system

Abstract

The system according to this embodiment aims to enable users to check the fit and appearance of clothing in real time when shopping online. [Solution] The system according to the embodiment comprises a creation unit, a simulation unit, and a recommendation unit. The creation unit creates an avatar based on the user's body shape data. The simulation unit has the avatar created by the creation unit try on fashion items and simulates the fit and appearance in real time. The recommendation unit recommends the optimal size based on the fit and appearance simulated by the simulation unit.

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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for a user to check the fit and appearance of clothes before purchasing in online shopping, resulting in low satisfaction.

[0005] The system according to the embodiment aims to enable a user to check the fit and appearance of clothes in real time in online shopping.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a creation unit, a simulation unit, and a recommendation unit. The creation unit creates an avatar based on the user's body shape data. The simulation unit has the avatar created by the creation unit try on fashion items and simulates the fit and appearance in real time. The recommendation unit recommends the optimal size based on the fit and appearance simulated by the simulation unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to check the fit and appearance of clothing in real time when shopping online. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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 is a server.

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Style Agent System according to an embodiment of the present invention is an AI technology that allows users to create their own avatars and virtually try on various fashion items. This Style Agent System improves online shopping satisfaction by allowing users to create avatars based on their own body shape data, and having a generating AI simulate the fit and appearance of clothing in real time. For example, a user first inputs their own body shape data to create an avatar. Next, the generating AI simulates the fit and appearance of clothing in real time based on the user's body shape data. This allows users to have an experience as if they were actually trying on clothes when shopping online. Specifically, the avatar tries on the fashion items selected by the user, and the generating AI simulates the fit and appearance. For example, if a user selects a dress, the generating AI calculates in real time how the dress will fit the user's body shape and displays it on the avatar. This allows the user to check the fit and appearance of the dress and make a purchase decision. The Style Agent System can also suggest multiple fashion styles, and users can try on and evaluate each style. For example, the system allows users to try on different styles of clothing, such as casual and formal styles, using a generating AI to simulate the fit and appearance of each. This enables users to find the style that best suits them. Furthermore, the style agent system recommends the optimal size based on the user's body shape data. For instance, if the size of the clothing the user has chosen is incorrect, the generating AI will recommend the optimal size based on the user's body shape data. This reduces the number of returns due to incorrect sizing. This mechanism allows users to have an experience similar to actually trying on clothes when shopping online, improving satisfaction. It also reduces returns due to incorrect sizing, increasing customer satisfaction. Moreover, the improved user experience enhances brand value. In summary, the style agent system can improve the user's online shopping experience.

[0029] The style agent system according to this embodiment comprises a creation unit, a simulation unit, and a recommendation unit. The creation unit creates an avatar based on the user's body shape data. The creation unit creates an avatar based on body shape data such as height, weight, body fat percentage, and 3D scan data entered by the user. The creation unit can generate an avatar based on the user's body shape using 3D modeling technology. For example, the creation unit receives the user's body shape data as input and generates an avatar using 3D modeling software. The creation unit can also create a detailed 3D model of the avatar based on the user's body shape data. The simulation unit uses a generation AI to have the avatar created by the creation unit try on fashion items and simulate the fit and appearance in real time. For example, the simulation unit has the avatar try on fashion items selected by the user, and the generation AI simulates the fit and appearance in real time. The simulation unit can use a generation AI to simulate the fit and appearance of the fashion items tried on by the avatar. For example, the simulation unit uses a generating AI to calculate the fit and appearance of fashion items in real time based on the user's body shape data and display them on the avatar. The simulation unit also uses the generating AI to suggest multiple fashion styles, and the user can try on and evaluate each style. For example, the simulation unit has the avatar try on clothes in different styles, such as casual and formal styles, and the generating AI simulates the fit and appearance of each. The recommendation unit recommends the optimal size based on the fit and appearance simulated by the simulation unit. For example, if the size of the clothes chosen by the user does not fit, the recommendation unit uses the generating AI to recommend the optimal size based on the user's body shape data. The recommendation unit can use the generating AI to recommend the optimal size based on the user's body shape data. For example, the recommendation unit receives the user's body shape data as input, the generating AI calculates the optimal size, and recommends it to the user.As a result, the style agent system according to this embodiment can improve online shopping satisfaction by creating an avatar based on the user's body shape data, simulating the fit and appearance in real time, and recommending the optimal size.

[0030] The creation unit creates avatars based on the user's body shape data. Specifically, it creates avatars based on body shape data such as height, weight, body fat percentage, and 3D scan data entered by the user. The creation unit can generate avatars based on the user's body shape using 3D modeling technology. For example, the creation unit receives the user's body shape data as input and generates an avatar using 3D modeling software. The body shape data provided by the user is collected through dedicated applications and devices. For example, the user can take a full-body photo using their smartphone camera and generate 3D scan data from that photo. It is also possible to obtain more detailed body shape data using a dedicated 3D scanner. This data is sent to a cloud server, which the creation unit accesses to generate avatars. The creation unit analyzes the collected body shape data and selects the 3D model that is closest to the user's body shape. Next, it reflects the user's body shape data onto that 3D model to create a detailed avatar. For example, it adjusts the overall proportions of the avatar based on the user's height and weight, and fine-tunes the shape of each part of the body based on the body fat percentage. Furthermore, the creation function allows users to customize the avatar's skin color, hairstyle, and facial features based on their body shape data. This enables users to create avatars that resemble themselves, resulting in a more realistic try-on experience. The creation function saves the generated avatar to the user's account for later reuse. This means that once a user creates an avatar, they can use the same avatar in subsequent try-on simulations.

[0031] The simulation unit uses generative AI to have the avatar created by the creation unit try on fashion items and simulate the fit and appearance in real time. Specifically, the user selects fashion items and has the avatar try them on, and the generative AI simulates the fit and appearance in real time. The simulation unit can use generative AI to simulate the fit and appearance of fashion items tried on by the avatar. For example, the simulation unit uses generative AI to calculate the fit and appearance of fashion items in real time based on the user's body shape data and displays it on the avatar. The generative AI uses deep learning technology to analyze the user's body shape data and the design data of the fashion items and calculate the optimal fit. For example, the generative AI receives the user's body shape data as input, adjusts the dimensions and shape of each part of the fashion item to provide the optimal fit for the avatar. The generative AI also considers the material, color, and design characteristics of the fashion items when simulating their appearance. For example, the generative AI realistically reproduces the texture of the material and light reflection of the fashion items and displays how they would look when tried on by the avatar in real time. Furthermore, the simulation unit uses generative AI to suggest multiple fashion styles, allowing users to try on and evaluate each style. For example, the simulation unit can have an avatar try on different styles of clothing, such as casual and formal styles, and the generative AI simulates the fit and appearance of each. This allows users to try on various styles and find the fashion items that best suit them. The simulation unit can collect user feedback and use it as training data for the generative AI to improve the accuracy of the simulation.

[0032] The recommendation unit recommends the optimal size based on the fit and appearance simulated by the simulation unit. Specifically, if the size of the clothing selected by the user does not fit, the generating AI recommends the optimal size based on the user's body shape data. The recommendation unit can use the generating AI to recommend the optimal size based on the user's body shape data. For example, the recommendation unit receives the user's body shape data as input, the generating AI calculates the optimal size, and recommends it to the user. The generating AI analyzes the user's body shape data and the size data of the fashion item to identify the optimal size. For example, the generating AI adjusts the dimensions of each part of the fashion item based on data such as the user's height, weight, and body fat percentage to calculate the optimal size. The generating AI also improves the accuracy of size recommendations by utilizing past fitting data and feedback from other users. For example, it can recommend a more appropriate size by referring to the size and fit evaluations of other users with the same body shape. Furthermore, the recommendation unit can also recommend sizes according to the user's preferences and style. For example, if the user prefers a loose fit, the generating AI will take that preference into consideration and recommend a slightly larger size. Furthermore, if a user prefers a tight fit, the generating AI will recommend a size that matches that preference. This allows users to find fashion items in the size that best suits their body type and preferences. The recommendation unit collects the results of users trying on the recommended sizes and uses this data as training data for the generating AI, thereby continuously improving the accuracy of size recommendations. As a result, the style agent system according to this embodiment can improve online shopping satisfaction by creating an avatar based on the user's body shape data, simulating the fit and appearance in real time, and recommending the optimal size.

[0033] The simulation unit can have an avatar try on fashion items selected by the user and simulate the fit and appearance in real time. For example, the simulation unit can have the avatar try on a dress selected by the user, and the generating AI calculates the fit and appearance of the dress in real time and displays it on the avatar. The simulation unit can also have the avatar try on a jacket selected by the user, and the generating AI simulates the fit and appearance of the jacket in real time. Furthermore, the simulation unit can have the avatar try on pants selected by the user, and the generating AI simulates the fit and appearance of the pants in real time. This allows for real-time simulation of the fit and appearance of fashion items selected by the user, thereby improving the satisfaction of online shopping. Real-time simulation is achieved, for example, by minimizing the update frequency and latency of the simulation. Some or all of the above-mentioned processes in the simulation unit are performed using the generating AI. For example, the simulation unit inputs image data of the fashion items selected by the user into the generating AI, and the generating AI simulates the fit and appearance in real time.

[0034] The recommendation system can recommend the optimal size based on the user's body shape data. For example, if the size of the clothing the user has chosen does not fit, the recommendation system's generating AI will recommend the optimal size based on the user's body shape data. The recommendation system can also have the generating AI calculate the optimal size based on the user's body shape data and recommend it to the user. Furthermore, by having the generating AI recommend the optimal size based on the user's body shape data, the recommendation system can reduce returns due to size mismatches. The recommendation of the optimal size is based, for example, on the degree of match with the user's body shape data and past purchase history. Some or all of the above processing in the recommendation system may be performed using the generating AI, or it may be performed without the generating AI. For example, the recommendation system can input the user's body shape data into the generating AI, which will calculate the optimal size and recommend it to the user.

[0035] The simulation unit proposes multiple fashion styles, allowing users to try on and evaluate each style. For example, the simulation unit can have an avatar try on different styles of clothing, such as casual and formal, and the generating AI simulates the fit and appearance of each. It can also have the avatar try on different styles of clothing, such as sporty and elegant, and the generating AI simulates the fit and appearance of each. Furthermore, it can have the avatar try on different styles of clothing, such as business and casual, and the generating AI simulates the fit and appearance of each. This allows the system to propose multiple fashion styles, enabling users to try on and evaluate each to find the optimal style. The proposal of multiple fashion styles is achieved, for example, by suggesting different styles of clothing, such as casual, formal, and sporty. Some or all of the above processing in the simulation unit is performed using the generating AI. For example, the simulation unit inputs image data of multiple fashion items selected by the user into the generating AI, which then simulates the fit and appearance of each.

[0036] The creation unit can analyze the user's past body shape data and automatically update the avatar in accordance with changes in body shape. For example, the creation unit can update the avatar to reflect weight gain or loss based on body shape data previously entered by the user. The creation unit can also update the avatar to reflect changes in muscle mass based on the user's past body shape data. Furthermore, the creation unit can analyze the user's past body shape data and update the avatar to reflect changes in height. This allows the system to provide a fitting experience that is always based on the user's most up-to-date body shape by analyzing the user's past body shape data and automatically updating the avatar in accordance with changes in body shape. The analysis of past body shape data is performed, for example, based on periodic measurement data or user self-reported data. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit inputs the user's past body shape data into a generation AI, which analyzes the changes in body shape and automatically updates the avatar.

[0037] The creation unit can customize the avatar's skin color and hairstyle according to the user's preferences during avatar creation. For example, the creation unit can create an avatar that reflects the skin color selected by the user. It can also create an avatar that reflects the hairstyle selected by the user. Furthermore, the creation unit can create an avatar that reflects the eye color and face shape selected by the user. This allows for a more personalized try-on experience by customizing the avatar's skin color and hairstyle according to the user's preferences. User preferences are obtained, for example, based on survey results or past selection history. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit inputs user preference data into a generation AI, and the generation AI customizes the avatar's skin color and hairstyle.

[0038] The creation unit can reflect region-specific fashion styles based on the user's geographical location information when creating an avatar. For example, if the user is in Japan, the creation unit will suggest a fashion style appropriate for the Japanese season. If the user is in the United States, the creation unit can also suggest a fashion style that reflects American trends. Furthermore, if the user is in Europe, the creation unit can suggest a traditional European fashion style. This allows for the selection of more appropriate fashion items by reflecting region-specific fashion styles based on the user's geographical location information. Geographical location information is obtained, for example, based on GPS data or IP address. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit inputs the user's geographical location information into the generation AI, and the generation AI reflects region-specific fashion styles.

[0039] The creation unit can analyze the user's social media activity and reflect popular fashion items when creating an avatar. For example, the creation unit can reflect the style of fashion influencers the user follows on Instagram. It can also reflect fashion items the user has shared on social media. Furthermore, it can reflect items from fashion brands the user has "liked" on social media. This allows for the selection of more appropriate fashion items by analyzing the user's social media activity and reflecting popular fashion items. The analysis of social media activity is based, for example, on post content, the number of likes, and the number of followers. Some or all of the above processing in the creation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the creation unit inputs the user's social media activity data into a generative AI, and the generative AI reflects popular fashion items.

[0040] The simulation unit can simulate the movement and texture of clothing in real time based on the user's body shape data during the simulation. For example, the simulation unit can simulate the movement of wrinkles and drapes of clothing in real time based on the user's body shape data. The simulation unit can also simulate the texture of clothing materials in real time based on the user's body shape data. Furthermore, the simulation unit can simulate the fit of clothing in real time based on the user's body shape data. This allows for a more realistic try-on experience by simulating the movement and texture of clothing in real time based on the user's body shape data. The simulation of movement and texture is performed using, for example, physical simulation or texture mapping. Some or all of the above processing in the simulation unit is performed using generative AI. For example, the simulation unit inputs the user's body shape data into the generative AI, which then simulates the movement and texture of clothing in real time.

[0041] The simulation unit can simulate the appearance of clothing under different lighting conditions during the simulation. For example, the simulation unit can simulate the appearance of clothing under natural daylight. It can also simulate the appearance of clothing under indoor lighting. Furthermore, it can simulate the appearance of clothing under streetlights at night. By simulating the appearance under different lighting conditions, a more realistic try-on experience can be provided. The simulation of different lighting conditions takes into account, for example, natural light, artificial light, and changes in time of day. Some or all of the above processing in the simulation unit is performed using a generative AI. For example, the simulation unit inputs the appearance of clothing under different lighting conditions into the generative AI, and the generative AI simulates that appearance.

[0042] The simulation unit can reflect region-specific fashion styles based on the user's geographical location information during simulation. For example, if the user is in Japan, the simulation unit will simulate fashion styles appropriate for the Japanese season. If the user is in the United States, the simulation unit can also simulate fashion styles reflecting American trends. Furthermore, if the user is in Europe, the simulation unit can simulate traditional European fashion styles. This allows for the selection of more appropriate fashion items by reflecting region-specific fashion styles based on the user's geographical location information. Geographical location information is obtained, for example, based on GPS data or IP addresses. Some or all of the above processing in the simulation unit is performed using a generative AI. For example, the simulation unit inputs the user's geographical location information into the generative AI, which then reflects region-specific fashion styles.

[0043] The simulation unit can analyze a user's social media activity during a simulation and reflect popular fashion items. For example, the simulation unit can simulate the style of fashion influencers that the user follows on Instagram. It can also simulate fashion items that the user has shared on social media. Furthermore, the simulation unit can simulate items from fashion brands that the user has "liked" on social media. This allows for the selection of more appropriate fashion items by analyzing the user's social media activity and reflecting popular fashion items. The analysis of social media activity is based, for example, on post content, the number of likes, and the number of followers. Some or all of the above processing in the simulation unit is performed using generative AI. For example, the simulation unit inputs the user's social media activity data into the generative AI, which then reflects popular fashion items.

[0044] The recommendation system can analyze a user's past purchase history to recommend the optimal size. For example, it can recommend the optimal size based on the sizes of items the user has purchased in the past. It can also analyze size trends from the user's past purchase history to recommend the optimal size. Furthermore, it can consider the sizes of items the user has returned in the past when recommending the optimal size. This allows for the recommendation of a more appropriate size by analyzing the user's past purchase history. The analysis of past purchase history is performed based on, for example, the date and time of purchase, the purchased items, and the frequency of purchase. Some or all of the above processing in the recommendation system may be performed using a generative AI, or it may be performed without a generative AI. For example, the recommendation system inputs the user's past purchase history data into a generative AI, and the generative AI recommends the optimal size.

[0045] The recommendation unit can compare sizes from different brands based on the user's body shape data and make recommendations. For example, the recommendation unit can compare sizes from different brands based on the user's body shape data and recommend the optimal size. The recommendation unit can also make recommendations considering the size differences between brands based on the user's body shape data. Furthermore, the recommendation unit can compare the fit of different brands based on the user's body shape data and make recommendations. This allows for the recommendation of a more appropriate size by comparing sizes from different brands based on the user's body shape data. The comparison of sizes from different brands is performed, for example, based on the size charts and fit differences of each brand. Some or all of the above processing in the recommendation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the recommendation unit inputs the user's body shape data into a generative AI, and the generative AI compares sizes from different brands and recommends the optimal size.

[0046] The recommendation unit can recommend region-specific sizes based on the user's geographical location information. For example, if the user is in Japan, the recommendation unit will recommend based on the standard Japanese size. Similarly, if the user is in the United States, it can recommend based on the standard American size. Furthermore, if the user is in Europe, it can recommend based on the standard European size. This allows for the provision of more appropriate sizes by recommending region-specific sizes based on the user's geographical location information. Geographical location information can be obtained, for example, based on GPS data or IP addresses. Some or all of the above processing in the recommendation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the recommendation unit inputs the user's geographical location information into a generative AI, which then recommends region-specific sizes.

[0047] The recommendation system can analyze a user's social media activity and recommend popular sizes. For example, it can recommend sizes based on the sizes of fashion influencers the user follows on Instagram. It can also recommend sizes based on fashion items the user has shared on social media. Furthermore, it can recommend sizes based on fashion brands the user has "liked" on social media. By analyzing the user's social media activity and recommending popular sizes, it can recommend more appropriate sizes. The analysis of social media activity is based, for example, on the content of posts, the number of likes, and the number of followers. Some or all of the above processing in the recommendation system may be performed using or without a generative AI. For example, the recommendation system inputs the user's social media activity data into a generative AI, which then recommends popular sizes.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The Style Agent system not only creates avatars based on the user's body shape data, but can also analyze the user's past purchase history and evaluate the fit and satisfaction level of purchased items. For example, it collects feedback on the size and fit of items the user has purchased in the past and recommends the best size and style for the next purchase. It can also analyze the reasons why the user has returned items in the past to prevent similar problems from occurring. Furthermore, if the user has shown high satisfaction with a particular brand or style, it can prioritize recommending that brand or style. This allows the system to leverage the user's past purchase history to provide a more personalized shopping experience.

[0050] The Style Agent system can suggest region-specific fashion styles based on the user's geographical location. For example, if the user is in Japan, it can suggest fashion items that match Japanese seasons and trends. If the user is in the United States, it can suggest fashion items that match American trends and culture. Furthermore, if the user is in Europe, it can suggest fashion items that match European traditions and trends. This allows for the suggestion of more appropriate fashion items based on the user's geographical location. Geographical location information is obtained based on GPS data and IP addresses.

[0051] The Style Agent system can analyze a user's social media activity and suggest popular fashion items. For example, it can suggest items that reflect the style of fashion influencers the user follows on Instagram. It can also suggest similar items based on fashion items the user has shared on social media. Furthermore, it can suggest items from fashion brands that the user has "liked" on social media. By analyzing the user's social media activity and suggesting popular fashion items, it can provide a more suitable shopping experience. The analysis of social media activity is based on the content of posts, the number of likes, and the number of followers.

[0052] The Style Agent system can analyze a user's past body shape data and automatically update their avatar in response to changes in their body shape. For example, it can update the avatar to reflect weight changes based on the user's previously entered body shape data. It can also update the avatar to reflect changes in muscle mass based on the user's past body shape data. Furthermore, it can analyze the user's past body shape data and update the avatar to reflect changes in height. This allows the system to always provide a fitting experience based on the user's most up-to-date body shape by analyzing the user's past body shape data and automatically updating the avatar in response to changes in body shape. The analysis of past body shape data is performed based on regular measurement data and user self-reported data.

[0053] The Style Agent system can reflect region-specific fashion styles based on the user's geographical location. For example, if the user is in Japan, it can suggest fashion styles appropriate for the Japanese season. If the user is in the United States, it can suggest fashion styles that reflect American trends. Furthermore, if the user is in Europe, it can suggest traditional European fashion styles. This allows users to select more appropriate fashion items by reflecting region-specific fashion styles based on their geographical location. Geographical location information is obtained based on GPS data and IP addresses.

[0054] The Style Agent system can analyze a user's social media activity and reflect popular fashion items. For example, it can reflect the styles of fashion influencers a user follows on Instagram. It can also reflect fashion items a user has shared on social media. Furthermore, it can reflect items from fashion brands a user has "liked" on social media. This allows the system to analyze a user's social media activity and reflect popular fashion items, enabling users to choose more appropriate fashion items. The analysis of social media activity is based on post content, the number of likes, and the number of followers.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The creation unit creates an avatar based on the user's body shape data. For example, it generates an avatar using 3D modeling technology based on body shape data such as height, weight, body fat percentage, and 3D scan data entered by the user. Step 2: The simulation unit has the avatar created by the creation unit try on fashion items and simulates the fit and appearance in real time. For example, it uses a generation AI to calculate the fit and appearance of the fashion items selected by the user and displays them on the avatar. Step 3: The recommendation unit recommends the optimal size based on the fit and appearance simulated by the simulation unit. For example, it uses generative AI to calculate the optimal size based on the user's body shape data and recommends it to the user.

[0057] (Example of form 2) The Style Agent System according to an embodiment of the present invention is an AI technology that allows users to create their own avatars and virtually try on various fashion items. This Style Agent System improves online shopping satisfaction by allowing users to create avatars based on their own body shape data, and having a generating AI simulate the fit and appearance of clothing in real time. For example, a user first inputs their own body shape data to create an avatar. Next, the generating AI simulates the fit and appearance of clothing in real time based on the user's body shape data. This allows users to have an experience as if they were actually trying on clothes when shopping online. Specifically, the avatar tries on the fashion items selected by the user, and the generating AI simulates the fit and appearance. For example, if a user selects a dress, the generating AI calculates in real time how the dress will fit the user's body shape and displays it on the avatar. This allows the user to check the fit and appearance of the dress and make a purchase decision. The Style Agent System can also suggest multiple fashion styles, and users can try on and evaluate each style. For example, the system allows users to try on different styles of clothing, such as casual and formal styles, using a generating AI to simulate the fit and appearance of each. This enables users to find the style that best suits them. Furthermore, the style agent system recommends the optimal size based on the user's body shape data. For instance, if the size of the clothing the user has chosen is incorrect, the generating AI will recommend the optimal size based on the user's body shape data. This reduces the number of returns due to incorrect sizing. This mechanism allows users to have an experience similar to actually trying on clothes when shopping online, improving satisfaction. It also reduces returns due to incorrect sizing, increasing customer satisfaction. Moreover, the improved user experience enhances brand value. In summary, the style agent system can improve the user's online shopping experience.

[0058] The style agent system according to this embodiment comprises a creation unit, a simulation unit, and a recommendation unit. The creation unit creates an avatar based on the user's body shape data. The creation unit creates an avatar based on body shape data such as height, weight, body fat percentage, and 3D scan data entered by the user. The creation unit can generate an avatar based on the user's body shape using 3D modeling technology. For example, the creation unit receives the user's body shape data as input and generates an avatar using 3D modeling software. The creation unit can also create a detailed 3D model of the avatar based on the user's body shape data. The simulation unit uses a generation AI to have the avatar created by the creation unit try on fashion items and simulate the fit and appearance in real time. For example, the simulation unit has the avatar try on fashion items selected by the user, and the generation AI simulates the fit and appearance in real time. The simulation unit can use a generation AI to simulate the fit and appearance of the fashion items tried on by the avatar. For example, the simulation unit uses a generating AI to calculate the fit and appearance of fashion items in real time based on the user's body shape data and display them on the avatar. The simulation unit also uses the generating AI to suggest multiple fashion styles, and the user can try on and evaluate each style. For example, the simulation unit has the avatar try on clothes in different styles, such as casual and formal styles, and the generating AI simulates the fit and appearance of each. The recommendation unit recommends the optimal size based on the fit and appearance simulated by the simulation unit. For example, if the size of the clothes chosen by the user does not fit, the recommendation unit uses the generating AI to recommend the optimal size based on the user's body shape data. The recommendation unit can use the generating AI to recommend the optimal size based on the user's body shape data. For example, the recommendation unit receives the user's body shape data as input, the generating AI calculates the optimal size, and recommends it to the user.As a result, the style agent system according to this embodiment can improve online shopping satisfaction by creating an avatar based on the user's body shape data, simulating the fit and appearance in real time, and recommending the optimal size.

[0059] The creation unit creates avatars based on the user's body shape data. Specifically, it creates avatars based on body shape data such as height, weight, body fat percentage, and 3D scan data entered by the user. The creation unit can generate avatars based on the user's body shape using 3D modeling technology. For example, the creation unit receives the user's body shape data as input and generates an avatar using 3D modeling software. The body shape data provided by the user is collected through dedicated applications and devices. For example, the user can take a full-body photo using their smartphone camera and generate 3D scan data from that photo. It is also possible to obtain more detailed body shape data using a dedicated 3D scanner. This data is sent to a cloud server, which the creation unit accesses to generate avatars. The creation unit analyzes the collected body shape data and selects the 3D model that is closest to the user's body shape. Next, it reflects the user's body shape data onto that 3D model to create a detailed avatar. For example, it adjusts the overall proportions of the avatar based on the user's height and weight, and fine-tunes the shape of each part of the body based on the body fat percentage. Furthermore, the creation function allows users to customize the avatar's skin color, hairstyle, and facial features based on their body shape data. This enables users to create avatars that resemble themselves, resulting in a more realistic try-on experience. The creation function saves the generated avatar to the user's account for later reuse. This means that once a user creates an avatar, they can use the same avatar in subsequent try-on simulations.

[0060] The simulation unit uses generative AI to have the avatar created by the creation unit try on fashion items and simulate the fit and appearance in real time. Specifically, the user selects fashion items and has the avatar try them on, and the generative AI simulates the fit and appearance in real time. The simulation unit can use generative AI to simulate the fit and appearance of fashion items tried on by the avatar. For example, the simulation unit uses generative AI to calculate the fit and appearance of fashion items in real time based on the user's body shape data and displays it on the avatar. The generative AI uses deep learning technology to analyze the user's body shape data and the design data of the fashion items and calculate the optimal fit. For example, the generative AI receives the user's body shape data as input, adjusts the dimensions and shape of each part of the fashion item to provide the optimal fit for the avatar. The generative AI also considers the material, color, and design characteristics of the fashion items when simulating their appearance. For example, the generative AI realistically reproduces the texture of the material and light reflection of the fashion items and displays how they would look when tried on by the avatar in real time. Furthermore, the simulation unit uses generative AI to suggest multiple fashion styles, allowing users to try on and evaluate each style. For example, the simulation unit can have an avatar try on different styles of clothing, such as casual and formal styles, and the generative AI simulates the fit and appearance of each. This allows users to try on various styles and find the fashion items that best suit them. The simulation unit can collect user feedback and use it as training data for the generative AI to improve the accuracy of the simulation.

[0061] The recommendation unit recommends the optimal size based on the fit and appearance simulated by the simulation unit. Specifically, if the size of the clothing selected by the user does not fit, the generating AI recommends the optimal size based on the user's body shape data. The recommendation unit can use the generating AI to recommend the optimal size based on the user's body shape data. For example, the recommendation unit receives the user's body shape data as input, the generating AI calculates the optimal size, and recommends it to the user. The generating AI analyzes the user's body shape data and the size data of the fashion item to identify the optimal size. For example, the generating AI adjusts the dimensions of each part of the fashion item based on data such as the user's height, weight, and body fat percentage to calculate the optimal size. The generating AI also improves the accuracy of size recommendations by utilizing past fitting data and feedback from other users. For example, it can recommend a more appropriate size by referring to the size and fit evaluations of other users with the same body shape. Furthermore, the recommendation unit can also recommend sizes according to the user's preferences and style. For example, if the user prefers a loose fit, the generating AI will take that preference into consideration and recommend a slightly larger size. Furthermore, if a user prefers a tight fit, the generating AI will recommend a size that matches that preference. This allows users to find fashion items in the size that best suits their body type and preferences. The recommendation unit collects the results of users trying on the recommended sizes and uses this data as training data for the generating AI, thereby continuously improving the accuracy of size recommendations. As a result, the style agent system according to this embodiment can improve online shopping satisfaction by creating an avatar based on the user's body shape data, simulating the fit and appearance in real time, and recommending the optimal size.

[0062] The simulation unit can have an avatar try on fashion items selected by the user and simulate the fit and appearance in real time. For example, the simulation unit can have the avatar try on a dress selected by the user, and the generating AI calculates the fit and appearance of the dress in real time and displays it on the avatar. The simulation unit can also have the avatar try on a jacket selected by the user, and the generating AI simulates the fit and appearance of the jacket in real time. Furthermore, the simulation unit can have the avatar try on pants selected by the user, and the generating AI simulates the fit and appearance of the pants in real time. This allows for real-time simulation of the fit and appearance of fashion items selected by the user, thereby improving the satisfaction of online shopping. Real-time simulation is achieved, for example, by minimizing the update frequency and latency of the simulation. Some or all of the above-mentioned processes in the simulation unit are performed using the generating AI. For example, the simulation unit inputs image data of the fashion items selected by the user into the generating AI, and the generating AI simulates the fit and appearance in real time.

[0063] The recommendation system can recommend the optimal size based on the user's body shape data. For example, if the size of the clothing the user has chosen does not fit, the recommendation system's generating AI will recommend the optimal size based on the user's body shape data. The recommendation system can also have the generating AI calculate the optimal size based on the user's body shape data and recommend it to the user. Furthermore, by having the generating AI recommend the optimal size based on the user's body shape data, the recommendation system can reduce returns due to size mismatches. The recommendation of the optimal size is based, for example, on the degree of match with the user's body shape data and past purchase history. Some or all of the above processing in the recommendation system may be performed using the generating AI, or it may be performed without the generating AI. For example, the recommendation system can input the user's body shape data into the generating AI, which will calculate the optimal size and recommend it to the user.

[0064] The simulation unit proposes multiple fashion styles, allowing users to try on and evaluate each style. For example, the simulation unit can have an avatar try on different styles of clothing, such as casual and formal, and the generating AI simulates the fit and appearance of each. It can also have the avatar try on different styles of clothing, such as sporty and elegant, and the generating AI simulates the fit and appearance of each. Furthermore, it can have the avatar try on different styles of clothing, such as business and casual, and the generating AI simulates the fit and appearance of each. This allows the system to propose multiple fashion styles, enabling users to try on and evaluate each to find the optimal style. The proposal of multiple fashion styles is achieved, for example, by suggesting different styles of clothing, such as casual, formal, and sporty. Some or all of the above processing in the simulation unit is performed using the generating AI. For example, the simulation unit inputs image data of multiple fashion items selected by the user into the generating AI, which then simulates the fit and appearance of each.

[0065] The creation unit can estimate the user's emotions and adjust the avatar's facial expressions and posture based on the estimated emotions. For example, if the user is relaxed, the creation unit can make the avatar's facial expression gentle and its posture relaxed. If the user is tense, the creation unit can make the avatar's facial expression serious and its posture straight. Furthermore, if the user is having fun, the creation unit can make the avatar's facial expression smile and its posture lively. By adjusting the avatar's facial expressions and posture according to the user's emotions, a more realistic try-on experience can be provided. Emotion estimation is performed using, for example, facial recognition, voice analysis, and user input data. Some or all of the above processing in the creation unit is performed using an emotion engine or generative AI. For example, the creation unit inputs the user's facial data into the generative AI, which estimates the user's emotions and adjusts the avatar's facial expressions and posture.

[0066] The creation unit can analyze the user's past body shape data and automatically update the avatar in accordance with changes in body shape. For example, the creation unit can update the avatar to reflect weight gain or loss based on body shape data previously entered by the user. The creation unit can also update the avatar to reflect changes in muscle mass based on the user's past body shape data. Furthermore, the creation unit can analyze the user's past body shape data and update the avatar to reflect changes in height. This allows the system to provide a fitting experience that is always based on the user's most up-to-date body shape by analyzing the user's past body shape data and automatically updating the avatar in accordance with changes in body shape. The analysis of past body shape data is performed, for example, based on periodic measurement data or user self-reported data. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit inputs the user's past body shape data into a generation AI, which analyzes the changes in body shape and automatically updates the avatar.

[0067] The creation unit can customize the avatar's skin color and hairstyle according to the user's preferences during avatar creation. For example, the creation unit can create an avatar that reflects the skin color selected by the user. It can also create an avatar that reflects the hairstyle selected by the user. Furthermore, the creation unit can create an avatar that reflects the eye color and face shape selected by the user. This allows for a more personalized try-on experience by customizing the avatar's skin color and hairstyle according to the user's preferences. User preferences are obtained, for example, based on survey results or past selection history. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit inputs user preference data into a generation AI, and the generation AI customizes the avatar's skin color and hairstyle.

[0068] The creation unit can estimate the user's emotions and suggest avatar clothing based on those emotions. For example, if the user is relaxed, the creation unit can suggest casual clothing. If the user is nervous, it can suggest formal clothing. Furthermore, if the user is having fun, it can suggest brightly colored clothing. This allows for the selection of more appropriate fashion items by suggesting avatar clothing according to the user's emotions. Emotion estimation is performed using methods such as facial recognition, voice analysis, and user input data. Some or all of the above processing in the creation unit is performed using an emotion engine or generative AI. For example, the creation unit inputs the user's facial data into the generative AI, which estimates the user's emotions and suggests avatar clothing.

[0069] The creation unit can reflect region-specific fashion styles based on the user's geographical location information when creating an avatar. For example, if the user is in Japan, the creation unit will suggest a fashion style appropriate for the Japanese season. If the user is in the United States, the creation unit can also suggest a fashion style that reflects American trends. Furthermore, if the user is in Europe, the creation unit can suggest a traditional European fashion style. This allows for the selection of more appropriate fashion items by reflecting region-specific fashion styles based on the user's geographical location information. Geographical location information is obtained, for example, based on GPS data or IP address. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit inputs the user's geographical location information into the generation AI, and the generation AI reflects region-specific fashion styles.

[0070] The creation unit can analyze the user's social media activity and reflect popular fashion items when creating an avatar. For example, the creation unit can reflect the style of fashion influencers the user follows on Instagram. It can also reflect fashion items the user has shared on social media. Furthermore, it can reflect items from fashion brands the user has "liked" on social media. This allows for the selection of more appropriate fashion items by analyzing the user's social media activity and reflecting popular fashion items. The analysis of social media activity is based, for example, on post content, the number of likes, and the number of followers. Some or all of the above processing in the creation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the creation unit inputs the user's social media activity data into a generative AI, and the generative AI reflects popular fashion items.

[0071] The simulation unit can estimate the user's emotions and adjust how the simulation results are displayed based on the estimated emotions. For example, if the user is relaxed, the generation AI will perform a simulation that proceeds at a leisurely pace. If the user is in a hurry, the generation AI can perform a simulation that emphasizes the shortest route. Furthermore, if the user is excited, the generation AI can perform a simulation that adds visually stimulating effects. By adjusting how the simulation results are displayed according to the user's emotions, more appropriate simulation results can be provided. Emotion estimation is performed using, for example, facial recognition, voice analysis, and user input data. Some or all of the above processing in the simulation unit is performed using an emotion engine or generation AI. For example, the simulation unit inputs the user's facial expression data into the generation AI, which estimates the user's emotions and adjusts how the simulation results are displayed.

[0072] The simulation unit can simulate the movement and texture of clothing in real time based on the user's body shape data during the simulation. For example, the simulation unit can simulate the movement of wrinkles and drapes of clothing in real time based on the user's body shape data. The simulation unit can also simulate the texture of clothing materials in real time based on the user's body shape data. Furthermore, the simulation unit can simulate the fit of clothing in real time based on the user's body shape data. This allows for a more realistic try-on experience by simulating the movement and texture of clothing in real time based on the user's body shape data. The simulation of movement and texture is performed using, for example, physical simulation or texture mapping. Some or all of the above processing in the simulation unit is performed using generative AI. For example, the simulation unit inputs the user's body shape data into the generative AI, which then simulates the movement and texture of clothing in real time.

[0073] The simulation unit can simulate the appearance of clothing under different lighting conditions during the simulation. For example, the simulation unit can simulate the appearance of clothing under natural daylight. It can also simulate the appearance of clothing under indoor lighting. Furthermore, it can simulate the appearance of clothing under streetlights at night. By simulating the appearance under different lighting conditions, a more realistic try-on experience can be provided. The simulation of different lighting conditions takes into account, for example, natural light, artificial light, and changes in time of day. Some or all of the above processing in the simulation unit is performed using a generative AI. For example, the simulation unit inputs the appearance of clothing under different lighting conditions into the generative AI, and the generative AI simulates that appearance.

[0074] The simulation unit can estimate the user's emotions and determine the priority of simulation results based on the estimated emotions. For example, if the user is relaxed, the simulation unit will prioritize displaying detailed simulation results. If the user is in a hurry, the simulation unit can also prioritize displaying concise simulation results. Furthermore, if the user is excited, the simulation unit can prioritize displaying visually stimulating simulation results. By prioritizing simulation results according to the user's emotions, more appropriate simulation results can be provided. Emotion estimation is performed using, for example, facial recognition, voice analysis, and user input data. Some or all of the above processing in the simulation unit is performed using an emotion engine or generative AI. For example, the simulation unit inputs the user's facial data into the generative AI, which estimates the user's emotions and determines the priority of simulation results.

[0075] The simulation unit can reflect region-specific fashion styles based on the user's geographical location information during simulation. For example, if the user is in Japan, the simulation unit will simulate fashion styles appropriate for the Japanese season. If the user is in the United States, the simulation unit can also simulate fashion styles reflecting American trends. Furthermore, if the user is in Europe, the simulation unit can simulate traditional European fashion styles. This allows for the selection of more appropriate fashion items by reflecting region-specific fashion styles based on the user's geographical location information. Geographical location information is obtained, for example, based on GPS data or IP addresses. Some or all of the above processing in the simulation unit is performed using a generative AI. For example, the simulation unit inputs the user's geographical location information into the generative AI, which then reflects region-specific fashion styles.

[0076] The simulation unit can analyze a user's social media activity during a simulation and reflect popular fashion items. For example, the simulation unit can simulate the style of fashion influencers that the user follows on Instagram. It can also simulate fashion items that the user has shared on social media. Furthermore, the simulation unit can simulate items from fashion brands that the user has "liked" on social media. This allows for the selection of more appropriate fashion items by analyzing the user's social media activity and reflecting popular fashion items. The analysis of social media activity is based, for example, on post content, the number of likes, and the number of followers. Some or all of the above processing in the simulation unit is performed using generative AI. For example, the simulation unit inputs the user's social media activity data into the generative AI, which then reflects popular fashion items.

[0077] The recommendation unit can estimate the user's emotions and adjust how recommended sizes are displayed based on those emotions. For example, if the user is relaxed, the recommendation unit can display detailed size information. If the user is in a hurry, it can display concise size information. Furthermore, if the user is excited, it can display visually stimulating size information. This allows for the provision of more appropriate size information by adjusting how recommended sizes are displayed according to the user's emotions. Emotion estimation is performed using, for example, facial recognition, voice analysis, and user input data. Some or all of the above processing in the recommendation unit is performed using an emotion engine or generative AI. For example, the recommendation unit inputs the user's facial data into the generative AI, which estimates the user's emotions and adjusts how recommended sizes are displayed.

[0078] The recommendation system can analyze a user's past purchase history to recommend the optimal size. For example, it can recommend the optimal size based on the sizes of items the user has purchased in the past. It can also analyze size trends from the user's past purchase history to recommend the optimal size. Furthermore, it can consider the sizes of items the user has returned in the past when recommending the optimal size. This allows for the recommendation of a more appropriate size by analyzing the user's past purchase history. The analysis of past purchase history is performed based on, for example, the date and time of purchase, the purchased items, and the frequency of purchase. Some or all of the above processing in the recommendation system may be performed using a generative AI, or it may be performed without a generative AI. For example, the recommendation system inputs the user's past purchase history data into a generative AI, and the generative AI recommends the optimal size.

[0079] The recommendation unit can compare sizes from different brands based on the user's body shape data and make recommendations. For example, the recommendation unit can compare sizes from different brands based on the user's body shape data and recommend the optimal size. The recommendation unit can also make recommendations considering the size differences between brands based on the user's body shape data. Furthermore, the recommendation unit can compare the fit of different brands based on the user's body shape data and make recommendations. This allows for the recommendation of a more appropriate size by comparing sizes from different brands based on the user's body shape data. The comparison of sizes from different brands is performed, for example, based on the size charts and fit differences of each brand. Some or all of the above processing in the recommendation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the recommendation unit inputs the user's body shape data into a generative AI, and the generative AI compares sizes from different brands and recommends the optimal size.

[0080] The recommendation system can estimate the user's emotions and prioritize the products it recommends based on those emotions. For example, if the user is relaxed, the recommendation system will prioritize displaying detailed product information. If the user is in a hurry, it can prioritize displaying concise product information. Furthermore, if the user is excited, it can prioritize displaying visually stimulating product information. This allows the system to provide more appropriate product information by prioritizing recommendations according to the user's emotions. Emotion estimation is performed using methods such as facial recognition, voice analysis, and user input data. Some or all of the above processing in the recommendation system is performed using an emotion engine or generative AI. For example, the recommendation system inputs the user's facial data into the generative AI, which estimates the user's emotions and determines the priority of the products to recommend.

[0081] The recommendation unit can recommend region-specific sizes based on the user's geographical location information. For example, if the user is in Japan, the recommendation unit will recommend based on the standard Japanese size. Similarly, if the user is in the United States, it can recommend based on the standard American size. Furthermore, if the user is in Europe, it can recommend based on the standard European size. This allows for the provision of more appropriate sizes by recommending region-specific sizes based on the user's geographical location information. Geographical location information can be obtained, for example, based on GPS data or IP addresses. Some or all of the above processing in the recommendation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the recommendation unit inputs the user's geographical location information into a generative AI, which then recommends region-specific sizes.

[0082] The recommendation system can analyze a user's social media activity and recommend popular sizes. For example, it can recommend sizes based on the sizes of fashion influencers the user follows on Instagram. It can also recommend sizes based on fashion items the user has shared on social media. Furthermore, it can recommend sizes based on fashion brands the user has "liked" on social media. By analyzing the user's social media activity and recommending popular sizes, it can recommend more appropriate sizes. The analysis of social media activity is based, for example, on the content of posts, the number of likes, and the number of followers. Some or all of the above processing in the recommendation system may be performed using or without a generative AI. For example, the recommendation system inputs the user's social media activity data into a generative AI, which then recommends popular sizes.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The Style Agent system not only creates avatars based on the user's body shape data, but can also analyze the user's past purchase history and evaluate the fit and satisfaction level of purchased items. For example, it collects feedback on the size and fit of items the user has purchased in the past and recommends the best size and style for the next purchase. It can also analyze the reasons why the user has returned items in the past to prevent similar problems from occurring. Furthermore, if the user has shown high satisfaction with a particular brand or style, it can prioritize recommending that brand or style. This allows the system to leverage the user's past purchase history to provide a more personalized shopping experience.

[0085] The style agent system can estimate a user's emotions and suggest fashion items based on those emotions. For example, if the user is relaxed, it can suggest casual and relaxed style items. If the user is stressed, it can suggest formal and calm style items. Furthermore, if the user is having fun, it can suggest items with bright and cheerful designs. By suggesting fashion items that match the user's emotions, it can provide a more appropriate shopping experience. Emotion estimation is performed using facial recognition, voice analysis, and user input data.

[0086] The Style Agent system can suggest region-specific fashion styles based on the user's geographical location. For example, if the user is in Japan, it can suggest fashion items that match Japanese seasons and trends. If the user is in the United States, it can suggest fashion items that match American trends and culture. Furthermore, if the user is in Europe, it can suggest fashion items that match European traditions and trends. This allows for the suggestion of more appropriate fashion items based on the user's geographical location. Geographical location information is obtained based on GPS data and IP addresses.

[0087] The Style Agent system can analyze a user's social media activity and suggest popular fashion items. For example, it can suggest items that reflect the style of fashion influencers the user follows on Instagram. It can also suggest similar items based on fashion items the user has shared on social media. Furthermore, it can suggest items from fashion brands that the user has "liked" on social media. By analyzing the user's social media activity and suggesting popular fashion items, it can provide a more suitable shopping experience. The analysis of social media activity is based on the content of posts, the number of likes, and the number of followers.

[0088] The Style Agent system can estimate the user's emotions and adjust the avatar's facial expressions and posture based on those estimates. For example, if the user is relaxed, the avatar's facial expression will be calmer and its posture relaxed. If the user is tense, the avatar's facial expression will be more serious and its posture straighter. Furthermore, if the user is having fun, the avatar's facial expression will be more cheerful and its posture more lively. By adjusting the avatar's facial expressions and posture according to the user's emotions, a more realistic try-on experience can be provided. Emotion estimation is performed using facial recognition, voice analysis, and user input data.

[0089] The Style Agent system can analyze a user's past body shape data and automatically update their avatar in response to changes in their body shape. For example, it can update the avatar to reflect weight changes based on the user's previously entered body shape data. It can also update the avatar to reflect changes in muscle mass based on the user's past body shape data. Furthermore, it can analyze the user's past body shape data and update the avatar to reflect changes in height. This allows the system to always provide a fitting experience based on the user's most up-to-date body shape by analyzing the user's past body shape data and automatically updating the avatar in response to changes in body shape. The analysis of past body shape data is performed based on regular measurement data and user self-reported data.

[0090] The Style Agent system can estimate the user's emotions and adjust how simulation results are displayed based on those emotions. For example, if the user is relaxed, the simulation will proceed at a leisurely pace. If the user is in a hurry, the simulation can emphasize the shortest route. Furthermore, if the user is excited, the simulation can include visually stimulating effects. By adjusting how simulation results are displayed according to the user's emotions, the system can provide more appropriate simulation results. Emotion estimation is performed using facial recognition, voice analysis, and user input data.

[0091] The Style Agent system can reflect region-specific fashion styles based on the user's geographical location. For example, if the user is in Japan, it can suggest fashion styles appropriate for the Japanese season. If the user is in the United States, it can suggest fashion styles that reflect American trends. Furthermore, if the user is in Europe, it can suggest traditional European fashion styles. This allows users to select more appropriate fashion items by reflecting region-specific fashion styles based on their geographical location. Geographical location information is obtained based on GPS data and IP addresses.

[0092] The Style Agent system can estimate a user's emotions and prioritize recommended products based on those emotions. For example, if a user is relaxed, it will prioritize displaying detailed product information. If a user is in a hurry, it can prioritize displaying concise product information. Furthermore, if a user is excited, it can prioritize displaying visually stimulating product information. By prioritizing recommended products according to the user's emotions, it can provide more appropriate product information. Emotion estimation is performed using facial recognition, voice analysis, and user input data.

[0093] The Style Agent system can analyze a user's social media activity and reflect popular fashion items. For example, it can reflect the styles of fashion influencers a user follows on Instagram. It can also reflect fashion items a user has shared on social media. Furthermore, it can reflect items from fashion brands a user has "liked" on social media. This allows the system to analyze a user's social media activity and reflect popular fashion items, enabling users to choose more appropriate fashion items. The analysis of social media activity is based on post content, the number of likes, and the number of followers.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The creation unit creates an avatar based on the user's body shape data. For example, it generates an avatar using 3D modeling technology based on body shape data such as height, weight, body fat percentage, and 3D scan data entered by the user. Step 2: The simulation unit has the avatar created by the creation unit try on fashion items and simulates the fit and appearance in real time. For example, it uses a generation AI to calculate the fit and appearance of the fashion items selected by the user and displays them on the avatar. Step 3: The recommendation unit recommends the optimal size based on the fit and appearance simulated by the simulation unit. For example, it uses generative AI to calculate the optimal size based on the user's body shape data and recommends it to the user.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] Each of the multiple elements described above, including the creation unit, simulation unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the creation unit is implemented by the control unit 46A of the smart device 14 and creates an avatar based on the user's body shape data. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses a generation AI to have the avatar try on fashion items and simulate the fit and appearance in real time. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the optimal size based on the fit and appearance simulated by the simulation unit. 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.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the creation unit, simulation unit, and recommendation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the creation unit is implemented by the control unit 46A of the smart glasses 214 and creates an avatar based on the user's body shape data. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses a generation AI to have the avatar try on fashion items and simulate the fit and appearance in real time. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the optimal size based on the fit and appearance simulated by the simulation unit. 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.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the creation unit, simulation unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the creation unit is implemented by the control unit 46A of the headset terminal 314 and creates an avatar based on the user's body shape data. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses a generation AI to have the avatar try on fashion items and simulate the fit and appearance in real time. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the optimal size based on the fit and appearance simulated by the simulation unit. 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.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the creation unit, simulation unit, and recommendation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the creation unit is implemented by the control unit 46A of the robot 414 and creates an avatar based on the user's body shape data. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses a generation AI to have the avatar try on fashion items and simulate the fit and appearance in real time. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the optimal size based on the fit and appearance simulated by the simulation unit. 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Note 1) A creation unit that creates avatars based on the user's body shape data, The simulation unit simulates the fit and appearance of the avatar created by the creation unit by having it try on fashion items in real time. The system includes a recommendation unit that recommends the optimal size based on the fit and appearance simulated by the aforementioned simulation unit. A system characterized by the following features. (Note 2) The aforementioned simulation unit, The system allows users to try on fashion items they've selected, simulating the fit and appearance in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned recommendation department, Based on the user's body shape data, the system recommends the optimal size. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned simulation unit, The system proposes multiple fashion styles, allowing users to try on and evaluate each style. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned creation unit, It estimates the user's emotions and adjusts the avatar's facial expressions and posture based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned creation unit, The system analyzes the user's past body shape data and automatically updates the avatar in response to changes in body shape. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned creation unit, When creating an avatar, users can customize the avatar's skin color and hairstyle according to their preferences. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned creation unit, It estimates the user's emotions and suggests avatar clothing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned creation unit, When creating an avatar, the system reflects region-specific fashion styles based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned creation unit, When creating an avatar, the system analyzes the user's social media activity and reflects popular fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned simulation unit, It estimates the user's emotions and adjusts how the simulation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned simulation unit, During the simulation, the movement and texture of clothing are simulated in real time based on the user's body shape data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned simulation unit, During the simulation, simulate how it looks under different lighting conditions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned simulation unit, It estimates the user's emotions and determines the priority of the simulation results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned simulation unit, During the simulation, the system reflects region-specific fashion styles based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned simulation unit, During the simulation, the system analyzes users' social media activity to reflect popular fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recommendation department, It estimates the user's sentiment and adjusts how recommended sizes are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recommendation department, When making recommendations, the system analyzes the user's past purchase history to suggest the optimal size. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recommendation department, During the recommendation process, the system compares sizes from different brands based on the user's body shape data to make recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recommendation department, It estimates the user's emotions and determines the priority of recommended products based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recommendation department, When making recommendations, the system will suggest region-specific sizes based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recommendation department, When making recommendations, the system analyzes the user's social media activity and recommends popular sizes. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0168] 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 creation unit that creates avatars based on the user's body shape data, The simulation unit simulates the fit and appearance of the avatar created by the creation unit by having it try on fashion items in real time. The system includes a recommendation unit that recommends the optimal size based on the fit and appearance simulated by the aforementioned simulation unit. A system characterized by the following features.

2. The aforementioned simulation unit, The system allows users to try on fashion items they've selected, simulating the fit and appearance in real time. The system according to feature 1.

3. The aforementioned recommendation department, Based on the user's body shape data, the system recommends the optimal size. The system according to feature 1.

4. The aforementioned simulation unit, The system suggests multiple fashion styles, allowing users to try on and evaluate each style. The system according to feature 1.

5. The aforementioned creation unit, It estimates the user's emotions and adjusts the avatar's facial expressions and posture based on those estimated emotions. The system according to feature 1.

6. The aforementioned creation unit, The system analyzes the user's past body shape data and automatically updates the avatar according to changes in body shape. The system according to feature 1.

7. The aforementioned creation unit, When creating an avatar, users can customize the avatar's skin color and hairstyle according to their preferences. The system according to feature 1.

8. The aforementioned creation unit, It estimates the user's emotions and suggests avatar clothing based on those estimated emotions. The system according to feature 1.

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