system

Abstract

The system according to this embodiment aims to suggest the most suitable clothing for the user and reduce the time and stress associated with choosing clothes. [Solution] The system according to the embodiment comprises a collection unit, a reception unit, an analysis unit, a suggestion unit, and a simulation unit. The collection unit collects data on the user's clothing. The reception unit receives information on TPO (Time, Place, Occasion) and hairstyle. The analysis unit analyzes trendy fashion data and fashion information from the internet. The suggestion unit suggests appropriate clothing. The simulation unit performs a simulation of the suggested clothing.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes time to choose clothes every day and it is difficult to choose appropriate clothing.

[0005] The system according to the embodiment aims to propose optimal clothing for a user and reduce the time and stress of choosing clothes.

Means for Solving the Problems

[0007] The system according to this embodiment can suggest the most suitable clothing for the user, reducing the time and stress associated with choosing clothes. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network). <00​​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 AI ​​fashion coordinator system according to an embodiment of the present invention is a system designed to reduce the time and stress spent choosing clothes for daily commutes and private use. The AI ​​fashion coordinator system takes photos of the clothes the user owns with their smartphone camera, and the AI ​​suggests an outfit that suits the "occasion for the day" and "hairstyle." This suggestion is based on "trendy fashion data" and "fashion information from the internet." For example, the AI ​​fashion coordinator system takes photos of the clothes the user owns with their smartphone camera and registers them in a database. Next, the user inputs information about the occasion and hairstyle for the day. Based on this information, the AI ​​refers to trendy fashion data and fashion information from the internet to suggest the most suitable outfit. For example, if the user inputs "business casual" and "short hair," the AI ​​selects and suggests an outfit from the clothes the user owns that is suitable for business casual. This suggestion is displayed on the user's smartphone, and the user can check the suggested outfit. This mechanism allows the user to reduce the time and stress spent choosing clothes every morning. Furthermore, because the AI ​​refers to trendy fashion data and fashion information from the internet, it can always suggest outfits that are in line with the latest trends. This allows the AI ​​fashion coordinator system to collect data on the user's clothing, suggest the most suitable outfit based on information such as the occasion and hairstyle, and perform simulations.

[0029] The AI ​​fashion coordinator system according to this embodiment comprises a data collection unit, a reception unit, an analysis unit, a proposal unit, and a simulation unit. The data collection unit collects data on the user's clothing. This data includes, but is not limited to, the type, color, size, and brand of the clothing. The data collection unit, for example, takes a picture of the user's clothing using a smartphone camera and registers the data in a database. The data collection unit can also manually input information about the clothing the user owns. For example, the data collection unit can input the type and color of the clothing the user owns by selecting from a list of options. Furthermore, the data collection unit has a function to automatically update the user's clothing data. For example, the data collection unit can automatically add information to the database when the user purchases new clothing. The reception unit receives information on TPO and hairstyle. TPO includes, but is not limited to, the time, place, occasion (event). Hairstyle information includes, but is not limited to, the length, color, and style of the hair. The reception unit provides, for example, an interface for the user to input information on the TPO and hairstyle for the day. The reception department can also refer to information about TPO (Time, Place, Occasion) and hairstyles that the user has entered in the past. For example, the reception department can automatically suggest TPO and hairstyle information for the day based on information the user has entered in the past. The analysis department analyzes trending fashion data and fashion information from the internet. Trending fashion data includes, but is not limited to, fashion magazines and online shop data. Fashion information from the internet includes, but is not limited to, blogs, social media, and fashion websites. The analysis department uses, for example, AI to collect and analyze trending fashion data and fashion information from the internet. The suggestion department suggests the most suitable outfit. The suggestion department uses, for example, AI to select and suggest the most suitable outfit based on the user's clothing data, TPO and hairstyle information, trending fashion data, and fashion information from the internet. For example, if the user enters "business casual" and "short hair," the suggestion department will suggest an outfit suitable for business casual.The simulation unit performs simulations of the proposed outfits. For example, the simulation unit uses AI to simulate how the proposed outfit combinations would look among the clothes the user already owns. The simulation unit uses technologies such as 3D modeling and virtual try-on to simulate the proposed outfits. As a result, the AI ​​fashion coordinator system according to this embodiment can collect data on the user's clothes, propose the most suitable outfit based on information about the occasion and hairstyle, and perform simulations.

[0030] The data collection unit collects data on the user's clothing. This data includes, but is not limited to, the type, color, size, and brand of the clothing. For example, the data collection unit takes pictures of the user's clothing using a smartphone camera and registers the data in a database. Specifically, images taken with a smartphone camera are analyzed using image recognition technology, and information such as the type, color, size, and brand of the clothing is automatically extracted. This information is centrally managed for each user and stored in the database. The data collection unit can also manually input information about the user's clothing. For example, the data collection unit can input the type and color of the clothing the user owns by selecting from a list of options. Users can easily add and edit clothing information through the application interface. Furthermore, the data collection unit has a function to automatically update the user's clothing data. For example, when a user purchases new clothing, the data collection unit can automatically add that information to the database. This includes integration with online shopping sites and the ability to take pictures of purchase receipts to import information. This allows the data collection unit to always know the user's latest clothing information and provide accurate data.

[0031] The reception desk receives information about TPO (Time, Place, Occasion) and hairstyle. TPO includes, but is not limited to, time, place, and occasion (event). Hairstyle information includes, but is not limited to, hair length, color, and style. The reception desk provides, for example, an interface for users to input information about their TPO and hairstyle for the day. Users can easily input their schedule and hairstyle information for the day through the application. The reception desk can also refer to TPO and hairstyle information that users have previously entered. For example, the reception desk can automatically suggest TPO and hairstyle information for the day based on information that users have previously entered. This allows users to easily input information while referring to past data. Furthermore, the reception desk can learn the user's preferences and tendencies to provide more personalized suggestions. For example, it can learn the style a user prefers when attending a particular event or the clothing tendencies that match a particular hairstyle, and reflect this in future suggestions. This allows the reception desk to respond flexibly to user needs and provide a more satisfying service.

[0032] The analysis department analyzes trending fashion data and online fashion information. Trending fashion data includes, but is not limited to, data from fashion magazines and online shops. Online fashion information includes, but is not limited to, blogs, social media, and fashion websites. The analysis department uses AI, for example, to collect and analyze trending fashion data and online fashion information. Specifically, the AI ​​uses natural language processing technology to analyze articles in fashion magazines and social media posts to extract current trends and popular styles. It also uses image recognition technology to analyze images of products in online shops to grasp trending designs and color trends. This allows the analysis department to grasp the latest fashion trends in real time and provide them to users. Furthermore, the analysis department can also use historical data and statistical information to perform long-term trend analysis and predictions. For example, based on fashion data from the past few years, it can predict future trend changes and provide users with proactive information. This allows the analysis department to ensure that users always have access to the latest fashion information and can provide more appropriate coordination suggestions.

[0033] The suggestion department proposes the most suitable outfit. For example, using AI, the suggestion department selects and proposes the most suitable outfit based on the user's clothing data, information on occasion and hairstyle, trending fashion data, and online fashion information. Specifically, the AI ​​selects appropriate items from the user's clothing database and puts together an outfit based on the occasion and hairstyle information. For example, if the user inputs "business casual" and "short hair," the AI ​​will select jackets, pants, shirts, etc. that are suitable for business casual and propose a style that suits short hair. It can also refer to trending fashion data and online fashion information to propose outfits that incorporate the latest trends. This allows the suggestion department to ensure that users can always enjoy the latest fashion. Furthermore, the suggestion department can learn the user's preferences and past choices to provide more personalized suggestions. For example, it can learn the user's preferred styles and color tendencies in the past and make suggestions based on that. This allows the suggestion department to provide the most suitable outfit that matches the user's personality and preferences.

[0034] The simulation unit performs simulations of suggested outfits. For example, the simulation unit uses AI to simulate how suggested outfit combinations would look with the user's existing wardrobe. Specifically, it uses technologies such as 3D modeling and virtual try-on to simulate suggested outfits. Users can have their avatar try on the suggested outfits through the application. The avatar reflects the user's body shape and facial features, providing a realistic try-on experience. This allows users to check whether the suggested outfits suit them without actually trying on the clothes. Furthermore, the simulation unit can set different lighting conditions and backgrounds, simulating how the outfits would look in various situations. For example, it can check how the outfits would look in different environments, such as natural light outdoors or artificial lighting indoors. This allows users to choose the outfit that is best suited to a particular situation. In addition, the simulation unit can continuously improve the accuracy and realism of the simulations based on user feedback. This enables the simulation unit to provide users with a more realistic and satisfying try-on experience.

[0035] The suggestion function can propose the most suitable outfit based on the user's preferences and past selection history. For example, the suggestion function can suggest outfits considering the user's preferences. For instance, it can suggest outfits that match the user's preferences based on data of outfits the user has previously selected. Furthermore, the suggestion function can also suggest outfits considering the user's past selection history. For example, it can analyze the user's past outfit selection history and suggest outfits that match the user's preferences. This allows for more appropriate outfit suggestions by considering the user's preferences and past selection history.

[0036] The external information unit can suggest the most appropriate clothing based on the user's schedule and weather forecast. For example, the external information unit can suggest clothing considering the user's schedule. For instance, it can suggest clothing that fits the user's schedule based on data from the user's calendar app or schedule planner. Furthermore, the external information unit can also suggest clothing considering the weather forecast. For example, it can suggest clothing appropriate for the weather based on data from the Japan Meteorological Agency or information from weather forecast apps. This allows for more appropriate clothing suggestions by considering both the user's schedule and the weather forecast.

[0037] The simulation unit can simulate how suggested clothing combinations would look with the user's existing wardrobe. For example, the simulation unit can use 3D modeling technology to simulate suggested clothing combinations. For instance, it can display suggested clothing combinations as 3D models based on the user's existing clothing data. The simulation unit can also simulate suggested clothing using virtual try-on technology. For example, it can virtually try on suggested clothing using the user's avatar. This makes it easier for users to confirm how suggested clothing combinations will look by simulating their appearance.

[0038] The collection unit can analyze the user's past clothing history and select the optimal collection method. For example, the collection unit can prioritize collecting clothes that the user has frequently worn in the past. The collection unit can also collect clothes suitable for a specific season or event from the user's past clothing history. Furthermore, the collection unit can analyze the user's past clothing history and select the most efficient collection method. This allows the optimal collection method to be selected by analyzing the user's past clothing history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past clothing history data into a generating AI and have the generating AI select the optimal collection method.

[0039] The data collection unit can filter the clothing data based on the user's current fashion trends and areas of interest. For example, the data collection unit can collect clothing data based on the fashion styles the user is currently interested in. The data collection unit can also prioritize the collection of relevant clothing data, taking into account the user's current fashion trends. Furthermore, the data collection unit can collect clothing data of specific brands or designs based on the user's areas of interest. This allows for the collection of highly relevant data by filtering based on the user's current fashion trends and areas of interest. 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 current fashion trend data into a generating AI and have the generating AI perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting clothing data. For example, if the user lives in a cold region, the data collection unit will prioritize the collection of data on winter clothing. If the user lives in an urban area, the data collection unit can also prioritize the collection of data on business casual clothing. Furthermore, if the user lives by the sea, the data collection unit can prioritize the collection of data on resort wear. This allows for the priority collection of highly relevant data 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 data into a generating AI and have the generating AI prioritize highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data when collecting clothing data. For example, the data collection unit can collect fashion items that the user has "liked" on social media. The data collection unit can also collect data by referencing the styles of fashion influencers that the user follows. Furthermore, the data collection unit can analyze fashion-related photos posted by the user and collect relevant data. In this way, relevant data 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 perform the collection of relevant data.

[0042] The reception desk can select the optimal reception method by referring to the user's past information input history when receiving information about TPO (Time, Place, Occasion) and hairstyle. For example, the reception desk can automatically display TPO and hairstyle information that the user has frequently entered in the past as suggestions. For example, the reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest TPO and hairstyle information to be used during a specific time period based on the user's past information input history. In this way, the optimal reception method can be selected by referring to the user's past information input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past information input history data into a generating AI and have the generating AI select the optimal reception method.

[0043] The reception unit can filter information about appropriate occasions (TPO) and hairstyles based on the user's current lifestyle and areas of interest. For example, the reception unit can receive TPO and hairstyle information based on events or activities the user is currently interested in. The reception unit can also prioritize receiving relevant TPO and hairstyle information, taking into account the user's current lifestyle. Furthermore, the reception unit can receive specific TPO and hairstyle information based on the user's areas of interest. This allows for the reception of highly relevant information by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current lifestyle data into a generating AI and have the generating AI perform the filtering.

[0044] The reception desk can prioritize receiving information on appropriate occasions (TPO) and hairstyles, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize receiving information on TPO and hairstyles related to that region. If the user is traveling, the reception desk can also prioritize receiving information on TPO and hairstyles related to the travel destination. Furthermore, if the user is attending a specific event, the reception desk can prioritize receiving information on TPO and hairstyles related to that event. This allows for the priority of receiving information on appropriate occasions by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI prioritize the most relevant information.

[0045] The reception desk can analyze the user's social media activity and receive relevant information when receiving information about TPO (Time, Place, Occasion) and hairstyle. For example, the reception desk can receive information about TPO and hairstyle related to events and activities that the user has "liked" on social media. The reception desk can also receive information by referencing the styles of influencers that the user follows. Furthermore, the reception desk can analyze photos and comments posted by the user and receive relevant information about TPO and hairstyle. In this way, relevant information can be received by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI perform the reception of relevant information.

[0046] The analysis unit can adjust the level of detail in its analysis based on the importance of the fashion data. For example, the analysis unit can analyze important fashion data in detail and reflect it in its recommendations. Alternatively, it can analyze general fashion data concisely and reflect it in its recommendations. Furthermore, the analysis unit can focus its analysis on fashion data related to specific events or seasons and reflect it in its recommendations. By adjusting the level of detail in the analysis based on the importance of the fashion data, more appropriate analysis results can be obtained. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the fashion data into a generating AI and have the generating AI adjust the level of detail in the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the category of fashion data during analysis. For example, the analysis unit can apply a casual fashion analysis algorithm to casual fashion data. The analysis unit can also apply a business fashion analysis algorithm to business fashion data. Furthermore, the analysis unit can apply a sports fashion analysis algorithm to sports fashion data. By applying the most appropriate analysis algorithm according to the category of fashion data, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of fashion data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0048] The analysis department can prioritize analysis based on the submission timing of fashion data. For example, the analysis department can prioritize the analysis of the latest fashion data and reflect it in its recommendations. The analysis department can also prioritize the analysis of the latest data while referring to past fashion data. Furthermore, the analysis department can prioritize the analysis of fashion data related to a specific season or event. This allows for analysis that emphasizes the latest data by prioritizing analysis based on the submission timing of fashion data. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the submission timing of fashion data into a generating AI and have the generating AI determine the analysis priorities.

[0049] The analysis unit can adjust the order of analysis based on the relevance of fashion data during the analysis process. For example, the analysis unit may prioritize analyzing fashion data related to the user's current interests. The analysis unit may also prioritize analyzing highly relevant fashion data based on the user's past selection history. Furthermore, the analysis unit may prioritize analyzing fashion data related to specific events or seasons. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of fashion data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of fashion data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0050] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the clothing. For example, it might provide detailed clothing suggestions for important events, while keeping everyday clothing suggestions concise. It might also focus on clothing suggestions related to specific seasons or events. By adjusting the level of detail based on the importance of the clothing, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the importance of the clothing into a generating AI and have the generating AI adjust the level of detail in the suggestions.

[0051] The suggestion unit can apply different suggestion algorithms depending on the clothing category when making suggestions. For example, the suggestion unit can apply a casual suggestion algorithm to casual clothing. For example, the suggestion unit can also apply a business suggestion algorithm to business attire. Furthermore, the suggestion unit can apply a sports suggestion algorithm to sports attire. By applying different suggestion algorithms depending on the clothing category, more appropriate suggestions can be made. 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 the clothing category into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0052] The proposal department can prioritize proposals based on the timing of clothing submissions. For example, the proposal department may prioritize the most recent clothing data. Alternatively, it may prioritize the latest data while referencing past clothing data. It may also prioritize clothing data related to specific seasons or events. By prioritizing proposals based on the timing of clothing submissions, it becomes possible to make proposals that prioritize the latest data. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input the timing of clothing submissions into a generating AI and have the generating AI determine the priority of proposals.

[0053] The suggestion unit can adjust the order of suggestions based on the relevance of the clothing items. For example, the suggestion unit may prioritize suggesting clothing data related to the user's current interests. The suggestion unit may also prioritize suggesting highly relevant clothing data based on the user's past selection history. Furthermore, the suggestion unit may prioritize suggesting clothing data related to specific events or seasons. This allows for the prioritization of highly relevant data 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 the relevance of the clothing items into a generating AI and have the generating AI adjust the order of suggestions.

[0054] The simulation unit can adjust the level of detail of the simulation based on the importance of clothing during the simulation. For example, the simulation unit will perform detailed clothing simulations for important events. For example, it can perform simpler clothing simulations for everyday wear. It can also focus on clothing simulations related to specific seasons or events. By adjusting the level of detail of the simulation based on the importance of clothing, a more appropriate simulation becomes possible. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the importance of clothing into a generating AI and have the generating AI perform the adjustment of the level of detail of the simulation.

[0055] The simulation unit can apply different simulation algorithms depending on the clothing category during the simulation. For example, the simulation unit can apply a simulation algorithm for casual wear to casual wear. The simulation unit can also apply a simulation algorithm for business wear to business wear. Furthermore, the simulation unit can apply a simulation algorithm for sports wear to sports wear. By applying different simulation algorithms depending on the clothing category, a more appropriate simulation becomes possible. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the clothing category into a generating AI and have the generating AI execute the application of different simulation algorithms.

[0056] The simulation unit can determine the priority of simulations based on the timing of clothing submissions. For example, the simulation unit may prioritize the simulation of the most recent clothing data. The simulation unit may also prioritize the simulation of the most recent data while referring to past clothing data. Furthermore, the simulation unit may prioritize the simulation of clothing data related to a specific season or event. This allows for simulations that prioritize the most recent data by determining the priority of simulations based on the timing of clothing submissions. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit may input the timing of clothing submissions into a generating AI and have the generating AI determine the priority of simulations.

[0057] The simulation unit can adjust the simulation order based on the relevance of clothing during the simulation. For example, the simulation unit may prioritize simulating clothing data related to the user's current interests. The simulation unit may also prioritize simulating clothing data that is highly relevant based on the user's past selection history. Furthermore, the simulation unit may prioritize simulating clothing data related to specific events or seasons. This allows for prioritizing the simulation of highly relevant data by adjusting the simulation order based on the relevance of clothing. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the relevance of clothing into a generating AI and have the generating AI perform the adjustment of the simulation order.

[0058] The External Information Unit can adjust the level of detail collected based on the importance of the information when gathering external information. For example, the External Information Unit can collect important external information in detail and reflect it in proposals. The External Information Unit can also collect general external information concisely and reflect it in proposals. Furthermore, the External Information Unit can focus on collecting external information related to specific events or seasons and reflect it in proposals. This allows for the collection of more appropriate external information by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the External Information Unit may be performed using AI, for example, or without AI. For example, the External Information Unit can input the importance of the information into a generating AI and have the generating AI perform the adjustment of the level of detail of the collection.

[0059] The external information unit can apply different collection algorithms depending on the category of information when collecting external information. For example, the external information unit can apply a fashion-specific collection algorithm to fashion information. For example, it can also apply a weather-specific collection algorithm to weather information. Furthermore, the external information unit can apply an event-specific collection algorithm to event information. By applying different collection algorithms depending on the category of information, more appropriate external information can be collected. Some or all of the above processing in the external information unit may be performed using AI, for example, or without AI. For example, the external information unit can input the category of information into a generating AI and have the generating AI execute the application of different collection algorithms.

[0060] The External Information Department can determine the priority of information collection based on the timing of information submission when collecting external information. For example, the External Information Department can prioritize the collection of the latest external information and reflect it in proposals. The External Information Department can also prioritize the collection of the latest information while referring to past external information. Furthermore, the External Information Department can prioritize the collection of external information related to a specific season or event. This makes it possible to prioritize the collection of the latest information by determining the priority of information collection based on the timing of information submission. Some or all of the above processing in the External Information Department may be performed using AI, for example, or not using AI. For example, the External Information Department can input the timing of information submission into a generating AI and have the generating AI perform the determination of the collection priority.

[0061] The external information unit can adjust the order of collection based on the relevance of the information when collecting external information. For example, the external information unit can prioritize collecting external information related to the user's current interests. The external information unit can also prioritize collecting highly relevant external information based on the user's past selection history. Furthermore, the external information unit can prioritize collecting external information related to specific events or seasons. This allows for the priority collection of highly relevant information by adjusting the order of collection based on the relevance of the information. Some or all of the above processing in the external information unit may be performed using AI, for example, or without AI. For example, the external information unit can input the relevance of the information into a generating AI and have the generating AI perform the adjustment of the collection order.

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

[0063] The suggestion unit can analyze the user's past selection history and select the optimal suggestion method. For example, it can prioritize suggesting clothing that the user has frequently selected in the past. The suggestion unit can also suggest clothing suitable for a specific season or event based on the user's past selection history. Furthermore, the suggestion unit can analyze the user's past selection history and select the most efficient suggestion method. In this way, the optimal suggestion method can be selected by analyzing the user's past selection history. 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 the user's past selection history data into a generating AI and have the generating AI select the optimal suggestion method.

[0064] The proposed function can filter clothing data based on the user's current fashion trends and areas of interest. For example, it can collect clothing data based on the fashion styles the user is currently interested in. The proposed function can also prioritize the collection of relevant clothing data, taking into account the user's current fashion trends. Furthermore, the proposed function can collect clothing data for specific brands or designs based on the user's areas of interest. This allows for the collection of highly relevant data by filtering based on the user's current fashion trends and areas of interest. Some or all of the above processing in the proposed function may be performed using AI, for example, or without AI. For example, the proposed function can input the user's current fashion trend data into a generating AI and have the generating AI perform the filtering.

[0065] The suggestion unit can prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user lives in a cold region, the suggestion unit will prioritize the collection of data on winter clothing. If the user lives in an urban area, the suggestion unit can also prioritize the collection of data on business casual attire. Furthermore, if the user lives by the sea, the suggestion unit can prioritize the collection of data on resort wear. In this way, by considering the user's geographical location, highly relevant data can be prioritized. 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 the user's geographical location data into a generating AI and have the generating AI prioritize highly relevant data.

[0066] The proposal unit can analyze a user's social media activity and collect relevant data. For example, it can collect fashion items that a user has "liked" on social media. The proposal unit can also collect data by referencing the styles of fashion influencers that the user follows. Furthermore, the proposal unit can analyze fashion-related photos posted by the user and collect relevant data. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0067] The suggestion unit can analyze the user's past selection history and select the optimal suggestion method. For example, it can prioritize suggesting clothing that the user has frequently selected in the past. The suggestion unit can also suggest clothing suitable for a specific season or event based on the user's past selection history. Furthermore, the suggestion unit can analyze the user's past selection history and select the most efficient suggestion method. In this way, the optimal suggestion method can be selected by analyzing the user's past selection history. 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 the user's past selection history data into a generating AI and have the generating AI select the optimal suggestion method.

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

[0069] Step 1: The data collection unit collects data on the user's clothing. This data includes the type, color, size, and brand of the clothing. The data collection unit takes pictures of the user's clothing using the smartphone camera and registers the data in the database. Users can also manually input information about the clothes they own. Furthermore, the data collection unit can automatically add information to the database when the user purchases new clothing. Step 2: The reception desk receives information about TPO and hairstyle. TPO includes time, place, occasion (event), etc., and hairstyle information includes hair length, color, style, etc. The reception desk provides an interface for users to input information about the day's TPO and hairstyle, and can also automatically suggest TPO and hairstyle information for the day based on previously entered information. Step 3: The analysis department analyzes trending fashion data and online fashion information. Trending fashion data includes data from fashion magazines and online shops, while online fashion information includes blogs, social media, and fashion websites. The analysis department uses AI to collect and analyze this data. Step 4: The suggestion department proposes the most suitable outfit. Using AI, the suggestion department selects and proposes the most suitable outfit based on the user's clothing data, information on occasion and hairstyle, trending fashion data, and online fashion information. For example, if the user inputs "business casual" and "short hair," the suggestion department will propose an outfit suitable for business casual. Step 5: The simulation unit simulates the proposed outfits. The simulation unit uses AI to simulate how the proposed outfit combinations would look with the user's existing wardrobe. Technologies such as 3D modeling and virtual try-on are used to simulate the proposed outfits.

[0070] (Example of form 2) The AI ​​fashion coordinator system according to an embodiment of the present invention is a system designed to reduce the time and stress spent choosing clothes for daily commutes and private use. The AI ​​fashion coordinator system takes photos of the clothes the user owns with their smartphone camera, and the AI ​​suggests an outfit that suits the "occasion for the day" and "hairstyle." This suggestion is based on "trendy fashion data" and "fashion information from the internet." For example, the AI ​​fashion coordinator system takes photos of the clothes the user owns with their smartphone camera and registers them in a database. Next, the user inputs information about the occasion and hairstyle for the day. Based on this information, the AI ​​refers to trendy fashion data and fashion information from the internet to suggest the most suitable outfit. For example, if the user inputs "business casual" and "short hair," the AI ​​selects and suggests an outfit from the clothes the user owns that is suitable for business casual. This suggestion is displayed on the user's smartphone, and the user can check the suggested outfit. This mechanism allows the user to reduce the time and stress spent choosing clothes every morning. Furthermore, because the AI ​​refers to trendy fashion data and fashion information from the internet, it can always suggest outfits that are in line with the latest trends. This allows the AI ​​fashion coordinator system to collect data on the user's clothing, suggest the most suitable outfit based on information such as the occasion and hairstyle, and perform simulations.

[0071] The AI ​​fashion coordinator system according to this embodiment comprises a data collection unit, a reception unit, an analysis unit, a proposal unit, and a simulation unit. The data collection unit collects data on the user's clothing. This data includes, but is not limited to, the type, color, size, and brand of the clothing. The data collection unit, for example, takes a picture of the user's clothing using a smartphone camera and registers the data in a database. The data collection unit can also manually input information about the clothing the user owns. For example, the data collection unit can input the type and color of the clothing the user owns by selecting from a list of options. Furthermore, the data collection unit has a function to automatically update the user's clothing data. For example, the data collection unit can automatically add information to the database when the user purchases new clothing. The reception unit receives information on TPO and hairstyle. TPO includes, but is not limited to, the time, place, occasion (event). Hairstyle information includes, but is not limited to, the length, color, and style of the hair. The reception unit provides, for example, an interface for the user to input information on the TPO and hairstyle for the day. The reception department can also refer to information about TPO (Time, Place, Occasion) and hairstyles that the user has entered in the past. For example, the reception department can automatically suggest TPO and hairstyle information for the day based on information the user has entered in the past. The analysis department analyzes trending fashion data and fashion information from the internet. Trending fashion data includes, but is not limited to, fashion magazines and online shop data. Fashion information from the internet includes, but is not limited to, blogs, social media, and fashion websites. The analysis department uses, for example, AI to collect and analyze trending fashion data and fashion information from the internet. The suggestion department suggests the most suitable outfit. The suggestion department uses, for example, AI to select and suggest the most suitable outfit based on the user's clothing data, TPO and hairstyle information, trending fashion data, and fashion information from the internet. For example, if the user enters "business casual" and "short hair," the suggestion department will suggest an outfit suitable for business casual.The simulation unit performs simulations of the proposed outfits. For example, the simulation unit uses AI to simulate how the proposed outfit combinations would look among the clothes the user already owns. The simulation unit uses technologies such as 3D modeling and virtual try-on to simulate the proposed outfits. As a result, the AI ​​fashion coordinator system according to this embodiment can collect data on the user's clothes, propose the most suitable outfit based on information about the occasion and hairstyle, and perform simulations.

[0072] The data collection unit collects data on the user's clothing. This data includes, but is not limited to, the type, color, size, and brand of the clothing. For example, the data collection unit takes pictures of the user's clothing using a smartphone camera and registers the data in a database. Specifically, images taken with a smartphone camera are analyzed using image recognition technology, and information such as the type, color, size, and brand of the clothing is automatically extracted. This information is centrally managed for each user and stored in the database. The data collection unit can also manually input information about the user's clothing. For example, the data collection unit can input the type and color of the clothing the user owns by selecting from a list of options. Users can easily add and edit clothing information through the application interface. Furthermore, the data collection unit has a function to automatically update the user's clothing data. For example, when a user purchases new clothing, the data collection unit can automatically add that information to the database. This includes integration with online shopping sites and the ability to take pictures of purchase receipts to import information. This allows the data collection unit to always know the user's latest clothing information and provide accurate data.

[0073] The reception desk receives information about TPO (Time, Place, Occasion) and hairstyle. TPO includes, but is not limited to, time, place, and occasion (event). Hairstyle information includes, but is not limited to, hair length, color, and style. The reception desk provides, for example, an interface for users to input information about their TPO and hairstyle for the day. Users can easily input their schedule and hairstyle information for the day through the application. The reception desk can also refer to TPO and hairstyle information that users have previously entered. For example, the reception desk can automatically suggest TPO and hairstyle information for the day based on information that users have previously entered. This allows users to easily input information while referring to past data. Furthermore, the reception desk can learn the user's preferences and tendencies to provide more personalized suggestions. For example, it can learn the style a user prefers when attending a particular event or the clothing tendencies that match a particular hairstyle, and reflect this in future suggestions. This allows the reception desk to respond flexibly to user needs and provide a more satisfying service.

[0074] The analysis department analyzes trending fashion data and online fashion information. Trending fashion data includes, but is not limited to, data from fashion magazines and online shops. Online fashion information includes, but is not limited to, blogs, social media, and fashion websites. The analysis department uses AI, for example, to collect and analyze trending fashion data and online fashion information. Specifically, the AI ​​uses natural language processing technology to analyze articles in fashion magazines and social media posts to extract current trends and popular styles. It also uses image recognition technology to analyze images of products in online shops to grasp trending designs and color trends. This allows the analysis department to grasp the latest fashion trends in real time and provide them to users. Furthermore, the analysis department can also use historical data and statistical information to perform long-term trend analysis and predictions. For example, based on fashion data from the past few years, it can predict future trend changes and provide users with proactive information. This allows the analysis department to ensure that users always have access to the latest fashion information and can provide more appropriate coordination suggestions.

[0075] The suggestion department proposes the most suitable outfit. For example, using AI, the suggestion department selects and proposes the most suitable outfit based on the user's clothing data, information on occasion and hairstyle, trending fashion data, and online fashion information. Specifically, the AI ​​selects appropriate items from the user's clothing database and puts together an outfit based on the occasion and hairstyle information. For example, if the user inputs "business casual" and "short hair," the AI ​​will select jackets, pants, shirts, etc. that are suitable for business casual and propose a style that suits short hair. It can also refer to trending fashion data and online fashion information to propose outfits that incorporate the latest trends. This allows the suggestion department to ensure that users can always enjoy the latest fashion. Furthermore, the suggestion department can learn the user's preferences and past choices to provide more personalized suggestions. For example, it can learn the user's preferred styles and color tendencies in the past and make suggestions based on that. This allows the suggestion department to provide the most suitable outfit that matches the user's personality and preferences.

[0076] The simulation unit performs simulations of suggested outfits. For example, the simulation unit uses AI to simulate how suggested outfit combinations would look with the user's existing wardrobe. Specifically, it uses technologies such as 3D modeling and virtual try-on to simulate suggested outfits. Users can have their avatar try on the suggested outfits through the application. The avatar reflects the user's body shape and facial features, providing a realistic try-on experience. This allows users to check whether the suggested outfits suit them without actually trying on the clothes. Furthermore, the simulation unit can set different lighting conditions and backgrounds, simulating how the outfits would look in various situations. For example, it can check how the outfits would look in different environments, such as natural light outdoors or artificial lighting indoors. This allows users to choose the outfit that is best suited to a particular situation. In addition, the simulation unit can continuously improve the accuracy and realism of the simulations based on user feedback. This enables the simulation unit to provide users with a more realistic and satisfying try-on experience.

[0077] The suggestion function can propose the most suitable outfit based on the user's preferences and past selection history. For example, the suggestion function can suggest outfits considering the user's preferences. For instance, it can suggest outfits that match the user's preferences based on data of outfits the user has previously selected. Furthermore, the suggestion function can also suggest outfits considering the user's past selection history. For example, it can analyze the user's past outfit selection history and suggest outfits that match the user's preferences. This allows for more appropriate outfit suggestions by considering the user's preferences and past selection history.

[0078] The external information unit can suggest the most appropriate clothing based on the user's schedule and weather forecast. For example, the external information unit can suggest clothing considering the user's schedule. For instance, it can suggest clothing that fits the user's schedule based on data from the user's calendar app or schedule planner. Furthermore, the external information unit can also suggest clothing considering the weather forecast. For example, it can suggest clothing appropriate for the weather based on data from the Japan Meteorological Agency or information from weather forecast apps. This allows for more appropriate clothing suggestions by considering both the user's schedule and the weather forecast.

[0079] The simulation unit can simulate how suggested clothing combinations would look with the user's existing wardrobe. For example, the simulation unit can use 3D modeling technology to simulate suggested clothing combinations. For instance, it can display suggested clothing combinations as 3D models based on the user's existing clothing data. The simulation unit can also simulate suggested clothing using virtual try-on technology. For example, it can virtually try on suggested clothing using the user's avatar. This makes it easier for users to confirm how suggested clothing combinations will look by simulating their appearance.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of clothing data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect clothing data during times when the user is relaxed. For example, if the user is busy, the data collection unit can also collect clothing data during times when the user is calm. Furthermore, if the user is relaxed, the data collection unit can collect clothing data at that time to reduce the user's burden. In this way, the user's burden can be reduced by adjusting the timing of clothing data collection 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The collection unit can analyze the user's past clothing history and select the optimal collection method. For example, the collection unit can prioritize collecting clothes that the user has frequently worn in the past. The collection unit can also collect clothes suitable for a specific season or event from the user's past clothing history. Furthermore, the collection unit can analyze the user's past clothing history and select the most efficient collection method. This allows the optimal collection method to be selected by analyzing the user's past clothing history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past clothing history data into a generating AI and have the generating AI select the optimal collection method.

[0082] The data collection unit can filter the clothing data based on the user's current fashion trends and areas of interest. For example, the data collection unit can collect clothing data based on the fashion styles the user is currently interested in. The data collection unit can also prioritize the collection of relevant clothing data, taking into account the user's current fashion trends. Furthermore, the data collection unit can collect clothing data of specific brands or designs based on the user's areas of interest. This allows for the collection of highly relevant data by filtering based on the user's current fashion trends and areas of interest. 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 current fashion trend data into a generating AI and have the generating AI perform the filtering.

[0083] The data collection unit can estimate the user's emotions and prioritize the clothing data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data on relaxing clothing. If the user is having fun, the data collection unit may also prioritize collecting data on casual clothing. If the user is in a hurry, the data collection unit may also prioritize collecting data on easy-to-wear clothing. This reduces the user's burden by prioritizing the clothing data to collect based on their emotions. 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 data collection unit may be performed using AI or not. 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 prioritize the collection of highly relevant data by considering the user's geographical location when collecting clothing data. For example, if the user lives in a cold region, the data collection unit will prioritize the collection of data on winter clothing. If the user lives in an urban area, the data collection unit can also prioritize the collection of data on business casual clothing. Furthermore, if the user lives by the sea, the data collection unit can prioritize the collection of data on resort wear. This allows for the priority collection of highly relevant data 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 data into a generating AI and have the generating AI prioritize highly relevant data.

[0085] The data collection unit can analyze the user's social media activity and collect relevant data when collecting clothing data. For example, the data collection unit can collect fashion items that the user has "liked" on social media. The data collection unit can also collect data by referencing the styles of fashion influencers that the user follows. Furthermore, the data collection unit can analyze fashion-related photos posted by the user and collect relevant data. In this way, relevant data 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 perform the collection of relevant data.

[0086] The reception desk can estimate the user's emotions and adjust the method of receiving information about the occasion (TPO) and hairstyle based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, for example, the reception desk can provide detailed input options and suggest a customizable input method. Also, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of TPO and hairstyle information. This reduces the burden on the user by adjusting the method of receiving TPO and hairstyle information 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 reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The reception desk can select the optimal reception method by referring to the user's past information input history when receiving information about TPO (Time, Place, Occasion) and hairstyle. For example, the reception desk can automatically display TPO and hairstyle information that the user has frequently entered in the past as suggestions. For example, the reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest TPO and hairstyle information to be used during a specific time period based on the user's past information input history. In this way, the optimal reception method can be selected by referring to the user's past information input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past information input history data into a generating AI and have the generating AI select the optimal reception method.

[0088] The reception unit can filter information about appropriate occasions (TPO) and hairstyles based on the user's current lifestyle and areas of interest. For example, the reception unit can receive TPO and hairstyle information based on events or activities the user is currently interested in. The reception unit can also prioritize receiving relevant TPO and hairstyle information, taking into account the user's current lifestyle. Furthermore, the reception unit can receive specific TPO and hairstyle information based on the user's areas of interest. This allows for the reception of highly relevant information by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current lifestyle data into a generating AI and have the generating AI perform the filtering.

[0089] The reception desk can estimate the user's emotions and prioritize the information to be received based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize receiving important information. If the user is relaxed, the reception desk may also prioritize receiving detailed information. Furthermore, if the user is in a hurry, the reception desk may prioritize receiving information that can be entered quickly. This reduces the user's burden by prioritizing the information to be received 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 reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The reception desk can prioritize receiving information on appropriate occasions (TPO) and hairstyles, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize receiving information on TPO and hairstyles related to that region. If the user is traveling, the reception desk can also prioritize receiving information on TPO and hairstyles related to the travel destination. Furthermore, if the user is attending a specific event, the reception desk can prioritize receiving information on TPO and hairstyles related to that event. This allows for the priority of receiving information on appropriate occasions by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI prioritize the most relevant information.

[0091] The reception desk can analyze the user's social media activity and receive relevant information when receiving information about TPO (Time, Place, Occasion) and hairstyle. For example, the reception desk can receive information about TPO and hairstyle related to events and activities that the user has "liked" on social media. The reception desk can also receive information by referencing the styles of influencers that the user follows. Furthermore, the reception desk can analyze photos and comments posted by the user and receive relevant information about TPO and hairstyle. In this way, relevant information can be received by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI perform the reception of relevant information.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis method of trendy fashion data and internet fashion information based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can analyze detailed fashion data and reflect it in its suggestions. If the user is in a hurry, the analysis unit can also analyze concise fashion data and make suggestions quickly. Furthermore, if the user is excited, the analysis unit can analyze visually stimulating fashion data and reflect it in its suggestions. By adjusting the analysis method based on the user's emotions, more appropriate analysis results can be obtained. 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 the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The analysis unit can adjust the level of detail in its analysis based on the importance of the fashion data. For example, the analysis unit can analyze important fashion data in detail and reflect it in its recommendations. Alternatively, it can analyze general fashion data concisely and reflect it in its recommendations. Furthermore, the analysis unit can focus its analysis on fashion data related to specific events or seasons and reflect it in its recommendations. By adjusting the level of detail in the analysis based on the importance of the fashion data, more appropriate analysis results can be obtained. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the fashion data into a generating AI and have the generating AI adjust the level of detail in the analysis.

[0094] The analysis unit can apply different analysis algorithms depending on the category of fashion data during analysis. For example, the analysis unit can apply a casual fashion analysis algorithm to casual fashion data. The analysis unit can also apply a business fashion analysis algorithm to business fashion data. Furthermore, the analysis unit can apply a sports fashion analysis algorithm to sports fashion data. By applying the most appropriate analysis algorithm according to the category of fashion data, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of fashion data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0095] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results based on the user's emotions, a user-friendly display can be achieved. 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 without 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 department can prioritize analysis based on the submission timing of fashion data. For example, the analysis department can prioritize the analysis of the latest fashion data and reflect it in its recommendations. The analysis department can also prioritize the analysis of the latest data while referring to past fashion data. Furthermore, the analysis department can prioritize the analysis of fashion data related to a specific season or event. This allows for analysis that emphasizes the latest data by prioritizing analysis based on the submission timing of fashion data. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the submission timing of fashion data into a generating AI and have the generating AI determine the analysis priorities.

[0097] The analysis unit can adjust the order of analysis based on the relevance of fashion data during the analysis process. For example, the analysis unit may prioritize analyzing fashion data related to the user's current interests. The analysis unit may also prioritize analyzing highly relevant fashion data based on the user's past selection history. Furthermore, the analysis unit may prioritize analyzing fashion data related to specific events or seasons. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of fashion data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of fashion data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0098] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions and offer many options. If the user is in a hurry, the suggestion unit can also provide concise and to-the-point suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually appealing suggestions. By adjusting the way suggestions are presented based on the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. 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 processing described above 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.

[0099] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the clothing. For example, it might provide detailed clothing suggestions for important events, while keeping everyday clothing suggestions concise. It might also focus on clothing suggestions related to specific seasons or events. By adjusting the level of detail based on the importance of the clothing, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the importance of the clothing into a generating AI and have the generating AI adjust the level of detail in the suggestions.

[0100] The suggestion unit can apply different suggestion algorithms depending on the clothing category when making suggestions. For example, the suggestion unit can apply a casual suggestion algorithm to casual clothing. For example, the suggestion unit can also apply a business suggestion algorithm to business attire. Furthermore, the suggestion unit can apply a sports suggestion algorithm to sports attire. By applying different suggestion algorithms depending on the clothing category, more appropriate suggestions can be made. 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 the clothing category into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0101] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide a short, concise suggestion. If the user is relaxed, the suggestion unit may provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit may provide a visually stimulating suggestion. By adjusting the length of the suggestion based on the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above 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.

[0102] The proposal department can prioritize proposals based on the timing of clothing submissions. For example, the proposal department may prioritize the most recent clothing data. Alternatively, it may prioritize the latest data while referencing past clothing data. It may also prioritize clothing data related to specific seasons or events. By prioritizing proposals based on the timing of clothing submissions, it becomes possible to make proposals that prioritize the latest data. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input the timing of clothing submissions into a generating AI and have the generating AI determine the priority of proposals.

[0103] The suggestion unit can adjust the order of suggestions based on the relevance of the clothing items. For example, the suggestion unit may prioritize suggesting clothing data related to the user's current interests. The suggestion unit may also prioritize suggesting highly relevant clothing data based on the user's past selection history. Furthermore, the suggestion unit may prioritize suggesting clothing data related to specific events or seasons. This allows for the prioritization of highly relevant data 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 the relevance of the clothing items into a generating AI and have the generating AI adjust the order of suggestions.

[0104] The simulation unit can estimate the user's emotions and adjust the display method of the simulation based on the estimated user emotions. For example, if the user is nervous, the simulation unit can provide a simple and highly visible display method. For example, if the user is relaxed, the simulation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the simulation unit can provide a display method that gets straight to the point. By adjusting the display method of the simulation based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. 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 simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0105] The simulation unit can adjust the level of detail of the simulation based on the importance of clothing during the simulation. For example, the simulation unit will perform detailed clothing simulations for important events. For example, it can perform simpler clothing simulations for everyday wear. It can also focus on clothing simulations related to specific seasons or events. By adjusting the level of detail of the simulation based on the importance of clothing, a more appropriate simulation becomes possible. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the importance of clothing into a generating AI and have the generating AI perform the adjustment of the level of detail of the simulation.

[0106] The simulation unit can apply different simulation algorithms depending on the clothing category during the simulation. For example, the simulation unit can apply a simulation algorithm for casual wear to casual wear. The simulation unit can also apply a simulation algorithm for business wear to business wear. Furthermore, the simulation unit can apply a simulation algorithm for sports wear to sports wear. By applying different simulation algorithms depending on the clothing category, a more appropriate simulation becomes possible. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the clothing category into a generating AI and have the generating AI execute the application of different simulation algorithms.

[0107] The simulation unit can estimate the user's emotions and adjust the display order of the simulation results based on the estimated emotions. For example, if the user is tense, the simulation unit provides a simple and highly visible display order. If the user is relaxed, the simulation unit can also provide a display order that includes detailed information. Furthermore, if the user is in a hurry, the simulation unit can provide a concise display order. By adjusting the display order of the simulation results based on the user's emotions, a user-friendly display is made possible. Emotion estimation is achieved using an emotion estimation function, for example, with 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 simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0108] The simulation unit can determine the priority of simulations based on the timing of clothing submissions. For example, the simulation unit may prioritize the simulation of the most recent clothing data. The simulation unit may also prioritize the simulation of the most recent data while referring to past clothing data. Furthermore, the simulation unit may prioritize the simulation of clothing data related to a specific season or event. This allows for simulations that prioritize the most recent data by determining the priority of simulations based on the timing of clothing submissions. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit may input the timing of clothing submissions into a generating AI and have the generating AI determine the priority of simulations.

[0109] The simulation unit can adjust the simulation order based on the relevance of clothing during the simulation. For example, the simulation unit may prioritize simulating clothing data related to the user's current interests. The simulation unit may also prioritize simulating clothing data that is highly relevant based on the user's past selection history. Furthermore, the simulation unit may prioritize simulating clothing data related to specific events or seasons. This allows for prioritizing the simulation of highly relevant data by adjusting the simulation order based on the relevance of clothing. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the relevance of clothing into a generating AI and have the generating AI perform the adjustment of the simulation order.

[0110] The external information unit can estimate the user's emotions and adjust the method of collecting external information based on the estimated user emotions. For example, if the user is relaxed, the external information unit can collect detailed external information and reflect it in its suggestions. If the user is in a hurry, the external information unit can also collect concise external information and make suggestions quickly. Furthermore, if the user is excited, the external information unit can collect visually stimulating external information and reflect it in its suggestions. This allows for the collection of more appropriate external information by adjusting the method of collecting external information 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 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 external information unit may be performed using AI or not using AI. For example, the external information unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0111] The External Information Unit can adjust the level of detail collected based on the importance of the information when gathering external information. For example, the External Information Unit can collect important external information in detail and reflect it in proposals. The External Information Unit can also collect general external information concisely and reflect it in proposals. Furthermore, the External Information Unit can focus on collecting external information related to specific events or seasons and reflect it in proposals. This allows for the collection of more appropriate external information by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the External Information Unit may be performed using AI, for example, or without AI. For example, the External Information Unit can input the importance of the information into a generating AI and have the generating AI perform the adjustment of the level of detail of the collection.

[0112] The external information unit can apply different collection algorithms depending on the category of information when collecting external information. For example, the external information unit can apply a fashion-specific collection algorithm to fashion information. For example, it can also apply a weather-specific collection algorithm to weather information. Furthermore, the external information unit can apply an event-specific collection algorithm to event information. By applying different collection algorithms depending on the category of information, more appropriate external information can be collected. Some or all of the above processing in the external information unit may be performed using AI, for example, or without AI. For example, the external information unit can input the category of information into a generating AI and have the generating AI execute the application of different collection algorithms.

[0113] The external information unit can estimate the user's emotions and determine the priority of external information to collect based on the estimated user emotions. For example, if the user is stressed, the external information unit may prioritize collecting information that helps them relax. For example, if the user is having fun, the external information unit may prioritize collecting entertainment information. Also, if the user is in a hurry, the external information unit may prioritize collecting information that can be collected quickly. This allows for the collection of more appropriate external information by prioritizing the information to collect 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 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 external information unit may be performed using AI or not using AI. For example, the external information unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0114] The External Information Department can determine the priority of information collection based on the timing of information submission when collecting external information. For example, the External Information Department can prioritize the collection of the latest external information and reflect it in proposals. The External Information Department can also prioritize the collection of the latest information while referring to past external information. Furthermore, the External Information Department can prioritize the collection of external information related to a specific season or event. This makes it possible to prioritize the collection of the latest information by determining the priority of information collection based on the timing of information submission. Some or all of the above processing in the External Information Department may be performed using AI, for example, or not using AI. For example, the External Information Department can input the timing of information submission into a generating AI and have the generating AI perform the determination of the collection priority.

[0115] The external information unit can adjust the order of collection based on the relevance of the information when collecting external information. For example, the external information unit can prioritize collecting external information related to the user's current interests. The external information unit can also prioritize collecting highly relevant external information based on the user's past selection history. Furthermore, the external information unit can prioritize collecting external information related to specific events or seasons. This allows for the priority collection of highly relevant information by adjusting the order of collection based on the relevance of the information. Some or all of the above processing in the external information unit may be performed using AI, for example, or without AI. For example, the external information unit can input the relevance of the information into a generating AI and have the generating AI perform the adjustment of the collection order.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can suggest clothing during a time when the user is relaxed. If the user is busy, the suggestion unit can also suggest clothing during a time when the user is calm. Furthermore, if the user is relaxed, the suggestion unit can suggest clothing at that time to reduce the user's burden. In this way, the user's burden can be reduced by adjusting the timing of suggestions 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 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.

[0118] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can suggest relaxing clothing. If the user is having fun, the suggestion unit can also suggest casual clothing. If the user is in a hurry, the suggestion unit can also suggest clothing that is easy to put on. By adjusting the content of suggestions based on the user's emotions, the burden on the user can be reduced. 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.

[0119] The suggestion unit can estimate the user's emotions and adjust the frequency of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can reduce the frequency of suggestions, and if the user is relaxed, it can increase the frequency. Similarly, if the user is in a hurry, the suggestion unit can reduce the frequency of suggestions, and if the user is enjoying themselves, it can increase the frequency. This reduces the user's burden by adjusting the frequency of suggestions based on their emotions. 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 using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0120] The suggestion unit can estimate the user's emotions and adjust the format of its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide suggestions in a simple format; if the user is relaxed, it can provide suggestions in a detailed format. Similarly, if the user is in a hurry, the suggestion unit can provide suggestions in a concise format; if the user is enjoying themselves, it can provide suggestions in a visually appealing format. This reduces the user's burden by adjusting the suggestion format based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the 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.

[0121] The suggestion unit can estimate the user's emotions and adjust the order of suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can prioritize important suggestions; if the user is relaxed, it can prioritize detailed suggestions. Similarly, if the user is in a hurry, it can prioritize concise suggestions; and if the user is enjoying themselves, it can prioritize visually appealing suggestions. This reduces the user's burden by adjusting the order of suggestions based on their emotions. 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.

[0122] The suggestion unit can analyze the user's past selection history and select the optimal suggestion method. For example, it can prioritize suggesting clothing that the user has frequently selected in the past. The suggestion unit can also suggest clothing suitable for a specific season or event based on the user's past selection history. Furthermore, the suggestion unit can analyze the user's past selection history and select the most efficient suggestion method. In this way, the optimal suggestion method can be selected by analyzing the user's past selection history. 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 the user's past selection history data into a generating AI and have the generating AI select the optimal suggestion method.

[0123] The proposed function can filter clothing data based on the user's current fashion trends and areas of interest. For example, it can collect clothing data based on the fashion styles the user is currently interested in. The proposed function can also prioritize the collection of relevant clothing data, taking into account the user's current fashion trends. Furthermore, the proposed function can collect clothing data for specific brands or designs based on the user's areas of interest. This allows for the collection of highly relevant data by filtering based on the user's current fashion trends and areas of interest. Some or all of the above processing in the proposed function may be performed using AI, for example, or without AI. For example, the proposed function can input the user's current fashion trend data into a generating AI and have the generating AI perform the filtering.

[0124] The suggestion unit can prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user lives in a cold region, the suggestion unit will prioritize the collection of data on winter clothing. If the user lives in an urban area, the suggestion unit can also prioritize the collection of data on business casual attire. Furthermore, if the user lives by the sea, the suggestion unit can prioritize the collection of data on resort wear. In this way, by considering the user's geographical location, highly relevant data can be prioritized. 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 the user's geographical location data into a generating AI and have the generating AI prioritize highly relevant data.

[0125] The proposal unit can analyze a user's social media activity and collect relevant data. For example, it can collect fashion items that a user has "liked" on social media. The proposal unit can also collect data by referencing the styles of fashion influencers that the user follows. Furthermore, the proposal unit can analyze fashion-related photos posted by the user and collect relevant data. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0126] The suggestion unit can analyze the user's past selection history and select the optimal suggestion method. For example, it can prioritize suggesting clothing that the user has frequently selected in the past. The suggestion unit can also suggest clothing suitable for a specific season or event based on the user's past selection history. Furthermore, the suggestion unit can analyze the user's past selection history and select the most efficient suggestion method. In this way, the optimal suggestion method can be selected by analyzing the user's past selection history. 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 the user's past selection history data into a generating AI and have the generating AI select the optimal suggestion method.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The data collection unit collects data on the user's clothing. This data includes the type, color, size, and brand of the clothing. The data collection unit takes pictures of the user's clothing using the smartphone camera and registers the data in the database. Users can also manually input information about the clothes they own. Furthermore, the data collection unit can automatically add information to the database when the user purchases new clothing. Step 2: The reception desk receives information about TPO and hairstyle. TPO includes time, place, occasion (event), etc., and hairstyle information includes hair length, color, style, etc. The reception desk provides an interface for users to input information about the day's TPO and hairstyle, and can also automatically suggest TPO and hairstyle information for the day based on previously entered information. Step 3: The analysis department analyzes trending fashion data and online fashion information. Trending fashion data includes data from fashion magazines and online shops, while online fashion information includes blogs, social media, and fashion websites. The analysis department uses AI to collect and analyze this data. Step 4: The suggestion department proposes the most suitable outfit. Using AI, the suggestion department selects and proposes the most suitable outfit based on the user's clothing data, information on occasion and hairstyle, trending fashion data, and online fashion information. For example, if the user inputs "business casual" and "short hair," the suggestion department will propose an outfit suitable for business casual. Step 5: The simulation unit simulates the proposed outfits. The simulation unit uses AI to simulate how the proposed outfit combinations would look with the user's existing wardrobe. Technologies such as 3D modeling and virtual try-on are used to simulate the proposed outfits.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, reception unit, analysis unit, proposal unit, simulation unit, and external information unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart device 14 to photograph the user's clothes and registers the data in the database 24 of the data processing unit 12. The reception unit uses the touch panel 38A of the smart device 14 to provide an interface for the user to input information about the day's occasion (TPO) and hairstyle. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to collect and analyze trendy fashion data and internet fashion information. The proposal unit uses the specific processing unit 290 of the data processing unit 12 to select and propose the most suitable outfit based on the user's clothing data, TPO and hairstyle information, trendy fashion data, and internet fashion information. The simulation unit uses the specific processing unit 290 of the data processing unit 12 to simulate the proposed outfit. The external information unit uses the specific processing unit 290 of the data processing unit 12 to propose an outfit considering the user's schedule and weather forecast. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, reception unit, analysis unit, proposal unit, simulation unit, and external information unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart glasses 214 to photograph the user's clothes and registers the data in the database 24 of the data processing unit 12. The reception unit uses the microphone 238 of the smart glasses 214 to provide an interface for the user to input information about the day's occasion (TPO) and hairstyle. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to collect and analyze trendy fashion data and internet fashion information. The proposal unit uses the specific processing unit 290 of the data processing unit 12 to select and propose the most suitable outfit based on the user's clothing data, TPO and hairstyle information, trendy fashion data, and internet fashion information. The simulation unit uses the specific processing unit 290 of the data processing unit 12 to simulate the outfit proposed. The external information unit uses the specific processing unit 290 of the data processing unit 12 to propose an outfit considering the user's schedule and weather forecast. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the collection unit, reception unit, analysis unit, proposal unit, simulation unit, and external information unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 of the headset terminal 314 to photograph the user's clothing and registers the data in the database 24 of the data processing unit 12. The reception unit uses the microphone 238 of the headset terminal 314 to provide an interface for the user to input information about the day's occasion (TPO) and hairstyle. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to collect and analyze trendy fashion data and internet fashion information. The proposal unit uses the specific processing unit 290 of the data processing unit 12 to select and propose the most suitable outfit based on the user's clothing data, TPO and hairstyle information, trendy fashion data, and internet fashion information. The simulation unit uses the specific processing unit 290 of the data processing unit 12 to simulate the proposed outfit. The external information unit uses the specific processing unit 290 of the data processing unit 12 to propose an outfit considering the user's schedule and weather forecast. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the collection unit, reception unit, analysis unit, proposal unit, simulation unit, and external information unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 of the robot 414 to photograph the user's clothes and registers the data in the database 24 of the data processing unit 12. The reception unit uses the microphone 238 of the robot 414 to provide an interface for the user to input information about the day's occasion (TPO) and hairstyle. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to collect and analyze trendy fashion data and internet fashion information. The proposal unit uses the specific processing unit 290 of the data processing unit 12 to select and propose the most suitable outfit based on the user's clothing data, TPO and hairstyle information, trendy fashion data, and internet fashion information. The simulation unit uses the specific processing unit 290 of the data processing unit 12 to simulate the outfit proposed by the specific processing unit 290. The external information unit uses the specific processing unit 290 of the data processing unit 12 to propose an outfit considering the user's schedule and weather forecast. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) A data collection unit that collects data on the user's clothing, A reception desk that accepts information regarding TPO (Time, Place, Occasion) and hairstyles, The analysis department analyzes trending fashion data and online fashion information, The proposal department suggests appropriate attire, It comprises a simulation unit that performs a simulation of the proposed clothing. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Based on the user's preferences and past selection history, it suggests appropriate clothing. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an external information unit based on the user's schedule and weather forecast. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned simulation unit, This simulates how the suggested outfit combinations would look with the user's existing wardrobe. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of clothing data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) 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 7) The aforementioned collection unit is When collecting clothing data, filtering is performed based on the user's current fashion trends and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the clothing data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting clothing data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting clothing data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system estimates the user's emotions and adjusts how information about the occasion (TPO) and hairstyle is received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving information about the occasion (TPO) and hairstyle, the system selects the most appropriate method of processing by referring to the user's past information input history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving information about TPO (Time, Place, Occasion) and hairstyles, the system filters the information based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is When receiving information about TPO (Time, Place, Occasion) and hairstyle, the system prioritizes receiving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reception unit is When receiving information about TPO (Time, Place, Occasion) and hairstyles, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is We estimate user sentiment and adjust the analysis methods for trending fashion data and internet fashion information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the fashion data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of fashion data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is During the analysis, we prioritize the analysis based on when the fashion data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the fashion data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) 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 25) 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 26) 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 27) The aforementioned proposal section is, When submitting proposals, prioritize them based on the submission deadline for clothing-related submissions. The system described in Appendix 1, characterized by the features described herein. (Note 28) 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 29) The aforementioned simulation unit, It estimates the user's emotions and adjusts how the simulation is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned simulation unit, During the simulation, adjust the level of detail based on the importance of clothing. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned simulation unit, During the simulation, different simulation algorithms are applied depending on the clothing category. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned simulation unit, It estimates the user's emotions and adjusts the display order of the simulation results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned simulation unit, During the simulation, the simulation priority is determined based on the timing of clothing submissions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned simulation unit, During the simulation, the order of the simulations is adjusted based on the relevance of the clothing. The system described in Appendix 1, characterized by the features described herein. (Note 35) The External Information Department, It estimates the user's emotions and adjusts how external information is collected based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The External Information Department, When collecting external information, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned external information unit is, When collecting external information, different collection algorithms are applied depending on the category of the information. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned external information unit is, It estimates the user's emotions and determines the priority of external information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned external information unit is, When collecting external information, prioritize the collection based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned external information unit is, When collecting external information, adjust the order of collection based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data on the user's clothing, A reception desk that accepts information regarding TPO (Time, Place, Occasion) and hairstyles, The analysis department analyzes trending fashion data and online fashion information, The proposal department suggests appropriate attire, It comprises a simulation unit that performs a simulation of the proposed clothing. A system characterized by the following features.

2. The aforementioned proposal section is, Based on the user's preferences and past selection history, it suggests appropriate clothing. The system according to feature 1.

3. It includes an external information unit based on the user's schedule and weather forecast. The system according to feature 1.

4. The aforementioned simulation unit, This simulates how the suggested outfit combinations would look with the user's existing wardrobe. The system according to feature 1.

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

6. The aforementioned collection unit is Analyze the user's past clothing history and select the optimal data collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting clothing data, filtering is performed based on the user's current fashion trends and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the clothing data to collect based on those estimated emotions. The system according to feature 1.

Images