system

Abstract

The system according to this embodiment aims to suggest the optimal outfit based on the user's body type, preferences, and purchase history, and to generate try-on images. [Solution] The system according to the embodiment comprises a collection unit, a learning unit, a suggestion unit, a generation unit, and a representation unit. The collection unit collects the user's body shape, preferences, and purchase history. The learning unit learns the data collected by the collection unit. The suggestion unit suggests the optimal outfit based on the data learned by the learning unit. The generation unit generates try-on images of the outfit suggested by the suggestion unit. The representation unit provides a visual representation of the fit based on the try-on images generated by the generation unit.

Description

Technical Field

[0006] , , , ,

[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, the method including 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] <00000​​​​​​​​​​​The system according to this embodiment comprises a collection unit, a learning unit, a suggestion unit, a generation unit, and a representation unit. The collection unit collects the user's body shape, preferences, and purchase history. The learning unit learns from the data collected by the collection unit. The suggestion unit suggests the optimal outfit based on the data learned by the learning unit. The generation unit generates try-on images of the outfit suggested by the suggestion unit. The representation unit provides a visual representation of the fit based on the try-on images generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal outfit based on the user's body type, preferences, and purchase history, and generate try-on images. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable 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 12 is a server.

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The personalized AI stylist according to an embodiment of the present invention is a system that learns the user's body type, preferences, and purchase history, and proposes the optimal outfit. This personalized AI stylist collects the user's body type, preferences, and purchase history, and the AI ​​learns this data. Next, the AI ​​proposes the optimal outfit to the user based on the learned data. For example, if the user is participating in a specific event, the AI ​​can suggest clothing suitable for that event. Furthermore, the AI ​​has a function to generate try-on images, allowing the user to check try-on images tailored to their body type and face. This eliminates the need to actually try on clothes. The AI ​​can also visually represent the comfort of the clothing, allowing the user to visually check the comfort of the clothes before purchasing. This service solves problems such as insufficient staff support and limitations of individual suggestions in stores, the difficulty of choosing sizes and styles in online shopping, and high return rates. By having the AI ​​learn the user's body type, preferences, and purchase history and propose the optimal outfit, it saves time while increasing purchase satisfaction and providing a next-generation shopping experience. For example, if a user inputs "I want you to find clothes that suit me," the AI ​​will suggest the most suitable clothes based on the user's body type, preferences, and purchase history. Furthermore, a function to generate try-on images allows users to see how the clothes look on them, tailored to their own body type and face. The AI ​​also offers suggestions for everyday outfits, outfits for events, and even advice on gifts for others. Thus, the personalized AI stylist is an innovative service that not only learns the user's body type, preferences, and purchase history to suggest the best outfits, but also generates try-on images and provides a visual representation of comfort. This reduces effort while increasing purchase satisfaction, providing a next-generation shopping experience. In short, the personalized AI stylist can increase purchase satisfaction by suggesting the best outfits based on the user's body type, preferences, and purchase history, and by generating try-on images and providing a visual representation of comfort.

[0029] The personalized AI stylist according to this embodiment comprises a collection unit, a learning unit, a suggestion unit, a generation unit, and a representation unit. The collection unit collects the user's body shape, preferences, and purchase history. The collection unit can collect body shape information such as the user's height, weight, and body fat percentage. The collection unit can also collect information such as the user's preferences for color, style, and brand. Furthermore, the collection unit can collect the user's purchase history for the past year and purchase history for specific categories. The learning unit learns from the data collected by the collection unit. The learning unit analyzes the collected data using AI, for example, and learns based on the user's body shape, preferences, and purchase history. The suggestion unit suggests the optimal outfit based on the data learned by the learning unit. The suggestion unit can suggest clothing suitable for the season and events based on the user's body shape, preferences, and purchase history. The generation unit generates try-on images of the outfits suggested by the suggestion unit. The generation unit can generate try-on images that reflect the user's body shape and facial features using 3D modeling or photorealistic rendering technology, for example. The expression unit provides a visual representation of the fit based on the try-on images generated by the generation unit. The expression unit can, for example, visually represent the texture of the material and changes in response to movement. As a result, the personalized AI stylist according to the embodiment can suggest the optimal outfit based on the user's body type, preferences, and purchase history, and increase purchase satisfaction by generating try-on images and providing a visual representation of the fit.

[0030] The data collection department collects user body type, preferences, and purchase history. Specifically, it can utilize data from smartphones and wearable devices to collect body type information such as the user's height, weight, and body fat percentage. These devices monitor the user's daily activities and update body type information in real time. The data collection department also analyzes items the user has purchased in the past and browsing history to collect information such as the user's color, style, and brand preferences. For example, it collects reviews of products the user has viewed and purchased on online shopping sites to understand the user's preferences in detail. Furthermore, the data collection department accesses the user's purchase history database to collect the user's purchase history for the past year and purchase history in specific categories. This allows the department to understand what items the user frequently purchases and which brands they prefer. The data collection department centrally manages this data and creates user profiles. User profiles include body type information, preferences, and purchase history, which other departments can then use as a basis for data utilization. By adjusting the frequency and accuracy of data collection, the data collection department can always maintain up-to-date user information and provide personalized services to individual users.

[0031] The learning unit learns from the data collected by the collection unit. Specifically, it uses AI to analyze the collected data and learns based on the user's body type, preferences, and purchase history. The AI ​​uses deep learning algorithms to identify patterns in the user's body type and preferences, and analyzes the user's profile in detail. For example, the AI ​​learns what styles and silhouettes are most suitable based on the user's body type information. It also analyzes the user's color preferences and style tendencies to identify the colors and designs the user likes. Furthermore, the AI ​​analyzes the user's purchase history to understand the characteristics and trends of items purchased in the past. This allows the learning unit to build a foundation for suggesting the optimal outfits for each individual user based on their body type, preferences, and purchase history. The learning unit can continuously learn from the collected data and adapt to changes in user preferences and trends. For example, when seasons change or new fashion trends emerge, the AI ​​incorporates new data and updates its learning. This allows the learning unit to always provide highly accurate suggestions based on the latest information.

[0032] The suggestion department proposes optimal outfits based on data learned by the learning department. Specifically, it suggests clothing suitable for the season and event based on the user's body type, preferences, and purchase history. The suggestion department utilizes the data learned by the AI ​​to select items that best suit the user's profile. For example, if a user is attending a summer event, the suggestion department will suggest clothing made of cool materials that suit the user's body type and items with designs that match the user's preferences. Also, if a user prefers a particular brand, the department can prioritize suggesting new and popular items from that brand. The suggestion department can also consider the user's past purchase history and suggest outfits that are easy to combine with items they already own. This allows users to enjoy coordinating outfits that harmonize with their existing wardrobe when purchasing new items. The suggestion department collects user feedback and continuously improves the accuracy and effectiveness of its suggestions. For example, it collects user satisfaction after purchasing suggested items and their impressions of actually wearing them, and incorporates this into future suggestions. This allows the suggestion department to better meet user needs.

[0033] The generation unit generates try-on images of the outfits suggested by the suggestion unit. Specifically, it uses 3D modeling and photorealistic rendering techniques to generate try-on images that reflect the user's body shape and facial features. Based on the user's body shape information, the generation unit creates a realistic 3D model and applies the suggested clothing to that model. This allows the user to visually check how the suggested clothing will look without actually trying it on. The generation unit uses photorealistic rendering techniques to realistically represent the material and texture of the clothing. For example, it faithfully reproduces the smooth texture of silk and the rough texture of denim, providing the user with a realistic try-on experience. The generation unit uses facial recognition technology to generate try-on images that reflect the user's facial features. This allows the user to check outfits that suit their face and body shape. When generating try-on images, the generation unit considers the user's preferences and style and selects the optimal angle and pose. This allows the user to check how the suggested outfits will look from multiple angles. The generation unit collects user feedback when generating try-on images and continuously improves the accuracy and realism of the generation algorithm. This allows the generation unit to provide users with a more realistic and satisfying try-on experience.

[0034] The rendering unit provides a visual representation of the fit and feel of the clothing based on the try-on images generated by the generation unit. Specifically, it visually represents the texture of the material and how it changes with movement. The rendering unit uses physically based rendering technology to realistically represent the material and texture of the clothing in the generated try-on images. For example, it faithfully reproduces the smooth movement of silk and the soft texture of wool, visually conveying a realistic fit to the user. The rendering unit simulates how the clothing changes when the user moves and displays it in real time. This allows the user to check the movement and fit when actually wearing the clothing. The rendering unit collects user feedback and continuously improves the accuracy and realism of the visual representation. For example, it collects user impressions of the try-on images and feedback on the fit of the clothing they actually purchased, and incorporates this into the next visual representation. This allows the rendering unit to provide users with a more realistic and satisfying try-on experience. Based on the generated try-on images, the rendering unit provides the user with a detailed explanation of the clothing's fit and feel. For example, it explains the material and design features of the clothing, providing information to help the user consider purchasing it. This allows the presentation unit to enhance user satisfaction with their purchase.

[0035] The suggestion department can offer daily outfit suggestions, outfit suggestions for events, and advice on gifts for others. For example, the suggestion department can suggest daily outfits based on the weather, day of the week, and the user's schedule. It can also suggest outfits for events such as weddings, parties, and business meetings. Furthermore, the suggestion department can advise on gifts for others based on the recipient's preferences, budget, and the type of event. In this way, the suggestion department can meet the diverse needs of users by offering daily outfit suggestions, outfit suggestions for events, and advice on gifts for others.

[0036] The data collection unit can analyze the user's past purchase history and determine the priority of the data to collect. For example, the data collection unit may prioritize collecting data on brands the user has purchased in the past. The data collection unit may also collect relevant data based on the style of items the user has purchased in the past. The data collection unit may also collect relevant data based on the color and material of items the user has purchased in the past. This allows the data collection unit to efficiently collect data by analyzing the user's past purchase history and determining the priority of the data to collect. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0037] The data collection unit can filter data according to the user's current fashion trends and the season during collection. For example, the data collection unit can collect data that matches the user's current fashion trends. The data collection unit can also collect data on clothing appropriate for the season. For example, the data collection unit can combine data on the user's current fashion trends and seasonal data. This allows the data collection unit to collect more relevant data by filtering the data according to the user's current fashion trends and the season. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0038] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during collection. For example, the data collection unit can collect data on clothing suitable for the climate of the area where the user lives. The data collection unit can also collect data on clothing suitable for places the user frequently visits. The data collection unit can also prioritize the collection of highly relevant data based on the user's geographical location information. This allows the data collection unit to prioritize the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0039] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, the data collection unit can collect data on clothing shared by the user on social media. The data collection unit can also collect data on brands followed by the user on social media. The data collection unit can also collect relevant data based on the user's social media activity. This allows the data collection unit to collect relevant data by analyzing the user's social media activity. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI.

[0040] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit optimizes the learning algorithm based on past learning data. The learning unit can also adjust the parameters of the learning algorithm by referring to past learning data. For example, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. In this way, the learning unit can optimize the learning algorithm and improve its accuracy by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI.

[0041] The learning unit can update the learned content during learning, taking into account changes in the user's fashion trends. For example, the learning unit updates the learned content in response to changes in the user's fashion trends. The learning unit can also analyze changes in the user's fashion trends and optimize the learned content. For example, the learning unit can adjust the learned content based on changes in the user's fashion trends. This allows the learning unit to appropriately update the learned content by taking changes in the user's fashion trends into account. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI.

[0042] The learning unit can weight the training data based on the user's lifestyle and occupation during training. For example, the learning unit can weight the training data based on the user's lifestyle. The learning unit can also weight the training data based on the user's occupation. The learning unit can also weight the training data based on both the user's lifestyle and occupation. This allows the learning unit to learn more appropriate data by weighting the training data based on the user's lifestyle and occupation. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI.

[0043] The learning unit can analyze the user's social media activity and incorporate relevant data into its learning process. For example, the learning unit can incorporate data on clothing shared by the user on social media. The learning unit can also incorporate data on brands followed by the user on social media. The learning unit can also incorporate relevant data based on the user's social media activity. This allows the learning unit to incorporate relevant data by analyzing the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI.

[0044] The suggestion unit can make optimal suggestions by referring to the user's past coordination history when making suggestions. For example, the suggestion unit makes optimal suggestions based on the user's past coordination history. For example, the suggestion unit can also make relevant suggestions by referring to the user's past coordination history. For example, the suggestion unit can analyze the user's past coordination history and make optimal suggestions. In this way, the suggestion unit can make optimal suggestions by referring to the user's past coordination history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0045] The suggestion unit can propose appropriate outfits based on the user's event schedule when making a suggestion. For example, the suggestion unit can propose appropriate outfits based on the user's event schedule. The suggestion unit can also, for example, refer to the user's event schedule and propose relevant outfits. The suggestion unit can also, for example, analyze the user's event schedule and propose the optimal outfit. In this way, the suggestion unit can propose the most suitable attire for the event by suggesting appropriate outfits based on the user's event schedule. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0046] The suggestion unit can propose the optimal coordination when making a suggestion, taking into account the user's geographical location information. For example, the suggestion unit can propose the optimal coordination based on the user's geographical location information. For example, the suggestion unit can also propose relevant coordination by taking into account the user's geographical location information. For example, the suggestion unit can analyze the user's geographical location information and propose the optimal coordination. In this way, the suggestion unit can propose the optimal coordination by taking into account the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0047] The suggestion unit can analyze the user's social media activity and suggest relevant outfits when making suggestions. For example, the suggestion unit suggests relevant outfits based on the user's social media activity. The suggestion unit can also suggest the optimal outfit based on data of clothing shared by the user on social media. The suggestion unit can also suggest the optimal outfit by analyzing the user's social media activity. In this way, the suggestion unit can suggest relevant outfits by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0048] The generation unit can generate try-on images that accurately reflect the user's body shape and facial features during the generation process. For example, the generation unit can generate try-on images that reflect the optimal fit based on the user's body shape. The generation unit can also generate try-on images that are balanced overall, taking into account the user's facial features. The generation unit can also generate realistic try-on images that accurately reflect the user's body shape and facial features. As a result, the generation unit can generate more realistic try-on images by accurately reflecting the user's body shape and facial features. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI.

[0049] The generation unit can generate try-on images by simulating different lighting conditions and backgrounds during the generation process. For example, the generation unit can simulate different lighting conditions and generate try-on images. The generation unit can also simulate different backgrounds and generate try-on images. For example, the generation unit can combine lighting conditions and backgrounds to generate realistic try-on images. In this way, the generation unit can generate more realistic try-on images by simulating different lighting conditions and backgrounds. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI.

[0050] The generation unit can generate highly relevant try-on images by considering the user's geographical location information during generation. For example, the generation unit generates highly relevant try-on images based on the user's geographical location information. The generation unit can also generate optimal try-on images by considering the user's geographical location information. For example, the generation unit can analyze the user's geographical location information and generate highly relevant try-on images. In this way, the generation unit can generate highly relevant try-on images by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0051] The generation unit can analyze the user's social media activity during generation and generate relevant try-on images. For example, the generation unit generates relevant try-on images based on the user's social media activity. The generation unit can also generate optimal try-on images based on clothing data shared by the user on social media. For example, the generation unit can analyze the user's social media activity and generate optimal try-on images. In this way, the generation unit can generate relevant try-on images by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0052] The expression unit can perform visual representations that accurately reflect the textures of different materials and fabrics during the expression process. For example, the expression unit can perform visual representations that accurately reflect the textures of different materials. The expression unit can also perform visual representations that accurately reflect the textures of different fabrics. For example, the expression unit can combine the textures of materials and fabrics to create realistic visual representations. As a result, the expression unit can perform more realistic visual representations by accurately reflecting the textures of different materials and fabrics. Some or all of the above-described processes in the expression unit may be performed using AI, for example, or without using AI.

[0053] The expression unit can simulate and represent changes in comfort based on the user's body shape and movements during the representation process. For example, the expression unit can simulate and represent changes in comfort based on the user's body shape. The expression unit can also simulate and represent changes in comfort based on the user's movements. For example, the expression unit can combine the user's body shape and movements to create a realistic visual representation of comfort. As a result, the expression unit can create a more realistic visual representation by simulating changes in comfort based on the user's body shape and movements. Some or all of the above-described processes in the expression unit may be performed using AI, for example, or without using AI.

[0054] The display unit can perform optimal visual representations while considering the user's geographical location information. For example, the display unit can perform optimal visual representations based on the user's geographical location information. The display unit can also perform relevant visual representations while considering the user's geographical location information. For example, the display unit can analyze the user's geographical location information and perform optimal visual representations. In this way, the display unit can perform optimal visual representations by considering the user's geographical location information. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI.

[0055] The expression unit can analyze the user's social media activity and create relevant visual representations during the expression process. For example, the expression unit can create relevant visual representations based on the user's social media activity. The expression unit can also create optimal visual representations based on, for example, data on clothing shared by the user on social media. The expression unit can also analyze the user's social media activity and create optimal visual representations. In this way, the expression unit can create relevant visual representations by analyzing the user's social media activity. Some or all of the above processing in the expression unit may be performed using AI, for example, or without using AI.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can collect data based on the user's lifestyle. For example, if the user has an active lifestyle, it can collect data on sportswear and casual clothing. If the user is a business person, it can also collect data on formal attire and business casual clothing. If the user spends a lot of time at home, it can also collect data on comfortable clothing. In this way, the data collection unit can collect more relevant data by collecting data based on the user's lifestyle.

[0058] The data collection unit can analyze a user's past purchase history and determine the priority of the data to collect. For example, it can prioritize collecting data on brands the user has purchased in the past. It can also collect relevant data based on the style of items the user has purchased in the past. It can also collect relevant data based on the color and material of items the user has purchased in the past. In this way, the data collection unit can analyze a user's past purchase history to determine the priority of the data to collect and collect data efficiently.

[0059] The data collection unit can filter data according to the user's current fashion trends and the season during collection. For example, it can collect data that matches the user's current fashion trends. It can also collect data on clothing appropriate for the season. It can even combine data on the user's current fashion trends with data appropriate for the season. As a result, the data collection unit can collect more relevant data by filtering the data according to the user's current fashion trends and the season.

[0060] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during the collection process. For example, it can collect data on clothing suitable for the climate of the area where the user lives. It can also collect data on clothing suitable for places the user frequently visits. Based on the user's geographical location information, it can also prioritize the collection of highly relevant data. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information.

[0061] The generation unit can generate try-on images by simulating different lighting conditions and backgrounds during the generation process. For example, it can simulate different lighting conditions and generate try-on images. It can also simulate different backgrounds and generate try-on images. It can even combine lighting conditions and backgrounds to generate realistic try-on images. In this way, the generation unit can generate more realistic try-on images by simulating different lighting conditions and backgrounds.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects the user's body type, preferences, and purchase history. For example, the data collection unit can collect body type information such as the user's height, weight, and body fat percentage. It can also collect information such as the user's color, style, and brand preferences. Furthermore, the data collection unit can collect the user's purchase history for the past year, as well as purchase history in specific categories. Step 2: The learning unit learns from the data collected by the collection unit. The learning unit, for example, uses AI to analyze the collected data and learns based on the user's body type, preferences, and purchase history. Step 3: The suggestion unit proposes the optimal outfit based on the data learned by the learning unit. For example, the suggestion unit can suggest clothing suitable for the season and event based on the user's body type, preferences, and purchase history. Step 4: The generation unit generates try-on images of the outfits proposed by the suggestion unit. The generation unit can generate try-on images that reflect the user's body shape and facial features, for example, using 3D modeling or photorealistic rendering technology. Step 5: The expression unit provides a visual representation of the fit based on the try-on images generated by the generation unit. For example, the expression unit can visually represent the texture of the material and how it changes in response to movement.

[0064] (Example of form 2) The personalized AI stylist according to an embodiment of the present invention is a system that learns the user's body type, preferences, and purchase history, and proposes the optimal outfit. This personalized AI stylist collects the user's body type, preferences, and purchase history, and the AI ​​learns this data. Next, the AI ​​proposes the optimal outfit to the user based on the learned data. For example, if the user is participating in a specific event, the AI ​​can suggest clothing suitable for that event. Furthermore, the AI ​​has a function to generate try-on images, allowing the user to check try-on images tailored to their body type and face. This eliminates the need to actually try on clothes. The AI ​​can also visually represent the comfort of the clothing, allowing the user to visually check the comfort of the clothes before purchasing. This service solves problems such as insufficient staff support and limitations of individual suggestions in stores, the difficulty of choosing sizes and styles in online shopping, and high return rates. By having the AI ​​learn the user's body type, preferences, and purchase history and propose the optimal outfit, it saves time while increasing purchase satisfaction and providing a next-generation shopping experience. For example, if a user inputs "I want you to find clothes that suit me," the AI ​​will suggest the most suitable clothes based on the user's body type, preferences, and purchase history. Furthermore, a function to generate try-on images allows users to see how the clothes look on them, tailored to their own body type and face. The AI ​​also offers suggestions for everyday outfits, outfits for events, and even advice on gifts for others. Thus, the personalized AI stylist is an innovative service that not only learns the user's body type, preferences, and purchase history to suggest the best outfits, but also generates try-on images and provides a visual representation of comfort. This reduces effort while increasing purchase satisfaction, providing a next-generation shopping experience. In short, the personalized AI stylist can increase purchase satisfaction by suggesting the best outfits based on the user's body type, preferences, and purchase history, and by generating try-on images and providing a visual representation of comfort.

[0065] The personalized AI stylist according to this embodiment comprises a collection unit, a learning unit, a suggestion unit, a generation unit, and a representation unit. The collection unit collects the user's body shape, preferences, and purchase history. The collection unit can collect body shape information such as the user's height, weight, and body fat percentage. The collection unit can also collect information such as the user's preferences for color, style, and brand. Furthermore, the collection unit can collect the user's purchase history for the past year and purchase history for specific categories. The learning unit learns from the data collected by the collection unit. The learning unit analyzes the collected data using AI, for example, and learns based on the user's body shape, preferences, and purchase history. The suggestion unit suggests the optimal outfit based on the data learned by the learning unit. The suggestion unit can suggest clothing suitable for the season and events based on the user's body shape, preferences, and purchase history. The generation unit generates try-on images of the outfits suggested by the suggestion unit. The generation unit can generate try-on images that reflect the user's body shape and facial features using 3D modeling or photorealistic rendering technology, for example. The expression unit provides a visual representation of the fit based on the try-on images generated by the generation unit. The expression unit can, for example, visually represent the texture of the material and changes in response to movement. As a result, the personalized AI stylist according to the embodiment can suggest the optimal outfit based on the user's body type, preferences, and purchase history, and increase purchase satisfaction by generating try-on images and providing a visual representation of the fit.

[0066] The data collection department collects user body type, preferences, and purchase history. Specifically, it can utilize data from smartphones and wearable devices to collect body type information such as the user's height, weight, and body fat percentage. These devices monitor the user's daily activities and update body type information in real time. The data collection department also analyzes items the user has purchased in the past and browsing history to collect information such as the user's color, style, and brand preferences. For example, it collects reviews of products the user has viewed and purchased on online shopping sites to understand the user's preferences in detail. Furthermore, the data collection department accesses the user's purchase history database to collect the user's purchase history for the past year and purchase history in specific categories. This allows the department to understand what items the user frequently purchases and which brands they prefer. The data collection department centrally manages this data and creates user profiles. User profiles include body type information, preferences, and purchase history, which other departments can then use as a basis for data utilization. By adjusting the frequency and accuracy of data collection, the data collection department can always maintain up-to-date user information and provide personalized services to individual users.

[0067] The learning unit learns from the data collected by the collection unit. Specifically, it uses AI to analyze the collected data and learns based on the user's body type, preferences, and purchase history. The AI ​​uses deep learning algorithms to identify patterns in the user's body type and preferences, and analyzes the user's profile in detail. For example, the AI ​​learns what styles and silhouettes are most suitable based on the user's body type information. It also analyzes the user's color preferences and style tendencies to identify the colors and designs the user likes. Furthermore, the AI ​​analyzes the user's purchase history to understand the characteristics and trends of items purchased in the past. This allows the learning unit to build a foundation for suggesting the optimal outfits for each individual user based on their body type, preferences, and purchase history. The learning unit can continuously learn from the collected data and adapt to changes in user preferences and trends. For example, when seasons change or new fashion trends emerge, the AI ​​incorporates new data and updates its learning. This allows the learning unit to always provide highly accurate suggestions based on the latest information.

[0068] The suggestion department proposes optimal outfits based on data learned by the learning department. Specifically, it suggests clothing suitable for the season and event based on the user's body type, preferences, and purchase history. The suggestion department utilizes the data learned by the AI ​​to select items that best suit the user's profile. For example, if a user is attending a summer event, the suggestion department will suggest clothing made of cool materials that suit the user's body type and items with designs that match the user's preferences. Also, if a user prefers a particular brand, the department can prioritize suggesting new and popular items from that brand. The suggestion department can also consider the user's past purchase history and suggest outfits that are easy to combine with items they already own. This allows users to enjoy coordinating outfits that harmonize with their existing wardrobe when purchasing new items. The suggestion department collects user feedback and continuously improves the accuracy and effectiveness of its suggestions. For example, it collects user satisfaction after purchasing suggested items and their impressions of actually wearing them, and incorporates this into future suggestions. This allows the suggestion department to better meet user needs.

[0069] The generation unit generates try-on images of the outfits suggested by the suggestion unit. Specifically, it uses 3D modeling and photorealistic rendering techniques to generate try-on images that reflect the user's body shape and facial features. Based on the user's body shape information, the generation unit creates a realistic 3D model and applies the suggested clothing to that model. This allows the user to visually check how the suggested clothing will look without actually trying it on. The generation unit uses photorealistic rendering techniques to realistically represent the material and texture of the clothing. For example, it faithfully reproduces the smooth texture of silk and the rough texture of denim, providing the user with a realistic try-on experience. The generation unit uses facial recognition technology to generate try-on images that reflect the user's facial features. This allows the user to check outfits that suit their face and body shape. When generating try-on images, the generation unit considers the user's preferences and style and selects the optimal angle and pose. This allows the user to check how the suggested outfits will look from multiple angles. The generation unit collects user feedback when generating try-on images and continuously improves the accuracy and realism of the generation algorithm. This allows the generation unit to provide users with a more realistic and satisfying try-on experience.

[0070] The rendering unit provides a visual representation of the fit and feel of the clothing based on the try-on images generated by the generation unit. Specifically, it visually represents the texture of the material and how it changes with movement. The rendering unit uses physically based rendering technology to realistically represent the material and texture of the clothing in the generated try-on images. For example, it faithfully reproduces the smooth movement of silk and the soft texture of wool, visually conveying a realistic fit to the user. The rendering unit simulates how the clothing changes when the user moves and displays it in real time. This allows the user to check the movement and fit when actually wearing the clothing. The rendering unit collects user feedback and continuously improves the accuracy and realism of the visual representation. For example, it collects user impressions of the try-on images and feedback on the fit of the clothing they actually purchased, and incorporates this into the next visual representation. This allows the rendering unit to provide users with a more realistic and satisfying try-on experience. Based on the generated try-on images, the rendering unit provides the user with a detailed explanation of the clothing's fit and feel. For example, it explains the material and design features of the clothing, providing information to help the user consider purchasing it. This allows the presentation unit to enhance user satisfaction with their purchase.

[0071] The suggestion department can offer daily outfit suggestions, outfit suggestions for events, and advice on gifts for others. For example, the suggestion department can suggest daily outfits based on the weather, day of the week, and the user's schedule. It can also suggest outfits for events such as weddings, parties, and business meetings. Furthermore, the suggestion department can advise on gifts for others based on the recipient's preferences, budget, and the type of event. In this way, the suggestion department can meet the diverse needs of users by offering daily outfit suggestions, outfit suggestions for events, and advice on gifts for others.

[0072] The data collection unit can estimate the user's emotions and adjust the type of data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data on relaxing clothing. For example, if the user is having fun, the data collection unit may also collect data on casual and fun clothing designs. For example, if the user is tired, the data collection unit may also collect data on comfortable and relaxing clothing. In this way, the data collection unit can collect more appropriate data by adjusting the type of data collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The data collection unit can analyze the user's past purchase history and determine the priority of the data to collect. For example, the data collection unit may prioritize collecting data on brands the user has purchased in the past. The data collection unit may also collect relevant data based on the style of items the user has purchased in the past. The data collection unit may also collect relevant data based on the color and material of items the user has purchased in the past. This allows the data collection unit to efficiently collect data by analyzing the user's past purchase history and determining the priority of the data to collect. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0074] The data collection unit can filter data according to the user's current fashion trends and the season during collection. For example, the data collection unit can collect data that matches the user's current fashion trends. The data collection unit can also collect data on clothing appropriate for the season. For example, the data collection unit can combine data on the user's current fashion trends and seasonal data. This allows the data collection unit to collect more relevant data by filtering the data according to the user's current fashion trends and the season. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0075] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit may frequently collect data on relaxing clothing. For example, if the user is having fun, the data collection unit may frequently collect data on casual and fun clothing designs. For example, if the user is tired, the data collection unit may frequently collect data on comfortable and relaxing clothing. This allows the data collection unit to collect data at a more appropriate time by adjusting the frequency of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during collection. For example, the data collection unit can collect data on clothing suitable for the climate of the area where the user lives. The data collection unit can also collect data on clothing suitable for places the user frequently visits. The data collection unit can also prioritize the collection of highly relevant data based on the user's geographical location information. This allows the data collection unit to prioritize the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0077] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, the data collection unit can collect data on clothing shared by the user on social media. The data collection unit can also collect data on brands followed by the user on social media. The data collection unit can also collect relevant data based on the user's social media activity. This allows the data collection unit to collect relevant data by analyzing the user's social media activity. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI.

[0078] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit will prioritize learning data on relaxing clothing. For example, if the user is having fun, the learning unit can also learn data on casual and fun clothing designs. For example, if the user is tired, the learning unit can also learn data on comfortable and relaxing clothing. In this way, the learning unit can learn more appropriate data by selecting training data based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit optimizes the learning algorithm based on past learning data. The learning unit can also adjust the parameters of the learning algorithm by referring to past learning data. For example, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. In this way, the learning unit can optimize the learning algorithm and improve its accuracy by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI.

[0080] The learning unit can update the learned content during learning, taking into account changes in the user's fashion trends. For example, the learning unit updates the learned content in response to changes in the user's fashion trends. The learning unit can also analyze changes in the user's fashion trends and optimize the learned content. For example, the learning unit can adjust the learned content based on changes in the user's fashion trends. This allows the learning unit to appropriately update the learned content by taking changes in the user's fashion trends into account. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI.

[0081] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit will frequently learn data on relaxing clothing. For example, if the user is having fun, the learning unit may also frequently learn data on casual and fun clothing designs. For example, if the user is tired, the learning unit may also frequently learn data on comfortable and relaxing clothing. This allows the learning unit to learn at more appropriate times by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The learning unit can weight the training data based on the user's lifestyle and occupation during training. For example, the learning unit can weight the training data based on the user's lifestyle. The learning unit can also weight the training data based on the user's occupation. The learning unit can also weight the training data based on both the user's lifestyle and occupation. This allows the learning unit to learn more appropriate data by weighting the training data based on the user's lifestyle and occupation. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI.

[0083] The learning unit can analyze the user's social media activity and incorporate relevant data into its learning process. For example, the learning unit can incorporate data on clothing shared by the user on social media. The learning unit can also incorporate data on brands followed by the user on social media. The learning unit can also incorporate relevant data based on the user's social media activity. This allows the learning unit to incorporate relevant data by analyzing the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI.

[0084] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function will present simple and highly visible suggestions. If the user is having fun, the suggestion function may present casual and fun design suggestions. If the user is tired, the suggestion function may present comfortable and relaxing suggestions. In this way, the suggestion function can provide more appropriate suggestions by adjusting the way it presents suggestions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The suggestion unit can make optimal suggestions by referring to the user's past coordination history when making suggestions. For example, the suggestion unit makes optimal suggestions based on the user's past coordination history. For example, the suggestion unit can also make relevant suggestions by referring to the user's past coordination history. For example, the suggestion unit can analyze the user's past coordination history and make optimal suggestions. In this way, the suggestion unit can make optimal suggestions by referring to the user's past coordination history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0086] The suggestion unit can propose appropriate outfits based on the user's event schedule when making a suggestion. For example, the suggestion unit can propose appropriate outfits based on the user's event schedule. The suggestion unit can also, for example, refer to the user's event schedule and propose relevant outfits. The suggestion unit can also, for example, analyze the user's event schedule and propose the optimal outfit. In this way, the suggestion unit can propose the most suitable attire for the event by suggesting appropriate outfits based on the user's event schedule. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0087] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion function will prioritize relaxing suggestions. If the user is having fun, the suggestion function may also prioritize casual and fun design suggestions. If the user is tired, the suggestion function may also prioritize comfortable and relaxing suggestions. This allows the suggestion function to provide more appropriate suggestions by prioritizing suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The suggestion unit can propose the optimal coordination when making a suggestion, taking into account the user's geographical location information. For example, the suggestion unit can propose the optimal coordination based on the user's geographical location information. For example, the suggestion unit can also propose relevant coordination by taking into account the user's geographical location information. For example, the suggestion unit can analyze the user's geographical location information and propose the optimal coordination. In this way, the suggestion unit can propose the optimal coordination by taking into account the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0089] The suggestion unit can analyze the user's social media activity and suggest relevant outfits when making suggestions. For example, the suggestion unit suggests relevant outfits based on the user's social media activity. The suggestion unit can also suggest the optimal outfit based on data of clothing shared by the user on social media. The suggestion unit can also suggest the optimal outfit by analyzing the user's social media activity. In this way, the suggestion unit can suggest relevant outfits by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0090] The generation unit can estimate the user's emotions and adjust the method of generating try-on images based on the estimated emotions. For example, if the user is stressed, the generation unit will generate simple and highly visible try-on images. For example, if the user is having fun, the generation unit can also generate casual and fun try-on images. For example, if the user is tired, the generation unit can also generate comfortable and relaxing try-on images. In this way, the generation unit can generate more appropriate try-on images by adjusting the method of generating try-on images based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The generation unit can generate try-on images that accurately reflect the user's body shape and facial features during the generation process. For example, the generation unit can generate try-on images that reflect the optimal fit based on the user's body shape. The generation unit can also generate try-on images that are balanced overall, taking into account the user's facial features. The generation unit can also generate realistic try-on images that accurately reflect the user's body shape and facial features. As a result, the generation unit can generate more realistic try-on images by accurately reflecting the user's body shape and facial features. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI.

[0092] The generation unit can generate try-on images by simulating different lighting conditions and backgrounds during the generation process. For example, the generation unit can simulate different lighting conditions and generate try-on images. The generation unit can also simulate different backgrounds and generate try-on images. For example, the generation unit can combine lighting conditions and backgrounds to generate realistic try-on images. In this way, the generation unit can generate more realistic try-on images by simulating different lighting conditions and backgrounds. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI.

[0093] The generation unit can estimate the user's emotions and adjust the display order of the try-on images based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize displaying relaxing try-on images. For example, if the user is having fun, the generation unit can also prioritize displaying casual and fun try-on images. For example, if the user is tired, the generation unit can also prioritize displaying comfortable and relaxing try-on images. In this way, the generation unit can display try-on images in a more appropriate order by adjusting the display order of the try-on images based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The generation unit can generate highly relevant try-on images by considering the user's geographical location information during generation. For example, the generation unit generates highly relevant try-on images based on the user's geographical location information. The generation unit can also generate optimal try-on images by considering the user's geographical location information. For example, the generation unit can analyze the user's geographical location information and generate highly relevant try-on images. In this way, the generation unit can generate highly relevant try-on images by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0095] The generation unit can analyze the user's social media activity during generation and generate relevant try-on images. For example, the generation unit generates relevant try-on images based on the user's social media activity. The generation unit can also generate optimal try-on images based on clothing data shared by the user on social media. For example, the generation unit can analyze the user's social media activity and generate optimal try-on images. In this way, the generation unit can generate relevant try-on images by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0096] The expression unit can estimate the user's emotions and adjust the visual representation of comfort based on the estimated emotions. For example, if the user is stressed, the expression unit will provide a visual representation of relaxing comfort. For example, if the user is having fun, the expression unit can also provide a visual representation of casual and fun comfort. For example, if the user is tired, the expression unit can provide a visual representation of comfortable and relaxing comfort. In this way, the expression unit can provide a more appropriate visual representation by adjusting the visual representation of comfort based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The expression unit can perform visual representations that accurately reflect the textures of different materials and fabrics during the expression process. For example, the expression unit can perform visual representations that accurately reflect the textures of different materials. The expression unit can also perform visual representations that accurately reflect the textures of different fabrics. For example, the expression unit can combine the textures of materials and fabrics to create realistic visual representations. As a result, the expression unit can perform more realistic visual representations by accurately reflecting the textures of different materials and fabrics. Some or all of the above-described processes in the expression unit may be performed using AI, for example, or without using AI.

[0098] The expression unit can simulate and represent changes in comfort based on the user's body shape and movements during the representation process. For example, the expression unit can simulate and represent changes in comfort based on the user's body shape. The expression unit can also simulate and represent changes in comfort based on the user's movements. For example, the expression unit can combine the user's body shape and movements to create a realistic visual representation of comfort. As a result, the expression unit can create a more realistic visual representation by simulating changes in comfort based on the user's body shape and movements. Some or all of the above-described processes in the expression unit may be performed using AI, for example, or without using AI.

[0099] The expression unit can estimate the user's emotions and determine the priority of visual representations of comfort based on the estimated user emotions. For example, if the user is stressed, the expression unit will prioritize visual representations of comfort that promote relaxation. For example, if the user is having fun, the expression unit may also prioritize visual representations of comfort with a casual and fun design. For example, if the user is tired, the expression unit may also prioritize visual representations of comfort that promote comfort and relaxation. In this way, the expression unit can provide more appropriate visual representations by determining the priority of visual representations of comfort based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The display unit can perform optimal visual representations while considering the user's geographical location information. For example, the display unit can perform optimal visual representations based on the user's geographical location information. The display unit can also perform relevant visual representations while considering the user's geographical location information. For example, the display unit can analyze the user's geographical location information and perform optimal visual representations. In this way, the display unit can perform optimal visual representations by considering the user's geographical location information. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI.

[0101] The expression unit can analyze the user's social media activity and create relevant visual representations during the expression process. For example, the expression unit can create relevant visual representations based on the user's social media activity. The expression unit can also create optimal visual representations based on, for example, data on clothing shared by the user on social media. The expression unit can also analyze the user's social media activity and create optimal visual representations. In this way, the expression unit can create relevant visual representations by analyzing the user's social media activity. Some or all of the above processing in the expression unit may be performed using AI, for example, or without using AI.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The suggestion function can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is feeling stressed, it can suggest relaxing clothing. If the user is having fun, it can suggest casual and fun clothing designs. If the user is tired, it can suggest comfortable and relaxing clothing. In this way, the suggestion function can make more appropriate suggestions by adjusting the content of its suggestions based on the user's emotions.

[0104] The data collection unit can collect data based on the user's lifestyle. For example, if the user has an active lifestyle, it can collect data on sportswear and casual clothing. If the user is a business person, it can also collect data on formal attire and business casual clothing. If the user spends a lot of time at home, it can also collect data on comfortable clothing. In this way, the data collection unit can collect more relevant data by collecting data based on the user's lifestyle.

[0105] The generation unit can estimate the user's emotions and adjust the method of generating try-on images based on those emotions. For example, if the user is stressed, it can generate simple and highly visible try-on images. If the user is having fun, it can generate casual and fun try-on images. If the user is tired, it can generate comfortable and relaxing try-on images. In this way, the generation unit can generate more appropriate try-on images by adjusting the generation method based on the user's emotions.

[0106] The data collection unit can analyze a user's past purchase history and determine the priority of the data to collect. For example, it can prioritize collecting data on brands the user has purchased in the past. It can also collect relevant data based on the style of items the user has purchased in the past. It can also collect relevant data based on the color and material of items the user has purchased in the past. In this way, the data collection unit can analyze a user's past purchase history to determine the priority of the data to collect and collect data efficiently.

[0107] The proposal department can estimate the user's emotions and adjust the way the proposal is presented based on those emotions. For example, if the user is stressed, it can present a simple and highly visible proposal. If the user is having fun, it can present a casual and enjoyable design. If the user is tired, it can present a comfortable and relaxing proposal. In this way, the proposal department can provide more appropriate proposals by adjusting the way the proposal is presented based on the user's emotions.

[0108] The data collection unit can filter data according to the user's current fashion trends and the season during collection. For example, it can collect data that matches the user's current fashion trends. It can also collect data on clothing appropriate for the season. It can even combine data on the user's current fashion trends with data appropriate for the season. As a result, the data collection unit can collect more relevant data by filtering the data according to the user's current fashion trends and the season.

[0109] The learning unit can estimate the user's emotions and select training data based on those estimated emotions. For example, if the user is stressed, it can prioritize learning data on relaxing clothing. If the user is having fun, it can learn data on casual and fun clothing designs. If the user is tired, it can learn data on comfortable and relaxing clothing. In this way, the learning unit can learn more appropriate data by selecting training data based on the user's emotions.

[0110] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during the collection process. For example, it can collect data on clothing suitable for the climate of the area where the user lives. It can also collect data on clothing suitable for places the user frequently visits. Based on the user's geographical location information, it can also prioritize the collection of highly relevant data. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information.

[0111] The generation unit can generate try-on images by simulating different lighting conditions and backgrounds during the generation process. For example, it can simulate different lighting conditions and generate try-on images. It can also simulate different backgrounds and generate try-on images. It can even combine lighting conditions and backgrounds to generate realistic try-on images. In this way, the generation unit can generate more realistic try-on images by simulating different lighting conditions and backgrounds.

[0112] The display unit can estimate the user's emotions and adjust the visual representation of comfort based on those emotions. For example, if the user is stressed, it can display a visual representation of relaxing comfort. If the user is having fun, it can display a visual representation of casual and fun design comfort. If the user is tired, it can display a visual representation of comfortable and relaxing comfort. In this way, the display unit can provide more appropriate visual representations by adjusting the visual representation of comfort based on the user's emotions.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects the user's body type, preferences, and purchase history. For example, the data collection unit can collect body type information such as the user's height, weight, and body fat percentage. It can also collect information such as the user's color, style, and brand preferences. Furthermore, the data collection unit can collect the user's purchase history for the past year, as well as purchase history in specific categories. Step 2: The learning unit learns from the data collected by the collection unit. The learning unit, for example, uses AI to analyze the collected data and learns based on the user's body type, preferences, and purchase history. Step 3: The suggestion unit proposes the optimal outfit based on the data learned by the learning unit. For example, the suggestion unit can suggest clothing suitable for the season and event based on the user's body type, preferences, and purchase history. Step 4: The generation unit generates try-on images of the outfits proposed by the suggestion unit. The generation unit can generate try-on images that reflect the user's body shape and facial features, for example, using 3D modeling or photorealistic rendering technology. Step 5: The expression unit provides a visual representation of the fit based on the try-on images generated by the generation unit. For example, the expression unit can visually represent the texture of the material and how it changes in response to movement.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the collection unit, learning unit, proposal unit, generation unit, and representation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's body shape, preferences, and purchase history using the camera 42 and microphone 38B of the smart device 14. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes and learns the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal outfit based on the learned data. The generation unit is implemented in the specific processing unit 46A of the smart device 14 and generates a try-on image of the proposed outfit. The representation unit is implemented in the specific processing unit 46A of the smart device 14 and provides a visual representation of the fit based on the generated try-on image. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

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

[0122] The 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.

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

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

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

[0126] Figure 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.

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

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

[0129] In the 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.

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0133] The data processing system 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.

[0134] Each of the multiple elements described above, including the collection unit, learning unit, proposal unit, generation unit, and representation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's body shape, preferences, and purchase history using the camera 42 and microphone 238 of the smart glasses 214. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes and learns the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes the optimal outfit based on the learned data. The generation unit is implemented in the control unit 46A of the smart glasses 214, for example, and generates a try-on image of the proposed outfit. The representation unit is implemented in the control unit 46A of the smart glasses 214, for example, and provides a visual representation of the fit based on the generated try-on image. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

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

[0138] The 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.

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

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

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

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the collection unit, learning unit, proposal unit, generation unit, and representation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's body shape, preferences, and purchase history using the camera 42 and microphone 238 of the headset terminal 314. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes and learns the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal outfit based on the learned data. The generation unit is implemented in the control unit 46A of the headset terminal 314 and generates a try-on image of the proposed outfit. The representation unit is implemented in the control unit 46A of the headset terminal 314 and provides a visual representation of the fit based on the generated try-on image. 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.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the collection unit, learning unit, proposal unit, generation unit, and representation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects the user's body shape, preferences, and purchase history using the camera 42 and microphone 238 of the robot 414. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes and learns from the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes the optimal outfit based on the learned data. The generation unit is implemented in the control unit 46A of the robot 414, for example, and generates a try-on image of the proposed outfit. The representation unit is implemented in the control unit 46A of the robot 414, for example, and provides a visual representation of the comfort of the outfit based on the generated try-on image. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) A data collection unit that collects user body type, preferences, and purchase history, A learning unit that learns from the data collected by the aforementioned collection unit, A proposal unit that proposes the optimal coordination based on the data learned by the aforementioned learning unit, A generation unit that generates try-on images of the outfit proposed by the proposal unit, The system includes an expression unit that provides a visual representation of the fit based on the try-on images generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We offer suggestions for everyday outfits, clothing recommendations for events, and advice on gifts for others. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We analyze the user's past purchase history to determine the priority of the data to collect. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is During data collection, the data is filtered according to the user's current fashion trends and the season. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the frequency of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, During the learning process, the learning content is updated to take into account changes in the user's fashion trends. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, During training, the training data is weighted based on the user's lifestyle and occupation. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, During training, the system analyzes users' social media activity and incorporates relevant data into the learning process. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making suggestions, we refer to the user's past styling history to provide the most suitable recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, we suggest appropriate coordination based on the user's event schedule. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration to suggest the most suitable coordination. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest relevant coordinated approaches. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is The system estimates the user's emotions and adjusts the method of generating try-on images based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During the generation process, try-on images are generated that accurately reflect the user's body shape and facial features. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, try-on images are created by simulating different lighting conditions and backgrounds. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is The system estimates the user's emotions and adjusts the display order of try-on images based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the system considers the user's geographical location to generate highly relevant try-on images. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, the system analyzes the user's social media activity and generates relevant try-on images. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned expression unit is The system estimates the user's emotions and adjusts the visual representation of comfort based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned expression unit is When creating visual representations, we aim to accurately reflect the textures of different materials and fabrics. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned expression unit is During the presentation, the system simulates and represents how the wearer's comfort changes according to the user's body shape and movements. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned expression unit is The system estimates the user's emotions and prioritizes the visual representation of comfort based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned expression unit is When displaying content, the user's geographical location information is taken into consideration to create the optimal visual representation. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned expression unit is When creating visual representations, we analyze users' social media activity and create relevant visual representations. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user body type, preferences, and purchase history, A learning unit that learns from the data collected by the aforementioned collection unit, A proposal unit that proposes the optimal coordination based on the data learned by the aforementioned learning unit, A generation unit that generates try-on images of the outfit proposed by the proposal unit, The system includes an expression unit that provides a visual representation of the fit based on the try-on images generated by the generation unit. A system characterized by the following features.

2. The aforementioned proposal section is, We offer suggestions for everyday outfits, clothing recommendations for events, and advice on gifts for others. The system according to feature 1.

3. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system according to feature 1.

4. The aforementioned collection unit is We analyze the user's past purchase history to determine the priority of the data to collect. The system according to feature 1.

5. The aforementioned collection unit is During data collection, the data is filtered according to the user's current fashion trends and the season. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the frequency of data collection based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system according to feature 1.

9. The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system according to feature 1.

10. The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system according to feature 1.

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