system

Abstract

The system according to this embodiment aims to suggest the optimal outfit based on the user's clothing information and weather information. [Solution] The system according to the embodiment comprises a collection unit, an acquisition unit, an analysis unit, a proposal unit, and a consultation unit. The collection unit collects information about the user's clothes. The acquisition unit acquires weather information based on the information collected by the collection unit. The analysis unit analyzes the weather information and fashion trend information acquired by the acquisition unit. The proposal unit proposes outfits based on the information analyzed by the analysis unit. The consultation unit provides advice on outfits proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it has not been fully done to propose an optimal coordination by combining the information of the clothes the user has and the weather information, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal coordination based on the information of the clothes the user has and the weather information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an acquisition unit, an analysis unit, a suggestion unit, and a consultation unit. The collection unit collects information about the user's clothing. The acquisition unit acquires weather information based on the information collected by the collection unit. The analysis unit analyzes the weather information and fashion trend information acquired by the acquisition unit. The suggestion unit suggests outfits based on the information analyzed by the analysis unit. The consultation unit provides advice on any concerns regarding the outfits suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal outfit based on the user's clothing information and weather information. [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, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The fashion coordination suggestion system according to an embodiment of the present invention is a system that utilizes multimodal AI to refer to fashion trend information and proposes a trendy outfit by combining the user's existing clothes. This fashion coordination suggestion system proposes the optimal outfit for the user by collecting information on the clothes the user owns, obtaining weather information, and collecting and analyzing fashion trend information. For example, it selects clothes from the user's existing wardrobe that are suitable for the day's temperature and proposes a trendy outfit. It can also suggest additional clothes that the user should purchase to match the clothes they already own. Furthermore, the system allows the user to consult about fashion concerns in a conversational format. For example, the user takes pictures of the clothes they own and uploads them to the system. The system analyzes these pictures and obtains information such as the type, color, and design of each garment. Next, the system obtains weather information for the user's current location in real time and collects information such as the temperature, humidity, and weather for the day. Furthermore, the system collects and analyzes the latest fashion trend information from fashion sites and social media on the internet. Based on this information, the system proposes the optimal outfit for the user. For example, it selects clothes from the user's existing wardrobe that are suitable for the day's temperature and proposes a trendy outfit. It can also suggest additional clothes that the user should purchase to match the clothes they already own. Furthermore, the system allows the user to consult about fashion concerns in a conversational format. Users can ask the system fashion-related questions, and the system will provide appropriate advice. This system allows even those unfamiliar with fashion to enjoy stylish outfits, and solves the problems of those who want to know what comfortable clothing to wear or who are unsure what clothes to buy. Furthermore, by connecting users to shopping sites through the system, advertising effects can also be expected. In this way, the fashion coordination suggestion system can solve users' fashion-related problems and suggest the best outfits.

[0029] The fashion coordination suggestion system according to this embodiment comprises a collection unit, an acquisition unit, an analysis unit, a suggestion unit, and a consultation unit. The collection unit collects information about the clothes owned by the user. For example, the collection unit takes pictures of the clothes owned by the user and uploads them to the system. The collection unit analyzes these pictures and obtains information such as the type, color, and design of each garment. The collection unit can analyze the type, color, and design of the clothes using image recognition technology. For example, the collection unit uses image recognition technology to identify the type of clothing and a color analysis algorithm to analyze the color of the clothing. The collection unit can also analyze the design of the clothing using design pattern recognition technology. The acquisition unit obtains weather information based on the information collected by the collection unit. For example, the acquisition unit obtains weather information for the user's current location in real time. The acquisition unit can obtain information such as the temperature, humidity, and weather at the user's current location from a weather information data source. For example, the acquisition unit obtains the temperature at the user's current location from a weather information data source and also obtains humidity and weather information. The acquisition unit can periodically update data in order to obtain weather information in real time. The analysis unit analyzes weather information and fashion trend information acquired by the acquisition unit. The analysis unit collects and analyzes the latest fashion trend information from sources such as fashion websites and social media on the internet. The analysis unit can use web scraping technology to collect fashion trend information. For example, the analysis unit uses web scraping technology to collect the latest trend information from fashion websites. The analysis unit can also analyze social media posts to collect trend information. The suggestion unit proposes outfits based on the information analyzed by the analysis unit. For example, the suggestion unit selects clothes from the user's wardrobe that are suitable for the day's temperature and proposes an outfit that aligns with current trends. The suggestion unit can combine information about the user's wardrobe with weather information to propose the optimal outfit. For example, the suggestion unit selects clothes from the user's wardrobe that are suitable for the temperature and proposes an outfit that aligns with current trends.Furthermore, the suggestion unit can also suggest additional clothing items that the user should purchase, based on the clothes they already own. The consultation unit can provide advice on any concerns regarding the outfits suggested by the suggestion unit. For example, the consultation unit can discuss the user's fashion concerns in a conversational format. When the user asks the system a question about fashion, the consultation unit can provide appropriate advice. For example, if the user asks, "What accessories would go with this outfit?", the consultation unit will suggest appropriate accessories. In this way, the fashion coordination suggestion system according to this embodiment can analyze weather information and fashion trend information based on the user's clothing information and suggest the optimal outfit.

[0030] The data collection unit collects information about the clothes owned by the user. Specifically, the user takes photos of their clothes and uploads them to the system. The data collection unit then analyzes these photos to obtain information such as the type, color, and design of each garment. Image recognition technology can be used to analyze the type, color, and design of the clothes. For example, the data collection unit uses image recognition technology to identify the type of clothing and a color analysis algorithm to analyze the color. It can also analyze the design of the clothes using design pattern recognition technology. Specifically, the image recognition technology uses a convolutional neural network (CNN) to extract features from images of clothes and classify the type of clothing based on these features. The color analysis algorithm analyzes the pixel data of the image to identify the main colors. The design pattern recognition technology analyzes the shapes and patterns in the image to identify designs such as stripes, dots, and florals. As a result, the data collection unit can accurately collect detailed information about the clothes owned by the user and provide it to the system. Furthermore, the data collection unit can update the information using the same procedure when the user purchases new clothes. For example, when a user purchases new clothing, they can take a photo of the clothing and upload it to the system. This allows the data collection unit to add new information and keep the database up-to-date. This provides the data collection unit with a foundation for always offering outfit suggestions based on the latest information.

[0031] The acquisition unit obtains weather information based on the information collected by the data collection unit. Specifically, it obtains weather information for the user's current location in real time. The acquisition unit can obtain information such as temperature, humidity, and weather for the user's current location from weather information data sources. For example, the acquisition unit obtains the temperature for the user's current location from the weather information data source, and also obtains humidity and weather information. The acquisition unit can periodically update data in order to obtain weather information in real time. Specifically, the acquisition unit uses a weather information API to obtain the latest weather data based on the user's current location. The weather information API is provided by a weather data provider and can obtain detailed weather information such as current temperature, humidity, weather, and wind speed. By periodically acquiring this information and reflecting it in the system, the acquisition unit can always provide coordination suggestions based on the latest weather information. In addition, the acquisition unit can use GPS technology to obtain the user's location information. It obtains location information from the user's smartphone or device and obtains weather information based on that location. This allows the acquisition unit to provide weather information that is most suitable for the user's current location. Furthermore, the acquisition unit can update data in real time in response to changes in weather information. For example, if the weather or temperature changes suddenly, the acquisition unit immediately obtains the new weather information and reflects it in the system. This allows the acquisition unit to always provide the user with the latest weather information and support optimal coordination suggestions.

[0032] The analysis unit analyzes weather information and fashion trend information acquired by the acquisition unit. Specifically, it collects and analyzes the latest fashion trend information from fashion websites and social media on the internet. The analysis unit can use web scraping technology to collect fashion trend information. For example, the analysis unit uses web scraping technology to collect the latest trend information from fashion websites. The analysis unit can also collect trend information by analyzing social media posts. Specifically, it uses web scraping technology to regularly collect new information from fashion blogs and online shops to grasp the latest trends. Furthermore, to analyze social media posts, it uses natural language processing (NLP) technology to extract fashion-related keywords and hashtags and identify trends. For example, it identifies items and styles that many users mention on social media and collects them as trend information. The analysis unit can integrate this information to grasp current fashion trends. Furthermore, the analysis unit can analyze past trend data and predict seasonal trend changes and trends related to specific events. As a result, the analysis unit can provide users with the latest trend information and suggest outfits that match the trends.

[0033] The suggestion department proposes outfits based on information analyzed by the analysis department. Specifically, it selects clothes from the user's wardrobe that are suitable for the day's temperature and proposes outfits that are in line with current trends. The suggestion department can combine information about the user's wardrobe with weather information to propose the optimal outfit. For example, it selects clothes from the user's wardrobe that are suitable for the temperature and proposes outfits that are in line with current trends. The suggestion department can also suggest additional clothes that the user should purchase to complement their existing wardrobe. Specifically, the suggestion department uses AI to analyze the user's wardrobe information and weather information to generate the optimal outfit. The AI ​​learns the user's past outfit history and preferred style and makes suggestions based on that. For example, the AI ​​analyzes patterns of outfits the user has chosen in the past and proposes new outfits based on similar weather conditions and trends. The suggestion department also identifies items that are missing from the user's wardrobe and makes purchase suggestions to complete those items. For example, it suggests accessories and shoes necessary for a particular outfit, and by purchasing them, the user can achieve a more complete outfit. In this way, the suggestion department can propose the optimal outfit to the user and provide them with the enjoyment of fashion.

[0034] The Consultation Department provides advice on outfit coordination problems suggested by the Proposal Department. Specifically, it offers conversational advice on users' fashion concerns. When a user asks the system a question about fashion, the Consultation Department can provide appropriate advice. For example, if a user asks, "What accessories would go with this outfit?", the Consultation Department will suggest appropriate accessories. The Consultation Department uses an AI chatbot to interact with users. The AI ​​chatbot uses natural language processing (NLP) technology to understand the user's questions and generate appropriate answers. For example, if a user asks, "What accessories would go with this outfit?", the AI ​​chatbot will suggest the most suitable accessories based on the user's existing accessories. The Consultation Department can also provide advice tailored to the user's preferences and style. For example, if a user asks, "I want a casual style, what kind of outfit would be good?", the AI ​​chatbot will suggest outfits from the user's existing wardrobe that are suitable for a casual style. Furthermore, the Consultation Department can collect user feedback to improve the system. For example, if a user provides feedback on a suggested outfit, the suggestion algorithm can be improved based on that feedback, allowing for more accurate suggestions. This enables the consultation department to provide users with appropriate advice and support them in resolving their fashion-related concerns.

[0035] The data collection unit can analyze photos of clothes owned by the user and obtain information such as the type, color, and design of each garment. For example, the data collection unit takes photos of clothes owned by the user and uploads them to the system. The data collection unit analyzes these photos and obtains information such as the type, color, and design of each garment. The data collection unit can use image recognition technology to analyze the type, color, and design of the clothes. For example, the data collection unit can use image recognition technology to identify the type of clothing and use a color analysis algorithm to analyze the color of the clothing. The data collection unit can also use design pattern recognition technology to analyze the design of the clothing. This allows for more accurate coordination suggestions by obtaining detailed information about the clothes owned by 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 can input photos of clothes owned by the user into a generating AI and have the generating AI analyze the information on the type, color, and design of the clothes.

[0036] The acquisition unit can acquire weather information for the user's current location in real time. For example, the acquisition unit can acquire weather information for the user's current location in real time. The acquisition unit can acquire information such as temperature, humidity, and weather for the user's current location from a weather information data source. For example, the acquisition unit can acquire the temperature for the user's current location from a weather information data source, and also acquire humidity and weather information. The acquisition unit can periodically update the data in order to acquire weather information in real time. This allows the system to propose the most suitable outfit for the user by acquiring weather information in real time. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input weather information for the user's current location into a generating AI and have the generating AI perform weather analysis.

[0037] The analysis unit can collect and analyze the latest fashion trend information from fashion websites and social media on the internet. For example, the analysis unit can collect and analyze the latest fashion trend information from fashion websites and social media on the internet. The analysis unit can use web scraping technology to collect fashion trend information. For example, the analysis unit can use web scraping technology to collect the latest trend information from fashion websites. The analysis unit can also analyze social media posts to collect trend information. By collecting and analyzing the latest fashion trend information, it becomes possible to suggest outfits that are in line with the trends. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input trend information collected from fashion websites and social media on the internet into a generating AI and have the generating AI perform the analysis of the trend information.

[0038] The suggestion unit can select clothes from the user's wardrobe that are suitable for the day's temperature and suggest a coordinated outfit that matches current trends. For example, the suggestion unit can select clothes from the user's wardrobe that are suitable for the day's temperature and suggest a coordinated outfit that matches current trends. The suggestion unit can combine information about the user's wardrobe with weather information to suggest the optimal outfit. For example, the suggestion unit can select clothes from the user's wardrobe that are suitable for the temperature and suggest a coordinated outfit that matches current trends. In this way, by selecting clothes suitable for the temperature and suggesting a coordinated outfit that matches current trends, the system can provide the user with the most suitable clothing. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about the user's wardrobe and weather information into a generating AI and have the generating AI suggest the optimal outfit.

[0039] The suggestion unit can suggest additional clothing items that the user should purchase, based on the clothes they already own. For example, the suggestion unit can suggest additional clothing items that the user should purchase, based on the clothes they already have. The suggestion unit can combine information about the clothes the user owns with trend information to suggest additional clothing items. For example, the suggestion unit can suggest new, trendy clothes that match the clothes the user already owns. The suggestion unit can also suggest items that are missing from the clothes the user owns. This allows for more complete outfits by suggesting additional clothing items that the user should purchase, based on the clothes they already own. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input information about the clothes the user owns and trend information into a generating AI and have the generating AI generate suggestions for additional clothing items to purchase.

[0040] The consultation department allows users to discuss their fashion concerns in a conversational format. For example, the consultation department can discuss a user's fashion concerns in a conversational format. When a user asks the system a question about fashion, the consultation department can provide appropriate advice in response. For example, if a user asks, "What accessories would go with this outfit?", the consultation department will suggest appropriate accessories. This allows for more appropriate advice to be provided by discussing the user's fashion concerns in a conversational format. Some or all of the above processes in the consultation department may be performed using AI, for example, or not. For example, the consultation department can input a user's question into a generating AI and have the generating AI execute appropriate advice.

[0041] The data collection unit can analyze the user's past clothing history and select the optimal data collection method. For example, the data collection unit can prioritize collecting information on clothes the user has frequently worn in the past. The data collection unit can also collect information on clothes worn in specific seasons from the user's past clothing history. The data collection unit can also analyze the user's past clothing history and collect information on clothes suitable for specific events or situations. This allows the optimal data collection method to be selected by analyzing the user's past clothing 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 can input the user's past clothing history data into a generating AI and have the generating AI select the optimal data collection method.

[0042] The data collection unit can filter the collected clothing information based on the user's current fashion trends and preferences. For example, the data collection unit can collect clothing information based on the fashion styles the user is currently interested in. The data collection unit can also prioritize the collection of information on specific brands or designs of clothing according to the user's preferences. The data collection unit can also analyze the user's current fashion trends and collect relevant clothing information. This allows for the collection of more relevant information by filtering based on the user's current fashion trends and preferences. 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 data on the user's current fashion trends and preferences into a generating AI and have the generating AI perform the filtering.

[0043] The data collection unit can prioritize the collection of highly relevant clothing information by considering the user's geographical location when collecting clothing information. For example, the data collection unit can collect information on clothing suitable for the climate of the area where the user is currently located. The data collection unit can also collect information on clothing the user will need at their travel destination. Based on the user's geographical location, the data collection unit can also collect information on clothing that matches the fashion trends of the region. This allows for the priority collection of highly relevant clothing information by considering 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 can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant clothing information.

[0044] The data collection unit can collect relevant clothing information by analyzing the user's social media activity when collecting clothing information. For example, the data collection unit can analyze posts from fashion influencers that the user follows on social media and collect relevant clothing information. The data collection unit can also collect information on clothing that the user has "liked" or commented on on social media. The data collection unit can also analyze the user's social media activity and collect information on clothing that the user might be interested in. In this way, relevant clothing information can be collected by analyzing 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 data into a generating AI and have the generating AI collect relevant clothing information.

[0045] The acquisition unit can analyze the user's past weather information usage history and select the optimal acquisition method. For example, the acquisition unit can prioritize the acquisition method for weather information that the user has frequently referenced in the past. The acquisition unit can also select a weather information acquisition method suitable for a specific season based on the user's past weather information usage history. The acquisition unit can also analyze the user's past weather information usage history and select a weather information acquisition method suitable for a specific event or situation. In this way, the optimal acquisition method can be selected by analyzing the user's past weather information usage history. 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 can input the user's past weather information usage history data into a generating AI and have the generating AI select the optimal acquisition method.

[0046] The acquisition unit can filter weather information based on the user's current activity plans and location. For example, if the user has an outdoor activity planned, the acquisition unit will prioritize acquiring weather information suitable for that activity. If the user has a trip planned, the acquisition unit can also prioritize acquiring weather information for the travel destination. The acquisition unit can also acquire weather information suitable for the user's current location. This allows for the acquisition of more relevant weather information by filtering based on the user's current activity plans and location. 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 can input data on the user's current activity plans and location into a generating AI and have the generating AI perform the filtering.

[0047] The acquisition unit can prioritize the acquisition of highly relevant weather information by considering the user's geographical location when acquiring weather information. For example, the acquisition unit can acquire weather information suitable for the climate of the area where the user is currently located. The acquisition unit can also acquire weather information that the user will need at their travel destination. Based on the user's geographical location, the acquisition unit can also acquire weather information that matches the weather trends of the region. This allows for the priority acquisition of highly relevant weather information by considering the user's geographical location. 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 can input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant weather information.

[0048] The acquisition unit can analyze the user's social media activity when acquiring weather information and acquire relevant weather information. For example, the acquisition unit can analyze posts from weather-related accounts that the user follows on social media and acquire relevant weather information. The acquisition unit can also acquire weather information that the user has "liked" or commented on on social media. The acquisition unit can also analyze the user's social media activity and acquire weather information that the user might be interested in. In this way, relevant weather information can be acquired by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant weather information.

[0049] The analysis unit can adjust the level of detail of the analysis based on the importance of the fashion items during the analysis. For example, the analysis unit can perform a detailed analysis on fashion information of high importance. The analysis unit can also perform a simplified analysis on fashion information of low importance. The analysis unit can also determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the fashion items. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input importance data of the fashion information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0050] The analysis unit can apply different analysis algorithms depending on the fashion category during analysis. For example, for casual fashion, the analysis unit applies an analysis algorithm specialized for casual fashion. For formal fashion, the analysis unit can also apply an analysis algorithm specialized for formal fashion. For sports fashion, the analysis unit can also apply an analysis algorithm specialized for sports fashion. This allows for more accurate analysis by applying the most suitable analysis algorithm according to the fashion category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input fashion category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0051] The analysis unit can determine the priority of analysis based on the timing of fashion submissions during the analysis process. For example, the analysis unit may prioritize the analysis of fashion information submitted at the beginning of a season. The analysis unit may also prioritize the analysis of fashion information submitted before an event. The analysis unit may also determine the priority of analysis based on the submission timing in response to changes in trends. This enables efficient analysis by determining the priority of analysis based on the timing of fashion submissions. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input fashion information submission timing data into a generating AI and have the generating AI determine the priority of analysis.

[0052] The analysis unit can adjust the order of analysis based on the relevance of fashion items during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant fashion information. The analysis unit may also postpone the analysis of less relevant fashion information. The analysis unit can also adjust the order of analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of fashion items. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevance data of fashion information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0053] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the clothing items. For example, it can provide detailed coordination suggestions for highly important clothing items, and simplified coordination suggestions for less important clothing items. The suggestion unit can also determine the priority of suggestions based on their importance. This allows for efficient suggestions by adjusting the level of detail based on the importance of the clothing items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input clothing importance data into a generating AI and have the generating AI adjust the level of detail in the suggestions.

[0054] The suggestion unit can apply different suggestion algorithms depending on the clothing category when making suggestions. For example, for casual fashion, the suggestion unit can apply a suggestion algorithm specialized for casual fashion. For formal fashion, the suggestion unit can also apply a suggestion algorithm specialized for formal fashion. For sports fashion, the suggestion unit can also apply a suggestion algorithm specialized for sports fashion. This allows for more accurate suggestions by applying the most suitable suggestion algorithm according to the clothing category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input clothing category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0055] The proposal department can prioritize proposals based on when the clothing was submitted. For example, the proposal department might prioritize proposals for clothing submitted at the beginning of the season. It might also prioritize proposals for clothing submitted before an event. The proposal department could also prioritize proposals based on submission timing in response to changing trends. This allows for more efficient proposals by prioritizing proposals based on when the clothing was submitted. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department could input clothing submission timing data into a generating AI and have the generating AI determine the priority of proposals.

[0056] The suggestion unit can adjust the order of suggestions based on the relevance of the clothing items. For example, the suggestion unit can prioritize suggesting highly relevant clothing items. It can also postpone suggesting less relevant clothing items. The suggestion unit can also adjust the order of suggestions according to their relevance. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of the clothing items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input clothing relevance data into a generating AI and have the generating AI adjust the order of suggestions.

[0057] The consultation unit can provide the best possible answer by referring to the user's past consultation history during a consultation. For example, the consultation unit can provide the best answer based on the content of the user's past consultations. The consultation unit can also provide relevant answers from the user's past consultation history. The consultation unit can also analyze the user's past consultation history and provide the most appropriate answer. In this way, the consultation unit can provide the best possible answer by referring to the user's past consultation history. Some or all of the above processes in the consultation unit may be performed using AI, for example, or not using AI. For example, the consultation unit can input the user's past consultation history data into a generating AI and have the generating AI perform the task of providing the best possible answer.

[0058] The consultation unit can customize its responses based on the user's current fashion trends during a consultation. For example, the consultation unit can customize responses based on the fashion styles the user is currently interested in. The consultation unit can also analyze the user's current fashion trends and provide relevant responses. The consultation unit can also provide the most appropriate response based on the user's current fashion trends. This allows for more appropriate responses by customizing them based on the user's current fashion trends. Some or all of the above processes in the consultation unit may be performed using AI, for example, or not using AI. For example, the consultation unit can input the user's current fashion trend data into a generating AI and have the generating AI perform the response customization.

[0059] The consultation unit can provide the most suitable answer by considering the user's geographical location information during a consultation. For example, the consultation unit can provide an answer based on the fashion trends of the area where the user is currently located. The consultation unit can also provide fashion information that the user needs at their travel destination. The consultation unit can also provide an answer tailored to the fashion trends of the region based on the user's geographical location information. In this way, the consultation unit can provide the most suitable answer by considering the user's geographical location information. Some or all of the above processing in the consultation unit may be performed using AI, for example, or without AI. For example, the consultation unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing the most suitable answer.

[0060] The consultation department can analyze the user's social media activity and customize the response during the consultation. For example, the consultation department can analyze posts from fashion influencers that the user follows on social media and provide relevant answers. The consultation department can also provide answers based on fashion information that the user has liked or commented on on social media. The consultation department can analyze the user's social media activity and provide answers based on fashion information that the user is likely to be interested in. In this way, by analyzing the user's social media activity, more appropriate answers can be provided. Some or all of the above processing in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the answer.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data collection unit can analyze the user's past clothing history and select the optimal data collection method. For example, it can prioritize collecting information on clothes the user has frequently worn in the past. It can also collect information on clothes worn in specific seasons from the user's past clothing history. By analyzing the user's past clothing history, it can also collect information on clothes suitable for specific events or situations. This allows the optimal data collection method to be selected by analyzing the user's past clothing 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 can input the user's past clothing history data into a generating AI and have the generating AI select the optimal data collection method.

[0063] The data collection unit can filter the collected clothing information based on the user's current fashion trends and preferences. For example, it can collect clothing information based on the fashion styles the user is currently interested in. It can also prioritize the collection of information on specific brands or designs of clothing according to the user's preferences. It can also analyze the user's current fashion trends and collect related clothing information. This allows for the collection of more relevant information by filtering based on the user's current fashion trends and preferences. 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 data on the user's current fashion trends and preferences into a generating AI and have the generating AI perform the filtering.

[0064] The acquisition unit can filter weather information based on the user's current activity plans and location. For example, if the user has an outdoor activity planned, the acquisition unit will prioritize acquiring weather information suitable for that activity. If the user has a trip planned, the acquisition unit can also prioritize acquiring weather information for the travel destination. It can also acquire weather information suitable for the user's current location. This allows for the acquisition of more relevant weather information by filtering based on the user's current activity plans and location. 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 can input data on the user's current activity plans and location into a generating AI and have the generating AI perform the filtering.

[0065] The analysis unit can apply different analysis algorithms depending on the fashion category during analysis. For example, for casual fashion, an analysis algorithm specialized for casual fashion can be applied. For formal fashion, an analysis algorithm specialized for formal fashion can also be applied. For sports fashion, an analysis algorithm specialized for sports fashion can also be applied. This allows for more accurate analysis by applying the most suitable analysis algorithm according to the fashion category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input fashion category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0066] The suggestion unit can apply different suggestion algorithms depending on the clothing category when making suggestions. For example, for casual fashion, a suggestion algorithm specialized for casual fashion can be applied. For formal fashion, a suggestion algorithm specialized for formal fashion can also be applied. For sports fashion, a suggestion algorithm specialized for sports fashion can also be applied. This allows for more accurate suggestions by applying the optimal suggestion algorithm according to the clothing category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input clothing category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0067] The consultation department can analyze the user's social media activity and customize the response during the consultation. For example, it can analyze posts from fashion influencers the user follows on social media and provide relevant answers. It can also provide answers based on fashion information the user has liked or commented on on social media. By analyzing the user's social media activity, it can provide answers based on fashion information that the user is likely to be interested in. In this way, by analyzing the user's social media activity, it is possible to provide more appropriate answers. Some or all of the above processes in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the answer.

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

[0069] Step 1: The collection unit collects information about the user's clothing. For example, the user takes photos of their clothes and uploads them to the system. The collection unit analyzes these photos to obtain information such as the type, color, and design of each garment. Image recognition technology can be used to analyze the type, color, and design of the clothing. Step 2: The acquisition unit acquires weather information based on the information collected by the collection unit. For example, it acquires weather information for the user's current location in real time. From the weather information data source, it can acquire information such as temperature, humidity, and weather for the user's current location. Step 3: The analysis unit analyzes the weather information and fashion trend information acquired by the acquisition unit. For example, it collects and analyzes the latest fashion trend information from fashion websites and social media on the internet. Web scraping technology can be used to collect the latest trend information from fashion websites. Step 4: The suggestion unit proposes outfits based on the information analyzed by the analysis unit. For example, it selects clothes from the user's wardrobe that are suitable for the day's temperature and proposes an outfit that matches current trends. By combining information about the user's wardrobe with weather information, it can propose the optimal outfit. Step 5: The consultation department addresses concerns regarding the outfits proposed by the proposal department. For example, it can discuss users' fashion concerns in a conversational format. When a user asks the system a question about fashion, the system can provide appropriate advice in response.

[0070] (Example of form 2) The fashion coordination suggestion system according to an embodiment of the present invention is a system that utilizes multimodal AI to refer to fashion trend information and proposes a trendy outfit by combining the user's existing clothes. This fashion coordination suggestion system proposes the optimal outfit for the user by collecting information on the clothes the user owns, obtaining weather information, and collecting and analyzing fashion trend information. For example, it selects clothes from the user's existing wardrobe that are suitable for the day's temperature and proposes a trendy outfit. It can also suggest additional clothes that the user should purchase to match the clothes they already own. Furthermore, the system allows the user to consult about fashion concerns in a conversational format. For example, the user takes pictures of the clothes they own and uploads them to the system. The system analyzes these pictures and obtains information such as the type, color, and design of each garment. Next, the system obtains weather information for the user's current location in real time and collects information such as the temperature, humidity, and weather for the day. Furthermore, the system collects and analyzes the latest fashion trend information from fashion sites and social media on the internet. Based on this information, the system proposes the optimal outfit for the user. For example, it selects clothes from the user's existing wardrobe that are suitable for the day's temperature and proposes a trendy outfit. It can also suggest additional clothes that the user should purchase to match the clothes they already own. Furthermore, the system allows the user to consult about fashion concerns in a conversational format. Users can ask the system fashion-related questions, and the system will provide appropriate advice. This system allows even those unfamiliar with fashion to enjoy stylish outfits, and solves the problems of those who want to know what comfortable clothing to wear or who are unsure what clothes to buy. Furthermore, by connecting users to shopping sites through the system, advertising effects can also be expected. In this way, the fashion coordination suggestion system can solve users' fashion-related problems and suggest the best outfits.

[0071] The fashion coordination suggestion system according to this embodiment comprises a collection unit, an acquisition unit, an analysis unit, a suggestion unit, and a consultation unit. The collection unit collects information about the clothes owned by the user. For example, the collection unit takes pictures of the clothes owned by the user and uploads them to the system. The collection unit analyzes these pictures and obtains information such as the type, color, and design of each garment. The collection unit can analyze the type, color, and design of the clothes using image recognition technology. For example, the collection unit uses image recognition technology to identify the type of clothing and a color analysis algorithm to analyze the color of the clothing. The collection unit can also analyze the design of the clothing using design pattern recognition technology. The acquisition unit obtains weather information based on the information collected by the collection unit. For example, the acquisition unit obtains weather information for the user's current location in real time. The acquisition unit can obtain information such as the temperature, humidity, and weather at the user's current location from a weather information data source. For example, the acquisition unit obtains the temperature at the user's current location from a weather information data source and also obtains humidity and weather information. The acquisition unit can periodically update data in order to obtain weather information in real time. The analysis unit analyzes weather information and fashion trend information acquired by the acquisition unit. The analysis unit collects and analyzes the latest fashion trend information from sources such as fashion websites and social media on the internet. The analysis unit can use web scraping technology to collect fashion trend information. For example, the analysis unit uses web scraping technology to collect the latest trend information from fashion websites. The analysis unit can also analyze social media posts to collect trend information. The suggestion unit proposes outfits based on the information analyzed by the analysis unit. For example, the suggestion unit selects clothes from the user's wardrobe that are suitable for the day's temperature and proposes an outfit that aligns with current trends. The suggestion unit can combine information about the user's wardrobe with weather information to propose the optimal outfit. For example, the suggestion unit selects clothes from the user's wardrobe that are suitable for the temperature and proposes an outfit that aligns with current trends.Furthermore, the suggestion unit can also suggest additional clothing items that the user should purchase, based on the clothes they already own. The consultation unit can provide advice on any concerns regarding the outfits suggested by the suggestion unit. For example, the consultation unit can discuss the user's fashion concerns in a conversational format. When the user asks the system a question about fashion, the consultation unit can provide appropriate advice. For example, if the user asks, "What accessories would go with this outfit?", the consultation unit will suggest appropriate accessories. In this way, the fashion coordination suggestion system according to this embodiment can analyze weather information and fashion trend information based on the user's clothing information and suggest the optimal outfit.

[0072] The data collection unit collects information about the clothes owned by the user. Specifically, the user takes photos of their clothes and uploads them to the system. The data collection unit then analyzes these photos to obtain information such as the type, color, and design of each garment. Image recognition technology can be used to analyze the type, color, and design of the clothes. For example, the data collection unit uses image recognition technology to identify the type of clothing and a color analysis algorithm to analyze the color. It can also analyze the design of the clothes using design pattern recognition technology. Specifically, the image recognition technology uses a convolutional neural network (CNN) to extract features from images of clothes and classify the type of clothing based on these features. The color analysis algorithm analyzes the pixel data of the image to identify the main colors. The design pattern recognition technology analyzes the shapes and patterns in the image to identify designs such as stripes, dots, and florals. As a result, the data collection unit can accurately collect detailed information about the clothes owned by the user and provide it to the system. Furthermore, the data collection unit can update the information using the same procedure when the user purchases new clothes. For example, when a user purchases new clothing, they can take a photo of the clothing and upload it to the system. This allows the data collection unit to add new information and keep the database up-to-date. This provides the data collection unit with a foundation for always offering outfit suggestions based on the latest information.

[0073] The acquisition unit obtains weather information based on the information collected by the data collection unit. Specifically, it obtains weather information for the user's current location in real time. The acquisition unit can obtain information such as temperature, humidity, and weather for the user's current location from weather information data sources. For example, the acquisition unit obtains the temperature for the user's current location from the weather information data source, and also obtains humidity and weather information. The acquisition unit can periodically update data in order to obtain weather information in real time. Specifically, the acquisition unit uses a weather information API to obtain the latest weather data based on the user's current location. The weather information API is provided by a weather data provider and can obtain detailed weather information such as current temperature, humidity, weather, and wind speed. By periodically acquiring this information and reflecting it in the system, the acquisition unit can always provide coordination suggestions based on the latest weather information. In addition, the acquisition unit can use GPS technology to obtain the user's location information. It obtains location information from the user's smartphone or device and obtains weather information based on that location. This allows the acquisition unit to provide weather information that is most suitable for the user's current location. Furthermore, the acquisition unit can update data in real time in response to changes in weather information. For example, if the weather or temperature changes suddenly, the acquisition unit immediately obtains the new weather information and reflects it in the system. This allows the acquisition unit to always provide the user with the latest weather information and support optimal coordination suggestions.

[0074] The analysis unit analyzes weather information and fashion trend information acquired by the acquisition unit. Specifically, it collects and analyzes the latest fashion trend information from fashion websites and social media on the internet. The analysis unit can use web scraping technology to collect fashion trend information. For example, the analysis unit uses web scraping technology to collect the latest trend information from fashion websites. The analysis unit can also collect trend information by analyzing social media posts. Specifically, it uses web scraping technology to regularly collect new information from fashion blogs and online shops to grasp the latest trends. Furthermore, to analyze social media posts, it uses natural language processing (NLP) technology to extract fashion-related keywords and hashtags and identify trends. For example, it identifies items and styles that many users mention on social media and collects them as trend information. The analysis unit can integrate this information to grasp current fashion trends. Furthermore, the analysis unit can analyze past trend data and predict seasonal trend changes and trends related to specific events. As a result, the analysis unit can provide users with the latest trend information and suggest outfits that match the trends.

[0075] The suggestion department proposes outfits based on information analyzed by the analysis department. Specifically, it selects clothes from the user's wardrobe that are suitable for the day's temperature and proposes outfits that are in line with current trends. The suggestion department can combine information about the user's wardrobe with weather information to propose the optimal outfit. For example, it selects clothes from the user's wardrobe that are suitable for the temperature and proposes outfits that are in line with current trends. The suggestion department can also suggest additional clothes that the user should purchase to complement their existing wardrobe. Specifically, the suggestion department uses AI to analyze the user's wardrobe information and weather information to generate the optimal outfit. The AI ​​learns the user's past outfit history and preferred style and makes suggestions based on that. For example, the AI ​​analyzes patterns of outfits the user has chosen in the past and proposes new outfits based on similar weather conditions and trends. The suggestion department also identifies items that are missing from the user's wardrobe and makes purchase suggestions to complete those items. For example, it suggests accessories and shoes necessary for a particular outfit, and by purchasing them, the user can achieve a more complete outfit. In this way, the suggestion department can propose the optimal outfit to the user and provide them with the enjoyment of fashion.

[0076] The Consultation Department provides advice on outfit coordination problems suggested by the Proposal Department. Specifically, it offers conversational advice on users' fashion concerns. When a user asks the system a question about fashion, the Consultation Department can provide appropriate advice. For example, if a user asks, "What accessories would go with this outfit?", the Consultation Department will suggest appropriate accessories. The Consultation Department uses an AI chatbot to interact with users. The AI ​​chatbot uses natural language processing (NLP) technology to understand the user's questions and generate appropriate answers. For example, if a user asks, "What accessories would go with this outfit?", the AI ​​chatbot will suggest the most suitable accessories based on the user's existing accessories. The Consultation Department can also provide advice tailored to the user's preferences and style. For example, if a user asks, "I want a casual style, what kind of outfit would be good?", the AI ​​chatbot will suggest outfits from the user's existing wardrobe that are suitable for a casual style. Furthermore, the Consultation Department can collect user feedback to improve the system. For example, if a user provides feedback on a suggested outfit, the suggestion algorithm can be improved based on that feedback, allowing for more accurate suggestions. This enables the consultation department to provide users with appropriate advice and support them in resolving their fashion-related concerns.

[0077] The data collection unit can analyze photos of clothes owned by the user and obtain information such as the type, color, and design of each garment. For example, the data collection unit takes photos of clothes owned by the user and uploads them to the system. The data collection unit analyzes these photos and obtains information such as the type, color, and design of each garment. The data collection unit can use image recognition technology to analyze the type, color, and design of the clothes. For example, the data collection unit can use image recognition technology to identify the type of clothing and use a color analysis algorithm to analyze the color of the clothing. The data collection unit can also use design pattern recognition technology to analyze the design of the clothing. This allows for more accurate coordination suggestions by obtaining detailed information about the clothes owned by 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 can input photos of clothes owned by the user into a generating AI and have the generating AI analyze the information on the type, color, and design of the clothes.

[0078] The acquisition unit can acquire weather information for the user's current location in real time. For example, the acquisition unit can acquire weather information for the user's current location in real time. The acquisition unit can acquire information such as temperature, humidity, and weather for the user's current location from a weather information data source. For example, the acquisition unit can acquire the temperature for the user's current location from a weather information data source, and also acquire humidity and weather information. The acquisition unit can periodically update the data in order to acquire weather information in real time. This allows the system to propose the most suitable outfit for the user by acquiring weather information in real time. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input weather information for the user's current location into a generating AI and have the generating AI perform weather analysis.

[0079] The analysis unit can collect and analyze the latest fashion trend information from fashion websites and social media on the internet. For example, the analysis unit can collect and analyze the latest fashion trend information from fashion websites and social media on the internet. The analysis unit can use web scraping technology to collect fashion trend information. For example, the analysis unit can use web scraping technology to collect the latest trend information from fashion websites. The analysis unit can also analyze social media posts to collect trend information. By collecting and analyzing the latest fashion trend information, it becomes possible to suggest outfits that are in line with the trends. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input trend information collected from fashion websites and social media on the internet into a generating AI and have the generating AI perform the analysis of the trend information.

[0080] The suggestion unit can select clothes from the user's wardrobe that are suitable for the day's temperature and suggest a coordinated outfit that matches current trends. For example, the suggestion unit can select clothes from the user's wardrobe that are suitable for the day's temperature and suggest a coordinated outfit that matches current trends. The suggestion unit can combine information about the user's wardrobe with weather information to suggest the optimal outfit. For example, the suggestion unit can select clothes from the user's wardrobe that are suitable for the temperature and suggest a coordinated outfit that matches current trends. In this way, by selecting clothes suitable for the temperature and suggesting a coordinated outfit that matches current trends, the system can provide the user with the most suitable clothing. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about the user's wardrobe and weather information into a generating AI and have the generating AI suggest the optimal outfit.

[0081] The suggestion unit can suggest additional clothing items that the user should purchase, based on the clothes they already own. For example, the suggestion unit can suggest additional clothing items that the user should purchase, based on the clothes they already have. The suggestion unit can combine information about the clothes the user owns with trend information to suggest additional clothing items. For example, the suggestion unit can suggest new, trendy clothes that match the clothes the user already owns. The suggestion unit can also suggest items that are missing from the clothes the user owns. This allows for more complete outfits by suggesting additional clothing items that the user should purchase, based on the clothes they already own. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input information about the clothes the user owns and trend information into a generating AI and have the generating AI generate suggestions for additional clothing items to purchase.

[0082] The consultation department allows users to discuss their fashion concerns in a conversational format. For example, the consultation department can discuss a user's fashion concerns in a conversational format. When a user asks the system a question about fashion, the consultation department can provide appropriate advice in response. For example, if a user asks, "What accessories would go with this outfit?", the consultation department will suggest appropriate accessories. This allows for more appropriate advice to be provided by discussing the user's fashion concerns in a conversational format. Some or all of the above processes in the consultation department may be performed using AI, for example, or not. For example, the consultation department can input a user's question into a generating AI and have the generating AI execute appropriate advice.

[0083] The data collection unit can estimate the user's emotions and adjust the timing of clothing information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect clothing information when the user is relaxed. If the user is busy, the data collection unit can collect clothing information when the user is calm. If the user is excited, the data collection unit can collect clothing information when the user has calmed down. By adjusting the timing of clothing information collection based on the user's emotions, information can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The data collection unit can analyze the user's past clothing history and select the optimal data collection method. For example, the data collection unit can prioritize collecting information on clothes the user has frequently worn in the past. The data collection unit can also collect information on clothes worn in specific seasons from the user's past clothing history. The data collection unit can also analyze the user's past clothing history and collect information on clothes suitable for specific events or situations. This allows the optimal data collection method to be selected by analyzing the user's past clothing 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 can input the user's past clothing history data into a generating AI and have the generating AI select the optimal data collection method.

[0085] The data collection unit can filter the collected clothing information based on the user's current fashion trends and preferences. For example, the data collection unit can collect clothing information based on the fashion styles the user is currently interested in. The data collection unit can also prioritize the collection of information on specific brands or designs of clothing according to the user's preferences. The data collection unit can also analyze the user's current fashion trends and collect relevant clothing information. This allows for the collection of more relevant information by filtering based on the user's current fashion trends and preferences. 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 data on the user's current fashion trends and preferences into a generating AI and have the generating AI perform the filtering.

[0086] The data collection unit can estimate the user's emotions and prioritize the clothing information to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting information on clothing that the user is likely to be interested in. If the user is stressed, the data collection unit may also prioritize collecting information on clothing that the user will find comfortable. If the user is excited, the data collection unit may also prioritize collecting information on clothing that will be useful after the user has calmed down. By prioritizing the clothing information to collect based on the user's emotions, more appropriate information can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The data collection unit can prioritize the collection of highly relevant clothing information by considering the user's geographical location when collecting clothing information. For example, the data collection unit can collect information on clothing suitable for the climate of the area where the user is currently located. The data collection unit can also collect information on clothing the user will need at their travel destination. Based on the user's geographical location, the data collection unit can also collect information on clothing that matches the fashion trends of the region. This allows for the priority collection of highly relevant clothing information by considering 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 can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant clothing information.

[0088] The data collection unit can collect relevant clothing information by analyzing the user's social media activity when collecting clothing information. For example, the data collection unit can analyze posts from fashion influencers that the user follows on social media and collect relevant clothing information. The data collection unit can also collect information on clothing that the user has "liked" or commented on on social media. The data collection unit can also analyze the user's social media activity and collect information on clothing that the user might be interested in. In this way, relevant clothing information can be collected by analyzing 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 data into a generating AI and have the generating AI collect relevant clothing information.

[0089] The acquisition unit can estimate the user's emotions and adjust the timing of weather information acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit will acquire weather information when the user is relaxed. If the user is busy, the acquisition unit can acquire weather information when the user is calm. If the user is excited, the acquisition unit can acquire weather information when the user has calmed down. By adjusting the timing of weather information acquisition based on the user's emotions, information can be acquired at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. 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 can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The acquisition unit can analyze the user's past weather information usage history and select the optimal acquisition method. For example, the acquisition unit can prioritize the acquisition method for weather information that the user has frequently referenced in the past. The acquisition unit can also select a weather information acquisition method suitable for a specific season based on the user's past weather information usage history. The acquisition unit can also analyze the user's past weather information usage history and select a weather information acquisition method suitable for a specific event or situation. In this way, the optimal acquisition method can be selected by analyzing the user's past weather information usage history. 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 can input the user's past weather information usage history data into a generating AI and have the generating AI select the optimal acquisition method.

[0091] The acquisition unit can filter weather information based on the user's current activity plans and location. For example, if the user has an outdoor activity planned, the acquisition unit will prioritize acquiring weather information suitable for that activity. If the user has a trip planned, the acquisition unit can also prioritize acquiring weather information for the travel destination. The acquisition unit can also acquire weather information suitable for the user's current location. This allows for the acquisition of more relevant weather information by filtering based on the user's current activity plans and location. 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 can input data on the user's current activity plans and location into a generating AI and have the generating AI perform the filtering.

[0092] The acquisition unit can estimate the user's emotions and determine the priority of weather information to acquire based on the estimated user emotions. For example, if the user is relaxed, the acquisition unit will prioritize acquiring weather information that the user is likely to be interested in. If the user is stressed, the acquisition unit may also prioritize acquiring weather information that will make the user feel comfortable. If the user is excited, the acquisition unit may also prioritize acquiring weather information that will be useful after the user has calmed down. In this way, by determining the priority of weather information to acquire based on the user's emotions, more appropriate information can be acquired preferentially. Emotion estimation is implemented 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 acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The acquisition unit can prioritize the acquisition of highly relevant weather information by considering the user's geographical location when acquiring weather information. For example, the acquisition unit can acquire weather information suitable for the climate of the area where the user is currently located. The acquisition unit can also acquire weather information that the user will need at their travel destination. Based on the user's geographical location, the acquisition unit can also acquire weather information that matches the weather trends of the region. This allows for the priority acquisition of highly relevant weather information by considering the user's geographical location. 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 can input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant weather information.

[0094] The acquisition unit can analyze the user's social media activity when acquiring weather information and acquire relevant weather information. For example, the acquisition unit can analyze posts from weather-related accounts that the user follows on social media and acquire relevant weather information. The acquisition unit can also acquire weather information that the user has "liked" or commented on on social media. The acquisition unit can also analyze the user's social media activity and acquire weather information that the user might be interested in. In this way, relevant weather information can be acquired by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant weather information.

[0095] The analysis unit can estimate the user's emotions and adjust the analysis method of fashion trend information based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can analyze detailed fashion trend information. If the user is in a hurry, the analysis unit can also analyze fashion trend information to the essentials. If the user is excited, the analysis unit can also analyze fashion trend information that is visually stimulating. By adjusting the analysis method based on the user's emotions, a more appropriate analysis becomes possible. 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The analysis unit can adjust the level of detail of the analysis based on the importance of the fashion items during the analysis. For example, the analysis unit can perform a detailed analysis on fashion information of high importance. The analysis unit can also perform a simplified analysis on fashion information of low importance. The analysis unit can also determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the fashion items. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input importance data of the fashion information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0097] The analysis unit can apply different analysis algorithms depending on the fashion category during analysis. For example, for casual fashion, the analysis unit applies an analysis algorithm specialized for casual fashion. For formal fashion, the analysis unit can also apply an analysis algorithm specialized for formal fashion. For sports fashion, the analysis unit can also apply an analysis algorithm specialized for sports fashion. This allows for more accurate analysis by applying the most suitable analysis algorithm according to the fashion category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input fashion category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0098] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit provides a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The analysis unit can determine the priority of analysis based on the timing of fashion submissions during the analysis process. For example, the analysis unit may prioritize the analysis of fashion information submitted at the beginning of a season. The analysis unit may also prioritize the analysis of fashion information submitted before an event. The analysis unit may also determine the priority of analysis based on the submission timing in response to changes in trends. This enables efficient analysis by determining the priority of analysis based on the timing of fashion submissions. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input fashion information submission timing data into a generating AI and have the generating AI determine the priority of analysis.

[0100] The analysis unit can adjust the order of analysis based on the relevance of fashion items during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant fashion information. The analysis unit may also postpone the analysis of less relevant fashion information. The analysis unit can also adjust the order of analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of fashion items. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevance data of fashion information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0101] The suggestion unit can estimate the user's emotions and adjust the way it presents coordination suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide detailed coordination suggestions. If the user is in a hurry, the suggestion unit can provide concise and to-the-point coordination suggestions. If the user is excited, the suggestion unit can provide visually stimulating coordination suggestions. By adjusting the presentation of coordination suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the clothing items. For example, it can provide detailed coordination suggestions for highly important clothing items, and simplified coordination suggestions for less important clothing items. The suggestion unit can also determine the priority of suggestions based on their importance. This allows for efficient suggestions by adjusting the level of detail based on the importance of the clothing items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input clothing importance data into a generating AI and have the generating AI adjust the level of detail in the suggestions.

[0103] The suggestion unit can apply different suggestion algorithms depending on the clothing category when making suggestions. For example, for casual fashion, the suggestion unit can apply a suggestion algorithm specialized for casual fashion. For formal fashion, the suggestion unit can also apply a suggestion algorithm specialized for formal fashion. For sports fashion, the suggestion unit can also apply a suggestion algorithm specialized for sports fashion. This allows for more accurate suggestions by applying the most suitable suggestion algorithm according to the clothing category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input clothing category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0104] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise and to-the-point suggestions. If the user is excited, the suggestion unit can provide visually stimulating suggestions. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0105] The proposal department can prioritize proposals based on when the clothing was submitted. For example, the proposal department might prioritize proposals for clothing submitted at the beginning of the season. It might also prioritize proposals for clothing submitted before an event. The proposal department could also prioritize proposals based on submission timing in response to changing trends. This allows for more efficient proposals by prioritizing proposals based on when the clothing was submitted. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department could input clothing submission timing data into a generating AI and have the generating AI determine the priority of proposals.

[0106] The suggestion unit can adjust the order of suggestions based on the relevance of the clothing items. For example, the suggestion unit can prioritize suggesting highly relevant clothing items. It can also postpone suggesting less relevant clothing items. The suggestion unit can also adjust the order of suggestions according to their relevance. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of the clothing items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input clothing relevance data into a generating AI and have the generating AI adjust the order of suggestions.

[0107] The consultation unit can estimate the user's emotions and adjust its response method based on the estimated emotions. For example, if the user is nervous, the consultation unit will respond in a calm voice. If the user is relaxed, the consultation unit will respond in a cheerful voice. If the user is in a hurry, the consultation unit can also provide a quick and concise response. By adjusting the response method based on the user's emotions, more appropriate responses become possible. 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 consultation unit may be performed using AI or not using AI. For example, the consultation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0108] The consultation unit can provide the best possible answer by referring to the user's past consultation history during a consultation. For example, the consultation unit can provide the best answer based on the content of the user's past consultations. The consultation unit can also provide relevant answers from the user's past consultation history. The consultation unit can also analyze the user's past consultation history and provide the most appropriate answer. In this way, the consultation unit can provide the best possible answer by referring to the user's past consultation history. Some or all of the above processes in the consultation unit may be performed using AI, for example, or not using AI. For example, the consultation unit can input the user's past consultation history data into a generating AI and have the generating AI perform the task of providing the best possible answer.

[0109] The consultation unit can customize its responses based on the user's current fashion trends during a consultation. For example, the consultation unit can customize responses based on the fashion styles the user is currently interested in. The consultation unit can also analyze the user's current fashion trends and provide relevant responses. The consultation unit can also provide the most appropriate response based on the user's current fashion trends. This allows for more appropriate responses by customizing them based on the user's current fashion trends. Some or all of the above processes in the consultation unit may be performed using AI, for example, or not using AI. For example, the consultation unit can input the user's current fashion trend data into a generating AI and have the generating AI perform the response customization.

[0110] The consultation unit can estimate the user's emotions and determine the priority of consultations based on the estimated emotions. For example, if the user is relaxed, the consultation unit will adjust the priority of consultations. If the user is stressed, the consultation unit may also adjust the priority of consultations. If the user is agitated, the consultation unit may also adjust the priority of consultations. This allows for more appropriate responses by determining the priority of consultations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consultation unit may be performed using AI, for example, or not using AI. For example, the consultation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0111] The consultation unit can provide the most suitable answer by considering the user's geographical location information during a consultation. For example, the consultation unit can provide an answer based on the fashion trends of the area where the user is currently located. The consultation unit can also provide fashion information that the user needs at their travel destination. The consultation unit can also provide an answer tailored to the fashion trends of the region based on the user's geographical location information. In this way, the consultation unit can provide the most suitable answer by considering the user's geographical location information. Some or all of the above processing in the consultation unit may be performed using AI, for example, or without AI. For example, the consultation unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing the most suitable answer.

[0112] The consultation department can analyze the user's social media activity and customize the response during the consultation. For example, the consultation department can analyze posts from fashion influencers that the user follows on social media and provide relevant answers. The consultation department can also provide answers based on fashion information that the user has liked or commented on on social media. The consultation department can analyze the user's social media activity and provide answers based on fashion information that the user is likely to be interested in. In this way, by analyzing the user's social media activity, more appropriate answers can be provided. Some or all of the above processing in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the answer.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The suggestion unit can estimate the user's emotions and adjust the way it presents outfit suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed outfit suggestions. If the user is in a hurry, the suggestion unit can provide concise and to-the-point outfit suggestions. If the user is excited, the suggestion unit can provide visually stimulating outfit suggestions. By adjusting the way outfit suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0115] The data collection unit can analyze the user's past clothing history and select the optimal data collection method. For example, it can prioritize collecting information on clothes the user has frequently worn in the past. It can also collect information on clothes worn in specific seasons from the user's past clothing history. By analyzing the user's past clothing history, it can also collect information on clothes suitable for specific events or situations. This allows the optimal data collection method to be selected by analyzing the user's past clothing 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 can input the user's past clothing history data into a generating AI and have the generating AI select the optimal data collection method.

[0116] The acquisition unit can estimate the user's emotions and adjust the timing of weather information acquisition based on the estimated user emotions. For example, if the user is stressed, the acquisition unit can acquire weather information when the user is relaxed. If the user is busy, the acquisition unit can also acquire weather information when the user is calm. If the user is excited, the acquisition unit can also acquire weather information when the user has calmed down. By adjusting the timing of weather information acquisition based on the user's emotions, information can be acquired at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0117] The analysis unit can estimate the user's emotions and adjust the analysis method of fashion trend information based on the estimated user emotions. For example, if the user is relaxed, the analysis unit will analyze detailed fashion trend information. If the user is in a hurry, the analysis unit can also analyze fashion trend information to the essentials. If the user is excited, the analysis unit can also analyze fashion trend information that is visually stimulating. By adjusting the analysis method based on the user's emotions, a more appropriate analysis becomes possible. 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0118] The consultation unit can estimate the user's emotions and adjust its response method based on the estimated emotions. For example, if the user is nervous, the consultation unit will respond in a calm voice. If the user is relaxed, the consultation unit will respond in a cheerful voice. If the user is in a hurry, the consultation unit can also provide a quick and concise response. By adjusting the response method based on the user's emotions, more appropriate responses become possible. Emotion estimation is achieved using an emotion estimation function, such as 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 consultation unit may be performed using AI or not. For example, the consultation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0119] The data collection unit can filter the collected clothing information based on the user's current fashion trends and preferences. For example, it can collect clothing information based on the fashion styles the user is currently interested in. It can also prioritize the collection of information on specific brands or designs of clothing according to the user's preferences. It can also analyze the user's current fashion trends and collect related clothing information. This allows for the collection of more relevant information by filtering based on the user's current fashion trends and preferences. 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 data on the user's current fashion trends and preferences into a generating AI and have the generating AI perform the filtering.

[0120] The acquisition unit can filter weather information based on the user's current activity plans and location. For example, if the user has an outdoor activity planned, the acquisition unit will prioritize acquiring weather information suitable for that activity. If the user has a trip planned, the acquisition unit can also prioritize acquiring weather information for the travel destination. It can also acquire weather information suitable for the user's current location. This allows for the acquisition of more relevant weather information by filtering based on the user's current activity plans and location. 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 can input data on the user's current activity plans and location into a generating AI and have the generating AI perform the filtering.

[0121] The analysis unit can apply different analysis algorithms depending on the fashion category during analysis. For example, for casual fashion, an analysis algorithm specialized for casual fashion can be applied. For formal fashion, an analysis algorithm specialized for formal fashion can also be applied. For sports fashion, an analysis algorithm specialized for sports fashion can also be applied. This allows for more accurate analysis by applying the most suitable analysis algorithm according to the fashion category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input fashion category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0122] The suggestion unit can apply different suggestion algorithms depending on the clothing category when making suggestions. For example, for casual fashion, a suggestion algorithm specialized for casual fashion can be applied. For formal fashion, a suggestion algorithm specialized for formal fashion can also be applied. For sports fashion, a suggestion algorithm specialized for sports fashion can also be applied. This allows for more accurate suggestions by applying the optimal suggestion algorithm according to the clothing category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input clothing category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0123] The consultation department can analyze the user's social media activity and customize the response during the consultation. For example, it can analyze posts from fashion influencers the user follows on social media and provide relevant answers. It can also provide answers based on fashion information the user has liked or commented on on social media. By analyzing the user's social media activity, it can provide answers based on fashion information that the user is likely to be interested in. In this way, by analyzing the user's social media activity, it is possible to provide more appropriate answers. Some or all of the above processes in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the answer.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The collection unit collects information about the user's clothing. For example, the user takes photos of their clothes and uploads them to the system. The collection unit analyzes these photos to obtain information such as the type, color, and design of each garment. Image recognition technology can be used to analyze the type, color, and design of the clothing. Step 2: The acquisition unit acquires weather information based on the information collected by the collection unit. For example, it acquires weather information for the user's current location in real time. From the weather information data source, it can acquire information such as temperature, humidity, and weather for the user's current location. Step 3: The analysis unit analyzes the weather information and fashion trend information acquired by the acquisition unit. For example, it collects and analyzes the latest fashion trend information from fashion websites and social media on the internet. Web scraping technology can be used to collect the latest trend information from fashion websites. Step 4: The suggestion unit proposes outfits based on the information analyzed by the analysis unit. For example, it selects clothes from the user's wardrobe that are suitable for the day's temperature and proposes an outfit that matches current trends. By combining information about the user's wardrobe with weather information, it can propose the optimal outfit. Step 5: The consultation department addresses concerns regarding the outfits proposed by the proposal department. For example, it can discuss users' fashion concerns in a conversational format. When a user asks the system a question about fashion, the system can provide appropriate advice in response.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the collection unit, acquisition unit, analysis unit, proposal unit, and consultation unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit takes a photograph of the user's clothes using the camera 42 of the smart device 14 and analyzes it using image recognition technology by the control unit 46A. The acquisition unit acquires weather information using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes fashion trend information using the identification processing unit 290 of the data processing unit 12. The proposal unit proposes outfits using the identification processing unit 290 of the data processing unit 12 and displays them using the control unit 46A of the smart device 14. The consultation unit provides appropriate advice to the user's questions using the control unit 46A 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 modified in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the collection unit, acquisition unit, analysis unit, proposal unit, and consultation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit takes a picture of the user's clothes using the camera 42 of the smart glasses 214 and analyzes it using image recognition technology by the control unit 46A. The acquisition unit acquires weather information using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes fashion trend information using the identification processing unit 290 of the data processing unit 12. The proposal unit proposes outfits using the identification processing unit 290 of the data processing unit 12 and displays them using the control unit 46A of the smart glasses 214. The consultation unit provides appropriate advice to the user's questions using the control unit 46A 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.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, acquisition unit, analysis unit, proposal unit, and consultation unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit takes a photograph of the user's clothing using the camera 42 of the headset terminal 314 and analyzes it using image recognition technology by the control unit 46A. The acquisition unit acquires weather information using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes fashion trend information using the identification processing unit 290 of the data processing unit 12. The proposal unit proposes outfits using the identification processing unit 290 of the data processing unit 12 and displays them using the control unit 46A of the headset terminal 314. The consultation unit provides appropriate advice to the user's questions using the control unit 46A 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.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the collection unit, acquisition unit, analysis unit, proposal unit, and consultation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit takes a photograph of the user's clothing using the camera 42 of the robot 414 and analyzes it using image recognition technology by the control unit 46A. The acquisition unit acquires weather information using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes fashion trend information using the identification processing unit 290 of the data processing unit 12. The proposal unit proposes outfits using the identification processing unit 290 of the data processing unit 12 and displays them using the control unit 46A of the robot 414. The consultation unit provides appropriate advice to the user's questions using the control unit 46A 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] (Note 1) A collection unit that collects information about the clothes the user owns, An acquisition unit that acquires weather information based on the information collected by the aforementioned acquisition unit, An analysis unit analyzes weather information and fashion trend information acquired by the acquisition unit, A proposal unit proposes a coordination based on the information analyzed by the aforementioned analysis unit, The system includes a consultation department for discussing concerns regarding the coordination proposed by the aforementioned proposal department. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system analyzes photos of the clothes the user owns and obtains information such as the type, color, and design of each garment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The acquisition unit is, Obtain real-time weather information for the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We collect and analyze the latest fashion trend information from online fashion sites, social media, and other sources. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, The system selects clothes from the user's wardrobe that are suitable for the day's temperature and suggests trendy outfits. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We suggest additional clothing items that the user should purchase based on the clothes they already own. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned consultation department, Users can discuss their fashion concerns in a conversational format. 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 clothing 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 clothing 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 gathering information about clothing, filtering is performed based on the user's current fashion trends and preferences. 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 prioritizes the clothing information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting information about clothing, the system prioritizes collecting information about clothing that is highly relevant to 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 gathering information about clothing, we analyze the user's social media activity and collect relevant clothing information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of weather information acquisition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The acquisition unit is, Analyze the user's past weather information usage history and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The acquisition unit is, When acquiring weather information, filtering is performed based on the user's current activity plans and location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The acquisition unit is, It estimates the user's sentiment and determines the priority of weather information to retrieve based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The acquisition unit is, When acquiring weather information, the system prioritizes acquiring highly relevant weather 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 acquiring weather information, the system analyzes the user's social media activity and retrieves relevant weather information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, We estimate user sentiment and adjust the analysis method of fashion trend information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of fashion. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the fashion category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During the analysis, the priority of the analysis 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 aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of fashion. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the way coordination suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the clothing. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the clothing category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the clothing samples will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the clothing. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned consultation department, The system estimates the user's emotions and adjusts the consultation response method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned consultation department, During consultations, we refer to the user's past consultation history to provide the most appropriate answer. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned consultation department, During the consultation, the response will be customized based on the user's current fashion trends. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned consultation department, It estimates the user's emotions and determines the priority of consultations based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned consultation department, When providing advice, we take the user's geographical location into consideration to provide the most appropriate answer. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned consultation department, During consultations, the system analyzes the user's social media activity to customize the response. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0198] 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 information about the clothes the user owns, An acquisition unit that acquires weather information based on the information collected by the aforementioned acquisition unit, An analysis unit analyzes weather information and fashion trend information acquired by the acquisition unit, A proposal unit proposes a coordination based on the information analyzed by the aforementioned analysis unit, The system includes a consultation department for discussing concerns regarding the coordination proposed by the aforementioned proposal department. A system characterized by the following features.

2. The aforementioned collection unit is The system analyzes photos of the clothes the user owns and obtains information such as the type, color, and design of each garment. The system according to feature 1.

3. The acquisition unit is, Obtain real-time weather information for the user's current location. The system according to feature 1.

4. The aforementioned analysis unit, We collect and analyze the latest fashion trend information from online fashion sites, social media, and other sources. The system according to feature 1.

5. The aforementioned proposal section is, The system selects clothes from the user's wardrobe that are suitable for the day's temperature and suggests trendy outfits. The system according to feature 1.

6. The aforementioned proposal section is, We suggest additional clothing items that the user should purchase based on the clothes they already own. The system according to feature 1.

7. The aforementioned consultation department, Users can discuss their fashion concerns in a conversational format. The system according to feature 1.

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

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