system

Abstract

The system according to this embodiment aims to suggest the optimal outfit by taking into account the clothes the user owns. [Solution] The system according to the embodiment comprises a registration unit, a reception unit, an analysis unit, a simulation unit, and a provision unit. The registration unit scans and registers the clothes owned by the user. The reception unit receives an abstract message from the user. The analysis unit analyzes information such as location, season, temperature, gender, age, trends, and preferences based on the information received by the reception unit. The simulation unit simulates the optimal outfit based on the information analyzed by the analysis unit. The provision unit provides the user with a 3D model generated by the simulation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 coordination considering the clothes the user has, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal coordination considering the clothes the user has.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a registration unit, a reception unit, an analysis unit, a simulation unit, and a provision unit. The registration unit scans and registers the clothes owned by the user. The reception unit receives abstract messages from the user. The analysis unit analyzes information such as location, season, temperature, gender, age, trends, and preferences based on the information received by the reception unit. The simulation unit simulates the optimal outfit based on the information analyzed by the analysis unit. The provision unit provides the user with the 3D model generated by the simulation unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal outfit by taking into account the clothes the user owns. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The fashion coordination suggestion system according to an embodiment of the present invention is a system that scans and registers the clothes owned by the user, receives an abstract message, analyzes the information, simulates the optimal coordination, and provides a 3D model. The fashion coordination suggestion system scans the clothes owned by the user and registers them in RAG. Next, the user inputs an abstract message via chat or voice, such as "Today I want to wear something like office casual clothes, so please choose some." This information is sent to an AI agent. The AI ​​agent simulates the optimal coordination, taking into account information such as location, season, temperature, gender, age, trends, and preferences. The simulation results are generated as a 3D model on a digital twin and provided to the user. This allows the user to easily find the optimal coordination from the clothes they own and resolve their fashion-related worries. In addition, apparel companies can utilize the user's trend information for marketing. For example, the fashion coordination suggestion system scans the clothes owned by the user and registers them in RAG. For example, the clothes owned by the user are read with a scanner and saved as image data. Then, the image data is converted into text data using OCR technology. Next, the fashion coordination suggestion system receives an abstract message from the user. For example, a user might enter a message via chat or voice, such as, "Today I want to wear something like office casual clothes, please help me choose some." This information is sent to the AI ​​agent. Next, the fashion coordination suggestion system uses the AI ​​agent to analyze information such as location, season, temperature, gender, age, trends, and preferences. For example, the AI ​​agent obtains the user's location information and considers the season and temperature of that area. The AI ​​agent also analyzes information such as the user's gender, age, trends, and preferences. Next, the fashion coordination suggestion system uses the AI ​​agent to simulate the optimal outfit. For example, the AI ​​agent simulates the optimal outfit based on the user's information.The simulation results are generated as a 3D model on a digital twin. Next, the fashion coordination suggestion system provides the user with the simulation results. For example, it provides the user with the 3D model generated on the digital twin. This allows the user to easily find the best outfit from their existing wardrobe. In this way, the fashion coordination suggestion system can resolve the user's fashion concerns and suggest the optimal outfit.

[0029] The fashion coordination suggestion system according to this embodiment comprises a registration unit, a reception unit, an analysis unit, a simulation unit, and a provision unit. The registration unit scans and registers the clothes owned by the user. The clothes owned by the user include, for example, handwritten clothes, clothes in digital format, etc., but are not limited to such examples. The registration unit can, for example, digitize and register handwritten clothes using scanning technology. The registration unit can also directly register clothes submitted in digital format. Furthermore, the registration unit can read printed clothes using OCR technology. For example, the registration unit scans handwritten clothes with a high-resolution scanner and converts them into text information using OCR technology. Clothes in digital format can be directly registered if submitted in a specific file format. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The reception unit receives abstract messages from the user. Abstract messages include, for example, text messages, voice messages, images, etc., but are not limited to such examples. For example, the reception unit receives messages from the user such as "I want to wear something like office casual clothes today, so please choose some for me" entered via chat or voice. The analysis unit analyzes information such as location, season, temperature, gender, age, trends, and preferences. For example, the analysis unit acquires the user's location information and considers the season and temperature of that region. The analysis unit also analyzes information such as the user's gender, age, trends, and preferences. For example, the analysis unit considers the season and temperature of that region based on the user's location information. The analysis unit analyzes information such as the user's gender, age, trends, and preferences. The simulation unit simulates the optimal outfit based on the information analyzed by the analysis unit. For example, the simulation unit simulates the optimal outfit based on the user's information. The simulation unit simulates the optimal outfit based on the user's information. The provision unit provides the user with the 3D model generated by the simulation unit. For example, the provision unit provides the user with the 3D model generated on the digital twin. The provision unit provides the user with the 3D model generated by the simulation unit.As a result, the fashion coordination suggestion system according to the embodiment can resolve the user's fashion-related concerns and suggest the optimal coordination.

[0030] The registration unit scans and registers the clothes owned by the user. These clothes include, but are not limited to, handwritten designs and digitally formatted designs. For example, the registration unit can digitize and register handwritten designs using scanning technology. Specifically, a high-resolution scanner is used to scan the handwritten clothing design and save it as image data. This image data is analyzed using OCR (Optical Character Recognition) technology, and the clothing design and features are extracted as digital text. This allows the handwritten clothing design to be registered digitally and made available within the system. The registration unit can also directly register clothing submitted in digital format. For example, if a user submits a digital clothing design in a specific file format (JPEG, PNG, PDF, etc.), these files can be registered directly into the system. Furthermore, the registration unit can read printed clothing using OCR technology. For example, clothing designs published in fashion magazines or catalogs can be scanned, and the text and design information can be digitized using OCR technology. This allows printed clothing designs to also be registered in the system, enriching the database of the user's clothing. The registration section centrally manages this data, allowing users to efficiently register and update information about the clothes they own.

[0031] The reception unit receives abstract messages from users. Abstract messages include, but are not limited to, text messages, voice messages, and images. For example, the reception unit can receive messages from users that say, "I want to wear something like office casual clothes today, please help me choose some," via chat or voice input. Specifically, users can send messages using a smartphone or computer via a chat application or voice recognition application. The reception unit receives these messages and, in the case of text messages, sends them directly to the analysis unit. In the case of voice messages, it uses voice recognition technology to convert the voice to text and sends it to the analysis unit. In the case of images, it uses image analysis technology to extract information from the image and sends it to the analysis unit. For example, if a user voice-inputs, "I want to wear casual clothes today," the reception unit uses voice recognition technology to convert this message to text and sends it to the analysis unit as the text message, "I want to wear casual clothes today." This allows the reception unit to receive and appropriately process messages in various formats from users.

[0032] The analysis unit analyzes information such as location, season, temperature, gender, age, trends, and preferences. For example, the analysis unit obtains the user's location information and considers the season and temperature of that region. Specifically, it obtains location information from the user's smartphone or device and links it with the local weather database to obtain the current season and temperature. This allows the analysis unit to understand the climate conditions of the area where the user is currently located and suggest appropriate fashion coordinates. The analysis unit also analyzes information such as the user's gender, age, trends, and preferences. For example, based on the gender and age information entered by the user during registration, it suggests fashion styles suitable for that age and gender. Furthermore, the analysis unit analyzes the user's past selection history and preference data to suggest coordinates that match the user's preferences. For example, it analyzes the design, color, and style of clothes the user has chosen in the past and suggests new coordinates based on that. The analysis unit comprehensively analyzes this information to generate basic data for providing the user with the optimal fashion coordinates.

[0033] The simulation unit simulates the optimal outfit based on the information analyzed by the analysis unit. Specifically, it simulates the optimal outfit based on information such as the user's location, season, temperature, gender, age, trends, and preferences. For example, the simulation unit generates a 3D model of the user using digital twin technology based on the user's information and simulates having the model try on various clothes. This allows the user to visually confirm how the suggested outfits look through a 3D model that closely resembles themselves. The simulation unit simulates the optimal outfit based on the user's information. For example, if the user inputs, "Today I want to wear something like office casual clothes," the simulation unit selects clothes suitable for office casual and has the user's 3D model try them on. Furthermore, the simulation unit simulates multiple outfits, taking into account the user's preferences and trends, and selects the best one. In this way, the simulation unit can propose the optimal fashion outfit to the user in a visually easy-to-understand manner.

[0034] The service provider provides users with 3D models generated by the simulation unit. Specifically, it provides users with 3D models generated on a digital twin. The service provider displays the 3D models on the user's smartphone or PC, allowing the user to check the suggested outfits. The service provider provides users with 3D models generated by the simulation unit. For example, a user can open a smartphone app and check the suggested outfits in 3D models. The service provider allows users to rotate and zoom the 3D models to check the suggested outfits in detail. This allows users to visually check how the suggested outfits look and choose fashion that suits them. Furthermore, the service provider also provides functions for users to save and share suggested outfits. For example, a user can save their favorite outfits and check them later. Users can also share suggested outfits on social media and exchange opinions with friends and family. In this way, the service provider can provide users with optimal fashion outfits in a visually easy-to-understand manner and resolve their fashion concerns.

[0035] The registration unit scans the user's clothes and registers them in RAG. For example, the registration unit scans the user's clothes and saves them as image data. Then, the registration unit uses OCR technology to convert the image data into text data. The registration unit can also write on the user's clothes with a dedicated digital pen, and the digital pen's writing can be converted into digital data in real time. For example, the movement of the pen can be detected by a sensor and saved as text data. As a result, by scanning the user's clothes and registering them in RAG, the system can understand the user's clothes and suggest the best outfit. RAG serves as a clothing database and registration system. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the image data acquired by the scanner into a generating AI and have the generating AI generate text data from the image data.

[0036] The reception desk receives abstract messages from users. For example, the reception desk can receive messages from users via chat or voice input, such as, "Today I want to wear something like office casual clothes, so please help me choose some." The reception desk can accept abstract messages in the form of text messages, voice messages, images, etc. By receiving abstract messages from users, the system can understand the user's preferences and suggest the most suitable outfit. Abstract messages include, but are not limited to, text messages, voice messages, and images. Some or all of the processing described above in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input a voice message from a user into a generating AI and have the generating AI perform the conversion of the voice message into text data.

[0037] The analysis unit analyzes information such as location, season, temperature, gender, age, trends, and preferences. For example, the analysis unit acquires the user's location information and considers the season and temperature of that region. The analysis unit also analyzes information such as the user's gender, age, trends, and preferences. For example, the analysis unit considers the season and temperature of that region based on the user's location information. The analysis unit analyzes information such as the user's gender, age, trends, and preferences. By analyzing information such as location, season, temperature, gender, age, trends, and preferences, the system can suggest the most suitable outfit for the user. Location information is acquired from, for example, GPS data or user input information. Seasons are used based on specific definitions such as spring, summer, autumn, and winter. Temperature is acquired from, for example, weather data or user input information. Gender is acquired from, for example, user input information or registration information. Age is acquired from, for example, user input information or registration information. Trends are acquired from, for example, fashion magazines or social media. Preferences are obtained, for example, from user input information and past selection history. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user location information and temperature data into a generating AI and have the generating AI perform the analysis.

[0038] The simulation unit simulates the optimal outfit based on the information analyzed by the analysis unit. The simulation unit simulates the optimal outfit based on user information, for example. The simulation unit simulates the optimal outfit based on user information, thereby enabling the system to suggest the most suitable fashion outfit to the user. The optimal outfit is, for example, an outfit based on the user's preferences, trends, season, etc. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the information analyzed by the analysis unit into a generating AI and have the generating AI perform the simulation.

[0039] The provisioning unit provides the user with a 3D model generated by the simulation unit. The provisioning unit provides the user with a 3D model generated on a digital twin, for example. The provisioning unit provides the user with a 3D model generated by the simulation unit. By providing the user with a 3D model generated by the simulation unit, the system can suggest the user the most visually optimal fashion coordination. The 3D model includes specific details and format, such as the design, color, and size of the clothing. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input the 3D model generated by the simulation unit into a generating AI and have the generating AI perform the provision of the 3D model.

[0040] The registration unit analyzes the user's past clothing registration history and selects the optimal registration method. For example, the registration unit automatically displays clothing categories that the user has frequently registered in the past as suggestions. The registration unit can also prioritize suggesting registration methods (voice, text, etc.) that the user has used in the past. The registration unit can also predict and suggest clothing categories to be registered at a specific time based on the user's past registration history. This allows for efficient clothing registration by selecting the optimal registration method through analysis of the user's past clothing registration history. The optimal registration method includes specific criteria and methods, such as the timing and procedure of registration. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's past registration history data into a generating AI and have the generating AI select the optimal registration method.

[0041] The registration unit filters clothing items based on the user's current fashion trends and areas of interest when they are registered. For example, the registration unit filters clothing items based on the fashion styles the user is currently interested in. The registration unit can also suggest clothing items to register by considering the trends of clothing the user has recently purchased. The registration unit can also filter clothing items by referring to the styles of fashion influencers the user follows. This allows the system to register clothing items that are best suited to the user by filtering them based on the user's current fashion trends and areas of interest. Fashion trends are obtained from sources such as fashion magazines and social media. Areas of interest are obtained from sources such as user input information and past selection history. Some or all of the above processing in the registration unit may be performed using AI, or not. For example, the registration unit can input the user's fashion trend data into a generating AI and have the generating AI perform the filtering.

[0042] The registration unit prioritizes registering clothing that is highly relevant to the user's geographical location when registering clothing. For example, if the user is in a cold region, the registration unit will prioritize registering warm clothing. If the user is in a tropical region, the registration unit can also prioritize registering cool clothing. If the user is in an urban area, the registration unit can also prioritize registering clothing that is in line with current trends. In this way, by prioritizing the registration of highly relevant clothing that is relevant to the user's situation, the system can register the most suitable clothing for the user. Geographical location information is obtained from, for example, GPS data or user input information. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant clothing.

[0043] The registration unit analyzes the user's social media activity when registering clothing and registers relevant clothing. For example, the registration unit prioritizes registering clothing from brands that the user follows on social media. The registration unit can also register clothing based on fashion styles shared by the user on social media. The registration unit can also prioritize registering clothing that the user has "liked" on social media. This allows the system to register relevant clothing and clothing that matches the user's preferences by analyzing the user's social media activity. Social media activity is obtained, for example, from the content of posts and the history of likes. Some or all of the above processing in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the user's social media activity data into a generating AI and have the generating AI select relevant clothing.

[0044] The reception unit, upon receiving a message, selects the optimal reception method by referring to the user's past message history. For example, the reception unit automatically displays message formats frequently used by the user in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) previously used by the user. The reception unit can also predict and suggest message formats to be used during specific time periods based on the user's past message history. This allows for efficient message reception by selecting the optimal reception method through the user's past message history. The optimal reception method includes specific criteria and methods, such as reception timing and reception procedures. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past message history data into a generating AI and have the generating AI select the optimal reception method.

[0045] The reception unit filters messages upon receipt based on the user's current situation and areas of interest. For example, the reception unit filters messages based on topics the user is currently interested in. The reception unit can also suggest relevant messages by considering keywords the user has recently searched for. The reception unit can also filter messages by referring to information about accounts the user follows. This allows the reception unit to receive messages that are most suitable for the user by filtering them based on the user's current situation and areas of interest. The current situation is obtained, for example, from user input information or real-time data. Areas of interest are obtained, for example, from user input information or past selection history. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input the user's current situation data into a generating AI and have the generating AI perform the filtering.

[0046] The analysis unit optimizes the analysis algorithm by referring to past analysis data during the analysis. For example, the analysis unit selects the optimal analysis algorithm based on past analysis data. The analysis unit can also extract specific patterns from past analysis data and optimize the analysis algorithm. The analysis unit can also analyze past analysis data to improve the accuracy of the analysis algorithm. In this way, by referring to past analysis data, the analysis algorithm can be optimized and the accuracy of the analysis can be improved. The analysis algorithm includes specific types and optimization methods, such as machine learning algorithms and data mining techniques. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0047] The analysis unit performs analysis while considering the user's attribute information. For example, the analysis unit may perform analysis based on the user's gender and age. The analysis unit may also perform analysis based on the user's fashion preferences. The analysis unit may also perform analysis while considering the user's past outfit history. By performing analysis while considering the user's attribute information, the system can provide the user with the most optimal analysis results. Attribute information is obtained, for example, from the user's input information or registration information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's attribute information data into a generating AI and have the generating AI perform the analysis.

[0048] The analysis unit considers the geographical distribution of users during analysis. For example, if a user is in a specific region, the analysis unit considers the fashion trends of that region. If a user is traveling, the analysis unit can also consider the fashion trends of the travel destination. If a user is in an urban area, the analysis unit can also consider the fashion trends of the urban area. By considering the geographical distribution of users during analysis, the analysis unit can provide optimal analysis results tailored to each region. Geographical distribution is obtained, for example, from regional user data or geographical trends. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of users into a generating AI and have the generating AI perform the analysis.

[0049] The analysis unit improves the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit improves the accuracy of its analysis by referring to the latest fashion research. The analysis unit can also improve the accuracy of its analysis by referring to past fashion data. The analysis unit can also improve the accuracy of its analysis by referring to coordination data from other users. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Relevant literature is obtained from, for example, academic papers and technical reports. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0050] The simulation unit optimizes the simulation algorithm by referring to past simulation data during the simulation. For example, the simulation unit selects the optimal simulation algorithm based on past simulation data. The simulation unit can also extract specific patterns from past simulation data and optimize the simulation algorithm. The simulation unit can also analyze past simulation data and improve the accuracy of the simulation algorithm. In this way, by referring to past simulation data, the simulation algorithm can be optimized and the accuracy of the simulation can be improved. The simulation algorithm includes specific types and optimization methods, such as physical simulations and statistical models. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input past simulation data into a generating AI and have the generating AI perform the optimization of the simulation algorithm.

[0051] The simulation unit performs simulations while considering the user's attribute information. For example, the simulation unit may perform simulations based on the user's gender and age. The simulation unit may also perform simulations based on the user's fashion preferences. The simulation unit may also perform simulations while considering the user's past outfit history. By performing simulations while considering the user's attribute information, the system can provide the user with the most optimal simulation results. Attribute information is obtained, for example, from the user's input information or registration information. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's attribute information data into a generating AI and have the generating AI execute the simulation.

[0052] The simulation unit performs simulations while considering the geographical distribution of users. For example, if a user is in a specific region, the simulation unit considers the fashion trends of that region. If a user is traveling, the simulation unit can also consider the fashion trends of the travel destination. If a user is in an urban area, the simulation unit can also consider the fashion trends of the urban area. By performing simulations while considering the geographical distribution of users, it is possible to provide optimal simulation results tailored to each region. Geographical distribution is obtained, for example, from regional user data or geographical trends. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the geographical distribution data of users into a generating AI and have the generating AI execute the simulation.

[0053] The simulation unit improves the accuracy of the simulation by referring to relevant literature during the simulation. For example, the simulation unit improves the accuracy of the simulation by referring to the latest fashion research. The simulation unit can also improve the accuracy of the simulation by referring to past fashion data. The simulation unit can also improve the accuracy of the simulation by referring to coordination data from other users. In this way, the accuracy of the simulation can be improved by referring to relevant literature. Relevant literature is obtained from, for example, academic papers and technical reports. Some or all of the above processing in the simulation unit may be performed using, for example, AI, or not using AI. For example, the simulation unit can input relevant literature data into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0054] The delivery unit selects the optimal delivery method by referring to the user's past delivery history at the time of delivery. For example, the delivery unit automatically displays delivery methods that the user has frequently used in the past as candidates. The delivery unit can also prioritize suggesting delivery methods (voice, text, etc.) that the user has used in the past. The delivery unit can also predict and suggest delivery methods to be used at specific time periods based on the user's past delivery history. This allows the delivery unit to select the optimal delivery method and provide information efficiently by referring to the user's past delivery history. The optimal delivery method includes specific criteria and methods such as the timing of delivery and the procedure for delivery. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's past delivery history data into a generating AI and have the generating AI select the optimal delivery method.

[0055] The information provider filters the information provided based on the user's current situation and areas of interest. For example, the provider filters the information based on topics the user is currently interested in. The provider can also provide relevant information by considering keywords the user has recently searched for. The provider can also filter the information provided by referring to information from accounts the user follows. This allows the provider to provide the user with the most relevant information by filtering information based on the user's current situation and areas of interest. The current situation is obtained, for example, from user input information or real-time data. Areas of interest are obtained, for example, from user input information or past selection history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the user's current situation data into a generating AI and have the generating AI perform the filtering.

[0056] The information provider prioritizes providing highly relevant information, taking into account the user's geographical location. For example, if the user is in a specific region, the provider prioritizes providing information related to that region. If the user is traveling, the provider can also prioritize providing information related to their travel destination. If the user is at an event venue, the provider can also prioritize providing information related to that event. By prioritizing highly relevant information while considering the user's geographical location, the provider can provide optimal information tailored to the user's situation. Geographical location information is obtained, for example, from GPS data or user input information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0057] The service provider analyzes the user's social media activity and provides relevant information at the time of delivery. For example, the service provider prioritizes providing information about accounts that the user follows on social media. The service provider can also provide information based on topics that the user has shared on social media. The service provider can also prioritize providing information related to posts that the user has "liked" on social media. In this way, by analyzing the user's social media activity, it is possible to provide relevant information and information that matches the user's preferences. Social media activity is obtained from, for example, the content of posts and the history of likes. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI select relevant information.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The registration unit can automatically recognize the material and brand information of clothing when a user registers their clothes, and add this information to the registration data. For example, the registration unit can identify the material of the clothing from image data acquired by a scanner and register it in the database. Furthermore, the registration unit can recognize the brand logo of the clothing using OCR technology and automatically add the brand information. This eliminates the need for users to manually enter detailed information about their clothing, allowing them to receive more accurate styling suggestions.

[0060] The analysis unit can analyze a user's past outfit history and adjust the analysis algorithm according to changes in trends. For example, the analysis unit can detect changes in the user's preferences from past outfit history and perform analysis based on the latest trends. Furthermore, the analysis unit can also suggest outfits suitable for specific seasons or events based on the user's past selection history. This allows the system to provide optimal outfits that match the user's preferences and changing trends.

[0061] The registration unit can record the condition and frequency of use of clothing when a user registers it, and add this information to a database. For example, the registration unit can evaluate the condition of clothing using image analysis technology and estimate the frequency of use from the user's input information and past registration history. This allows users to receive outfit suggestions based on the condition and frequency of use of their clothing, making clothing management easier.

[0062] The reception system can select the optimal reception method by referring to the user's past message history. For example, it can automatically display message formats that the user has frequently used in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest message formats that the user will use at a specific time of day based on their past message history. This allows for efficient message reception by selecting the optimal reception method based on the user's past message history.

[0063] The analysis unit can perform analysis while considering the geographical distribution of users. For example, if a user is in a specific region, the analysis can be performed while considering the fashion trends of that region. If a user is traveling, the analysis can also be performed while considering the fashion trends of their travel destination. If a user is in an urban area, the analysis can also be performed while considering the fashion trends of that urban area. By considering the geographical distribution of users, the analysis can be provided with optimal results tailored to each region.

[0064] The simulation unit can improve the accuracy of simulations by referring to relevant literature during the simulation process. For example, it can improve the accuracy of simulations by referring to the latest fashion research. It can also improve the accuracy of simulations by referring to past fashion data. It can also improve the accuracy of simulations by referring to coordination data from other users. In this way, the accuracy of simulations can be improved by referring to relevant literature.

[0065] The service provider can analyze users' social media activity and provide relevant information. For example, it can prioritize providing information about accounts that users follow on social media. It can also provide information based on topics that users have shared on social media. It can also prioritize providing information related to posts that users have "liked" on social media. In this way, by analyzing users' social media activity, it is possible to provide relevant information and information that is tailored to the user's preferences.

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

[0067] Step 1: The registration unit scans and registers the clothes owned by the user. These clothes include handwritten descriptions and digital descriptions. The registration unit digitizes handwritten descriptions using scanning technology and registers them. It can also directly register clothes submitted in digital format. Furthermore, it can read printed clothing using OCR technology. Step 2: The reception desk receives abstract messages from users. Abstract messages can include text messages, voice messages, images, etc. For example, it can accept messages from users such as, "I want to wear something like office casual clothes today, please help me choose some." via chat or voice. Step 3: The analysis unit analyzes information such as location, season, temperature, gender, age, trends, and preferences. For example, it obtains the user's location information and considers the season and temperature of that region. It also analyzes information such as the user's gender, age, trends, and preferences. Step 4: The simulation unit simulates the optimal coordination based on the information analyzed by the analysis unit. For example, it simulates the optimal coordination based on user information. Step 5: The provisioning unit provides the user with the 3D model generated by the simulation unit. For example, it provides the user with a 3D model generated on a digital twin.

[0068] (Example of form 2) The fashion coordination suggestion system according to an embodiment of the present invention is a system that scans and registers the clothes owned by the user, receives an abstract message, analyzes the information, simulates the optimal coordination, and provides a 3D model. The fashion coordination suggestion system scans the clothes owned by the user and registers them in RAG. Next, the user inputs an abstract message via chat or voice, such as "Today I want to wear something like office casual clothes, so please choose some." This information is sent to an AI agent. The AI ​​agent simulates the optimal coordination, taking into account information such as location, season, temperature, gender, age, trends, and preferences. The simulation results are generated as a 3D model on a digital twin and provided to the user. This allows the user to easily find the optimal coordination from the clothes they own and resolve their fashion-related worries. In addition, apparel companies can utilize the user's trend information for marketing. For example, the fashion coordination suggestion system scans the clothes owned by the user and registers them in RAG. For example, the clothes owned by the user are read with a scanner and saved as image data. Then, the image data is converted into text data using OCR technology. Next, the fashion coordination suggestion system receives an abstract message from the user. For example, a user might enter a message via chat or voice, such as, "Today I want to wear something like office casual clothes, please help me choose some." This information is sent to the AI ​​agent. Next, the fashion coordination suggestion system uses the AI ​​agent to analyze information such as location, season, temperature, gender, age, trends, and preferences. For example, the AI ​​agent obtains the user's location information and considers the season and temperature of that area. The AI ​​agent also analyzes information such as the user's gender, age, trends, and preferences. Next, the fashion coordination suggestion system uses the AI ​​agent to simulate the optimal outfit. For example, the AI ​​agent simulates the optimal outfit based on the user's information.The simulation results are generated as a 3D model on a digital twin. Next, the fashion coordination suggestion system provides the user with the simulation results. For example, it provides the user with the 3D model generated on the digital twin. This allows the user to easily find the best outfit from their existing wardrobe. In this way, the fashion coordination suggestion system can resolve the user's fashion concerns and suggest the optimal outfit.

[0069] The fashion coordination suggestion system according to this embodiment comprises a registration unit, a reception unit, an analysis unit, a simulation unit, and a provision unit. The registration unit scans and registers the clothes owned by the user. The clothes owned by the user include, for example, handwritten clothes, clothes in digital format, etc., but are not limited to such examples. The registration unit can, for example, digitize and register handwritten clothes using scanning technology. The registration unit can also directly register clothes submitted in digital format. Furthermore, the registration unit can read printed clothes using OCR technology. For example, the registration unit scans handwritten clothes with a high-resolution scanner and converts them into text information using OCR technology. Clothes in digital format can be directly registered if submitted in a specific file format. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The reception unit receives abstract messages from the user. Abstract messages include, for example, text messages, voice messages, images, etc., but are not limited to such examples. For example, the reception unit receives messages from the user such as "I want to wear something like office casual clothes today, so please choose some for me" entered via chat or voice. The analysis unit analyzes information such as location, season, temperature, gender, age, trends, and preferences. For example, the analysis unit acquires the user's location information and considers the season and temperature of that region. The analysis unit also analyzes information such as the user's gender, age, trends, and preferences. For example, the analysis unit considers the season and temperature of that region based on the user's location information. The analysis unit analyzes information such as the user's gender, age, trends, and preferences. The simulation unit simulates the optimal outfit based on the information analyzed by the analysis unit. For example, the simulation unit simulates the optimal outfit based on the user's information. The simulation unit simulates the optimal outfit based on the user's information. The provision unit provides the user with the 3D model generated by the simulation unit. For example, the provision unit provides the user with the 3D model generated on the digital twin. The provision unit provides the user with the 3D model generated by the simulation unit.As a result, the fashion coordination suggestion system according to the embodiment can resolve the user's fashion-related concerns and suggest the optimal coordination.

[0070] The registration unit scans and registers the clothes owned by the user. These clothes include, but are not limited to, handwritten designs and digitally formatted designs. For example, the registration unit can digitize and register handwritten designs using scanning technology. Specifically, a high-resolution scanner is used to scan the handwritten clothing design and save it as image data. This image data is analyzed using OCR (Optical Character Recognition) technology, and the clothing design and features are extracted as digital text. This allows the handwritten clothing design to be registered digitally and made available within the system. The registration unit can also directly register clothing submitted in digital format. For example, if a user submits a digital clothing design in a specific file format (JPEG, PNG, PDF, etc.), these files can be registered directly into the system. Furthermore, the registration unit can read printed clothing using OCR technology. For example, clothing designs published in fashion magazines or catalogs can be scanned, and the text and design information can be digitized using OCR technology. This allows printed clothing designs to also be registered in the system, enriching the database of the user's clothing. The registration section centrally manages this data, allowing users to efficiently register and update information about the clothes they own.

[0071] The reception unit receives abstract messages from users. Abstract messages include, but are not limited to, text messages, voice messages, and images. For example, the reception unit can receive messages from users that say, "I want to wear something like office casual clothes today, please help me choose some," via chat or voice input. Specifically, users can send messages using a smartphone or computer via a chat application or voice recognition application. The reception unit receives these messages and, in the case of text messages, sends them directly to the analysis unit. In the case of voice messages, it uses voice recognition technology to convert the voice to text and sends it to the analysis unit. In the case of images, it uses image analysis technology to extract information from the image and sends it to the analysis unit. For example, if a user voice-inputs, "I want to wear casual clothes today," the reception unit uses voice recognition technology to convert this message to text and sends it to the analysis unit as the text message, "I want to wear casual clothes today." This allows the reception unit to receive and appropriately process messages in various formats from users.

[0072] The analysis unit analyzes information such as location, season, temperature, gender, age, trends, and preferences. For example, the analysis unit obtains the user's location information and considers the season and temperature of that region. Specifically, it obtains location information from the user's smartphone or device and links it with the local weather database to obtain the current season and temperature. This allows the analysis unit to understand the climate conditions of the area where the user is currently located and suggest appropriate fashion coordinates. The analysis unit also analyzes information such as the user's gender, age, trends, and preferences. For example, based on the gender and age information entered by the user during registration, it suggests fashion styles suitable for that age and gender. Furthermore, the analysis unit analyzes the user's past selection history and preference data to suggest coordinates that match the user's preferences. For example, it analyzes the design, color, and style of clothes the user has chosen in the past and suggests new coordinates based on that. The analysis unit comprehensively analyzes this information to generate basic data for providing the user with the optimal fashion coordinates.

[0073] The simulation unit simulates the optimal outfit based on the information analyzed by the analysis unit. Specifically, it simulates the optimal outfit based on information such as the user's location, season, temperature, gender, age, trends, and preferences. For example, the simulation unit generates a 3D model of the user using digital twin technology based on the user's information and simulates having the model try on various clothes. This allows the user to visually confirm how the suggested outfits look through a 3D model that closely resembles themselves. The simulation unit simulates the optimal outfit based on the user's information. For example, if the user inputs, "Today I want to wear something like office casual clothes," the simulation unit selects clothes suitable for office casual and has the user's 3D model try them on. Furthermore, the simulation unit simulates multiple outfits, taking into account the user's preferences and trends, and selects the best one. In this way, the simulation unit can propose the optimal fashion outfit to the user in a visually easy-to-understand manner.

[0074] The service provider provides users with 3D models generated by the simulation unit. Specifically, it provides users with 3D models generated on a digital twin. The service provider displays the 3D models on the user's smartphone or PC, allowing the user to check the suggested outfits. The service provider provides users with 3D models generated by the simulation unit. For example, a user can open a smartphone app and check the suggested outfits in 3D models. The service provider allows users to rotate and zoom the 3D models to check the suggested outfits in detail. This allows users to visually check how the suggested outfits look and choose fashion that suits them. Furthermore, the service provider also provides functions for users to save and share suggested outfits. For example, a user can save their favorite outfits and check them later. Users can also share suggested outfits on social media and exchange opinions with friends and family. In this way, the service provider can provide users with optimal fashion outfits in a visually easy-to-understand manner and resolve their fashion concerns.

[0075] The registration unit scans the user's clothes and registers them in RAG. For example, the registration unit scans the user's clothes and saves them as image data. Then, the registration unit uses OCR technology to convert the image data into text data. The registration unit can also write on the user's clothes with a dedicated digital pen, and the digital pen's writing can be converted into digital data in real time. For example, the movement of the pen can be detected by a sensor and saved as text data. As a result, by scanning the user's clothes and registering them in RAG, the system can understand the user's clothes and suggest the best outfit. RAG serves as a clothing database and registration system. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the image data acquired by the scanner into a generating AI and have the generating AI generate text data from the image data.

[0076] The reception desk receives abstract messages from users. For example, the reception desk can receive messages from users via chat or voice input, such as, "Today I want to wear something like office casual clothes, so please help me choose some." The reception desk can accept abstract messages in the form of text messages, voice messages, images, etc. By receiving abstract messages from users, the system can understand the user's preferences and suggest the most suitable outfit. Abstract messages include, but are not limited to, text messages, voice messages, and images. Some or all of the processing described above in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input a voice message from a user into a generating AI and have the generating AI perform the conversion of the voice message into text data.

[0077] The analysis unit analyzes information such as location, season, temperature, gender, age, trends, and preferences. For example, the analysis unit acquires the user's location information and considers the season and temperature of that region. The analysis unit also analyzes information such as the user's gender, age, trends, and preferences. For example, the analysis unit considers the season and temperature of that region based on the user's location information. The analysis unit analyzes information such as the user's gender, age, trends, and preferences. By analyzing information such as location, season, temperature, gender, age, trends, and preferences, the system can suggest the most suitable outfit for the user. Location information is acquired from, for example, GPS data or user input information. Seasons are used based on specific definitions such as spring, summer, autumn, and winter. Temperature is acquired from, for example, weather data or user input information. Gender is acquired from, for example, user input information or registration information. Age is acquired from, for example, user input information or registration information. Trends are acquired from, for example, fashion magazines or social media. Preferences are obtained, for example, from user input information and past selection history. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user location information and temperature data into a generating AI and have the generating AI perform the analysis.

[0078] The simulation unit simulates the optimal outfit based on the information analyzed by the analysis unit. The simulation unit simulates the optimal outfit based on user information, for example. The simulation unit simulates the optimal outfit based on user information, thereby enabling the system to suggest the most suitable fashion outfit to the user. The optimal outfit is, for example, an outfit based on the user's preferences, trends, season, etc. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the information analyzed by the analysis unit into a generating AI and have the generating AI perform the simulation.

[0079] The provisioning unit provides the user with a 3D model generated by the simulation unit. The provisioning unit provides the user with a 3D model generated on a digital twin, for example. The provisioning unit provides the user with a 3D model generated by the simulation unit. By providing the user with a 3D model generated by the simulation unit, the system can suggest the user the most visually optimal fashion coordination. The 3D model includes specific details and format, such as the design, color, and size of the clothing. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input the 3D model generated by the simulation unit into a generating AI and have the generating AI perform the provision of the 3D model.

[0080] The registration unit estimates the user's emotions and adjusts the timing of clothing registration based on the estimated emotions. For example, if the user is stressed, the registration unit simplifies the registration process to allow for quick registration. If the user is relaxed, the registration unit can also provide detailed registration options and suggest a customizable registration method. If the user is in a hurry, the registration unit can prioritize voice input to allow for quick clothing registration. This allows for clothing registration at the optimal time according to the user's situation by adjusting the timing of clothing registration based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not using AI. For example, the registration unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The registration unit analyzes the user's past clothing registration history and selects the optimal registration method. For example, the registration unit automatically displays clothing categories that the user has frequently registered in the past as suggestions. The registration unit can also prioritize suggesting registration methods (voice, text, etc.) that the user has used in the past. The registration unit can also predict and suggest clothing categories to be registered at a specific time based on the user's past registration history. This allows for efficient clothing registration by selecting the optimal registration method through analysis of the user's past clothing registration history. The optimal registration method includes specific criteria and methods, such as the timing and procedure of registration. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's past registration history data into a generating AI and have the generating AI select the optimal registration method.

[0082] The registration unit filters clothing items based on the user's current fashion trends and areas of interest when they are registered. For example, the registration unit filters clothing items based on the fashion styles the user is currently interested in. The registration unit can also suggest clothing items to register by considering the trends of clothing the user has recently purchased. The registration unit can also filter clothing items by referring to the styles of fashion influencers the user follows. This allows the system to register clothing items that are best suited to the user by filtering them based on the user's current fashion trends and areas of interest. Fashion trends are obtained from sources such as fashion magazines and social media. Areas of interest are obtained from sources such as user input information and past selection history. Some or all of the above processing in the registration unit may be performed using AI, or not. For example, the registration unit can input the user's fashion trend data into a generating AI and have the generating AI perform the filtering.

[0083] The registration unit estimates the user's emotions and determines the priority of clothing to register based on the estimated emotions. For example, if the user is stressed, the registration unit will prioritize registering simple and easy-to-wear clothing. If the user is relaxed, the registration unit may also prioritize registering casual clothing. If the user is in a hurry, the registration unit may also prioritize registering clothing that can be put on immediately. In this way, by determining the priority of clothing to register based on the user's emotions, the optimal clothing for the user's situation can be registered. 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 registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The registration unit prioritizes registering clothing that is highly relevant to the user's geographical location when registering clothing. For example, if the user is in a cold region, the registration unit will prioritize registering warm clothing. If the user is in a tropical region, the registration unit can also prioritize registering cool clothing. If the user is in an urban area, the registration unit can also prioritize registering clothing that is in line with current trends. In this way, by prioritizing the registration of highly relevant clothing that is relevant to the user's situation, the system can register the most suitable clothing for the user. Geographical location information is obtained from, for example, GPS data or user input information. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant clothing.

[0085] The registration unit analyzes the user's social media activity when registering clothing and registers relevant clothing. For example, the registration unit prioritizes registering clothing from brands that the user follows on social media. The registration unit can also register clothing based on fashion styles shared by the user on social media. The registration unit can also prioritize registering clothing that the user has "liked" on social media. This allows the system to register relevant clothing and clothing that matches the user's preferences by analyzing the user's social media activity. Social media activity is obtained, for example, from the content of posts and the history of likes. Some or all of the above processing in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the user's social media activity data into a generating AI and have the generating AI select relevant clothing.

[0086] The reception unit estimates the user's emotions and adjusts the message reception method based on the estimated emotions. For example, if the user is stressed, the reception unit provides a simple interface and minimizes the input steps. If the user is relaxed, the reception unit may also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception unit may prioritize voice input to allow for quick message reception. This allows for message reception in the most appropriate way for the user's situation by adjusting the message reception method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The reception unit, upon receiving a message, selects the optimal reception method by referring to the user's past message history. For example, the reception unit automatically displays message formats frequently used by the user in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) previously used by the user. The reception unit can also predict and suggest message formats to be used during specific time periods based on the user's past message history. This allows for efficient message reception by selecting the optimal reception method through the user's past message history. The optimal reception method includes specific criteria and methods, such as reception timing and reception procedures. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past message history data into a generating AI and have the generating AI select the optimal reception method.

[0088] The reception unit filters messages upon receipt based on the user's current situation and areas of interest. For example, the reception unit filters messages based on topics the user is currently interested in. The reception unit can also suggest relevant messages by considering keywords the user has recently searched for. The reception unit can also filter messages by referring to information about accounts the user follows. This allows the reception unit to receive messages that are most suitable for the user by filtering them based on the user's current situation and areas of interest. The current situation is obtained, for example, from user input information or real-time data. Areas of interest are obtained, for example, from user input information or past selection history. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input the user's current situation data into a generating AI and have the generating AI perform the filtering.

[0089] The analysis unit estimates the user's emotions and adjusts the analysis criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit uses simple analysis criteria. If the user is relaxed, the analysis unit may also use detailed analysis criteria. If the user is in a hurry, the analysis unit may also use criteria that allow for quick analysis results. By adjusting the analysis criteria based on the user's emotions, the analysis can be performed using the most appropriate criteria for the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The analysis unit optimizes the analysis algorithm by referring to past analysis data during the analysis. For example, the analysis unit selects the optimal analysis algorithm based on past analysis data. The analysis unit can also extract specific patterns from past analysis data and optimize the analysis algorithm. The analysis unit can also analyze past analysis data to improve the accuracy of the analysis algorithm. In this way, by referring to past analysis data, the analysis algorithm can be optimized and the accuracy of the analysis can be improved. The analysis algorithm includes specific types and optimization methods, such as machine learning algorithms and data mining techniques. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0091] The analysis unit performs analysis while considering the user's attribute information. For example, the analysis unit may perform analysis based on the user's gender and age. The analysis unit may also perform analysis based on the user's fashion preferences. The analysis unit may also perform analysis while considering the user's past outfit history. By performing analysis while considering the user's attribute information, the system can provide the user with the most optimal analysis results. Attribute information is obtained, for example, from the user's input information or registration information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's attribute information data into a generating AI and have the generating AI perform the analysis.

[0092] The analysis unit considers the geographical distribution of users during analysis. For example, if a user is in a specific region, the analysis unit considers the fashion trends of that region. If a user is traveling, the analysis unit can also consider the fashion trends of the travel destination. If a user is in an urban area, the analysis unit can also consider the fashion trends of the urban area. By considering the geographical distribution of users during analysis, the analysis unit can provide optimal analysis results tailored to each region. Geographical distribution is obtained, for example, from regional user data or geographical trends. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of users into a generating AI and have the generating AI perform the analysis.

[0093] The analysis unit improves the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit improves the accuracy of its analysis by referring to the latest fashion research. The analysis unit can also improve the accuracy of its analysis by referring to past fashion data. The analysis unit can also improve the accuracy of its analysis by referring to coordination data from other users. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Relevant literature is obtained from, for example, academic papers and technical reports. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0094] The simulation unit estimates the user's emotions and adjusts the simulation criteria based on the estimated emotions. For example, if the user is stressed, the simulation unit uses simple simulation criteria. If the user is relaxed, the simulation unit can also use detailed simulation criteria. If the user is in a hurry, the simulation unit can also use criteria that allow for quick simulation results. By adjusting the simulation criteria based on the user's emotions, the system can provide optimal simulation results tailored to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the simulation unit may be performed using AI, or not using AI. For example, the simulation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The simulation unit optimizes the simulation algorithm by referring to past simulation data during the simulation. For example, the simulation unit selects the optimal simulation algorithm based on past simulation data. The simulation unit can also extract specific patterns from past simulation data and optimize the simulation algorithm. The simulation unit can also analyze past simulation data and improve the accuracy of the simulation algorithm. In this way, by referring to past simulation data, the simulation algorithm can be optimized and the accuracy of the simulation can be improved. The simulation algorithm includes specific types and optimization methods, such as physical simulations and statistical models. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input past simulation data into a generating AI and have the generating AI perform the optimization of the simulation algorithm.

[0096] The simulation unit performs simulations while considering the user's attribute information. For example, the simulation unit may perform simulations based on the user's gender and age. The simulation unit may also perform simulations based on the user's fashion preferences. The simulation unit may also perform simulations while considering the user's past outfit history. By performing simulations while considering the user's attribute information, the system can provide the user with the most optimal simulation results. Attribute information is obtained, for example, from the user's input information or registration information. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's attribute information data into a generating AI and have the generating AI execute the simulation.

[0097] The simulation unit estimates the user's emotions and adjusts the display method of the simulation results based on the estimated user emotions. For example, if the user is stressed, the simulation unit provides a simple and highly visible display method. If the user is relaxed, the simulation unit can also provide a display method that includes detailed information. If the user is in a hurry, the simulation unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of the simulation results based on the user's emotions, the simulation results can be provided in the most optimal display method according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0098] The simulation unit performs simulations while considering the geographical distribution of users. For example, if a user is in a specific region, the simulation unit considers the fashion trends of that region. If a user is traveling, the simulation unit can also consider the fashion trends of the travel destination. If a user is in an urban area, the simulation unit can also consider the fashion trends of the urban area. By performing simulations while considering the geographical distribution of users, it is possible to provide optimal simulation results tailored to each region. Geographical distribution is obtained, for example, from regional user data or geographical trends. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the geographical distribution data of users into a generating AI and have the generating AI execute the simulation.

[0099] The simulation unit improves the accuracy of the simulation by referring to relevant literature during the simulation. For example, the simulation unit improves the accuracy of the simulation by referring to the latest fashion research. The simulation unit can also improve the accuracy of the simulation by referring to past fashion data. The simulation unit can also improve the accuracy of the simulation by referring to coordination data from other users. In this way, the accuracy of the simulation can be improved by referring to relevant literature. Relevant literature is obtained from, for example, academic papers and technical reports. Some or all of the above processing in the simulation unit may be performed using, for example, AI, or not using AI. For example, the simulation unit can input relevant literature data into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0100] The delivery unit estimates the user's emotions and adjusts the delivery method based on the estimated emotions. For example, if the user is stressed, the delivery unit provides a simple and highly visible delivery method. If the user is relaxed, the delivery unit may also provide a delivery method that includes detailed information. If the user is in a hurry, the delivery unit may also provide a delivery method that gets straight to the point. By adjusting the delivery method based on the user's emotions, information can be delivered in the most optimal way according to the user's situation. 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 delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0101] The delivery unit selects the optimal delivery method by referring to the user's past delivery history at the time of delivery. For example, the delivery unit automatically displays delivery methods that the user has frequently used in the past as candidates. The delivery unit can also prioritize suggesting delivery methods (voice, text, etc.) that the user has used in the past. The delivery unit can also predict and suggest delivery methods to be used at specific time periods based on the user's past delivery history. This allows the delivery unit to select the optimal delivery method and provide information efficiently by referring to the user's past delivery history. The optimal delivery method includes specific criteria and methods such as the timing of delivery and the procedure for delivery. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's past delivery history data into a generating AI and have the generating AI select the optimal delivery method.

[0102] The information provider filters the information provided based on the user's current situation and areas of interest. For example, the provider filters the information based on topics the user is currently interested in. The provider can also provide relevant information by considering keywords the user has recently searched for. The provider can also filter the information provided by referring to information from accounts the user follows. This allows the provider to provide the user with the most relevant information by filtering information based on the user's current situation and areas of interest. The current situation is obtained, for example, from user input information or real-time data. Areas of interest are obtained, for example, from user input information or past selection history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the user's current situation data into a generating AI and have the generating AI perform the filtering.

[0103] The service provider estimates the user's emotions and prioritizes the information to be provided based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing important information. If the user is relaxed, the service provider may also prioritize providing casual information. If the user is in a hurry, the service provider may also prioritize providing information that requires a quick response. In this way, by prioritizing the information to be provided based on the user's emotions, the service provider can provide optimal information tailored to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0104] The information provider prioritizes providing highly relevant information, taking into account the user's geographical location. For example, if the user is in a specific region, the provider prioritizes providing information related to that region. If the user is traveling, the provider can also prioritize providing information related to their travel destination. If the user is at an event venue, the provider can also prioritize providing information related to that event. By prioritizing highly relevant information while considering the user's geographical location, the provider can provide optimal information tailored to the user's situation. Geographical location information is obtained, for example, from GPS data or user input information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0105] The service provider analyzes the user's social media activity and provides relevant information at the time of delivery. For example, the service provider prioritizes providing information about accounts that the user follows on social media. The service provider can also provide information based on topics that the user has shared on social media. The service provider can also prioritize providing information related to posts that the user has "liked" on social media. In this way, by analyzing the user's social media activity, it is possible to provide relevant information and information that matches the user's preferences. Social media activity is obtained from, for example, the content of posts and the history of likes. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI select relevant information.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The registration unit can automatically recognize the material and brand information of clothing when a user registers their clothes, and add this information to the registration data. For example, the registration unit can identify the material of the clothing from image data acquired by a scanner and register it in the database. Furthermore, the registration unit can recognize the brand logo of the clothing using OCR technology and automatically add the brand information. This eliminates the need for users to manually enter detailed information about their clothing, allowing them to receive more accurate styling suggestions.

[0108] The reception system can estimate the user's emotions and adjust how messages are received based on that estimation. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can offer detailed input options and suggest customizable input methods. If the user is in a hurry, it can prioritize voice input to allow for quick message reception. This ensures that messages are received in the most appropriate way according to the user's emotions.

[0109] The analysis unit can analyze a user's past outfit history and adjust the analysis algorithm according to changes in trends. For example, the analysis unit can detect changes in the user's preferences from past outfit history and perform analysis based on the latest trends. Furthermore, the analysis unit can also suggest outfits suitable for specific seasons or events based on the user's past selection history. This allows the system to provide optimal outfits that match the user's preferences and changing trends.

[0110] The simulation unit can estimate the user's emotions and adjust the simulation criteria based on those emotions. For example, if the user is stressed, a simple simulation criterion can be used. If the user is relaxed, a more detailed simulation criterion can be used. If the user is in a hurry, a criterion that provides a quick simulation result can be used. This allows the system to provide the optimal simulation result tailored to the user's emotions.

[0111] The information delivery system can estimate the user's emotions and prioritize the information provided based on those emotions. For example, if a user is stressed, important information can be prioritized. If a user is relaxed, casual information can be prioritized. If a user is in a hurry, information requiring immediate attention can be prioritized. This allows the system to provide optimal information tailored to the user's emotions.

[0112] The registration unit can record the condition and frequency of use of clothing when a user registers it, and add this information to a database. For example, the registration unit can evaluate the condition of clothing using image analysis technology and estimate the frequency of use from the user's input information and past registration history. This allows users to receive outfit suggestions based on the condition and frequency of use of their clothing, making clothing management easier.

[0113] The reception system can select the optimal reception method by referring to the user's past message history. For example, it can automatically display message formats that the user has frequently used in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest message formats that the user will use at a specific time of day based on their past message history. This allows for efficient message reception by selecting the optimal reception method based on the user's past message history.

[0114] The analysis unit can perform analysis while considering the geographical distribution of users. For example, if a user is in a specific region, the analysis can be performed while considering the fashion trends of that region. If a user is traveling, the analysis can also be performed while considering the fashion trends of their travel destination. If a user is in an urban area, the analysis can also be performed while considering the fashion trends of that urban area. By considering the geographical distribution of users, the analysis can be provided with optimal results tailored to each region.

[0115] The simulation unit can improve the accuracy of simulations by referring to relevant literature during the simulation process. For example, it can improve the accuracy of simulations by referring to the latest fashion research. It can also improve the accuracy of simulations by referring to past fashion data. It can also improve the accuracy of simulations by referring to coordination data from other users. In this way, the accuracy of simulations can be improved by referring to relevant literature.

[0116] The service provider can analyze users' social media activity and provide relevant information. For example, it can prioritize providing information about accounts that users follow on social media. It can also provide information based on topics that users have shared on social media. It can also prioritize providing information related to posts that users have "liked" on social media. In this way, by analyzing users' social media activity, it is possible to provide relevant information and information that is tailored to the user's preferences.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The registration unit scans and registers the clothes owned by the user. These clothes include handwritten descriptions and digital descriptions. The registration unit digitizes handwritten descriptions using scanning technology and registers them. It can also directly register clothes submitted in digital format. Furthermore, it can read printed clothing using OCR technology. Step 2: The reception desk receives abstract messages from users. Abstract messages can include text messages, voice messages, images, etc. For example, it can accept messages from users such as, "I want to wear something like office casual clothes today, please help me choose some." via chat or voice. Step 3: The analysis unit analyzes information such as location, season, temperature, gender, age, trends, and preferences. For example, it obtains the user's location information and considers the season and temperature of that region. It also analyzes information such as the user's gender, age, trends, and preferences. Step 4: The simulation unit simulates the optimal coordination based on the information analyzed by the analysis unit. For example, it simulates the optimal coordination based on user information. Step 5: The provisioning unit provides the user with the 3D model generated by the simulation unit. For example, it provides the user with a 3D model generated on a digital twin.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the registration unit, reception unit, analysis unit, simulation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the registration unit scans the user's clothing using the scanner and camera 42 of the smart device 14 and converts it into digital data using OCR technology by the identification processing unit 290 of the data processing unit 12. The reception unit receives messages from the user using the microphone 38B and touch panel 38A of the smart device 14 and analyzes them by the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the user's location information, season, temperature, etc. by the identification processing unit 290 of the data processing unit 12. The simulation unit simulates the optimal outfit using the identification processing unit 290 of the data processing unit 12, and the provision unit provides a 3D model to the user using the display 40A of the smart device 14. 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.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the registration unit, reception unit, analysis unit, simulation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit scans the user's clothing using the camera 42 of the smart glasses 214 and converts it into digital data using OCR technology by the identification processing unit 290 of the data processing unit 12. The reception unit receives messages from the user using the microphone 238 of the smart glasses 214 and analyzes them by the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the user's location information, season, temperature, etc. by the identification processing unit 290 of the data processing unit 12. The simulation unit simulates the optimal outfit using the identification processing unit 290 of the data processing unit 12, and the provision unit provides the user with a 3D model using the display of the smart glasses 214. 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.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the registration unit, reception unit, analysis unit, simulation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit scans the user's clothing using the camera 42 of the headset terminal 314 and converts it into digital data using OCR technology by the identification processing unit 290 of the data processing unit 12. The reception unit receives messages from the user using the microphone 238 of the headset terminal 314 and analyzes them using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the user's location information, season, temperature, etc. using the identification processing unit 290 of the data processing unit 12. The simulation unit simulates the optimal outfit using the identification processing unit 290 of the data processing unit 12, and the provision unit provides a 3D model to the user using the display 343 of the headset terminal 314. 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.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the registration unit, reception unit, analysis unit, simulation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the registration unit scans the user's clothing using the camera 42 of the robot 414 and converts it into digital data using OCR technology by the identification processing unit 290 of the data processing unit 12. The reception unit receives messages from the user using the microphone 238 of the robot 414 and analyzes them by the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the user's location information, season, temperature, etc. by the identification processing unit 290 of the data processing unit 12. The simulation unit simulates the optimal outfit using the identification processing unit 290 of the data processing unit 12, and the provision unit provides a 3D model to the user using the display of the robot 414. 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) A registration unit that scans and registers the clothes the user owns, A reception desk that receives abstract messages from users, An analysis unit analyzes information such as location information, season, temperature, gender, age, trends, and preferences based on the information received by the aforementioned reception unit. A simulation unit simulates the optimal coordination based on the information analyzed by the aforementioned analysis unit, The system includes a provisioning unit that provides the user with a 3D model generated by the simulation unit. A system characterized by the following features. (Note 2) The aforementioned registration unit is The user scans their clothes and registers them in RAG. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Accepts abstract messages from users. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, It analyzes information such as location, season, temperature, gender, age, trends, and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned simulation unit, Based on the information analyzed by the aforementioned analysis unit, the optimal coordination is simulated. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, The 3D model generated by the simulation unit is provided to the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of clothing registration based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is The system analyzes the user's past clothing registration history and selects the optimal registration method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is When registering clothing items, filtering is performed based on the user's current fashion trends and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is It estimates the user's emotions and determines the priority of clothing items to register based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is When registering clothing items, the system prioritizes registering items that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned registration unit is When registering clothing items, the system analyzes the user's social media activity and registers related clothing items. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is It estimates the user's emotions and adjusts how messages are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When receiving a message, the system will refer to the user's past message history to select the most suitable receiving method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is When receiving messages, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, user attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, the geographical distribution of users will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned simulation unit, During simulation, the simulation algorithm is optimized by referring to past simulation data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned simulation unit, During the simulation, the user's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned simulation unit, It estimates the user's emotions and adjusts how the simulation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned simulation unit, During the simulation, the geographical distribution of users is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned simulation unit, During simulations, we refer to relevant literature to improve the accuracy of the simulations. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the system will refer to the user's past service history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing content, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing information, we prioritize providing highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0191] 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 registration unit that scans and registers the clothes the user owns, A reception desk that receives abstract messages from users, An analysis unit analyzes information such as location information, season, temperature, gender, age, trends, and preferences based on the information received by the aforementioned reception unit. A simulation unit simulates the optimal coordination based on the information analyzed by the aforementioned analysis unit, The system includes a provisioning unit that provides the user with a 3D model generated by the simulation unit. A system characterized by the following features.

2. The aforementioned registration unit is The user scans their clothes and registers them in RAG. The system according to feature 1.

3. The aforementioned reception unit is Accepts abstract messages from users. The system according to feature 1.

4. The aforementioned analysis unit, It analyzes information such as location, season, temperature, gender, age, trends, and preferences. The system according to feature 1.

5. The aforementioned simulation unit, Based on the information analyzed by the aforementioned analysis unit, the optimal coordination is simulated. The system according to feature 1.

6. The aforementioned supply unit is, The 3D model generated by the simulation unit is provided to the user. The system according to feature 1.

7. The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of clothing registration based on those emotions. The system according to feature 1.

8. The aforementioned registration unit is The system analyzes the user's past clothing registration history and selects the optimal registration method. The system according to feature 1.

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