System

Abstract

An object of a system according to an embodiment is to manage clothes that a user has and propose clothes suitable for the temperature.SOLUTION: A system includes a clothing registration unit, an air temperature acquisition unit, and a clothing proposal unit. The clothing registration unit registers clothing that the user has. The air temperature acquisition unit acquires air temperature data from a weather forecast application. The clothing proposal unit proposes clothing on the basis of the clothing data registered in the clothing registration unit and the temperature data acquired by the temperature acquisition 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 a user owns and suggest outfits appropriate for the temperature, so there is room for improvement.

[0005] The system according to the embodiment aims to manage the clothes owned by the user and suggest clothes that suit the temperature. [Means for solving the problem]

[0006] The system according to the embodiment includes a clothing registration unit, a temperature acquisition unit, and an outfit suggestion unit. The clothing registration unit registers the clothing owned by the user. The temperature acquisition unit acquires temperature data from a weather forecast app. The outfit suggestion unit suggests outfits based on the clothing data registered in the clothing registration unit and the temperature data acquired by the temperature acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment can manage the clothes owned by the user and suggest clothes that suit the temperature. [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 clothing suggestion system according to an embodiment of the present invention is a system that utilizes a generation AI to manage the clothes owned by a user and suggests the most suitable clothes for the day's temperature in cooperation with a weather forecast app. As a result, the clothing suggestion system can manage the clothes owned by a user and suggest the most suitable clothes for the day's temperature in cooperation with a weather forecast app.

[0029] The clothing suggestion system according to the embodiment includes a clothing registration unit, a temperature acquisition unit, and a clothing suggestion unit. The clothing registration unit registers the clothing owned by the user. For example, the user completes registration by taking a photo of the clothing and sending it to a chatbot. The generation AI analyzes the registered clothing information and manages data such as the type, color, and seasonal appropriate clothing. The temperature acquisition unit acquires temperature data from a weather forecast app. For example, it acquires the current temperature and predicted maximum and minimum temperatures using an API provided by the weather forecast app. The clothing suggestion unit suggests clothing based on the clothing data registered in the clothing registration unit and the temperature data acquired by the temperature acquisition unit. For example, the generation AI suggests, "The temperature today is 20 degrees, so it would be good to wear a light jacket and a long-sleeved shirt." This allows the clothing suggestion system to manage the user's clothing and, in cooperation with the weather forecast app, suggest optimal clothing for the day's temperature.

[0030] The clothing registration unit completes registration by taking a photo of the clothing and sending it to the chatbot. For example, when the clothing registration unit takes a photo of the clothing and sends it to the chatbot, the generation AI automatically recognizes the clothing's material and brand information and adds it to the database. For example, information such as a 100% cotton shirt or Nike sneakers is automatically extracted. The generation AI also reads the information written on the clothing tag using image analysis technology and adds that information to the database. For example, detailed information such as washing instructions and country of manufacture is automatically registered. The generation AI also recognizes the color and design pattern from the clothing photo and adds it to the database along with brand information. For example, it automatically classifies information such as a striped shirt or a checked skirt. This makes it easy for users to register clothing.

[0031] The temperature acquisition unit can acquire the current temperature and the predicted maximum or minimum temperatures using an API provided by the weather forecast app. The temperature acquisition unit, for example, acquires the current temperature and the predicted maximum and minimum temperatures using an API provided by the weather forecast app. For example, the weather forecast app's API can be used to acquire detailed weather information such as humidity and wind speed in addition to temperature, and the generation AI can make clothing suggestions based on that data. For example, on humid days, breathable clothing can be suggested, and on windy days, a windproof jacket can be suggested. This makes it possible to acquire detailed temperature data from the weather forecast app.

[0032] The clothing suggestion unit can suggest specific outfits based on temperature data. For example, the generation AI may suggest, "The temperature is 20 degrees today, so you should wear a light jacket and a long-sleeved shirt." The generation AI may also analyze the user's preferences and past clothing history to suggest individually customized outfits. For example, it may suggest, "You like the color blue, so you should wear this blue shirt today." This allows specific outfits to be suggested based on the temperature.

[0033] The clothing suggestion unit can make suggestions to avoid being perceived as wearing the same clothes when meeting the same person on the same day of the week. The clothing suggestion unit makes suggestions to avoid being perceived as wearing the same clothes when meeting the same person on the same day of the week. For example, the generation AI makes suggestions in the form of "You wore this outfit last Monday, so it would be good to wear different clothes today." The generation AI also manages the user's clothing history and makes suggestions to avoid being perceived as wearing the same clothes when meeting the same person on the same day of the week. This makes it possible to make suggestions to avoid being perceived as wearing the same clothes when meeting the same person on the same day of the week.

[0034] The clothing suggestion unit can analyze the user's preferences and past clothing history and make individually customized clothing suggestions. For example, the clothing suggestion unit analyzes the user's preferences and past clothing history and makes individually customized clothing suggestions. For example, it may make a suggestion in the form of, "Since you like the color blue, you might want to wear this blue shirt today." The generation AI also analyzes the user's past clothing history and makes suggestions based on specific patterns and preferences. For example, it makes suggestions based on colors and styles that the user frequently wears. This allows for customized clothing suggestions based on the user's preferences and past clothing history.

[0035] The clothing registration unit can automatically recognize clothing material and brand information and add it to the database. For example, when a photo of clothing is taken and sent to the chatbot, the generation AI automatically recognizes the clothing material and brand information and adds it to the database. For example, information such as a 100% cotton shirt or Nike sneakers is automatically extracted. The generation AI also reads the information written on the clothing tag using image analysis technology and adds that information to the database. For example, detailed information such as washing instructions and country of manufacture is automatically registered. The generation AI also recognizes colors and design patterns from photos of clothing and adds them to the database along with brand information. For example, it automatically classifies information such as striped shirts and checked skirts. This allows clothing material and brand information to be automatically recognized and added to the database.

[0036] The clothing registration unit can analyze photos and automatically classify color combinations and design patterns. For example, the clothing registration unit takes a photo of clothing, and the generation AI analyzes the photo to automatically classify color combinations and design patterns. For example, information such as red and white stripes or blue and green checkered patterns is registered in a database. The generation AI also analyzes color combinations from photos of clothing and suggests the combination that best suits the user's clothing. For example, it automatically suggests a combination such as a blue shirt and white pants. It also adds a function to analyze photos of clothing and automatically classify design patterns. For example, it recognizes design patterns such as floral and geometric patterns and registers them in a database. This makes it possible to analyze photos of clothing and automatically classify color combinations and design patterns.

[0037] The clothing registration section adds a function that allows users to input their clothing purchase history and frequency of use, allowing the generation AI to make suggestions based on that data. For example, when registering clothing, the clothing registration section adds a function that allows users to input their clothing purchase history and frequency of use. For example, by inputting the purchase date and number of times worn, the generation AI will make suggestions based on that data. The generation AI also analyzes the user's clothing purchase history and frequency of use to make optimal outfit suggestions. For example, it will prioritize suggestions for clothes that have been recently purchased or frequently worn. Furthermore, when registering clothing, the user can input their clothing purchase history and frequency of use, allowing the generation AI to make suggestions based on that data. For example, if a particular piece of clothing is frequently used, the generation AI will suggest new outfits that go well with that clothing. This allows users to input their clothing purchase history and frequency of use and make suggestions based on that information.

[0038] The clothing registration unit can link the registered clothing information with other fashion apps to allow the user to receive styling suggestions for the clothes they own. The clothing registration unit, for example, links the registered clothing information with other fashion apps to allow the user to receive styling suggestions for the clothes they own. For example, it links with a fashion coordination app to suggest styling that suits the user's clothes. It also links with other fashion apps to share information about the clothes the user owns, allowing the user to receive a wider variety of styling suggestions. For example, it integrates and displays suggestions from different apps. It also links the registered clothing information with other fashion apps to allow the user to receive styling suggestions for the clothes they own. For example, it automatically obtains styling suggestions from other apps based on the clothes registered by the user. In this way, it is possible to link the registered clothing information with other fashion apps to allow the user to receive styling suggestions.

[0039] The temperature acquisition unit adds detailed weather information such as humidity and wind speed to the data acquired from a weather forecast app, enabling more accurate clothing suggestions. For example, the temperature acquisition unit adds detailed weather information such as humidity and wind speed to the data acquired from a weather forecast app, allowing the generation AI to make more accurate clothing suggestions. For example, on days with high humidity, breathable clothing is suggested. In addition, the weather forecast app's API is used to acquire detailed weather information such as not only temperature but also humidity and wind speed, and the generation AI makes clothing suggestions based on that data. For example, a windproof jacket is suggested on days with strong winds. In addition, detailed weather information such as humidity and wind speed is added to the data acquired from the weather forecast app, and the generation AI makes clothing suggestions based on that data. For example, on days with low humidity, clothing made of materials with a moisturizing effect is suggested. In this way, detailed weather information such as humidity and wind speed can be added, allowing more accurate clothing suggestions to be made.

[0040] The temperature acquisition unit can compare data from a weather forecast app with past weather data to suggest clothing trends for each season. The temperature acquisition unit adds a function to suggest clothing trends for each season based on data from a weather forecast app, for example, by comparing it with past weather data. For example, it analyzes past data to suggest clothing that will be popular this spring. Also, it adds a function to suggest clothing trends for each season based on data from the weather forecast app, by comparing it with past weather data. For example, it suggests clothing that will be suitable for this summer based on past data. Also, it adds a function to suggest clothing trends for each season based on data from a weather forecast app, by comparing it with past weather data. For example, it analyzes past data to suggest clothing that will be popular this fall. In this way, it is possible to suggest clothing trends for each season by comparing it with past weather data.

[0041] The temperature acquisition unit can work in conjunction with a weather forecast app to suggest clothing suitable for a specific event or activity. The temperature acquisition unit, for example, adds a function to work in conjunction with a weather forecast app to suggest clothing suitable for a specific event or activity. For example, it suggests clothing suitable for a sporting event or outdoor activity. Furthermore, it also adds a function to work in conjunction with a weather forecast app to suggest clothing suitable for a specific event or activity. For example, it suggests clothing suitable for a wedding or a party. Furthermore, it also adds a function to work in conjunction with a weather forecast app to suggest clothing suitable for a specific event or activity. For example, it suggests clothing suitable for a business meeting or a casual gathering. This makes it possible to suggest clothing suitable for a specific event or activity.

[0042] The temperature acquisition unit can make clothing suggestions according to the weather conditions of different regions based on data from a weather forecast app. The temperature acquisition unit adds a function to make clothing suggestions according to the weather conditions of different regions based on data from a weather forecast app. For example, it suggests clothing that matches the weather conditions of a travel destination. Also, it suggests clothing that matches the weather conditions of a business trip destination based on data from the weather forecast app. Also, it adds a function to make clothing suggestions according to the weather conditions of different regions based on data from the weather forecast app. For example, it suggests clothing that matches the weather conditions of a different city or country. This makes it possible to make clothing suggestions according to the weather conditions of different regions.

[0043] The clothing suggestion unit uses the generation AI to analyze the user's past clothing history and make suggestions based on specific patterns and preferences. For example, the generation AI analyzes the user's past clothing history and makes suggestions based on specific patterns and preferences. For example, suggestions are made based on the colors and styles frequently worn by the user. The generation AI also analyzes the user's past clothing history and makes suggestions suitable for specific events or situations. For example, suggestions are made based on the clothes the user would wear to a business meeting. The generation AI also analyzes the user's past clothing history and makes suggestions based on specific patterns and preferences. For example, if the user prefers a specific brand, clothes from that brand are preferentially suggested. This makes it possible to analyze the user's past clothing history and make suggestions based on specific patterns and preferences.

[0044] The clothing suggestion unit allows the generation AI to consider the user's body type and size information and make clothing suggestions that provide an optimal fit. For example, the clothing suggestion unit allows the generation AI to consider the user's body type and size information and make clothing suggestions that provide an optimal fit. For example, it suggests clothes of the optimal size based on the user's height and weight. It also analyzes the user's body type and size information and suggests clothes of a specific brand or design. For example, it prioritizes suggestions of clothes from a brand that suits the user's body type. It also allows the generation AI to consider the user's body type and size information and make clothing suggestions that provide an optimal fit. For example, it suggests clothing that is customized to fit the user's body type. This allows it to consider the user's body type and size information and make clothing suggestions that provide an optimal fit.

[0045] The clothing suggestion unit allows the generation AI to link with the clothing data of the user's friends and family and make group coordination suggestions. The clothing suggestion unit adds a function, for example, where the generation AI links with the clothing data of the user's friends and family and makes group coordination suggestions. For example, it may suggest outfits for the whole family in a coordinated style. The generation AI also analyzes the clothing data of the user's friends and family and makes group coordination suggestions. For example, it may suggest matching outfits with friends. The generation AI also adds a function where the generation AI links with the clothing data of the user's friends and family and makes group coordination suggestions. For example, it may suggest group coordination for a specific event. This allows the generation AI to link with the clothing data of the user's friends and family and make group coordination suggestions.

[0046] The clothing suggestion unit allows the generation AI to make clothing suggestions based on the user's lifestyle and hobbies. The clothing suggestion unit adds, for example, a function that allows the generation AI to make clothing suggestions based on the user's lifestyle and hobbies. For example, it suggests clothing suitable for a user who likes the outdoors. It also analyzes the user's lifestyle and hobbies and makes clothing suggestions based on that. For example, it suggests activewear suitable for a user who plays sports. It also adds a function that allows the generation AI to make clothing suggestions based on the user's lifestyle and hobbies. For example, it suggests clothing suitable for a user who is attending a music festival. This makes it possible to make clothing suggestions based on the user's lifestyle and hobbies.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The clothing recommendation system can further include an activity monitoring unit that monitors the user's activity level. For example, it can record the amount of exercise and number of steps the user takes daily and suggest appropriate clothing based on that data. On days when the user is highly active, it can suggest clothing made of breathable materials and designed for easy movement. The activity monitoring unit can also analyze the user's activity patterns and suggest clothing suitable for specific activities. For example, on days when the user plans to run, it can suggest sportswear that is moisture-wicking and quick-drying. This makes it possible to suggest optimal clothing according to the user's activity level.

[0049] The clothing suggestion system can further include a health monitoring unit that monitors the user's health condition. For example, the system measures the user's body temperature and heart rate and suggests appropriate clothing based on that data. On days when the user's body temperature is high, it can suggest clothing made of cool materials. The health monitoring unit also analyzes the user's health condition and suggests clothing suitable for a specific health condition. For example, on days when the user has a cold, it can suggest clothing with high thermal insulation. This makes it possible to suggest optimal clothing according to the user's health condition.

[0050] The clothing suggestion system can further include a lifestyle analysis unit that suggests clothing based on the user's lifestyle. For example, if the user likes outdoor activities, clothing suitable for outdoor activities can be suggested. Alternatively, if the user places importance on business attire, formal clothing can be suggested. The lifestyle analysis unit can analyze the user's lifestyle data and suggest clothing suitable for specific occasions. This makes it possible to suggest optimal clothing that suits the user's lifestyle.

[0051] The clothing suggestion system can further include a preference analysis unit that analyzes the user's fashion preferences. For example, the preference analysis unit can analyze data on clothing previously chosen by the user to identify their preferred colors and designs. The preference analysis unit can also analyze the styles of fashion influencers the user follows and suggest clothing based on those styles. This allows the system to suggest optimal clothing that matches the user's fashion preferences.

[0052] The clothing suggestion system can further include a history analysis unit that analyzes the user's clothing history. For example, the system can analyze data on clothing worn by the user in the past and make suggestions based on specific patterns or preferences. The history analysis unit can also record the clothing worn by the user at a specific event and make suggestions for clothing for the next event. This allows the system to suggest optimal clothing based on the user's clothing history.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The clothing registration section registers the clothes the user owns. For example, the user can take a photo of the clothes and send it to the chatbot to complete registration. The generation AI analyzes the registered clothing information and manages data such as type, color, and seasonal appropriateness. Step 2: The temperature acquisition unit acquires temperature data from a weather forecast app. For example, it uses an API provided by the weather forecast app to acquire the current temperature and the predicted maximum and minimum temperatures. Step 3: The clothing suggestion unit suggests clothing based on the clothing data registered in the clothing registration unit and the temperature data acquired by the temperature acquisition unit. For example, the generation AI might suggest, "Today the temperature is 20 degrees, so it would be good to wear a light jacket and a long-sleeved shirt."

[0055] (Example 2) The clothing suggestion system according to an embodiment of the present invention is a system that utilizes a generation AI to manage the clothes owned by a user and suggests the most suitable clothes for the day's temperature in cooperation with a weather forecast app. As a result, the clothing suggestion system can manage the clothes owned by a user and suggest the most suitable clothes for the day's temperature in cooperation with a weather forecast app.

[0056] The clothing suggestion system according to the embodiment includes a clothing registration unit, a temperature acquisition unit, and a clothing suggestion unit. The clothing registration unit registers the clothing owned by the user. For example, the user completes registration by taking a photo of the clothing and sending it to a chatbot. The generation AI analyzes the registered clothing information and manages data such as the type, color, and seasonal appropriate clothing. The temperature acquisition unit acquires temperature data from a weather forecast app. For example, it acquires the current temperature and predicted maximum and minimum temperatures using an API provided by the weather forecast app. The clothing suggestion unit suggests clothing based on the clothing data registered in the clothing registration unit and the temperature data acquired by the temperature acquisition unit. For example, the generation AI suggests, "The temperature today is 20 degrees, so it would be good to wear a light jacket and a long-sleeved shirt." This allows the clothing suggestion system to manage the user's clothing and, in cooperation with the weather forecast app, suggest optimal clothing for the day's temperature.

[0057] The clothing registration unit completes registration by taking a photo of the clothing and sending it to the chatbot. For example, when the clothing registration unit takes a photo of the clothing and sends it to the chatbot, the generation AI automatically recognizes the clothing's material and brand information and adds it to the database. For example, information such as a 100% cotton shirt or Nike sneakers is automatically extracted. The generation AI also reads the information written on the clothing tag using image analysis technology and adds that information to the database. For example, detailed information such as washing instructions and country of manufacture is automatically registered. The generation AI also recognizes the color and design pattern from the clothing photo and adds it to the database along with brand information. For example, it automatically classifies information such as a striped shirt or a checked skirt. This makes it easy for users to register clothing.

[0058] The temperature acquisition unit can acquire the current temperature and the predicted maximum or minimum temperatures using an API provided by the weather forecast app. The temperature acquisition unit, for example, acquires the current temperature and the predicted maximum and minimum temperatures using an API provided by the weather forecast app. For example, the weather forecast app's API can be used to acquire detailed weather information such as humidity and wind speed in addition to temperature, and the generation AI can make clothing suggestions based on that data. For example, on humid days, breathable clothing can be suggested, and on windy days, a windproof jacket can be suggested. This makes it possible to acquire detailed temperature data from the weather forecast app.

[0059] The clothing suggestion unit can suggest specific outfits based on temperature data. For example, the generation AI may suggest, "The temperature is 20 degrees today, so you should wear a light jacket and a long-sleeved shirt." The generation AI may also analyze the user's preferences and past clothing history to suggest individually customized outfits. For example, it may suggest, "You like the color blue, so you should wear this blue shirt today." This allows specific outfits to be suggested based on the temperature.

[0060] The clothing suggestion unit can make suggestions to avoid being perceived as wearing the same clothes when meeting the same person on the same day of the week. The clothing suggestion unit makes suggestions to avoid being perceived as wearing the same clothes when meeting the same person on the same day of the week. For example, the generation AI makes suggestions in the form of "You wore this outfit last Monday, so it would be good to wear different clothes today." The generation AI also manages the user's clothing history and makes suggestions to avoid being perceived as wearing the same clothes when meeting the same person on the same day of the week. This makes it possible to make suggestions to avoid being perceived as wearing the same clothes when meeting the same person on the same day of the week.

[0061] The clothing suggestion unit can analyze the user's preferences and past clothing history and make individually customized clothing suggestions. For example, the clothing suggestion unit analyzes the user's preferences and past clothing history and makes individually customized clothing suggestions. For example, it may make a suggestion in the form of, "Since you like the color blue, you might want to wear this blue shirt today." The generation AI also analyzes the user's past clothing history and makes suggestions based on specific patterns and preferences. For example, it makes suggestions based on colors and styles that the user frequently wears. This allows for customized clothing suggestions based on the user's preferences and past clothing history.

[0062] The clothing registration unit can automatically recognize clothing material and brand information and add it to the database. For example, when a photo of clothing is taken and sent to the chatbot, the generation AI automatically recognizes the clothing material and brand information and adds it to the database. For example, information such as a 100% cotton shirt or Nike sneakers is automatically extracted. The generation AI also reads the information written on the clothing tag using image analysis technology and adds that information to the database. For example, detailed information such as washing instructions and country of manufacture is automatically registered. The generation AI also recognizes colors and design patterns from photos of clothing and adds them to the database along with brand information. For example, it automatically classifies information such as striped shirts and checked skirts. This allows clothing material and brand information to be automatically recognized and added to the database.

[0063] The clothing registration unit can analyze photos and automatically classify color combinations and design patterns. For example, the clothing registration unit takes a photo of clothing, and the generation AI analyzes the photo to automatically classify color combinations and design patterns. For example, information such as red and white stripes or blue and green checkered patterns is registered in a database. The generation AI also analyzes color combinations from photos of clothing and suggests the combination that best suits the user's clothing. For example, it automatically suggests a combination such as a blue shirt and white pants. It also adds a function to analyze photos of clothing and automatically classify design patterns. For example, it recognizes design patterns such as floral and geometric patterns and registers them in a database. This makes it possible to analyze photos of clothing and automatically classify color combinations and design patterns.

[0064] The clothing registration unit can use the emotion estimation function to analyze the emotions of the user when registering clothes and provide an interface for eliciting positive emotions. The clothing registration unit adds a function for analyzing the user's facial expressions and voice and estimating emotions when registering clothes, for example. For example, if the user is registering clothes with a smile, a message for eliciting positive emotions is displayed. The emotion estimation function can also be used to analyze the emotions of the user when registering clothes and provide an interface for eliciting positive emotions. For example, a game element can be added that allows the user to enjoy registering clothes. Furthermore, when registering clothes, feedback can be provided in real time based on the emotion estimation data, and advice can be provided to strengthen positive emotions. For example, compliments can be displayed for the clothes registered by the user. This makes it possible to provide an interface for eliciting positive emotions when the user registers clothes.

[0065] The clothing registration section adds a function that allows users to input their clothing purchase history and frequency of use, allowing the generation AI to make suggestions based on that data. For example, when registering clothing, the clothing registration section adds a function that allows users to input their clothing purchase history and frequency of use. For example, by inputting the purchase date and number of times worn, the generation AI will make suggestions based on that data. The generation AI also analyzes the user's clothing purchase history and frequency of use to make optimal outfit suggestions. For example, it will prioritize suggestions for clothes that have been recently purchased or frequently worn. Furthermore, when registering clothing, the user can input their clothing purchase history and frequency of use, allowing the generation AI to make suggestions based on that data. For example, if a particular piece of clothing is frequently used, the generation AI will suggest new outfits that go well with that clothing. This allows users to input their clothing purchase history and frequency of use and make suggestions based on that information.

[0066] The clothing registration unit can link the registered clothing information with other fashion apps to allow the user to receive styling suggestions for the clothes they own. The clothing registration unit, for example, links the registered clothing information with other fashion apps to allow the user to receive styling suggestions for the clothes they own. For example, it links with a fashion coordination app to suggest styling that suits the user's clothes. It also links with other fashion apps to share information about the clothes the user owns, allowing the user to receive a wider variety of styling suggestions. For example, it integrates and displays suggestions from different apps. It also links the registered clothing information with other fashion apps to allow the user to receive styling suggestions for the clothes they own. For example, it automatically obtains styling suggestions from other apps based on the clothes registered by the user. In this way, it is possible to link the registered clothing information with other fashion apps to allow the user to receive styling suggestions.

[0067] The clothing registration unit can use the emotion estimation function to analyze the emotion of the user when registering clothing and evaluate the emotional value of the registered clothing. The clothing registration unit adds a function to estimate the user's emotion when registering clothing and evaluate the emotional value of the registered clothing based on the emotion data, for example. For example, clothing that the user particularly likes is registered in the database as highly rated. Furthermore, the emotion estimation function is used to analyze the emotion of the user when registering clothing and evaluate the emotional value of the registered clothing. For example, clothing registered by the user with a smile is stored in the database as having special value. Furthermore, when registering clothing, a function is added to provide feedback in real time based on the emotion estimation data and evaluate the emotional value of the registered clothing. For example, clothing that the user has positive emotions about is preferentially suggested. This makes it possible to analyze the emotion of the user when registering clothing and evaluate the emotional value.

[0068] The temperature acquisition unit adds detailed weather information such as humidity and wind speed to the data acquired from a weather forecast app, enabling more accurate clothing suggestions. For example, the temperature acquisition unit adds detailed weather information such as humidity and wind speed to the data acquired from a weather forecast app, allowing the generation AI to make more accurate clothing suggestions. For example, on days with high humidity, breathable clothing is suggested. In addition, the weather forecast app's API is used to acquire detailed weather information such as not only temperature but also humidity and wind speed, and the generation AI makes clothing suggestions based on that data. For example, a windproof jacket is suggested on days with strong winds. In addition, detailed weather information such as humidity and wind speed is added to the data acquired from the weather forecast app, and the generation AI makes clothing suggestions based on that data. For example, on days with low humidity, clothing made of materials with a moisturizing effect is suggested. In this way, detailed weather information such as humidity and wind speed can be added, allowing more accurate clothing suggestions to be made.

[0069] The temperature acquisition unit can compare data from a weather forecast app with past weather data to suggest clothing trends for each season. The temperature acquisition unit adds a function to suggest clothing trends for each season based on data from a weather forecast app, for example, by comparing it with past weather data. For example, it analyzes past data to suggest clothing that will be popular this spring. Also, it adds a function to suggest clothing trends for each season based on data from the weather forecast app, by comparing it with past weather data. For example, it suggests clothing that will be suitable for this summer based on past data. Also, it adds a function to suggest clothing trends for each season based on data from a weather forecast app, by comparing it with past weather data. For example, it analyzes past data to suggest clothing that will be popular this fall. In this way, it is possible to suggest clothing trends for each season by comparing it with past weather data.

[0070] The temperature acquisition unit uses the emotion estimation function to analyze the user's emotion regarding the weather forecast and make emotional clothing suggestions according to the weather conditions. The temperature acquisition unit uses the emotion estimation function to analyze the user's emotion based on, for example, data from a weather forecast app and makes emotional clothing suggestions according to the weather conditions. For example, on a rainy day, bright colored clothes that will lift your spirits are suggested. The emotion estimation function is also used to analyze the user's emotion regarding the weather forecast and makes emotional clothing suggestions according to the weather conditions. For example, on a cold day, clothes that will make you feel warm are suggested. The emotion estimation function is also used to analyze the user's emotion based on data from the weather forecast app and makes emotional clothing suggestions according to the weather conditions. For example, on a sunny day, clothes that will make you feel refreshed are suggested. In this way, the user's emotion regarding the weather forecast can be analyzed and emotional clothing suggestions according to the weather conditions can be made.

[0071] The temperature acquisition unit can work in conjunction with a weather forecast app to suggest clothing suitable for a specific event or activity. The temperature acquisition unit, for example, adds a function to work in conjunction with a weather forecast app to suggest clothing suitable for a specific event or activity. For example, it suggests clothing suitable for a sporting event or outdoor activity. Furthermore, it also adds a function to work in conjunction with a weather forecast app to suggest clothing suitable for a specific event or activity. For example, it suggests clothing suitable for a wedding or a party. Furthermore, it also adds a function to work in conjunction with a weather forecast app to suggest clothing suitable for a specific event or activity. For example, it suggests clothing suitable for a business meeting or a casual gathering. This makes it possible to suggest clothing suitable for a specific event or activity.

[0072] The temperature acquisition unit can make clothing suggestions according to the weather conditions of different regions based on data from a weather forecast app. The temperature acquisition unit adds a function to make clothing suggestions according to the weather conditions of different regions based on data from a weather forecast app. For example, it suggests clothing that matches the weather conditions of a travel destination. Also, it suggests clothing that matches the weather conditions of a business trip destination based on data from the weather forecast app. Also, it adds a function to make clothing suggestions according to the weather conditions of different regions based on data from the weather forecast app. For example, it suggests clothing that matches the weather conditions of a different city or country. This makes it possible to make clothing suggestions according to the weather conditions of different regions.

[0073] The temperature acquisition unit uses the emotion estimation function to analyze the user's emotion regarding the weather forecast and make emotional clothing suggestions according to the weather conditions. The temperature acquisition unit uses the emotion estimation function to analyze the user's emotion based on, for example, data from a weather forecast app and makes emotional clothing suggestions according to the weather conditions. For example, on a rainy day, bright colored clothes that will lift your spirits are suggested. The emotion estimation function is also used to analyze the user's emotion regarding the weather forecast and makes emotional clothing suggestions according to the weather conditions. For example, on a cold day, clothes that will make you feel warm are suggested. The emotion estimation function is also used to analyze the user's emotion based on data from the weather forecast app and makes emotional clothing suggestions according to the weather conditions. For example, on a sunny day, clothes that will make you feel refreshed are suggested. In this way, the user's emotion regarding the weather forecast can be analyzed and emotional clothing suggestions according to the weather conditions can be made.

[0074] The clothing suggestion unit uses the generation AI to analyze the user's past clothing history and make suggestions based on specific patterns and preferences. For example, the generation AI analyzes the user's past clothing history and makes suggestions based on specific patterns and preferences. For example, suggestions are made based on the colors and styles frequently worn by the user. The generation AI also analyzes the user's past clothing history and makes suggestions suitable for specific events or situations. For example, suggestions are made based on the clothes the user would wear to a business meeting. The generation AI also analyzes the user's past clothing history and makes suggestions based on specific patterns and preferences. For example, if the user prefers a specific brand, clothes from that brand are preferentially suggested. This makes it possible to analyze the user's past clothing history and make suggestions based on specific patterns and preferences.

[0075] The clothing suggestion unit allows the generation AI to consider the user's body type and size information and make clothing suggestions that provide an optimal fit. For example, the clothing suggestion unit allows the generation AI to consider the user's body type and size information and make clothing suggestions that provide an optimal fit. For example, it suggests clothes of the optimal size based on the user's height and weight. It also analyzes the user's body type and size information and suggests clothes of a specific brand or design. For example, it prioritizes suggestions of clothes from a brand that suits the user's body type. It also allows the generation AI to consider the user's body type and size information and make clothing suggestions that provide an optimal fit. For example, it suggests clothing that is customized to fit the user's body type. This allows it to consider the user's body type and size information and make clothing suggestions that provide an optimal fit.

[0076] The clothing suggestion unit can use the emotion estimation function to make emotional clothing suggestions based on the user's mood and plans for that day. The clothing suggestion unit, for example, uses the emotion estimation function to make emotional clothing suggestions based on the user's mood and plans for that day. For example, it suggests comfortable clothes for a day when the user wants to relax. It also analyzes the user's mood and plans for that day to make emotional clothing suggestions. For example, it suggests clothes that will make the user feel confident on a day when the user has an important presentation coming up. It also uses the emotion estimation function to make emotional clothing suggestions based on the user's mood and plans for that day. For example, it suggests special clothes for the user on a date. In this way, it is possible to make emotional clothing suggestions based on the user's mood and plans for that day.

[0077] The clothing suggestion unit allows the generation AI to link with the clothing data of the user's friends and family and make group coordination suggestions. The clothing suggestion unit adds a function, for example, where the generation AI links with the clothing data of the user's friends and family and makes group coordination suggestions. For example, it may suggest outfits for the whole family in a coordinated style. The generation AI also analyzes the clothing data of the user's friends and family and makes group coordination suggestions. For example, it may suggest matching outfits with friends. The generation AI also adds a function where the generation AI links with the clothing data of the user's friends and family and makes group coordination suggestions. For example, it may suggest group coordination for a specific event. This allows the generation AI to link with the clothing data of the user's friends and family and make group coordination suggestions.

[0078] The clothing suggestion unit allows the generation AI to make clothing suggestions based on the user's lifestyle and hobbies. The clothing suggestion unit adds, for example, a function that allows the generation AI to make clothing suggestions based on the user's lifestyle and hobbies. For example, it suggests clothing suitable for a user who likes the outdoors. It also analyzes the user's lifestyle and hobbies and makes clothing suggestions based on that. For example, it suggests activewear suitable for a user who plays sports. It also adds a function that allows the generation AI to make clothing suggestions based on the user's lifestyle and hobbies. For example, it suggests clothing suitable for a user who is attending a music festival. This makes it possible to make clothing suggestions based on the user's lifestyle and hobbies.

[0079] The clothing suggestion unit can use the emotion estimation function to make emotional clothing suggestions based on the user's mood and plans for that day. The clothing suggestion unit, for example, uses the emotion estimation function to make emotional clothing suggestions based on the user's mood and plans for that day. For example, it suggests comfortable clothes for a day when the user wants to relax. It also analyzes the user's mood and plans for that day to make emotional clothing suggestions. For example, it suggests clothes that will make the user feel confident on a day when the user has an important presentation coming up. It also uses the emotion estimation function to make emotional clothing suggestions based on the user's mood and plans for that day. For example, it suggests special clothes for the user on a date. In this way, it is possible to make emotional clothing suggestions based on the user's mood and plans for that day.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The clothing recommendation system can further include an activity monitoring unit that monitors the user's activity level. For example, it can record the amount of exercise and number of steps the user takes daily and suggest appropriate clothing based on that data. On days when the user is highly active, it can suggest clothing made of breathable materials and designed for easy movement. The activity monitoring unit can also analyze the user's activity patterns and suggest clothing suitable for specific activities. For example, on days when the user plans to run, it can suggest sportswear that is moisture-wicking and quick-drying. This makes it possible to suggest optimal clothing according to the user's activity level.

[0082] The clothing suggestion system can further include an emotion estimation unit that estimates the user's emotion and suggests clothing based on the estimated emotion. For example, if the user is feeling stressed, clothing made of soft materials that will help them relax can be suggested. Also, if the user is feeling cheerful, clothing with bright colors and designs can be suggested. The emotion estimation unit can analyze the user's facial expressions and voice and estimate the emotion in real time. This makes it possible to suggest optimal clothing based on the user's emotion.

[0083] The clothing suggestion system can further include a health monitoring unit that monitors the user's health condition. For example, the system measures the user's body temperature and heart rate and suggests appropriate clothing based on that data. On days when the user's body temperature is high, it can suggest clothing made of cool materials. The health monitoring unit also analyzes the user's health condition and suggests clothing suitable for a specific health condition. For example, on days when the user has a cold, it can suggest clothing with high thermal insulation. This makes it possible to suggest optimal clothing according to the user's health condition.

[0084] The clothing suggestion system can further include an emotion estimation unit that estimates the user's emotion and suggests clothing based on the estimated emotion. For example, if the user is nervous, it can suggest clothing with a relaxing design. Also, if the user is in a happy mood, it can suggest clothing with a colorful design. The emotion estimation unit can analyze the user's facial expressions and voice and estimate the emotion in real time. This makes it possible to suggest optimal clothing according to the user's emotion.

[0085] The clothing suggestion system can further include a lifestyle analysis unit that suggests clothing based on the user's lifestyle. For example, if the user likes outdoor activities, clothing suitable for outdoor activities can be suggested. Alternatively, if the user places importance on business attire, formal clothing can be suggested. The lifestyle analysis unit can analyze the user's lifestyle data and suggest clothing suitable for specific occasions. This makes it possible to suggest optimal clothing that suits the user's lifestyle.

[0086] The clothing suggestion system can further include an emotion estimation unit that estimates the user's emotion and suggests clothing based on the estimated emotion. For example, if the user is feeling down, bright-colored clothing that will lift their spirits can be suggested. Also, if the user wants to relax, clothing made of comfortable materials can be suggested. The emotion estimation unit can analyze the user's facial expressions and voice and estimate their emotion in real time. This makes it possible to suggest optimal clothing based on the user's emotion.

[0087] The clothing suggestion system can further include a preference analysis unit that analyzes the user's fashion preferences. For example, the preference analysis unit can analyze data on clothing previously chosen by the user to identify their preferred colors and designs. The preference analysis unit can also analyze the styles of fashion influencers the user follows and suggest clothing based on those styles. This allows the system to suggest optimal clothing that matches the user's fashion preferences.

[0088] The clothing suggestion system can further include an emotion estimation unit that estimates the user's emotion and suggests clothing based on the estimated emotion. For example, if the user is tired, it can suggest clothing with a relaxing design. On the other hand, if the user is feeling energetic, it can suggest clothing with an active design. The emotion estimation unit can analyze the user's facial expressions and voice and estimate the emotion in real time. This makes it possible to suggest optimal clothing according to the user's emotion.

[0089] The clothing suggestion system can further include a history analysis unit that analyzes the user's clothing history. For example, the system can analyze data on clothing worn by the user in the past and make suggestions based on specific patterns or preferences. The history analysis unit can also record the clothing worn by the user at a specific event and make suggestions for clothing for the next event. This allows the system to suggest optimal clothing based on the user's clothing history.

[0090] The clothing suggestion system can further include an emotion estimation unit that estimates the user's emotion and suggests clothing based on the estimated emotion. For example, if the user is nervous, it can suggest clothing with a relaxing design. Also, if the user is in a happy mood, it can suggest clothing with a colorful design. The emotion estimation unit can analyze the user's facial expressions and voice and estimate the emotion in real time. This makes it possible to suggest optimal clothing according to the user's emotion.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The clothing registration section registers the clothes the user owns. For example, the user can take a photo of the clothes and send it to the chatbot to complete registration. The generation AI analyzes the registered clothing information and manages data such as type, color, and seasonal appropriateness. Step 2: The temperature acquisition unit acquires temperature data from a weather forecast app. For example, it uses an API provided by the weather forecast app to acquire the current temperature and the predicted maximum and minimum temperatures. Step 3: The clothing suggestion unit suggests clothing based on the clothing data registered in the clothing registration unit and the temperature data acquired by the temperature acquisition unit. For example, the generation AI might suggest, "Today the temperature is 20 degrees, so it would be good to wear a light jacket and a long-sleeved shirt."

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 AI 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 AI 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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 AI 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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 clothing registration unit for registering the clothing owned by the user; a temperature acquisition unit that acquires temperature data from a weather forecast application; and a clothing suggestion unit that suggests clothing based on the clothing data registered in the clothing registration unit and the temperature data acquired by the temperature acquisition unit. A system characterized by:

2. The clothing registration unit Registration is completed by taking a photo of the clothes and sending it to the chatbot.

2. The system of claim 1.

3. The temperature acquisition unit Using the API provided by the weather forecast app, the current temperature and the predicted maximum or minimum temperature are obtained.

2. The system of claim 1.

4. The clothing suggestion unit Recommend specific clothing based on the temperature data 2. The system of claim 1.

5. The clothing suggestion unit Suggestions for avoiding the appearance of wearing the same clothes when meeting the same person on the same day of the week 2. The system of claim 1.

6. The clothing suggestion unit Analyzing the user's preferences and past clothing history and proposing individually customized clothing 2. The system of claim 1.

7. The clothing registration unit Automatically recognize clothing material and brand information and add it to the database 2. The system of claim 1.

8. The clothing registration unit Analyzes photos and automatically classifies color combinations and design patterns 2. The system of claim 1.

Images