system

Abstract

The system according to this embodiment aims to analyze the user's style and suggest clothing suitable for the event. [Solution] The system according to the embodiment comprises a scanning unit, a learning unit, and a suggestion unit. The scanning unit scans the user's clothes to create a digital closet. The learning unit learns the user's history and trends based on the digital closet created by the scanning unit and suggests personalized styles. The suggestion unit suggests outfits in real time that are appropriate for the weather and events, based on the styles suggested by the learning unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, 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 was a problem that it took time to choose daily clothing and one lacked confidence in one's sense.

[0005] The system according to the embodiment aims to analyze the user's style and propose clothing suitable for an event.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a scanning unit, a learning unit, and a suggestion unit. The scanning unit scans the user's clothes to create a digital closet. The learning unit learns the user's history and trends based on the digital closet created by the scanning unit and suggests personalized styles. The suggestion unit suggests outfits in real time that are appropriate for the weather and events, based on the styles suggested by the learning unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's style and suggest clothing suitable for the event. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F 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).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Smart Coordination AI Agent according to an embodiment of the present invention is a system that analyzes an individual's style data and proposes a style suitable for an event. The Smart Coordination AI Agent scans the user's existing clothes and creates a digital closet. Next, the Smart Coordination AI Agent learns the user's history and trends and proposes a personalized style. Furthermore, the Smart Coordination AI Agent proposes clothing in real time that is appropriate for the weather and the event. This mechanism can shorten the time spent choosing clothes and improve style. For example, the Smart Coordination AI Agent scans the user's existing clothes and creates a digital closet. At this time, information such as the type, color, and material of each garment is digitized and registered in a database. For example, information on shirts, pants, jackets, etc. that the user owns is scanned and registered in the digital closet. Next, the Smart Coordination AI Agent learns the user's history and trends and proposes a personalized style. The Smart Coordination AI Agent learns the outfits the user has chosen in the past and current fashion trends. For example, it analyzes data on outfits the user has chosen in the past to understand the user's preferences and style. It also learns current fashion trends and proposes the most suitable style for the user. Furthermore, the Smart Coordination AI Agent suggests outfits in real time, tailored to the weather and events. It acquires current weather information and information about events the user plans to attend, and then suggests the optimal outfit based on that information. For example, it might suggest a waterproof jacket on a rainy day or a formal suit for a business meeting. This system allows users to reduce the time spent choosing clothes daily and improve their style. It is designed for a wide range of users, particularly busy business professionals, trend-conscious young people, stylists, and fashion industry professionals. It also enables rapid outfit suggestions for stylists and streamlines inventory management through a digital closet.This allows the smart coordination AI agent to reduce the time users spend choosing clothes and improve their style.

[0029] The smart coordination AI agent according to this embodiment comprises a scanning unit, a learning unit, and a suggestion unit. The scanning unit scans the user's clothes to create a digital closet. For example, the scanning unit scans the user's clothes with a scanner and saves them as digital data. The scanning unit digitizes information such as the type, color, and material of each garment and registers it in a database. For example, the scanning unit scans information on shirts, pants, jackets, etc., and registers it in the digital closet. The learning unit learns the user's history and trends and suggests personalized styles. For example, the learning unit analyzes data on outfits the user has previously chosen to understand the user's preferences and style. The learning unit learns current fashion trends and suggests the most suitable style for the user. For example, the learning unit collects data from fashion magazines and social media to learn trends. The suggestion unit suggests outfits in real time that are appropriate for the weather and events, based on the styles suggested by the learning unit. For example, the suggestion unit obtains current weather information and information on events the user plans to attend, and suggests the most suitable outfit based on that information. The suggestion department would suggest a waterproof jacket for rainy days and a formal suit for business meetings. This allows the smart coordination AI agent, according to the embodiment, to reduce the user's time spent choosing clothes and improve their style.

[0030] The scanning unit creates a digital closet by scanning the user's existing clothing. Specifically, the scanning unit uses a high-resolution camera or a dedicated scanner to scan the user's clothing in detail. The scanned data includes detailed information such as the shape, color, material, brand, and size of the clothing. This information is analyzed using image processing technology and stored as digital data. The scanning unit digitizes information such as the type, color, and material of each garment and registers it in a database. For example, it scans shirts, pants, jackets, dresses, and skirts, and registers detailed attribute information for each item in the database. Furthermore, the scanning unit also records information such as the condition of the clothing and how often it is used, allowing it to understand which clothes the user uses and to what extent. As a result, the scanning unit can accurately digitize the user's existing clothing and create a digital closet.

[0031] The learning unit learns the user's history and trends to suggest personalized styles. Specifically, the learning unit collects data on outfits the user has previously chosen and analyzes the user's preferences and style using machine learning algorithms. For example, if a user tends to prefer certain colors or brands, the learning unit uses this information to understand the user's style. In addition, to learn current fashion trends, the learning unit collects data from fashion magazines and social media and analyzes trend information using natural language processing technology. This allows the learning unit to combine the user's preferences with the latest trends to suggest personalized styles. Furthermore, the learning unit collects user feedback to continuously improve the accuracy of its suggestions. For example, by having users evaluate the suggested outfits, the learning unit adjusts its algorithm based on that evaluation to make suggestions that are more suitable for the user. In this way, the learning unit can suggest personalized styles based on the user's preferences and trends, increasing user satisfaction.

[0032] The suggestion department provides real-time clothing recommendations based on the style suggested by the learning department, taking into account the weather and the event. Specifically, the suggestion department acquires current weather information and information about events the user plans to attend, and then suggests the optimal outfit based on that information. For example, the suggestion department acquires weather forecast data and suggests waterproof jackets and boots on rainy days, and breathable, lightweight clothing on hot days. Also, if the user is attending a business meeting, it suggests a formal suit or dress, and for a casual event, it suggests a relaxed style. The suggestion department can suggest appropriate clothing in real time according to the user's schedule and the type of event. Furthermore, the suggestion department collects user feedback to improve the accuracy of its suggestions. For example, by having the user evaluate the suggested outfits, the suggestion department can adjust its algorithm based on that evaluation to provide more user-friendly suggestions. As a result, the suggestion department can suggest the optimal outfit in real time according to the user's needs, shorten the time the user spends choosing clothes, and improve their style.

[0033] The Rapid Proposal Department provides stylists with quick outfit suggestions. For example, the Rapid Proposal Department allows stylists to quickly propose outfits in response to client requests. The Rapid Proposal Department uses AI to suggest the optimal outfit tailored to the client's preferences and the event. For example, the Rapid Proposal Department analyzes the client's past outfit history and quickly provides the best suggestion. The Rapid Proposal Department can also adjust the timing of suggestions to match the client's schedule. This enables the Rapid Proposal Department to provide stylists with quick outfit suggestions.

[0034] The Inventory Management Department manages inventory using a digital closet. For example, the Inventory Management Department registers and manages the inventory of users' clothing in the digital closet. The Inventory Management Department digitizes information such as the type, color, and material of each garment and registers it in a database. For example, the Inventory Management Department scans information on shirts, pants, jackets, etc., and registers it in the digital closet. The Inventory Management Department can perform appropriate management according to the frequency of inventory updates and the types of inventory. This allows the Inventory Management Department to streamline inventory management using the digital closet.

[0035] The scanning unit digitizes information such as the type, color, and material of each garment and registers it in a database. For example, the scanning unit scans the user's clothes with a scanner and saves them as digital data. The scanning unit digitizes information such as the type, color, and material of each garment and registers it in a database. For example, the scanning unit scans information on shirts, pants, jackets, etc., and registers it in the digital closet. By digitizing the information of each garment and registering it in the database, the scanning unit can create a digital closet.

[0036] The learning unit learns about the user's past outfit choices and current fashion trends. For example, the learning unit analyzes data on the user's past outfit choices to understand the user's preferences and style. The learning unit learns about current fashion trends and suggests the most suitable style for the user. For example, the learning unit collects data from fashion magazines and social media to learn about trends. By learning about the user's past outfits and current fashion trends, the learning unit can suggest a personalized style.

[0037] The suggestion department acquires current weather information and information about events the user plans to attend, and then proposes the most suitable outfit based on that information. For example, the suggestion department might suggest a waterproof jacket for rainy days or a formal suit for business meetings. By suggesting the most suitable outfit based on current weather and event information, the suggestion department can support users in choosing their clothes.

[0038] The scanning unit prioritizes scanning high-priority clothing, taking into account how often it is worn. For example, it prioritizes scanning clothing that the user frequently wears and registers it in the digital closet. The scanning unit can also prioritize scanning clothing that is frequently worn each season, allowing for timely suggestions. It can also prioritize scanning clothing used for specific events or occasions, providing event-specific suggestions. In this way, the scanning unit can prioritize the management of important clothing by scanning based on its usage frequency.

[0039] The scanning unit detects the degree of deterioration of clothing during scanning and identifies clothing that is significantly deteriorated. For example, the scanning unit can detect fading and stains on clothing to identify clothing that is significantly deteriorated. The scanning unit can also detect wear and tear on the fabric of clothing to identify clothing that needs repair. The scanning unit can also detect changes in the shape of clothing to identify clothing that needs to be replaced. In this way, by detecting the degree of deterioration of clothing, the scanning unit can identify clothing that is significantly deteriorated and manage it appropriately.

[0040] The scanning unit prioritizes scanning newer clothing items, taking into account when they were purchased. For example, it prioritizes scanning recently purchased clothing items and registering them in the digital closet. The scanning unit can also prioritize scanning newly purchased clothing items each season, allowing for timely suggestions. It can also prioritize scanning clothing items purchased for specific events or occasions, providing event-specific suggestions. This allows the scanning unit to prioritize the management of new clothing items by considering when they were purchased.

[0041] The scanning unit acquires brand information of clothing during scanning and classifies it by brand. For example, the scanning unit recognizes the brand logo of clothing during scanning and classifies it by brand. Based on the brand information, the scanning unit can also analyze the user's preferences and style and reflect this in its suggestions. The scanning unit can also consider the characteristics of clothing from each brand and suggest the optimal outfit. In this way, by acquiring brand information of clothing and classifying it by brand, the scanning unit makes it easier to analyze the user's preferences and style.

[0042] The learning unit analyzes the user's past outfit history during training to detect changes in preferences. For example, the learning unit analyzes data on outfits the user has previously chosen to detect changes in preferences. The learning unit can also detect seasonal changes in preferences from the user's past outfit history. Based on the user's past outfit history, the learning unit can also make suggestions that respond to changing trends. In this way, by analyzing the user's past outfit history, the learning unit can detect changes in preferences and suggest more appropriate styles.

[0043] The learning unit, during the learning process, proposes the optimal style while considering the user's changes in body shape. For example, the learning unit can detect changes in the user's body shape and propose a style that suits that body shape. Based on the user's changes in body shape, the learning unit can also propose clothing that fits well. The learning unit can also consider changes in the user's body shape and propose trendy styles that suit that body shape. In this way, the learning unit can propose a style that fits better by considering the user's changes in body shape.

[0044] The learning unit analyzes the user's social media activity during learning to obtain trend information. For example, the learning unit analyzes the user's social media posts to obtain current trend information. The learning unit can also analyze posts from the user's followers and friends to obtain trend information. The learning unit can also analyze social media hashtags to obtain trend information. As a result, by analyzing the user's social media activity, the learning unit can obtain current trend information and suggest more appropriate styles.

[0045] The learning unit makes suggestions during the learning process by referencing the styles of the user's friends and family. For example, the learning unit analyzes the styles of the user's friends and family and suggests styles that suit the user. The learning unit can also make suggestions by referencing the coordination history of the user's friends and family. The learning unit can also suggest the optimal style based on trend information from the user's friends and family. In this way, the learning unit can suggest more appropriate styles by referencing the styles of the user's friends and family.

[0046] The proposal team considers the user's schedule and makes proposals at the optimal time. For example, the proposal team can refer to the user's calendar information and make proposals at the appropriate time. The proposal team can also adjust the timing of proposals to match the user's schedule. The proposal team can also make proposals at the optimal time based on the user's schedule. In this way, the proposal team can make proposals at a more appropriate time by adjusting the timing of proposals to match the user's schedule.

[0047] The proposal department improves the accuracy of its proposals by incorporating past user feedback. For example, the proposal department analyzes past user feedback to improve the accuracy of its proposals. The proposal department can also adjust the content of its proposals based on user feedback. The proposal department can also make optimal proposals by incorporating past user feedback. In this way, the proposal department can improve the accuracy of its proposals by incorporating past user feedback.

[0048] The suggestion department proposes the optimal outfit when considering the user's geographical location. For example, the suggestion department proposes the optimal outfit based on the weather information of the user's current location. The suggestion department can also propose outfits that match local trends based on the user's geographical location. The suggestion department can also propose the optimal outfit based on the weather information of the user's destination. In this way, the suggestion department can propose more appropriate outfits by considering the user's geographical location.

[0049] The proposal team considers the event information of the user's friends and family when making proposals. For example, the proposal team will propose the most suitable coordination based on the event information of the user's friends and family. The proposal team can also adjust the content of the proposal to match the schedules of the user's friends and family. The proposal team can also consider the event information of the user's friends and family and make proposals at the optimal time. In this way, the proposal team can make more appropriate proposals by considering the event information of the user's friends and family.

[0050] The rapid suggestion function makes optimal suggestions by referring to the user's past coordination history when making rapid suggestions. For example, the rapid suggestion function makes optimal suggestions quickly based on the user's past coordination history. The rapid suggestion function can also make rapid suggestions that reflect the user's preferred style based on their past coordination history. The rapid suggestion function can also make rapid suggestions that are in line with current trends by referring to the user's past coordination history. In this way, the rapid suggestion function can make optimal suggestions quickly by referring to the user's past coordination history.

[0051] The rapid suggestion department considers the user's device information to provide the most appropriate suggestion during the rapid suggestion process. For example, if the user is using a smartphone, the rapid suggestion department will provide a suggestion tailored to the screen size. If the user is using a tablet, the rapid suggestion department can also provide a rapid suggestion optimized for the larger screen. If the user is using a smartwatch, the rapid suggestion department can provide a concise and highly visible rapid suggestion. In this way, the rapid suggestion department can provide more appropriate suggestions by considering the user's device information.

[0052] The inventory management department updates inventory while considering the condition of the clothing. For example, the inventory management department can detect the condition of clothing and prioritize replacing clothing that is severely deteriorated. The inventory management department can also detect fading and stains on clothing and replace clothing that needs repair. The inventory management department can also detect wear and tear on the fabric of clothing and replace clothing that needs to be replaced. In this way, the inventory management department can perform more appropriate inventory management by updating inventory while considering the condition of the clothing.

[0053] The inventory management department updates inventory while considering the purchase date of clothing. For example, the inventory management department prioritizes inventory management of recently purchased clothing and registers it in the digital closet. The inventory management department can also prioritize inventory management of newly purchased clothing each season and make suggestions at the appropriate time. The inventory management department can also prioritize inventory management of clothing newly purchased for specific events or occasions and make suggestions tailored to those events. In this way, the inventory management department can perform more appropriate inventory management by updating inventory while considering the purchase date of clothing.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The scanning unit can prioritize scanning high-priority clothing by considering how often it is worn. For example, it can prioritize scanning clothes that the user wears frequently and register them in the digital closet. It can also prioritize scanning clothes that are frequently worn each season, allowing for timely suggestions. It can even prioritize scanning clothes used for specific events or occasions, providing suggestions tailored to those events. In this way, the scanning unit can prioritize the management of important clothing by considering how often it is worn.

[0056] The scanning unit can detect the degree of deterioration of clothing during scanning and identify clothing that is significantly deteriorated. For example, it can detect fading and stains on clothing to identify clothing that is significantly deteriorated. It can also detect wear and tear on the fabric of clothing to identify clothing that needs repair. It can also detect changes in the shape of clothing to identify clothing that needs to be replaced. In this way, the scanning unit can detect the degree of deterioration of clothing, identify clothing that is significantly deteriorated, and manage it appropriately.

[0057] The proposal department can make proposals at the optimal time, taking into account the user's schedule. For example, it can refer to the user's calendar information and make proposals at the appropriate time. It can also adjust the timing of proposals to match the user's schedule. It can make proposals at the optimal time based on the user's schedule. In this way, the proposal department can make proposals at a more appropriate time by adjusting the timing of proposals to match the user's schedule.

[0058] The inventory management department can update inventory while considering the purchase date of clothing. For example, it can prioritize inventory management of recently purchased clothing and register it in the digital closet. It can also prioritize inventory management of newly purchased clothing each season so that it can be suggested at the appropriate time. It can also prioritize inventory management of clothing newly purchased for specific events or occasions so that suggestions can be made that match the event. In this way, the inventory management department can perform more appropriate inventory management by updating inventory while considering the purchase date of clothing.

[0059] The learning unit can analyze users' social media activity and obtain trend information during the learning process. For example, it can analyze users' social media posts to obtain current trend information. It can also analyze posts from users' followers and friends to obtain trend information. It can also analyze social media hashtags to obtain trend information. As a result, the learning unit can obtain current trend information by analyzing users' social media activity and suggest more appropriate styles.

[0060] The proposal department can suggest the most suitable outfit by considering the user's geographical location. For example, it can suggest the best outfit based on the weather information of the user's current location. It can also suggest outfits that match local trends based on the user's geographical location. It can also suggest the best outfit based on the weather information of the user's destination. In this way, the proposal department can suggest more appropriate outfits by considering the user's geographical location.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The scanning unit scans the user's clothes to create a digital closet. The scanning unit scans the user's clothes with a scanner and saves them as digital data. Information such as the type, color, and material of each garment is digitized and registered in the database. For example, information on shirts, pants, and jackets is scanned and registered in the digital closet. Step 2: The learning unit learns the user's history and trends to suggest personalized styles. The learning unit analyzes data on outfits the user has previously chosen to understand the user's preferences and style. Furthermore, it learns current fashion trends to suggest the most suitable styles for the user. For example, it collects data from fashion magazines and social media to learn trends. Step 3: The suggestion department provides real-time clothing suggestions based on the style proposed by the learning department, taking into account the weather and event. The suggestion department obtains current weather information and information about events the user plans to attend, and then suggests the optimal outfit based on that information. For example, it might suggest a waterproof jacket on a rainy day or a formal suit for a business meeting.

[0063] (Example of form 2) The Smart Coordination AI Agent according to an embodiment of the present invention is a system that analyzes an individual's style data and proposes a style suitable for an event. The Smart Coordination AI Agent scans the user's existing clothes and creates a digital closet. Next, the Smart Coordination AI Agent learns the user's history and trends and proposes a personalized style. Furthermore, the Smart Coordination AI Agent proposes clothing in real time that is appropriate for the weather and the event. This mechanism can shorten the time spent choosing clothes and improve style. For example, the Smart Coordination AI Agent scans the user's existing clothes and creates a digital closet. At this time, information such as the type, color, and material of each garment is digitized and registered in a database. For example, information on shirts, pants, jackets, etc. that the user owns is scanned and registered in the digital closet. Next, the Smart Coordination AI Agent learns the user's history and trends and proposes a personalized style. The Smart Coordination AI Agent learns the outfits the user has chosen in the past and current fashion trends. For example, it analyzes data on outfits the user has chosen in the past to understand the user's preferences and style. It also learns current fashion trends and proposes the most suitable style for the user. Furthermore, the Smart Coordination AI Agent suggests outfits in real time, tailored to the weather and events. It acquires current weather information and information about events the user plans to attend, and then suggests the optimal outfit based on that information. For example, it might suggest a waterproof jacket on a rainy day or a formal suit for a business meeting. This system allows users to reduce the time spent choosing clothes daily and improve their style. It is designed for a wide range of users, particularly busy business professionals, trend-conscious young people, stylists, and fashion industry professionals. It also enables rapid outfit suggestions for stylists and streamlines inventory management through a digital closet.This allows the smart coordination AI agent to reduce the time users spend choosing clothes and improve their style.

[0064] The smart coordination AI agent according to this embodiment comprises a scanning unit, a learning unit, and a suggestion unit. The scanning unit scans the user's clothes to create a digital closet. For example, the scanning unit scans the user's clothes with a scanner and saves them as digital data. The scanning unit digitizes information such as the type, color, and material of each garment and registers it in a database. For example, the scanning unit scans information on shirts, pants, jackets, etc., and registers it in the digital closet. The learning unit learns the user's history and trends and suggests personalized styles. For example, the learning unit analyzes data on outfits the user has previously chosen to understand the user's preferences and style. The learning unit learns current fashion trends and suggests the most suitable style for the user. For example, the learning unit collects data from fashion magazines and social media to learn trends. The suggestion unit suggests outfits in real time that are appropriate for the weather and events, based on the styles suggested by the learning unit. For example, the suggestion unit obtains current weather information and information on events the user plans to attend, and suggests the most suitable outfit based on that information. The suggestion department would suggest a waterproof jacket for rainy days and a formal suit for business meetings. This allows the smart coordination AI agent, according to the embodiment, to reduce the user's time spent choosing clothes and improve their style.

[0065] The scanning unit creates a digital closet by scanning the user's existing clothing. Specifically, the scanning unit uses a high-resolution camera or a dedicated scanner to scan the user's clothing in detail. The scanned data includes detailed information such as the shape, color, material, brand, and size of the clothing. This information is analyzed using image processing technology and stored as digital data. The scanning unit digitizes information such as the type, color, and material of each garment and registers it in a database. For example, it scans shirts, pants, jackets, dresses, and skirts, and registers detailed attribute information for each item in the database. Furthermore, the scanning unit also records information such as the condition of the clothing and how often it is used, allowing it to understand which clothes the user uses and to what extent. As a result, the scanning unit can accurately digitize the user's existing clothing and create a digital closet.

[0066] The learning unit learns the user's history and trends to suggest personalized styles. Specifically, the learning unit collects data on outfits the user has previously chosen and analyzes the user's preferences and style using machine learning algorithms. For example, if a user tends to prefer certain colors or brands, the learning unit uses this information to understand the user's style. In addition, to learn current fashion trends, the learning unit collects data from fashion magazines and social media and analyzes trend information using natural language processing technology. This allows the learning unit to combine the user's preferences with the latest trends to suggest personalized styles. Furthermore, the learning unit collects user feedback to continuously improve the accuracy of its suggestions. For example, by having users evaluate the suggested outfits, the learning unit adjusts its algorithm based on that evaluation to make suggestions that are more suitable for the user. In this way, the learning unit can suggest personalized styles based on the user's preferences and trends, increasing user satisfaction.

[0067] The suggestion department provides real-time clothing recommendations based on the style suggested by the learning department, taking into account the weather and the event. Specifically, the suggestion department acquires current weather information and information about events the user plans to attend, and then suggests the optimal outfit based on that information. For example, the suggestion department acquires weather forecast data and suggests waterproof jackets and boots on rainy days, and breathable, lightweight clothing on hot days. Also, if the user is attending a business meeting, it suggests a formal suit or dress, and for a casual event, it suggests a relaxed style. The suggestion department can suggest appropriate clothing in real time according to the user's schedule and the type of event. Furthermore, the suggestion department collects user feedback to improve the accuracy of its suggestions. For example, by having the user evaluate the suggested outfits, the suggestion department can adjust its algorithm based on that evaluation to provide more user-friendly suggestions. As a result, the suggestion department can suggest the optimal outfit in real time according to the user's needs, shorten the time the user spends choosing clothes, and improve their style.

[0068] The Rapid Proposal Department provides stylists with quick outfit suggestions. For example, the Rapid Proposal Department allows stylists to quickly propose outfits in response to client requests. The Rapid Proposal Department uses AI to suggest the optimal outfit tailored to the client's preferences and the event. For example, the Rapid Proposal Department analyzes the client's past outfit history and quickly provides the best suggestion. The Rapid Proposal Department can also adjust the timing of suggestions to match the client's schedule. This enables the Rapid Proposal Department to provide stylists with quick outfit suggestions.

[0069] The Inventory Management Department manages inventory using a digital closet. For example, the Inventory Management Department registers and manages the inventory of users' clothing in the digital closet. The Inventory Management Department digitizes information such as the type, color, and material of each garment and registers it in a database. For example, the Inventory Management Department scans information on shirts, pants, jackets, etc., and registers it in the digital closet. The Inventory Management Department can perform appropriate management according to the frequency of inventory updates and the types of inventory. This allows the Inventory Management Department to streamline inventory management using the digital closet.

[0070] The scanning unit digitizes information such as the type, color, and material of each garment and registers it in a database. For example, the scanning unit scans the user's clothes with a scanner and saves them as digital data. The scanning unit digitizes information such as the type, color, and material of each garment and registers it in a database. For example, the scanning unit scans information on shirts, pants, jackets, etc., and registers it in the digital closet. By digitizing the information of each garment and registering it in the database, the scanning unit can create a digital closet.

[0071] The learning unit learns about the user's past outfit choices and current fashion trends. For example, the learning unit analyzes data on the user's past outfit choices to understand the user's preferences and style. The learning unit learns about current fashion trends and suggests the most suitable style for the user. For example, the learning unit collects data from fashion magazines and social media to learn about trends. By learning about the user's past outfits and current fashion trends, the learning unit can suggest a personalized style.

[0072] The suggestion department acquires current weather information and information about events the user plans to attend, and then proposes the most suitable outfit based on that information. For example, the suggestion department might suggest a waterproof jacket for rainy days or a formal suit for business meetings. By suggesting the most suitable outfit based on current weather and event information, the suggestion department can support users in choosing their clothes.

[0073] The scanning unit estimates the user's emotions and adjusts the timing of the scan based on the estimated emotions. For example, if the user is relaxed, the scanning unit will delay the scan and start it when the user is calm. If the user is in a hurry, the scanning unit can also speed up the scan and complete it quickly. If the user is stressed, the scanning unit can adjust the timing of the scan and wait until the user is relaxed. In this way, the scanning unit can reduce the burden on the user by adjusting the timing of the scan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The scanning unit prioritizes scanning high-priority clothing, taking into account how often it is worn. For example, it prioritizes scanning clothing that the user frequently wears and registers it in the digital closet. The scanning unit can also prioritize scanning clothing that is frequently worn each season, allowing for timely suggestions. It can also prioritize scanning clothing used for specific events or occasions, providing event-specific suggestions. In this way, the scanning unit can prioritize the management of important clothing by scanning based on its usage frequency.

[0075] The scanning unit detects the degree of deterioration of clothing during scanning and identifies clothing that is significantly deteriorated. For example, the scanning unit can detect fading and stains on clothing to identify clothing that is significantly deteriorated. The scanning unit can also detect wear and tear on the fabric of clothing to identify clothing that needs repair. The scanning unit can also detect changes in the shape of clothing to identify clothing that needs to be replaced. In this way, by detecting the degree of deterioration of clothing, the scanning unit can identify clothing that is significantly deteriorated and manage it appropriately.

[0076] The scanning unit estimates the user's emotions and determines the priority of clothing to scan based on the estimated emotions. For example, if the user is relaxed, the scanning unit will prioritize scanning everyday clothes. If the user is in a hurry, the scanning unit can also prioritize scanning work clothes or formal clothing. If the user is stressed, the scanning unit can also prioritize scanning comfortable clothing. In this way, the scanning unit can reduce the user's burden by determining the priority of clothing to scan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The scanning unit prioritizes scanning newer clothing items, taking into account when they were purchased. For example, it prioritizes scanning recently purchased clothing items and registering them in the digital closet. The scanning unit can also prioritize scanning newly purchased clothing items each season, allowing for timely suggestions. It can also prioritize scanning clothing items purchased for specific events or occasions, providing event-specific suggestions. This allows the scanning unit to prioritize the management of new clothing items by considering when they were purchased.

[0078] The scanning unit acquires brand information of clothing during scanning and classifies it by brand. For example, the scanning unit recognizes the brand logo of clothing during scanning and classifies it by brand. Based on the brand information, the scanning unit can also analyze the user's preferences and style and reflect this in its suggestions. The scanning unit can also consider the characteristics of clothing from each brand and suggest the optimal outfit. In this way, by acquiring brand information of clothing and classifying it by brand, the scanning unit makes it easier to analyze the user's preferences and style.

[0079] The learning unit estimates the user's emotions and selects training data based on the estimated emotions. For example, if the user is relaxed, the learning unit selects training data based on past outfit history. If the user is in a hurry, the learning unit can also select training data based on recent trend information. If the user is stressed, the learning unit can also select training data to suggest a comfortable style. In this way, the learning unit can suggest a more appropriate style by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The learning unit analyzes the user's past outfit history during training to detect changes in preferences. For example, the learning unit analyzes data on outfits the user has previously chosen to detect changes in preferences. The learning unit can also detect seasonal changes in preferences from the user's past outfit history. Based on the user's past outfit history, the learning unit can also make suggestions that respond to changing trends. In this way, by analyzing the user's past outfit history, the learning unit can detect changes in preferences and suggest more appropriate styles.

[0081] The learning unit, during the learning process, proposes the optimal style while considering the user's changes in body shape. For example, the learning unit can detect changes in the user's body shape and propose a style that suits that body shape. Based on the user's changes in body shape, the learning unit can also propose clothing that fits well. The learning unit can also consider changes in the user's body shape and propose trendy styles that suit that body shape. In this way, the learning unit can propose a style that fits better by considering the user's changes in body shape.

[0082] The learning unit estimates the user's emotions and adjusts the learning frequency based on the estimated emotions. For example, if the user is relaxed, the learning unit increases the learning frequency and collects more detailed data. If the user is in a hurry, the learning unit can also decrease the learning frequency and provide suggestions more quickly. If the user is stressed, the learning unit can adjust the learning frequency to reduce the user's burden. In this way, the learning unit can reduce the user's burden by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The learning unit analyzes the user's social media activity during learning to obtain trend information. For example, the learning unit analyzes the user's social media posts to obtain current trend information. The learning unit can also analyze posts from the user's followers and friends to obtain trend information. The learning unit can also analyze social media hashtags to obtain trend information. As a result, by analyzing the user's social media activity, the learning unit can obtain current trend information and suggest more appropriate styles.

[0084] The learning unit makes suggestions during the learning process by referencing the styles of the user's friends and family. For example, the learning unit analyzes the styles of the user's friends and family and suggests styles that suit the user. The learning unit can also make suggestions by referencing the coordination history of the user's friends and family. The learning unit can also suggest the optimal style based on trend information from the user's friends and family. In this way, the learning unit can suggest more appropriate styles by referencing the styles of the user's friends and family.

[0085] The suggestion function estimates the user's emotions and adjusts the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function may offer suggestions with detailed explanations. If the user is in a hurry, the suggestion function may offer concise and to-the-point suggestions. If the user is stressed, the suggestion function may offer visually relaxing suggestions. In this way, the suggestion function can provide more appropriate suggestions by adjusting the way it presents suggestions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The proposal team considers the user's schedule and makes proposals at the optimal time. For example, the proposal team can refer to the user's calendar information and make proposals at the appropriate time. The proposal team can also adjust the timing of proposals to match the user's schedule. The proposal team can also make proposals at the optimal time based on the user's schedule. In this way, the proposal team can make proposals at a more appropriate time by adjusting the timing of proposals to match the user's schedule.

[0087] The proposal department improves the accuracy of its proposals by incorporating past user feedback. For example, the proposal department analyzes past user feedback to improve the accuracy of its proposals. The proposal department can also adjust the content of its proposals based on user feedback. The proposal department can also make optimal proposals by incorporating past user feedback. In this way, the proposal department can improve the accuracy of its proposals by incorporating past user feedback.

[0088] The suggestion unit estimates the user's emotions and prioritizes suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will prioritize suggestions for casual wear. If the user is in a hurry, the suggestion unit may prioritize suggestions for work clothes or formal attire. If the user is stressed, the suggestion unit may prioritize suggestions for comfortable clothing. In this way, the suggestion unit can provide more appropriate suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The suggestion department proposes the optimal outfit when considering the user's geographical location. For example, the suggestion department proposes the optimal outfit based on the weather information of the user's current location. The suggestion department can also propose outfits that match local trends based on the user's geographical location. The suggestion department can also propose the optimal outfit based on the weather information of the user's destination. In this way, the suggestion department can propose more appropriate outfits by considering the user's geographical location.

[0090] The proposal team considers the event information of the user's friends and family when making proposals. For example, the proposal team will propose the most suitable coordination based on the event information of the user's friends and family. The proposal team can also adjust the content of the proposal to match the schedules of the user's friends and family. The proposal team can also consider the event information of the user's friends and family and make proposals at the optimal time. In this way, the proposal team can make more appropriate proposals by considering the event information of the user's friends and family.

[0091] The rapid suggestion unit estimates the user's emotions and adjusts its rapid suggestion method based on the estimated emotions. For example, if the user is relaxed, the rapid suggestion unit will provide a rapid suggestion with a detailed explanation. If the user is in a hurry, the rapid suggestion unit can also provide a concise and to-the-point rapid suggestion. If the user is stressed, the rapid suggestion unit can also provide a visually relaxing rapid suggestion. In this way, the rapid suggestion unit can provide more appropriate rapid suggestions by adjusting its method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The rapid suggestion function makes optimal suggestions by referring to the user's past coordination history when making rapid suggestions. For example, the rapid suggestion function makes optimal suggestions quickly based on the user's past coordination history. The rapid suggestion function can also make rapid suggestions that reflect the user's preferred style based on their past coordination history. The rapid suggestion function can also make rapid suggestions that are in line with current trends by referring to the user's past coordination history. In this way, the rapid suggestion function can make optimal suggestions quickly by referring to the user's past coordination history.

[0093] The rapid suggestion unit estimates the user's emotions and prioritizes rapid suggestions based on those emotions. For example, if the user is relaxed, the rapid suggestion unit will prioritize rapid suggestions for casual wear. If the user is in a hurry, the rapid suggestion unit may also prioritize rapid suggestions for work clothes or formal wear. If the user is stressed, the rapid suggestion unit may also prioritize rapid suggestions for comfortable clothing. In this way, the rapid suggestion unit can provide more appropriate rapid suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The rapid suggestion department considers the user's device information to provide the most appropriate suggestion during the rapid suggestion process. For example, if the user is using a smartphone, the rapid suggestion department will provide a suggestion tailored to the screen size. If the user is using a tablet, the rapid suggestion department can also provide a rapid suggestion optimized for the larger screen. If the user is using a smartwatch, the rapid suggestion department can provide a concise and highly visible rapid suggestion. In this way, the rapid suggestion department can provide more appropriate suggestions by considering the user's device information.

[0095] The inventory management department estimates the user's emotions and adjusts its inventory management methods based on those estimates. For example, if the user is relaxed, the inventory management department performs detailed inventory management. If the user is in a hurry, the inventory management department can also perform concise and rapid inventory management. If the user is stressed, the inventory management department can also perform visually relaxing inventory management. In this way, the inventory management department can perform more appropriate inventory management by adjusting its methods according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The inventory management department updates inventory while considering the condition of the clothing. For example, the inventory management department can detect the condition of clothing and prioritize replacing clothing that is severely deteriorated. The inventory management department can also detect fading and stains on clothing and replace clothing that needs repair. The inventory management department can also detect wear and tear on the fabric of clothing and replace clothing that needs to be replaced. In this way, the inventory management department can perform more appropriate inventory management by updating inventory while considering the condition of the clothing.

[0097] The inventory management department estimates the user's emotions and determines inventory management priorities based on the estimated emotions. For example, if the user is relaxed, the inventory management department will prioritize inventory management of everyday clothes. If the user is in a hurry, the inventory management department may also prioritize inventory management of work clothes or formal wear. If the user is stressed, the inventory management department may also prioritize inventory management of comfortable clothes. In this way, the inventory management department can perform more appropriate inventory management by determining inventory management priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The inventory management department updates inventory while considering the purchase date of clothing. For example, the inventory management department prioritizes inventory management of recently purchased clothing and registers it in the digital closet. The inventory management department can also prioritize inventory management of newly purchased clothing each season and make suggestions at the appropriate time. The inventory management department can also prioritize inventory management of clothing newly purchased for specific events or occasions and make suggestions tailored to those events. In this way, the inventory management department can perform more appropriate inventory management by updating inventory while considering the purchase date of clothing.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The suggestion function can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is relaxed, it can suggest a casual style. If the user is in a hurry, it can suggest a simple style that can be put on quickly. If the user is stressed, it can suggest a comfortable and relaxing style. In this way, the suggestion function can suggest a more appropriate style by adjusting the content of its suggestions according to the user's emotions.

[0101] The learning unit can estimate the user's emotions and select training data based on those emotions. For example, if the user is relaxed, it can select training data based on their past outfit history. If the user is in a hurry, it can select training data based on recent trend information. If the user is stressed, it can select training data to suggest a comfortable style. In this way, the learning unit can suggest a more appropriate style by selecting training data according to the user's emotions.

[0102] The scanning unit can prioritize scanning high-priority clothing by considering how often it is worn. For example, it can prioritize scanning clothes that the user wears frequently and register them in the digital closet. It can also prioritize scanning clothes that are frequently worn each season, allowing for timely suggestions. It can even prioritize scanning clothes used for specific events or occasions, providing suggestions tailored to those events. In this way, the scanning unit can prioritize the management of important clothing by considering how often it is worn.

[0103] The scanning unit can detect the degree of deterioration of clothing during scanning and identify clothing that is significantly deteriorated. For example, it can detect fading and stains on clothing to identify clothing that is significantly deteriorated. It can also detect wear and tear on the fabric of clothing to identify clothing that needs repair. It can also detect changes in the shape of clothing to identify clothing that needs to be replaced. In this way, the scanning unit can detect the degree of deterioration of clothing, identify clothing that is significantly deteriorated, and manage it appropriately.

[0104] The proposal department can make proposals at the optimal time, taking into account the user's schedule. For example, it can refer to the user's calendar information and make proposals at the appropriate time. It can also adjust the timing of proposals to match the user's schedule. It can make proposals at the optimal time based on the user's schedule. In this way, the proposal department can make proposals at a more appropriate time by adjusting the timing of proposals to match the user's schedule.

[0105] The rapid suggestion unit can estimate the user's emotions and adjust the rapid suggestion method based on those emotions. For example, if the user is relaxed, it can provide a rapid suggestion that includes a detailed explanation. If the user is in a hurry, it can provide a concise and to-the-point rapid suggestion. If the user is stressed, it can provide a visually relaxing rapid suggestion. In this way, the rapid suggestion unit can provide more appropriate rapid suggestions by adjusting the method according to the user's emotions.

[0106] The inventory management department can update inventory while considering the purchase date of clothing. For example, it can prioritize inventory management of recently purchased clothing and register it in the digital closet. It can also prioritize inventory management of newly purchased clothing each season so that it can be suggested at the appropriate time. It can also prioritize inventory management of clothing newly purchased for specific events or occasions so that suggestions can be made that match the event. In this way, the inventory management department can perform more appropriate inventory management by updating inventory while considering the purchase date of clothing.

[0107] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is relaxed, it can prioritize suggestions for casual wear. If the user is in a hurry, it can prioritize suggestions for work clothes or formal attire. If the user is stressed, it can prioritize suggestions for comfortable clothing. In this way, the suggestion function can provide more appropriate suggestions by prioritizing suggestions according to the user's emotions.

[0108] The learning unit can analyze users' social media activity and obtain trend information during the learning process. For example, it can analyze users' social media posts to obtain current trend information. It can also analyze posts from users' followers and friends to obtain trend information. It can also analyze social media hashtags to obtain trend information. As a result, the learning unit can obtain current trend information by analyzing users' social media activity and suggest more appropriate styles.

[0109] The proposal department can suggest the most suitable outfit by considering the user's geographical location. For example, it can suggest the best outfit based on the weather information of the user's current location. It can also suggest outfits that match local trends based on the user's geographical location. It can also suggest the best outfit based on the weather information of the user's destination. In this way, the proposal department can suggest more appropriate outfits by considering the user's geographical location.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The scanning unit scans the user's clothes to create a digital closet. The scanning unit scans the user's clothes with a scanner and saves them as digital data. Information such as the type, color, and material of each garment is digitized and registered in the database. For example, information on shirts, pants, and jackets is scanned and registered in the digital closet. Step 2: The learning unit learns the user's history and trends to suggest personalized styles. The learning unit analyzes data on outfits the user has previously chosen to understand the user's preferences and style. Furthermore, it learns current fashion trends to suggest the most suitable styles for the user. For example, it collects data from fashion magazines and social media to learn trends. Step 3: The suggestion department provides real-time clothing suggestions based on the style proposed by the learning department, taking into account the weather and event. The suggestion department obtains current weather information and information about events the user plans to attend, and then suggests the optimal outfit based on that information. For example, it might suggest a waterproof jacket on a rainy day or a formal suit for a business meeting.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the scanning unit, learning unit, suggestion unit, rapid suggestion unit, and inventory management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the scanning unit uses the camera 42 of the smart device 14 to scan the user's clothes and create a digital closet. The learning unit uses the specific processing unit 290 of the data processing unit 12 to learn the user's history and trends and suggest personalized styles. The suggestion unit uses the specific processing unit 290 of the data processing unit 12 to suggest outfits in real time that are appropriate for the weather and events. The rapid suggestion unit uses the specific processing unit 290 of the data processing unit 12 to provide rapid outfit suggestions for stylists. The inventory management unit uses the camera 42 of the smart device 14 to register and manage the inventory of the user's clothes in the digital closet. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the scanning unit, learning unit, suggestion unit, rapid suggestion unit, and inventory management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the scanning unit uses the camera 42 of the smart glasses 214 to scan the user's clothes and create a digital closet. The learning unit uses the identification processing unit 290 of the data processing unit 12 to learn the user's history and trends and suggest personalized styles. The suggestion unit uses the identification processing unit 290 of the data processing unit 12 to suggest outfits in real time that are appropriate for the weather and events. The rapid suggestion unit uses the identification processing unit 290 of the data processing unit 12 to provide rapid outfit suggestions for stylists. The inventory management unit uses the camera 42 of the smart glasses 214 to register and manage the inventory of the user's clothes in the digital closet. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the scanning unit, learning unit, suggestion unit, rapid suggestion unit, and inventory management unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the scanning unit uses the camera 42 of the headset terminal 314 to scan the user's clothes and create a digital closet. The learning unit uses the identification processing unit 290 of the data processing unit 12 to learn the user's history and trends and suggest personalized styles. The suggestion unit uses the identification processing unit 290 of the data processing unit 12 to suggest clothing in real time that is appropriate for the weather and events. The rapid suggestion unit uses the identification processing unit 290 of the data processing unit 12 to provide rapid outfit suggestions for stylists. The inventory management unit uses the camera 42 of the headset terminal 314 to register and manage the inventory of the user's clothes in the digital closet. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[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 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.

[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 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.

[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 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.

[0164] Each of the multiple elements described above, including the scanning unit, learning unit, suggestion unit, rapid suggestion unit, and inventory management unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the scanning unit uses the camera 42 of the robot 414 to scan the user's clothes and create a digital closet. The learning unit uses the identification processing unit 290 of the data processing unit 12 to learn the user's history and trends and suggest personalized styles. The suggestion unit uses the identification processing unit 290 of the data processing unit 12 to suggest outfits in real time that are appropriate for the weather and events. The rapid suggestion unit uses the identification processing unit 290 of the data processing unit 12 to provide rapid outfit suggestions for stylists. The inventory management unit uses the camera 42 of the robot 414 to register and manage the inventory of the user's clothes in the digital closet. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A scanning unit that scans the user's clothes to create a digital closet, A learning unit learns the user's history and trends based on the digital closet created by the scanning unit and proposes a personalized style. The system includes a suggestion unit that, based on the style proposed by the learning unit, suggests clothing in real time that is appropriate for the weather and event. A system characterized by the following features. (Note 2) It has a rapid proposal department that provides quick styling suggestions for stylists. The system described in Appendix 1, characterized by the features described herein. (Note 3) The company has an inventory management department that manages inventory using a digital closet. The system described in Appendix 1, characterized by the features described herein. (Note 4) The scanning unit is Information such as the type, color, and material of each garment is digitized and registered in a database. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned learning unit, It learns the user's past outfit choices and current fashion trends. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, The system retrieves current weather information and information about events the user plans to attend, and then suggests the most suitable outfit based on that information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The scanning unit is During scanning, the system prioritizes scanning of clothing items based on their frequency of use, prioritizing those with higher importance. The system described in Appendix 1, characterized by the features described herein. (Note 9) The scanning unit is During scanning, the system detects the degree of deterioration of clothing and identifies garments that are severely deteriorated. The system described in Appendix 1, characterized by the features described herein. (Note 10) The scanning unit is It estimates the user's emotions and determines the priority of clothing to scan based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The scanning unit is During scanning, the system prioritizes scanning newer clothing items, taking into account when they were purchased. The system described in Appendix 1, characterized by the features described herein. (Note 12) The scanning unit is During scanning, the brand information of the clothing is obtained and categorized by brand. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, During training, the system analyzes the user's past outfit history to detect changes in their preferences. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, During learning, the system suggests the optimal style while taking into account changes in the user's body shape. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, During training, the system analyzes users' social media activity and obtains trend information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, During the learning process, suggestions are made based on the styles of the user's friends and family. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The aforementioned proposal section is, When making a proposal, we take the user's schedule into consideration and make the proposal at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, we improve the accuracy of the suggestions by incorporating past user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration to suggest the most suitable coordination. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, we take into account event information from the user's friends and family. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned rapid proposal unit, It estimates the user's emotions and adjusts the rapid suggestion method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned rapid proposal unit, When providing quick suggestions, the system refers to the user's past coordination history to make the most suitable proposals. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned rapid proposal unit, It estimates the user's emotions and prioritizes rapid suggestions based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned rapid proposal unit, When providing rapid suggestions, we take the user's device information into consideration to make the most optimal suggestions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned inventory management department, It estimates user sentiment and adjusts inventory management methods based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned inventory management department, When managing inventory, update the stock list while taking into account the condition of the clothing. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned inventory management department, The system estimates user sentiment and prioritizes inventory management based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned inventory management department, When managing inventory, update the stock list considering the purchase date of the clothing. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0184] 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 scanning unit that scans the user's clothes to create a digital closet, A learning unit learns the user's history and trends based on the digital closet created by the scanning unit and proposes a personalized style. The system includes a suggestion unit that, based on the style proposed by the learning unit, suggests clothing in real time that is appropriate for the weather and event. A system characterized by the following features.

2. It has a rapid proposal department that provides quick styling suggestions for stylists. The system according to feature 1.

3. The company has an inventory management department that manages inventory using a digital closet. The system according to feature 1.

4. The scanning unit is Information such as the type, color, and material of each garment is digitized and registered in a database. The system according to feature 1.

5. The aforementioned learning unit, It learns the user's past outfit choices and current fashion trends. The system according to feature 1.

6. The aforementioned proposal section is, The system retrieves current weather information and information about events the user plans to attend, and then suggests the most suitable outfit based on that information. The system according to feature 1.

7. The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system according to feature 1.

8. The scanning unit is During scanning, the system prioritizes scanning of clothing items based on their frequency of use, prioritizing those with higher importance. The system according to feature 1.

9. The scanning unit is During scanning, the system detects the degree of deterioration of clothing and identifies garments that are severely deteriorated. The system according to feature 1.

10. The scanning unit is It estimates the user's emotions and determines the priority of clothing to scan based on the estimated emotions. The system according to feature 1.

Images