system

Abstract

The system according to the embodiment aims to provide a simple and easy way to perform a personal color diagnosis. [Solution] A system according to an embodiment includes a photographing unit, an analysis unit, and a providing unit. The photographing unit automatically starts photographing when a user enters a Box. The analysis unit analyzes the image photographed by the photographing unit and diagnoses the user's personal color. The providing unit provides the user with the results of the diagnosis by the analysis unit.

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 technology requires face-to-face personal color diagnosis, which makes it expensive and difficult to use.

[0005] The system according to the embodiment aims to provide a simple and easy way to perform a personal color diagnosis. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an analysis unit, and a providing unit. The photographing unit automatically starts photographing when a user enters a Box. The analysis unit analyzes the image photographed by the photographing unit and diagnoses the user's personal color. The providing unit provides the user with the results of the diagnosis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows for easy personal color diagnosis. [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) A personal color diagnosis system according to an embodiment of the present invention automatically diagnoses a user's personal color and provides the results simply by entering a box. The system automatically starts capturing images when the user enters the box, and AI analyzes the captured images to diagnose the user's personal color. For example, the AI ​​uses deep learning image analysis technology to analyze characteristics such as skin color, hair color, and eye color to identify the user's optimal personal color. The diagnosis results are then digitized and provided to the user. The user can view the diagnosis results via a smartphone app or website. Based on the diagnosis results, optimal fashion and makeup advice is also provided. For example, if spring colors are appropriate, bright pink or pastel-colored clothing is recommended. This system allows users to easily receive a personal color diagnosis. Installing the system in a specific location can also generate buzz and attract customers. For example, installing the system in a shopping mall or beauty salon can increase accessibility. This allows users to easily receive a personal color diagnosis and view the diagnosis results via a smartphone app or website. Based on the diagnosis results, optimal fashion and makeup advice is also provided. This allows users to know the colors and styles that suit them best, which can be useful in choosing fashion and makeup. Furthermore, by installing it in a specific location, it can be expected to create a buzz and attract customers.

[0029] A personal color diagnosis system according to an embodiment includes a camera unit, an analyzer, and a provider. The camera unit automatically starts capturing images when a user enters a Box. For example, the camera unit may detect the user's entry using a sensor and automatically start capturing images. The camera unit can also detect the user's movements and capture images at the optimal timing. For example, it may start capturing images when the user is relaxed. The analyzer uses AI to analyze images captured by the camera unit and diagnose the user's personal color. The analyzer uses deep learning-based image analysis technology to analyze characteristics such as skin color, hair color, and eye color. For example, the analyzer may diagnose that a user with light skin tone would be suited to spring-type colors. The provider provides the results of the analysis to the user. The provider can provide the diagnosis results via a smartphone app or website. The provider can also provide optimal fashion and makeup advice based on the diagnosis results. For example, if spring-type colors are suited, the provider may recommend bright pink or pastel-colored clothing. As a result, the personal color diagnosis system according to the embodiment automatically performs a personal color diagnosis and provides the results to the user simply by the user entering the box.

[0030] The providing unit can provide the diagnosis results via a smartphone app or a website. For example, the providing unit provides the diagnosis results via a smartphone app. Smartphone apps include iOS apps and Android apps. The providing unit can also provide the diagnosis results via a website. For example, the diagnosis results can be checked by accessing a specific website. This allows the user to check the diagnosis results on the smartphone app or website.

[0031] The analysis unit can analyze the characteristics of skin color, hair color, and eye color using image analysis technology that uses deep learning. The analysis unit can analyze skin color, for example, using image analysis technology that uses deep learning. Deep learning technologies include CNN (convolutional neural network) and RNN (recurrent neural network). The analysis unit can also analyze hair color using image analysis technology that uses deep learning. For example, the analysis unit extracts hair color characteristics and identifies the optimal personal color. The analysis unit can also analyze eye color using image analysis technology that uses deep learning. For example, the analysis unit extracts eye color characteristics and identifies the optimal personal color. This enables more accurate personal color diagnosis using deep learning.

[0032] The providing unit can provide fashion and makeup advice based on the diagnosis results. The providing unit, for example, provides optimal fashion advice based on the diagnosis results. For example, if spring-type colors are suitable, the providing unit recommends bright pink or pastel-colored clothing. The providing unit can also provide optimal makeup advice based on the diagnosis results. For example, if spring-type colors are suitable, the providing unit recommends bright-colored lipstick and blush. This allows the user to receive optimal fashion and makeup advice based on the diagnosis results.

[0033] The system can be installed in a specific location. For example, the system can be installed in a shopping mall. By installing the system in a shopping mall, it can be used by many people. The system can also be installed in a beauty salon. By installing the system in a beauty salon, it can be used by people who are interested in beauty. Furthermore, the system can also be installed in an event venue. By installing the system at an event venue, it is expected to create a buzz and attract customers. By installing the system in a specific location, it is expected to create a buzz and attract customers.

[0034] The photographing unit can analyze the user's past photographing history and select a photographing method. For example, the photographing unit analyzes the user's past photographing history and selects the optimal photographing method. For example, the photographing unit automatically applies photographing settings that the user has previously preferred. The photographing unit can also select the most successful settings from the user's past photographing history. The photographing unit can also suggest a new photographing method based on the user's past photographing history. For example, the photographing unit analyzes the user's past photographing history and selects the optimal photographing method. In this way, the optimal photographing method can be selected based on the user's past photographing history.

[0035] The photographing unit can automatically adjust photographing settings based on the user's current ambient light or background when photographing. For example, the photographing unit automatically adjusts exposure when the ambient light is bright. For example, the photographing unit automatically adjusts exposure when the ambient light is bright. The photographing unit can also automatically use a flash when the background is dark. For example, the photographing unit automatically uses a flash when the background is dark. The photographing unit can also adjust settings in real time when the ambient light fluctuates. For example, the photographing unit adjusts settings in real time when the ambient light fluctuates. This makes it possible to automatically adjust optimal photographing settings according to the ambient light and background.

[0036] When taking a photograph, the photographing unit can detect the direction of the user's face and facial expression and photograph at an angle. For example, if the user's face is facing forward, the photographing unit photographs from the front. For example, if the user's face is facing forward, the photographing unit photographs from the front. Furthermore, if the user's face is facing at an angle, the photographing unit can also photograph from an oblique angle. For example, if the user's face is facing at an angle, the photographing unit can also photograph from an oblique angle. Furthermore, if the user's facial expression is smiling, the photographing unit can photograph at an angle that emphasizes the smile. For example, if the user's facial expression is smiling, the photographing unit photographs at an angle that emphasizes the smile. This makes it possible to photograph at the optimal angle depending on the direction of the user's face and facial expression.

[0037] When taking a photograph, the photographing unit can select a highly relevant background based on the user's geographical location information. For example, when the user is in an urban area, the photographing unit selects an urban background. For example, when the user is in an urban area, the photographing unit selects an urban background. Furthermore, when the user is in a natural environment, the photographing unit can select a natural background. For example, when the user is in a natural environment, the photographing unit can select a natural background. Furthermore, when the user is in a specific tourist destination, the photographing unit can select a background of the tourist destination. For example, when the user is in a specific tourist destination, the photographing unit selects a background of the tourist destination. This makes it possible to select an optimal background based on the user's geographical location information.

[0038] The photography unit can analyze the user's social media activity at the time of photography and suggest a related photography style. The photography unit can, for example, suggest a filter that the user often uses on social media. For example, the photography unit can suggest a filter that the user often uses on social media. The photography unit can also suggest a style that the user often posts on social media. For example, the photography unit can suggest a style that the user often posts on social media. The photography unit can also suggest a related photography style by referring to the activities of the user's friends on social media. For example, the photography unit can suggest a related photography style by referring to the activities of the user's friends on social media. This makes it possible to suggest an optimal photography style based on the user's social media activity.

[0039] The photographing unit can customize the photographing method by reflecting the user's past feedback when photographing. For example, the photographing unit automatically applies photographing settings that the user has previously preferred. For example, the photographing unit automatically applies photographing settings that the user has previously preferred. The photographing unit can also select the most successful settings from the user's past feedback. For example, the photographing unit selects the most successful settings from the user's past feedback. The photographing unit can also suggest a new photographing method based on the user's past feedback. For example, the photographing unit suggests a new photographing method based on the user's past feedback. This makes it possible to customize the optimal photographing method based on the user's past feedback.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis according to the importance of the image. For example, the analysis unit performs a detailed analysis for an important image. For example, the analysis unit performs a detailed analysis for an important image. The analysis unit can also perform a simplified analysis for an image of low importance. For example, the analysis unit performs a simplified analysis for an image of low importance. The analysis unit can also perform an analysis with a moderate level of detail for an image of medium importance. For example, the analysis unit performs an analysis with a moderate level of detail for an image of medium importance. This allows analysis to be performed with an optimal level of detail according to the importance of the image.

[0041] During analysis, the analysis unit can apply different analysis algorithms based on the category of the image. For example, when analyzing skin color, the analysis unit uses a dedicated algorithm. For example, when analyzing skin color, the analysis unit uses a dedicated algorithm. In addition, the analysis unit can also use a dedicated algorithm when analyzing hair color. For example, when analyzing hair color, the analysis unit uses a dedicated algorithm. In addition, the analysis unit can also use a dedicated algorithm when analyzing eye color. For example, when analyzing eye color, the analysis unit uses a dedicated algorithm. This makes it possible to apply the optimal analysis algorithm depending on the category of the image.

[0042] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit, for example, performs the current analysis based on the user's past analysis results. For example, the analysis unit performs the current analysis based on the user's past analysis results. The analysis unit can also select the most successful analysis method from the user's past analysis results. For example, the analysis unit selects the most successful analysis method from the user's past analysis results. The analysis unit can also suggest a new analysis method based on the user's past analysis results. For example, the analysis unit suggests a new analysis method based on the user's past analysis results. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time the images were taken. For example, the analysis unit prioritizes analyzing the most recent image. For example, the analysis unit prioritizes analyzing the most recent image. The analysis unit can also postpone analyzing older images. For example, the analysis unit postpones analyzing older images. The analysis unit can also prioritize analyzing images taken at a specific time. For example, the analysis unit prioritizes analyzing images taken at a specific time. This allows analysis to be performed with optimal priority based on the time the images were taken.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the images. For example, the analysis unit prioritizes analyzing highly relevant images. For example, the analysis unit prioritizes analyzing highly relevant images. The analysis unit can also postpone analyzing less relevant images. For example, the analysis unit postpones analyzing less relevant images. The analysis unit can also prioritize analyzing images that belong to a specific category. For example, the analysis unit prioritizes analyzing images that belong to a specific category. This allows analysis to be performed in an optimal order based on the relevance of the images.

[0045] During analysis, the analysis unit can adjust the use of technical terms for the analysis based on the user's level of expertise. For example, the analysis unit uses detailed technical terms for a user with high expertise. For example, the analysis unit uses detailed technical terms for a user with high expertise. The analysis unit can also use simple terms for a user with low expertise. For example, the analysis unit uses simple terms for a user with low expertise. The analysis unit can also use appropriate technical terms for a user with medium expertise. For example, the analysis unit uses appropriate technical terms for a user with medium expertise. This allows the analysis to be performed using optimal technical terms according to the user's level of expertise.

[0046] The providing unit can adjust the level of detail to be provided depending on the importance of the diagnostic result when providing the diagnostic result. For example, in the case of an important diagnostic result, the providing unit provides detailed information. For example, in the case of an important diagnostic result, the providing unit provides detailed information. Furthermore, the providing unit can also provide simplified information in the case of a diagnostic result with low importance. For example, in the case of a diagnostic result with low importance, the providing unit provides simplified information. Furthermore, in the case of a diagnostic result with medium importance, the providing unit can also provide information with a moderate level of detail. For example, in the case of a diagnostic result with medium importance, the providing unit provides information with a moderate level of detail. This makes it possible to provide information with an optimal level of detail depending on the importance of the diagnostic result.

[0047] The providing unit can apply different providing algorithms based on the category of the diagnostic result when providing the result. For example, in the case of personal color diagnosis, the providing unit uses a dedicated algorithm. For example, in the case of personal color diagnosis, the providing unit uses a dedicated algorithm. The providing unit can also use a dedicated algorithm for fashion advice. For example, in the case of fashion advice, the providing unit can also use a dedicated algorithm for makeup advice. For example, in the case of makeup advice, the providing unit uses a dedicated algorithm. This makes it possible to apply the optimal providing algorithm depending on the category of the diagnostic result.

[0048] The providing unit can improve the accuracy of provision by referring to the user's past provision results when providing the information. The providing unit, for example, performs the current provision based on the user's past provision results. For example, the providing unit performs the current provision based on the user's past provision results. The providing unit can also select the most successful provision method from the user's past provision results. For example, the providing unit selects the most successful provision method from the user's past provision results. The providing unit can also propose a new provision method based on the user's past provision results. For example, the providing unit proposes a new provision method based on the user's past provision results. This makes it possible to improve the accuracy of provision based on the user's past provision results.

[0049] At the time of providing, the providing unit can determine the priority of provision based on the time of submission of the diagnostic results. For example, the providing unit provides the latest diagnostic results preferentially. For example, the providing unit provides the latest diagnostic results preferentially. The providing unit can also postpone older diagnostic results. For example, the providing unit postpones older diagnostic results. The providing unit can also preferentially provide results diagnosed at a specific time period. For example, the providing unit preferentially provides results diagnosed at a specific time period. This makes it possible to provide information with optimal priority based on the time of submission of the diagnostic results.

[0050] The providing unit can adjust the order of providing the diagnostic results based on the relevance of the diagnostic results when providing them. For example, the providing unit provides diagnostic results with high relevance preferentially. For example, the providing unit provides diagnostic results with high relevance preferentially. The providing unit can also postpone diagnostic results with low relevance. For example, the providing unit postpones diagnostic results with low relevance. The providing unit can also prioritize providing diagnostic results that belong to a specific category. For example, the providing unit prioritizes providing diagnostic results that belong to a specific category. This makes it possible to provide information in an optimal order based on the relevance of the diagnostic results.

[0051] The providing unit can adjust the use of technical terminology to be provided based on the user's level of expertise when providing information. For example, the providing unit uses detailed technical terminology for a user with high expertise. For example, the providing unit uses detailed technical terminology for a user with high expertise. The providing unit can also use simple terminology for a user with low expertise. For example, the providing unit uses simple terminology for a user with low expertise. The providing unit can also use moderate technical terminology for a user with medium expertise. For example, the providing unit uses moderate technical terminology for a user with medium expertise. This makes it possible to provide information using optimal technical terminology according to the user's level of expertise.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The analysis unit can analyze the user's past fashion history and suggest the optimal fashion style based on the diagnosis results. For example, the analysis unit can analyze the colors and styles of clothes the user has purchased in the past and suggest new fashion items based on the diagnosis results. The analysis unit can also suggest styles suitable for specific seasons or events based on the user's past fashion history. Furthermore, the analysis unit can suggest styles that match the latest trends based on the user's past fashion history. This allows the user to find the fashion style that best suits them.

[0054] The camera unit can analyze the user's movements in real time and suggest optimal poses. For example, if the user is standing in a natural posture, the camera unit can suggest that the user maintain that posture. Also, if the user is in an unnatural posture, the camera unit can provide specific instructions for correcting the posture. Furthermore, the camera unit can analyze the user's movements and suggest the most attractive pose, allowing the user to take a photo in the most optimal pose.

[0055] The providing unit can visually display changes in the diagnostic results based on the user's past diagnostic results. For example, the providing unit can compare the user's past diagnostic results with their current diagnostic results and display the changes in a graph or chart. The providing unit can also present areas for improvement and points to note based on the user's past diagnostic results. Furthermore, the providing unit can provide future predictions based on the user's past diagnostic results. This allows the user to visually confirm changes in their personal color.

[0056] The providing unit can suggest optimal fashion items based on the user's current location information. For example, if the user is in a cold region, it can suggest clothes made of warm materials. Also, if the user is in a warm region, it can suggest lightweight, breathable clothes. Furthermore, if the user is attending a specific event, it can suggest fashion items suitable for that event. This allows the user to select optimal fashion items based on the user's current location information.

[0057] The providing unit can analyze the user's past purchase history and suggest optimal items to purchase based on the diagnosis results. For example, the providing unit can analyze the color and style of items the user has previously purchased and suggest new items based on the diagnosis results. The providing unit can also suggest items suitable for a particular season or event based on the user's past purchase history. Furthermore, the providing unit can suggest trendy items based on the user's past purchase history. This allows the user to find the optimal items to purchase.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The camera unit automatically starts taking pictures when the user enters the Box. The camera unit uses a sensor to detect that the user has entered the Box and automatically starts taking pictures. It can also detect the user's movements and take pictures at the optimal timing. For example, it can start taking pictures when the user is relaxing. Step 2: The analysis unit uses AI to analyze the image captured by the camera unit and diagnose the user's personal color. The analysis unit uses image analysis technology based on deep learning to analyze characteristics such as skin color, hair color, and eye color. For example, if the user has light skin, it will diagnose that spring-type colors are suitable. Step 3: The provider provides the results of the diagnosis made by the analyzer to the user. The provider can provide the diagnosis results via a smartphone app or website. It can also provide optimal fashion and makeup advice based on the diagnosis results. For example, if spring colors are appropriate, it can recommend bright pink or pastel-colored clothing.

[0060] (Example 2) A personal color diagnosis system according to an embodiment of the present invention automatically diagnoses a user's personal color and provides the results simply by entering a box. The system automatically starts capturing images when the user enters the box, and AI analyzes the captured images to diagnose the user's personal color. For example, the AI ​​uses deep learning image analysis technology to analyze characteristics such as skin color, hair color, and eye color to identify the user's optimal personal color. The diagnosis results are then digitized and provided to the user. The user can view the diagnosis results via a smartphone app or website. Based on the diagnosis results, optimal fashion and makeup advice is also provided. For example, if spring colors are appropriate, bright pink or pastel-colored clothing is recommended. This system allows users to easily receive a personal color diagnosis. Installing the system in a specific location can also generate buzz and attract customers. For example, installing the system in a shopping mall or beauty salon can increase accessibility. This allows users to easily receive a personal color diagnosis and view the diagnosis results via a smartphone app or website. Based on the diagnosis results, optimal fashion and makeup advice is also provided. This allows users to know the colors and styles that suit them best, which can be useful in choosing fashion and makeup. Furthermore, by installing it in a specific location, it can be expected to create a buzz and attract customers.

[0061] A personal color diagnosis system according to an embodiment includes a camera unit, an analyzer, and a provider. The camera unit automatically starts capturing images when a user enters a Box. For example, the camera unit may detect the user's entry using a sensor and automatically start capturing images. The camera unit can also detect the user's movements and capture images at the optimal timing. For example, it may start capturing images when the user is relaxed. The analyzer uses AI to analyze images captured by the camera unit and diagnose the user's personal color. The analyzer uses deep learning-based image analysis technology to analyze characteristics such as skin color, hair color, and eye color. For example, the analyzer may diagnose that a user with light skin tone would be suited to spring-type colors. The provider provides the results of the analysis to the user. The provider can provide the diagnosis results via a smartphone app or website. The provider can also provide optimal fashion and makeup advice based on the diagnosis results. For example, if spring-type colors are suited, the provider may recommend bright pink or pastel-colored clothing. As a result, the personal color diagnosis system according to the embodiment automatically performs a personal color diagnosis and provides the results to the user simply by the user entering the box.

[0062] The providing unit can provide the diagnosis results via a smartphone app or a website. For example, the providing unit provides the diagnosis results via a smartphone app. Smartphone apps include iOS apps and Android apps. The providing unit can also provide the diagnosis results via a website. For example, the diagnosis results can be checked by accessing a specific website. This allows the user to check the diagnosis results on the smartphone app or website.

[0063] The analysis unit can analyze the characteristics of skin color, hair color, and eye color using image analysis technology that uses deep learning. The analysis unit can analyze skin color, for example, using image analysis technology that uses deep learning. Deep learning technologies include CNN (convolutional neural network) and RNN (recurrent neural network). The analysis unit can also analyze hair color using image analysis technology that uses deep learning. For example, the analysis unit extracts hair color characteristics and identifies the optimal personal color. The analysis unit can also analyze eye color using image analysis technology that uses deep learning. For example, the analysis unit extracts eye color characteristics and identifies the optimal personal color. This enables more accurate personal color diagnosis using deep learning.

[0064] The providing unit can provide fashion and makeup advice based on the diagnosis results. The providing unit, for example, provides optimal fashion advice based on the diagnosis results. For example, if spring-type colors are suitable, the providing unit recommends bright pink or pastel-colored clothing. The providing unit can also provide optimal makeup advice based on the diagnosis results. For example, if spring-type colors are suitable, the providing unit recommends bright-colored lipstick and blush. This allows the user to receive optimal fashion and makeup advice based on the diagnosis results.

[0065] The system can be installed in a specific location. For example, the system can be installed in a shopping mall. By installing the system in a shopping mall, it can be used by many people. The system can also be installed in a beauty salon. By installing the system in a beauty salon, it can be used by people who are interested in beauty. Furthermore, the system can also be installed in an event venue. By installing the system at an event venue, it is expected to create a buzz and attract customers. By installing the system in a specific location, it is expected to create a buzz and attract customers.

[0066] The image capturing unit can estimate the user's emotion and adjust the timing of capturing images based on the estimated user emotion. For example, the image capturing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the image capturing unit calculates an emotion score based on changes in facial expression and adjusts the timing of capturing images. The image capturing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the image capturing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of capturing images. The image capturing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the image capturing unit calculates an emotion score based on heart rate fluctuations and adjusts the timing of capturing images. This allows capturing images at the optimal timing depending on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] The photographing unit can analyze the user's past photographing history and select a photographing method. For example, the photographing unit analyzes the user's past photographing history and selects the optimal photographing method. For example, the photographing unit automatically applies photographing settings that the user has previously preferred. The photographing unit can also select the most successful settings from the user's past photographing history. The photographing unit can also suggest a new photographing method based on the user's past photographing history. For example, the photographing unit analyzes the user's past photographing history and selects the optimal photographing method. In this way, the optimal photographing method can be selected based on the user's past photographing history.

[0068] The photographing unit can automatically adjust photographing settings based on the user's current ambient light or background when photographing. For example, the photographing unit automatically adjusts exposure when the ambient light is bright. For example, the photographing unit automatically adjusts exposure when the ambient light is bright. The photographing unit can also automatically use a flash when the background is dark. For example, the photographing unit automatically uses a flash when the background is dark. The photographing unit can also adjust settings in real time when the ambient light fluctuates. For example, the photographing unit adjusts settings in real time when the ambient light fluctuates. This makes it possible to automatically adjust optimal photographing settings according to the ambient light and background.

[0069] When taking a photograph, the photographing unit can detect the direction of the user's face and facial expression and photograph at an angle. For example, if the user's face is facing forward, the photographing unit photographs from the front. For example, if the user's face is facing forward, the photographing unit photographs from the front. Furthermore, if the user's face is facing at an angle, the photographing unit can also photograph from an oblique angle. For example, if the user's face is facing at an angle, the photographing unit can also photograph from an oblique angle. Furthermore, if the user's facial expression is smiling, the photographing unit can photograph at an angle that emphasizes the smile. For example, if the user's facial expression is smiling, the photographing unit photographs at an angle that emphasizes the smile. This makes it possible to photograph at the optimal angle depending on the direction of the user's face and facial expression.

[0070] The image capturing unit can estimate the user's emotions and determine the priority of images to be captured based on the estimated user's emotions. For example, if the user is nervous, the image capturing unit prioritizes images with a relaxed expression. For example, if the user is nervous, the image capturing unit prioritizes images with a relaxed expression. The image capturing unit can also prioritize images with a natural expression if the user is relaxed. For example, if the user is relaxed, the image capturing unit prioritizes images with a natural expression. The image capturing unit can also prioritize images captured quickly if the user is in a hurry. For example, if the user is in a hurry, the image capturing unit prioritizes images captured quickly. This makes it possible to prioritize capturing optimal images according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] When taking a photograph, the photographing unit can select a highly relevant background based on the user's geographical location information. For example, when the user is in an urban area, the photographing unit selects an urban background. For example, when the user is in an urban area, the photographing unit selects an urban background. Furthermore, when the user is in a natural environment, the photographing unit can select a natural background. For example, when the user is in a natural environment, the photographing unit can select a natural background. Furthermore, when the user is in a specific tourist destination, the photographing unit can select a background of the tourist destination. For example, when the user is in a specific tourist destination, the photographing unit selects a background of the tourist destination. This makes it possible to select an optimal background based on the user's geographical location information.

[0072] The photography unit can analyze the user's social media activity at the time of photography and suggest a related photography style. The photography unit can, for example, suggest a filter that the user often uses on social media. For example, the photography unit can suggest a filter that the user often uses on social media. The photography unit can also suggest a style that the user often posts on social media. For example, the photography unit can suggest a style that the user often posts on social media. The photography unit can also suggest a related photography style by referring to the activities of the user's friends on social media. For example, the photography unit can suggest a related photography style by referring to the activities of the user's friends on social media. This makes it possible to suggest an optimal photography style based on the user's social media activity.

[0073] The photographing unit can customize the photographing method by reflecting the user's past feedback when photographing. For example, the photographing unit automatically applies photographing settings that the user has previously preferred. For example, the photographing unit automatically applies photographing settings that the user has previously preferred. The photographing unit can also select the most successful settings from the user's past feedback. For example, the photographing unit selects the most successful settings from the user's past feedback. The photographing unit can also suggest a new photographing method based on the user's past feedback. For example, the photographing unit suggests a new photographing method based on the user's past feedback. This makes it possible to customize the optimal photographing method based on the user's past feedback.

[0074] The analysis unit can estimate the user's emotions and adjust the way the analysis is expressed based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a summary analysis result if the user is in a hurry. For example, if the user is in a hurry, the analysis unit provides a summary analysis result. This makes it possible to provide the analysis result in an optimal expression method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] During analysis, the analysis unit can adjust the level of detail of the analysis according to the importance of the image. For example, the analysis unit performs a detailed analysis for an important image. For example, the analysis unit performs a detailed analysis for an important image. The analysis unit can also perform a simplified analysis for an image of low importance. For example, the analysis unit performs a simplified analysis for an image of low importance. The analysis unit can also perform an analysis with a moderate level of detail for an image of medium importance. For example, the analysis unit performs an analysis with a moderate level of detail for an image of medium importance. This allows analysis to be performed with an optimal level of detail according to the importance of the image.

[0076] During analysis, the analysis unit can apply different analysis algorithms based on the category of the image. For example, when analyzing skin color, the analysis unit uses a dedicated algorithm. For example, when analyzing skin color, the analysis unit uses a dedicated algorithm. In addition, the analysis unit can also use a dedicated algorithm when analyzing hair color. For example, when analyzing hair color, the analysis unit uses a dedicated algorithm. In addition, the analysis unit can also use a dedicated algorithm when analyzing eye color. For example, when analyzing eye color, the analysis unit uses a dedicated algorithm. This makes it possible to apply the optimal analysis algorithm depending on the category of the image.

[0077] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit, for example, performs the current analysis based on the user's past analysis results. For example, the analysis unit performs the current analysis based on the user's past analysis results. The analysis unit can also select the most successful analysis method from the user's past analysis results. For example, the analysis unit selects the most successful analysis method from the user's past analysis results. The analysis unit can also suggest a new analysis method based on the user's past analysis results. For example, the analysis unit suggests a new analysis method based on the user's past analysis results. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit performs a short and to-the-point analysis. For example, if the user is in a hurry, the analysis unit performs a short and to-the-point analysis. The analysis unit can also perform a detailed analysis if the user is relaxed. For example, if the user is relaxed, the analysis unit performs a detailed analysis. The analysis unit can also perform an analysis that adds visually stimulating effects if the user is excited. For example, if the user is excited, the analysis unit performs an analysis that adds visually stimulating effects. This allows the analysis to be performed at an optimal length depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] During analysis, the analysis unit can determine the priority of analysis based on the time the images were taken. For example, the analysis unit prioritizes analyzing the most recent image. For example, the analysis unit prioritizes analyzing the most recent image. The analysis unit can also postpone analyzing older images. For example, the analysis unit postpones analyzing older images. The analysis unit can also prioritize analyzing images taken at a specific time. For example, the analysis unit prioritizes analyzing images taken at a specific time. This allows analysis to be performed with optimal priority based on the time the images were taken.

[0080] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the images. For example, the analysis unit prioritizes analyzing highly relevant images. For example, the analysis unit prioritizes analyzing highly relevant images. The analysis unit can also postpone analyzing less relevant images. For example, the analysis unit postpones analyzing less relevant images. The analysis unit can also prioritize analyzing images that belong to a specific category. For example, the analysis unit prioritizes analyzing images that belong to a specific category. This allows analysis to be performed in an optimal order based on the relevance of the images.

[0081] During analysis, the analysis unit can adjust the use of technical terms for the analysis based on the user's level of expertise. For example, the analysis unit uses detailed technical terms for a user with high expertise. For example, the analysis unit uses detailed technical terms for a user with high expertise. The analysis unit can also use simple terms for a user with low expertise. For example, the analysis unit uses simple terms for a user with low expertise. The analysis unit can also use appropriate technical terms for a user with medium expertise. For example, the analysis unit uses appropriate technical terms for a user with medium expertise. This allows the analysis to be performed using optimal technical terms according to the user's level of expertise.

[0082] The providing unit can estimate the user's emotions and adjust the way in which information is presented based on the estimated user's emotions. For example, when the user is nervous, the providing unit provides simple, highly visible information. For example, when the user is nervous, the providing unit provides simple, highly visible information. The providing unit can also provide detailed information when the user is relaxed. For example, when the user is relaxed, the providing unit provides detailed information. The providing unit can also provide information that focuses on the main points when the user is in a hurry. For example, when the user is in a hurry, the providing unit provides information that focuses on the main points. This makes it possible to provide information in an optimal way of presentation depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The providing unit can adjust the level of detail to be provided depending on the importance of the diagnostic result when providing the diagnostic result. For example, in the case of an important diagnostic result, the providing unit provides detailed information. For example, in the case of an important diagnostic result, the providing unit provides detailed information. Furthermore, the providing unit can also provide simplified information in the case of a diagnostic result with low importance. For example, in the case of a diagnostic result with low importance, the providing unit provides simplified information. Furthermore, in the case of a diagnostic result with medium importance, the providing unit can also provide information with a moderate level of detail. For example, in the case of a diagnostic result with medium importance, the providing unit provides information with a moderate level of detail. This makes it possible to provide information with an optimal level of detail depending on the importance of the diagnostic result.

[0084] The providing unit can apply different providing algorithms based on the category of the diagnostic result when providing the result. For example, in the case of personal color diagnosis, the providing unit uses a dedicated algorithm. For example, in the case of personal color diagnosis, the providing unit uses a dedicated algorithm. The providing unit can also use a dedicated algorithm for fashion advice. For example, in the case of fashion advice, the providing unit can also use a dedicated algorithm for makeup advice. For example, in the case of makeup advice, the providing unit uses a dedicated algorithm. This makes it possible to apply the optimal providing algorithm depending on the category of the diagnostic result.

[0085] The providing unit can improve the accuracy of provision by referring to the user's past provision results when providing the information. The providing unit, for example, performs the current provision based on the user's past provision results. For example, the providing unit performs the current provision based on the user's past provision results. The providing unit can also select the most successful provision method from the user's past provision results. For example, the providing unit selects the most successful provision method from the user's past provision results. The providing unit can also propose a new provision method based on the user's past provision results. For example, the providing unit proposes a new provision method based on the user's past provision results. This makes it possible to improve the accuracy of provision based on the user's past provision results.

[0086] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the estimated user's emotions. For example, when the user is in a hurry, the providing unit provides short, to-the-point information. For example, when the user is in a hurry, the providing unit provides short, to-the-point information. The providing unit can also provide detailed information when the user is relaxed. For example, when the user is relaxed, the providing unit provides detailed information. The providing unit can also provide information with a visually stimulating effect when the user is excited. For example, when the user is excited, the providing unit provides information with a visually stimulating effect. This makes it possible to provide information with an optimal length depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] At the time of providing, the providing unit can determine the priority of provision based on the time of submission of the diagnostic results. For example, the providing unit provides the latest diagnostic results preferentially. For example, the providing unit provides the latest diagnostic results preferentially. The providing unit can also postpone older diagnostic results. For example, the providing unit postpones older diagnostic results. The providing unit can also preferentially provide results diagnosed at a specific time period. For example, the providing unit preferentially provides results diagnosed at a specific time period. This makes it possible to provide information with optimal priority based on the time of submission of the diagnostic results.

[0088] The providing unit can adjust the order of providing the diagnostic results based on the relevance of the diagnostic results when providing them. For example, the providing unit provides diagnostic results with high relevance preferentially. For example, the providing unit provides diagnostic results with high relevance preferentially. The providing unit can also postpone diagnostic results with low relevance. For example, the providing unit postpones diagnostic results with low relevance. The providing unit can also prioritize providing diagnostic results that belong to a specific category. For example, the providing unit prioritizes providing diagnostic results that belong to a specific category. This makes it possible to provide information in an optimal order based on the relevance of the diagnostic results.

[0089] The providing unit can adjust the use of technical terminology to be provided based on the user's level of expertise when providing information. For example, the providing unit uses detailed technical terminology for a user with high expertise. For example, the providing unit uses detailed technical terminology for a user with high expertise. The providing unit can also use simple terminology for a user with low expertise. For example, the providing unit uses simple terminology for a user with low expertise. The providing unit can also use moderate technical terminology for a user with medium expertise. For example, the providing unit uses moderate technical terminology for a user with medium expertise. This makes it possible to provide information using optimal technical terminology according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, and providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the smart device 14, and automatically starts taking pictures when the user enters the Box. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the captured image using AI to diagnose the user's personal color. The providing unit is realized, for example, by the control unit 46A of the smart device 14, and provides the diagnosis results via a smartphone app or website. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned photographing unit, analysis unit, and providing unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the smart glasses 214, and automatically starts capturing images when the user enters the Box. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the captured image using AI to diagnose the user's personal color. The providing unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the diagnosis results via a smartphone app or website. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, and providing unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the headset-type terminal 314, and automatically starts taking pictures when the user enters the Box. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the captured image using AI to diagnose the user's personal color. The providing unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides the diagnosis results via a smartphone app or website. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, and providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the robot 414, and automatically starts taking pictures when the user enters the Box. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the captured image using AI to diagnose the user's personal color. The providing unit is realized, for example, by the control unit 46A of the robot 414, and provides the diagnosis results via a smartphone app or website.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The analysis unit can analyze the user's past fashion history and suggest the optimal fashion style based on the diagnosis results. For example, the analysis unit can analyze the colors and styles of clothes the user has purchased in the past and suggest new fashion items based on the diagnosis results. The analysis unit can also suggest styles suitable for specific seasons or events based on the user's past fashion history. Furthermore, the analysis unit can suggest styles that match the latest trends based on the user's past fashion history. This allows the user to find the fashion style that best suits them.

[0092] The providing unit can estimate the user's emotions and adjust the tone of the fashion advice provided based on the estimated user's emotions. For example, if the user is nervous, simple and easy-to-understand advice can be provided. If the user is relaxed, detailed and specific advice can be provided. Furthermore, if the user is excited, visually appealing advice can be provided. In this way, fashion advice can be provided in an optimal tone depending on the user's emotions.

[0093] The camera unit can analyze the user's movements in real time and suggest optimal poses. For example, if the user is standing in a natural posture, the camera unit can suggest that the user maintain that posture. Also, if the user is in an unnatural posture, the camera unit can provide specific instructions for correcting the posture. Furthermore, the camera unit can analyze the user's movements and suggest the most attractive pose, allowing the user to take a photo in the most optimal pose.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be selected. If the user is relaxed, detailed analysis results can be displayed. Furthermore, if the user is excited, a visually appealing display method can be selected. This makes it possible to provide analysis results in the optimal display method according to the user's emotions.

[0095] The providing unit can visually display changes in the diagnostic results based on the user's past diagnostic results. For example, the providing unit can compare the user's past diagnostic results with their current diagnostic results and display the changes in a graph or chart. The providing unit can also present areas for improvement and points to note based on the user's past diagnostic results. Furthermore, the providing unit can provide future predictions based on the user's past diagnostic results. This allows the user to visually confirm changes in their personal color.

[0096] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, if the user is nervous, a simplified analysis can be performed. If the user is relaxed, a detailed analysis can be performed. Furthermore, if the user is excited, a visually appealing analysis can be performed. This allows the analysis to be performed with optimal accuracy according to the user's emotions.

[0097] The providing unit can suggest optimal fashion items based on the user's current location information. For example, if the user is in a cold region, it can suggest clothes made of warm materials. Also, if the user is in a warm region, it can suggest lightweight, breathable clothes. Furthermore, if the user is attending a specific event, it can suggest fashion items suitable for that event. This allows the user to select optimal fashion items based on the user's current location information.

[0098] The analysis unit can estimate the user's emotions and adjust the speed of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis can be performed quickly. If the user is relaxed, the analysis can be performed in detail. Furthermore, if the user is excited, the analysis can be performed in a visually appealing manner. This allows the analysis to be performed at an optimal speed depending on the user's emotions.

[0099] The providing unit can analyze the user's past purchase history and suggest optimal items to purchase based on the diagnosis results. For example, the providing unit can analyze the color and style of items the user has previously purchased and suggest new items based on the diagnosis results. The providing unit can also suggest items suitable for a particular season or event based on the user's past purchase history. Furthermore, the providing unit can suggest trendy items based on the user's past purchase history. This allows the user to find the optimal items to purchase.

[0100] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is nervous, important analysis can be prioritized. If the user is relaxed, detailed analysis can be performed. Furthermore, if the user is excited, visually appealing analysis can be prioritized. This allows analysis to be performed with optimal priority according to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The camera unit automatically starts taking pictures when the user enters the Box. The camera unit uses a sensor to detect that the user has entered the Box and automatically starts taking pictures. It can also detect the user's movements and take pictures at the optimal timing. For example, it can start taking pictures when the user is relaxing. Step 2: The analysis unit uses AI to analyze the image captured by the camera unit and diagnose the user's personal color. The analysis unit uses image analysis technology based on deep learning to analyze characteristics such as skin color, hair color, and eye color. For example, if the user has light skin, it will diagnose that spring-type colors are suitable. Step 3: The provider provides the results of the diagnosis made by the analyzer to the user. The provider can provide the diagnosis results via a smartphone app or website. It can also provide optimal fashion and makeup advice based on the diagnosis results. For example, if spring colors are appropriate, it can recommend bright pink or pastel-colored clothing.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A camera that automatically starts taking pictures when a user enters the Box; an analysis unit that analyzes the image captured by the image capturing unit and diagnoses the user's personal color; a providing unit that provides a user with the results of the diagnosis by the analysis unit. A system characterized by:

2. The providing unit Diagnostic results are provided via a smartphone app or website 2. The system of claim 1.

3. The analysis unit Deep learning-based image analysis technology is used to analyze skin, hair, and eye color characteristics.

2. The system of claim 1.

4. The providing unit Providing fashion and makeup advice based on diagnostic results 2. The system of claim 1.

5. The system comprises: placed in a specific location 2. The system of claim 1.

6. The imaging unit is Estimate the user's emotions and adjust the timing of taking photos based on the estimated user emotions.

2. The system of claim 1.

7. The imaging unit is Analyze the user's past photography history and select the photography method 2. The system of claim 1.

8. The imaging unit is When taking a photo, automatically adjust capture settings based on the user's current ambient light or background 2. The system of claim 1.

Images