience. For example, it can provide a function for users to leave ratings and comments on the try-on videos and use that feedback to improve the system. This allows the service provider to provide users with high-quality try-on videos and improve their satisfaction.

[0034] The simulation unit simulates changes in body shape and aging. For example, when a user specifies their future body shape and age, the simulation unit uses a generative AI to update the avatar and recreate that future appearance. Specifically, when a user specifies changes in body shape such as weight gain or loss or muscle gain, the generative AI generates an avatar that reflects those changes. Also, when a user specifies their age, the generative AI simulates changes in face and body shape due to aging and updates the avatar. This allows the user to realistically see what they will look like in the future. Furthermore, the simulation unit can also update the avatar by considering changes in health status and lifestyle. For example, it can simulate changes in body shape if a user adopts an exercise routine or follows a specific diet. In this way, the simulation unit can realistically recreate the user's future appearance and provide information that is useful for health management and lifestyle improvement.

[0035] The acquisition unit can acquire information that reproduces the user's facial features and body shape in detail. For example, the acquisition unit can extract the user's facial features from high-resolution photographs and reproduce the body shape in detail using photographs from multiple angles. For example, the acquisition unit can analyze points of facial features and reproduce the body shape in detail using a body shape measurement method. By acquiring information that reproduces the user's facial features and body shape in detail, a more sophisticated avatar can be generated. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can extract the user's facial features from high-resolution photographs and reproduce the body shape in detail using AI.

[0036] The generation unit can generate sophisticated avatars based on the user's facial features and body shape. For example, the generation unit uses a generation AI to generate sophisticated avatars based on the user's facial features and body shape. The generation unit uses a text generation AI (e.g., LLM) to reproduce the user's facial features in detail. The generation unit can also use a multimodal generation AI to reproduce the user's body shape in detail. For example, the generation unit uses a generation AI to reproduce the user's facial features in detail and reproduces the body shape in detail using photographs from multiple angles. This allows for the generation of sophisticated avatars based on the user's facial features and body shape, providing a realistic try-on experience. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit can extract the user's facial features from high-resolution photographs and reproduce the body shape in detail using a generation AI.

[0037] The fitting room function can have an avatar try on items selected by the user and generate a realistic virtual fitting video of the process. For example, the fitting room function uses a generation AI to have an avatar try on items selected by the user and generate a realistic virtual fitting video of the process. For example, the fitting room function can have an avatar try on dresses or accessories selected by the user and display the process as a video. For example, the fitting room function uses a generation AI to have an avatar try on items selected by the user and generate a realistic virtual fitting video of the process. This allows the system to provide users with a realistic fitting experience by having an avatar try on items selected by the user and generating a realistic virtual fitting video of the process. Some or all of the above-described processes in the fitting room function are performed using a generation AI. For example, the fitting room function can have an avatar try on items selected by the user and generate a realistic virtual fitting video of the process using a generation AI.

[0038] The service provider can provide the generated try-on video to the user. The service provider can, for example, provide the generated try-on video to the user. The service provider can, for example, display the try-on video through a web application or a mobile application. The service provider can, for example, provide the user with the generated try-on video, allowing the user to have a realistic experience as if they were actually trying on the product. By providing the user with the generated try-on video, the user can have a realistic experience as if they were actually trying on the product. Some or all of the above processing in the service provider is performed using a generation AI. For example, the service provider can provide the generated try-on video to the user using a generation AI.

[0039] The simulation unit can simulate changes in the user's body shape and aging, and generate an avatar that reproduces their future appearance. For example, if the user specifies their future body shape and age, the simulation unit updates the avatar using a generation AI to reproduce their future appearance. The simulation unit simulates changes in the user's body shape and aging, and updates the avatar based on that information. The simulation unit uses a generation AI to simulate changes in the user's body shape and aging, and generates an avatar that reproduces their future appearance. In this way, by simulating changes in the user's body shape and aging and generating an avatar that reproduces their future appearance, it is possible to provide a future fitting image. Some or all of the above processing in the simulation unit is performed using a generation AI. For example, the simulation unit can use a generation AI to simulate changes in the user's body shape and aging, and generate an avatar that reproduces their future appearance.

[0040] The acquisition unit can analyze the user's past photos and profile information and select the optimal acquisition method. For example, the acquisition unit can extract facial features from the user's past photos and suggest the optimal angle and lighting conditions. For example, the acquisition unit can automatically complete necessary information based on the user's past profile information. For example, the acquisition unit can compare the user's past photos with profile information to acquire consistent data. This allows information to be acquired in the most optimal way by analyzing the user's past information. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's past photos into AI and have the AI ​​select the optimal acquisition method.

[0041] The data acquisition unit can filter data based on the user's current living situation and areas of interest during the acquisition process. For example, if the user inputs their current living situation, the data acquisition unit filters the data to be acquired based on that information. For example, the data acquisition unit can acquire only relevant information based on the user's areas of interest. For example, the data acquisition unit can analyze the user's living situation and areas of interest in real time to acquire the most relevant data. This allows for the acquisition of highly relevant information by filtering information based on the user's living situation and areas of interest. Some or all of the above-described processes in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input the user's living situation data into an AI and have the AI ​​perform the filtering.

[0042] The data acquisition unit can prioritize acquiring highly relevant information based on the user's geographical location information during the acquisition process. For example, if the user is in a specific region, the data acquisition unit will prioritize acquiring information related to that region. For example, the data acquisition unit will prioritize acquiring information about locations close to the user's current location. For example, the data acquisition unit will filter and acquire highly relevant information based on the user's geographical location information. This allows for the provision of more appropriate information by acquiring highly relevant information based on the user's geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input the user's geographical location information into an AI and have the AI ​​acquire highly relevant information.

[0043] The data acquisition unit can analyze the user's social media activity and obtain relevant information during the acquisition process. For example, the data acquisition unit can analyze the user's social media posts and obtain information based on their interests. For example, the data acquisition unit can obtain relevant information by referring to the activities of the user's followers and friends. For example, the data acquisition unit can obtain optimal information based on the user's social media activity history. In this way, relevant information can be obtained by analyzing the user's social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input the user's social media data into AI and have the AI ​​acquire relevant information.

[0044] The generation unit can adjust the level of detail of the user's facial features and body shape during generation. For example, the generation unit uses high-resolution photographs to accurately reproduce the user's facial features. For example, the generation unit uses photographs from multiple angles to accurately reproduce the user's body shape. For example, the generation unit adjusts the algorithm for generating a realistic avatar based on the user's facial features and body shape. By adjusting the level of detail of the user's facial features and body shape, a more sophisticated avatar can be generated. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can extract the user's facial features from high-resolution photographs and use AI to reproduce the body shape in detail.

[0045] The generation unit can apply different generation algorithms depending on the user's category during generation. For example, if the user is young, the generation unit applies an algorithm that generates an avatar with a youthful appearance. For example, if the user is middle-aged, the generation unit applies an algorithm that generates an avatar with a calm appearance. For example, if the user is elderly, the generation unit applies an algorithm that generates an avatar with an age-appropriate appearance. In this way, by applying different generation algorithms depending on the user's category, a more appropriate avatar can be generated. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can apply different generation algorithms using AI depending on the user's category.

[0046] The generation unit can determine the generation priority based on the user's submission timing during generation. For example, if the user submits early, the generation unit will prioritize generating the avatar. For example, if the user is close to the submission deadline, the generation unit will quickly generate the avatar. For example, the generation unit will adjust the generation schedule based on the user's submission timing. This allows for more efficient avatar generation by determining the generation priority based on the user's submission timing. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user submission timing data into the AI ​​and have the AI ​​determine the generation priority.

[0047] The generation unit can adjust the generation order based on user relevance during generation. For example, the generation unit prioritizes generating avatars for users who use the service frequently. The generation unit adjusts the generation order based on user relevance, for example. The generation unit determines the optimal generation order based on the user's usage history, for example. By adjusting the generation order based on user relevance, avatars can be generated in a more appropriate order. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user usage history data into the AI ​​and have the AI ​​adjust the generation order.

[0048] The fitting room unit can perform fittings while considering the attribute information of the person submitting the product. For example, the fitting room unit may consider the age and gender of the person submitting the product when performing fittings. For example, the fitting room unit may consider the body type and style of the person submitting the product when performing fittings. For example, the fitting room unit may consider the preferences and interests of the person submitting the product when performing fittings. By considering the attribute information of the person submitting the product, a more appropriate fitting experience can be provided. Some or all of the above processing in the fitting room unit may be performed using AI or not. For example, the fitting room unit may input the attribute information of the person submitting the product into the AI ​​and have the AI ​​perform the fitting.

[0049] The fitting room unit can perform fittings while considering the geographical distribution of the products. For example, the fitting room unit can select the optimal fitting method considering the geographical distribution of the products. For example, the fitting room unit can adjust the order of fittings based on the geographical distribution of the products. For example, the fitting room unit can suggest a highly relevant fitting method based on the geographical distribution of the products. In this way, a more appropriate fitting experience can be provided by considering the geographical distribution of the products. Some or all of the above processes in the fitting room unit may be performed using AI or not. For example, the fitting room unit can input geographical distribution data of the products into AI and have the AI ​​select a fitting method.

[0050] The fitting unit can improve the accuracy of the fitting process by referring to relevant literature on the product during the fitting. For example, the fitting unit can improve the accuracy of the fitting by referring to relevant literature on the product. For example, the fitting unit can select the optimal fitting method based on relevant literature on the product. For example, the fitting unit can adjust the order of fitting based on relevant literature on the product. In this way, the accuracy of the fitting can be improved by referring to relevant literature on the product. Some or all of the above processes in the fitting unit may be performed using AI or not. For example, the fitting unit can input relevant literature data on the product into the AI ​​and have the AI ​​select the fitting method.

[0051] The delivery unit can optimize the current delivery by referring to past delivery data at the time of delivery. For example, the delivery unit can select the optimal delivery method based on past delivery data. For example, the delivery unit can adjust the delivery order by referring to past delivery data. For example, the delivery unit can improve the accuracy of the delivery based on past delivery data. In this way, the current delivery can be optimized by referring to past delivery data. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input past delivery data into AI and have the AI ​​optimize the current delivery.

[0052] The service provider can apply different service delivery methods depending on the user's category at the time of delivery. For example, if the user is young, the service provider can apply a youthful style of service delivery. For example, if the user is middle-aged, the service provider can apply a calm style of service delivery. For example, if the user is elderly, the service provider can apply an age-appropriate style of service delivery. By applying different service delivery methods to each user's category, more appropriate information can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user category data into AI and have AI apply the service delivery method.

[0053] The service provider can analyze changes in the service based on the user's submission timing at the time of service provision. For example, if a user submits early, the service provider can quickly analyze changes in the service. For example, if a user is close to the submission deadline, the service provider can quickly reflect changes in the service. For example, the service provider can schedule changes in the service based on the user's submission timing. This allows for the provision of more appropriate information by analyzing changes in the service based on the user's submission timing. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input user submission timing data into AI and have the AI ​​analyze changes in the service.

[0054] The delivery unit can analyze its deliveries by referring to the user's relevant market data at the time of delivery. For example, the delivery unit can select the optimal delivery method based on the user's relevant market data. For example, the delivery unit can adjust the delivery order by referring to the user's relevant market data. For example, the delivery unit can improve the accuracy of its deliveries based on the user's relevant market data. This allows the delivery unit to provide more appropriate information by referring to the user's relevant market data. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's relevant market data into AI and have the AI ​​analyze the deliveries.

[0055] The simulation unit can optimize the simulation algorithm by referring to the user's past data during simulation. For example, the simulation unit can select the optimal simulation algorithm based on the user's past data. For example, the simulation unit can improve the accuracy of the simulation by referring to the user's past data. For example, the simulation unit can adjust the order of simulations based on the user's past data. In this way, the simulation algorithm can be optimized by referring to the user's past data. Some or all of the above processes in the simulation unit may be performed using AI or not. For example, the simulation unit can input the user's past data into AI and have the AI ​​optimize the simulation algorithm.

[0056] The simulation unit can customize the simulation methods based on the user's current living situation during the simulation. For example, the simulation unit can select the optimal simulation method based on the user's current living situation. For example, the simulation unit can adjust the simulation order based on the user's current living situation. For example, the simulation unit can improve the accuracy of the simulation based on the user's current living situation. By customizing the simulation methods based on the user's current living situation, more appropriate simulation results can be provided. Some or all of the above processes in the simulation unit may be performed using AI or not. For example, the simulation unit can input the user's current living situation data into the AI ​​and have the AI ​​customize the simulation methods.

[0057] The simulation unit can select the optimal simulation method by considering the user's geographical location information during simulation. For example, the simulation unit selects the optimal simulation method based on the user's geographical location information. For example, the simulation unit adjusts the simulation order based on the user's geographical location information. For example, the simulation unit improves the accuracy of the simulation based on the user's geographical location information. This allows for the provision of more appropriate simulation results by considering the user's geographical location information. Some or all of the above-described processes in the simulation unit may be performed using AI or not. For example, the simulation unit can input the user's geographical location data into AI and have AI select the simulation method.

[0058] The simulation unit can analyze the user's social media activity during simulation and propose simulation methods. For example, the simulation unit proposes the optimal simulation method based on the user's social media activity. For example, the simulation unit adjusts the simulation order based on the user's social media activity. For example, the simulation unit improves the accuracy of the simulation based on the user's social media activity. This allows for the proposal of more appropriate simulation methods by analyzing the user's social media activity. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input the user's social media data into AI and have AI propose simulation methods.

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

[0060] The acquisition unit can analyze the user's past photos and profile information and select the optimal acquisition method. For example, the acquisition unit can extract facial features from the user's past photos and suggest the optimal angle and lighting conditions. For example, the acquisition unit can automatically complete necessary information based on the user's past profile information. For example, the acquisition unit can compare the user's past photos with profile information to acquire consistent data. This allows information to be acquired in the most optimal way by analyzing the user's past information. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's past photos into AI and have the AI ​​select the optimal acquisition method.

[0061] The generation unit can adjust the level of detail of the user's facial features and body shape during generation. For example, the generation unit uses high-resolution photographs to accurately reproduce the user's facial features. For example, the generation unit uses photographs from multiple angles to accurately reproduce the user's body shape. For example, the generation unit adjusts the algorithm for generating a realistic avatar based on the user's facial features and body shape. By adjusting the level of detail of the user's facial features and body shape, a more sophisticated avatar can be generated. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can extract the user's facial features from high-resolution photographs and use AI to reproduce the body shape in detail.

[0062] The generation unit can apply different generation algorithms depending on the user's category during generation. For example, if the user is young, the generation unit applies an algorithm that generates an avatar with a youthful appearance. For example, if the user is middle-aged, the generation unit applies an algorithm that generates an avatar with a calm appearance. For example, if the user is elderly, the generation unit applies an algorithm that generates an avatar with an age-appropriate appearance. In this way, by applying different generation algorithms depending on the user's category, a more appropriate avatar can be generated. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can apply different generation algorithms using AI depending on the user's category.

[0063] The generation unit can determine the generation priority based on the user's submission timing during generation. For example, if the user submits early, the generation unit will prioritize generating the avatar. For example, if the user is close to the submission deadline, the generation unit will quickly generate the avatar. For example, the generation unit will adjust the generation schedule based on the user's submission timing. This allows for more efficient avatar generation by determining the generation priority based on the user's submission timing. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user submission timing data into the AI ​​and have the AI ​​determine the generation priority.

[0064] The fitting room unit can perform fittings while considering the attribute information of the person submitting the product. For example, the fitting room unit may consider the age and gender of the person submitting the product when performing fittings. For example, the fitting room unit may consider the body type and style of the person submitting the product when performing fittings. For example, the fitting room unit may consider the preferences and interests of the person submitting the product when performing fittings. By considering the attribute information of the person submitting the product, a more appropriate fitting experience can be provided. Some or all of the above processing in the fitting room unit may be performed using AI or not. For example, the fitting room unit may input the attribute information of the person submitting the product into the AI ​​and have the AI ​​perform the fitting.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The acquisition unit acquires the user's photo and profile information. The acquisition unit acquires information that, for example, reproduces the user's facial features and body shape in detail. For example, the acquisition unit extracts the user's facial features from high-resolution photos and reproduces the body shape in detail using photos from multiple angles. Step 2: The generation unit generates an avatar based on the information acquired by the acquisition unit. The generation unit generates a sophisticated avatar based on the user's facial features and body shape, for example, using a generation AI. The generation unit reproduces the user's facial features in detail, for example, using a text generation AI (e.g., LLM). The generation unit can also reproduce the user's body shape in detail, using a multimodal generation AI. Step 3: The fitting unit has the avatar generated by the generation unit try on clothes and accessories from the online shop. For example, the fitting unit has the avatar try on items selected by the user and generates a realistic virtual try-on video of the process. For example, the fitting unit uses generation AI to have the avatar try on dresses and accessories selected by the user and displays the process as a video. Step 4: The provider unit provides the fitting video generated by the fitting unit. The provider unit provides the generated fitting video to the user, for example. The provider unit displays the fitting video, for example, through a web application or a mobile application. Step 5: The simulation unit simulates changes in body shape and aging. For example, when the user specifies their future body shape and age, the simulation unit uses a generative AI to update the avatar and recreate that future appearance. For example, the simulation unit simulates changes in the user's body shape and aging, and updates the avatar based on that information.

[0067] (Example of form 2) The system according to an embodiment of the present invention is a system that creates a sophisticated AI avatar based on a user's personal photo and profile information. This system creates a sophisticated AI avatar based on the user's personal photo and profile information. The system then has the AI ​​avatar try on clothes and accessories from an online shop, generating a realistic virtual try-on video. Through the generated try-on video, the user can have a realistic experience as if they were actually trying on the products. Furthermore, the system creates avatars that simulate changes in body shape and aging, providing try-on images that anticipate future appearances. First, the user provides a personal photo and profile information. This information is input into the generating AI. The generating AI analyzes the provided information and generates a sophisticated avatar of the user. For example, an avatar that accurately reproduces the user's facial features and body shape is generated. Next, the generated avatar is made to try on clothes and accessories from an online shop. The generating AI has the avatar try on the items selected by the user and generates a realistic virtual try-on video of the process. For example, the avatar tries on a dress or accessory selected by the user, and the process is displayed as a video. Furthermore, the generating AI also creates avatars that simulate changes in body shape and aging. For example, if a user specifies their future body shape and age, the generating AI updates the avatar based on that information to recreate their future appearance. It then generates a try-on video for this avatar as well and provides it to the user. This system allows users to have a realistic try-on experience from the comfort of their homes. For instance, users can view a video that simulates them actually trying on clothes before purchasing them from an online shop. In addition, since try-on images simulating future body shape and age are also provided, users can make purchase decisions from a long-term perspective. In this way, the system can generate sophisticated avatars based on the user's photos and profile information and provide realistic virtual try-on videos.

[0068] The system according to this embodiment comprises an acquisition unit, a generation unit, a fitting unit, a provision unit, and a simulation unit. The acquisition unit acquires the user's photograph and profile information. The acquisition unit acquires information that reproduces the user's facial features and body shape in detail, for example. The acquisition unit extracts the user's facial features from high-resolution photographs and reproduces the body shape in detail using photographs from multiple angles. The generation unit generates an avatar based on the information acquired by the acquisition unit. The generation unit generates an elaborate avatar based on the user's facial features and body shape, for example, using a generation AI. The generation unit reproduces the user's facial features in detail, for example, using a text generation AI (e.g., LLM). The generation unit can also reproduce the user's body shape in detail using a multimodal generation AI. The fitting unit has the avatar generated by the generation unit try on clothes and accessories from an online shop. The fitting unit has the avatar try on items selected by the user and generates a realistic virtual fitting video of the process. The fitting unit, for example, uses a generation AI to have an avatar try on dresses and accessories selected by the user, and displays the process as a video. The provision unit provides the fitting video generated by the fitting unit. The provision unit provides the generated fitting video to the user. The provision unit displays the fitting video through, for example, a web application or mobile application. The simulation unit simulates changes in body shape and aging. For example, if the user specifies their future body shape and age, the simulation unit updates the avatar using a generation AI to recreate that future appearance. For example, the simulation unit simulates changes in the user's body shape and aging, and updates the avatar based on that information. As a result, the system can generate a sophisticated avatar based on the user's photos and profile information and provide realistic virtual fitting videos.

[0069] The acquisition unit retrieves the user's photos and profile information. For example, it acquires information that precisely reproduces the user's facial features and body shape. Specifically, it uses high-resolution photos provided by the user to extract detailed facial features. These features include eye shape and position, nose height, mouth shape, skin color, and texture. To reproduce body shape, users are required to provide full-body photos taken from multiple angles. This allows the acquisition unit to accurately understand the user's body shape and obtain detailed measurements such as height, weight, shoulder width, waist, and hips. Furthermore, the acquisition unit also collects the user's profile information. This profile information includes age, gender, preferred fashion style, and past purchase history. This information plays a crucial role in subsequent processing in the generation and fitting units. The acquisition unit centrally manages this data and stores it using encryption technology to ensure security. This allows for efficient collection of necessary information while protecting user privacy.

[0070] The generation unit generates avatars based on information acquired by the acquisition unit. For example, the generation unit uses a generation AI to create sophisticated avatars based on the user's facial features and body shape. Specifically, it uses a text generation AI (e.g., LLM) to reproduce the user's facial features in detail. The text generation AI generates text data describing the user's facial features and creates a 3D model of the face based on that data. It can also reproduce the user's body shape in detail using a multimodal generation AI. The multimodal generation AI integrates and processes image data and text data to generate a 3D model that accurately reproduces the user's body shape. The generation unit combines these AI technologies to generate sophisticated avatars that integrate the user's face and body shape. Furthermore, the generation unit can also customize the avatar's clothing and accessory style, taking into account the user's profile information. This allows the generation unit to provide avatars tailored to the user's personality and preferences.

[0071] The fitting section allows users to try on clothes and accessories from online shops using avatars generated by the generation section. For example, the fitting section can have the avatar try on items selected by the user and generate a realistic virtual try-on video. Specifically, it uses generation AI to have the avatar try on dresses and accessories selected by the user and displays the result as a video. The generation AI accurately reproduces the shape, color, and texture of the clothes and accessories, and makes them fit naturally to the avatar's body shape. The fitting section can also generate 360-degree rotating videos so that users can view the avatar from different angles. This allows users to check in detail how the selected items look on them. Furthermore, the fitting section also provides a function that allows users to try on multiple items simultaneously. This allows users to try on different combinations of clothes and accessories to find the best outfit.

[0072] The service provider provides the try-on videos generated by the try-on service provider. The service provider provides the generated try-on videos to users, for example, by displaying them through web or mobile applications. The service provider provides an easily accessible interface for users and can play the try-on videos in high resolution. The service provider also provides functions for users to save and share the try-on videos. This allows users to share try-on videos with friends and family and get their opinions. Furthermore, the service provider can collect user feedback and continuously improve the quality of the try-on videos and the user experience. For example, it can provide a function for users to leave ratings and comments on the try-on videos and use that feedback to improve the system. This allows the service provider to provide users with high-quality try-on videos and improve their satisfaction.

[0073] The simulation unit simulates changes in body shape and aging. For example, when a user specifies their future body shape and age, the simulation unit uses a generative AI to update the avatar and recreate that future appearance. Specifically, when a user specifies changes in body shape such as weight gain or loss or muscle gain, the generative AI generates an avatar that reflects those changes. Also, when a user specifies their age, the generative AI simulates changes in face and body shape due to aging and updates the avatar. This allows the user to realistically see what they will look like in the future. Furthermore, the simulation unit can also update the avatar by considering changes in health status and lifestyle. For example, it can simulate changes in body shape if a user adopts an exercise routine or follows a specific diet. In this way, the simulation unit can realistically recreate the user's future appearance and provide information that is useful for health management and lifestyle improvement.

[0074] The acquisition unit can acquire information that reproduces the user's facial features and body shape in detail. For example, the acquisition unit can extract the user's facial features from high-resolution photographs and reproduce the body shape in detail using photographs from multiple angles. For example, the acquisition unit can analyze points of facial features and reproduce the body shape in detail using a body shape measurement method. By acquiring information that reproduces the user's facial features and body shape in detail, a more sophisticated avatar can be generated. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can extract the user's facial features from high-resolution photographs and reproduce the body shape in detail using AI.

[0075] The generation unit can generate sophisticated avatars based on the user's facial features and body shape. For example, the generation unit uses a generation AI to generate sophisticated avatars based on the user's facial features and body shape. The generation unit uses a text generation AI (e.g., LLM) to reproduce the user's facial features in detail. The generation unit can also use a multimodal generation AI to reproduce the user's body shape in detail. For example, the generation unit uses a generation AI to reproduce the user's facial features in detail and reproduces the body shape in detail using photographs from multiple angles. This allows for the generation of sophisticated avatars based on the user's facial features and body shape, providing a realistic try-on experience. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit can extract the user's facial features from high-resolution photographs and reproduce the body shape in detail using a generation AI.

[0076] The fitting room function can have an avatar try on items selected by the user and generate a realistic virtual fitting video of the process. For example, the fitting room function uses a generation AI to have an avatar try on items selected by the user and generate a realistic virtual fitting video of the process. For example, the fitting room function can have an avatar try on dresses or accessories selected by the user and display the process as a video. For example, the fitting room function uses a generation AI to have an avatar try on items selected by the user and generate a realistic virtual fitting video of the process. This allows the system to provide users with a realistic fitting experience by having an avatar try on items selected by the user and generating a realistic virtual fitting video of the process. Some or all of the above-described processes in the fitting room function are performed using a generation AI. For example, the fitting room function can have an avatar try on items selected by the user and generate a realistic virtual fitting video of the process using a generation AI.

[0077] The service provider can provide the generated try-on video to the user. The service provider can, for example, provide the generated try-on video to the user. The service provider can, for example, display the try-on video through a web application or a mobile application. The service provider can, for example, provide the user with the generated try-on video, allowing the user to have a realistic experience as if they were actually trying on the product. By providing the user with the generated try-on video, the user can have a realistic experience as if they were actually trying on the product. Some or all of the above processing in the service provider is performed using a generation AI. For example, the service provider can provide the generated try-on video to the user using a generation AI.

[0078] The simulation unit can simulate changes in the user's body shape and aging, and generate an avatar that reproduces their future appearance. For example, if the user specifies their future body shape and age, the simulation unit updates the avatar using a generation AI to reproduce their future appearance. The simulation unit simulates changes in the user's body shape and aging, and updates the avatar based on that information. The simulation unit uses a generation AI to simulate changes in the user's body shape and aging, and generates an avatar that reproduces their future appearance. In this way, by simulating changes in the user's body shape and aging and generating an avatar that reproduces their future appearance, it is possible to provide a future fitting image. Some or all of the above processing in the simulation unit is performed using a generation AI. For example, the simulation unit can use a generation AI to simulate changes in the user's body shape and aging, and generate an avatar that reproduces their future appearance.

[0079] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring photos and profile information based on the estimated emotions. For example, if the user is relaxed, the acquisition unit will acquire photos and profile information immediately. If the user is stressed, the acquisition unit will delay the acquisition timing and wait for the user to calm down. If the user is in a hurry, the acquisition unit will acquire the necessary information in the shortest possible time. In this way, by adjusting the timing of information acquisition based on the user's emotions, information can be acquired at a more appropriate time. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the acquisition unit is performed using an emotion engine or generative AI. For example, the acquisition unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0080] The acquisition unit can analyze the user's past photos and profile information and select the optimal acquisition method. For example, the acquisition unit can extract facial features from the user's past photos and suggest the optimal angle and lighting conditions. For example, the acquisition unit can automatically complete necessary information based on the user's past profile information. For example, the acquisition unit can compare the user's past photos with profile information to acquire consistent data. This allows information to be acquired in the most optimal way by analyzing the user's past information. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's past photos into AI and have the AI ​​select the optimal acquisition method.

[0081] The data acquisition unit can filter data based on the user's current living situation and areas of interest during the acquisition process. For example, if the user inputs their current living situation, the data acquisition unit filters the data to be acquired based on that information. For example, the data acquisition unit can acquire only relevant information based on the user's areas of interest. For example, the data acquisition unit can analyze the user's living situation and areas of interest in real time to acquire the most relevant data. This allows for the acquisition of highly relevant information by filtering information based on the user's living situation and areas of interest. Some or all of the above-described processes in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input the user's living situation data into an AI and have the AI ​​perform the filtering.

[0082] The data acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated emotions. For example, if the user is excited, the data acquisition unit will prioritize acquiring important information. For example, if the user is relaxed, the data acquisition unit will prioritize acquiring detailed information. For example, if the user is stressed, the data acquisition unit will prioritize acquiring simple information. In this way, by prioritizing information based on the user's emotions, important information can be acquired preferentially. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit is performed using an emotion engine or generative AI. For example, the data acquisition unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0083] The data acquisition unit can prioritize acquiring highly relevant information based on the user's geographical location information during the acquisition process. For example, if the user is in a specific region, the data acquisition unit will prioritize acquiring information related to that region. For example, the data acquisition unit will prioritize acquiring information about locations close to the user's current location. For example, the data acquisition unit will filter and acquire highly relevant information based on the user's geographical location information. This allows for the provision of more appropriate information by acquiring highly relevant information based on the user's geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input the user's geographical location information into an AI and have the AI ​​acquire highly relevant information.

[0084] The data acquisition unit can analyze the user's social media activity and obtain relevant information during the acquisition process. For example, the data acquisition unit can analyze the user's social media posts and obtain information based on their interests. For example, the data acquisition unit can obtain relevant information by referring to the activities of the user's followers and friends. For example, the data acquisition unit can obtain optimal information based on the user's social media activity history. In this way, relevant information can be obtained by analyzing the user's social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input the user's social media data into AI and have the AI ​​acquire relevant information.

[0085] The generation unit can estimate the user's emotions and adjust the avatar's expression based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate an avatar with a soft expression. If the user is excited, the generation unit will generate an avatar with an energetic expression. If the user is stressed, the generation unit will generate an avatar with a calm expression. By adjusting the avatar's expression based on the user's emotions, a more realistic avatar can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using an emotion engine or a generation AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0086] The generation unit can adjust the level of detail of the user's facial features and body shape during generation. For example, the generation unit uses high-resolution photographs to accurately reproduce the user's facial features. For example, the generation unit uses photographs from multiple angles to accurately reproduce the user's body shape. For example, the generation unit adjusts the algorithm for generating a realistic avatar based on the user's facial features and body shape. By adjusting the level of detail of the user's facial features and body shape, a more sophisticated avatar can be generated. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can extract the user's facial features from high-resolution photographs and use AI to reproduce the body shape in detail.

[0087] The generation unit can apply different generation algorithms depending on the user's category during generation. For example, if the user is young, the generation unit applies an algorithm that generates an avatar with a youthful appearance. For example, if the user is middle-aged, the generation unit applies an algorithm that generates an avatar with a calm appearance. For example, if the user is elderly, the generation unit applies an algorithm that generates an avatar with an age-appropriate appearance. In this way, by applying different generation algorithms depending on the user's category, a more appropriate avatar can be generated. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can apply different generation algorithms using AI depending on the user's category.

[0088] The generation unit can estimate the user's emotions and adjust the length of the avatar based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a longer avatar video. For example, if the user is in a hurry, the generation unit will generate a shorter avatar video. For example, if the user is excited, the generation unit will generate an avatar video with visually stimulating effects. By adjusting the length of the avatar based on the user's emotions, a more appropriate avatar video can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using an emotion engine or a generation AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0089] The generation unit can determine the generation priority based on the user's submission timing during generation. For example, if the user submits early, the generation unit will prioritize generating the avatar. For example, if the user is close to the submission deadline, the generation unit will quickly generate the avatar. For example, the generation unit will adjust the generation schedule based on the user's submission timing. This allows for more efficient avatar generation by determining the generation priority based on the user's submission timing. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user submission timing data into the AI ​​and have the AI ​​determine the generation priority.

[0090] The generation unit can adjust the generation order based on user relevance during generation. For example, the generation unit prioritizes generating avatars for users who use the service frequently. The generation unit adjusts the generation order based on user relevance, for example. The generation unit determines the optimal generation order based on the user's usage history, for example. By adjusting the generation order based on user relevance, avatars can be generated in a more appropriate order. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user usage history data into the AI ​​and have the AI ​​adjust the generation order.

[0091] The fitting room unit can estimate the user's emotions and adjust the fitting criteria based on those emotions. For example, if the user is relaxed, the fitting room unit applies detailed fitting criteria. If the user is in a hurry, for example, the fitting room unit applies simplified fitting criteria. If the user is excited, for example, the fitting room unit applies visually stimulating fitting criteria. By adjusting the fitting criteria based on the user's emotions, a more appropriate fitting experience can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the fitting room unit is performed using an emotion engine or generative AI. For example, the fitting room unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0092] The fitting room unit can perform fittings while considering the attribute information of the person submitting the product. For example, the fitting room unit may consider the age and gender of the person submitting the product when performing fittings. For example, the fitting room unit may consider the body type and style of the person submitting the product when performing fittings. For example, the fitting room unit may consider the preferences and interests of the person submitting the product when performing fittings. By considering the attribute information of the person submitting the product, a more appropriate fitting experience can be provided. Some or all of the above processing in the fitting room unit may be performed using AI or not. For example, the fitting room unit may input the attribute information of the person submitting the product into the AI ​​and have the AI ​​perform the fitting.

[0093] The fitting room unit can estimate the user's emotions and adjust the order in which fitting room results are displayed based on the estimated emotions. For example, if the user is relaxed, the fitting room unit will prioritize displaying detailed fitting room results. If the user is in a hurry, the fitting room unit will prioritize displaying concise fitting room results. If the user is excited, the fitting room unit will prioritize displaying visually stimulating fitting room results. By adjusting the order in which fitting room results are displayed based on the user's emotions, more appropriate fitting room results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the fitting room unit is performed using an emotion engine or generative AI. For example, the fitting room unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0094] The fitting room unit can perform fittings while considering the geographical distribution of the products. For example, the fitting room unit can select the optimal fitting method considering the geographical distribution of the products. For example, the fitting room unit can adjust the order of fittings based on the geographical distribution of the products. For example, the fitting room unit can suggest a highly relevant fitting method based on the geographical distribution of the products. In this way, a more appropriate fitting experience can be provided by considering the geographical distribution of the products. Some or all of the above processes in the fitting room unit may be performed using AI or not. For example, the fitting room unit can input geographical distribution data of the products into AI and have the AI ​​select a fitting method.

[0095] The fitting unit can improve the accuracy of the fitting process by referring to relevant literature on the product during the fitting. For example, the fitting unit can improve the accuracy of the fitting by referring to relevant literature on the product. For example, the fitting unit can select the optimal fitting method based on relevant literature on the product. For example, the fitting unit can adjust the order of fitting based on relevant literature on the product. In this way, the accuracy of the fitting can be improved by referring to relevant literature on the product. Some or all of the above processes in the fitting unit may be performed using AI or not. For example, the fitting unit can input relevant literature data on the product into the AI ​​and have the AI ​​select the fitting method.

[0096] The service provider can estimate the user's emotions and adjust the way the service is displayed based on the estimated emotions. For example, if the user is relaxed, the service provider may provide a display method that includes detailed information. If the user is in a hurry, the service provider may provide a display method that gets straight to the point. If the user is excited, the service provider may provide a display method that is visually stimulating. By adjusting the way the service is displayed based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider is performed using an emotion engine or a generative AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The delivery unit can optimize the current delivery by referring to past delivery data at the time of delivery. For example, the delivery unit can select the optimal delivery method based on past delivery data. For example, the delivery unit can adjust the delivery order by referring to past delivery data. For example, the delivery unit can improve the accuracy of the delivery based on past delivery data. In this way, the current delivery can be optimized by referring to past delivery data. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input past delivery data into AI and have the AI ​​optimize the current delivery.

[0098] The service provider can apply different service delivery methods depending on the user's category at the time of delivery. For example, if the user is young, the service provider can apply a youthful style of service delivery. For example, if the user is middle-aged, the service provider can apply a calm style of service delivery. For example, if the user is elderly, the service provider can apply an age-appropriate style of service delivery. By applying different service delivery methods to each user's category, more appropriate information can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user category data into AI and have AI apply the service delivery method.

[0099] The service provider can estimate the user's emotions and adjust the importance of the offerings based on the estimated emotions. For example, if the user is relaxed, the service provider will prioritize providing detailed information. If the user is in a hurry, the service provider will prioritize providing concise information. If the user is excited, the service provider will prioritize providing visually stimulating information. By adjusting the importance of the offerings based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using an emotion engine or generative AI. For example, the service provider can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0100] The service provider can analyze changes in the service based on the user's submission timing at the time of service provision. For example, if a user submits early, the service provider can quickly analyze changes in the service. For example, if a user is close to the submission deadline, the service provider can quickly reflect changes in the service. For example, the service provider can schedule changes in the service based on the user's submission timing. This allows for the provision of more appropriate information by analyzing changes in the service based on the user's submission timing. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input user submission timing data into AI and have the AI ​​analyze changes in the service.

[0101] The delivery unit can analyze its deliveries by referring to the user's relevant market data at the time of delivery. For example, the delivery unit can select the optimal delivery method based on the user's relevant market data. For example, the delivery unit can adjust the delivery order by referring to the user's relevant market data. For example, the delivery unit can improve the accuracy of its deliveries based on the user's relevant market data. This allows the delivery unit to provide more appropriate information by referring to the user's relevant market data. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's relevant market data into AI and have the AI ​​analyze the deliveries.

[0102] The simulation unit can estimate the user's emotions and adjust the simulation method based on the estimated emotions. For example, if the user is relaxed, the simulation unit applies a detailed simulation method. For example, if the user is in a hurry, the simulation unit applies a simplified simulation method. For example, if the user is excited, the simulation unit applies a visually stimulating simulation method. By adjusting the simulation method based on the user's emotions, it is possible to provide more appropriate simulation results. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit is performed using an emotion engine or generative AI. For example, the simulation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0103] The simulation unit can optimize the simulation algorithm by referring to the user's past data during simulation. For example, the simulation unit can select the optimal simulation algorithm based on the user's past data. For example, the simulation unit can improve the accuracy of the simulation by referring to the user's past data. For example, the simulation unit can adjust the order of simulations based on the user's past data. In this way, the simulation algorithm can be optimized by referring to the user's past data. Some or all of the above processes in the simulation unit may be performed using AI or not. For example, the simulation unit can input the user's past data into AI and have the AI ​​optimize the simulation algorithm.

[0104] The simulation unit can customize the simulation methods based on the user's current living situation during the simulation. For example, the simulation unit can select the optimal simulation method based on the user's current living situation. For example, the simulation unit can adjust the simulation order based on the user's current living situation. For example, the simulation unit can improve the accuracy of the simulation based on the user's current living situation. By customizing the simulation methods based on the user's current living situation, more appropriate simulation results can be provided. Some or all of the above processes in the simulation unit may be performed using AI or not. For example, the simulation unit can input the user's current living situation data into the AI ​​and have the AI ​​customize the simulation methods.

[0105] The simulation unit can estimate the user's emotions and determine the priority of simulations based on the estimated emotions. For example, if the user is relaxed, the simulation unit will prioritize detailed simulations. If the user is in a hurry, the simulation unit will prioritize simplified simulations. If the user is excited, the simulation unit will prioritize visually stimulating simulations. By prioritizing simulations based on the user's emotions, more appropriate simulation results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit is performed using an emotion engine or generative AI. For example, the simulation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0106] The simulation unit can select the optimal simulation method by considering the user's geographical location information during simulation. For example, the simulation unit selects the optimal simulation method based on the user's geographical location information. For example, the simulation unit adjusts the simulation order based on the user's geographical location information. For example, the simulation unit improves the accuracy of the simulation based on the user's geographical location information. This allows for the provision of more appropriate simulation results by considering the user's geographical location information. Some or all of the above-described processes in the simulation unit may be performed using AI or not. For example, the simulation unit can input the user's geographical location data into AI and have AI select the simulation method.

[0107] The simulation unit can analyze the user's social media activity during simulation and propose simulation methods. For example, the simulation unit proposes the optimal simulation method based on the user's social media activity. For example, the simulation unit adjusts the simulation order based on the user's social media activity. For example, the simulation unit improves the accuracy of the simulation based on the user's social media activity. This allows for the proposal of more appropriate simulation methods by analyzing the user's social media activity. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input the user's social media data into AI and have AI propose simulation methods.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring photos and profile information based on the estimated emotions. For example, if the user is relaxed, the acquisition unit will acquire photos and profile information immediately. If the user is stressed, the acquisition unit will delay the acquisition timing and wait for the user to calm down. If the user is in a hurry, the acquisition unit will acquire the necessary information in the shortest possible time. In this way, by adjusting the timing of information acquisition based on the user's emotions, information can be acquired at a more appropriate time. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the acquisition unit is performed using an emotion engine or generative AI. For example, the acquisition unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0110] The acquisition unit can analyze the user's past photos and profile information and select the optimal acquisition method. For example, the acquisition unit can extract facial features from the user's past photos and suggest the optimal angle and lighting conditions. For example, the acquisition unit can automatically complete necessary information based on the user's past profile information. For example, the acquisition unit can compare the user's past photos with profile information to acquire consistent data. This allows information to be acquired in the most optimal way by analyzing the user's past information. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's past photos into AI and have the AI ​​select the optimal acquisition method.

[0111] The generation unit can estimate the user's emotions and adjust the avatar's expression based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate an avatar with a soft expression. If the user is excited, the generation unit will generate an avatar with an energetic expression. If the user is stressed, the generation unit will generate an avatar with a calm expression. By adjusting the avatar's expression based on the user's emotions, a more realistic avatar can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using an emotion engine or a generation AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0112] The generation unit can adjust the level of detail of the user's facial features and body shape during generation. For example, the generation unit uses high-resolution photographs to accurately reproduce the user's facial features. For example, the generation unit uses photographs from multiple angles to accurately reproduce the user's body shape. For example, the generation unit adjusts the algorithm for generating a realistic avatar based on the user's facial features and body shape. By adjusting the level of detail of the user's facial features and body shape, a more sophisticated avatar can be generated. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can extract the user's facial features from high-resolution photographs and use AI to reproduce the body shape in detail.

[0113] The generation unit can apply different generation algorithms depending on the user's category during generation. For example, if the user is young, the generation unit applies an algorithm that generates an avatar with a youthful appearance. For example, if the user is middle-aged, the generation unit applies an algorithm that generates an avatar with a calm appearance. For example, if the user is elderly, the generation unit applies an algorithm that generates an avatar with an age-appropriate appearance. In this way, by applying different generation algorithms depending on the user's category, a more appropriate avatar can be generated. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can apply different generation algorithms using AI depending on the user's category.

[0114] The generation unit can estimate the user's emotions and adjust the length of the avatar based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a longer avatar video. For example, if the user is in a hurry, the generation unit will generate a shorter avatar video. For example, if the user is excited, the generation unit will generate an avatar video with visually stimulating effects. By adjusting the length of the avatar based on the user's emotions, a more appropriate avatar video can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using an emotion engine or a generation AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0115] The generation unit can determine the generation priority based on the user's submission timing during generation. For example, if the user submits early, the generation unit will prioritize generating the avatar. For example, if the user is close to the submission deadline, the generation unit will quickly generate the avatar. For example, the generation unit will adjust the generation schedule based on the user's submission timing. This allows for more efficient avatar generation by determining the generation priority based on the user's submission timing. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user submission timing data into the AI ​​and have the AI ​​determine the generation priority.

[0116] The fitting room unit can estimate the user's emotions and adjust the fitting criteria based on those emotions. For example, if the user is relaxed, the fitting room unit applies detailed fitting criteria. If the user is in a hurry, for example, the fitting room unit applies simplified fitting criteria. If the user is excited, for example, the fitting room unit applies visually stimulating fitting criteria. By adjusting the fitting criteria based on the user's emotions, a more appropriate fitting experience can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the fitting room unit is performed using an emotion engine or generative AI. For example, the fitting room unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0117] The fitting room unit can perform fittings while considering the attribute information of the person submitting the product. For example, the fitting room unit may consider the age and gender of the person submitting the product when performing fittings. For example, the fitting room unit may consider the body type and style of the person submitting the product when performing fittings. For example, the fitting room unit may consider the preferences and interests of the person submitting the product when performing fittings. By considering the attribute information of the person submitting the product, a more appropriate fitting experience can be provided. Some or all of the above processing in the fitting room unit may be performed using AI or not. For example, the fitting room unit may input the attribute information of the person submitting the product into the AI ​​and have the AI ​​perform the fitting.

[0118] The service provider can estimate the user's emotions and adjust the way the service is displayed based on the estimated emotions. For example, if the user is relaxed, the service provider may provide a display method that includes detailed information. If the user is in a hurry, the service provider may provide a display method that gets straight to the point. If the user is excited, the service provider may provide a display method that is visually stimulating. By adjusting the way the service is displayed based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider is performed using an emotion engine or a generative AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The acquisition unit acquires the user's photo and profile information. The acquisition unit acquires information that, for example, reproduces the user's facial features and body shape in detail. For example, the acquisition unit extracts the user's facial features from high-resolution photos and reproduces the body shape in detail using photos from multiple angles. Step 2: The generation unit generates an avatar based on the information acquired by the acquisition unit. The generation unit generates a sophisticated avatar based on the user's facial features and body shape, for example, using a generation AI. The generation unit reproduces the user's facial features in detail, for example, using a text generation AI (e.g., LLM). The generation unit can also reproduce the user's body shape in detail, using a multimodal generation AI. Step 3: The fitting unit has the avatar generated by the generation unit try on clothes and accessories from the online shop. For example, the fitting unit has the avatar try on items selected by the user and generates a realistic virtual try-on video of the process. For example, the fitting unit uses generation AI to have the avatar try on dresses and accessories selected by the user and displays the process as a video. Step 4: The provider unit provides the fitting video generated by the fitting unit. The provider unit provides the generated fitting video to the user, for example. The provider unit displays the fitting video, for example, through a web application or a mobile application. Step 5: The simulation unit simulates changes in body shape and aging. For example, when the user specifies their future body shape and age, the simulation unit uses a generative AI to update the avatar and recreate that future appearance. For example, the simulation unit simulates changes in the user's body shape and aging, and updates the avatar based on that information.

[0121] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0124] Each of the multiple elements described above, including the acquisition unit, generation unit, try-on unit, provision unit, and simulation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires a photograph of the user using the camera 42 of the smart device 14 and analyzes the profile information using the specific processing unit 290 of the data processing unit 12. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates an elaborate avatar based on the acquired information. The try-on unit is implemented in the control unit 46A of the smart device 14 and allows the generated avatar to try on clothes and accessories from an online shop. The provision unit provides the user with a try-on video using the output device 40 of the smart device 14. The simulation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates an avatar that simulates changes in body shape and aging. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the acquisition unit, generation unit, try-on unit, provision unit, and simulation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires a photograph of the user using the camera 42 of the smart glasses 214 and analyzes the profile information using the identification processing unit 290 of the data processing unit 12. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates an elaborate avatar based on the acquired information. The try-on unit is implemented, for example, by the control unit 46A of the smart glasses 214 and allows the generated avatar to try on clothes and accessories from an online shop. The provision unit provides the user with a try-on video using the speaker 240 of the smart glasses 214. The simulation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates an avatar that simulates changes in body shape and aging. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the acquisition unit, generation unit, fitting unit, provision unit, and simulation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires a photograph of the user using the camera 42 of the headset terminal 314 and analyzes the profile information using the specific processing unit 290 of the data processing unit 12. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates an elaborate avatar based on the acquired information. The fitting unit is implemented in the control unit 46A of the headset terminal 314 and allows the generated avatar to try on clothes and accessories from an online shop. The provision unit provides the user with a fitting video using the display 343 of the headset terminal 314. The simulation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates an avatar that simulates changes in body shape and aging. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the acquisition unit, generation unit, fitting unit, provision unit, and simulation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires a photograph of the user using the camera 42 of the robot 414 and analyzes the profile information using the specific processing unit 290 of the data processing unit 12. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates an elaborate avatar based on the acquired information. The fitting unit is implemented, for example, by the control unit 46A of the robot 414 and allows the generated avatar to try on clothes and accessories from an online shop. The provision unit provides the user with a fitting video using the speaker 240 of the robot 414. The simulation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates an avatar that simulates changes in body shape and aging. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0174] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) An acquisition unit that acquires the user's photo and profile information, A generation unit generates an avatar based on the information acquired by the acquisition unit, A fitting unit that allows the avatar generated by the generation unit to try on clothes and accessories from an online shop, A providing unit that provides the fitting video generated by the fitting unit, It includes a simulation unit that simulates changes in body shape and aging. A system characterized by the following features. (Note 2) The acquisition unit is, Obtain information that accurately reproduces the user's facial features and body shape. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is It generates sophisticated avatars based on the user's facial features and body type. The system described in Appendix 1, characterized by the features described herein. (Note 4) The fitting area is, The system allows users to select items and have their avatar try them on, generating a realistic virtual try-on video. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the user with the generated try-on video. The system described in Appendix 1, characterized by the features described herein. (Note 6) The simulation unit, It simulates changes in the user's body shape and aging, and generates an avatar that recreates their future appearance. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, It estimates the user's emotions and adjusts the timing of photo and profile information acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze the user's past photos and profile information to select the optimal method of acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When retrieving data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When retrieving data, the system prioritizes retrieving highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring data, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the avatar's representation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, adjust the level of detail in the user's facial features and body shape. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, different generation algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the avatar based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the generation priority is determined based on when the user submitted the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the generation order is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The fitting area is, The system estimates the user's emotions and adjusts the fitting criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The fitting area is, When trying on clothes, the personal information of the person submitting the product will be taken into consideration during the fitting process. The system described in Appendix 1, characterized by the features described herein. (Note 21) The fitting area is, The system estimates the user's emotions and adjusts the order in which the try-on results are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The fitting area is, When trying on clothes, take into consideration the geographical distribution of the products. The system described in Appendix 1, characterized by the features described herein. (Note 23) The fitting area is, When trying on clothes, refer to relevant literature on the product to improve the accuracy of the fitting. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing a service, we optimize the current service by referring to past service data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, different delivery methods will be applied depending on the user category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the importance of the offering based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, At the time of delivery, we analyze changes in delivery based on when the user submitted the data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we analyze the service by referring to relevant market data from the user. The system described in Appendix 1, characterized by the features described herein. (Note 30) The simulation unit, We estimate the user's emotions and adjust the simulation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The simulation unit, During simulation, the simulation algorithm is optimized by referencing the user's past data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The simulation unit, During the simulation, the simulation method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The simulation unit, It estimates the user's emotions and determines the simulation priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The simulation unit, During simulation, the optimal simulation method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 35) The simulation unit, During the simulation, we analyze the user's social media activity and propose simulation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires the user's photo and profile information, A generation unit generates an avatar based on the information acquired by the acquisition unit, A fitting unit that allows the avatar generated by the generation unit to try on clothes and accessories from an online shop, A providing unit that provides the fitting video generated by the fitting unit, It includes a simulation unit that simulates changes in body shape and aging. A system characterized by the following features.

2. The acquisition unit is, Obtain information that accurately reproduces the user's facial features and body shape. The system according to feature 1.

3. The generating unit is It generates sophisticated avatars based on the user's facial features and body type. The system according to feature 1.

4. The fitting area is, The system allows users to select items and have their avatar try them on, generating a realistic virtual try-on video. The system according to feature 1.

5. The aforementioned supply unit is, Provide the user with the generated try-on video. The system according to feature 1.

6. The simulation unit, It simulates changes in the user's body shape and aging, and generates an avatar that recreates their future appearance. The system according to feature 1.

7. The acquisition unit is, It estimates the user's emotions and adjusts the timing of photo and profile information acquisition based on the estimated user emotions. The system according to feature 1.

8. The acquisition unit is, Analyze the user's past photos and profile information to select the optimal method of acquisition. The system according to feature 1.

9. The acquisition unit is, When retrieving data, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system according to feature 1.

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