System

Abstract

An object of a system according to an embodiment is to smoothly link try-on in a virtual space with an online shopping platform.SOLUTION: A system includes an avatar try-on part, an interlocking part, a privilege providing part, and an interface providing part. The avatar try-on unit causes the avatar of the user to try on the clothes for sale in the virtual space. The interworking unit interworks the clothes tried on by the avatar try-on unit with the online shopping platform. The benefit providing unit provides a benefit or promotion for the purchase of the clothing linked by the linking unit. The interface provider may allow the user to smoothly perform a try-on in a virtual space or a purchase in an online shopping platform.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately integrate virtual try-on experiences with online shopping platforms, leaving room for improvement in user experience.

[0005] The system according to the embodiment aims to smoothly link virtual try-on clothing with an online shopping platform. [Means for solving the problem]

[0006] The system according to the embodiment includes an avatar trying-on unit, a linking unit, a reward providing unit, and an interface providing unit. The avatar trying-on unit allows a user's avatar to try on clothes available for sale in a virtual space. The linking unit links the clothes tried on by the avatar trying-on unit with an online shopping platform. The reward providing unit provides a reward or promotion for purchasing the clothes linked by the linking unit. The interface providing unit enables a user to smoothly try on clothes in the virtual space or purchase them on the online shopping platform. [Effects of the Invention]

[0007] The system according to the embodiment can smoothly link virtual try-on sessions with an online shopping platform. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The virtual space try-on system according to an embodiment of the present invention is a system that aims to create SoftBank fans by having users try on clothes for sale on their avatars and linking it with Yahoo Shopping. As a result, the virtual space try-on system allows users to smoothly make purchases on Yahoo Shopping while enjoying the experience of trying on clothes in a virtual space.

[0029] A virtual space try-on system according to an embodiment includes an avatar try-on unit, a linking unit, a reward provision unit, and an interface provision unit. The avatar try-on unit allows a user's avatar to try on clothes available for sale. For example, a user can try on various clothes on their avatar in the virtual space. The avatar try-on unit also uses a generation AI to apply selected clothes to the avatar and display the fitting results in real time. For example, if a user instructs the system to "try on a red dress," the generation AI analyzes the instruction and dresses the avatar in the red dress. The linking unit links the clothes tried on by the avatar try-on unit with an online shopping platform. For example, clothes tried on in the virtual space are linked with Yahoo Shopping, allowing the user to purchase the clothes directly from Yahoo Shopping after checking the fitting results. The generation AI automatically adds information about the tried-on clothes to a Yahoo Shopping cart and supports the purchase process. The reward provision unit offers rewards and promotions for purchasing clothes linked by the linking unit. For example, when a user purchases a specific piece of clothing, SoftBank points are awarded or a discount coupon for SoftBank services is provided. The generation AI analyzes the user's purchase history and behavior to suggest optimal benefits and promotions. The interface providing unit enables the user to smoothly try on clothes in a virtual space or purchase on an online shopping platform. For example, the generation AI analyzes the user's operations and provides optimal navigation and support. As a result, the virtual space try-on system according to the embodiment enables the user to smoothly make a purchase on Yahoo Shopping while enjoying the experience of trying on clothes in a virtual space.

[0030] The avatar fitting unit can perform a real-time fit simulation based on the user's body shape or posture. The avatar fitting unit, for example, uses a generation AI to analyze the user's body shape data and simulate the fit of clothes in a virtual space in real time. For example, it realistically reproduces the wrinkles and stretching of clothes when the user moves. The avatar fitting unit also uses the generation AI to adjust the fit of the clothes based on the user's posture data, displaying the fitting results more realistically. For example, it changes the appearance of the clothes depending on the user's movements such as sitting and standing. The avatar fitting unit also uses the generation AI to analyze the user's body shape and posture in real time and dynamically adjust the fit of the clothes being tried on. For example, it simulates the movement of clothes as the user walks. This allows the fitting results to be displayed more realistically.

[0031] The avatar fitting unit can simulate different lighting conditions or backgrounds, allowing the user to check how clothes look from multiple angles. The avatar fitting unit adds a function that allows the user to simulate different lighting conditions in a virtual space and check how the color and texture of clothes change. For example, natural daylight and artificial nightlight can be reproduced. The avatar fitting unit also provides a function that allows the user to check how clothes look from multiple angles by changing the background. For example, an outdoor landscape or indoor interior can be set as the background. The avatar fitting unit also allows the user to freely select lighting conditions and backgrounds, simulating how the clothes look from multiple angles after trying them on. For example, the user can compare how the clothes look in different scenes. This allows the user to check how the clothes look from multiple angles.

[0032] The avatar fitting unit can be expanded to include other fashion items such as accessories or shoes, allowing users to try on complete outfits. For example, the avatar fitting unit extends the fitting system in the virtual space to accessories, allowing users to try on necklaces, earrings, etc. For example, it simulates combinations of clothes and accessories. The avatar fitting unit also adds a shoe fitting function, allowing users to try on shoes in the virtual space. For example, it checks how the clothes and shoes are coordinated. The avatar fitting unit also extends the fitting system in the virtual space to allow users to try on complete outfits, allowing users to try on clothes, accessories, and shoes at the same time. For example, it simulates full-body outfits, allowing users to try on complete outfits.

[0033] The avatar fitting unit adds a function that allows the results of trying on clothes to be shared on social media, allowing feedback from other users to be obtained. The avatar fitting unit adds a function that allows the results of trying on clothes in a virtual space to be shared on social media, allowing comments and ratings from other users to be obtained. For example, an image of the clothes tried on can be posted. The avatar fitting unit also uses the social media sharing function to share the results of trying on clothes with friends and followers and collect feedback. For example, opinions and advice on the clothes tried on can be received. The avatar fitting unit also adds a hashtag or tagging function when sharing the results of trying on clothes on social media, encouraging feedback from other users. For example, tags related to specific brands or styles can be added. This allows feedback from other users to be obtained.

[0034] The interlocking unit can use the generation AI to analyze the user's past purchase history or browsing history and suggest individually customized recommended products. The interlocking unit, for example, uses the generation AI to analyze the user's past purchase history and suggest individually customized recommended products. For example, it suggests products similar to items purchased in the past. The interlocking unit also uses the generation AI to suggest individually customized recommended products based on the user's browsing history. For example, it prioritizes displaying products from frequently viewed brands or categories. The interlocking unit also integrates the purchase history and browsing history to build a system in which the generation AI suggests optimal recommended products. For example, it suggests products based on past purchasing trends and current interests. This makes it possible to suggest individually customized recommended products.

[0035] The interlocking unit can automatically collect reviews or ratings of clothes tried on in the virtual space and reflect them on the Yahoo Shopping purchase page. The interlocking unit, for example, builds a system that automatically collects reviews and ratings of clothes tried on in the virtual space and reflects them on the Yahoo Shopping purchase page. For example, it automatically collects impressions and ratings at the time of trying on. The interlocking unit also records reviews of clothes tried on by users in the virtual space and displays that information on the Yahoo Shopping purchase page. For example, it reflects evaluations of the fit and design at the time of trying on. The interlocking unit also develops a system that automatically collects evaluation data of clothes tried on and reflects them on the Yahoo Shopping purchase page in real time. For example, it displays the user's evaluation score on the purchase page. This allows reviews and ratings of clothes tried on to be automatically collected and reflected on the purchase page.

[0036] The interlocking unit can extend the interlocking with the online shopping platform to other online shopping platforms, allowing users to make purchases on multiple sites. For example, the interlocking unit can extend the interlocking with Yahoo Shopping to other online shopping platforms, building a system that allows users to make purchases on multiple sites. For example, it can realize interlocking with Amazon and Rakuten Ichiba. The interlocking unit can also interlock with multiple online shopping platforms, allowing users to try on clothes in a single virtual space and purchase them on multiple sites. For example, the try-on results can be added to the cart on each platform. The interlocking unit can also interlock with other online shopping platforms, allowing users to check the best price and stock status. For example, it can integrate and display information from each platform, allowing users to make purchases on multiple sites.

[0037] The interlocking unit can display the inventory status of clothes tried on in the virtual space in real time, allowing the user to instantly check available sizes or colors. For example, the interlocking unit can build a system that displays the inventory status of clothes tried on in the virtual space in real time, allowing the user to instantly check available sizes and colors. For example, the interlocking unit can update inventory information in real time. The interlocking unit can also add a function that allows the user to check the inventory status of clothes tried on in the virtual space and display available sizes and colors. For example, the interlocking unit can display an alert if stock is low. The interlocking unit can also develop a system that displays inventory status in real time, allowing the user to instantly check available options for the clothes tried on. For example, the interlocking unit can dynamically display size and color options. This allows the user to instantly check available sizes and colors.

[0038] The reward provision unit uses the generation AI to analyze the user's purchase history and behavioral data, and can provide rewards and promotions at the optimal timing. The reward provision unit, for example, uses the generation AI to analyze the user's purchase history and build a system that provides rewards and promotions at the optimal timing. For example, rewards are suggested based on purchase frequency and timing. The reward provision unit also provides promotions at the optimal timing based on the user's behavioral data. For example, rewards are suggested to coincide with specific time periods or events. The reward provision unit also integrates the purchase history and behavioral data, and develops a system in which the generation AI provides the optimal rewards and promotions to the user. For example, rewards are suggested based on past purchasing trends and current behavior. This allows rewards and promotions to be provided at the optimal timing.

[0039] The reward provision unit allows users to try on limited items or collaboration products related to SoftBank's brand or services in a virtual space, thereby attracting the interest of fans. The reward provision unit, for example, allows users to try on limited items related to SoftBank's brand or services in a virtual space, thereby building a system that attracts the interest of fans. For example, limited edition clothing or accessories are provided. The reward provision unit also allows users to try on collaboration products in a virtual space, thereby attracting the interest of users. For example, collaboration products with popular brands or characters can be tried on. The reward provision unit also adds a function that allows users to try on limited edition items or collaboration products in a virtual space, thereby attracting the interest of users. For example, limited edition items are provided in conjunction with specific events or campaigns. This can attract the interest of fans.

[0040] The benefit provision unit can link with other SoftBank services to provide a comprehensive benefit package. For example, the benefit provision unit links with SoftBank's communication plans to provide benefits such as a virtual try-on experience or a purchase on Yahoo Shopping. For example, it provides exclusive benefits to communication plan subscribers. The benefit provision unit also links with SoftBank's entertainment services to provide benefits such as a virtual try-on experience or a purchase on Yahoo Shopping. For example, it provides discount coupons for movies and music. The benefit provision unit also links with other SoftBank services to build a system that provides a comprehensive benefit package. For example, it provides benefits for communication plans, entertainment, and shopping all together. This makes it possible to provide a comprehensive benefit package.

[0041] The reward provision unit links the try-on experience in the virtual space with the try-on experience at a SoftBank physical store, thereby providing a seamless online or offline purchasing experience. For example, the reward provision unit links the try-on experience in the virtual space with the try-on experience at a SoftBank physical store, allowing a user to have a seamless purchasing experience online and offline. For example, the reward provision unit allows a user to try on clothes tried on in the virtual space at the physical store. The reward provision unit also links the try-on results in the virtual space with inventory information at the physical store, allowing a user to check clothes available for try-on at the physical store. For example, the reward provision unit checks the inventory status of clothes tried on in the virtual space at the physical store. The reward provision unit also links the online and offline try-on experiences, allowing a user to purchase clothes tried on in the virtual space at the physical store. For example, the reward provision unit reserves clothes tried on in the virtual space at the physical store. This allows a seamless online and offline purchasing experience to be provided.

[0042] The interface providing unit can use the generation AI to analyze the user's operation history and propose the optimal navigation path in real time. The interface providing unit, for example, uses the generation AI to build a system that analyzes the user's operation history and proposes the optimal navigation path in real time. For example, it proposes the next step based on past operation history. The interface providing unit also has the generation AI propose the optimal navigation path in real time based on the user's operation history. For example, it prioritizes displaying frequently used functions. The interface providing unit also analyzes the operation history and develops a system in which the generation AI proposes the optimal navigation path to the user. For example, it learns the user's operation patterns and proposes the optimal operation procedure. This makes it possible to propose the optimal navigation path in real time.

[0043] The interface providing unit can add a voice recognition function to the user interface, enabling operation by voice commands. The interface providing unit, for example, adds a voice recognition function to the user interface and builds a system that enables operation by voice commands. For example, instructions for trying on clothes or purchasing are given by voice. The interface providing unit also uses the voice recognition function to provide an interface that the user can operate by voice commands. For example, navigation or searches are performed by voice. The interface providing unit also adds a voice recognition function, enabling the user to operate by voice commands. For example, trying on specific clothes or completing purchase procedures by voice. This enables operation by voice commands.

[0044] The interface providing unit can adapt the user interface to different devices, enabling use on multiple devices. The interface providing unit, for example, adapts the user interface to smartphones and tablets, building a system that enables use on multiple devices. For example, it allows seamless operation between different devices. The interface providing unit also provides a user interface compatible with VR headsets, making the experience of trying on clothes in a virtual space more realistic. For example, it optimizes operation in a VR environment. The interface providing unit also develops user interfaces compatible with different devices, allowing users to operate smoothly on any device. For example, it provides the optimal layout for each device. This enables use on multiple devices.

[0045] The interface providing unit can add a real-time chat function with other users to the user interface to promote communication. The interface providing unit, for example, adds a real-time chat function to the user interface to build a system that promotes communication with other users. For example, sharing try-on results and exchanging opinions. The interface providing unit also uses the real-time chat function to provide an interface that allows users to communicate directly with other users. For example, sharing try-on results with friends and followers. The interface providing unit also adds a chat function to the user interface to enable real-time exchange of opinions with other users. For example, receiving feedback on clothes tried on. This promotes communication with other users.

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

[0047] The avatar fitting unit can simulate the fit of clothes in a virtual space in real time based on the user's body shape data. For example, it can realistically reproduce the wrinkles and stretching of clothes when the user moves. The avatar fitting unit can also adjust the fit of clothes based on the user's posture data, allowing the fitting results to be displayed more realistically. For example, it can change the appearance of clothes depending on the user's movements such as sitting and standing. The avatar fitting unit can also analyze the user's body shape and posture in real time, dynamically adjusting the fit of the clothes being tried on. For example, it can simulate the movement of clothes when the user walks. This allows the fitting results to be displayed more realistically.

[0048] The avatar fitting unit can simulate different lighting conditions or backgrounds, allowing users to check how clothes look from multiple angles. For example, natural daylight or artificial nightlight can be reproduced in the virtual space, allowing users to check how the color and texture of clothes change. Changing the background can also provide a function that allows users to check how clothes look from multiple angles. For example, an outdoor landscape or indoor interior can be set as the background. Users can also freely select lighting conditions and backgrounds, allowing users to simulate how clothes look when tried on from multiple angles. For example, it can compare how clothes look in different scenes. This allows users to check how clothes look from multiple angles.

[0049] The avatar fitting unit can be expanded to include other fashion items such as accessories or shoes, allowing users to try on complete outfits. For example, the fitting system in the virtual space can be expanded to include accessories, allowing users to try on necklaces, earrings, etc. For example, it can simulate combinations of clothes and accessories. Also, a shoe fitting function can be added, allowing users to try on shoes in the virtual space. For example, it can be possible to check how the clothes and shoes are coordinated. Furthermore, the fitting system in the virtual space can be expanded to allow users to try on complete outfits, allowing users to try on clothes, accessories, and shoes at the same time. For example, it can simulate outfits for the entire body, allowing users to try on complete outfits.

[0050] The avatar fitting unit can add a function that allows the results of trying on clothes to be shared on social media, allowing feedback from other users to be obtained. For example, a function can be added that allows the results of trying on clothes in a virtual space to be shared on social media, allowing comments and ratings from other users to be obtained. For example, an image of the clothes tried on can be posted. The social media sharing function can also be used to share the results of trying on clothes with friends and followers to collect feedback. For example, opinions and advice on the clothes tried on can be received. When sharing the results of trying on clothes on social media, a hashtag or tagging function can be added to encourage feedback from other users. For example, tags related to specific brands or styles can be added. This allows feedback from other users to be obtained.

[0051] The interlocking unit can use the generation AI to analyze a user's past purchase history or browsing history and suggest individually customized recommended products. For example, the generation AI can analyze a user's past purchase history and suggest individually customized recommended products. For example, it can suggest products similar to items previously purchased. The generation AI can also suggest individually customized recommended products based on the user's browsing history. For example, it can prioritize the display of products from frequently viewed brands or categories. It can also build a system that integrates purchase history and browsing history and allows the generation AI to suggest optimal recommended products. For example, it can suggest products based on past purchasing trends and current interests. This makes it possible to suggest individually customized recommended products.

[0052] The interlocking unit can automatically collect reviews or ratings of clothes tried on in the virtual space and reflect them on the Yahoo Shopping purchase page. For example, a system can be constructed that automatically collects reviews and ratings of clothes tried on in the virtual space and reflects them on the Yahoo Shopping purchase page. For example, impressions and ratings at the time of trying on can be automatically collected. Furthermore, reviews of clothes tried on by users in the virtual space can be recorded and the information can be displayed on the Yahoo Shopping purchase page. For example, evaluations of the fit and design at the time of trying on can be reflected. Furthermore, a system can be developed that automatically collects evaluation data of clothes tried on and reflects them on the Yahoo Shopping purchase page in real time. For example, the user's evaluation score can be displayed on the purchase page. This allows reviews and ratings of clothes tried on to be automatically collected and reflected on the purchase page.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The avatar fitting unit has the user's avatar try on clothes that are on sale. For example, a user can try on various clothes on their avatar in a virtual space. The avatar fitting unit also uses a generation AI to apply the selected clothes to the avatar and display the fitting results in real time. For example, if a user instructs the unit to "try on a red dress," the generation AI analyzes the instruction and puts the red dress on the avatar. Step 2: The linking unit links the clothes tried on by the avatar fitting unit to an online shopping platform. For example, clothes tried on in the virtual space are linked to Yahoo Shopping, and after checking the fitting results, the user can purchase the clothes directly on Yahoo Shopping. The generation AI automatically adds the information about the clothes tried on to the Yahoo Shopping cart and assists with the purchase process. Step 3: The reward provision unit offers rewards and promotions for the purchase of clothes linked by the linking unit. For example, when a specific piece of clothing is purchased, SoftBank points are awarded or discount coupons for SoftBank services are provided. The generation AI analyzes the user's purchase history and behavior and suggests optimal rewards and promotions. Step 4: The interface providing unit enables the user to smoothly try on clothes in the virtual space and make purchases on the online shopping platform. For example, the generating AI analyzes the user's operations and provides optimal navigation and support. As a result, the virtual space try-on system according to the embodiment allows the user to smoothly make purchases on Yahoo Shopping while enjoying the experience of trying on clothes in the virtual space.

[0055] (Example 2) The virtual space try-on system according to an embodiment of the present invention is a system that aims to create SoftBank fans by having users try on clothes for sale on their avatars and linking it with Yahoo Shopping. As a result, the virtual space try-on system allows users to smoothly make purchases on Yahoo Shopping while enjoying the experience of trying on clothes in a virtual space.

[0056] A virtual space try-on system according to an embodiment includes an avatar try-on unit, a linking unit, a reward provision unit, and an interface provision unit. The avatar try-on unit allows a user's avatar to try on clothes available for sale. For example, a user can try on various clothes on their avatar in the virtual space. The avatar try-on unit also uses a generation AI to apply selected clothes to the avatar and display the fitting results in real time. For example, if a user instructs the system to "try on a red dress," the generation AI analyzes the instruction and dresses the avatar in the red dress. The linking unit links the clothes tried on by the avatar try-on unit with an online shopping platform. For example, clothes tried on in the virtual space are linked with Yahoo Shopping, allowing the user to purchase the clothes directly from Yahoo Shopping after checking the fitting results. The generation AI automatically adds information about the tried-on clothes to a Yahoo Shopping cart and supports the purchase process. The reward provision unit offers rewards and promotions for purchasing clothes linked by the linking unit. For example, when a user purchases a specific piece of clothing, SoftBank points are awarded or a discount coupon for SoftBank services is provided. The generation AI analyzes the user's purchase history and behavior to suggest optimal benefits and promotions. The interface providing unit enables the user to smoothly try on clothes in a virtual space or purchase on an online shopping platform. For example, the generation AI analyzes the user's operations and provides optimal navigation and support. As a result, the virtual space try-on system according to the embodiment enables the user to smoothly make a purchase on Yahoo Shopping while enjoying the experience of trying on clothes in a virtual space.

[0057] The avatar fitting unit can perform a real-time fit simulation based on the user's body shape or posture. The avatar fitting unit, for example, uses a generation AI to analyze the user's body shape data and simulate the fit of clothes in a virtual space in real time. For example, it realistically reproduces the wrinkles and stretching of clothes when the user moves. The avatar fitting unit also uses the generation AI to adjust the fit of the clothes based on the user's posture data, displaying the fitting results more realistically. For example, it changes the appearance of the clothes depending on the user's movements such as sitting and standing. The avatar fitting unit also uses the generation AI to analyze the user's body shape and posture in real time and dynamically adjust the fit of the clothes being tried on. For example, it simulates the movement of clothes as the user walks. This allows the fitting results to be displayed more realistically.

[0058] The avatar fitting unit can simulate different lighting conditions or backgrounds, allowing the user to check how clothes look from multiple angles. The avatar fitting unit adds a function that allows the user to simulate different lighting conditions in a virtual space and check how the color and texture of clothes change. For example, natural daylight and artificial nightlight can be reproduced. The avatar fitting unit also provides a function that allows the user to check how clothes look from multiple angles by changing the background. For example, an outdoor landscape or indoor interior can be set as the background. The avatar fitting unit also allows the user to freely select lighting conditions and backgrounds, simulating how the clothes look from multiple angles after trying them on. For example, the user can compare how the clothes look in different scenes. This allows the user to check how the clothes look from multiple angles.

[0059] The avatar fitting unit uses the emotion estimation function to analyze the user's facial expression or reaction while trying on clothes, and can suggest clothes based on the user's preferences and satisfaction level. The avatar fitting unit, for example, uses the emotion estimation function to analyze the user's facial expression while trying on clothes and evaluate the user's preferences and satisfaction level. For example, it detects smiling or surprised expressions and suggests clothes based on the results. The avatar fitting unit also analyzes the user's reactions while trying on clothes in real time and calculates an emotion score. For example, it prioritizes suggesting clothes that receive a lot of positive reactions. The avatar fitting unit also builds a system that suggests clothes based on the user's preferences and satisfaction level based on the emotion estimation data. For example, it suggests the most suitable clothes based on past fitting data. This makes it possible to suggest clothes based on the user's preferences and satisfaction level.

[0060] The avatar fitting unit can be expanded to include other fashion items such as accessories or shoes, allowing users to try on complete outfits. For example, the avatar fitting unit extends the fitting system in the virtual space to accessories, allowing users to try on necklaces, earrings, etc. For example, it simulates combinations of clothes and accessories. The avatar fitting unit also adds a shoe fitting function, allowing users to try on shoes in the virtual space. For example, it checks how the clothes and shoes are coordinated. The avatar fitting unit also extends the fitting system in the virtual space to allow users to try on complete outfits, allowing users to try on clothes, accessories, and shoes at the same time. For example, it simulates full-body outfits, allowing users to try on complete outfits.

[0061] The avatar fitting unit adds a function that allows the results of trying on clothes to be shared on social media, allowing feedback from other users to be obtained. The avatar fitting unit adds a function that allows the results of trying on clothes in a virtual space to be shared on social media, allowing comments and ratings from other users to be obtained. For example, an image of the clothes tried on can be posted. The avatar fitting unit also uses the social media sharing function to share the results of trying on clothes with friends and followers and collect feedback. For example, opinions and advice on the clothes tried on can be received. The avatar fitting unit also adds a hashtag or tagging function when sharing the results of trying on clothes on social media, encouraging feedback from other users. For example, tags related to specific brands or styles can be added. This allows feedback from other users to be obtained.

[0062] The avatar fitting unit can use the emotion estimation function to analyze the user's emotions regarding the clothes they have tried on and provide styling advice to elicit positive emotions. The avatar fitting unit can, for example, use the emotion estimation function to analyze the user's emotions regarding the clothes they have tried on and provide styling advice to elicit positive emotions. For example, it can suggest optimal outfits based on the user's facial expressions and reactions. The avatar fitting unit can also provide styling advice to elicit positive emotions in real time based on the user's emotion data. For example, it can suggest combinations of colors and designs. The avatar fitting unit can also analyze the user's emotions regarding the clothes they have tried on based on the emotion estimation data and provide specific advice to elicit positive emotions. For example, it can suggest combinations of accessories and shoes. This makes it possible to provide styling advice to elicit positive emotions.

[0063] The interlocking unit can use the generation AI to analyze the user's past purchase history or browsing history and suggest individually customized recommended products. The interlocking unit, for example, uses the generation AI to analyze the user's past purchase history and suggest individually customized recommended products. For example, it suggests products similar to items purchased in the past. The interlocking unit also uses the generation AI to suggest individually customized recommended products based on the user's browsing history. For example, it prioritizes displaying products from frequently viewed brands or categories. The interlocking unit also integrates the purchase history and browsing history to build a system in which the generation AI suggests optimal recommended products. For example, it suggests products based on past purchasing trends and current interests. This makes it possible to suggest individually customized recommended products.

[0064] The interlocking unit can automatically collect reviews or ratings of clothes tried on in the virtual space and reflect them on the Yahoo Shopping purchase page. The interlocking unit, for example, builds a system that automatically collects reviews and ratings of clothes tried on in the virtual space and reflects them on the Yahoo Shopping purchase page. For example, it automatically collects impressions and ratings at the time of trying on. The interlocking unit also records reviews of clothes tried on by users in the virtual space and displays that information on the Yahoo Shopping purchase page. For example, it reflects evaluations of the fit and design at the time of trying on. The interlocking unit also develops a system that automatically collects evaluation data of clothes tried on and reflects them on the Yahoo Shopping purchase page in real time. For example, it displays the user's evaluation score on the purchase page. This allows reviews and ratings of clothes tried on to be automatically collected and reflected on the purchase page.

[0065] The interlocking unit can extend the interlocking with the online shopping platform to other online shopping platforms, allowing users to make purchases on multiple sites. For example, the interlocking unit can extend the interlocking with Yahoo Shopping to other online shopping platforms, building a system that allows users to make purchases on multiple sites. For example, it can realize interlocking with Amazon and Rakuten Ichiba. The interlocking unit can also interlock with multiple online shopping platforms, allowing users to try on clothes in a single virtual space and purchase them on multiple sites. For example, the try-on results can be added to the cart on each platform. The interlocking unit can also interlock with other online shopping platforms, allowing users to check the best price and stock status. For example, it can integrate and display information from each platform, allowing users to make purchases on multiple sites.

[0066] The interlocking unit can display the inventory status of clothes tried on in the virtual space in real time, allowing the user to instantly check available sizes or colors. For example, the interlocking unit can build a system that displays the inventory status of clothes tried on in the virtual space in real time, allowing the user to instantly check available sizes and colors. For example, the interlocking unit can update inventory information in real time. The interlocking unit can also add a function that allows the user to check the inventory status of clothes tried on in the virtual space and display available sizes and colors. For example, the interlocking unit can display an alert if stock is low. The interlocking unit can also develop a system that displays inventory status in real time, allowing the user to instantly check available options for the clothes tried on. For example, the interlocking unit can dynamically display size and color options. This allows the user to instantly check available sizes and colors.

[0067] The interlocking unit can use the emotion estimation function to provide customer support that elicits positive emotions when a user is unsure about a purchase. For example, the interlocking unit uses the emotion estimation function to build a system that provides customer support that elicits positive emotions when a user is unsure about a purchase. For example, a chatbot provides support according to the emotions. The interlocking unit also displays a support message based on the user's emotion data to elicit positive emotions when the user is unsure about a purchase. For example, it presents encouraging messages or success stories. The interlocking unit also provides specific advice based on the emotion estimation data to elicit positive emotions when the user is unsure about a purchase. For example, it highlights the advantages and benefits of the product. In this way, it is possible to provide customer support that elicits positive emotions when the user is unsure about a purchase.

[0068] The reward provision unit uses the generation AI to analyze the user's purchase history and behavioral data, and can provide rewards and promotions at the optimal timing. The reward provision unit, for example, uses the generation AI to analyze the user's purchase history and build a system that provides rewards and promotions at the optimal timing. For example, rewards are suggested based on purchase frequency and timing. The reward provision unit also provides promotions at the optimal timing based on the user's behavioral data. For example, rewards are suggested to coincide with specific time periods or events. The reward provision unit also integrates the purchase history and behavioral data, and develops a system in which the generation AI provides the optimal rewards and promotions to the user. For example, rewards are suggested based on past purchasing trends and current behavior. This allows rewards and promotions to be provided at the optimal timing.

[0069] The reward provision unit allows users to try on limited items or collaboration products related to SoftBank's brand or services in a virtual space, thereby attracting the interest of fans. The reward provision unit, for example, allows users to try on limited items related to SoftBank's brand or services in a virtual space, thereby building a system that attracts the interest of fans. For example, limited edition clothing or accessories are provided. The reward provision unit also allows users to try on collaboration products in a virtual space, thereby attracting the interest of users. For example, collaboration products with popular brands or characters can be tried on. The reward provision unit also adds a function that allows users to try on limited edition items or collaboration products in a virtual space, thereby attracting the interest of users. For example, limited edition items are provided in conjunction with specific events or campaigns. This can attract the interest of fans.

[0070] The reward provision unit can use the emotion estimation function to propose personalized rewards or promotions based on the user's emotions. The reward provision unit, for example, uses the emotion estimation function to build a system that proposes personalized rewards and promotions based on the user's emotions. For example, the reward provision unit proposes optimal rewards based on the emotion score. The reward provision unit also proposes personalized rewards and promotions in real time based on the user's emotion data. For example, the reward provision unit provides rewards that elicit positive emotions. The reward provision unit also develops a system that dynamically adjusts rewards and promotions according to the user's emotions based on the emotion estimation data. For example, the reward content is changed according to the emotion score. This makes it possible to propose personalized rewards and promotions based on the user's emotions.

[0071] The benefit provision unit can link with other SoftBank services to provide a comprehensive benefit package. For example, the benefit provision unit links with SoftBank's communication plans to provide benefits such as a virtual try-on experience or a purchase on Yahoo Shopping. For example, it provides exclusive benefits to communication plan subscribers. The benefit provision unit also links with SoftBank's entertainment services to provide benefits such as a virtual try-on experience or a purchase on Yahoo Shopping. For example, it provides discount coupons for movies and music. The benefit provision unit also links with other SoftBank services to build a system that provides a comprehensive benefit package. For example, it provides benefits for communication plans, entertainment, and shopping all together. This makes it possible to provide a comprehensive benefit package.

[0072] The reward provision unit links the try-on experience in the virtual space with the try-on experience at a SoftBank physical store, thereby providing a seamless online or offline purchasing experience. For example, the reward provision unit links the try-on experience in the virtual space with the try-on experience at a SoftBank physical store, allowing a user to have a seamless purchasing experience online and offline. For example, the reward provision unit allows a user to try on clothes tried on in the virtual space at the physical store. The reward provision unit also links the try-on results in the virtual space with inventory information at the physical store, allowing a user to check clothes available for try-on at the physical store. For example, the reward provision unit checks the inventory status of clothes tried on in the virtual space at the physical store. The reward provision unit also links the online and offline try-on experiences, allowing a user to purchase clothes tried on in the virtual space at the physical store. For example, the reward provision unit reserves clothes tried on in the virtual space at the physical store. This allows a seamless online and offline purchasing experience to be provided.

[0073] The reward provision unit can use the emotion estimation function to analyze how users feel about rewards and promotions, and formulate an optimal marketing strategy. For example, the reward provision unit uses the emotion estimation function to build a system that analyzes how users feel about rewards and promotions. For example, the reward provision unit adjusts the marketing strategy based on the emotion score. The reward provision unit also analyzes the emotional response to rewards and promotions based on the user's emotion data, and formulates an optimal marketing strategy. For example, the reward provision unit implements promotions that elicit positive emotions. The reward provision unit also develops a system that dynamically adjusts the marketing strategy according to the user's emotions based on the emotion estimation data. For example, the promotion content is changed according to the emotion score. This makes it possible to formulate an optimal marketing strategy.

[0074] The interface providing unit can use the generation AI to analyze the user's operation history and propose the optimal navigation path in real time. The interface providing unit, for example, uses the generation AI to build a system that analyzes the user's operation history and proposes the optimal navigation path in real time. For example, it proposes the next step based on past operation history. The interface providing unit also has the generation AI propose the optimal navigation path in real time based on the user's operation history. For example, it prioritizes displaying frequently used functions. The interface providing unit also analyzes the operation history and develops a system in which the generation AI proposes the optimal navigation path to the user. For example, it learns the user's operation patterns and proposes the optimal operation procedure. This makes it possible to propose the optimal navigation path in real time.

[0075] The interface providing unit can add a voice recognition function to the user interface, enabling operation by voice commands. The interface providing unit, for example, adds a voice recognition function to the user interface and builds a system that enables operation by voice commands. For example, instructions for trying on clothes or purchasing are given by voice. The interface providing unit also uses the voice recognition function to provide an interface that the user can operate by voice commands. For example, navigation or searches are performed by voice. The interface providing unit also adds a voice recognition function, enabling the user to operate by voice commands. For example, trying on specific clothes or completing purchase procedures by voice. This enables operation by voice commands.

[0076] The interface providing unit can adapt the user interface to different devices, enabling use on multiple devices. The interface providing unit, for example, adapts the user interface to smartphones and tablets, building a system that enables use on multiple devices. For example, it allows seamless operation between different devices. The interface providing unit also provides a user interface compatible with VR headsets, making the experience of trying on clothes in a virtual space more realistic. For example, it optimizes operation in a VR environment. The interface providing unit also develops user interfaces compatible with different devices, allowing users to operate smoothly on any device. For example, it provides the optimal layout for each device. This enables use on multiple devices.

[0077] The interface providing unit can add a real-time chat function with other users to the user interface to promote communication. The interface providing unit, for example, adds a real-time chat function to the user interface to build a system that promotes communication with other users. For example, sharing try-on results and exchanging opinions. The interface providing unit also uses the real-time chat function to provide an interface that allows users to communicate directly with other users. For example, sharing try-on results with friends and followers. The interface providing unit also adds a chat function to the user interface to enable real-time exchange of opinions with other users. For example, receiving feedback on clothes tried on. This promotes communication with other users.

[0078] The interface providing unit can use the emotion estimation function to detect stress or frustration felt by the user during operation in real time and provide instantaneous support. The interface providing unit, for example, uses the emotion estimation function to build a system that detects stress or frustration felt by the user during operation in real time and provides instantaneous support. For example, a help message is displayed when stress is felt. The interface providing unit also detects stress or frustration felt by the user during operation based on the user's emotion data and provides support in real time. For example, a guide is displayed when operation is difficult. The interface providing unit also develops a system that detects stress or frustration felt by the user during operation in real time based on the emotion estimation data and provides instantaneous support. For example, the support content is adjusted according to the emotion score. This makes it possible to detect stress or frustration felt by the user during operation in real time and provide instantaneous support.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The avatar fitting unit can simulate the fit of clothes in a virtual space in real time based on the user's body shape data. For example, it can realistically reproduce the wrinkles and stretching of clothes when the user moves. The avatar fitting unit can also adjust the fit of clothes based on the user's posture data, allowing the fitting results to be displayed more realistically. For example, it can change the appearance of clothes depending on the user's movements such as sitting and standing. The avatar fitting unit can also analyze the user's body shape and posture in real time, dynamically adjusting the fit of the clothes being tried on. For example, it can simulate the movement of clothes when the user walks. This allows the fitting results to be displayed more realistically.

[0081] The avatar fitting unit can simulate different lighting conditions or backgrounds, allowing users to check how clothes look from multiple angles. For example, natural daylight or artificial nightlight can be reproduced in the virtual space, allowing users to check how the color and texture of clothes change. Changing the background can also provide a function that allows users to check how clothes look from multiple angles. For example, an outdoor landscape or indoor interior can be set as the background. Users can also freely select lighting conditions and backgrounds, allowing users to simulate how clothes look when tried on from multiple angles. For example, it can compare how clothes look in different scenes. This allows users to check how clothes look from multiple angles.

[0082] The avatar fitting unit can use the emotion estimation function to analyze the user's facial expression or reaction while trying on clothes and suggest clothes based on their preferences and satisfaction. For example, the emotion estimation function can be used to analyze the user's facial expression while trying on clothes and evaluate their preferences and satisfaction. For example, smiling or surprised expressions can be detected and clothes can be suggested based on the results. The avatar fitting unit can also analyze the user's reaction while trying on clothes in real time and calculate an emotion score. For example, clothes that receive a lot of positive reactions can be suggested preferentially. Furthermore, a system can be built that suggests clothes based on the user's preferences and satisfaction based on the emotion estimation data. For example, the most suitable clothes can be suggested based on past fitting data. This makes it possible to suggest clothes based on the user's preferences and satisfaction.

[0083] The avatar fitting unit can be expanded to include other fashion items such as accessories or shoes, allowing users to try on complete outfits. For example, the fitting system in the virtual space can be expanded to include accessories, allowing users to try on necklaces, earrings, etc. For example, it can simulate combinations of clothes and accessories. Also, a shoe fitting function can be added, allowing users to try on shoes in the virtual space. For example, it can be possible to check how the clothes and shoes are coordinated. Furthermore, the fitting system in the virtual space can be expanded to allow users to try on complete outfits, allowing users to try on clothes, accessories, and shoes at the same time. For example, it can simulate outfits for the entire body, allowing users to try on complete outfits.

[0084] The avatar fitting unit can add a function that allows the results of trying on clothes to be shared on social media, allowing feedback from other users to be obtained. For example, a function can be added that allows the results of trying on clothes in a virtual space to be shared on social media, allowing comments and ratings from other users to be obtained. For example, an image of the clothes tried on can be posted. The social media sharing function can also be used to share the results of trying on clothes with friends and followers to collect feedback. For example, opinions and advice on the clothes tried on can be received. When sharing the results of trying on clothes on social media, a hashtag or tagging function can be added to encourage feedback from other users. For example, tags related to specific brands or styles can be added. This allows feedback from other users to be obtained.

[0085] The avatar fitting unit can use the emotion estimation function to analyze the user's emotions regarding the clothes they have tried on and provide styling advice to elicit positive emotions. For example, the emotion estimation function can be used to analyze the user's emotions regarding the clothes they have tried on and provide styling advice to elicit positive emotions. For example, the optimal outfit can be suggested based on the user's facial expressions and reactions. Furthermore, styling advice to elicit positive emotions can be provided in real time based on the user's emotion data. For example, combinations of colors and designs can be suggested. Furthermore, the emotion estimation data can be used to analyze the user's emotions regarding the clothes they have tried on and provide specific advice to elicit positive emotions. For example, combinations of accessories and shoes can be suggested. This makes it possible to provide styling advice to elicit positive emotions.

[0086] The interlocking unit can use the generation AI to analyze a user's past purchase history or browsing history and suggest individually customized recommended products. For example, the generation AI can analyze a user's past purchase history and suggest individually customized recommended products. For example, it can suggest products similar to items previously purchased. The generation AI can also suggest individually customized recommended products based on the user's browsing history. For example, it can prioritize the display of products from frequently viewed brands or categories. It can also build a system that integrates purchase history and browsing history and allows the generation AI to suggest optimal recommended products. For example, it can suggest products based on past purchasing trends and current interests. This makes it possible to suggest individually customized recommended products.

[0087] The interlocking unit can automatically collect reviews or ratings of clothes tried on in the virtual space and reflect them on the Yahoo Shopping purchase page. For example, a system can be constructed that automatically collects reviews and ratings of clothes tried on in the virtual space and reflects them on the Yahoo Shopping purchase page. For example, impressions and ratings at the time of trying on can be automatically collected. Furthermore, reviews of clothes tried on by users in the virtual space can be recorded and the information can be displayed on the Yahoo Shopping purchase page. For example, evaluations of the fit and design at the time of trying on can be reflected. Furthermore, a system can be developed that automatically collects evaluation data of clothes tried on and reflects them on the Yahoo Shopping purchase page in real time. For example, the user's evaluation score can be displayed on the purchase page. This allows reviews and ratings of clothes tried on to be automatically collected and reflected on the purchase page.

[0088] The linking unit can use the emotion estimation function to provide customer support that elicits positive emotions when a user is unsure about a purchase. For example, the emotion estimation function can be used to build a system that provides customer support that elicits positive emotions when a user is unsure about a purchase. For example, a chatbot can provide support according to the user's emotions. Furthermore, based on the user's emotion data, a support message can be displayed that elicits positive emotions when the user is unsure about a purchase. For example, encouraging messages or success stories can be presented. Furthermore, based on the emotion estimation data, specific advice can be provided that elicits positive emotions when the user is unsure about a purchase. For example, the advantages and benefits of the product can be emphasized. In this way, customer support can be provided that elicits positive emotions when the user is unsure about a purchase.

[0089] The reward provision unit can use the emotion estimation function to propose personalized rewards or promotions based on the user's emotions. For example, a system can be built that uses the emotion estimation function to propose personalized rewards and promotions based on the user's emotions. For example, optimal rewards can be proposed based on the emotion score. Furthermore, personalized rewards and promotions can be proposed in real time based on the user's emotion data. For example, rewards that elicit positive emotions can be provided. Furthermore, a system can be developed that dynamically adjusts rewards and promotions according to the user's emotions based on the emotion estimation data. For example, the reward content can be changed according to the emotion score. This makes it possible to propose personalized rewards and promotions based on the user's emotions.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The avatar fitting unit has the user's avatar try on clothes that are on sale. For example, a user can try on various clothes on their avatar in a virtual space. The avatar fitting unit also uses a generation AI to apply the selected clothes to the avatar and display the fitting results in real time. For example, if a user instructs the unit to "try on a red dress," the generation AI analyzes the instruction and puts the red dress on the avatar. Step 2: The linking unit links the clothes tried on by the avatar fitting unit to an online shopping platform. For example, clothes tried on in the virtual space are linked to Yahoo Shopping, and after checking the fitting results, the user can purchase the clothes directly on Yahoo Shopping. The generation AI automatically adds the information about the clothes tried on to the Yahoo Shopping cart and assists with the purchase process. Step 3: The reward provision unit offers rewards and promotions for the purchase of clothes linked by the linking unit. For example, when a specific piece of clothing is purchased, SoftBank points are awarded or discount coupons for SoftBank services are provided. The generation AI analyzes the user's purchase history and behavior and suggests optimal rewards and promotions. Step 4: The interface providing unit enables the user to smoothly try on clothes in the virtual space and make purchases on the online shopping platform. For example, the generating AI analyzes the user's operations and provides optimal navigation and support. As a result, the virtual space try-on system according to the embodiment allows the user to smoothly make purchases on Yahoo Shopping while enjoying the experience of trying on clothes in the virtual space.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

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

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] 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.

[0151] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an avatar fitting unit that allows a user's avatar to try on clothes available for sale in the virtual space; a linking unit that links the clothes tried on by the avatar fitting unit with an online shopping platform; a reward providing unit that provides a reward or promotion for the purchase of clothes linked by the linking unit; and an interface providing unit that enables users to smoothly try on clothes in a virtual space or purchase items on an online shopping platform. A system characterized by:

2. The interlocking portion is Using a generative AI, the user's past purchase history or browsing history is analyzed to suggest individually customized recommended products.

2. The system of claim 1.

3. The benefit providing unit: Using a generation AI, the purchase history and behavioral data of the user are analyzed, and the special offer or promotion is provided at the optimal timing.

2. The system of claim 1.

4. The interface providing unit Using generative AI, the user's operation history is analyzed and the optimal navigation path is proposed in real time.

2. The system of claim 1.

5. The avatar fitting section includes: Analyzing the facial expression or reaction of the user when trying on clothes, and suggesting clothes based on the user's preferences and satisfaction level 2. The system of claim 1.

Images