system

Abstract

An object of a system according to an embodiment is to manage clothing in a closet and propose appropriate clothes.SOLUTION: A system according to an embodiment includes a camera, an RFID tag, a generation AI, and a suggestion unit. The camera is installed in a closet. An RFID tag is attached to each garment. The production AI analyzes the date from the camera and the RFID tag. The suggestion unit suggests clothes based on the AI analyzed by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately manage the clothes in a closet or suggest appropriate outfits, and there is room for improvement.

[0005] The system according to the embodiment aims to manage clothes in a closet and suggest appropriate outfits. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, an RFID tag, a generation AI, and a suggestion unit. The camera is installed in a closet. An RFID tag is attached to each piece of clothing. The generation AI analyzes data from the camera and the RFID tag. The suggestion unit suggests outfits based on the data analyzed by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can manage clothes in a closet and suggest suitable outfits. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The coordination assistance system according to the embodiment of the present invention is a system that allows a user to efficiently manage clothes and enjoy optimal coordination. As a result, the coordination assistance system allows a user to efficiently manage clothes and enjoy optimal coordination.

[0029] A coordination assistance system according to an embodiment includes a camera, an RFID tag, a generation AI, and a suggestion unit. The camera is installed in a closet and captures images of clothes. For example, the camera may be a wide-angle camera mounted on the ceiling and capture the entire closet. Alternatively, the camera may be a fixed camera mounted on a shelf and capture a specific area. Alternatively, multiple cameras may be installed to track the location of clothes in the closet. An RFID tag is attached to each piece of clothing and provides information about the clothing. For example, the RFID tag may be a passive RFID tag attached to the tag portion of the clothing. Alternatively, the RFID tag may be an active RFID tag attached to a pocket of the clothing. The RFID tag may also provide information such as the material and color of the clothing. The generation AI analyzes data from the camera and the RFID tag. For example, the generation AI may use deep learning to analyze the condition of the clothing from camera images. The generation AI may also use natural language processing to analyze the information in the RFID tag. The generation AI may also use image recognition technology to identify the type and color of clothing. The suggestion unit suggests outfits based on the data analyzed by the generation AI. For example, the suggestion unit suggests optimal outfits to the user through a user interface. The suggestion unit can also suggest outfits to the user using a notification method. The suggestion unit can also set suggestion criteria and suggest outfits that match the user's preferences. This allows the coordination assistance system according to the embodiment to enable the user to efficiently manage clothes and enjoy optimal coordination. For example, the user can always keep track of the status of the clothes in their closet and receive suggestions for optimal outfits based on the weather and schedule. The user can also enjoy creating new outfits based on their preferences and avoid purchasing duplicate items. Furthermore, the user can visually check how new clothes match with their existing collection.

[0030] Generative AI can analyze camera images and RFID tag data to predict the deterioration state of clothing and suggest the timing for repairs or replacement. For example, generative AI can analyze camera images and RFID tag data from inside a closet to predict the deterioration state of clothing. For example, it can detect fading and wear on the fabric and suggest the timing for repairs or replacement. Generative AI can also predict the progression of deterioration based on how often the clothing is used and how many times it is washed, and suggest an appropriate maintenance schedule. For example, if a particular piece of clothing is used frequently, it can predict that the clothing will deteriorate more quickly. Generative AI can also predict the progression of deterioration by taking into account the material and brand information of the clothing. For example, it can predict that luxury brand clothing will deteriorate more slowly and suggest the timing for repairs or replacement. This makes clothing management more efficient by predicting the deterioration state of clothing and suggesting repairs or replacement at the appropriate time.

[0031] Generative AI can analyze camera images, track changes in clothing material and color, and record changes over time. For example, generative AI can analyze camera images from inside a closet to track changes in clothing material and color. For example, it can detect fading and stains and record changes over time. Generative AI can also analyze clothing material information based on RFID tag data and record changes over time. For example, if a particular material is prone to deterioration, it can record those changes in detail. Generative AI can also predict and record changes over time, taking into account the purchase date and frequency of use of the clothing. For example, it can regularly record changes in clothing that have occurred a certain period of time since purchase. This makes clothing management more efficient by tracking changes in clothing material and color and recording changes over time.

[0032] Generative AI analyzes camera images and RFID tag data to manage the condition of not only clothes but also shoes and accessories, supporting overall coordination. For example, generative AI analyzes camera images and RFID tag data from within a closet to manage the condition of not only clothes but also shoes and accessories. For example, it detects the deterioration of shoes or missing accessories. Generative AI also centrally manages all items in a closet and supports overall coordination. For example, it suggests shoes and accessories that go well with specific clothes. Generative AI also analyzes the frequency of use and condition of items in the closet to optimize overall coordination. For example, it suggests coordination to suit a specific event. This improves user satisfaction by managing the condition of not only clothes but also shoes and accessories, supporting overall coordination.

[0033] Generative AI can link clothing management data with smart home devices and automatically configure washing machines and dryers. Generative AI, for example, automatically configures washing machines and dryers based on clothing management data. For example, if a specific garment needs washing, it sends that information to the washing machine, which automatically starts the wash. Generative AI also suggests optimal washing settings based on the garment's material and color and connects with smart home devices. For example, it can set low-temperature washing for delicate garments. Generative AI also analyzes the frequency and condition of garment use and automatically configures washing and drying schedules. For example, it can set frequently used garments to be washed regularly. This allows clothing management data to be linked with other smart home devices and automatically configures washing machines and dryers, improving user convenience.

[0034] The generation AI can suggest comfortable clothing based on the user's physical condition data. For example, the generation AI analyzes the user's physical condition data and suggests comfortable clothing. For example, if the heart rate or body temperature is high, it will suggest clothing with good breathability. The generation AI also collects the user's physical condition data in real time and suggests optimal clothing based on that data. For example, if the body temperature is low, it will suggest clothing with good heat retention. The generation AI also analyzes the user's physical condition data over a long period of time and suggests clothing that suits the seasons and changes in physical condition. For example, it will suggest clothing that is suitable for the change of seasons. In this way, the user's comfort is improved by suggesting comfortable clothing based on the user's physical condition data.

[0035] The generation AI can analyze a user's past clothing history and suggest the best outfit for a particular event or season. For example, the generation AI may analyze a user's past clothing history and suggest the best outfit for a particular event. For example, it may suggest new outfits for a party based on outfits worn at past parties. The generation AI may also suggest the best outfit for each season based on the user's past clothing history. For example, it may suggest outfits suitable for this winter based on outfits worn in past winters. The generation AI may also analyze a user's past clothing history over a long period of time and suggest the best outfit for a particular event or season. For example, it may suggest outfits suitable for an annual event. In this way, user satisfaction is improved by suggesting the best outfit based on the user's past clothing history.

[0036] The generation AI can analyze the clothing data of the user's friends and family and suggest group coordinations. For example, the generation AI may analyze the clothing data of the user's friends and family and suggest group coordinations. For example, it may suggest that all family members wear the same themed outfits. The generation AI may also suggest the best outfits for group events based on the clothing data of the user's friends and family. For example, it may suggest outfits for a party with friends. The generation AI may also analyze the clothing data of the user's friends and family over a long period of time to optimize group coordinations. For example, it may suggest outfits suitable for a family trip. In this way, the generation AI may refer to the clothing data of the user's friends and family to suggest group coordinations, thereby improving user satisfaction.

[0037] The generation AI can suggest optimal clothing based on the user's hobbies and activities. For example, the generation AI analyzes the user's hobby and activity data to suggest the optimal clothing for that activity. For example, it suggests clothing suitable for playing sports. The generation AI also suggests the optimal clothing for a specific event based on the user's hobbies and activities. For example, it suggests clothing suitable for a music festival. The generation AI also analyzes the user's hobby and activity data over a long period of time to suggest the optimal clothing for that activity. For example, it suggests clothing suitable for a hobby that is performed regularly. This improves user satisfaction by suggesting clothing that matches the user's hobbies and activities.

[0038] The generation AI can analyze the user's body shape data and suggest outfits that correspond to changes in body shape. For example, the generation AI can analyze the user's body shape data and suggest outfits that correspond to changes in body shape. For example, it can suggest outfits that correspond to weight gain or loss. The generation AI can also collect the user's body shape data in real time and suggest optimal outfits based on that data. For example, it can suggest outfits that fit the body shape. The generation AI can also analyze the user's body shape data over a long period of time and suggest outfits that correspond to changes in body shape. For example, it can suggest outfits that match changes in body shape with each season. This improves user satisfaction by suggesting outfits that correspond to changes in the user's body shape.

[0039] The generation AI can analyze other users' feedback on the user's past outfits and suggest popular outfits. For example, the generation AI analyzes other users' feedback on the user's past outfits and suggests popular outfits. For example, suggestions are made based on "likes" and comments on social media. The generation AI also suggests optimal outfits based on feedback data from other users. For example, it prioritizes suggestions of outfits that have received a lot of positive feedback. The generation AI also analyzes feedback on the user's past outfits over a long period of time and suggests popular outfits. For example, it suggests popular outfits for each season. In this way, user satisfaction is improved by suggesting popular outfits based on feedback from other users.

[0040] The generation AI can suggest optimal outfits based on the culture and climate of the user's travel or business trip destination. For example, the generation AI analyzes climate data for the user's travel or business trip destination to suggest the optimal outfit for that location. For example, it will suggest cold weather gear for trips to cold regions. The generation AI also suggests appropriate outfits based on cultural information about the user's travel or business trip destination. For example, it will suggest clothing that is suitable for a specific culture. The generation AI also analyzes data about the user's travel or business trip destination over a long period of time to suggest the optimal outfit for that location. For example, it will suggest clothing that is suitable for travel destinations in each season. This improves user satisfaction by suggesting outfits that match the culture and climate of the user's travel or business trip destination.

[0041] The generation AI can suggest optimal outfits based on the dress code of the user's workplace or school. For example, the generation AI analyzes the dress code of the user's workplace or school and suggests outfits that suit that dress code. For example, it suggests outfits that are suitable for business casual. The generation AI also suggests optimal outfits based on the dress code of the user's workplace or school. For example, it suggests outfits that are suitable for formal events. The generation AI also analyzes the dress code of the user's workplace or school over a long period of time and suggests outfits that suit that dress code. For example, it suggests outfits that are suitable for each season's dress code. This improves user satisfaction by suggesting outfits that match the dress code of the user's workplace or school.

[0042] The generation AI can analyze a user's purchasing history and make suggestions to prevent duplication of previously purchased items. For example, the generation AI can analyze a user's purchasing history and make suggestions to prevent duplication of previously purchased items. For example, it can suggest not purchasing items of the same color or design. The generation AI can also identify duplicate items based on the user's purchasing history and make suggestions to prevent the purchase of those items. For example, it can suggest not purchasing items similar to items already owned. The generation AI can also analyze a user's purchasing history over a long period of time and make suggestions to prevent duplication of previously purchased items. For example, it can make suggestions based on seasonal purchasing history. In this way, by analyzing a user's purchasing history and making suggestions to prevent duplication of previously purchased items, user satisfaction can be improved.

[0043] The generation AI can analyze a user's budget and spending patterns to suggest the optimal timing for a purchase. For example, the generation AI can analyze a user's budget data to suggest the optimal timing for a purchase. For example, it can suggest making a purchase during a sale period. The generation AI can also suggest the optimal timing for a purchase based on the user's spending patterns. For example, if most spending occurs at the end of the month, it can suggest making a purchase at the beginning of the month. The generation AI can also analyze a user's budget and spending patterns over a long period of time to suggest the optimal timing for a purchase. For example, it can suggest making a purchase after a bonus is paid. In this way, user satisfaction can be improved by taking the user's budget and spending patterns into consideration and suggesting the optimal timing for a purchase.

[0044] The generation AI can analyze the purchasing history of the user's friends and family and suggest the best items to make gifts. For example, the generation AI can analyze the purchasing history of the user's friends and family and suggest the best items to make gifts. For example, it can suggest items that friends want. The generation AI can also suggest the best items to make gifts based on the purchasing history of the user's friends and family. For example, it can suggest items that are suitable for a family member's birthday. The generation AI can also analyze the purchasing history of the user's friends and family over a long period of time and suggest the best items to make gifts. For example, it can suggest gift items for each season. In this way, the generation AI can also refer to the purchasing history of the user's friends and family to suggest the best items to make gifts, thereby improving user satisfaction.

[0045] The generation AI can suggest optimal items based on the user's life events. For example, the generation AI analyzes the user's life event data and suggests the optimal items for that event. For example, it suggests gift items suitable for a birthday. The generation AI also suggests the optimal items for a specific event based on the user's life events. For example, it suggests gift items suitable for a wedding. The generation AI also analyzes the user's life event data over a long period of time and suggests the optimal items for that event. For example, it suggests items suitable for seasonal life events. This improves user satisfaction by suggesting items that match the user's life events.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The coordination assistance system can further include a voice recognition unit. The voice recognition unit can analyze the user's voice commands and suggest specific clothing and outfits. For example, if the user commands by voice, "Tell me what clothes go well with today's weather," the voice recognition unit analyzes the command and works with the generation AI to suggest the most suitable outfit. The voice recognition unit can also make more personalized suggestions by taking into account the user's preferences and past selection history. Furthermore, the voice recognition unit can also respond when the user is looking for clothing to match a specific event or situation. For example, it can suggest appropriate outfits in response to a question such as, "What should I wear to the party this weekend?" This allows users to easily receive outfit suggestions through voice commands, improving convenience.

[0048] The coordination assistance system can further include a weather information acquisition unit. The weather information acquisition unit acquires current weather information via the Internet, and the generation AI uses that information to suggest optimal clothing. For example, it can suggest waterproof clothing and shoes on rainy days, and breathable clothing on hot days. The weather information acquisition unit can also link with the user's schedule to suggest clothing that matches the weather at a specific time of day. For example, on days when it is cold in the morning and warm in the afternoon, it can suggest layering. This allows the user to choose clothing that is appropriate for the weather and stay comfortable.

[0049] The coordination assistance system may further include a social media linking unit. The social media linking unit links with the user's social media account to collect information on other users' coordinations and trends. For example, it may obtain popular coordinations from Instagram or Pinterest and suggest them to the user. The social media linking unit may also support the user when posting their own coordinations on social media. For example, it may provide a function that allows the user to post suggested coordinations as they are. This allows the user to enjoy coordinating coordinations that incorporate the latest trends and also promotes interaction with other users.

[0050] The coordination assistance system can also suggest comfortable clothing based on the user's physical condition data. The generation AI, for example, analyzes the user's physical condition data and suggests comfortable clothing. For example, if the heart rate or body temperature is high, it will suggest clothing with good breathability. The generation AI also collects the user's physical condition data in real time and suggests optimal clothing based on that data. For example, if the body temperature is low, it will suggest clothing with good heat retention. The generation AI also analyzes the user's physical condition data over a long period of time and suggests clothing that suits the seasons and changes in physical condition. For example, it will suggest clothing that is suitable for the change of seasons. In this way, by suggesting comfortable clothing based on the user's physical condition data, the user's comfort is improved.

[0051] The coordination assistance system can further analyze the user's purchasing history and make suggestions to prevent duplication of previously purchased items. The generation AI, for example, analyzes the user's purchasing history and makes suggestions to prevent duplication of previously purchased items. For example, it suggests not purchasing items of the same color or design. The generation AI can also identify duplicate items based on the user's purchasing history and make suggestions to prevent the purchase of those items. For example, it suggests not purchasing items similar to items already owned. The generation AI can also analyze the user's purchasing history over a long period of time and make suggestions to prevent duplication of previously purchased items. For example, it makes suggestions based on seasonal purchasing history. In this way, by analyzing the user's purchasing history and making suggestions to prevent duplication of previously purchased items, user satisfaction is improved.

[0052] The coordination assistance system can further analyze the user's budget and spending patterns to suggest the optimal timing for purchases. The generation AI, for example, analyzes the user's budget data to suggest the optimal timing for purchases. For example, it may suggest purchasing during a sale period. The generation AI also suggests the optimal timing for purchases based on the user's spending patterns. For example, if most spending occurs at the end of the month, it may suggest purchasing at the beginning of the month. The generation AI also analyzes the user's budget and spending patterns over a long period of time to suggest the optimal timing for purchases. For example, it may suggest purchasing after a bonus is paid. In this way, by taking the user's budget and spending patterns into consideration and suggesting the optimal timing for purchases, user satisfaction is improved.

[0053] The coordination assistance system can further analyze the clothing data of the user's friends and family and suggest group coordinations. For example, the generation AI may analyze the clothing data of the user's friends and family and suggest group coordinations. For example, it may suggest that all family members wear clothing of the same theme. The generation AI may also suggest the best coordination for group events based on the clothing data of the user's friends and family. For example, it may suggest outfits for a party with friends. The generation AI may also analyze the clothing data of the user's friends and family over a long period of time to optimize group coordinations. For example, it may suggest outfits suitable for a family trip. In this way, the generation AI may refer to the clothing data of the user's friends and family to suggest group coordinations, thereby improving user satisfaction.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: A camera is installed in the closet to capture images of the clothes. For example, the camera can be a wide-angle camera mounted on the ceiling that captures the entire closet. Alternatively, the camera can be a fixed camera mounted on a shelf that captures a specific area. Furthermore, multiple cameras can be installed to track the location of clothes in the closet. Step 2: An RFID tag is attached to each garment to provide information about the garment. For example, the RFID tag may be a passive RFID tag attached to the tag portion of the garment. Alternatively, the RFID tag may be an active RFID tag attached to a pocket of the garment. Furthermore, the RFID tag may provide information about the material and color of the garment. Step 3: The generative AI analyzes the data from the camera and RFID tag. For example, the generative AI uses deep learning to analyze the condition of the clothing from the camera image. The generative AI can also use natural language processing to analyze the information from the RFID tag. Furthermore, the generative AI can use image recognition technology to identify the type and color of the clothing. Step 4: The suggestion unit suggests outfits based on the data analyzed by the generative AI. For example, the suggestion unit suggests the most suitable outfits to the user through a user interface. The suggestion unit can also suggest outfits to the user using a notification method. Furthermore, the suggestion unit can set criteria for suggestions and suggest outfits that match the user's preferences.

[0056] (Example 2) The coordination assistance system according to the embodiment of the present invention is a system that allows a user to efficiently manage clothes and enjoy optimal coordination. As a result, the coordination assistance system allows a user to efficiently manage clothes and enjoy optimal coordination.

[0057] A coordination assistance system according to an embodiment includes a camera, an RFID tag, a generation AI, and a suggestion unit. The camera is installed in a closet and captures images of clothes. For example, the camera may be a wide-angle camera mounted on the ceiling and capture the entire closet. Alternatively, the camera may be a fixed camera mounted on a shelf and capture a specific area. Alternatively, multiple cameras may be installed to track the location of clothes in the closet. An RFID tag is attached to each piece of clothing and provides information about the clothing. For example, the RFID tag may be a passive RFID tag attached to the tag portion of the clothing. Alternatively, the RFID tag may be an active RFID tag attached to a pocket of the clothing. The RFID tag may also provide information such as the material and color of the clothing. The generation AI analyzes data from the camera and the RFID tag. For example, the generation AI may use deep learning to analyze the condition of the clothing from camera images. The generation AI may also use natural language processing to analyze the information in the RFID tag. The generation AI may also use image recognition technology to identify the type and color of clothing. The suggestion unit suggests outfits based on the data analyzed by the generation AI. For example, the suggestion unit suggests optimal outfits to the user through a user interface. The suggestion unit can also suggest outfits to the user using a notification method. The suggestion unit can also set suggestion criteria and suggest outfits that match the user's preferences. This allows the coordination assistance system according to the embodiment to enable the user to efficiently manage clothes and enjoy optimal coordination. For example, the user can always keep track of the status of the clothes in their closet and receive suggestions for optimal outfits based on the weather and schedule. The user can also enjoy creating new outfits based on their preferences and avoid purchasing duplicate items. Furthermore, the user can visually check how new clothes match with their existing collection.

[0058] Generative AI can analyze camera images and RFID tag data to predict the deterioration state of clothing and suggest the timing for repairs or replacement. For example, generative AI can analyze camera images and RFID tag data from inside a closet to predict the deterioration state of clothing. For example, it can detect fading and wear on the fabric and suggest the timing for repairs or replacement. Generative AI can also predict the progression of deterioration based on how often the clothing is used and how many times it is washed, and suggest an appropriate maintenance schedule. For example, if a particular piece of clothing is used frequently, it can predict that the clothing will deteriorate more quickly. Generative AI can also predict the progression of deterioration by taking into account the material and brand information of the clothing. For example, it can predict that luxury brand clothing will deteriorate more slowly and suggest the timing for repairs or replacement. This makes clothing management more efficient by predicting the deterioration state of clothing and suggesting repairs or replacement at the appropriate time.

[0059] Generative AI can analyze camera images, track changes in clothing material and color, and record changes over time. For example, generative AI can analyze camera images from inside a closet to track changes in clothing material and color. For example, it can detect fading and stains and record changes over time. Generative AI can also analyze clothing material information based on RFID tag data and record changes over time. For example, if a particular material is prone to deterioration, it can record those changes in detail. Generative AI can also predict and record changes over time, taking into account the purchase date and frequency of use of the clothing. For example, it can regularly record changes in clothing that have occurred a certain period of time since purchase. This makes clothing management more efficient by tracking changes in clothing material and color and recording changes over time.

[0060] The generative AI can analyze a user's emotional data, evaluate their emotions toward specific clothing, and prioritize the management of emotionally significant clothing. For example, the generative AI can analyze a user's emotional data and evaluate their emotions toward specific clothing. For example, it can prioritize the management of emotionally significant clothing based on the emotion score when the user wears that clothing. The generative AI can also use its emotion estimation function to analyze the user's emotions toward specific clothing in real time and suggest maintenance for emotionally significant clothing. For example, it can prioritize repairs for clothing that the user cherishes. The generative AI can also analyze a user's emotional history to identify emotionally significant clothing and prioritize its management. For example, it can specially manage clothing worn by the user at a specific event. This improves user satisfaction by prioritizing the management of emotionally significant clothing.

[0061] Generative AI analyzes camera images and RFID tag data to manage the condition of not only clothes but also shoes and accessories, supporting overall coordination. For example, generative AI analyzes camera images and RFID tag data from within a closet to manage the condition of not only clothes but also shoes and accessories. For example, it detects the deterioration of shoes or missing accessories. Generative AI also centrally manages all items in a closet and supports overall coordination. For example, it suggests shoes and accessories that go well with specific clothes. Generative AI also analyzes the frequency of use and condition of items in the closet to optimize overall coordination. For example, it suggests coordination to suit a specific event. This improves user satisfaction by managing the condition of not only clothes but also shoes and accessories, supporting overall coordination.

[0062] Generative AI can link clothing management data with smart home devices and automatically configure washing machines and dryers. Generative AI, for example, automatically configures washing machines and dryers based on clothing management data. For example, if a specific garment needs washing, it sends that information to the washing machine, which automatically starts the wash. Generative AI also suggests optimal washing settings based on the garment's material and color and connects with smart home devices. For example, it can set low-temperature washing for delicate garments. Generative AI also analyzes the frequency and condition of garment use and automatically configures washing and drying schedules. For example, it can set frequently used garments to be washed regularly. This allows clothing management data to be linked with other smart home devices and automatically configures washing machines and dryers, improving user convenience.

[0063] The generation AI can analyze a user's emotional data, record the emotions felt when wearing specific clothing, and recommend clothing that elicits positive emotions. For example, the generation AI analyzes a user's emotional data and records the emotions felt when wearing specific clothing. For example, it saves the emotion score when the user wears specific clothing. The generation AI also uses an emotion estimation function to record the emotions felt when the user wears specific clothing in real time and recommends clothing that elicits positive emotions. For example, it suggests clothing that makes the user feel joy. The generation AI also analyzes a user's emotional history, identifies clothing that elicits positive emotions, and preferentially suggests those clothing. For example, it recommends clothing that the user wore at a specific event. This improves user satisfaction by recommending clothing that elicits positive emotions.

[0064] The generation AI can suggest comfortable clothing based on the user's physical condition data. For example, the generation AI analyzes the user's physical condition data and suggests comfortable clothing. For example, if the heart rate or body temperature is high, it will suggest clothing with good breathability. The generation AI also collects the user's physical condition data in real time and suggests optimal clothing based on that data. For example, if the body temperature is low, it will suggest clothing with good heat retention. The generation AI also analyzes the user's physical condition data over a long period of time and suggests clothing that suits the seasons and changes in physical condition. For example, it will suggest clothing that is suitable for the change of seasons. In this way, the user's comfort is improved by suggesting comfortable clothing based on the user's physical condition data.

[0065] The generation AI can analyze a user's past clothing history and suggest the best outfit for a particular event or season. For example, the generation AI may analyze a user's past clothing history and suggest the best outfit for a particular event. For example, it may suggest new outfits for a party based on outfits worn at past parties. The generation AI may also suggest the best outfit for each season based on the user's past clothing history. For example, it may suggest outfits suitable for this winter based on outfits worn in past winters. The generation AI may also analyze a user's past clothing history over a long period of time and suggest the best outfit for a particular event or season. For example, it may suggest outfits suitable for an annual event. In this way, user satisfaction is improved by suggesting the best outfit based on the user's past clothing history.

[0066] The generation AI can analyze the user's current emotional state and suggest clothing that matches that emotional state. For example, the generation AI can analyze the user's current emotional state and suggest clothing that matches that emotion. For example, if the user is feeling stressed, it can suggest clothing that is relaxing. The generation AI can also use its emotion estimation function to analyze the user's emotional state in real time and suggest clothing that best suits that emotion. For example, if the user is feeling happy, it can suggest brightly colored clothing. The generation AI can also analyze the user's emotional history and suggest clothing that matches their current emotional state. For example, it can refer to clothing that the user wore in the past when they felt a particular emotion. This improves user satisfaction by suggesting clothing that matches the user's current emotional state.

[0067] The generation AI can analyze the clothing data of the user's friends and family and suggest group coordinations. For example, the generation AI may analyze the clothing data of the user's friends and family and suggest group coordinations. For example, it may suggest that all family members wear the same themed outfits. The generation AI may also suggest the best outfits for group events based on the clothing data of the user's friends and family. For example, it may suggest outfits for a party with friends. The generation AI may also analyze the clothing data of the user's friends and family over a long period of time to optimize group coordinations. For example, it may suggest outfits suitable for a family trip. In this way, the generation AI may refer to the clothing data of the user's friends and family to suggest group coordinations, thereby improving user satisfaction.

[0068] The generation AI can suggest optimal clothing based on the user's hobbies and activities. For example, the generation AI analyzes the user's hobby and activity data to suggest the optimal clothing for that activity. For example, it suggests clothing suitable for playing sports. The generation AI also suggests the optimal clothing for a specific event based on the user's hobbies and activities. For example, it suggests clothing suitable for a music festival. The generation AI also analyzes the user's hobby and activity data over a long period of time to suggest the optimal clothing for that activity. For example, it suggests clothing suitable for a hobby that is performed regularly. This improves user satisfaction by suggesting clothing that matches the user's hobbies and activities.

[0069] The generation AI can predict the emotions a user will feel when wearing specific clothing and suggest clothing that elicits positive emotions. For example, the generation AI can analyze a user's emotional data and predict the emotions they will feel when wearing specific clothing. For example, it can make suggestions based on clothing that the user has previously felt joy in. The generation AI can also use its emotion estimation function to predict the emotions a user will feel when wearing specific clothing in real time and suggest clothing that elicits positive emotions. For example, it can suggest clothing that makes the user feel relaxed. The generation AI can also analyze a user's emotional history, identify clothing that elicits positive emotions, and prioritize suggesting those clothing. For example, it can recommend clothing that the user wore to a specific event. In this way, the generation AI can predict the emotions a user will feel when wearing specific clothing and suggest clothing that elicits positive emotions, thereby improving user satisfaction.

[0070] The generation AI can analyze the user's body shape data and suggest outfits that correspond to changes in body shape. For example, the generation AI can analyze the user's body shape data and suggest outfits that correspond to changes in body shape. For example, it can suggest outfits that correspond to weight gain or loss. The generation AI can also collect the user's body shape data in real time and suggest optimal outfits based on that data. For example, it can suggest outfits that fit the body shape. The generation AI can also analyze the user's body shape data over a long period of time and suggest outfits that correspond to changes in body shape. For example, it can suggest outfits that match changes in body shape with each season. This improves user satisfaction by suggesting outfits that correspond to changes in the user's body shape.

[0071] The generation AI can analyze other users' feedback on the user's past outfits and suggest popular outfits. For example, the generation AI analyzes other users' feedback on the user's past outfits and suggests popular outfits. For example, suggestions are made based on "likes" and comments on social media. The generation AI also suggests optimal outfits based on feedback data from other users. For example, it prioritizes suggestions of outfits that have received a lot of positive feedback. The generation AI also analyzes feedback on the user's past outfits over a long period of time and suggests popular outfits. For example, it suggests popular outfits for each season. In this way, user satisfaction is improved by suggesting popular outfits based on feedback from other users.

[0072] The generation AI can analyze the emotions a user has toward a specific outfit and suggest emotionally positive outfits. For example, the generation AI can analyze the user's emotional data and evaluate their emotions toward a specific outfit. For example, it can make suggestions based on the emotion score when the user wears a specific outfit. The generation AI can also use its emotion estimation function to analyze the emotions a user has toward a specific outfit in real time and suggest outfits that elicit positive emotions. For example, it can suggest outfits that make the user feel happy. The generation AI can also analyze the user's emotional history, identify emotionally positive outfits, and prioritize suggesting those outfits. For example, it can recommend outfits that the user wore at a specific event. In this way, by analyzing the emotions a user has toward a specific outfit and suggesting emotionally positive outfits, user satisfaction is improved.

[0073] The generation AI can suggest optimal outfits based on the culture and climate of the user's travel or business trip destination. For example, the generation AI analyzes climate data for the user's travel or business trip destination to suggest the optimal outfit for that location. For example, it will suggest cold weather gear for trips to cold regions. The generation AI also suggests appropriate outfits based on cultural information about the user's travel or business trip destination. For example, it will suggest clothing that is suitable for a specific culture. The generation AI also analyzes data about the user's travel or business trip destination over a long period of time to suggest the optimal outfit for that location. For example, it will suggest clothing that is suitable for travel destinations in each season. This improves user satisfaction by suggesting outfits that match the culture and climate of the user's travel or business trip destination.

[0074] The generation AI can suggest optimal outfits based on the dress code of the user's workplace or school. For example, the generation AI analyzes the dress code of the user's workplace or school and suggests outfits that suit that dress code. For example, it suggests outfits that are suitable for business casual. The generation AI also suggests optimal outfits based on the dress code of the user's workplace or school. For example, it suggests outfits that are suitable for formal events. The generation AI also analyzes the dress code of the user's workplace or school over a long period of time and suggests outfits that suit that dress code. For example, it suggests outfits that are suitable for each season's dress code. This improves user satisfaction by suggesting outfits that match the dress code of the user's workplace or school.

[0075] The generation AI can predict the emotions a user will feel when wearing a specific outfit and suggest outfits that will elicit positive emotions. For example, the generation AI can analyze a user's emotional data to predict the emotions they will feel when wearing a specific outfit. For example, it can make suggestions based on outfits that the user has previously found enjoyable. The generation AI can also use its emotion estimation function to predict the emotions a user will feel when wearing a specific outfit in real time and suggest outfits that will elicit positive emotions. For example, it can suggest outfits that will help the user relax. The generation AI can also analyze a user's emotional history to identify outfits that elicit positive emotions and prioritize suggesting those outfits. For example, it can recommend outfits that the user wore at a specific event. This improves user satisfaction by predicting the emotions a user will feel when wearing a specific outfit and suggesting outfits that will elicit positive emotions.

[0076] The generation AI can analyze a user's purchasing history and make suggestions to prevent duplication of previously purchased items. For example, the generation AI can analyze a user's purchasing history and make suggestions to prevent duplication of previously purchased items. For example, it can suggest not purchasing items of the same color or design. The generation AI can also identify duplicate items based on the user's purchasing history and make suggestions to prevent the purchase of those items. For example, it can suggest not purchasing items similar to items already owned. The generation AI can also analyze a user's purchasing history over a long period of time and make suggestions to prevent duplication of previously purchased items. For example, it can make suggestions based on seasonal purchasing history. In this way, by analyzing a user's purchasing history and making suggestions to prevent duplication of previously purchased items, user satisfaction can be improved.

[0077] The generation AI can analyze a user's budget and spending patterns to suggest the optimal timing for a purchase. For example, the generation AI can analyze a user's budget data to suggest the optimal timing for a purchase. For example, it can suggest making a purchase during a sale period. The generation AI can also suggest the optimal timing for a purchase based on the user's spending patterns. For example, if most spending occurs at the end of the month, it can suggest making a purchase at the beginning of the month. The generation AI can also analyze a user's budget and spending patterns over a long period of time to suggest the optimal timing for a purchase. For example, it can suggest making a purchase after a bonus is paid. In this way, user satisfaction can be improved by taking the user's budget and spending patterns into consideration and suggesting the optimal timing for a purchase.

[0078] The generative AI can predict the emotions a user will have when purchasing a specific item and suggest items that elicit positive emotions. For example, the generative AI analyzes a user's emotional data to predict the emotions they will have when purchasing a specific item. For example, it can make suggestions based on items that the user has previously felt joy in. The generative AI can also use its emotion estimation function to predict the emotions a user will have when purchasing a specific item in real time and suggest items that elicit positive emotions. For example, it can suggest items that will help the user relax. The generative AI can also analyze a user's emotional history to identify items that elicit positive emotions and prioritize suggesting those items. For example, it can recommend items that the user purchased at a specific event. This improves user satisfaction by predicting the emotions a user will have when purchasing a specific item and suggesting items that elicit positive emotions.

[0079] The generation AI can analyze the purchasing history of the user's friends and family and suggest the best items to make gifts. For example, the generation AI can analyze the purchasing history of the user's friends and family and suggest the best items to make gifts. For example, it can suggest items that friends want. The generation AI can also suggest the best items to make gifts based on the purchasing history of the user's friends and family. For example, it can suggest items that are suitable for a family member's birthday. The generation AI can also analyze the purchasing history of the user's friends and family over a long period of time and suggest the best items to make gifts. For example, it can suggest gift items for each season. In this way, the generation AI can also refer to the purchasing history of the user's friends and family to suggest the best items to make gifts, thereby improving user satisfaction.

[0080] The generation AI can suggest optimal items based on the user's life events. For example, the generation AI analyzes the user's life event data and suggests the optimal items for that event. For example, it suggests gift items suitable for a birthday. The generation AI also suggests the optimal items for a specific event based on the user's life events. For example, it suggests gift items suitable for a wedding. The generation AI also analyzes the user's life event data over a long period of time and suggests the optimal items for that event. For example, it suggests items suitable for seasonal life events. This improves user satisfaction by suggesting items that match the user's life events.

[0081] The generative AI can predict the emotions a user will have when purchasing a specific item and suggest items that elicit positive emotions. For example, the generative AI analyzes a user's emotional data to predict the emotions they will have when purchasing a specific item. For example, it can make suggestions based on items that the user has previously felt joy in. The generative AI can also use its emotion estimation function to predict the emotions a user will have when purchasing a specific item in real time and suggest items that elicit positive emotions. For example, it can suggest items that will help the user relax. The generative AI can also analyze a user's emotional history to identify items that elicit positive emotions and prioritize suggesting those items. For example, it can recommend items that the user purchased at a specific event. This improves user satisfaction by predicting the emotions a user will have when purchasing a specific item and suggesting items that elicit positive emotions.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The coordination assistance system can further include a voice recognition unit. The voice recognition unit can analyze the user's voice commands and suggest specific clothing and outfits. For example, if the user commands by voice, "Tell me what clothes go well with today's weather," the voice recognition unit analyzes the command and works with the generation AI to suggest the most suitable outfit. The voice recognition unit can also make more personalized suggestions by taking into account the user's preferences and past selection history. Furthermore, the voice recognition unit can also respond when the user is looking for clothing to match a specific event or situation. For example, it can suggest appropriate outfits in response to a question such as, "What should I wear to the party this weekend?" This allows users to easily receive outfit suggestions through voice commands, improving convenience.

[0084] The coordination assistance system can further include a weather information acquisition unit. The weather information acquisition unit acquires current weather information via the Internet, and the generation AI uses that information to suggest optimal clothing. For example, it can suggest waterproof clothing and shoes on rainy days, and breathable clothing on hot days. The weather information acquisition unit can also link with the user's schedule to suggest clothing that matches the weather at a specific time of day. For example, on days when it is cold in the morning and warm in the afternoon, it can suggest layering. This allows the user to choose clothing that is appropriate for the weather and stay comfortable.

[0085] The coordination assistance system may further include a social media linking unit. The social media linking unit links with the user's social media account to collect information on other users' coordinations and trends. For example, it may obtain popular coordinations from Instagram or Pinterest and suggest them to the user. The social media linking unit may also support the user when posting their own coordinations on social media. For example, it may provide a function that allows the user to post suggested coordinations as they are. This allows the user to enjoy coordinating coordinations that incorporate the latest trends and also promotes interaction with other users.

[0086] The coordination assistance system can further analyze the user's emotional data, evaluate their emotions toward specific clothing, and prioritize the management of emotionally significant clothing. The generation AI, for example, analyzes the user's emotional data and evaluates their emotions toward specific clothing. For example, it prioritizes the management of emotionally significant clothing based on the emotion score when the user wears that clothing. The generation AI also uses an emotion estimation function to analyze the user's emotions toward specific clothing in real time and suggest maintenance for emotionally significant clothing. For example, it prioritizes repairing clothing that the user cherishes. The generation AI also analyzes the user's emotional history, identifies emotionally significant clothing, and prioritizes its management. For example, it specially manages clothing worn by the user at a specific event. This improves user satisfaction by prioritizing the management of emotionally significant clothing.

[0087] The coordination assistance system can also suggest comfortable clothing based on the user's physical condition data. The generation AI, for example, analyzes the user's physical condition data and suggests comfortable clothing. For example, if the heart rate or body temperature is high, it will suggest clothing with good breathability. The generation AI also collects the user's physical condition data in real time and suggests optimal clothing based on that data. For example, if the body temperature is low, it will suggest clothing with good heat retention. The generation AI also analyzes the user's physical condition data over a long period of time and suggests clothing that suits the seasons and changes in physical condition. For example, it will suggest clothing that is suitable for the change of seasons. In this way, by suggesting comfortable clothing based on the user's physical condition data, the user's comfort is improved.

[0088] The coordination assistance system can further analyze the user's emotional data, record the emotions felt when wearing specific clothing, and recommend clothing that elicits positive emotions. The generation AI, for example, analyzes the user's emotional data and records the emotions felt when wearing specific clothing. For example, it saves the emotion score when the user wears specific clothing. The generation AI also uses an emotion estimation function to record the emotions felt when the user wears specific clothing in real time and recommends clothing that elicits positive emotions. For example, it suggests clothing that makes the user feel joy. The generation AI also analyzes the user's emotional history, identifies clothing that elicits positive emotions, and preferentially suggests those clothing. For example, it recommends clothing that the user wore at a specific event. This improves user satisfaction by recommending clothing that elicits positive emotions.

[0089] The coordination assistance system can further analyze the user's purchasing history and make suggestions to prevent duplication of previously purchased items. The generation AI, for example, analyzes the user's purchasing history and makes suggestions to prevent duplication of previously purchased items. For example, it suggests not purchasing items of the same color or design. The generation AI can also identify duplicate items based on the user's purchasing history and make suggestions to prevent the purchase of those items. For example, it suggests not purchasing items similar to items already owned. The generation AI can also analyze the user's purchasing history over a long period of time and make suggestions to prevent duplication of previously purchased items. For example, it makes suggestions based on seasonal purchasing history. In this way, by analyzing the user's purchasing history and making suggestions to prevent duplication of previously purchased items, user satisfaction is improved.

[0090] The coordination assistance system can further analyze the user's budget and spending patterns to suggest the optimal timing for purchases. The generation AI, for example, analyzes the user's budget data to suggest the optimal timing for purchases. For example, it may suggest purchasing during a sale period. The generation AI also suggests the optimal timing for purchases based on the user's spending patterns. For example, if most spending occurs at the end of the month, it may suggest purchasing at the beginning of the month. The generation AI also analyzes the user's budget and spending patterns over a long period of time to suggest the optimal timing for purchases. For example, it may suggest purchasing after a bonus is paid. In this way, by taking the user's budget and spending patterns into consideration and suggesting the optimal timing for purchases, user satisfaction is improved.

[0091] The coordination assistance system can further analyze the clothing data of the user's friends and family and suggest group coordinations. For example, the generation AI may analyze the clothing data of the user's friends and family and suggest group coordinations. For example, it may suggest that all family members wear clothing of the same theme. The generation AI may also suggest the best coordination for group events based on the clothing data of the user's friends and family. For example, it may suggest outfits for a party with friends. The generation AI may also analyze the clothing data of the user's friends and family over a long period of time to optimize group coordinations. For example, it may suggest outfits suitable for a family trip. In this way, the generation AI may refer to the clothing data of the user's friends and family to suggest group coordinations, thereby improving user satisfaction.

[0092] The coordination assistance system can also analyze the user's current emotional state and suggest clothing that matches that emotional state. For example, the generation AI can analyze the user's current emotional state and suggest clothing that matches that emotion. For example, if the user is feeling stressed, it can suggest clothing that is relaxing. The generation AI can also use its emotion estimation function to analyze the user's emotional state in real time and suggest clothing that best suits that emotion. For example, if the user is feeling happy, it can suggest brightly colored clothing. The generation AI can also analyze the user's emotional history and suggest clothing that matches their current emotional state. For example, it can refer to clothing that the user wore in the past when they felt a particular emotion. This improves user satisfaction by suggesting clothing that matches the user's current emotional state.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: A camera is installed in the closet to capture images of the clothes. For example, the camera can be a wide-angle camera mounted on the ceiling that captures the entire closet. Alternatively, the camera can be a fixed camera mounted on a shelf that captures a specific area. Furthermore, multiple cameras can be installed to track the location of clothes in the closet. Step 2: An RFID tag is attached to each garment to provide information about the garment. For example, the RFID tag may be a passive RFID tag attached to the tag portion of the garment. Alternatively, the RFID tag may be an active RFID tag attached to a pocket of the garment. Furthermore, the RFID tag may provide information about the material and color of the garment. Step 3: The generative AI analyzes the data from the camera and RFID tag. For example, the generative AI uses deep learning to analyze the condition of the clothing from the camera image. The generative AI can also use natural language processing to analyze the information from the RFID tag. Furthermore, the generative AI can use image recognition technology to identify the type and color of the clothing. Step 4: The suggestion unit suggests outfits based on the data analyzed by the generative AI. For example, the suggestion unit suggests the most suitable outfits to the user through a user interface. The suggestion unit can also suggest outfits to the user using a notification method. Furthermore, the suggestion unit can set criteria for suggestions and suggest outfits that match the user's preferences.

[0095] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0136] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0145] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0146] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0147] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0148] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0149] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0150] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0151] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0152] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0153] 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.

[0154] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0155] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0156] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0157] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0158] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0159] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A camera installed in the closet, RFID tags attached to each garment, a generation AI that analyzes data from the camera and the RFID tag; a suggestion unit that suggests clothing based on the data analyzed by the generation AI. A system characterized by:

2. The generated AI is The system analyzes camera images and data from the RFID tags to predict the deterioration of the clothing and suggest when it is time to repair or replace it.

2. The system of claim 1.

3. The generated AI is Analyzing camera images, the system tracks changes in the material and color of the clothing and records changes over time.

2. The system of claim 1.

4. The generated AI is Analyze user emotional data, evaluate emotions towards specific clothing items, and prioritize the management of emotionally significant clothing items.

2. The system of claim 1.

5. The generated AI is By analyzing camera images and data from the RFID tags, the system manages the status of not only the clothing but also the shoes and accessories, supporting overall coordination.

2. The system of claim 1.

6. The generated AI is The clothing management data will be linked to smart home devices to automatically set washing machines and dryers.

2. The system of claim 1.

7. The generated AI is Analyzing the user's emotional data, recording the emotions felt when wearing the clothing, and recommending the clothing that elicits positive emotions.

2. The system of claim 1.

8. The generated AI is Suggesting comfortable clothing based on the user's physical condition data 2. The system of claim 1.

Images