system

Abstract

The system according to this embodiment aims to suggest the optimal clothing combination based on the user's body shape data. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a virtual fitting unit. The data collection unit collects body shape data. The analysis unit analyzes the data collected by the data collection unit in real time. The suggestion unit suggests the optimal clothing combination based on the analysis results obtained by the analysis unit. The virtual fitting unit allows the user to virtually try on the clothes suggested by the suggestion unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to propose an optimal combination of clothes suitable for the user's body shape, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal combination of clothes based on the user's body shape data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a virtual fitting unit. The data collection unit collects body shape data. The analysis unit analyzes the data collected by the data collection unit in real time. The suggestion unit suggests the optimal clothing combination based on the analysis results obtained by the analysis unit. The virtual fitting unit allows the user to virtually try on the clothes suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal clothing combination based on the user's body shape data. [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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 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 fashion advice system according to an embodiment of the present invention is a system that collects, analyzes, and proposes body shape data, and provides the user with optimal fashion advice through virtual try-on. This fashion advice system combines the high-precision body shape measurement technology of a measurement suit such as ZOZOSUIT (registered trademark) with the autonomous judgment ability of an AI agent to provide the user with optimal fashion advice. Specifically, it consists of the following steps: First, the body shape is measured periodically using a measurement suit, and the AI ​​agent analyzes the changes in real time. Next, the AI ​​agent considers the user's body shape data, preferences, lifestyle, and budget, and proposes the optimal combination of clothes. Furthermore, it provides a function that allows the user to virtually try on clothes recommended by the AI ​​based on 3D data measured with the measurement suit. This service employs a subscription system that can be used for a monthly fee, and there is also an affiliate revenue model in which the system earns a commission from partner brands when a user purchases clothes recommended by the AI. In addition, anonymized fashion trend data is provided to apparel brands for use in product development, creating a new revenue stream. This service enables users to choose clothes that suit their individual body types, reducing unnecessary purchases and waste of clothing, improving users' self-esteem, and promoting sustainability in the fashion industry. The fashion advice system collects, analyzes, suggests, and provides optimal fashion advice through virtual try-ons based on users' body shape data.

[0029] The fashion advice system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a virtual fitting unit. The data collection unit collects body shape data. The data collection unit can periodically measure body shape using, for example, a measuring suit. The data collection unit needs to clarify the specific types of body shape data and the method of collection. For example, this includes height, weight, waist size, etc. The analysis unit analyzes the collected body shape data in real time. The analysis unit needs to clarify the specific methods and criteria for real-time analysis. For example, this includes the data update frequency and the algorithm to be used. The suggestion unit proposes the optimal clothing combination based on the analysis results obtained by the analysis unit. The suggestion unit proposes the optimal clothing combination considering the user's body shape data, preferences, lifestyle, and budget. The suggestion unit needs to clarify the specific criteria and methods for proposing the optimal clothing combination. For example, this includes sizes and color combinations that fit the body shape. The virtual fitting unit provides a function that allows the user to virtually try on the clothes suggested by the suggestion unit. The virtual fitting unit provides a function that allows the user to virtually try on clothes recommended by AI based on 3D data measured with a measuring suit. The virtual fitting section needs to clearly define the specific methods and technologies for virtually trying on clothes. Examples include 3D modeling technology and virtual reality technology. This allows the fashion advice system, according to the embodiment, to collect, analyze, suggest, and provide optimal fashion advice through virtual try-on based on the user's body shape data.

[0030] The data collection unit collects body shape data. For example, the data collection unit can periodically measure body shape using a measurement suit. The measurement suit is a special suit that, when worn by the user, allows for accurate measurement of the entire body's dimensions. The data collection unit needs to clearly define the specific types of body shape data and how it will be collected. Examples include height, weight, waist size, hip size, bust size, arm length, leg length, shoulder width, and neck circumference. This data is obtained when the user wears the measurement suit and takes photos with a smartphone using a dedicated application. The captured images are analyzed by the application to generate detailed body shape data. The data collection unit sends this data to a cloud server and stores it in a central database. Furthermore, the data collection unit can track changes in body shape by periodically updating the user's body shape data. For example, by regularly using the measurement suit for measurements, changes in weight and muscle mass can be tracked. This allows the data collection unit to always maintain the user's latest body shape data, making it available to the analysis and proposal units. By adjusting the frequency and accuracy of data collection, the data collection unit can flexibly respond to user needs and lifestyles. For example, for users on a diet, more frequent measurements allow for detailed tracking of changes in body shape. This enables the data collection unit to efficiently and accurately collect body shape data, improving the overall system performance.

[0031] The analytics department analyzes collected body shape data in real time. The analytics department needs to clearly define the specific methods and criteria for real-time analysis, such as the data update frequency and the algorithms used. Based on the collected body shape data, the analytics department analyzes the characteristics and changes in users' body shapes in detail. Specifically, it uses AI to process data in real time and grasp statistical information and trends related to users' body shapes. For example, it analyzes changes in users' height and weight, and increases and decreases in waist and hip sizes to track changes in body shape. Using machine learning algorithms, the AI ​​can predict changes in users' body shapes by comparing them with past data. This allows the analytics department to quickly and accurately analyze users' body shape data and provide up-to-date information. Furthermore, the analytics department can understand body shape characteristics and trends by comparing users' body shape data with data from other users. For example, by comparing it with the average body shape data of users of the same age and gender, it can assess how standard a user's body shape is. This allows the analytics department to provide detailed information about users' body shapes and provide the foundational data for the recommendation department to suggest optimal clothing combinations.

[0032] The Proposal Department proposes the optimal clothing combination based on the analysis results obtained by the Analysis Department. The Proposal Department proposes the optimal clothing combination considering the user's body shape data, preferences, lifestyle, and budget. The Proposal Department needs to clarify the specific criteria and methods for proposing the optimal clothing combination. For example, this includes sizes that fit the body shape, color combinations, and styles that are appropriate for the season and trends. The Proposal Department uses AI to analyze the user's body shape data and evaluate the optimal clothing size and fit. Furthermore, based on the user's preferences and lifestyle, it proposes the optimal clothing combination considering elements such as color, design, and material. For example, if the user prefers a casual style, it will propose a combination of items such as denim, T-shirts, and sneakers. If a style suitable for a business setting is proposed, it will propose a combination of items such as suits, shirts, and leather shoes. The Proposal Department also considers the user's budget and selects the optimal items according to the price range. This allows the Proposal Department to provide optimal fashion advice that meets the user's needs and preferences. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, if a user actually purchases the suggested clothing and provides feedback on their satisfaction and fit, the suggestion team can incorporate this feedback into future suggestions. This allows the suggestion team to provide more appropriate fashion advice to users and improve their satisfaction.

[0033] The virtual fitting department provides a function that allows users to virtually try on clothes suggested by the suggestion department. Based on 3D data measured with a measurement suit, the virtual fitting department provides a function that allows users to virtually try on clothes recommended by AI. The virtual fitting department needs to clearly define the specific methods and technologies for virtual try-on. These include, for example, 3D modeling technology, virtual reality technology, and augmented reality technology. The virtual fitting department allows users to try on suggested clothes in a virtual space based on their 3D body shape data. Specifically, users access the virtual fitting application using devices such as smartphones, tablets, or PCs. The application reads the user's 3D body shape data and displays suggested clothes in real time. Users can see how their avatar looks wearing the suggested clothes on the screen. Furthermore, the virtual fitting department provides functions to change the color, design, and size of the clothes, allowing users to try on various combinations. For example, a user can try on shirts and pants of different colors to find the best combination. Furthermore, the virtual fitting service uses technology to realistically reproduce the material and texture of clothing, providing users with an experience as if they were actually trying on the clothes. This allows the virtual fitting service to help users choose the perfect clothes from the comfort of their homes and improve satisfaction by allowing them to try on clothes before purchasing. In addition, the virtual fitting service can collect user feedback and continuously improve the accuracy and realism of the fitting experience. This enables the virtual fitting service to provide users with a more realistic and satisfying fitting experience.

[0034] The data collection unit can periodically measure body shape using a measurement suit. The data collection unit needs to clarify the specific frequency and method of periodic body shape measurements. For example, this could include weekly or monthly measurements. This enables highly accurate body shape measurements by using the measurement suit. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input body shape data measured by the measurement suit into a generative AI, which can then analyze the body shape data.

[0035] The analysis unit can analyze the collected body shape data in real time. For example, the analysis unit analyzes the collected body shape data in real time. The analysis unit needs to clarify the specific methods and criteria for real-time analysis. For example, this includes the data update frequency and the algorithms used. This will enable suggestions based on the latest body shape data through real-time analysis. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the collected body shape data into a generative AI, which can then analyze the data in real time.

[0036] The suggestion unit can propose the optimal clothing combination by considering the user's body shape data, preferences, lifestyle, and budget. For example, the suggestion unit proposes the optimal clothing combination by considering the user's body shape data, preferences, lifestyle, and budget. The suggestion unit needs to clearly define the specific criteria and methods for proposing the optimal clothing combination. For example, this includes appropriate sizes and color combinations for the user's body shape. This enables the suggestion unit to propose the optimal clothing that meets the user's individual needs. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's body shape data, preferences, lifestyle, and budget into a generative AI, which can then propose the optimal clothing combination.

[0037] The virtual fitting unit can provide a function that allows users to virtually try on clothes recommended by AI based on 3D data measured by a measurement suit. The virtual fitting unit needs to clearly define the specific methods and technologies for virtual try-on. These include, for example, 3D modeling technology and virtual reality technology. This allows users to check the fit of clothes before actually trying them on. Some or all of the above-described processes in the virtual fitting unit may be performed using, for example, generative AI, or without generative AI. For example, the virtual fitting unit can input 3D data measured by a measurement suit into a generative AI, which can then perform the virtual try-on.

[0038] The collection unit can collect the user's purchase history. For example, the collection unit collects the user's purchase history. The collection unit needs to clarify the specific types of purchase history and the method of collection. For example, this may include past purchased items and purchase dates. By collecting the user's purchase history, it becomes possible to provide more personalized suggestions. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collection unit can input the user's purchase history into a generative AI, which can then analyze the purchase history.

[0039] A revenue management department is established, which manages revenue to obtain commissions from partner brands. The revenue management department needs to clearly define the specific methods and standards for revenue management. For example, this includes the method for calculating revenue and the method for setting commissions. This will stabilize revenue by managing commission income from partner brands. Some or all of the above processes in the revenue management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the revenue management department can input commission income data from partner brands into a generative AI, and the generative AI can perform revenue management.

[0040] The data collection unit can analyze the user's past body shape data and select the optimal collection method. For example, the data collection unit analyzes the user's past body shape data and selects the optimal collection method. The data collection unit needs to clarify the specific types and collection methods of past body shape data. For example, this includes past measurement data and recorded body shape information. For example, the data collection unit selects the most accurate measurement method based on the user's past body shape data. For example, the data collection unit can analyze the user's body shape change trends and determine an appropriate measurement frequency. For example, the data collection unit can customize the measurement method considering fluctuations in the user's body shape data. This allows the optimal collection method to be selected by analyzing past body shape data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input the user's past body shape data into a generative AI, which can then select the optimal collection method.

[0041] The data collection unit can filter body shape data based on the user's current health status and lifestyle. For example, the data collection unit can filter body shape data based on the user's current health status and lifestyle. The data collection unit needs to clarify the specific types of health status and lifestyle and how to collect them. For example, this includes blood pressure, heart rate, and body fat percentage. For example, the data collection unit can adjust the frequency of body shape data collection considering the user's health status. For example, the data collection unit can determine the optimal collection timing based on the user's lifestyle. For example, the data collection unit can select the types of data to collect according to the user's health status and lifestyle. This allows for the collection of more appropriate body shape data by filtering the data based on health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's health status and lifestyle data into a generative AI, which can then perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting body shape data. For example, when collecting body shape data, the data collection unit prioritizes the collection of highly relevant data by considering the user's geographical location information. The data collection unit needs to clarify the specific types and methods of collection of geographical location information. For example, this includes GPS data, address information, etc. For example, if the user is in a specific region, the data collection unit will prioritize the collection of body shape data related to that region. For example, the data collection unit can determine the optimal collection timing based on the user's geographical location information. For example, the data collection unit can select the types of data to collect according to the user's geographical location information. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, which can then prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data when collecting body shape data. For example, the data collection unit analyzes the user's social media activity and collects relevant data when collecting body shape data. The data collection unit needs to clarify the specific types of social media activity and how to collect them. For example, this includes post content, the number of likes, and the number of followers. The data collection unit can adjust the timing of body shape data collection based on the user's social media activity. The data collection unit can select the types of data to collect based on the user's social media activity. The data collection unit can analyze the user's social media activity and prioritize the collection of relevant body shape data. This allows for the collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, which can then collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the body shape data during the analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the body shape data during the analysis. The analysis unit needs to clarify how to evaluate the importance of the body shape data. For example, this includes data accuracy, relevance, etc. The analysis unit can perform a detailed analysis for important body shape data. For example, the analysis unit can perform a concise analysis for less important body shape data. The analysis unit can adjust the level of detail of the analysis in stages according to the importance of the body shape data. This makes efficient analysis possible by adjusting the level of detail of the analysis according to the importance of the body shape data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the importance of the body shape data into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of body shape data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of body shape data during analysis. The analysis unit needs to clarify the specific types and classification methods of the body shape data categories. For example, these include height, weight, waist size, etc. The analysis unit selects the optimal analysis algorithm depending on the category of body shape data. For example, the analysis unit can apply different analysis methods to the body shape data for each category. For example, the analysis unit can customize the analysis algorithm for each category of body shape data. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the category of body shape data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the body shape data categories into a generative AI, and the generative AI can apply the optimal analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the timing of body shape data collection during the analysis. For example, the analysis unit can determine the priority of analysis based on the timing of body shape data collection during the analysis. The analysis unit needs to clarify the specific criteria and methods for the collection timing. For example, this includes the frequency of data updates and the timing of collection. For example, the analysis unit can prioritize the analysis of the most recent body shape data. For example, the analysis unit can postpone the analysis of older body shape data collected at a later date. For example, the analysis unit can adjust the priority of analysis in stages according to the timing of body shape data collection. This allows for prioritizing the analysis of the most recent data by determining the priority of analysis based on the timing of body shape data collection. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the timing of body shape data collection into a generative AI, and the generative AI can determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of body type data during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of body type data during analysis. The analysis unit needs to clarify specific criteria and evaluation methods for relevance. For example, this includes data correlation and commonalities. For example, the analysis unit can prioritize the analysis of highly relevant body type data. For example, the analysis unit can postpone the analysis of less relevant body type data. For example, the analysis unit can adjust the order of analysis step by step according to the relevance of the body type data. This makes efficient analysis possible by adjusting the order of analysis based on the relevance of body type data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the relevance of body type data into a generative AI, and the generative AI can adjust the order of analysis.

[0048] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the clothing items. For example, the suggestion unit can adjust the level of detail in its suggestions based on the importance of the clothing items. The suggestion unit needs to clarify how it evaluates the importance of the clothing items. For example, this could include the popularity of the design or the frequency of use. For example, the suggestion unit can provide detailed suggestions for important clothing items. For example, the suggestion unit can provide concise suggestions for less important clothing items. For example, the suggestion unit can adjust the level of detail in its suggestions in stages according to the importance of the clothing items. This allows for efficient suggestions by adjusting the level of detail according to the importance of the clothing items. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the importance of the clothing items into a generative AI, which can then adjust the level of detail in its suggestions.

[0049] The suggestion unit can apply different suggestion algorithms depending on the clothing category when making suggestions. For example, the suggestion unit applies different suggestion algorithms depending on the clothing category when making suggestions. The suggestion unit needs to clarify the specific types and classification methods of clothing categories. For example, these include casual wear, formal wear, and sportswear. The suggestion unit selects the optimal suggestion algorithm depending on the clothing category. For example, the suggestion unit can apply different suggestion methods to clothing in each category. For example, the suggestion unit can customize the suggestion algorithm for each clothing category. This improves the accuracy of suggestions by applying the optimal suggestion algorithm according to the clothing category. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input the clothing category into a generative AI, and the generative AI can apply the optimal suggestion algorithm.

[0050] The proposal department can determine the priority of proposals based on the submission timing of the clothing items when submitting a proposal. For example, the proposal department can determine the priority of proposals based on the submission timing of the clothing items when submitting a proposal. The proposal department needs to clarify the specific criteria and methods for submission timing. For example, this includes the frequency of data updates and the timing of submission. The proposal department can, for example, prioritize proposing the newest clothing items. The proposal department can, for example, postpone proposing older clothing items. The proposal department can, for example, adjust the priority of proposals in stages according to the submission timing of the clothing items. This allows for prioritizing the proposal of the newest clothing items by determining the priority of proposals based on the submission timing of the clothing items. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the submission timing of the clothing items into a generative AI, and the generative AI can determine the priority of proposals.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the clothing items when making suggestions. For example, the suggestion unit can adjust the order of suggestions based on the relevance of the clothing items when making suggestions. The suggestion unit needs to clarify specific criteria and evaluation methods for relevance. For example, this includes data correlation and commonalities. For example, the suggestion unit can prioritize suggesting highly relevant clothing items. For example, the suggestion unit can postpone suggesting less relevant clothing items. For example, the suggestion unit can adjust the order of suggestions in stages according to the relevance of the clothing items. This makes it possible to make efficient suggestions by adjusting the order of suggestions based on the relevance of the clothing items. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the relevance of the clothing items into a generative AI, and the generative AI can adjust the order of suggestions.

[0052] The virtual fitting unit can select the optimal display method by referring to the user's past fitting history during virtual fitting. For example, the virtual fitting unit selects the optimal display method by referring to the user's past fitting history during virtual fitting. The virtual fitting unit needs to clarify the specific types and collection methods of past fitting history. For example, this includes the type of clothing tried on and the date and time of the try-on. The virtual fitting unit selects the optimal display method based on the user's past fitting history. For example, the virtual fitting unit can suggest a display method that suits the user's preferences based on their fitting history. For example, the virtual fitting unit can analyze the user's past fitting history and select the most effective display method. This allows the optimal display method to be selected by referring to past fitting history. Some or all of the above processing in the virtual fitting unit may be performed using, for example, a generation AI, or without a generation AI. For example, the virtual fitting unit can input the user's past fitting history into a generation AI, and the generation AI can select the optimal display method.

[0053] The virtual fitting unit can improve fitting accuracy based on the user's body shape data during virtual fitting. For example, the virtual fitting unit can improve fitting accuracy based on the user's body shape data during virtual fitting. The virtual fitting unit needs to clearly define the specific types and methods of collecting body shape data. For example, this includes height, weight, and waist size. The virtual fitting unit can improve fitting accuracy based on the user's body shape data. For example, the virtual fitting unit can improve fitting accuracy by reflecting the user's body shape data in real time. For example, the virtual fitting unit can adjust the fitting display method according to the user's body shape data. This allows for more accurate fitting information to be provided by improving fitting accuracy based on body shape data. Some or all of the above processing in the virtual fitting unit may be performed using, for example, a generating AI, or without a generating AI. For example, the virtual fitting unit can input the user's body shape data into a generating AI, which can then improve fitting accuracy.

[0054] The virtual fitting unit can select the optimal display method during virtual fitting by considering the user's device information. For example, the virtual fitting unit selects the optimal display method during virtual fitting by considering the user's device information. The virtual fitting unit needs to clearly define the specific types and methods of collecting device information. This includes, for example, the device type and OS version. For example, if the user is using a smartphone, the virtual fitting unit can provide a display method adapted to the screen size. For example, if the user is using a tablet, the virtual fitting unit can provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the virtual fitting unit can provide a concise and highly visible display method. This allows the optimal display method to be provided by considering device information. Some or all of the above processing in the virtual fitting unit may be performed using, for example, a generation AI, or without a generation AI. For example, the virtual fitting unit can input the user's device information into a generation AI, which can then select the optimal display method.

[0055] The virtual fitting unit can analyze the user's social media activity during virtual fitting and provide relevant fitting information. For example, the virtual fitting unit needs to clearly define the specific types and methods of collection of social media activity. This includes, for example, post content, number of likes, and number of followers. The virtual fitting unit can provide relevant fitting information based on the user's social media activity. For example, the virtual fitting unit can suggest optimal fitting information based on the user's social media activity. The virtual fitting unit can analyze the user's social media activity and customize the fitting information. This allows it to provide relevant fitting information by analyzing social media activity. Some or all of the above processing in the virtual fitting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the virtual fitting unit can input the user's social media activity data into a generative AI, which can then provide relevant fitting information.

[0056] The revenue management department can analyze past revenue data of partner brands to select the optimal management method during revenue management. For example, the revenue management department can analyze past revenue data of partner brands to select the optimal management method during revenue management. The revenue management department needs to clarify the specific types and collection methods of past revenue data. For example, this includes sales data, profit data, etc. The revenue management department can select the optimal management method based on past revenue data of partner brands. For example, the revenue management department can analyze the revenue data of partner brands to improve the efficiency of revenue management. For example, the revenue management department can customize its revenue management methods by referring to past revenue data of partner brands. This allows it to select the optimal management method by analyzing past revenue data. Some or all of the above processes in the revenue management department may be performed using, for example, a generation AI, or not using a generation AI. For example, the revenue management department can input past revenue data of partner brands into a generation AI, and the generation AI can select the optimal management method.

[0057] The revenue management department can customize its revenue management methods based on the current market conditions of partner brands when performing revenue management. For example, the revenue management department can customize its revenue management methods based on the current market conditions of partner brands when performing revenue management. The revenue management department needs to clarify the specific types of market conditions and how they are collected. For example, this includes competitor trends and consumer trends. The revenue management department can customize its revenue management methods based on the current market conditions of partner brands. For example, the revenue management department can analyze the market conditions of partner brands to improve the efficiency of revenue management. The revenue management department can adjust its revenue management methods according to the current market conditions of partner brands. This improves the efficiency of revenue management by customizing revenue management methods based on current market conditions. Some or all of the above processes in the revenue management department may be performed using, for example, a generating AI, or not using a generating AI. For example, the revenue management department can input current market conditions data of partner brands into a generating AI, and the generating AI can customize the revenue management methods.

[0058] The revenue management department can select the optimal management method when managing revenue, taking into account the geographical location information of partner brands. For example, the revenue management department selects the optimal management method when managing revenue, taking into account the geographical location information of partner brands. The revenue management department needs to clarify the specific types and collection methods of geographical location information. For example, this includes GPS data, address information, etc. The revenue management department selects the optimal management method based on the geographical location information of partner brands. For example, the revenue management department can analyze the geographical location information of partner brands to improve the efficiency of revenue management. For example, the revenue management department can adjust the revenue management method according to the geographical location information of partner brands. This allows for the selection of the optimal management method by taking geographical location information into consideration. Some or all of the above processes in the revenue management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the revenue management department can input the geographical location information of partner brands into a generative AI, and the generative AI can select the optimal management method.

[0059] The revenue management department can analyze the social media activities of partner brands and propose revenue management methods when managing revenue. For example, the revenue management department can analyze the social media activities of partner brands and propose revenue management methods when managing revenue. The revenue management department needs to clarify the specific types and collection methods of social media activities. For example, this includes post content, number of likes, number of followers, etc. The revenue management department can propose revenue management methods based on the social media activities of partner brands. For example, the revenue management department can analyze the social media activities of partner brands and improve the efficiency of revenue management. For example, the revenue management department can adjust the revenue management methods according to the social media activities of partner brands. This allows the department to propose revenue management methods by analyzing social media activities. Some or all of the above processing in the revenue management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the revenue management department can input social media activity data of partner brands into a generative AI, and the generative AI can propose revenue management methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can collect not only the user's body shape data but also their health data. For example, by collecting health data such as the user's blood pressure, heart rate, and body fat percentage, and analyzing this data in combination with body shape data, it is possible to provide more accurate fashion advice. The data collection unit needs to clearly define the method and frequency of health data collection. For example, if the user is using a smartwatch, that data can be collected regularly. This makes it possible to provide fashion advice tailored to the user's health condition and contribute to maintaining the user's health.

[0062] The analysis department can analyze a user's posture and gait characteristics based on their body shape data. For example, if a user has poor posture, it can provide advice on how to improve it. It can also analyze a user's gait characteristics and suggest shoes suitable for walking. This can contribute not only to improving the user's body shape but also to improving their posture and gait. The analysis department needs to clarify specific methods and criteria for analyzing posture and gait characteristics. These may include, for example, posture angles and walking rhythm.

[0063] The suggestion department can suggest fashion items that match the user's skin tone and hair color based on the user's body shape data. For example, by suggesting clothes in colors that match the user's skin tone, a more attractive fashion style can be achieved. It can also suggest accessories that match the user's hair color. This allows for fashion advice that considers not only the user's body shape but also the overall coordination. The suggestion department needs to clearly define the specific methods and criteria for analyzing skin tone and hair color. For example, this should include color tone and brightness.

[0064] The virtual fitting unit can provide virtual fitting tailored to the user's movements based on the user's body shape data. For example, it can simulate movements such as walking and sitting, and allow users to check the fit of clothing in accordance with those movements. This allows users to check the fit of clothing in actual movements in advance. The virtual fitting unit needs to clearly define the specific methods and technologies that enable fitting tailored to movement. These include, for example, motion capture technology and real-time rendering technology.

[0065] The Revenue Management Department can generate revenue not only from commission income from partner brands, but also by analyzing user purchase data and displaying advertisements based on purchasing trends. For example, displaying advertisements related to products that users have previously purchased can enhance the effectiveness of advertising. Furthermore, it can create new revenue streams by providing anonymized user purchase data to apparel brands for use in product development. This will lead to diversification and stabilization of revenue. The Revenue Management Department needs to clarify the methods for analyzing purchase data and the criteria for displaying advertisements.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects body shape data. For example, body shape can be measured regularly using a measuring suit. The data collection unit collects specific body shape data such as height, weight, and waist size. Step 2: The analysis department analyzes the collected body shape data in real time. The analysis department clarifies the specific methods and criteria for real-time analysis, such as the data update frequency and the algorithms to be used. Step 3: The proposal department proposes the optimal clothing combination based on the analysis results obtained by the analysis department. The proposal department proposes the optimal clothing combination considering the user's body shape data, preferences, lifestyle, and budget. Specifically, it proposes size and color combinations that suit the body shape. Step 4: The virtual fitting department provides a function that allows users to virtually try on clothes suggested by the suggestion department. The virtual fitting department provides a function that allows users to virtually try on clothes recommended by AI based on 3D data measured with a measurement suit. Specifically, it uses 3D modeling technology and virtual reality technology.

[0068] (Example of form 2) The fashion advice system according to an embodiment of the present invention is a system that collects, analyzes, and proposes body shape data, and provides the user with optimal fashion advice through virtual try-on. This fashion advice system combines the high-precision body shape measurement technology of a measurement suit such as ZOZOSUIT with the autonomous judgment ability of an AI agent to provide the user with optimal fashion advice. Specifically, it consists of the following steps: First, the body shape is measured periodically using a measurement suit, and the AI ​​agent analyzes the changes in real time. Next, the AI ​​agent considers the user's body shape data, preferences, lifestyle, and budget, and proposes the optimal combination of clothes. Furthermore, it provides a function that allows the user to virtually try on clothes recommended by the AI ​​based on 3D data measured with the measurement suit. This service employs a subscription model that can be used for a monthly fee, and there is also an affiliate revenue model in which the system earns a commission from partner brands when a user purchases clothes recommended by the AI. In addition, anonymized fashion trend data is provided to apparel brands for use in product development, creating a new revenue stream. This service enables users to choose clothes that suit their individual body types, reducing unnecessary purchases and waste of clothing, improving users' self-esteem, and promoting sustainability in the fashion industry. The fashion advice system collects, analyzes, suggests, and provides optimal fashion advice through virtual try-ons based on users' body shape data.

[0069] The fashion advice system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a virtual fitting unit. The data collection unit collects body shape data. The data collection unit can periodically measure body shape using, for example, a measuring suit. The data collection unit needs to clarify the specific types of body shape data and the method of collection. For example, this includes height, weight, waist size, etc. The analysis unit analyzes the collected body shape data in real time. The analysis unit needs to clarify the specific methods and criteria for real-time analysis. For example, this includes the data update frequency and the algorithm to be used. The suggestion unit proposes the optimal clothing combination based on the analysis results obtained by the analysis unit. The suggestion unit proposes the optimal clothing combination considering the user's body shape data, preferences, lifestyle, and budget. The suggestion unit needs to clarify the specific criteria and methods for proposing the optimal clothing combination. For example, this includes sizes and color combinations that fit the body shape. The virtual fitting unit provides a function that allows the user to virtually try on the clothes suggested by the suggestion unit. The virtual fitting unit provides a function that allows the user to virtually try on clothes recommended by AI based on 3D data measured with a measuring suit. The virtual fitting section needs to clearly define the specific methods and technologies for virtually trying on clothes. Examples include 3D modeling technology and virtual reality technology. This allows the fashion advice system, according to the embodiment, to collect, analyze, suggest, and provide optimal fashion advice through virtual try-on based on the user's body shape data.

[0070] The data collection unit collects body shape data. For example, the data collection unit can periodically measure body shape using a measurement suit. The measurement suit is a special suit that, when worn by the user, allows for accurate measurement of the entire body's dimensions. The data collection unit needs to clearly define the specific types of body shape data and how it will be collected. Examples include height, weight, waist size, hip size, bust size, arm length, leg length, shoulder width, and neck circumference. This data is obtained when the user wears the measurement suit and takes photos with a smartphone using a dedicated application. The captured images are analyzed by the application to generate detailed body shape data. The data collection unit sends this data to a cloud server and stores it in a central database. Furthermore, the data collection unit can track changes in body shape by periodically updating the user's body shape data. For example, by regularly using the measurement suit for measurements, changes in weight and muscle mass can be tracked. This allows the data collection unit to always maintain the user's latest body shape data, making it available to the analysis and proposal units. By adjusting the frequency and accuracy of data collection, the data collection unit can flexibly respond to user needs and lifestyles. For example, for users on a diet, more frequent measurements allow for detailed tracking of changes in body shape. This enables the data collection unit to efficiently and accurately collect body shape data, improving the overall system performance.

[0071] The analytics department analyzes collected body shape data in real time. The analytics department needs to clearly define the specific methods and criteria for real-time analysis, such as the data update frequency and the algorithms used. Based on the collected body shape data, the analytics department analyzes the characteristics and changes in users' body shapes in detail. Specifically, it uses AI to process data in real time and grasp statistical information and trends related to users' body shapes. For example, it analyzes changes in users' height and weight, and increases and decreases in waist and hip sizes to track changes in body shape. Using machine learning algorithms, the AI ​​can predict changes in users' body shapes by comparing them with past data. This allows the analytics department to quickly and accurately analyze users' body shape data and provide up-to-date information. Furthermore, the analytics department can understand body shape characteristics and trends by comparing users' body shape data with data from other users. For example, by comparing it with the average body shape data of users of the same age and gender, it can assess how standard a user's body shape is. This allows the analytics department to provide detailed information about users' body shapes and provide the foundational data for the recommendation department to suggest optimal clothing combinations.

[0072] The Proposal Department proposes the optimal clothing combination based on the analysis results obtained by the Analysis Department. The Proposal Department proposes the optimal clothing combination considering the user's body shape data, preferences, lifestyle, and budget. The Proposal Department needs to clarify the specific criteria and methods for proposing the optimal clothing combination. For example, this includes sizes that fit the body shape, color combinations, and styles that are appropriate for the season and trends. The Proposal Department uses AI to analyze the user's body shape data and evaluate the optimal clothing size and fit. Furthermore, based on the user's preferences and lifestyle, it proposes the optimal clothing combination considering elements such as color, design, and material. For example, if the user prefers a casual style, it will propose a combination of items such as denim, T-shirts, and sneakers. If a style suitable for a business setting is proposed, it will propose a combination of items such as suits, shirts, and leather shoes. The Proposal Department also considers the user's budget and selects the optimal items according to the price range. This allows the Proposal Department to provide optimal fashion advice that meets the user's needs and preferences. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, if a user actually purchases the suggested clothing and provides feedback on their satisfaction and fit, the suggestion team can incorporate this feedback into future suggestions. This allows the suggestion team to provide more appropriate fashion advice to users and improve their satisfaction.

[0073] The virtual fitting department provides a function that allows users to virtually try on clothes suggested by the suggestion department. Based on 3D data measured with a measurement suit, the virtual fitting department provides a function that allows users to virtually try on clothes recommended by AI. The virtual fitting department needs to clearly define the specific methods and technologies for virtual try-on. These include, for example, 3D modeling technology, virtual reality technology, and augmented reality technology. The virtual fitting department allows users to try on suggested clothes in a virtual space based on their 3D body shape data. Specifically, users access the virtual fitting application using devices such as smartphones, tablets, or PCs. The application reads the user's 3D body shape data and displays suggested clothes in real time. Users can see how their avatar looks wearing the suggested clothes on the screen. Furthermore, the virtual fitting department provides functions to change the color, design, and size of the clothes, allowing users to try on various combinations. For example, a user can try on shirts and pants of different colors to find the best combination. Furthermore, the virtual fitting service uses technology to realistically reproduce the material and texture of clothing, providing users with an experience as if they were actually trying on the clothes. This allows the virtual fitting service to help users choose the perfect clothes from the comfort of their homes and improve satisfaction by allowing them to try on clothes before purchasing. In addition, the virtual fitting service can collect user feedback and continuously improve the accuracy and realism of the fitting experience. This enables the virtual fitting service to provide users with a more realistic and satisfying fitting experience.

[0074] The data collection unit can periodically measure body shape using a measurement suit. The data collection unit needs to clarify the specific frequency and method of periodic body shape measurements. For example, this could include weekly or monthly measurements. This enables highly accurate body shape measurements by using the measurement suit. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input body shape data measured by the measurement suit into a generative AI, which can then analyze the body shape data.

[0075] The analysis unit can analyze the collected body shape data in real time. For example, the analysis unit analyzes the collected body shape data in real time. The analysis unit needs to clarify the specific methods and criteria for real-time analysis. For example, this includes the data update frequency and the algorithms used. This will enable suggestions based on the latest body shape data through real-time analysis. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the collected body shape data into a generative AI, which can then analyze the data in real time.

[0076] The suggestion unit can propose the optimal clothing combination by considering the user's body shape data, preferences, lifestyle, and budget. For example, the suggestion unit proposes the optimal clothing combination by considering the user's body shape data, preferences, lifestyle, and budget. The suggestion unit needs to clearly define the specific criteria and methods for proposing the optimal clothing combination. For example, this includes appropriate sizes and color combinations for the user's body shape. This enables the suggestion unit to propose the optimal clothing that meets the user's individual needs. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's body shape data, preferences, lifestyle, and budget into a generative AI, which can then propose the optimal clothing combination.

[0077] The virtual fitting unit can provide a function that allows users to virtually try on clothes recommended by AI based on 3D data measured by a measurement suit. The virtual fitting unit needs to clearly define the specific methods and technologies for virtual try-on. These include, for example, 3D modeling technology and virtual reality technology. This allows users to check the fit of clothes before actually trying them on. Some or all of the above-described processes in the virtual fitting unit may be performed using, for example, generative AI, or without generative AI. For example, the virtual fitting unit can input 3D data measured by a measurement suit into a generative AI, which can then perform the virtual try-on.

[0078] The collection unit can collect the user's purchase history. For example, the collection unit collects the user's purchase history. The collection unit needs to clarify the specific types of purchase history and the method of collection. For example, this may include past purchased items and purchase dates. By collecting the user's purchase history, it becomes possible to provide more personalized suggestions. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collection unit can input the user's purchase history into a generative AI, which can then analyze the purchase history.

[0079] A revenue management department is established, which manages revenue to obtain commissions from partner brands. The revenue management department needs to clearly define the specific methods and standards for revenue management. For example, this includes the method for calculating revenue and the method for setting commissions. This will stabilize revenue by managing commission income from partner brands. Some or all of the above processes in the revenue management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the revenue management department can input commission income data from partner brands into a generative AI, and the generative AI can perform revenue management.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of body shape data collection based on the estimated emotions. The data collection unit needs to clearly define specific methods and criteria for estimating emotions. Examples include facial recognition and voice analysis. For example, if the user is stressed, the data collection unit can delay the collection timing and perform measurements when the user is relaxed. For example, if the user is relaxed, the data collection unit can immediately collect body shape data. For example, if the user is in a hurry, the data collection unit can collect body shape data in a short amount of time. By adjusting the collection timing according to the user's emotions, more accurate body shape data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can input user emotion data into a generating AI, which can then adjust the timing of data collection.

[0081] The data collection unit can analyze the user's past body shape data and select the optimal collection method. For example, the data collection unit analyzes the user's past body shape data and selects the optimal collection method. The data collection unit needs to clarify the specific types and collection methods of past body shape data. For example, this includes past measurement data and recorded body shape information. For example, the data collection unit selects the most accurate measurement method based on the user's past body shape data. For example, the data collection unit can analyze the user's body shape change trends and determine an appropriate measurement frequency. For example, the data collection unit can customize the measurement method considering fluctuations in the user's body shape data. This allows the optimal collection method to be selected by analyzing past body shape data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input the user's past body shape data into a generative AI, which can then select the optimal collection method.

[0082] The data collection unit can filter body shape data based on the user's current health status and lifestyle. For example, the data collection unit can filter body shape data based on the user's current health status and lifestyle. The data collection unit needs to clarify the specific types of health status and lifestyle and how to collect them. For example, this includes blood pressure, heart rate, and body fat percentage. For example, the data collection unit can adjust the frequency of body shape data collection considering the user's health status. For example, the data collection unit can determine the optimal collection timing based on the user's lifestyle. For example, the data collection unit can select the types of data to collect according to the user's health status and lifestyle. This allows for the collection of more appropriate body shape data by filtering the data based on health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's health status and lifestyle data into a generative AI, which can then perform the filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of body shape data to collect based on the estimated user emotions. The data collection unit needs to clarify the specific methods and criteria for estimating emotions. Examples include facial recognition and voice analysis. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. For example, if the user is relaxed, the data collection unit can collect detailed body shape data. For example, if the user is in a hurry, the data collection unit can prioritize collecting only the minimum necessary data. This allows for the priority collection of important data by determining data priority according to 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. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can input user emotion data into a generating AI, which can then determine the priority of the data.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting body shape data. For example, when collecting body shape data, the data collection unit prioritizes the collection of highly relevant data by considering the user's geographical location information. The data collection unit needs to clarify the specific types and methods of collection of geographical location information. For example, this includes GPS data, address information, etc. For example, if the user is in a specific region, the data collection unit will prioritize the collection of body shape data related to that region. For example, the data collection unit can determine the optimal collection timing based on the user's geographical location information. For example, the data collection unit can select the types of data to collect according to the user's geographical location information. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, which can then prioritize the collection of highly relevant data.

[0085] The data collection unit can analyze the user's social media activity and collect relevant data when collecting body shape data. For example, the data collection unit analyzes the user's social media activity and collects relevant data when collecting body shape data. The data collection unit needs to clarify the specific types of social media activity and how to collect them. For example, this includes post content, the number of likes, and the number of followers. The data collection unit can adjust the timing of body shape data collection based on the user's social media activity. The data collection unit can select the types of data to collect based on the user's social media activity. The data collection unit can analyze the user's social media activity and prioritize the collection of relevant body shape data. This allows for the collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, which can then collect relevant data.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. The analysis unit needs to clarify the specific methods and criteria for estimating emotions. For example, this includes facial recognition and voice analysis. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. For example, if the user is stressed, the analysis unit can provide visually easy-to-understand analysis results. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user emotion data into a generating AI, which can then adjust how the analysis is presented.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the body shape data during the analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the body shape data during the analysis. The analysis unit needs to clarify how to evaluate the importance of the body shape data. For example, this includes data accuracy, relevance, etc. The analysis unit can perform a detailed analysis for important body shape data. For example, the analysis unit can perform a concise analysis for less important body shape data. The analysis unit can adjust the level of detail of the analysis in stages according to the importance of the body shape data. This makes efficient analysis possible by adjusting the level of detail of the analysis according to the importance of the body shape data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the importance of the body shape data into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of body shape data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of body shape data during analysis. The analysis unit needs to clarify the specific types and classification methods of the body shape data categories. For example, these include height, weight, waist size, etc. The analysis unit selects the optimal analysis algorithm depending on the category of body shape data. For example, the analysis unit can apply different analysis methods to the body shape data for each category. For example, the analysis unit can customize the analysis algorithm for each category of body shape data. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the category of body shape data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the body shape data categories into a generative AI, and the generative AI can apply the optimal analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit needs to clearly define the specific methods and criteria for estimating emotions. These include, for example, facial recognition and voice analysis. The analysis unit can provide a short, concise analysis result if the user is in a hurry. The analysis unit can provide a detailed analysis result if the user is relaxed. The analysis unit can provide a visually easy-to-understand analysis result if the user is stressed. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with the most optimal analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user emotion data into a generating AI, which can then adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the timing of body shape data collection during the analysis. For example, the analysis unit can determine the priority of analysis based on the timing of body shape data collection during the analysis. The analysis unit needs to clarify the specific criteria and methods for the collection timing. For example, this includes the frequency of data updates and the timing of collection. For example, the analysis unit can prioritize the analysis of the most recent body shape data. For example, the analysis unit can postpone the analysis of older body shape data collected at a later date. For example, the analysis unit can adjust the priority of analysis in stages according to the timing of body shape data collection. This allows for prioritizing the analysis of the most recent data by determining the priority of analysis based on the timing of body shape data collection. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the timing of body shape data collection into a generative AI, and the generative AI can determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of body type data during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of body type data during analysis. The analysis unit needs to clarify specific criteria and evaluation methods for relevance. For example, this includes data correlation and commonalities. For example, the analysis unit can prioritize the analysis of highly relevant body type data. For example, the analysis unit can postpone the analysis of less relevant body type data. For example, the analysis unit can adjust the order of analysis step by step according to the relevance of the body type data. This makes efficient analysis possible by adjusting the order of analysis based on the relevance of body type data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the relevance of body type data into a generative AI, and the generative AI can adjust the order of analysis.

[0092] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, the suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. The suggestion unit needs to clearly define the specific methods and criteria for estimating emotions. These include, for example, facial recognition and voice analysis. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, if the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. For example, if the user is stressed, the suggestion unit can provide visually easy-to-understand suggestions. By adjusting the way it presents suggestions according to the user's emotions, it becomes possible to provide suggestions that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, generative AI, or without using generative AI. For example, the proposal unit can input user emotion data into a generating AI, which can then adjust how the proposal is expressed.

[0093] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the clothing items. For example, the suggestion unit can adjust the level of detail in its suggestions based on the importance of the clothing items. The suggestion unit needs to clarify how it evaluates the importance of the clothing items. For example, this could include the popularity of the design or the frequency of use. For example, the suggestion unit can provide detailed suggestions for important clothing items. For example, the suggestion unit can provide concise suggestions for less important clothing items. For example, the suggestion unit can adjust the level of detail in its suggestions in stages according to the importance of the clothing items. This allows for efficient suggestions by adjusting the level of detail according to the importance of the clothing items. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the importance of the clothing items into a generative AI, which can then adjust the level of detail in its suggestions.

[0094] The suggestion unit can apply different suggestion algorithms depending on the clothing category when making suggestions. For example, the suggestion unit applies different suggestion algorithms depending on the clothing category when making suggestions. The suggestion unit needs to clarify the specific types and classification methods of clothing categories. For example, these include casual wear, formal wear, and sportswear. The suggestion unit selects the optimal suggestion algorithm depending on the clothing category. For example, the suggestion unit can apply different suggestion methods to clothing in each category. For example, the suggestion unit can customize the suggestion algorithm for each clothing category. This improves the accuracy of suggestions by applying the optimal suggestion algorithm according to the clothing category. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input the clothing category into a generative AI, and the generative AI can apply the optimal suggestion algorithm.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. The suggestion unit needs to clarify the specific methods and criteria for estimating emotions. For example, these include facial recognition and voice analysis. For example, if the user is in a hurry, the suggestion unit can make a short, to-the-point suggestion. For example, if the user is relaxed, the suggestion unit can make a detailed suggestion. For example, if the user is stressed, the suggestion unit can make a visually easy-to-understand suggestion. By adjusting the length of the suggestion according to the user's emotions, it becomes possible to make the most appropriate suggestion for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or not using generative AI. For example, the suggestion unit can input user emotion data into a generating AI, which can then adjust the length of the suggestion.

[0096] The proposal department can determine the priority of proposals based on the submission timing of the clothing items when submitting a proposal. For example, the proposal department can determine the priority of proposals based on the submission timing of the clothing items when submitting a proposal. The proposal department needs to clarify the specific criteria and methods for submission timing. For example, this includes the frequency of data updates and the timing of submission. The proposal department can, for example, prioritize proposing the newest clothing items. The proposal department can, for example, postpone proposing older clothing items. The proposal department can, for example, adjust the priority of proposals in stages according to the submission timing of the clothing items. This allows for prioritizing the proposal of the newest clothing items by determining the priority of proposals based on the submission timing of the clothing items. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the submission timing of the clothing items into a generative AI, and the generative AI can determine the priority of proposals.

[0097] The suggestion unit can adjust the order of suggestions based on the relevance of the clothing items when making suggestions. For example, the suggestion unit can adjust the order of suggestions based on the relevance of the clothing items when making suggestions. The suggestion unit needs to clarify specific criteria and evaluation methods for relevance. For example, this includes data correlation and commonalities. For example, the suggestion unit can prioritize suggesting highly relevant clothing items. For example, the suggestion unit can postpone suggesting less relevant clothing items. For example, the suggestion unit can adjust the order of suggestions in stages according to the relevance of the clothing items. This makes it possible to make efficient suggestions by adjusting the order of suggestions based on the relevance of the clothing items. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the relevance of the clothing items into a generative AI, and the generative AI can adjust the order of suggestions.

[0098] The virtual fitting unit can estimate the user's emotions and adjust the display method of the virtual fitting based on the estimated user emotions. For example, the virtual fitting unit needs to clearly define the specific methods and criteria for estimating emotions. These include, for example, facial recognition and voice analysis. For example, the virtual fitting unit can display detailed fitting information when the user is relaxed. For example, the virtual fitting unit can display concise fitting information when the user is in a hurry. For example, the virtual fitting unit can display visually easy-to-understand fitting information when the user is stressed. This allows for more easily understood fitting information by adjusting the display method according to 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. Some or all of the above-described processes in the virtual fitting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the virtual fitting unit can input user emotion data into the generative AI, which can then adjust the display method.

[0099] The virtual fitting unit can select the optimal display method by referring to the user's past fitting history during virtual fitting. For example, the virtual fitting unit selects the optimal display method by referring to the user's past fitting history during virtual fitting. The virtual fitting unit needs to clarify the specific types and collection methods of past fitting history. For example, this includes the type of clothing tried on and the date and time of the try-on. The virtual fitting unit selects the optimal display method based on the user's past fitting history. For example, the virtual fitting unit can suggest a display method that suits the user's preferences based on their fitting history. For example, the virtual fitting unit can analyze the user's past fitting history and select the most effective display method. This allows the optimal display method to be selected by referring to past fitting history. Some or all of the above processing in the virtual fitting unit may be performed using, for example, a generation AI, or without a generation AI. For example, the virtual fitting unit can input the user's past fitting history into a generation AI, and the generation AI can select the optimal display method.

[0100] The virtual fitting unit can improve fitting accuracy based on the user's body shape data during virtual fitting. For example, the virtual fitting unit can improve fitting accuracy based on the user's body shape data during virtual fitting. The virtual fitting unit needs to clearly define the specific types and methods of collecting body shape data. For example, this includes height, weight, and waist size. The virtual fitting unit can improve fitting accuracy based on the user's body shape data. For example, the virtual fitting unit can improve fitting accuracy by reflecting the user's body shape data in real time. For example, the virtual fitting unit can adjust the fitting display method according to the user's body shape data. This allows for more accurate fitting information to be provided by improving fitting accuracy based on body shape data. Some or all of the above processing in the virtual fitting unit may be performed using, for example, a generating AI, or without a generating AI. For example, the virtual fitting unit can input the user's body shape data into a generating AI, which can then improve fitting accuracy.

[0101] The virtual fitting unit can estimate the user's emotions and adjust the virtual fitting procedure based on the estimated user emotions. For example, the virtual fitting unit needs to clearly define the specific methods and criteria for estimating emotions. Examples include facial recognition and voice analysis. For example, the virtual fitting unit can provide detailed instructions when the user is relaxed. For example, it can provide concise instructions when the user is in a hurry. For example, it can provide visually clear instructions when the user is stressed. By adjusting the procedure according to the user's emotions, a more user-friendly fitting experience can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the virtual fitting unit may be performed using, for example, generative AI, or without generative AI. For example, the virtual fitting unit can input user emotion data into a generating AI, which can then adjust the operating procedure.

[0102] The virtual fitting unit can select the optimal display method during virtual fitting by considering the user's device information. For example, the virtual fitting unit selects the optimal display method during virtual fitting by considering the user's device information. The virtual fitting unit needs to clearly define the specific types and methods of collecting device information. This includes, for example, the device type and OS version. For example, if the user is using a smartphone, the virtual fitting unit can provide a display method adapted to the screen size. For example, if the user is using a tablet, the virtual fitting unit can provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the virtual fitting unit can provide a concise and highly visible display method. This allows the optimal display method to be provided by considering device information. Some or all of the above processing in the virtual fitting unit may be performed using, for example, a generation AI, or without a generation AI. For example, the virtual fitting unit can input the user's device information into a generation AI, which can then select the optimal display method.

[0103] The virtual fitting unit can analyze the user's social media activity during virtual fitting and provide relevant fitting information. For example, the virtual fitting unit needs to clearly define the specific types and methods of collection of social media activity. This includes, for example, post content, number of likes, and number of followers. The virtual fitting unit can provide relevant fitting information based on the user's social media activity. For example, the virtual fitting unit can suggest optimal fitting information based on the user's social media activity. The virtual fitting unit can analyze the user's social media activity and customize the fitting information. This allows it to provide relevant fitting information by analyzing social media activity. Some or all of the above processing in the virtual fitting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the virtual fitting unit can input the user's social media activity data into a generative AI, which can then provide relevant fitting information.

[0104] The revenue management unit can estimate the user's emotions and adjust the revenue management method based on the estimated user emotions. The revenue management unit needs to clarify the specific methods and criteria for estimating emotions. Examples include facial recognition and voice analysis. The revenue management unit can provide a detailed revenue management method if the user is relaxed. The revenue management unit can provide a concise revenue management method if the user is in a hurry. The revenue management unit can provide a visually easy-to-understand revenue management method if the user is stressed. This allows for more effective revenue management by adjusting the revenue management method according to 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. Some or all of the above processing in the revenue management unit may be performed using generative AI, for example, or without generative AI. For example, the revenue management department can input user sentiment data into a generating AI, which can then adjust the revenue management method.

[0105] The revenue management department can analyze past revenue data of partner brands to select the optimal management method during revenue management. For example, the revenue management department can analyze past revenue data of partner brands to select the optimal management method during revenue management. The revenue management department needs to clarify the specific types and collection methods of past revenue data. For example, this includes sales data, profit data, etc. The revenue management department can select the optimal management method based on past revenue data of partner brands. For example, the revenue management department can analyze the revenue data of partner brands to improve the efficiency of revenue management. For example, the revenue management department can customize its revenue management methods by referring to past revenue data of partner brands. This allows it to select the optimal management method by analyzing past revenue data. Some or all of the above processes in the revenue management department may be performed using, for example, a generation AI, or not using a generation AI. For example, the revenue management department can input past revenue data of partner brands into a generation AI, and the generation AI can select the optimal management method.

[0106] The revenue management department can customize its revenue management methods based on the current market conditions of partner brands when performing revenue management. For example, the revenue management department can customize its revenue management methods based on the current market conditions of partner brands when performing revenue management. The revenue management department needs to clarify the specific types of market conditions and how they are collected. For example, this includes competitor trends and consumer trends. The revenue management department can customize its revenue management methods based on the current market conditions of partner brands. For example, the revenue management department can analyze the market conditions of partner brands to improve the efficiency of revenue management. The revenue management department can adjust its revenue management methods according to the current market conditions of partner brands. This improves the efficiency of revenue management by customizing revenue management methods based on current market conditions. Some or all of the above processes in the revenue management department may be performed using, for example, a generating AI, or not using a generating AI. For example, the revenue management department can input current market conditions data of partner brands into a generating AI, and the generating AI can customize the revenue management methods.

[0107] The revenue management department can estimate the user's emotions and determine revenue management priorities based on the estimated emotions. For example, the revenue management department needs to clarify the specific methods and criteria for estimating emotions. These include, for example, facial recognition and voice analysis. For example, the revenue management department might prioritize detailed revenue management if the user is relaxed. For example, the revenue management department might prioritize concise revenue management if the user is in a hurry. For example, the revenue management department might prioritize visually easy-to-understand revenue management if the user is stressed. This allows for more effective revenue management by determining revenue management priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the revenue management department may be performed using, for example, generative AI, or not using generative AI. For example, the revenue management department can input user sentiment data into a generating AI, which can then determine the priorities for revenue management.

[0108] The revenue management department can select the optimal management method when managing revenue, taking into account the geographical location information of partner brands. For example, the revenue management department selects the optimal management method when managing revenue, taking into account the geographical location information of partner brands. The revenue management department needs to clarify the specific types and collection methods of geographical location information. For example, this includes GPS data, address information, etc. The revenue management department selects the optimal management method based on the geographical location information of partner brands. For example, the revenue management department can analyze the geographical location information of partner brands to improve the efficiency of revenue management. For example, the revenue management department can adjust the revenue management method according to the geographical location information of partner brands. This allows for the selection of the optimal management method by taking geographical location information into consideration. Some or all of the above processes in the revenue management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the revenue management department can input the geographical location information of partner brands into a generative AI, and the generative AI can select the optimal management method.

[0109] The revenue management department can analyze the social media activities of partner brands and propose revenue management methods when managing revenue. For example, the revenue management department can analyze the social media activities of partner brands and propose revenue management methods when managing revenue. The revenue management department needs to clarify the specific types and collection methods of social media activities. For example, this includes post content, number of likes, number of followers, etc. The revenue management department can propose revenue management methods based on the social media activities of partner brands. For example, the revenue management department can analyze the social media activities of partner brands and improve the efficiency of revenue management. For example, the revenue management department can adjust the revenue management methods according to the social media activities of partner brands. This allows the department to propose revenue management methods by analyzing social media activities. Some or all of the above processing in the revenue management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the revenue management department can input social media activity data of partner brands into a generative AI, and the generative AI can propose revenue management methods.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The data collection unit can collect not only the user's body shape data but also their health data. For example, by collecting health data such as the user's blood pressure, heart rate, and body fat percentage, and analyzing this data in combination with body shape data, it is possible to provide more accurate fashion advice. The data collection unit needs to clearly define the method and frequency of health data collection. For example, if the user is using a smartwatch, that data can be collected regularly. This makes it possible to provide fashion advice tailored to the user's health condition and contribute to maintaining the user's health.

[0112] The analysis department can analyze a user's posture and gait characteristics based on their body shape data. For example, if a user has poor posture, it can provide advice on how to improve it. It can also analyze a user's gait characteristics and suggest shoes suitable for walking. This can contribute not only to improving the user's body shape but also to improving their posture and gait. The analysis department needs to clarify specific methods and criteria for analyzing posture and gait characteristics. These may include, for example, posture angles and walking rhythm.

[0113] The suggestion department can suggest fashion items that match the user's skin tone and hair color based on the user's body shape data. For example, by suggesting clothes in colors that match the user's skin tone, a more attractive fashion style can be achieved. It can also suggest accessories that match the user's hair color. This allows for fashion advice that considers not only the user's body shape but also the overall coordination. The suggestion department needs to clearly define the specific methods and criteria for analyzing skin tone and hair color. For example, this should include color tone and brightness.

[0114] The virtual fitting unit can provide virtual fitting tailored to the user's movements based on the user's body shape data. For example, it can simulate movements such as walking and sitting, and allow users to check the fit of clothing in accordance with those movements. This allows users to check the fit of clothing in actual movements in advance. The virtual fitting unit needs to clearly define the specific methods and technologies that enable fitting tailored to movement. These include, for example, motion capture technology and real-time rendering technology.

[0115] The Revenue Management Department can generate revenue not only from commission income from partner brands, but also by analyzing user purchase data and displaying advertisements based on purchasing trends. For example, displaying advertisements related to products that users have previously purchased can enhance the effectiveness of advertising. Furthermore, it can create new revenue streams by providing anonymized user purchase data to apparel brands for use in product development. This will lead to diversification and stabilization of revenue. The Revenue Management Department needs to clarify the methods for analyzing purchase data and the criteria for displaying advertisements.

[0116] The data collection unit can estimate the user's emotions and adjust the method of collecting body shape data based on the estimated emotions. For example, if the user is relaxed, detailed body shape data can be collected. Conversely, if the user is stressed, concise body shape data can be collected. By adjusting the collection method according to the user's emotions, more accurate body shape data can be collected. Emotion estimation can be performed using technologies such as facial recognition and voice analysis. The data collection unit needs to clearly define the specific methods and criteria for emotion estimation.

[0117] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on those estimated emotions. For example, if the user is relaxed, detailed analysis results can be provided. Conversely, if the user is in a hurry, concise analysis results focusing on the key points can be provided. By adjusting the presentation of the analysis results according to the user's emotions, more easily understandable results can be provided. Emotion estimation can be performed using technologies such as facial recognition and voice analysis. The analysis unit needs to clearly define the specific methods and criteria for emotion estimation.

[0118] The suggestion function can estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions that get straight to the point. By adjusting the suggestions according to the user's emotions, more effective suggestions can be made. Emotion estimation can be performed using technologies such as facial recognition and voice analysis. The suggestion function needs to clearly define the specific methods and criteria for emotion estimation.

[0119] The virtual fitting unit can estimate the user's emotions and adjust the display method of the virtual fitting based on the estimated emotions. For example, if the user is relaxed, detailed fitting information can be displayed. If the user is in a hurry, concise fitting information can be displayed. In this way, by adjusting the display method according to the user's emotions, fitting information that is easier to understand can be provided. Emotion estimation can be performed using technologies such as facial recognition and voice analysis. The virtual fitting unit needs to clearly define the specific methods and criteria for emotion estimation.

[0120] The revenue management department can estimate user emotions and adjust revenue management methods based on those estimates. For example, if a user is relaxed, a detailed revenue management method can be provided. Conversely, if a user is in a hurry, a concise revenue management method can be provided. This allows for more effective revenue management by adjusting revenue management methods according to user emotions. Emotion estimation can be performed using technologies such as facial recognition and voice analysis. The revenue management department needs to clearly define the specific methods and criteria for emotion estimation.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects body shape data. For example, body shape can be measured regularly using a measuring suit. The data collection unit collects specific body shape data such as height, weight, and waist size. Step 2: The analysis department analyzes the collected body shape data in real time. The analysis department clarifies the specific methods and criteria for real-time analysis, such as the data update frequency and the algorithms to be used. Step 3: The proposal department proposes the optimal clothing combination based on the analysis results obtained by the analysis department. The proposal department proposes the optimal clothing combination considering the user's body shape data, preferences, lifestyle, and budget. Specifically, it proposes size and color combinations that suit the body shape. Step 4: The virtual fitting department provides a function that allows users to virtually try on clothes suggested by the suggestion department. The virtual fitting department provides a function that allows users to virtually try on clothes recommended by AI based on 3D data measured with a measurement suit. Specifically, it uses 3D modeling technology and virtual reality technology.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, and virtual fitting unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects body shape data using the camera 42 and sensors of the smart device 14 and processes the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected body shape data in real time. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and suggests the optimal clothing combination based on the analysis results. The virtual fitting unit is implemented, for example, by the control unit 46A of the smart device 14 and provides virtual try-on based on 3D data. The revenue management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and manages commission revenue from partner brands. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, and virtual fitting unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects body shape data using the camera 42 and sensors of the smart glasses 214 and processes the data with the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected body shape data in real time. The suggestion unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and suggests the optimal clothing combination based on the analysis results. The virtual fitting unit is implemented, for example, in the control unit 46A of the smart glasses 214 and provides virtual try-on based on 3D data. The revenue management unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and manages commission revenue from partner brands. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, and virtual fitting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects body shape data using the camera 42 and sensors of the headset terminal 314 and processes the data with the control unit 46A. The analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12 and analyzes the collected body shape data. The suggestion unit is implemented in real time by the specific processing unit 290 of the data processing unit 12 and suggests the optimal clothing combination based on the analysis results. The virtual fitting unit is implemented in real time by the control unit 46A of the headset terminal 314 and provides virtual try-on based on 3D data. The revenue management unit is implemented in real time by the specific processing unit 290 of the data processing unit 12 and manages commission revenue from partner brands. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, and virtual fitting unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects body shape data using the camera 42 and sensors of the robot 414 and processes the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected body shape data in real time. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and suggests the optimal clothing combination based on the analysis results. The virtual fitting unit is implemented, for example, by the control unit 46A of the robot 414 and provides virtual try-on based on 3D data. The revenue management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and manages commission revenue from partner brands. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A collection unit that collects body shape data, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal clothing combination. The system includes a virtual fitting unit that allows users to virtually try on the clothes proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Regularly measure your body shape using a measuring suit. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyze collected body shape data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We suggest the optimal clothing combination based on the user's body type data, preferences, lifestyle, and budget. The system described in Appendix 1, characterized by the features described herein. (Note 5) The virtual fitting section is, This feature allows users to virtually try on clothes recommended by AI based on 3D data measured using a measurement suit. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Collect user purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 7) Equipped with a revenue management department, The aforementioned revenue management department manages revenue to obtain commissions from partner brands. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of body shape data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past body shape data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting body shape data, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and determines the priority of body shape data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting body shape data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting body shape data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of body shape data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of body shape data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the body shape data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of body shape data. The system described in Appendix 1, characterized by the features described herein. (Note 20) 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 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the clothing. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the clothing category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the clothing samples will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the clothing. The system described in Appendix 1, characterized by the features described herein. (Note 26) The virtual fitting section is, It estimates the user's emotions and adjusts how the virtual fitting is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The virtual fitting section is, During virtual fitting, the system selects the optimal display method by referring to the user's past fitting history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The virtual fitting section is, During virtual fitting, the accuracy of the fitting is improved based on the user's body shape data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The virtual fitting section is, It estimates the user's emotions and adjusts the virtual fitting procedure based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The virtual fitting section is, During virtual fitting, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The virtual fitting section is, During virtual fitting, the system analyzes the user's social media activity and provides relevant fitting information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned revenue management department, We estimate user sentiment and adjust revenue management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned revenue management department, When managing revenue, we analyze past revenue data from partner brands to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned revenue management department, When managing revenue, customize revenue management methods based on the current market conditions of partner brands. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned revenue management department, Estimate user sentiment and determine revenue management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned revenue management department, When managing revenue, we select the optimal management method by considering the geographical location information of partner brands. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned revenue management department, When managing revenue, we analyze the social media activities of partner brands and propose revenue management strategies. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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 collection unit that collects body shape data, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal clothing combination. The system includes a virtual fitting unit that allows users to virtually try on the clothes proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Regularly measure your body shape using a measuring suit. The system according to feature 1.

3. The aforementioned analysis unit is Analyze collected body shape data in real time. The system according to feature 1.

4. The aforementioned proposal section is, We suggest the optimal clothing combination based on the user's body type data, preferences, lifestyle, and budget. The system according to feature 1.

5. The virtual fitting section is, This service provides a function that allows users to virtually try on clothes recommended by AI, based on 3D data measured using a measurement suit. The system according to feature 1.

6. The aforementioned collection unit is Collect user purchase history. The system according to feature 1.

7. Equipped with a revenue management department, The aforementioned revenue management department manages revenue to obtain commissions from partner brands. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of body shape data collection based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past body shape data and select the optimal data collection method. The system according to feature 1.

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