system

Abstract

The system according to this embodiment aims to allow customers to receive optimal fashion advice while at home. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects the customer's body shape data and preferences. The generation unit generates a 3D avatar and fashion advice based on the data collected by the collection unit. The provision unit provides the avatar and advice generated by the generation unit to the customer.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for customers to receive optimal fashion advice while staying at home.

[0005] The system according to the embodiment aims to enable customers to receive optimal fashion advice while staying at home.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects the body type data and preferences of customers. The generation unit generates a 3D avatar and fashion advice based on the data collected by the collection unit. The provision unit provides the avatar and advice generated by the generation unit to the customers. [Effects of the Invention]

[0007] The system according to this embodiment allows customers to receive optimal fashion advice from the comfort of their own homes. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The apparel e-commerce platform according to an embodiment of the present invention is an innovative system that combines VR technology and generative AI. This system allows customers to try on various clothes in a VR space from the comfort of their homes and receive optimal fashion advice from an AI personal stylist. Furthermore, the AI ​​learns the customer's body shape data and preferences, enabling personalized product suggestions and virtual fashion shows. For example, a customer wears a VR headset at home and accesses the apparel e-commerce platform. Next, a precise 3D avatar is generated from the customer's photograph. This 3D avatar reproduces the customer's body shape and skin texture, providing an experience close to actual try-on. The customer can try on various clothes in the VR space. For example, by dressing the 3D avatar in the clothes the customer has selected, they can see how the clothes actually look. Furthermore, the AI ​​personal stylist analyzes the customer's body shape, preferences, and lifestyle to provide optimal fashion advice. For example, the AI ​​analyzes the customer's past purchase history and social media data to suggest outfits that reflect the latest trends. The AI ​​also learns the customer's body shape data and preferences to provide personalized product suggestions. For example, AI can suggest clothing sizes and styles that suit a customer's body type, reducing anxiety associated with online shopping. Furthermore, regular virtual fashion shows can be held, allowing customers to experience the latest collections in a VR space. This enables customers to enjoy the latest fashion trends from the comfort of their homes. The platform also includes social features, allowing customers to shop together with friends in the VR space. For instance, they can watch virtual fashion shows together or exchange opinions about clothes they've tried on. This service allows customers to shop comfortably and efficiently from home, finding the perfect fashion for them. Businesses also benefit from reduced return rates, improved customer engagement, and optimized inventory management. Moreover, it can revolutionize the apparel e-commerce industry and create a new shopping experience.This allows apparel e-commerce platforms to collect customer body shape data and preferences, and then generate and provide 3D avatars and fashion advice based on that data.

[0029] The apparel e-commerce platform according to this embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects customer body shape data and preferences. Customer body shape data and preferences include, but are not limited to, height, weight, clothing size, color preferences, and style preferences. For example, the collection unit takes a photograph of the customer and analyzes the data to obtain the customer's body shape data. The collection unit can also analyze the customer's past purchase history and social media data to understand the customer's preferences. For example, the collection unit identifies preferred styles and sizes from the customer's past purchase history and narrows down the data to be collected. The generation unit generates 3D avatars and fashion advice based on the data collected by the collection unit. The generation unit generates a precise 3D avatar from a customer's photograph, for example, using a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and can reproduce the customer's body shape and skin texture. The generation unit can also use the generation AI to analyze the customer's body shape, preferences, and lifestyle and provide optimal fashion advice. For example, the generation unit analyzes the customer's past purchase history and social media data to suggest outfits that reflect the latest trends. The delivery unit provides the customer with the avatar and advice generated by the generation unit. The delivery unit can, for example, have the generated 3D avatar try on various clothes. The delivery unit can, for example, have the customer see how the clothes they selected look on the 3D avatar. The delivery unit can also hold virtual fashion shows regularly, allowing customers to experience the latest collections in VR space. For example, the delivery unit can hold a virtual fashion show once a week to provide customers with the latest fashion. Furthermore, the delivery unit can implement social functions that allow customers to enjoy shopping together with friends in VR space. For example, the delivery unit can allow customers to watch virtual fashion shows with friends and exchange opinions about the clothes they have tried on. In this way, the apparel e-commerce platform according to this embodiment can collect customer body shape data and preferences, and generate and provide 3D avatars and fashion advice based on that data.

[0030] The data collection unit collects customer body shape data and preferences. This data includes, but is not limited to, height, weight, clothing size, color preferences, and style preferences. For example, to obtain customer body shape data, the data collection unit takes photos of customers and analyzes that data. Specifically, customers take full-body photos of themselves using a dedicated application and upload the image data to the cloud. The data collection unit uses image analysis technology to accurately measure the customer's body shape and automatically calculates height, weight, and dimensions of each body part. The data collection unit can also analyze customers' past purchase history and social media data to understand their preferences. For example, it stores information such as the brand, color, and style of items that customers have purchased in the past in a database and uses this to identify customer preferences. Furthermore, it collects information on social media posts and items that customers have "liked" to analyze customers' fashion preferences and trend tendencies. As a result, the data collection unit can comprehensively understand customer body shape data and preferences and provide basic data for making optimal fashion suggestions to individual customers. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the generation and provision units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The generation unit generates 3D avatars and fashion advice based on data collected by the collection unit. For example, the generation unit uses a generation AI to generate a precise 3D avatar from a customer's photograph. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, capable of reproducing the customer's body shape and skin texture. Specifically, the generation AI analyzes a full-body photograph of the customer and uses an algorithm that accurately reproduces the dimensions and shape of each part. Furthermore, it reproduces details such as skin texture, hairstyle, and facial features, generating a realistic 3D avatar that makes it seem as if the customer is actually trying on the clothes. The generation unit can also use the generation AI to analyze the customer's body shape, preferences, and lifestyle to provide optimal fashion advice. For example, the generation unit analyzes the customer's past purchase history and social media data to suggest outfits that reflect the latest trends. The generation AI uses a text generation AI to generate fashion advice tailored to the customer's preferences and lifestyle, and suggests specific items and outfits. In this way, the generation unit can provide personalized fashion advice to customers and improve customer satisfaction. Furthermore, the generation unit can use the generated 3D avatar to simulate trying on the clothes selected by the customer. This allows customers to see how their chosen clothes will look without actually trying them on. Through these functions, the generation unit can provide customers with a more realistic and personalized fashion experience.

[0032] The service provider offers customers avatars and advice generated by the generation unit. For example, the service provider can have the generated 3D avatar try on various outfits. Specifically, by dressing the 3D avatar in the clothes the customer has chosen, they can see how the clothes actually look. The service provider simulates the avatar's movements in real time, reproducing the fit of the clothes and how they look in response to movement. The service provider can also regularly hold virtual fashion shows, allowing customers to experience the latest collections in VR space. For example, the service provider could hold a virtual fashion show once a week, offering customers the latest fashion trends. Using a VR headset, customers can experience a sense of immersion as if they were participating in a real fashion show. Furthermore, the service provider can implement social features that allow customers to enjoy shopping together with friends in VR space. For example, the service provider can allow customers to watch virtual fashion shows with friends and exchange opinions about the clothes they have tried on. This allows customers to share a fun shopping experience with their friends. Through these features, the service provider can provide customers with a richer and more interactive fashion experience. Furthermore, the service department can collect customer feedback and use it to improve its services. For example, they can gather opinions from customers about the fit and design of clothes they have tried on and incorporate them into future recommendations. This allows the service department to provide services that meet customer needs and improve customer satisfaction.

[0033] The generation unit can generate a precise 3D avatar from a customer's photograph using a generation AI. For example, the generation unit uses a generation AI to generate a precise 3D avatar from a customer's photograph. The generation AI could be a text generation AI (e.g., LLM) or a multimodal generation AI, capable of reproducing the customer's body shape and skin texture. For example, the generation unit could input a prompt to the generation AI, such as "Generate a 3D avatar from the customer's photograph," and the generation AI would analyze the customer's photograph and generate a 3D avatar. This allows for the generation of a precise 3D avatar from the customer's photograph. A precise 3D avatar includes, but is not limited to, the degree of reproduction of facial features and the level of detail in body shape. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can generate a 3D avatar using a generation AI model that takes a customer's photograph as input and outputs a 3D avatar.

[0034] The generation unit can analyze the customer's body type, preferences, and lifestyle using a generation AI and provide optimal fashion advice. For example, the generation unit uses a generation AI to analyze the customer's body type, preferences, and lifestyle and provide optimal fashion advice. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, capable of analyzing the customer's body type, preferences, and lifestyle. For example, the generation unit can input a prompt to the generation AI such as, "Analyze the customer's body type, preferences, and lifestyle, and provide optimal fashion advice," and the generation AI will analyze the customer's data and provide fashion advice. This allows the generation unit to analyze the customer's body type, preferences, and lifestyle and provide optimal fashion advice. Optimal fashion advice includes, but is not limited to, suggestions based on the individual customer's preferences and lifestyle. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can provide fashion advice using a generation AI model that takes customer data as input and outputs fashion advice.

[0035] The service provider can have the generated 3D avatar try on various clothes. For example, the service provider can have the generated 3D avatar try on various clothes. The service provider can, for example, have the customer choose clothes and have the 3D avatar wear them to see how the clothes actually look. Various clothes include, but are not limited to, casual wear, formal wear, and sportswear. This allows the generated 3D avatar to try on various clothes. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can perform clothing try-on using an AI model that takes 3D avatar and clothing data as input and outputs try-on results.

[0036] The service provider can hold virtual fashion shows on a regular basis. The service provider can hold virtual fashion shows on a regular basis, for example. "Regularly" includes, but is not limited to, weekly or monthly events. This allows the service provider to hold virtual fashion shows on a regular basis. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can hold a virtual fashion show using an AI model that takes the schedule and content of a fashion show as input and outputs the holding of the show.

[0037] The service provider can implement social features that allow users to enjoy shopping together with friends in a VR space. For example, the service provider can implement social features that allow users to enjoy shopping together with friends in a VR space. These social features include, but are not limited to, chat functions and group buying functions. This enables the implementation of social features that allow users to enjoy shopping together with friends in a VR space. Some or all of the above-described processes in the service provider may be performed using, for example, AI, or not. For example, the service provider can implement social features using an AI model that takes social feature settings and user data as input and outputs social features.

[0038] The data collection unit can analyze customers' past purchase history and social media data to select the optimal data collection method. For example, the data collection unit can identify preferred styles and sizes from customers' past purchase history and narrow down the data to be collected. The data collection unit can also analyze customers' social media data to collect data that reflects the latest trends and preferences. For example, the data collection unit can combine customers' past purchase history and social media data to collect more accurate data. This allows the system to analyze customers' past purchase history and social media data and select the optimal data collection method. Optimal data collection methods include, but are not limited to, surveys and behavioral analysis. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer purchase history data into AI and have the AI ​​select the optimal data collection method.

[0039] The data collection unit can filter data based on the customer's current lifestyle and the season. For example, if a customer has an active lifestyle, the collection unit will prioritize collecting sportswear and casual wear. The collection unit can also prioritize collecting light clothing in summer and warm clothing in winter, depending on the season. The collection unit can also collect data categorized by the customer's lifestyle, such as for work or personal use. This allows for filtering based on the customer's current lifestyle and the season. Filtering includes, but is not limited to, seasonal trends and lifestyle changes. Some or all of the processing described above in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input customer lifestyle data into AI and have the AI ​​perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data, taking into account the customer's geographical location. For example, if the customer lives in an urban area, the data collection unit can prioritize the collection of urban-style data. If the customer lives in a suburban area, the data collection unit can also prioritize the collection of casual and relaxed-style data. If the customer plans to travel to a specific region, the data collection unit can also prioritize the collection of data appropriate to the climate and culture of that region. This allows for the priority collection of highly relevant data, taking into account the customer's geographical location. Highly relevant data includes, but is not limited to, geographical location-based trend information. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's geographical location information into AI and have the AI ​​collect highly relevant data.

[0041] The data collection unit can analyze the customer's social media activity and collect relevant data during the collection process. For example, the data collection unit can collect fashion items that the customer frequently shares on social media. The data collection unit can also collect data by referencing the styles of influencers that the customer follows. The data collection unit can also analyze trends in fashion communities that the customer participates in and collect relevant data. This allows for the analysis of the customer's social media activity and the collection of relevant data. Relevant data includes, but is not limited to, interests on social media. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the customer's social media data into AI and have AI collect relevant data.

[0042] The generation unit can adjust the level of detail of the avatar based on the customer's body shape data during generation. For example, if the customer's body shape data is detailed, the generation unit can generate a precise 3D avatar. For example, if the customer's body shape data is simple, the generation unit can also generate a basic 3D avatar. The generation unit can also adjust the level of detail of specific parts (e.g., shoulder width or waist) based on the customer's body shape data. This allows the level of detail of the avatar to be adjusted based on the customer's body shape data. The level of detail of the avatar includes, but is not limited to, facial features and the degree of body shape reproduction. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input the customer's body shape data into a generation AI and have the generation AI adjust the level of detail of the avatar.

[0043] The generation unit can apply different generation algorithms during generation according to the customer's preferences and lifestyle. For example, if the customer prefers a casual style, the generation unit can apply a generation algorithm suitable for casual attire. If the customer prefers a formal style, the generation unit can also apply a generation algorithm suitable for formal attire. The generation unit can also apply an appropriate generation algorithm according to the customer's lifestyle (e.g., an active lifestyle). This allows for the application of different generation algorithms according to the customer's preferences and lifestyle. Different generation algorithms include, but are not limited to, deep learning and rule-based generation. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input customer preference and lifestyle data into a generation AI and have the generation AI apply an appropriate generation algorithm.

[0044] The generation unit can determine the priority of items to be generated based on the customer's past purchase history. For example, the generation unit can prioritize the generation of items relevant to the customer based on items the customer has purchased in the past. The generation unit can also prioritize the generation of specific brands or styles based on the customer's past purchase history. The generation unit can also analyze the customer's past purchase history and prioritize the generation of the items they purchase most frequently. This allows the generation priority to be determined based on the customer's past purchase history. The generation priority includes, but is not limited to, importance based on past purchase history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input customer purchase history data into a generation AI and have the generation AI determine the generation priority.

[0045] The generation unit can adjust the order of generation based on customer relevance during the generation process. For example, the generation unit can prioritize generating highly relevant items based on customer preferences and lifestyles. The generation unit can also prioritize generating highly relevant items based on customer past purchase history, for example. The generation unit can also prioritize generating highly relevant items based on customer current lifestyles and seasons, for example. This allows the generation order to be adjusted based on customer relevance. The generation order includes, but is not limited to, items in order of relevance or importance. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input customer relevance data into a generation AI and have the generation AI adjust the generation order.

[0046] The service provider can select the optimal display method by referring to the customer's past operation history at the time of delivery. For example, the service provider may prioritize providing the display method that the customer has preferred to use in the past. The service provider may also select the most efficient display method from the customer's past operation history. The service provider may also analyze the customer's past operation history and provide the most visually appealing display method. This allows the service provider to select the optimal display method by referring to the customer's past operation history. The optimal display method includes, but is not limited to, customization based on the user's operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input customer operation history data into AI and have the AI ​​select the optimal display method.

[0047] The service provider can customize the offerings based on the customer's current lifestyle at the time of delivery. For example, if the customer has an active lifestyle, the service provider may prioritize providing sportswear or casual wear. For example, if the customer is a business person, the service provider may also prioritize providing formal attire. The service provider may also provide items suitable for specific occasions based on the customer's lifestyle. This allows for customization of the offerings based on the customer's current lifestyle. Customization of the offerings includes, but is not limited to, changes to suggestions based on lifestyle. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input customer lifestyle data into AI and have the AI ​​customize the offerings.

[0048] The service provider can select the optimal delivery method at the time of delivery, taking into account the customer's geographical location. For example, if the customer lives in an urban area, the service provider may prioritize providing urban-style items. If the customer lives in a suburban area, the service provider may also prioritize providing casual and relaxed-style items. If the customer plans to travel to a specific region, the service provider may also provide items suitable for the climate and culture of that region. This allows the service provider to select the optimal delivery method, taking into account the customer's geographical location. The optimal delivery method includes, but is not limited to, customization based on geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the customer's geographical location into AI and have the AI ​​select the optimal delivery method.

[0049] The service provider can analyze the customer's social media activity and adjust the content of the offerings at the time of delivery. For example, the service provider may prioritize offering fashion items that the customer frequently shares on social media. The service provider may also offer items based on the styles of influencers that the customer follows. The service provider may also analyze the trends of fashion communities that the customer participates in and offer relevant items. This allows the service provider to analyze the customer's social media activity and adjust the content of the offerings. Adjustments to the content of the offerings include, but are not limited to, changing the content of suggestions based on social media activity. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the customer's social media data into AI and have the AI ​​adjust the content of the offerings.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can collect customer health data in addition to body shape data and preferences. For example, it can collect health data such as steps taken, heart rate, and sleep patterns, and provide optimal fashion advice based on this data. If a customer has an active lifestyle, the data collection unit can prioritize suggesting sportswear or casual wear. The data collection unit can also analyze customer health data and provide fashion advice that adapts to changes in body shape. For example, if a customer has lost weight, it can suggest new sizes of clothing. This allows for the provision of personalized fashion advice based on the customer's health status.

[0052] The generation unit, using AI to create precise 3D avatars from customer photos, can adjust the avatar's appearance based on the customer's age and gender. For example, if the customer is young, the generation unit can create a trendy style avatar. If the customer is elderly, it can create a more subdued style avatar. Furthermore, depending on the customer's gender, the generation unit can create male, female, or gender-neutral avatars. This allows for the provision of more personalized 3D avatars based on the customer's age and gender.

[0053] The generation unit uses AI to analyze a customer's body type, preferences, and lifestyle, and provides optimal fashion advice, adjusting the advice based on the customer's occupation. For example, if the customer is a business person, the generation unit can prioritize suggesting formal attire or business casual. If the customer is in a creative profession, the generation unit can suggest individual and unique styles. Furthermore, if the customer works remotely, the generation unit can suggest comfortable and relaxed clothing. This allows the system to provide optimal fashion advice tailored to each customer's occupation.

[0054] The system can prioritize which clothes a generated 3D avatar tries on based on the customer's past purchase history. For example, it can prioritize trying on brands and styles the customer has previously purchased. It can also prioritize trying on items the customer frequently buys (e.g., jeans or shirts). Furthermore, it can analyze the customer's past purchase history and make new suggestions based on the items that the customer was most satisfied with. This allows for a more personalized try-on experience based on the customer's past purchase history.

[0055] The provider can reflect the regional fashion trends of its customers when regularly hosting virtual fashion shows. For example, it can host urban-style fashion shows for customers in urban areas. It can also host casual and relaxed-style fashion shows for customers in suburban or rural areas. Furthermore, it can host fashion shows tailored to the climate and culture of specific regions. This allows the provider to offer virtual fashion shows that reflect the regional fashion trends of its customers.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects customer body shape data and preferences. This data includes, for example, height, weight, clothing size, color preferences, and style preferences. The data collection unit obtains body shape data by taking photos of customers and analyzing that data. It also understands customer preferences by analyzing their past purchase history and social media data. Step 2: The generation unit generates 3D avatars and fashion advice based on the data collected by the collection unit. The generation unit uses generation AI to create precise 3D avatars from customer photos, reproducing the customer's body shape and skin texture. The generation unit also analyzes the customer's body shape, preferences, and lifestyle to provide optimal fashion advice. Step 3: The service provider provides customers with avatars and advice generated by the generation unit. The service provider can have the generated 3D avatar try on various clothes, allowing customers to see how the clothes they choose will actually look on the avatar. The service provider can also regularly hold virtual fashion shows, allowing customers to experience the latest collections in VR space. Furthermore, the service provider can implement social features that allow customers to enjoy shopping together with friends in VR space.

[0058] (Example of form 2) The apparel e-commerce platform according to an embodiment of the present invention is an innovative system that combines VR technology and generative AI. This system allows customers to try on various clothes in a VR space from the comfort of their homes and receive optimal fashion advice from an AI personal stylist. Furthermore, the AI ​​learns the customer's body shape data and preferences, enabling personalized product suggestions and virtual fashion shows. For example, a customer wears a VR headset at home and accesses the apparel e-commerce platform. Next, a precise 3D avatar is generated from the customer's photograph. This 3D avatar reproduces the customer's body shape and skin texture, providing an experience close to actual try-on. The customer can try on various clothes in the VR space. For example, by dressing the 3D avatar in the clothes the customer has selected, they can see how the clothes actually look. Furthermore, the AI ​​personal stylist analyzes the customer's body shape, preferences, and lifestyle to provide optimal fashion advice. For example, the AI ​​analyzes the customer's past purchase history and social media data to suggest outfits that reflect the latest trends. The AI ​​also learns the customer's body shape data and preferences to provide personalized product suggestions. For example, AI can suggest clothing sizes and styles that suit a customer's body type, reducing anxiety associated with online shopping. Furthermore, regular virtual fashion shows can be held, allowing customers to experience the latest collections in a VR space. This enables customers to enjoy the latest fashion trends from the comfort of their homes. The platform also includes social features, allowing customers to shop together with friends in the VR space. For instance, they can watch virtual fashion shows together or exchange opinions about clothes they've tried on. This service allows customers to shop comfortably and efficiently from home, finding the perfect fashion for them. Businesses also benefit from reduced return rates, improved customer engagement, and optimized inventory management. Moreover, it can revolutionize the apparel e-commerce industry and create a new shopping experience.This allows apparel e-commerce platforms to collect customer body shape data and preferences, and then generate and provide 3D avatars and fashion advice based on that data.

[0059] The apparel e-commerce platform according to this embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects customer body shape data and preferences. Customer body shape data and preferences include, but are not limited to, height, weight, clothing size, color preferences, and style preferences. For example, the collection unit takes a photograph of the customer and analyzes the data to obtain the customer's body shape data. The collection unit can also analyze the customer's past purchase history and social media data to understand the customer's preferences. For example, the collection unit identifies preferred styles and sizes from the customer's past purchase history and narrows down the data to be collected. The generation unit generates 3D avatars and fashion advice based on the data collected by the collection unit. The generation unit generates a precise 3D avatar from a customer's photograph, for example, using a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and can reproduce the customer's body shape and skin texture. The generation unit can also use the generation AI to analyze the customer's body shape, preferences, and lifestyle and provide optimal fashion advice. For example, the generation unit analyzes the customer's past purchase history and social media data to suggest outfits that reflect the latest trends. The delivery unit provides the customer with the avatar and advice generated by the generation unit. The delivery unit can, for example, have the generated 3D avatar try on various clothes. The delivery unit can, for example, have the customer see how the clothes they selected look on the 3D avatar. The delivery unit can also hold virtual fashion shows regularly, allowing customers to experience the latest collections in VR space. For example, the delivery unit can hold a virtual fashion show once a week to provide customers with the latest fashion. Furthermore, the delivery unit can implement social functions that allow customers to enjoy shopping together with friends in VR space. For example, the delivery unit can allow customers to watch virtual fashion shows with friends and exchange opinions about the clothes they have tried on. In this way, the apparel e-commerce platform according to this embodiment can collect customer body shape data and preferences, and generate and provide 3D avatars and fashion advice based on that data.

[0060] The data collection unit collects customer body shape data and preferences. This data includes, but is not limited to, height, weight, clothing size, color preferences, and style preferences. For example, to obtain customer body shape data, the data collection unit takes photos of customers and analyzes that data. Specifically, customers take full-body photos of themselves using a dedicated application and upload the image data to the cloud. The data collection unit uses image analysis technology to accurately measure the customer's body shape and automatically calculates height, weight, and dimensions of each body part. The data collection unit can also analyze customers' past purchase history and social media data to understand their preferences. For example, it stores information such as the brand, color, and style of items that customers have purchased in the past in a database and uses this to identify customer preferences. Furthermore, it collects information on social media posts and items that customers have "liked" to analyze customers' fashion preferences and trend tendencies. As a result, the data collection unit can comprehensively understand customer body shape data and preferences and provide basic data for making optimal fashion suggestions to individual customers. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the generation and provision units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The generation unit generates 3D avatars and fashion advice based on data collected by the collection unit. For example, the generation unit uses a generation AI to generate a precise 3D avatar from a customer's photograph. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, capable of reproducing the customer's body shape and skin texture. Specifically, the generation AI analyzes a full-body photograph of the customer and uses an algorithm that accurately reproduces the dimensions and shape of each part. Furthermore, it reproduces details such as skin texture, hairstyle, and facial features, generating a realistic 3D avatar that makes it seem as if the customer is actually trying on the clothes. The generation unit can also use the generation AI to analyze the customer's body shape, preferences, and lifestyle to provide optimal fashion advice. For example, the generation unit analyzes the customer's past purchase history and social media data to suggest outfits that reflect the latest trends. The generation AI uses a text generation AI to generate fashion advice tailored to the customer's preferences and lifestyle, and suggests specific items and outfits. In this way, the generation unit can provide personalized fashion advice to customers and improve customer satisfaction. Furthermore, the generation unit can use the generated 3D avatar to simulate trying on the clothes selected by the customer. This allows customers to see how their chosen clothes will look without actually trying them on. Through these functions, the generation unit can provide customers with a more realistic and personalized fashion experience.

[0062] The service provider offers customers avatars and advice generated by the generation unit. For example, the service provider can have the generated 3D avatar try on various outfits. Specifically, by dressing the 3D avatar in the clothes the customer has chosen, they can see how the clothes actually look. The service provider simulates the avatar's movements in real time, reproducing the fit of the clothes and how they look in response to movement. The service provider can also regularly hold virtual fashion shows, allowing customers to experience the latest collections in VR space. For example, the service provider could hold a virtual fashion show once a week, offering customers the latest fashion trends. Using a VR headset, customers can experience a sense of immersion as if they were participating in a real fashion show. Furthermore, the service provider can implement social features that allow customers to enjoy shopping together with friends in VR space. For example, the service provider can allow customers to watch virtual fashion shows with friends and exchange opinions about the clothes they have tried on. This allows customers to share a fun shopping experience with their friends. Through these features, the service provider can provide customers with a richer and more interactive fashion experience. Furthermore, the service department can collect customer feedback and use it to improve its services. For example, they can gather opinions from customers about the fit and design of clothes they have tried on and incorporate them into future recommendations. This allows the service department to provide services that meet customer needs and improve customer satisfaction.

[0063] The generation unit can generate a precise 3D avatar from a customer's photograph using a generation AI. For example, the generation unit uses a generation AI to generate a precise 3D avatar from a customer's photograph. The generation AI could be a text generation AI (e.g., LLM) or a multimodal generation AI, capable of reproducing the customer's body shape and skin texture. For example, the generation unit could input a prompt to the generation AI, such as "Generate a 3D avatar from the customer's photograph," and the generation AI would analyze the customer's photograph and generate a 3D avatar. This allows for the generation of a precise 3D avatar from the customer's photograph. A precise 3D avatar includes, but is not limited to, the degree of reproduction of facial features and the level of detail in body shape. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can generate a 3D avatar using a generation AI model that takes a customer's photograph as input and outputs a 3D avatar.

[0064] The generation unit can analyze the customer's body type, preferences, and lifestyle using a generation AI and provide optimal fashion advice. For example, the generation unit uses a generation AI to analyze the customer's body type, preferences, and lifestyle and provide optimal fashion advice. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, capable of analyzing the customer's body type, preferences, and lifestyle. For example, the generation unit can input a prompt to the generation AI such as, "Analyze the customer's body type, preferences, and lifestyle, and provide optimal fashion advice," and the generation AI will analyze the customer's data and provide fashion advice. This allows the generation unit to analyze the customer's body type, preferences, and lifestyle and provide optimal fashion advice. Optimal fashion advice includes, but is not limited to, suggestions based on the individual customer's preferences and lifestyle. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can provide fashion advice using a generation AI model that takes customer data as input and outputs fashion advice.

[0065] The service provider can have the generated 3D avatar try on various clothes. For example, the service provider can have the generated 3D avatar try on various clothes. The service provider can, for example, have the customer choose clothes and have the 3D avatar wear them to see how the clothes actually look. Various clothes include, but are not limited to, casual wear, formal wear, and sportswear. This allows the generated 3D avatar to try on various clothes. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can perform clothing try-on using an AI model that takes 3D avatar and clothing data as input and outputs try-on results.

[0066] The service provider can hold virtual fashion shows on a regular basis. The service provider can hold virtual fashion shows on a regular basis, for example. "Regularly" includes, but is not limited to, weekly or monthly events. This allows the service provider to hold virtual fashion shows on a regular basis. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can hold a virtual fashion show using an AI model that takes the schedule and content of a fashion show as input and outputs the holding of the show.

[0067] The service provider can implement social features that allow users to enjoy shopping together with friends in a VR space. For example, the service provider can implement social features that allow users to enjoy shopping together with friends in a VR space. These social features include, but are not limited to, chat functions and group buying functions. This enables the implementation of social features that allow users to enjoy shopping together with friends in a VR space. Some or all of the above-described processes in the service provider may be performed using, for example, AI, or not. For example, the service provider can implement social features using an AI model that takes social feature settings and user data as input and outputs social features.

[0068] The data collection unit can estimate the customer's emotions and adjust the collection timing of body shape data and preferred data collection based on the estimated emotions. For example, if the customer is relaxed, the data collection unit can perform a long scan to collect detailed body shape data. If the customer is in a hurry, for example, the data collection unit can perform a quick scan and later supplement with detailed data. If the customer is stressed, for example, the data collection unit can temporarily suspend collection, provide a relaxing environment, and then resume. This allows for adjustment of body shape data and preferred data collection timing based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input customer facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0069] The data collection unit can analyze customers' past purchase history and social media data to select the optimal data collection method. For example, the data collection unit can identify preferred styles and sizes from customers' past purchase history and narrow down the data to be collected. The data collection unit can also analyze customers' social media data to collect data that reflects the latest trends and preferences. For example, the data collection unit can combine customers' past purchase history and social media data to collect more accurate data. This allows the system to analyze customers' past purchase history and social media data and select the optimal data collection method. Optimal data collection methods include, but are not limited to, surveys and behavioral analysis. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer purchase history data into AI and have the AI ​​select the optimal data collection method.

[0070] The data collection unit can filter data based on the customer's current lifestyle and the season. For example, if a customer has an active lifestyle, the collection unit will prioritize collecting sportswear and casual wear. The collection unit can also prioritize collecting light clothing in summer and warm clothing in winter, depending on the season. The collection unit can also collect data categorized by the customer's lifestyle, such as for work or personal use. This allows for filtering based on the customer's current lifestyle and the season. Filtering includes, but is not limited to, seasonal trends and lifestyle changes. Some or all of the processing described above in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input customer lifestyle data into AI and have the AI ​​perform the filtering.

[0071] The data collection unit can estimate the customer's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the customer is excited, the data collection unit may prioritize collecting the latest trendy items. If the customer is relaxed, the data collection unit may also prioritize collecting classic or basic items. If the customer is stressed, the data collection unit may minimize the amount of data collected and prioritize collecting simple items. This allows the data collection unit to determine the priority of data to collect based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input customer emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0072] The data collection unit can prioritize the collection of highly relevant data, taking into account the customer's geographical location. For example, if the customer lives in an urban area, the data collection unit can prioritize the collection of urban-style data. If the customer lives in a suburban area, the data collection unit can also prioritize the collection of casual and relaxed-style data. If the customer plans to travel to a specific region, the data collection unit can also prioritize the collection of data appropriate to the climate and culture of that region. This allows for the priority collection of highly relevant data, taking into account the customer's geographical location. Highly relevant data includes, but is not limited to, geographical location-based trend information. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's geographical location information into AI and have the AI ​​collect highly relevant data.

[0073] The data collection unit can analyze the customer's social media activity and collect relevant data during the collection process. For example, the data collection unit can collect fashion items that the customer frequently shares on social media. The data collection unit can also collect data by referencing the styles of influencers that the customer follows. The data collection unit can also analyze trends in fashion communities that the customer participates in and collect relevant data. This allows for the analysis of the customer's social media activity and the collection of relevant data. Relevant data includes, but is not limited to, interests on social media. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the customer's social media data into AI and have AI collect relevant data.

[0074] The generation unit can estimate the customer's emotions and adjust the representation of the 3D avatar based on the estimated emotions. For example, if the customer is relaxed, the generation unit will generate a 3D avatar with natural facial expressions and poses. For example, if the customer is excited, the generation unit can also generate a 3D avatar with dynamic poses and facial expressions. For example, if the customer is stressed, the generation unit can also generate a 3D avatar with calm facial expressions and poses. This allows the representation of the 3D avatar to be adjusted based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer emotion data into the generation AI and have the generation AI adjust the representation of the 3D avatar.

[0075] The generation unit can adjust the level of detail of the avatar based on the customer's body shape data during generation. For example, if the customer's body shape data is detailed, the generation unit can generate a precise 3D avatar. For example, if the customer's body shape data is simple, the generation unit can also generate a basic 3D avatar. The generation unit can also adjust the level of detail of specific parts (e.g., shoulder width or waist) based on the customer's body shape data. This allows the level of detail of the avatar to be adjusted based on the customer's body shape data. The level of detail of the avatar includes, but is not limited to, facial features and the degree of body shape reproduction. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input the customer's body shape data into a generation AI and have the generation AI adjust the level of detail of the avatar.

[0076] The generation unit can apply different generation algorithms during generation according to the customer's preferences and lifestyle. For example, if the customer prefers a casual style, the generation unit can apply a generation algorithm suitable for casual attire. If the customer prefers a formal style, the generation unit can also apply a generation algorithm suitable for formal attire. The generation unit can also apply an appropriate generation algorithm according to the customer's lifestyle (e.g., an active lifestyle). This allows for the application of different generation algorithms according to the customer's preferences and lifestyle. Different generation algorithms include, but are not limited to, deep learning and rule-based generation. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input customer preference and lifestyle data into a generation AI and have the generation AI apply an appropriate generation algorithm.

[0077] The generation unit can estimate the customer's emotions and adjust the length and size of the generated avatar based on the estimated emotions. For example, if the customer is relaxed, the generation unit will generate a standard-sized avatar. If the customer is excited, the generation unit may also generate a slightly larger avatar. If the customer is stressed, the generation unit may also generate a slightly smaller avatar. This allows the length and size of the generated avatar to be adjusted based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input customer emotion data into the generation AI and have the generation AI adjust the length and size of the avatar.

[0078] The generation unit can determine the priority of items to be generated based on the customer's past purchase history. For example, the generation unit can prioritize the generation of items relevant to the customer based on items the customer has purchased in the past. The generation unit can also prioritize the generation of specific brands or styles based on the customer's past purchase history. The generation unit can also analyze the customer's past purchase history and prioritize the generation of the items they purchase most frequently. This allows the generation priority to be determined based on the customer's past purchase history. The generation priority includes, but is not limited to, importance based on past purchase history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input customer purchase history data into a generation AI and have the generation AI determine the generation priority.

[0079] The generation unit can adjust the order of generation based on customer relevance during the generation process. For example, the generation unit can prioritize generating highly relevant items based on customer preferences and lifestyles. The generation unit can also prioritize generating highly relevant items based on customer past purchase history, for example. The generation unit can also prioritize generating highly relevant items based on customer current lifestyles and seasons, for example. This allows the generation order to be adjusted based on customer relevance. The generation order includes, but is not limited to, items in order of relevance or importance. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input customer relevance data into a generation AI and have the generation AI adjust the generation order.

[0080] The service provider can estimate the customer's emotions and adjust the display of avatars and advice based on the estimated emotions. For example, if the customer is relaxed, the service provider can display detailed advice. For example, if the customer is in a hurry, the service provider can also display concise advice. For example, if the customer is stressed, the service provider can also provide a visually calming display. This allows the service provider to adjust the display of avatars and advice based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input customer emotion data into a generative AI and have the generative AI adjust the display method.

[0081] The service provider can select the optimal display method by referring to the customer's past operation history at the time of delivery. For example, the service provider may prioritize providing the display method that the customer has preferred to use in the past. The service provider may also select the most efficient display method from the customer's past operation history. The service provider may also analyze the customer's past operation history and provide the most visually appealing display method. This allows the service provider to select the optimal display method by referring to the customer's past operation history. The optimal display method includes, but is not limited to, customization based on the user's operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input customer operation history data into AI and have the AI ​​select the optimal display method.

[0082] The service provider can customize the offerings based on the customer's current lifestyle at the time of delivery. For example, if the customer has an active lifestyle, the service provider may prioritize providing sportswear or casual wear. For example, if the customer is a business person, the service provider may also prioritize providing formal attire. The service provider may also provide items suitable for specific occasions based on the customer's lifestyle. This allows for customization of the offerings based on the customer's current lifestyle. Customization of the offerings includes, but is not limited to, changes to suggestions based on lifestyle. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input customer lifestyle data into AI and have the AI ​​customize the offerings.

[0083] The service provider can estimate the customer's emotions and determine the priority of avatars and advice to provide based on the estimated emotions. For example, if the customer is relaxed, the service provider may prioritize detailed advice. If the customer is in a hurry, the service provider may also prioritize concise advice. If the customer is stressed, the service provider may also prioritize visually calming avatars. This allows the service provider to determine the priority of avatars and advice to provide based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input customer emotion data into a generative AI and have the generative AI determine the priorities.

[0084] The service provider can select the optimal delivery method at the time of delivery, taking into account the customer's geographical location. For example, if the customer lives in an urban area, the service provider may prioritize providing urban-style items. If the customer lives in a suburban area, the service provider may also prioritize providing casual and relaxed-style items. If the customer plans to travel to a specific region, the service provider may also provide items suitable for the climate and culture of that region. This allows the service provider to select the optimal delivery method, taking into account the customer's geographical location. The optimal delivery method includes, but is not limited to, customization based on geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the customer's geographical location into AI and have the AI ​​select the optimal delivery method.

[0085] The service provider can analyze the customer's social media activity and adjust the content of the offerings at the time of delivery. For example, the service provider may prioritize offering fashion items that the customer frequently shares on social media. The service provider may also offer items based on the styles of influencers that the customer follows. The service provider may also analyze the trends of fashion communities that the customer participates in and offer relevant items. This allows the service provider to analyze the customer's social media activity and adjust the content of the offerings. Adjustments to the content of the offerings include, but are not limited to, changing the content of suggestions based on social media activity. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the customer's social media data into AI and have the AI ​​adjust the content of the offerings.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The data collection unit can collect customer health data in addition to body shape data and preferences. For example, it can collect health data such as steps taken, heart rate, and sleep patterns, and provide optimal fashion advice based on this data. If a customer has an active lifestyle, the data collection unit can prioritize suggesting sportswear or casual wear. The data collection unit can also analyze customer health data and provide fashion advice that adapts to changes in body shape. For example, if a customer has lost weight, it can suggest new sizes of clothing. This allows for the provision of personalized fashion advice based on the customer's health status.

[0088] The generation unit, using AI to create precise 3D avatars from customer photos, can adjust the avatar's appearance based on the customer's age and gender. For example, if the customer is young, the generation unit can create a trendy style avatar. If the customer is elderly, it can create a more subdued style avatar. Furthermore, depending on the customer's gender, the generation unit can create male, female, or gender-neutral avatars. This allows for the provision of more personalized 3D avatars based on the customer's age and gender.

[0089] The generation unit uses AI to analyze a customer's body type, preferences, and lifestyle, and provides optimal fashion advice, adjusting the advice based on the customer's occupation. For example, if the customer is a business person, the generation unit can prioritize suggesting formal attire or business casual. If the customer is in a creative profession, the generation unit can suggest individual and unique styles. Furthermore, if the customer works remotely, the generation unit can suggest comfortable and relaxed clothing. This allows the system to provide optimal fashion advice tailored to each customer's occupation.

[0090] The system can prioritize which clothes a generated 3D avatar tries on based on the customer's past purchase history. For example, it can prioritize trying on brands and styles the customer has previously purchased. It can also prioritize trying on items the customer frequently buys (e.g., jeans or shirts). Furthermore, it can analyze the customer's past purchase history and make new suggestions based on the items that the customer was most satisfied with. This allows for a more personalized try-on experience based on the customer's past purchase history.

[0091] The provider can reflect the regional fashion trends of its customers when regularly hosting virtual fashion shows. For example, it can host urban-style fashion shows for customers in urban areas. It can also host casual and relaxed-style fashion shows for customers in suburban or rural areas. Furthermore, it can host fashion shows tailored to the climate and culture of specific regions. This allows the provider to offer virtual fashion shows that reflect the regional fashion trends of its customers.

[0092] The data collection unit can estimate the customer's emotions and adjust the timing of body shape data collection and preferred data collection based on those emotions. For example, if the customer is relaxed, the data collection unit can perform a longer scan to collect detailed body shape data. If the customer is in a hurry, the data collection unit can perform a quick scan and supplement the detailed data later. Furthermore, if the customer is stressed, the data collection unit can temporarily suspend collection, provide a relaxing environment, and then resume. This allows for adjustment of body shape data and preferred data collection timing based on the customer's emotions.

[0093] The generation unit can estimate the customer's emotions and adjust the way the 3D avatar is represented based on those estimated emotions. For example, if the customer is relaxed, the generation unit can generate a 3D avatar with natural facial expressions and poses. If the customer is excited, the generation unit can generate a 3D avatar with dynamic poses and facial expressions. Furthermore, if the customer is stressed, the generation unit can generate a 3D avatar with calm facial expressions and poses. This allows the representation of the 3D avatar to be adjusted based on the customer's emotions.

[0094] The generation unit can estimate the customer's emotions and adjust the length and size of the generated avatar based on those estimated emotions. For example, if the customer is relaxed, the generation unit can generate a standard-sized avatar. If the customer is excited, it can generate a slightly larger avatar. Furthermore, if the customer is stressed, it can generate a slightly smaller avatar. This allows the generation unit to adjust the length and size of the generated avatar based on the customer's emotions.

[0095] The service provider can estimate the customer's emotions and adjust the display of avatars and advice based on those estimated emotions. For example, if the customer is relaxed, the service provider can display detailed advice. If the customer is in a hurry, it can display concise advice. Furthermore, if the customer is stressed, the service provider can provide a visually calming display. This allows the service provider to adjust the display of avatars and advice based on the customer's emotions.

[0096] The service provider can estimate the customer's emotions and prioritize the avatars and advice it provides based on those estimates. For example, if the customer is relaxed, the service provider can prioritize detailed advice. If the customer is in a hurry, it can prioritize concise advice. Furthermore, if the customer is stressed, it can prioritize visually calming avatars. This allows the service provider to prioritize avatars and advice based on the customer's emotions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects customer body shape data and preferences. This data includes, for example, height, weight, clothing size, color preferences, and style preferences. The data collection unit obtains body shape data by taking photos of customers and analyzing that data. It also understands customer preferences by analyzing their past purchase history and social media data. Step 2: The generation unit generates 3D avatars and fashion advice based on the data collected by the collection unit. The generation unit uses generation AI to create precise 3D avatars from customer photos, reproducing the customer's body shape and skin texture. The generation unit also analyzes the customer's body shape, preferences, and lifestyle to provide optimal fashion advice. Step 3: The service provider provides customers with avatars and advice generated by the generation unit. The service provider can have the generated 3D avatar try on various clothes, allowing customers to see how the clothes they choose will actually look on the avatar. The service provider can also regularly hold virtual fashion shows, allowing customers to experience the latest collections in VR space. Furthermore, the service provider can implement social features that allow customers to enjoy shopping together with friends in VR space.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects customer body shape data and preferences using the camera 42 and microphone 38B of the smart device 14 and transmits them to the data processing unit 12 via the control unit 46A. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a 3D avatar and fashion advice based on the collected data. The provision unit is implemented in the control unit 46A of the smart device 14 and allows the generated 3D avatar to try on clothes or holds a virtual fashion show. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the customer's body shape data and preferences using the camera 42 and microphone 238 of the smart glasses 214 and transmits them to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates a 3D avatar and fashion advice based on the collected data. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and allows the generated 3D avatar to try on clothes or hold a virtual fashion show. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects customer body shape data and preferences using the camera 42 and microphone 238 of the headset terminal 314 and transmits them to the data processing unit 12 via the control unit 46A. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a 3D avatar and fashion advice based on the collected data. The provision unit is implemented in the control unit 46A of the headset terminal 314 and allows the generated 3D avatar to try on clothes or holds a virtual fashion show. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects customer body shape data and preferences using the camera 42 and microphone 238 of the robot 414 and transmits them to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates a 3D avatar and fashion advice based on the collected data. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and allows the generated 3D avatar to try on clothes or hold a virtual fashion show. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A data collection unit that collects customer body shape data and preferences, A generation unit generates 3D avatars and fashion advice based on the data collected by the aforementioned collection unit, The system includes a provisioning unit that provides customers with avatars and advice generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generative AI creates precise 3D avatars from customer photos. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The AI ​​generates data to analyze the customer's body type, preferences, and lifestyle, providing optimal fashion advice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Try on various clothes for the generated 3D avatar. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We regularly hold virtual fashion shows. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Implement social features that allow friends to enjoy shopping together in a VR space. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate customer emotions and adjust the timing of collecting body shape data and preferences based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We analyze customers' past purchase history and social media data to select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the customer's current lifestyle and the season. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate customer emotions and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, we analyze customers' social media activity and gather relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the customer's emotions and adjusts the way the 3D avatar is represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the level of detail of the avatar is adjusted based on the customer's body shape data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, different generation algorithms are applied according to the customer's preferences and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the customer's emotions and adjusts the length and size of the avatar generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the generation priority is determined based on the customer's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the generation order is adjusted based on customer relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates customer emotions and adjusts how avatars and advice are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the optimal display method is selected by referring to the customer's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, we customize the offering based on the customer's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the customer's emotions and determines the priority of avatars and advice to provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, We analyze the customer's social media activity and adjust the content of the service accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects customer body shape data and preferences, A generation unit generates 3D avatars and fashion advice based on the data collected by the aforementioned collection unit, The system includes a provisioning unit that provides customers with avatars and advice generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generative AI creates precise 3D avatars from customer photos. The system according to feature 1.

3. The generating unit is Using AI-generated data, we analyze customers' body types, preferences, and lifestyles to provide optimal fashion advice. The system according to feature 1.

4. The aforementioned supply unit is, Try on various clothes for the generated 3D avatar. The system according to feature 1.

5. The aforementioned supply unit is, We regularly hold virtual fashion shows. The system according to feature 1.

6. The aforementioned supply unit is, Implement social features that allow friends to enjoy shopping together in a VR space. The system according to feature 1.

7. The aforementioned collection unit is We estimate customer emotions and adjust the timing of collecting body shape data and preferences based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is We analyze customers' past purchase history and social media data to select the most suitable data collection method. The system according to feature 1.

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