system

Abstract

The system according to this embodiment aims to provide optimal fashion coordination based on the user's profile information, weather, and schedule. [Solution] The system according to the embodiment comprises a collection unit, an acquisition unit, a generation unit, an analysis unit, and a provision unit. The collection unit collects user profile information. The acquisition unit acquires weather and schedule information based on the information collected by the collection unit. The generation unit generates an optimal fashion coordination based on the information acquired by the acquisition unit. The analysis unit analyzes a full-body image of the clothing worn by the user. The provision unit presents improvement suggestions based on the results analyzed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully achieved to provide an optimal fashion coordination based on user profile information, weather, and schedules, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal fashion coordination based on user profile information, weather, and schedules.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an acquisition unit, a generation unit, an analysis unit, and a provision unit. The collection unit collects user profile information. The acquisition unit acquires weather and schedule information based on the information collected by the collection unit. The generation unit generates an optimal fashion coordination based on the information acquired by the acquisition unit. The analysis unit analyzes a full-body image of the clothing worn by the user. The provision unit presents improvement suggestions based on the results analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide optimal fashion coordination based on the user's profile information, weather, and schedule. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The fashion coordination system according to an embodiment of the present invention is a system that utilizes generative AI to provide an optimal fashion coordination based on the user's profile information (body type, age, gender, preferences, etc.), the weather and schedule for the day, and the latest fashion trends. This fashion coordination system collects the user's profile information, obtains the weather and schedule based on that information, and generates an optimal fashion coordination. It also analyzes a full-body image of the clothing the user is wearing, checks whether it is in line with the weather, season, and trends, and suggests improvements. For example, if the user has plans to go out on a rainy day, the generative AI will suggest waterproof clothing. It will also suggest a coordination that incorporates the latest fashion trends. Furthermore, it analyzes a full-body image of the clothing the user is wearing using a multimodal function. The generative AI analyzes the image and checks whether it is in line with the weather, season, and trends. For example, if the user is wearing a thick coat on a summer day, the generative AI will determine that the clothing is inappropriate for the season and suggest an appropriate improvement. In this way, the user can easily choose their daily outfits and enjoy an optimal coordination that suits their lifestyle and the trends. For example, it will suggest business casual attire on days when the user goes to work and relaxed casual attire on holidays. Furthermore, if there is a special event, the service will suggest appropriate attire for that event. This service can increase user satisfaction by providing personalized fashion coordination tailored to the individual needs of each user. For example, if a user prefers a particular brand or style, the service can suggest coordination that matches that preference. Also, if a user wants to try a new style, the generative AI will suggest coordination that incorporates the latest trends. In addition, the generative AI can improve the accuracy of its coordination based on user feedback. For example, by having users rate the suggested coordination as "good" or "bad," the generative AI can learn from that evaluation and reflect it in future suggestions. This makes it possible to provide coordination that is more suitable to the user's preferences and needs.This allows the fashion coordination system to provide optimal fashion coordinates based on the user's profile information, weather, schedule, and the latest fashion trends, making it easier for users to choose their outfits.

[0029] The fashion coordination system according to the embodiment comprises a collection unit, an acquisition unit, a generation unit, an analysis unit, and a provision unit. The collection unit collects user profile information. The collection unit collects information such as the user's body type, age, gender, and preferences. For example, the collection unit collects the user's body type information through an input form. The collection unit can also collect the user's age information by calculating it from their date of birth. Furthermore, the collection unit can collect the user's gender information by having them select it from a list of options. For example, the collection unit collects the user's body type information through an input form and stores it in a database. The collection unit collects the user's age information by calculating it from their date of birth and stores it in a database. The collection unit collects the user's gender information by having them select it from a list of options and stores it in a database. The acquisition unit acquires weather and schedule information based on the information collected by the collection unit. For example, the acquisition unit acquires weather information from a weather database. The acquisition unit can also acquire schedule information from a calendar application. Furthermore, the acquisition unit can acquire weather information based on the user's location information. For example, the acquisition unit acquires weather information from a weather database and stores it in the database. The acquisition unit acquires schedule information from a calendar application and stores it in the database. The acquisition unit acquires weather information based on the user's location information and stores it in the database. The generation unit generates the optimal fashion coordination based on the information acquired by the acquisition unit. The generation unit generates coordination considering the weather, schedule, and the latest fashion trends, for example, using a generation AI. The generation unit can also have the generation AI suggest the optimal coordination based on the user's profile information, for example. Furthermore, the generation unit can have the generation AI learn from user feedback and reflect it in future suggestions. For example, the generation unit uses a generation AI to generate coordination considering the weather, schedule, and the latest fashion trends and stores it in the database. The generation unit has the generation AI suggest the optimal coordination based on the user's profile information and stores it in the database. The generation unit has the generation AI learn from user feedback and reflect it in future suggestions. The analysis unit analyzes a full-body image of the clothing worn by the user.The analysis unit extracts clothing features using, for example, an image analysis algorithm. The analysis unit can also use, for example, an image analysis algorithm to analyze the color and shape of the clothing. Furthermore, the analysis unit can also use, for example, an image analysis algorithm to analyze the material and design of the clothing. For example, the analysis unit extracts clothing features using an image analysis algorithm and stores them in a database. The analysis unit uses, for example, an image analysis algorithm to analyze the color and shape of the clothing and stores them in a database. The analysis unit uses, for example, an image analysis algorithm to analyze the material and design of the clothing and stores them in a database. The provision unit presents improvement suggestions based on the results analyzed by the analysis unit. The provision unit generates improvement suggestions using, for example, a generative AI. The provision unit can also use, for example, a generative AI to propose the optimal improvement suggestion based on the analysis results. Furthermore, the provision unit can have the generative AI learn from user feedback and reflect it in future improvement suggestions. For example, the provision unit generates improvement suggestions using a generative AI and stores them in a database. The provision unit has the generative AI propose the optimal improvement suggestion based on the analysis results and stores it in a database. The service provider uses a generating AI to learn from user feedback and incorporate it into future improvement suggestions. As a result, the fashion coordination system according to this embodiment provides optimal fashion coordination based on the user's profile information, weather, schedule, and the latest fashion trends, making it easier for the user to choose their outfit.

[0030] The data collection unit collects user profile information. For example, it collects information such as the user's body type, age, gender, and preferences. For instance, it collects the user's body type information through an input form. It can also calculate and collect the user's age information from their date of birth. Furthermore, it can collect the user's gender information by having them select from a list of options. For example, the data collection unit collects the user's body type information through an input form and stores it in a database. It also calculates and collects the user's age information from their date of birth and stores it in a database. It also collects and stores the user's gender information by having them select from a list of options. The data collection unit also collects information about the user's preferences and style. For example, it collects information such as items the user has purchased in the past, their browsing history on online stores, and brands they "like" or follow on social media. This allows the system to understand the user's fashion preferences and interest in trends. Furthermore, the data collection unit can conduct surveys to find out what styles the user prefers for specific events or seasons. This enables more personalized outfit suggestions tailored to the user's needs. The data collection unit centrally manages this information and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The acquisition unit obtains weather and schedule information based on the information collected by the data collection unit. For example, the acquisition unit can obtain weather information from a weather database. The acquisition unit can also obtain schedule information from a calendar application. Furthermore, the acquisition unit can obtain weather information based on the user's location information. For example, the acquisition unit obtains weather information from a weather database and stores it in the database. The acquisition unit obtains schedule information from a calendar application and stores it in the database. The acquisition unit obtains weather information based on the user's location information and stores it in the database. The acquisition unit can update this information in real time to respond to the user's latest situation. For example, if the user is traveling, the acquisition unit will obtain weather information for the travel destination and suggest an outfit that matches the schedule. The acquisition unit can also suggest appropriate clothing based on the type and location of events registered in the user's calendar. For example, it can provide outfits suitable for different situations, such as business meetings and casual parties. Furthermore, the acquisition unit can obtain more personalized information by considering the user's past behavior history and preferences. For example, if a user has previously favored a particular brand or style, the system can use that information to retrieve the latest trends and new product information and incorporate it into its recommendations. This allows the retrieval unit to provide optimal information tailored to the user's lifestyle and preferences, improving the overall accuracy of the system and user satisfaction.

[0032] The generation unit generates the optimal fashion coordination based on the information acquired by the acquisition unit. For example, the generation unit uses a generation AI to generate coordination considering the weather, schedule, and the latest fashion trends. The generation unit can also use a generation AI to suggest the optimal coordination based on the user's profile information. Furthermore, the generation unit can have the generation AI learn from user feedback and reflect it in future suggestions. For example, the generation unit uses a generation AI to generate coordination considering the weather, schedule, and the latest fashion trends and saves it to a database. The generation unit has a generation AI that suggests the optimal coordination based on the user's profile information and saves it to a database. The generation unit has a generation AI that learns from user feedback and reflects it in future suggestions. The generation unit uses a generation AI to learn the user's preferences and past choices and provides more personalized coordination. For example, the generation AI analyzes coordinations that the user has highly rated in the past and styles that have been frequently selected, and makes new suggestions based on that. In addition, the generation AI incorporates the latest fashion trends and seasonal trends to always provide the user with fresh and attractive coordination. Furthermore, the generation unit continuously improves the accuracy of its suggestions by incorporating user feedback in real time. For example, when a user rates a suggested outfit as "good" or "bad," the generation AI learns from that rating and incorporates it into future suggestions. This allows the generation unit to provide optimal outfits tailored to the user's preferences and needs, thereby increasing user satisfaction.

[0033] The analysis unit analyzes a full-body image of the clothing worn by the user. The analysis unit extracts features of the clothing using, for example, an image analysis algorithm. The analysis unit can also use the image analysis algorithm to analyze the color and shape of the clothing. Furthermore, the analysis unit can use the image analysis algorithm to analyze the material and design of the clothing. For example, the analysis unit extracts features of the clothing using an image analysis algorithm and stores them in a database. The analysis unit uses the image analysis algorithm to analyze the color and shape of the clothing and store them in a database. The analysis unit uses the image analysis algorithm to analyze the material and design of the clothing and store them in a database. When analyzing a full-body image of the clothing worn by the user, the analysis unit uses an image analysis algorithm to extract detailed features. For example, it can analyze not only the color and shape of the clothing, but also patterns, the presence or absence of accessories, and the fit of the clothing. Furthermore, the analysis unit can evaluate how the clothing looks based on the user's body type and posture. This allows for a more accurate understanding of how the clothing looks and gives the impression when the user actually wears it. The analysis unit stores this information in a database, making it accessible to the generation and provision units. For example, the feature information extracted by the analysis unit is used by the generation unit when making the next outfit suggestion. Furthermore, the analysis unit can improve its analysis results based on user feedback and reflect this in the next analysis. This allows the analysis unit to provide detailed information about the user's clothing, improving the overall system accuracy and user satisfaction.

[0034] The service provider presents improvement proposals based on the results analyzed by the analysis unit. The service provider generates improvement proposals using, for example, generative AI. The service provider can also have the generative AI propose the optimal improvement proposal based on the analysis results. Furthermore, the service provider can have the generative AI learn from user feedback and incorporate it into future improvement proposals. For example, the service provider generates improvement proposals using generative AI and saves them in a database. The service provider has the generative AI propose the optimal improvement proposal based on the analysis results and saves it in a database. The service provider has the generative AI learn from user feedback and incorporate it into future improvement proposals. When presenting improvement proposals based on the results analyzed by the analysis unit, the service provider uses generative AI to make the best suggestions for the user. For example, the generative AI learns from the user's past coordination and feedback and incorporates it into future suggestions. This allows the service provider to provide improvement proposals tailored to the user's preferences and needs. Furthermore, the service provider can have the generative AI learn from the user's evaluation of the proposed improvement proposals and incorporate it into future suggestions. For example, improvement suggestions that users rate as "good" will be considered in future proposals, while those rated as "bad" will be excluded. This allows the service provider to continuously improve the accuracy of their suggestions based on user feedback. Furthermore, the service provider can increase the user's acceptance of suggestions by explaining the reasons and background behind them. For example, they can explain why a particular color or style suits the user, or the background of suggestions based on the latest fashion trends. This allows the service provider to make more reliable suggestions to users and improve user satisfaction.

[0035] The data collection unit can collect information such as the user's body type, age, gender, and preferences. For example, the data collection unit can collect the user's body type information through an input form. The data collection unit can also collect the user's age information by calculating it from their date of birth. The data collection unit can also collect the user's gender information by having them select it from a list of options. For example, the data collection unit collects the user's body type information through an input form and stores it in a database. The data collection unit collects the user's age information by calculating it from their date of birth and stores it in a database. The data collection unit collects the user's gender information by having them select it from a list of options and stores it in a database. This allows for the collection of more personalized outfits by collecting detailed profile information about the user. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit collects the user's body type information through an input form and stores it in a database using AI.

[0036] The acquisition unit can acquire weather and schedule information. For example, the acquisition unit can acquire weather information from a weather database. The acquisition unit can also acquire schedule information from a calendar application. For example, the acquisition unit can acquire weather information based on the user's location information. For example, the acquisition unit acquires weather information from a weather database and stores it in the database. The acquisition unit acquires schedule information from a calendar application and stores it in the database. The acquisition unit acquires weather information based on the user's location information and stores it in the database. This allows for the suggestion of appropriate coordination based on weather and schedule. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit acquires weather information from a weather database and stores it in the database using AI.

[0037] The generation unit can generate optimal fashion coordinates considering weather, schedule, and the latest fashion trends. For example, the generation unit uses a generation AI to generate coordinates considering weather, schedule, and the latest fashion trends. The generation unit can also use a generation AI to suggest optimal coordinates based on the user's profile information. The generation unit can also use a generation AI to learn from user feedback and reflect it in future suggestions. For example, the generation unit uses a generation AI to generate coordinates considering weather, schedule, and the latest fashion trends and saves them in a database. The generation unit uses a generation AI to suggest optimal coordinates based on the user's profile information and saves them in a database. The generation unit uses a generation AI to learn from user feedback and reflect it in future suggestions. This allows the system to provide coordinates that incorporate the latest fashion trends. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs weather, schedule, and the latest fashion trends into the generation AI and generates optimal fashion coordinates.

[0038] The analysis unit can analyze a full-body image of the clothing worn by the user and check whether it is in line with the weather, season, and trends. The analysis unit can extract features of the clothing using, for example, an image analysis algorithm. The analysis unit can also use, for example, an image analysis algorithm to analyze the color and shape of the clothing. The analysis unit can also use, for example, an image analysis algorithm to analyze the material and design of the clothing. For example, the analysis unit extracts features of the clothing using an image analysis algorithm and stores them in a database. The analysis unit uses an image analysis algorithm to analyze the color and shape of the clothing and stores them in a database. The analysis unit uses an image analysis algorithm to analyze the material and design of the clothing and stores them in a database. This allows the system to verify whether the user's clothing is in line with the weather, season, and trends. 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 inputs image data into AI and uses AI to extract features of the clothing.

[0039] The service provider can present appropriate improvement suggestions based on the results analyzed by the analysis unit. The service provider can generate improvement suggestions using, for example, a generative AI. The service provider can also have the generative AI propose the optimal improvement suggestion based on the analysis results. The service provider can also have the generative AI learn from user feedback and incorporate it into future improvement suggestions. For example, the service provider generates improvement suggestions using a generative AI and saves them in a database. The service provider has the generative AI propose the optimal improvement suggestion based on the analysis results and saves it in a database. The service provider has the generative AI learn from user feedback and incorporate it into future improvement suggestions. This allows the service provider to provide users with appropriate fashion improvement suggestions. Some or all of the above-described processes in the service provider may be performed using, for example, AI, or without AI. For example, the service provider inputs the analysis results into an AI and generates improvement suggestions using the AI.

[0040] The generation unit can learn from user feedback and incorporate it into future suggestions. For example, the generation unit learns from user feedback using a generation AI. The generation unit can also improve future suggestions based on user evaluations using a generation AI. For example, the generation unit can learn from user preferences and needs using a generation AI and incorporate them into future suggestions. For example, the generation unit learns from user feedback using a generation AI and incorporates it into future suggestions. The generation unit improves future suggestions based on user evaluations using a generation AI and saves them in a database. The generation unit learns from user preferences and needs using a generation AI and incorporates them into future suggestions. This allows for more accurate coordination by incorporating user feedback. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs user feedback into the generation AI and generates future suggestions.

[0041] The data collection unit can analyze the user's past fashion history and select the optimal data collection method. For example, the data collection unit can analyze past purchase history. The data collection unit can also analyze past wear history. The data collection unit can also analyze past event participation history. For example, the data collection unit analyzes past purchase history and saves it to a database. The data collection unit analyzes past wear history and saves it to a database. The data collection unit analyzes past event participation history and saves it to a database. This allows for the collection of more appropriate profile information based on the user's past fashion history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs past fashion history into AI and uses AI to select the optimal data collection method.

[0042] The data collection unit can filter the collected profile information based on the user's current lifestyle and areas of interest. For example, the data collection unit may consider the user's occupation and hobbies. The data collection unit may also consider the user's daily activities. The data collection unit may also consider the user's favorite brands and topics of interest. For example, the data collection unit considers the user's occupation and hobbies and stores them in the database. The data collection unit considers the user's daily activities and stores them in the database. The data collection unit considers the user's favorite brands and topics of interest and stores them in the database. This allows for the collection of more relevant profile information based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit inputs the user's lifestyle and areas of interest into AI and uses AI to perform filtering.

[0043] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting profile information. For example, the data collection unit can acquire the user's GPS data. The data collection unit can also acquire the user's address information. The data collection unit can also collect relevant information based on the user's location information. For example, the data collection unit acquires the user's GPS data and stores it in a database. The data collection unit acquires the user's address information and stores it in a database. The data collection unit collects relevant information based on the user's location information and stores it in a database. This allows for the collection of more relevant profile information based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs the user's geographical location information into AI and uses AI to collect highly relevant information.

[0044] The data collection unit can analyze a user's social media activity and collect relevant information when collecting profile information. For example, the data collection unit can analyze the content of a user's posts. The data collection unit can also analyze the number of likes a user receives. The data collection unit can also analyze the number of followers a user has. For example, the data collection unit analyzes the content of a user's posts and stores it in a database. The data collection unit analyzes the number of likes a user receives and stores it in a database. The data collection unit analyzes the number of followers a user has and stores it in a database. This allows for the collection of more relevant profile information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI and use AI to collect relevant information.

[0045] The data acquisition unit can analyze the user's past behavioral history and select the optimal data acquisition method. For example, the data acquisition unit can analyze past travel history. The data acquisition unit can also analyze past activity logs. The data acquisition unit can also analyze past event participation history. For example, the data acquisition unit analyzes past travel history and saves it to a database. The data acquisition unit analyzes past activity logs and saves it to a database. The data acquisition unit analyzes past event participation history and saves it to a database. This allows for the acquisition of more appropriate weather and schedule information based on the user's past behavioral history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit inputs past behavioral history into AI and uses AI to select the optimal data acquisition method.

[0046] The data acquisition unit can filter weather and schedule information based on the user's current lifestyle and areas of interest. For example, the data acquisition unit may consider the user's occupation and hobbies. The data acquisition unit may also consider the user's daily activities. The data acquisition unit may also consider the user's favorite brands and topics of interest. For example, the data acquisition unit may consider the user's occupation and hobbies and store them in the database. The data acquisition unit may consider the user's daily activities and store them in the database. The data acquisition unit may consider the user's favorite brands and topics of interest and store them in the database. This allows for the acquisition of more relevant weather and schedule information based on the user's lifestyle and areas of interest. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit may input the user's lifestyle and areas of interest into AI and perform filtering using AI.

[0047] The data acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring weather and schedule information. For example, the data acquisition unit can acquire the user's GPS data. The data acquisition unit can also acquire the user's address information. The data acquisition unit can also acquire relevant information based on the user's location information. For example, the data acquisition unit acquires the user's GPS data and stores it in a database. The data acquisition unit acquires the user's address information and stores it in a database. The data acquisition unit acquires relevant information based on the user's location information and stores it in a database. This allows the user to acquire more relevant weather and schedule information based on their geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit inputs the user's geographical location information into AI and uses AI to acquire highly relevant information.

[0048] The data acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring weather and schedule information. For example, the data acquisition unit can analyze the content of the user's posts. The data acquisition unit can also analyze the number of likes the user has. The data acquisition unit can also analyze the number of followers the user has. For example, the data acquisition unit analyzes the content of the user's posts and stores it in a database. The data acquisition unit analyzes the number of likes the user has and stores it in a database. The data acquisition unit analyzes the number of followers the user has and stores it in a database. This allows for the acquisition of more relevant weather and schedule information based on the user's social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit inputs the user's social media activity into AI and uses AI to acquire relevant information.

[0049] The generation unit can adjust the level of detail generated based on the importance of fashion items when generating outfits. For example, the generation unit can generate detailed outfits that focus on important fashion items. The generation unit can also simplify the generation of outfits that have low importance. The generation unit can also generate outfits with different levels of detail depending on their importance. For example, the generation unit generates detailed outfits that focus on important fashion items and saves them to the database. The generation unit simplifies the generation of outfits that have low importance and saves them to the database. The generation unit generates outfits with different levels of detail depending on their importance and saves them to the database. This allows for the provision of detailed outfits according to the importance of fashion items. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the importance of fashion items into the generation AI and adjusts the level of detail of the generation.

[0050] The generation unit can apply different generation algorithms depending on the category of fashion items when generating outfits. For example, the generation unit can apply a specific generation algorithm to outerwear. The generation unit can also apply a different generation algorithm to innerwear. The generation unit can also apply yet another different generation algorithm to accessories. For example, the generation unit applies a specific generation algorithm to outerwear and saves it to the database. The generation unit applies another generation algorithm to innerwear and saves it to the database. The generation unit applies yet another different generation algorithm to accessories and saves it to the database. This allows the optimal generation algorithm to be applied according to the category of fashion items. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the category of fashion items into the generation AI and applies different generation algorithms.

[0051] The generation unit can determine the generation priority based on the submission date of fashion items when generating outfits. For example, the generation unit can prioritize incorporating newly submitted fashion items into outfits. The generation unit can also, for example, postpone the inclusion of older fashion items. The generation unit can also generate outfits with different priorities depending on the submission date. For example, the generation unit prioritizes incorporating newly submitted fashion items into outfits and saves them in the database. The generation unit postpones the inclusion of older fashion items and saves them in the database. The generation unit generates outfits with different priorities depending on the submission date and saves them in the database. This allows for the provision of more appropriate outfits by determining priorities according to the submission date of fashion items. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the submission date of fashion items into the generation AI and determines the generation priority.

[0052] The generation unit can adjust the generation order based on the relevance of fashion items when generating outfits. For example, the generation unit can prioritize incorporating highly relevant fashion items into the outfit. For example, the generation unit can postpone the generation of less relevant fashion items. For example, the generation unit can generate outfits in different orders depending on their relevance. For example, the generation unit can prioritize incorporating highly relevant fashion items into the outfit and save them in the database. For example, the generation unit can postpone the generation of less relevant fashion items and save them in the database. For example, the generation unit can generate outfits in different orders depending on their relevance and save them in the database. This allows the generation unit to provide more appropriate outfits by adjusting the generation order according to the relevance of fashion items. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the relevance of fashion items into the generation AI and adjusts the generation order.

[0053] The analysis unit can improve the accuracy of image analysis by considering the interrelationships of fashion items. For example, the analysis unit can analyze combinations of tops and bottoms. The analysis unit can also analyze combinations of accessories and main items. The analysis unit can also analyze shoes and the overall outfit. For example, the analysis unit analyzes combinations of tops and bottoms and saves them to a database. The analysis unit analyzes combinations of accessories and main items and saves them to a database. The analysis unit analyzes shoes and the overall outfit and saves them to a database. This improves the accuracy of the analysis by considering the interrelationships of fashion items. 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 inputs the interrelationships of fashion items into AI and uses AI to improve the accuracy of the analysis.

[0054] The analysis unit can perform image analysis while considering the user's attribute information. For example, the analysis unit may consider the user's body type information. The analysis unit may also consider the user's age information. The analysis unit may also consider the user's gender information. For example, the analysis unit may consider the user's body type information and store it in the database. The analysis unit may consider the user's age information and store it in the database. The analysis unit may consider the user's gender information and store it in the database. By considering the user's attribute information, more appropriate analysis results can be provided. 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 may input the user's attribute information into the AI ​​and perform the analysis using the AI.

[0055] The analysis unit can perform image analysis while considering the geographical distribution of fashion items. For example, the analysis unit can consider fashion items that are popular in urban areas. The analysis unit can also consider fashion items that are popular in suburban areas. The analysis unit can also consider fashion items that are popular overseas. For example, the analysis unit considers fashion items that are popular in urban areas and stores them in a database. The analysis unit considers fashion items that are popular in suburban areas and stores them in a database. The analysis unit considers fashion items that are popular overseas and stores them in a database. By considering the geographical distribution of fashion items, more appropriate analysis results can be provided. 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 inputs the geographical distribution of fashion items into AI and performs analysis using AI.

[0056] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on fashion items during image analysis. For example, the analysis unit may refer to articles from the latest fashion magazines. The analysis unit may also refer to fashion research papers. The analysis unit may also refer to posts from fashion blogs or influencers. For example, the analysis unit may refer to articles from the latest fashion magazines and save them in a database. The analysis unit may refer to fashion research papers and save them in a database. The analysis unit may refer to posts from fashion blogs or influencers and save them in a database. This allows the accuracy of the analysis to be improved by referring to relevant literature on fashion items. 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 may input relevant literature on fashion items into AI and use AI to improve the accuracy of the analysis.

[0057] The service provider can provide optimal improvement suggestions by referring to the user's past fashion history when presenting improvement suggestions. For example, the service provider can provide relevant improvement suggestions based on styles the user has preferred to wear in the past. The service provider can also provide relevant improvement suggestions by referring to the user's past purchase history. The service provider can also provide relevant improvement suggestions by referring to information about events the user has attended in the past. For example, the service provider can provide relevant improvement suggestions based on styles the user has preferred to wear in the past and store them in a database. The service provider can provide relevant improvement suggestions by referring to the user's past purchase history and store them in a database. The service provider can provide relevant improvement suggestions based on information about events the user has attended in the past and store them in a database. This allows the service provider to provide more appropriate improvement suggestions based on the user's past fashion history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past fashion history into AI and use AI to provide optimal improvement suggestions.

[0058] The service provider can customize the means of improvement suggestions based on the user's current lifestyle when presenting them. For example, if the user likes sports, the service provider will provide sports-related improvement suggestions. For example, if the user is a business person, the service provider can also provide business-related improvement suggestions. For example, if the user is interested in art, the service provider can also provide art-related improvement suggestions. For example, if the user likes sports, the service provider will provide sports-related improvement suggestions and save them in the database. If the user is a business person, the service provider will provide business-related improvement suggestions and save them in the database. If the user is interested in art, the service provider will provide art-related improvement suggestions and save them in the database. This allows the service provider to provide improvement suggestions tailored to the user's lifestyle. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's lifestyle into AI and use AI to customize the means of improvement suggestions.

[0059] The service provider can provide optimal improvement suggestions by considering the user's geographical location information when presenting improvement suggestions. For example, if the user lives in an urban area, the service provider will provide improvement suggestions related to urban areas. For example, if the user lives in a suburban area, the service provider can also provide improvement suggestions related to suburban areas. For example, if the user lives abroad, the service provider can also provide improvement suggestions related to the culture and climate of that country. For example, if the user lives in an urban area, the service provider will provide improvement suggestions related to urban areas and store them in the database. If the user lives in a suburban area, the service provider will provide improvement suggestions related to suburban areas and store them in the database. If the user lives abroad, the service provider will provide improvement suggestions related to the culture and climate of that country and store them in the database. This allows the service provider to provide more appropriate improvement suggestions based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's geographical location information into AI and use AI to provide optimal improvement suggestions.

[0060] The service provider can analyze the user's social media activity and propose methods for improvement when presenting improvement suggestions. For example, the service provider can analyze fashion-related posts shared by the user on social media. The service provider can also provide relevant improvement suggestions based on information about fashion influencers followed by the user. The service provider can also provide relevant improvement suggestions based on information about fashion-related groups and communities in which the user participates. For example, the service provider analyzes fashion-related posts shared by the user on social media and stores them in a database. The service provider provides relevant improvement suggestions based on information about fashion influencers followed by the user and stores them in a database. The service provider provides relevant improvement suggestions based on information about fashion-related groups and communities in which the user participates and stores them in a database. This allows the service provider to provide more appropriate improvement suggestions based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider inputs the user's social media activity into AI and uses AI to propose methods for improvement.

[0061] The service provider can improve the accuracy of improvement proposals based on user feedback when presenting them. For example, the service provider can reflect the user's evaluation of the proposed improvement proposal as "good" or "bad" in the next improvement proposal. The service provider can also adjust the content of the improvement proposal based on user feedback. The service provider can also learn from user feedback to improve the accuracy of the next improvement proposal. For example, the service provider evaluates the proposed improvement proposal as "good" or "bad" and saves it in a database. The service provider adjusts the content of the improvement proposal based on user feedback and saves it in a database. The service provider learns from user feedback to improve the accuracy of the next improvement proposal and saves it in a database. This allows the service provider to provide more accurate improvement proposals by reflecting user feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider inputs user feedback into AI and uses AI to improve the accuracy of the improvement proposal.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The fashion coordination system can analyze the user's past feedback and learn their preferences for specific seasons and events. For example, based on the styles the user preferred at past summer events, it can suggest outfits suitable for the next summer event. Furthermore, if the user prefers specific brands or designs, it can suggest related items based on that information. It can also suggest outfits suitable for similar events based on information about past events the user has attended. This allows for more accurate coordination by leveraging the user's past feedback. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit inputs the user's past feedback into an AI, which then learns the user's preferences.

[0064] The fashion coordination system can suggest outfits suitable for specific activities based on the user's lifestyle. For example, if the user enjoys outdoor activities, the generation unit can suggest waterproof and durable items. If the user primarily works in an office, it can also suggest business casual styles. Furthermore, if the user travels frequently, it can suggest comfortable clothing suitable for travel. This allows for more practical fashion suggestions by providing outfits tailored to the user's lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs the user's lifestyle information into the AI ​​and generates outfits using the AI.

[0065] A fashion coordination system can analyze a user's past purchase history and learn their preferences for specific brands and designs. For example, if a user has purchased many items from a particular brand in the past, the generation unit can suggest new items from that brand. Furthermore, if a user prefers specific designs or colors, the system can suggest related items based on that information. It can also suggest new items that are easy to combine with items the user has purchased in the past. This allows the system to provide more accurate coordination by leveraging the user's past purchase history. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit inputs the user's past purchase history into an AI and uses the AI ​​to learn their preferences.

[0066] The fashion coordination system can suggest outfits that reflect regional fashion trends, taking into account the user's geographical location. For example, it can suggest outfits incorporating the latest urban trends to users living in urban areas. It can also suggest casual and relaxed styles to users living in suburban areas. Furthermore, it can suggest outfits suitable for the culture and climate of the region to users living abroad. This enables more appropriate fashion suggestions based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs the user's geographical location information into the AI ​​and generates outfits using the AI.

[0067] The fashion coordination system can analyze a user's social media activity and suggest outfits based on information about influencers and brands the user follows. For example, if a user follows a specific fashion influencer, the system can suggest outfits incorporating styles recommended by that influencer. Similarly, if a user follows a specific brand, the system can suggest new items from that brand. Furthermore, it can suggest relevant outfits based on information about fashion-related groups and communities the user participates in. This enables more personalized fashion suggestions based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit inputs the user's social media activity into an AI and uses the AI ​​to generate outfits.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects user profile information. The data collection unit collects information such as the user's body type, age, gender, and preferences. For example, the data collection unit collects the user's body type information through an input form and stores it in the database. The data collection unit calculates and collects the user's age information from their date of birth and stores it in the database. The data collection unit collects the user's gender information by having them select it from a list of options and stores it in the database. Step 2: The acquisition unit obtains weather and schedule information based on the information collected by the collection unit. For example, the acquisition unit obtains weather information from a weather database and saves it to the database. The acquisition unit obtains schedule information from a calendar application and saves it to the database. The acquisition unit obtains weather information based on the user's location information and saves it to the database. Step 3: The generation unit generates the optimal fashion coordination based on the information acquired by the acquisition unit. For example, the generation unit uses a generation AI to generate coordination considering the weather, schedule, and the latest fashion trends, and saves it to the database. The generation unit has the generation AI suggest the optimal coordination based on the user's profile information and saves it to the database. The generation unit has the generation AI learn from the user's feedback and incorporate it into future suggestions. Step 4: The analysis unit analyzes a full-body image of the clothing worn by the user. The analysis unit extracts features of the clothing using, for example, an image analysis algorithm and stores them in a database. The analysis unit uses an image analysis algorithm to analyze the color and shape of the clothing and stores them in a database. The analysis unit uses an image analysis algorithm to analyze the material and design of the clothing and stores them in a database. Step 5: The service provider presents improvement proposals based on the results analyzed by the analysis unit. For example, the service provider generates improvement proposals using generative AI and saves them in a database. The service provider has the generative AI propose the optimal improvement proposal based on the analysis results and saves it in the database. The service provider has the generative AI learn from user feedback and incorporate it into the next improvement proposal.

[0070] (Example of form 2) The fashion coordination system according to an embodiment of the present invention is a system that utilizes generative AI to provide an optimal fashion coordination based on the user's profile information (body type, age, gender, preferences, etc.), the weather and schedule for the day, and the latest fashion trends. This fashion coordination system collects the user's profile information, obtains the weather and schedule based on that information, and generates an optimal fashion coordination. It also analyzes a full-body image of the clothing the user is wearing, checks whether it is in line with the weather, season, and trends, and suggests improvements. For example, if the user has plans to go out on a rainy day, the generative AI will suggest waterproof clothing. It will also suggest a coordination that incorporates the latest fashion trends. Furthermore, it analyzes a full-body image of the clothing the user is wearing using a multimodal function. The generative AI analyzes the image and checks whether it is in line with the weather, season, and trends. For example, if the user is wearing a thick coat on a summer day, the generative AI will determine that the clothing is inappropriate for the season and suggest an appropriate improvement. In this way, the user can easily choose their daily outfits and enjoy an optimal coordination that suits their lifestyle and the trends. For example, it will suggest business casual attire on days when the user goes to work and relaxed casual attire on holidays. Furthermore, if there is a special event, the service will suggest appropriate attire for that event. This service can increase user satisfaction by providing personalized fashion coordination tailored to the individual needs of each user. For example, if a user prefers a particular brand or style, the service can suggest coordination that matches that preference. Also, if a user wants to try a new style, the generative AI will suggest coordination that incorporates the latest trends. In addition, the generative AI can improve the accuracy of its coordination based on user feedback. For example, by having users rate the suggested coordination as "good" or "bad," the generative AI can learn from that evaluation and reflect it in future suggestions. This makes it possible to provide coordination that is more suitable to the user's preferences and needs.This allows the fashion coordination system to provide optimal fashion coordinates based on the user's profile information, weather, schedule, and the latest fashion trends, making it easier for users to choose their outfits.

[0071] The fashion coordination system according to the embodiment comprises a collection unit, an acquisition unit, a generation unit, an analysis unit, and a provision unit. The collection unit collects user profile information. The collection unit collects information such as the user's body type, age, gender, and preferences. For example, the collection unit collects the user's body type information through an input form. The collection unit can also collect the user's age information by calculating it from their date of birth. Furthermore, the collection unit can collect the user's gender information by having them select it from a list of options. For example, the collection unit collects the user's body type information through an input form and stores it in a database. The collection unit collects the user's age information by calculating it from their date of birth and stores it in a database. The collection unit collects the user's gender information by having them select it from a list of options and stores it in a database. The acquisition unit acquires weather and schedule information based on the information collected by the collection unit. For example, the acquisition unit acquires weather information from a weather database. The acquisition unit can also acquire schedule information from a calendar application. Furthermore, the acquisition unit can acquire weather information based on the user's location information. For example, the acquisition unit acquires weather information from a weather database and stores it in the database. The acquisition unit acquires schedule information from a calendar application and stores it in the database. The acquisition unit acquires weather information based on the user's location information and stores it in the database. The generation unit generates the optimal fashion coordination based on the information acquired by the acquisition unit. The generation unit generates coordination considering the weather, schedule, and the latest fashion trends, for example, using a generation AI. The generation unit can also have the generation AI suggest the optimal coordination based on the user's profile information, for example. Furthermore, the generation unit can have the generation AI learn from user feedback and reflect it in future suggestions. For example, the generation unit uses a generation AI to generate coordination considering the weather, schedule, and the latest fashion trends and stores it in the database. The generation unit has the generation AI suggest the optimal coordination based on the user's profile information and stores it in the database. The generation unit has the generation AI learn from user feedback and reflect it in future suggestions. The analysis unit analyzes a full-body image of the clothing worn by the user.The analysis unit extracts clothing features using, for example, an image analysis algorithm. The analysis unit can also use, for example, an image analysis algorithm to analyze the color and shape of the clothing. Furthermore, the analysis unit can also use, for example, an image analysis algorithm to analyze the material and design of the clothing. For example, the analysis unit extracts clothing features using an image analysis algorithm and stores them in a database. The analysis unit uses, for example, an image analysis algorithm to analyze the color and shape of the clothing and stores them in a database. The analysis unit uses, for example, an image analysis algorithm to analyze the material and design of the clothing and stores them in a database. The provision unit presents improvement suggestions based on the results analyzed by the analysis unit. The provision unit generates improvement suggestions using, for example, a generative AI. The provision unit can also use, for example, a generative AI to propose the optimal improvement suggestion based on the analysis results. Furthermore, the provision unit can have the generative AI learn from user feedback and reflect it in future improvement suggestions. For example, the provision unit generates improvement suggestions using a generative AI and stores them in a database. The provision unit has the generative AI propose the optimal improvement suggestion based on the analysis results and stores it in a database. The service provider uses a generating AI to learn from user feedback and incorporate it into future improvement suggestions. As a result, the fashion coordination system according to this embodiment provides optimal fashion coordination based on the user's profile information, weather, schedule, and the latest fashion trends, making it easier for the user to choose their outfit.

[0072] The data collection unit collects user profile information. For example, it collects information such as the user's body type, age, gender, and preferences. For instance, it collects the user's body type information through an input form. It can also calculate and collect the user's age information from their date of birth. Furthermore, it can collect the user's gender information by having them select from a list of options. For example, the data collection unit collects the user's body type information through an input form and stores it in a database. It also calculates and collects the user's age information from their date of birth and stores it in a database. It also collects and stores the user's gender information by having them select from a list of options. The data collection unit also collects information about the user's preferences and style. For example, it collects information such as items the user has purchased in the past, their browsing history on online stores, and brands they "like" or follow on social media. This allows the system to understand the user's fashion preferences and interest in trends. Furthermore, the data collection unit can conduct surveys to find out what styles the user prefers for specific events or seasons. This enables more personalized outfit suggestions tailored to the user's needs. The data collection unit centrally manages this information and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0073] The acquisition unit obtains weather and schedule information based on the information collected by the data collection unit. For example, the acquisition unit can obtain weather information from a weather database. The acquisition unit can also obtain schedule information from a calendar application. Furthermore, the acquisition unit can obtain weather information based on the user's location information. For example, the acquisition unit obtains weather information from a weather database and stores it in the database. The acquisition unit obtains schedule information from a calendar application and stores it in the database. The acquisition unit obtains weather information based on the user's location information and stores it in the database. The acquisition unit can update this information in real time to respond to the user's latest situation. For example, if the user is traveling, the acquisition unit will obtain weather information for the travel destination and suggest an outfit that matches the schedule. The acquisition unit can also suggest appropriate clothing based on the type and location of events registered in the user's calendar. For example, it can provide outfits suitable for different situations, such as business meetings and casual parties. Furthermore, the acquisition unit can obtain more personalized information by considering the user's past behavior history and preferences. For example, if a user has previously favored a particular brand or style, the system can use that information to retrieve the latest trends and new product information and incorporate it into its recommendations. This allows the retrieval unit to provide optimal information tailored to the user's lifestyle and preferences, improving the overall accuracy of the system and user satisfaction.

[0074] The generation unit generates the optimal fashion coordination based on the information acquired by the acquisition unit. For example, the generation unit uses a generation AI to generate coordination considering the weather, schedule, and the latest fashion trends. The generation unit can also use a generation AI to suggest the optimal coordination based on the user's profile information. Furthermore, the generation unit can have the generation AI learn from user feedback and reflect it in future suggestions. For example, the generation unit uses a generation AI to generate coordination considering the weather, schedule, and the latest fashion trends and saves it to a database. The generation unit has a generation AI that suggests the optimal coordination based on the user's profile information and saves it to a database. The generation unit has a generation AI that learns from user feedback and reflects it in future suggestions. The generation unit uses a generation AI to learn the user's preferences and past choices and provides more personalized coordination. For example, the generation AI analyzes coordinations that the user has highly rated in the past and styles that have been frequently selected, and makes new suggestions based on that. In addition, the generation AI incorporates the latest fashion trends and seasonal trends to always provide the user with fresh and attractive coordination. Furthermore, the generation unit continuously improves the accuracy of its suggestions by incorporating user feedback in real time. For example, when a user rates a suggested outfit as "good" or "bad," the generation AI learns from that rating and incorporates it into future suggestions. This allows the generation unit to provide optimal outfits tailored to the user's preferences and needs, thereby increasing user satisfaction.

[0075] The analysis unit analyzes a full-body image of the clothing worn by the user. The analysis unit extracts features of the clothing using, for example, an image analysis algorithm. The analysis unit can also use the image analysis algorithm to analyze the color and shape of the clothing. Furthermore, the analysis unit can use the image analysis algorithm to analyze the material and design of the clothing. For example, the analysis unit extracts features of the clothing using an image analysis algorithm and stores them in a database. The analysis unit uses the image analysis algorithm to analyze the color and shape of the clothing and store them in a database. The analysis unit uses the image analysis algorithm to analyze the material and design of the clothing and store them in a database. When analyzing a full-body image of the clothing worn by the user, the analysis unit uses an image analysis algorithm to extract detailed features. For example, it can analyze not only the color and shape of the clothing, but also patterns, the presence or absence of accessories, and the fit of the clothing. Furthermore, the analysis unit can evaluate how the clothing looks based on the user's body type and posture. This allows for a more accurate understanding of how the clothing looks and gives the impression when the user actually wears it. The analysis unit stores this information in a database, making it accessible to the generation and provision units. For example, the feature information extracted by the analysis unit is used by the generation unit when making the next outfit suggestion. Furthermore, the analysis unit can improve its analysis results based on user feedback and reflect this in the next analysis. This allows the analysis unit to provide detailed information about the user's clothing, improving the overall system accuracy and user satisfaction.

[0076] The service provider presents improvement proposals based on the results analyzed by the analysis unit. The service provider generates improvement proposals using, for example, generative AI. The service provider can also have the generative AI propose the optimal improvement proposal based on the analysis results. Furthermore, the service provider can have the generative AI learn from user feedback and incorporate it into future improvement proposals. For example, the service provider generates improvement proposals using generative AI and saves them in a database. The service provider has the generative AI propose the optimal improvement proposal based on the analysis results and saves it in a database. The service provider has the generative AI learn from user feedback and incorporate it into future improvement proposals. When presenting improvement proposals based on the results analyzed by the analysis unit, the service provider uses generative AI to make the best suggestions for the user. For example, the generative AI learns from the user's past coordination and feedback and incorporates it into future suggestions. This allows the service provider to provide improvement proposals tailored to the user's preferences and needs. Furthermore, the service provider can have the generative AI learn from the user's evaluation of the proposed improvement proposals and incorporate it into future suggestions. For example, improvement suggestions that users rate as "good" will be considered in future proposals, while those rated as "bad" will be excluded. This allows the service provider to continuously improve the accuracy of their suggestions based on user feedback. Furthermore, the service provider can increase the user's acceptance of suggestions by explaining the reasons and background behind them. For example, they can explain why a particular color or style suits the user, or the background of suggestions based on the latest fashion trends. This allows the service provider to make more reliable suggestions to users and improve user satisfaction.

[0077] The data collection unit can collect information such as the user's body type, age, gender, and preferences. For example, the data collection unit can collect the user's body type information through an input form. The data collection unit can also collect the user's age information by calculating it from their date of birth. The data collection unit can also collect the user's gender information by having them select it from a list of options. For example, the data collection unit collects the user's body type information through an input form and stores it in a database. The data collection unit collects the user's age information by calculating it from their date of birth and stores it in a database. The data collection unit collects the user's gender information by having them select it from a list of options and stores it in a database. This allows for the collection of more personalized outfits by collecting detailed profile information about the user. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit collects the user's body type information through an input form and stores it in a database using AI.

[0078] The acquisition unit can acquire weather and schedule information. For example, the acquisition unit can acquire weather information from a weather database. The acquisition unit can also acquire schedule information from a calendar application. For example, the acquisition unit can acquire weather information based on the user's location information. For example, the acquisition unit acquires weather information from a weather database and stores it in the database. The acquisition unit acquires schedule information from a calendar application and stores it in the database. The acquisition unit acquires weather information based on the user's location information and stores it in the database. This allows for the suggestion of appropriate coordination based on weather and schedule. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit acquires weather information from a weather database and stores it in the database using AI.

[0079] The generation unit can generate optimal fashion coordinates considering weather, schedule, and the latest fashion trends. For example, the generation unit uses a generation AI to generate coordinates considering weather, schedule, and the latest fashion trends. The generation unit can also use a generation AI to suggest optimal coordinates based on the user's profile information. The generation unit can also use a generation AI to learn from user feedback and reflect it in future suggestions. For example, the generation unit uses a generation AI to generate coordinates considering weather, schedule, and the latest fashion trends and saves them in a database. The generation unit uses a generation AI to suggest optimal coordinates based on the user's profile information and saves them in a database. The generation unit uses a generation AI to learn from user feedback and reflect it in future suggestions. This allows the system to provide coordinates that incorporate the latest fashion trends. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs weather, schedule, and the latest fashion trends into the generation AI and generates optimal fashion coordinates.

[0080] The analysis unit can analyze a full-body image of the clothing worn by the user and check whether it is in line with the weather, season, and trends. The analysis unit can extract features of the clothing using, for example, an image analysis algorithm. The analysis unit can also use, for example, an image analysis algorithm to analyze the color and shape of the clothing. The analysis unit can also use, for example, an image analysis algorithm to analyze the material and design of the clothing. For example, the analysis unit extracts features of the clothing using an image analysis algorithm and stores them in a database. The analysis unit uses an image analysis algorithm to analyze the color and shape of the clothing and stores them in a database. The analysis unit uses an image analysis algorithm to analyze the material and design of the clothing and stores them in a database. This allows the system to verify whether the user's clothing is in line with the weather, season, and trends. 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 inputs image data into AI and uses AI to extract features of the clothing.

[0081] The service provider can present appropriate improvement suggestions based on the results analyzed by the analysis unit. The service provider can generate improvement suggestions using, for example, a generative AI. The service provider can also have the generative AI propose the optimal improvement suggestion based on the analysis results. The service provider can also have the generative AI learn from user feedback and incorporate it into future improvement suggestions. For example, the service provider generates improvement suggestions using a generative AI and saves them in a database. The service provider has the generative AI propose the optimal improvement suggestion based on the analysis results and saves it in a database. The service provider has the generative AI learn from user feedback and incorporate it into future improvement suggestions. This allows the service provider to provide users with appropriate fashion improvement suggestions. Some or all of the above-described processes in the service provider may be performed using, for example, AI, or without AI. For example, the service provider inputs the analysis results into an AI and generates improvement suggestions using the AI.

[0082] The generation unit can learn from user feedback and incorporate it into future suggestions. For example, the generation unit learns from user feedback using a generation AI. The generation unit can also improve future suggestions based on user evaluations using a generation AI. For example, the generation unit can learn from user preferences and needs using a generation AI and incorporate them into future suggestions. For example, the generation unit learns from user feedback using a generation AI and incorporates it into future suggestions. The generation unit improves future suggestions based on user evaluations using a generation AI and saves them in a database. The generation unit learns from user preferences and needs using a generation AI and incorporates them into future suggestions. This allows for more accurate coordination by incorporating user feedback. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs user feedback into the generation AI and generates future suggestions.

[0083] The data collection unit can estimate the user's emotions and adjust the timing of profile information collection based on the estimated user emotions. The data collection unit estimates the user's emotions using, for example, an emotion estimation algorithm. The data collection unit can also use, for example, an emotion estimation algorithm to analyze the user's facial expressions and voice. The data collection unit can also use, for example, an emotion estimation algorithm to analyze the user's text data. For example, the data collection unit estimates the user's emotions using an emotion estimation algorithm and stores it in a database. The data collection unit uses an emotion estimation algorithm to analyze the user's facial expressions and voice and stores it in a database. The data collection unit uses an emotion estimation algorithm to analyze the user's text data and stores it in a database. This allows for the collection of more appropriate information by adjusting the timing of profile information collection according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit inputs an emotion estimation algorithm into the AI, which then uses the AI ​​to estimate emotions.

[0084] The data collection unit can analyze the user's past fashion history and select the optimal data collection method. For example, the data collection unit can analyze past purchase history. The data collection unit can also analyze past wear history. The data collection unit can also analyze past event participation history. For example, the data collection unit analyzes past purchase history and saves it to a database. The data collection unit analyzes past wear history and saves it to a database. The data collection unit analyzes past event participation history and saves it to a database. This allows for the collection of more appropriate profile information based on the user's past fashion history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs past fashion history into AI and uses AI to select the optimal data collection method.

[0085] The data collection unit can filter the collected profile information based on the user's current lifestyle and areas of interest. For example, the data collection unit may consider the user's occupation and hobbies. The data collection unit may also consider the user's daily activities. The data collection unit may also consider the user's favorite brands and topics of interest. For example, the data collection unit considers the user's occupation and hobbies and stores them in the database. The data collection unit considers the user's daily activities and stores them in the database. The data collection unit considers the user's favorite brands and topics of interest and stores them in the database. This allows for the collection of more relevant profile information based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit inputs the user's lifestyle and areas of interest into AI and uses AI to perform filtering.

[0086] The data collection unit can estimate the user's emotions and determine the priority of profile information to collect based on the estimated user emotions. The data collection unit estimates the user's emotions using, for example, an emotion estimation algorithm. The data collection unit can also use, for example, an emotion estimation algorithm to analyze the user's facial expressions and voice. The data collection unit can also use, for example, an emotion estimation algorithm to analyze the user's text data. For example, the data collection unit estimates the user's emotions using an emotion estimation algorithm and stores it in a database. The data collection unit uses an emotion estimation algorithm to analyze the user's facial expressions and voice and stores it in a database. The data collection unit uses an emotion estimation algorithm to analyze the user's text data and stores it in a database. This allows for the collection of more appropriate information by determining the priority of profile information according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, 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-described processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit inputs an emotion estimation algorithm into the AI, which then uses the AI ​​to estimate emotions.

[0087] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting profile information. For example, the data collection unit can acquire the user's GPS data. The data collection unit can also acquire the user's address information. The data collection unit can also collect relevant information based on the user's location information. For example, the data collection unit acquires the user's GPS data and stores it in a database. The data collection unit acquires the user's address information and stores it in a database. The data collection unit collects relevant information based on the user's location information and stores it in a database. This allows for the collection of more relevant profile information based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs the user's geographical location information into AI and uses AI to collect highly relevant information.

[0088] The data collection unit can analyze a user's social media activity and collect relevant information when collecting profile information. For example, the data collection unit can analyze the content of a user's posts. The data collection unit can also analyze the number of likes a user receives. The data collection unit can also analyze the number of followers a user has. For example, the data collection unit analyzes the content of a user's posts and stores it in a database. The data collection unit analyzes the number of likes a user receives and stores it in a database. The data collection unit analyzes the number of followers a user has and stores it in a database. This allows for the collection of more relevant profile information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI and use AI to collect relevant information.

[0089] The acquisition unit can estimate the user's emotions and adjust the timing of weather and schedule acquisition based on the estimated user emotions. The acquisition unit estimates the user's emotions using, for example, an emotion estimation algorithm. The acquisition unit can also analyze the user's facial expressions and voice using, for example, an emotion estimation algorithm. The acquisition unit can also analyze the user's text data using, for example, an emotion estimation algorithm. For example, the acquisition unit estimates the user's emotions using an emotion estimation algorithm and stores it in a database. The acquisition unit analyzes the user's facial expressions and voice using an emotion estimation algorithm and stores it in a database. The acquisition unit analyzes the user's text data using an emotion estimation algorithm and stores it in a database. This allows for the acquisition of more appropriate information by adjusting the timing of weather and schedule acquisition according to the user's emotions. Emotion estimation is implemented using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit inputs an emotion estimation algorithm into the AI ​​and uses the AI ​​to estimate emotions.

[0090] The data acquisition unit can analyze the user's past behavioral history and select the optimal data acquisition method. For example, the data acquisition unit can analyze past travel history. The data acquisition unit can also analyze past activity logs. The data acquisition unit can also analyze past event participation history. For example, the data acquisition unit analyzes past travel history and saves it to a database. The data acquisition unit analyzes past activity logs and saves it to a database. The data acquisition unit analyzes past event participation history and saves it to a database. This allows for the acquisition of more appropriate weather and schedule information based on the user's past behavioral history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit inputs past behavioral history into AI and uses AI to select the optimal data acquisition method.

[0091] The data acquisition unit can filter weather and schedule information based on the user's current lifestyle and areas of interest. For example, the data acquisition unit may consider the user's occupation and hobbies. The data acquisition unit may also consider the user's daily activities. The data acquisition unit may also consider the user's favorite brands and topics of interest. For example, the data acquisition unit may consider the user's occupation and hobbies and store them in the database. The data acquisition unit may consider the user's daily activities and store them in the database. The data acquisition unit may consider the user's favorite brands and topics of interest and store them in the database. This allows for the acquisition of more relevant weather and schedule information based on the user's lifestyle and areas of interest. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit may input the user's lifestyle and areas of interest into AI and perform filtering using AI.

[0092] The acquisition unit can estimate the user's emotions and determine the priority of weather and schedules to acquire based on the estimated user emotions. The acquisition unit estimates the user's emotions using, for example, an emotion estimation algorithm. The acquisition unit can also use, for example, an emotion estimation algorithm to analyze the user's facial expressions and voice. The acquisition unit can also use, for example, an emotion estimation algorithm to analyze the user's text data. For example, the acquisition unit estimates the user's emotions using an emotion estimation algorithm and stores it in a database. The acquisition unit uses an emotion estimation algorithm to analyze the user's facial expressions and voice and stores it in a database. The acquisition unit uses an emotion estimation algorithm to analyze the user's text data and stores it in a database. This allows for the acquisition of more appropriate information by determining the priority of weather and schedules according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, 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-described processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit inputs an emotion estimation algorithm into the AI ​​and uses the AI ​​to estimate emotions.

[0093] The data acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring weather and schedule information. For example, the data acquisition unit can acquire the user's GPS data. The data acquisition unit can also acquire the user's address information. The data acquisition unit can also acquire relevant information based on the user's location information. For example, the data acquisition unit acquires the user's GPS data and stores it in a database. The data acquisition unit acquires the user's address information and stores it in a database. The data acquisition unit acquires relevant information based on the user's location information and stores it in a database. This allows the user to acquire more relevant weather and schedule information based on their geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit inputs the user's geographical location information into AI and uses AI to acquire highly relevant information.

[0094] The data acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring weather and schedule information. For example, the data acquisition unit can analyze the content of the user's posts. The data acquisition unit can also analyze the number of likes the user has. The data acquisition unit can also analyze the number of followers the user has. For example, the data acquisition unit analyzes the content of the user's posts and stores it in a database. The data acquisition unit analyzes the number of likes the user has and stores it in a database. The data acquisition unit analyzes the number of followers the user has and stores it in a database. This allows for the acquisition of more relevant weather and schedule information based on the user's social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit inputs the user's social media activity into AI and uses AI to acquire relevant information.

[0095] The generation unit can estimate the user's emotions and adjust the way the coordination is presented based on the estimated emotions. For example, the generation unit estimates the user's emotions using an emotion estimation algorithm. For example, the generation unit can also use the emotion estimation algorithm to analyze the user's facial expressions and voice. For example, the generation unit can also use the emotion estimation algorithm to analyze the user's text data. For example, the generation unit estimates the user's emotions using an emotion estimation algorithm and stores it in a database. The generation unit uses the emotion estimation algorithm to analyze the user's facial expressions and voice and stores it in a database. The generation unit uses the emotion estimation algorithm to analyze the user's text data and stores it in a database. This allows for the provision of more appropriate coordination by adjusting the way the coordination is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's emotions into the generation AI and adjusts how the outfit is expressed.

[0096] The generation unit can adjust the level of detail generated based on the importance of fashion items when generating outfits. For example, the generation unit can generate detailed outfits that focus on important fashion items. The generation unit can also simplify the generation of outfits that have low importance. The generation unit can also generate outfits with different levels of detail depending on their importance. For example, the generation unit generates detailed outfits that focus on important fashion items and saves them to the database. The generation unit simplifies the generation of outfits that have low importance and saves them to the database. The generation unit generates outfits with different levels of detail depending on their importance and saves them to the database. This allows for the provision of detailed outfits according to the importance of fashion items. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the importance of fashion items into the generation AI and adjusts the level of detail of the generation.

[0097] The generation unit can apply different generation algorithms depending on the category of fashion items when generating outfits. For example, the generation unit can apply a specific generation algorithm to outerwear. The generation unit can also apply a different generation algorithm to innerwear. The generation unit can also apply yet another different generation algorithm to accessories. For example, the generation unit applies a specific generation algorithm to outerwear and saves it to the database. The generation unit applies another generation algorithm to innerwear and saves it to the database. The generation unit applies yet another different generation algorithm to accessories and saves it to the database. This allows the optimal generation algorithm to be applied according to the category of fashion items. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the category of fashion items into the generation AI and applies different generation algorithms.

[0098] The generation unit can estimate the user's emotions and adjust the length of the coordination based on the estimated emotions. The generation unit estimates the user's emotions using, for example, an emotion estimation algorithm. The generation unit can also use, for example, an emotion estimation algorithm to analyze the user's facial expressions and voice. The generation unit can also use, for example, an emotion estimation algorithm to analyze the user's text data. For example, the generation unit estimates the user's emotions using the emotion estimation algorithm and stores it in a database. The generation unit uses the emotion estimation algorithm to analyze the user's facial expressions and voice and stores it in a database. The generation unit uses the emotion estimation algorithm to analyze the user's text data and stores it in a database. This allows for the provision of more appropriate coordination by adjusting the length of the coordination according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's emotions into the generation AI and adjusts the length of the coordination.

[0099] The generation unit can determine the generation priority based on the submission date of fashion items when generating outfits. For example, the generation unit can prioritize incorporating newly submitted fashion items into outfits. The generation unit can also, for example, postpone the inclusion of older fashion items. The generation unit can also generate outfits with different priorities depending on the submission date. For example, the generation unit prioritizes incorporating newly submitted fashion items into outfits and saves them in the database. The generation unit postpones the inclusion of older fashion items and saves them in the database. The generation unit generates outfits with different priorities depending on the submission date and saves them in the database. This allows for the provision of more appropriate outfits by determining priorities according to the submission date of fashion items. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the submission date of fashion items into the generation AI and determines the generation priority.

[0100] The generation unit can adjust the generation order based on the relevance of fashion items when generating outfits. For example, the generation unit can prioritize incorporating highly relevant fashion items into the outfit. For example, the generation unit can postpone the generation of less relevant fashion items. For example, the generation unit can generate outfits in different orders depending on their relevance. For example, the generation unit can prioritize incorporating highly relevant fashion items into the outfit and save them in the database. For example, the generation unit can postpone the generation of less relevant fashion items and save them in the database. For example, the generation unit can generate outfits in different orders depending on their relevance and save them in the database. This allows the generation unit to provide more appropriate outfits by adjusting the generation order according to the relevance of fashion items. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the relevance of fashion items into the generation AI and adjusts the generation order.

[0101] The analysis unit can estimate the user's emotions and adjust the image analysis criteria based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, an emotion estimation algorithm. The analysis unit can also use, for example, an emotion estimation algorithm to analyze the user's facial expressions and voice. The analysis unit can also use, for example, an emotion estimation algorithm to analyze the user's text data. For example, the analysis unit estimates the user's emotions using the emotion estimation algorithm and stores it in a database. The analysis unit uses the emotion estimation algorithm to analyze the user's facial expressions and voice and stores it in a database. The analysis unit uses the emotion estimation algorithm to analyze the user's text data and stores it in a database. This allows for more appropriate analysis results by adjusting the image analysis criteria according to the user's emotions. Emotion estimation is achieved using, for example, 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-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit inputs an emotion estimation algorithm into the AI ​​and uses the AI ​​to estimate emotions.

[0102] The analysis unit can improve the accuracy of image analysis by considering the interrelationships of fashion items. For example, the analysis unit can analyze combinations of tops and bottoms. The analysis unit can also analyze combinations of accessories and main items. The analysis unit can also analyze shoes and the overall outfit. For example, the analysis unit analyzes combinations of tops and bottoms and saves them to a database. The analysis unit analyzes combinations of accessories and main items and saves them to a database. The analysis unit analyzes shoes and the overall outfit and saves them to a database. This improves the accuracy of the analysis by considering the interrelationships of fashion items. 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 inputs the interrelationships of fashion items into AI and uses AI to improve the accuracy of the analysis.

[0103] The analysis unit can perform image analysis while considering the user's attribute information. For example, the analysis unit may consider the user's body type information. The analysis unit may also consider the user's age information. The analysis unit may also consider the user's gender information. For example, the analysis unit may consider the user's body type information and store it in the database. The analysis unit may consider the user's age information and store it in the database. The analysis unit may consider the user's gender information and store it in the database. By considering the user's attribute information, more appropriate analysis results can be provided. 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 may input the user's attribute information into the AI ​​and perform the analysis using the AI.

[0104] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, an emotion estimation algorithm. The analysis unit can also use, for example, the emotion estimation algorithm to analyze the user's facial expressions and voice. The analysis unit can also use, for example, the emotion estimation algorithm to analyze the user's text data. For example, the analysis unit estimates the user's emotions using the emotion estimation algorithm and stores it in a database. The analysis unit uses the emotion estimation algorithm to analyze the user's facial expressions and voice and stores it in a database. The analysis unit uses the emotion estimation algorithm to analyze the user's text data and stores it in a database. This allows for the provision of more appropriate information by adjusting the display order of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs the user's emotions into the generating AI and adjusts the display order of the analysis results.

[0105] The analysis unit can perform image analysis while considering the geographical distribution of fashion items. For example, the analysis unit can consider fashion items that are popular in urban areas. The analysis unit can also consider fashion items that are popular in suburban areas. The analysis unit can also consider fashion items that are popular overseas. For example, the analysis unit considers fashion items that are popular in urban areas and stores them in a database. The analysis unit considers fashion items that are popular in suburban areas and stores them in a database. The analysis unit considers fashion items that are popular overseas and stores them in a database. By considering the geographical distribution of fashion items, more appropriate analysis results can be provided. 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 inputs the geographical distribution of fashion items into AI and performs analysis using AI.

[0106] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on fashion items during image analysis. For example, the analysis unit may refer to articles from the latest fashion magazines. The analysis unit may also refer to fashion research papers. The analysis unit may also refer to posts from fashion blogs or influencers. For example, the analysis unit may refer to articles from the latest fashion magazines and save them in a database. The analysis unit may refer to fashion research papers and save them in a database. The analysis unit may refer to posts from fashion blogs or influencers and save them in a database. This allows the accuracy of the analysis to be improved by referring to relevant literature on fashion items. 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 may input relevant literature on fashion items into AI and use AI to improve the accuracy of the analysis.

[0107] The service provider can estimate the user's emotions and adjust the method of presenting improvement suggestions based on the estimated emotions. For example, the service provider estimates the user's emotions using an emotion estimation algorithm. For example, the service provider can also have the emotion estimation algorithm analyze the user's facial expressions and voice. For example, the service provider can have the emotion estimation algorithm analyze the user's text data. For example, the service provider estimates the user's emotions using an emotion estimation algorithm and stores it in a database. The service provider has the emotion estimation algorithm analyze the user's facial expressions and voice and store it in a database. The service provider has the emotion estimation algorithm analyze the user's text data and store it in a database. This allows the service provider to provide more appropriate improvement suggestions by adjusting the method of presenting improvement suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider inputs the user's emotions into the generating AI and adjusts how improvement suggestions are presented.

[0108] The service provider can provide optimal improvement suggestions by referring to the user's past fashion history when presenting improvement suggestions. For example, the service provider can provide relevant improvement suggestions based on styles the user has preferred to wear in the past. The service provider can also provide relevant improvement suggestions by referring to the user's past purchase history. The service provider can also provide relevant improvement suggestions by referring to information about events the user has attended in the past. For example, the service provider can provide relevant improvement suggestions based on styles the user has preferred to wear in the past and store them in a database. The service provider can provide relevant improvement suggestions by referring to the user's past purchase history and store them in a database. The service provider can provide relevant improvement suggestions based on information about events the user has attended in the past and store them in a database. This allows the service provider to provide more appropriate improvement suggestions based on the user's past fashion history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past fashion history into AI and use AI to provide optimal improvement suggestions.

[0109] The service provider can customize the means of improvement suggestions based on the user's current lifestyle when presenting them. For example, if the user likes sports, the service provider will provide sports-related improvement suggestions. For example, if the user is a business person, the service provider can also provide business-related improvement suggestions. For example, if the user is interested in art, the service provider can also provide art-related improvement suggestions. For example, if the user likes sports, the service provider will provide sports-related improvement suggestions and save them in the database. If the user is a business person, the service provider will provide business-related improvement suggestions and save them in the database. If the user is interested in art, the service provider will provide art-related improvement suggestions and save them in the database. This allows the service provider to provide improvement suggestions tailored to the user's lifestyle. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's lifestyle into AI and use AI to customize the means of improvement suggestions.

[0110] The service provider can estimate the user's emotions and determine the priority of improvement proposals based on the estimated emotions. For example, the service provider estimates the user's emotions using an emotion estimation algorithm. For example, the service provider can also have the emotion estimation algorithm analyze the user's facial expressions and voice. For example, the service provider can have the emotion estimation algorithm analyze the user's text data. For example, the service provider estimates the user's emotions using an emotion estimation algorithm and stores it in a database. The service provider has the emotion estimation algorithm analyze the user's facial expressions and voice and store it in a database. The service provider has the emotion estimation algorithm analyze the user's text data and store it in a database. This allows the service provider to provide more appropriate improvement proposals by determining the priority of improvement proposals according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider inputs the user's emotions into the generating AI to determine the priority of improvement suggestions.

[0111] The service provider can provide optimal improvement suggestions by considering the user's geographical location information when presenting improvement suggestions. For example, if the user lives in an urban area, the service provider will provide improvement suggestions related to urban areas. For example, if the user lives in a suburban area, the service provider can also provide improvement suggestions related to suburban areas. For example, if the user lives abroad, the service provider can also provide improvement suggestions related to the culture and climate of that country. For example, if the user lives in an urban area, the service provider will provide improvement suggestions related to urban areas and store them in the database. If the user lives in a suburban area, the service provider will provide improvement suggestions related to suburban areas and store them in the database. If the user lives abroad, the service provider will provide improvement suggestions related to the culture and climate of that country and store them in the database. This allows the service provider to provide more appropriate improvement suggestions based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's geographical location information into AI and use AI to provide optimal improvement suggestions.

[0112] The service provider can analyze the user's social media activity and propose methods for improvement when presenting improvement suggestions. For example, the service provider can analyze fashion-related posts shared by the user on social media. The service provider can also provide relevant improvement suggestions based on information about fashion influencers followed by the user. The service provider can also provide relevant improvement suggestions based on information about fashion-related groups and communities in which the user participates. For example, the service provider analyzes fashion-related posts shared by the user on social media and stores them in a database. The service provider provides relevant improvement suggestions based on information about fashion influencers followed by the user and stores them in a database. The service provider provides relevant improvement suggestions based on information about fashion-related groups and communities in which the user participates and stores them in a database. This allows the service provider to provide more appropriate improvement suggestions based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider inputs the user's social media activity into AI and uses AI to propose methods for improvement.

[0113] The service provider can improve the accuracy of improvement proposals based on user feedback when presenting them. For example, the service provider can reflect the user's evaluation of the proposed improvement proposal as "good" or "bad" in the next improvement proposal. The service provider can also adjust the content of the improvement proposal based on user feedback. The service provider can also learn from user feedback to improve the accuracy of the next improvement proposal. For example, the service provider evaluates the proposed improvement proposal as "good" or "bad" and saves it in a database. The service provider adjusts the content of the improvement proposal based on user feedback and saves it in a database. The service provider learns from user feedback to improve the accuracy of the next improvement proposal and saves it in a database. This allows the service provider to provide more accurate improvement proposals by reflecting user feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider inputs user feedback into AI and uses AI to improve the accuracy of the improvement proposal.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] A fashion coordination system can estimate a user's emotions and adjust the colors of the outfit based on those emotions. For example, if a user is feeling stressed, the generation unit can suggest relaxing colors. If the user is feeling energetic, it can suggest bright or lively colors. Furthermore, if a user is nervous about a particular event, it can suggest calming colors. This allows for more personalized fashion suggestions by providing color coordination that matches the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using generative AI. For example, the generation unit inputs the user's emotions into the generative AI and adjusts the colors of the outfit.

[0116] The fashion coordination system can analyze the user's past feedback and learn their preferences for specific seasons and events. For example, based on the styles the user preferred at past summer events, it can suggest outfits suitable for the next summer event. Furthermore, if the user prefers specific brands or designs, it can suggest related items based on that information. It can also suggest outfits suitable for similar events based on information about past events the user has attended. This allows for more accurate coordination by leveraging the user's past feedback. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit inputs the user's past feedback into an AI, which then learns the user's preferences.

[0117] A fashion coordination system can estimate a user's emotions and select accessories for their outfit based on those emotions. For example, if a user is feeling down, the generation unit can suggest glamorous accessories to lift their spirits. If the user wants to feel more confident, it can suggest simple and elegant accessories. Furthermore, if the user wants to relax, it can suggest accessories made of natural materials. This allows for more personalized fashion suggestions by selecting accessories that match the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using generative AI. For example, the generation unit inputs the user's emotions into the generative AI and selects accessories.

[0118] The fashion coordination system can suggest outfits suitable for specific activities based on the user's lifestyle. For example, if the user enjoys outdoor activities, the generation unit can suggest waterproof and durable items. If the user primarily works in an office, it can also suggest business casual styles. Furthermore, if the user travels frequently, it can suggest comfortable clothing suitable for travel. This allows for more practical fashion suggestions by providing outfits tailored to the user's lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs the user's lifestyle information into the AI ​​and generates outfits using the AI.

[0119] A fashion coordination system can estimate a user's emotions and select materials for their outfit based on those emotions. For example, if a user wants to relax, the system can suggest items made of soft and comfortable materials. If the user is feeling active, it can suggest items made of breathable and easy-to-move-in materials. Furthermore, if a user is nervous about a particular event, it can suggest items made of materials that provide a sense of security. By selecting materials according to the user's emotions, the system can provide more personalized fashion suggestions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI may include, 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 generation unit are performed using generative AI. For example, the generation unit inputs the user's emotions into the generative AI and selects materials.

[0120] A fashion coordination system can analyze a user's past purchase history and learn their preferences for specific brands and designs. For example, if a user has purchased many items from a particular brand in the past, the generation unit can suggest new items from that brand. Furthermore, if a user prefers specific designs or colors, the system can suggest related items based on that information. It can also suggest new items that are easy to combine with items the user has purchased in the past. This allows the system to provide more accurate coordination by leveraging the user's past purchase history. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit inputs the user's past purchase history into an AI and uses the AI ​​to learn their preferences.

[0121] A fashion coordination system can estimate a user's emotions and adjust the style of the outfit based on those emotions. For example, if the user wants to relax, the generation unit can suggest a casual and relaxed style. If the user wants to feel confident, it can suggest an elegant and sophisticated style. Furthermore, if the user is nervous about a particular event, it can suggest a formal and reassuring style. By adjusting the style according to the user's emotions, more personalized fashion suggestions become possible. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using generative AI. For example, the generation unit inputs the user's emotions into the generative AI and adjusts the style accordingly.

[0122] The fashion coordination system can suggest outfits that reflect regional fashion trends, taking into account the user's geographical location. For example, it can suggest outfits incorporating the latest urban trends to users living in urban areas. It can also suggest casual and relaxed styles to users living in suburban areas. Furthermore, it can suggest outfits suitable for the culture and climate of the region to users living abroad. This enables more appropriate fashion suggestions based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs the user's geographical location information into the AI ​​and generates outfits using the AI.

[0123] The fashion coordination system can analyze a user's social media activity and suggest outfits based on information about influencers and brands the user follows. For example, if a user follows a specific fashion influencer, the system can suggest outfits incorporating styles recommended by that influencer. Similarly, if a user follows a specific brand, the system can suggest new items from that brand. Furthermore, it can suggest relevant outfits based on information about fashion-related groups and communities the user participates in. This enables more personalized fashion suggestions based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit inputs the user's social media activity into an AI and uses the AI ​​to generate outfits.

[0124] A fashion coordination system can estimate a user's emotions and select accessories for their outfit based on those emotions. For example, if a user is feeling down, the generation unit can suggest glamorous accessories to lift their spirits. If the user wants to feel more confident, it can suggest simple and elegant accessories. Furthermore, if the user wants to relax, it can suggest accessories made of natural materials. This allows for more personalized fashion suggestions by selecting accessories that match the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using generative AI. For example, the generation unit inputs the user's emotions into the generative AI and selects accessories.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The data collection unit collects user profile information. The data collection unit collects information such as the user's body type, age, gender, and preferences. For example, the data collection unit collects the user's body type information through an input form and stores it in the database. The data collection unit calculates and collects the user's age information from their date of birth and stores it in the database. The data collection unit collects the user's gender information by having them select it from a list of options and stores it in the database. Step 2: The acquisition unit obtains weather and schedule information based on the information collected by the collection unit. For example, the acquisition unit obtains weather information from a weather database and saves it to the database. The acquisition unit obtains schedule information from a calendar application and saves it to the database. The acquisition unit obtains weather information based on the user's location information and saves it to the database. Step 3: The generation unit generates the optimal fashion coordination based on the information acquired by the acquisition unit. For example, the generation unit uses a generation AI to generate coordination considering the weather, schedule, and the latest fashion trends, and saves it to the database. The generation unit has the generation AI suggest the optimal coordination based on the user's profile information and saves it to the database. The generation unit has the generation AI learn from the user's feedback and incorporate it into future suggestions. Step 4: The analysis unit analyzes a full-body image of the clothing worn by the user. The analysis unit extracts features of the clothing using, for example, an image analysis algorithm and stores them in a database. The analysis unit uses an image analysis algorithm to analyze the color and shape of the clothing and stores them in a database. The analysis unit uses an image analysis algorithm to analyze the material and design of the clothing and stores them in a database. Step 5: The service provider presents improvement proposals based on the results analyzed by the analysis unit. For example, the service provider generates improvement proposals using generative AI and saves them in a database. The service provider has the generative AI propose the optimal improvement proposal based on the analysis results and saves it in the database. The service provider has the generative AI learn from user feedback and incorporate it into the next improvement proposal.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, acquisition unit, generation unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user profile information using the camera 42 and microphone 38B of the smart device 14 and stores it in a database using the control unit 46A. The acquisition unit acquires weather and schedule information using the specific processing unit 290 of the data processing unit 12 and stores it in a database. The generation unit generates an optimal fashion coordination using generation AI using the specific processing unit 290 of the data processing unit 12 and stores it in a database. The analysis unit takes a full-body image of the user using the camera 42 of the smart device 14 and performs image analysis using the specific processing unit 290 of the data processing unit 12. The provision unit generates improvement suggestions based on the analysis results using the specific processing unit 290 of the data processing unit 12 and presents them to the user through the display 40A and speaker 40B 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.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, acquisition unit, generation unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user profile information using the camera 42 and microphone 238 of the smart glasses 214 and stores it in a database using the control unit 46A. The acquisition unit acquires weather and schedule information using the specific processing unit 290 of the data processing unit 12 and stores it in a database. The generation unit generates an optimal fashion coordination using generation AI using the specific processing unit 290 of the data processing unit 12 and stores it in a database. The analysis unit captures a full-body image of the user using the camera 42 of the smart glasses 214 and performs image analysis using the specific processing unit 290 of the data processing unit 12. The provision unit generates improvement suggestions based on the analysis results using the specific processing unit 290 of the data processing unit 12 and presents them to the user through the speaker 240 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 modified in various ways.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the collection unit, acquisition unit, generation unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user profile information using the camera 42 and microphone 238 of the headset terminal 314 and stores it in a database using the control unit 46A. The acquisition unit acquires weather and schedule information using the specific processing unit 290 of the data processing unit 12 and stores it in a database. The generation unit generates an optimal fashion coordination using generation AI using the specific processing unit 290 of the data processing unit 12 and stores it in a database. The analysis unit captures a full-body image of the user using the camera 42 of the headset terminal 314 and performs image analysis using the specific processing unit 290 of the data processing unit 12. The provision unit generates improvement suggestions based on the analysis results using the specific processing unit 290 of the data processing unit 12 and presents them to the user through the display 343 and speaker 240 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 modified in various ways.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Each of the multiple elements described above, including the collection unit, acquisition unit, generation unit, analysis unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user profile information using the camera 42 and microphone 238 of the robot 414 and stores it in a database using the control unit 46A. The acquisition unit acquires weather and schedule information using the specific processing unit 290 of the data processing unit 12 and stores it in a database. The generation unit generates an optimal fashion coordination using generation AI using the specific processing unit 290 of the data processing unit 12 and stores it in a database. The analysis unit takes a full-body image of the user using the camera 42 of the robot 414 and performs image analysis using the specific processing unit 290 of the data processing unit 12. The provision unit generates improvement suggestions based on the analysis results using the specific processing unit 290 of the data processing unit 12 and presents them to the user through the speaker 240 and display device 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 modified in various ways.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] (Note 1) A collection unit that collects user profile information, An acquisition unit that acquires weather and schedules based on the information collected by the aforementioned acquisition unit, A generation unit generates an optimal fashion coordination based on the information acquired by the acquisition unit, An analysis unit that analyzes a full-body image of the clothing worn by the user, The system includes a provisioning unit that provides improvement suggestions based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information such as the user's body type, age, gender, and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The acquisition unit is, Get weather and schedule information The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is It generates the optimal fashion coordinate considering the weather, schedule, and the latest fashion trends. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The system analyzes full-body images of the user's clothing to check if it is in line with the weather, season, and current trends. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Based on the results analyzed by the aforementioned analysis unit, an appropriate improvement plan is presented. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is Learn from user feedback and incorporate it into future suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of profile information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past fashion history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting profile information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and determines the priority of profile information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting profile information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting profile information, we analyze the user's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The acquisition unit is, It estimates the user's emotions and adjusts the timing of weather and schedule acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The acquisition unit is, Analyze the user's past behavior history and select the optimal method for acquiring it. The system described in Appendix 1, characterized by the features described herein. (Note 16) The acquisition unit is, When retrieving weather and schedule information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 17) The acquisition unit is, It estimates the user's emotions and determines the priority of weather and schedule information to retrieve based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The acquisition unit is, When retrieving weather and schedule information, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The acquisition unit is, When obtaining weather and schedule information, the system analyzes the user's social media activity to retrieve relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts the way the outfit is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating outfits, adjust the level of detail based on the importance of the fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating outfits, different generation algorithms are applied depending on the category of the fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and adjusts the length of the outfit based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating outfits, the generation priority is determined based on when the fashion items were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating outfits, the order of creation is adjusted based on the relationships between fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, It estimates the user's emotions and adjusts the image analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit, When analyzing images, consider the interrelationships between fashion items to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit, When analyzing images, the analysis is performed while taking into account the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit, It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit, During image analysis, the geographical distribution of fashion items is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit, During image analysis, we improve the accuracy of the analysis by referring to relevant literature on fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way improvement suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When suggesting improvements, the system refers to the user's past fashion history to provide the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When presenting improvement suggestions, customize the methods of improvement based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, The system estimates user emotions and prioritizes improvement suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When presenting improvement proposals, we will provide the most suitable proposals by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned supply unit is, When proposing improvement plans, we analyze users' social media activity and suggest methods for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned supply unit is, When presenting improvement proposals, we improve the accuracy of those proposals based on user feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user profile information, An acquisition unit that acquires weather and schedules based on the information collected by the aforementioned acquisition unit, A generation unit generates an optimal fashion coordination based on the information acquired by the acquisition unit, An analysis unit that analyzes a full-body image of the clothing worn by the user, The system includes a provisioning unit that provides improvement suggestions based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect information such as the user's body type, age, gender, and preferences. The system according to feature 1.

3. The acquisition unit is, Get weather and schedule information The system according to feature 1.

4. The generating unit is It generates the optimal fashion coordinate considering the weather, schedule, and the latest fashion trends. The system according to feature 1.

5. The aforementioned analysis unit, The system analyzes full-body images of the user's clothing to check if it is in line with the weather, season, and current trends. The system according to feature 1.

6. The aforementioned supply unit is, Based on the results analyzed by the aforementioned analysis unit, an appropriate improvement plan is presented. The system according to feature 1.

7. The generating unit is Learn from user feedback and incorporate it into future suggestions. The system according to feature 1.

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

Images