System

Abstract

To provide a system which enables a user to receive an accurate diagnosis of a personal color, the skeleton, and a face type, while being at home.SOLUTION: A system includes an imaging unit, an analysis unit, and a display unit. The imaging unit enables a user to take a face photograph. The analysis unit analyzes the face photograph taken by the imaging unit, and diagnoses a personal color, the skeleton, and a face type respectively. The display unit instantly displays a diagnosis result acquired by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, accurate diagnosis of personal color, bone structure, and face type required a visit to a specialized salon, which was time-consuming and expensive.

[0005] The system according to the embodiment aims to enable a user to receive an accurate diagnosis of personal color, bone structure, and face type from the comfort of their own home. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an analysis unit, and a display unit. The photographing unit photographs a face photo of a user. The analysis unit analyzes the face photo photographed by the photographing unit and diagnoses the personal color, bone structure, and face type. The display unit instantly displays the diagnosis results obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment allows a user to receive accurate diagnosis of personal color, bone structure, and face type from the comfort of their own home. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The diagnostic system according to the embodiment of the present invention is a system that allows a user to easily receive a personal color diagnosis, bone structure diagnosis, and face type diagnosis at home. This diagnostic system can provide accurate diagnosis and advice to customers by using AI face recognition and image processing technology. For example, when a user starts the app and takes a face photo, the AI ​​analyzes the face photo and diagnoses the personal color, bone structure, and face type. The diagnosis results are displayed in real time, and the user can ask questions on the spot. The AI ​​provides answers to the user's questions in real time. This mechanism allows users to receive accurate diagnosis at home without going to a salon. In addition, since it is an AI diagnosis, there is no labor cost, and it is possible to monetize. For example, when a user starts the app and takes a face photo, the AI ​​analyzes the face photo and diagnoses the personal color. Next, a bone structure diagnosis is performed, and finally a face type diagnosis is performed. The diagnosis results are displayed in real time, and the user can ask, "What is my personal color?" on the spot. The AI ​​answers, "Your personal color is a spring type." In this way, the user can easily receive a diagnosis, and the effort and cost of going to a salon can be saved. In addition, the use of AI facial recognition and image processing technology enables accurate diagnosis and increases user satisfaction. This allows the diagnostic system to provide an accurate diagnosis while the user is at home.

[0029] The diagnostic system according to the embodiment includes a photographing unit, an analysis unit, and a display unit. The photographing unit photographs a face photo of a user. When the user photographs a face photo, for example, a smartphone camera can be used. The photographing unit can perform optimal photographing in consideration of the resolution, photographing angle, lighting conditions, and the like of the face photo. For example, the photographing unit can automatically set appropriate lighting conditions to keep the resolution of the face photo high. In addition, the photographing unit can provide appropriate instructions to the user to adjust the photographing angle of the face photo. The analysis unit uses AI to analyze the face photo photographed by the photographing unit and diagnose the personal color, bone structure, and face type. For example, the analysis unit can analyze the face photo using deep learning technology and diagnose the personal color. In addition, the analysis unit can analyze the face photo using a neural network and diagnose the bone structure. Furthermore, the analysis unit can extract features of the face photo and diagnose the face type. The display unit displays the diagnosis result obtained by the analysis unit in real time. For example, the display unit can display the diagnosis result within a few seconds. The display unit can also display the diagnosis results in a format suitable for the user's device. For example, a display format optimized for a smartphone screen can be provided. As a result, the diagnostic system according to the embodiment allows the user to easily receive a diagnosis of personal color, bone structure, and face type while at home. A part or all of the above-mentioned processing in the diagnostic system may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform a diagnosis using an AI model that inputs a face photo and outputs a diagnosis result of personal color, bone structure, and face type. As a result, the diagnostic system allows the user to receive an accurate diagnosis while at home.

[0030] The photographing unit photographs the user's face. When the user photographs the face, for example, the camera of a smartphone can be used. The photographing unit can take the optimal photograph by considering the resolution, shooting angle, lighting conditions, etc. of the face photograph. Specifically, the smartphone's camera app has a function of automatically detecting the face and focusing appropriately. Furthermore, the photographing unit can automatically set appropriate lighting conditions to keep the resolution of the face photograph high. For example, the camera app detects the amount of light in the surroundings and instructs the user to use a flash or use natural light as necessary. The photographing unit can also provide appropriate instructions to the user to adjust the shooting angle of the face photograph. For example, the app displays voice guidance or on-screen instructions such as "Turn your face slightly to the left" or "Adjust the camera to eye level" to the user. This allows the user to easily take the optimal face photograph. Furthermore, the photographing unit also has a function of taking multiple photographs in succession and automatically selecting the most suitable photograph from among them. This allows the user to obtain a high-quality face photograph with a single operation. The photographing unit is designed to enable the user to easily take high-quality face photographs by combining these functions.

[0031] The analysis unit uses AI to analyze the face photo taken by the photographing unit and diagnoses the personal color, bone structure, and face type. Specifically, it can analyze the face photo using deep learning technology and diagnose the personal color. The deep learning model is trained using a large amount of face photo data and its personal color labels, and can identify the face tone and skin tone with high accuracy. The analysis unit can also analyze the face photo using a neural network and diagnose the bone structure. The neural network analyzes the facial contour and bone structure to identify the user's bone structure type. Furthermore, the analysis unit can extract features of the face photo and diagnose the face type. An algorithm is used to diagnose the face type, which analyzes features such as the shape and position of the eyes, the shape of the nose, and the shape of the mouth. This allows the analysis unit to extract multifaceted information from the user's face photo and perform a comprehensive diagnosis. Furthermore, the analysis unit has a verification process to check the accuracy of the results internally before providing the diagnosis result to the user. For example, the same face photo is analyzed using multiple AI models to check whether the results match. In this way, the analysis unit can provide diagnostic results with high reliability and accuracy.

[0032] The display unit displays the diagnosis results obtained by the analysis unit in real time. For example, the display unit can display the diagnosis results within a few seconds. Specifically, the display unit quickly processes the data sent from the analysis unit and displays it in a format suitable for the user's device. For example, a display format optimized for a smartphone screen can be provided. The diagnosis results are displayed on the smartphone screen in a visually easy-to-understand manner. For example, a palette of colors that suit the user best is displayed as a result of a personal color diagnosis. Fashion advice based on the user's bone structure type is displayed as a result of a bone structure diagnosis. Makeup suggestions based on the user's facial features are displayed as a result of a face type diagnosis. Furthermore, the display unit has a function that allows the user to easily share the diagnosis results. For example, a button for sharing the diagnosis results via SNS or email is displayed. The display unit also has a function that allows the user to save the diagnosis results and check them again later. This allows the user to check and utilize the diagnosis results at any time. The display unit is designed with maximum consideration given to the user's convenience, and provides an intuitive and easy-to-use interface.

[0033] The analysis unit can analyze the face photo using AI and diagnose the personal color. The analysis unit can analyze the face photo using, for example, deep learning technology and diagnose the personal color. Deep learning technology can learn a large amount of data and extract features of the face photo. For example, the analysis unit can diagnose the personal color based on information such as the hue, brightness, and saturation of the face photo. The analysis unit can also analyze the face photo using a neural network and diagnose the personal color. The neural network is a model with multiple layers and can hierarchically extract features of the face photo. For example, the analysis unit inputs information such as the hue, brightness, and saturation of the face photo into the neural network and diagnoses the personal color. Furthermore, the analysis unit can develop an algorithm for extracting features of the face photo and diagnosing the personal color. For example, the analysis unit develops an algorithm for diagnosing the personal color based on information such as the hue, brightness, and saturation of the face photo. As a result, the analysis unit can improve the accuracy of the personal color diagnosis by using AI. Some or all of the above-mentioned processes in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may perform a diagnosis using an AI model that inputs a face photo and outputs a diagnosis result of personal color.

[0034] The analysis unit can analyze the facial photo using AI and diagnose the skeleton. The analysis unit can analyze the facial photo using, for example, deep learning technology and diagnose the skeleton. Deep learning technology can learn a large amount of data and extract features of the facial photo. For example, the analysis unit diagnoses the skeleton based on information such as the shape of the face and the structure of the bones. The analysis unit can also analyze the facial photo using a neural network and diagnose the skeleton. A neural network is a model with multiple layers and can hierarchically extract features of the facial photo. For example, the analysis unit inputs information such as the shape of the face and the structure of the bones into the neural network and diagnoses the skeleton. Furthermore, the analysis unit can develop an algorithm for extracting features of the facial photo and diagnosing the skeleton. For example, the analysis unit develops an algorithm for diagnosing the skeleton based on information such as the shape of the face and the structure of the bones. As a result, the analysis unit can improve the accuracy of diagnosis of the skeleton by using AI. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can make a diagnosis using an AI model that takes a facial photo as input and outputs a skeletal diagnosis result.

[0035] The analysis unit can analyze the face photo using AI and diagnose the face type. The analysis unit can analyze the face photo using, for example, deep learning technology and diagnose the face type. Deep learning technology can learn a large amount of data and extract features of the face photo. For example, the analysis unit diagnoses the face type based on information such as the arrangement and balance of the facial parts. The analysis unit can also analyze the face photo using a neural network and diagnose the face type. A neural network is a model with multiple layers and can hierarchically extract features of the face photo. For example, the analysis unit inputs information such as the arrangement and balance of the facial parts into the neural network and diagnoses the face type. Furthermore, the analysis unit can develop an algorithm for extracting features of the face photo and diagnosing the face type. For example, the analysis unit develops an algorithm for diagnosing the face type based on information such as the arrangement and balance of the facial parts. As a result, the analysis unit can improve the accuracy of diagnosing the face type by using AI. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can make a diagnosis using an AI model that takes a facial photo as input and outputs a diagnosis result for the face type.

[0036] The display unit can instantly display the diagnostic result. The display unit, for example, displays the diagnostic result within a few seconds. The display unit can display the diagnostic result in a format suitable for the user's device. For example, a display format optimized for a smartphone screen can be provided. The display unit can also use an appropriate technique to display the diagnostic result in real time. For example, the display unit updates the diagnostic result in real time so that the user can immediately check the result. As a result, the display unit displays the diagnostic result in real time so that the user can immediately check the result. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can display the diagnostic result using an AI model that inputs the diagnostic result and outputs a display format.

[0037] The diagnostic system includes an answering unit that provides an answer to a user's question instantly. The answering unit provides an answer to the user's question in real time, for example. The answering unit can use AI to analyze the user's question and generate an appropriate answer. For example, the answering unit uses natural language processing technology to analyze the user's question and generate an appropriate answer. The answering unit can also use an appropriate technology to provide an answer to the user's question instantly. For example, the answering unit analyzes the user's question in real time and generates an answer instantly. This allows the answering unit to provide an answer to the question in real time. A part or all of the above-mentioned processing in the answering unit may be performed, for example, using AI, or may be performed without using AI. For example, the answering unit can provide an answer using an AI model that receives the user's question as an input and outputs an answer.

[0038] The photographing unit can analyze the user's past photographing history and select the optimal photographing method. The photographing unit automatically selects the optimal photographing setting based on, for example, the photographing angle and lighting that the user has preferred in the past. The photographing unit can also photograph with the same setting based on the setting that was most highly rated among the photos taken by the user in the past. Furthermore, the photographing unit can analyze the timing at which the most natural facial expression can be captured from the user's past photographing history and photograph at that timing. This allows the photographing unit to select the optimal photographing method based on the past photographing history. A part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can select the photographing method using an AI model that inputs the user's past photographing data and outputs the optimal photographing method.

[0039] The photographing unit can automatically set optimal photographing conditions in consideration of the user's current environmental light and background when photographing. For example, when the user photographs indoors, the photographing unit automatically adjusts the exposure and white balance of the camera according to the lighting conditions in the room. In addition, when the user photographs outdoors, the photographing unit can also set optimal photographing settings according to the intensity and direction of natural light. Furthermore, when the user's background is complex, the photographing unit can automatically apply a setting to blur the background and photograph so that the face stands out. This allows the photographing unit to automatically set optimal photographing conditions according to the user's environment. Part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can automatically set photographing conditions using an AI model that inputs the user's environmental data and outputs optimal photographing conditions.

[0040] The photographing unit can propose an optimal photographing background in consideration of the geographical location information of the user at the time of photographing. For example, when the user is at a tourist spot, the photographing unit proposes a characteristic background of the place. In addition, when the user is at home, the photographing unit can also propose a simple and calm background. Furthermore, when the user is in an office, the photographing unit can also propose a background with a professional atmosphere. This allows the photographing unit to propose an optimal photographing background based on the geographical location information of the user. Part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can propose a background using an AI model that inputs the geographical location information of the user and outputs an optimal photographing background.

[0041] The photographing unit can analyze the user's social media activities and suggest related photographing poses at the time of photographing. The photographing unit can suggest popular poses based on trends of social media frequently used by the user, for example. The photographing unit can also suggest similar poses based on the poses that received the most positive responses in photos posted by the user in the past. Furthermore, the photographing unit can analyze poses preferred by the user's followers and suggest the poses. This allows the photographing unit to suggest optimal photographing poses based on the user's social media activities. Some or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI, or may be performed without using AI. For example, the photographing unit can suggest poses using an AI model that inputs the user's social media data and outputs optimal photographing poses.

[0042] The analysis unit can appropriately adjust the analysis algorithm based on the resolution and quality of the facial photograph during the analysis. For example, when a high-resolution facial photograph is used, the analysis unit performs a detailed analysis to provide a highly accurate diagnosis result. In addition, when a low-resolution facial photograph is used, the analysis unit can perform a simple analysis to quickly provide a diagnosis result. Furthermore, when the quality of the facial photograph is low, the analysis unit can perform the analysis after performing noise removal and correction. This allows the analysis unit to provide an optimal analysis algorithm according to the resolution and quality of the facial photograph. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the algorithm using an AI model that inputs the resolution and quality data of the facial photograph and outputs the analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past diagnosis results during the analysis. The analysis unit corrects the current analysis result based on, for example, the diagnosis results the user has received in the past. The analysis unit can also compare the user's past diagnosis results with the current face photo to provide a consistent analysis result. Furthermore, the analysis unit can also perform an analysis that emphasizes specific features by referring to the user's past diagnosis results. This allows the analysis unit to improve the accuracy of the analysis based on the user's past diagnosis results. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy by using an AI model that inputs the user's past diagnosis data and outputs the analysis accuracy.

[0044] During the analysis, the analysis unit can provide a region-specific diagnosis result by taking into account the geographical location information of the user. The analysis unit can, for example, suggest an optimal personal color based on the climate and environment of the region in which the user lives. The analysis unit can also provide an optimal bone structure diagnosis based on the culture and trends of the region in which the user lives. Furthermore, the analysis unit can also provide an optimal face type diagnosis based on the beauty trends of the region in which the user lives. This allows the analysis unit to provide an optimal diagnosis result based on the geographical location information of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can provide a diagnosis result using an AI model that inputs the geographical location information of the user and outputs a diagnosis result.

[0045] During the analysis, the analysis unit can analyze the user's social media activity and provide a related diagnosis result. For example, the analysis unit can analyze the tendency of photos frequently posted by the user and suggest an optimal personal color based on the analysis. The analysis unit can also provide an optimal bone structure diagnosis by referring to the style of an influencer the user follows. Furthermore, the analysis unit can provide an optimal face type diagnosis based on the style that has received the most positive response from the user's social media activity. This allows the analysis unit to provide an optimal diagnosis result based on the user's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can provide a diagnosis result using an AI model that inputs the user's social media data and outputs a diagnosis result.

[0046] When displaying the diagnostic result, the display unit can select an appropriate display method by referring to the user's past diagnostic history. For example, the display unit displays the diagnostic result in a similar format based on the display format that the user has previously preferred. The display unit can also select the most understandable display method from the user's past diagnostic history. Furthermore, the display unit can also provide a consistent display method by referring to the user's past diagnostic history. This allows the display unit to provide an optimal display method based on the user's past diagnostic history. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can select a display method using an AI model that inputs the user's past diagnostic data and outputs a display method.

[0047] The display unit can automatically adjust the display layout based on the user's current environment and device when displaying the diagnosis result. For example, when the user is using a smartphone, the display unit provides a display layout that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display layout optimized for a large screen. Furthermore, when the user is using a desktop, the display unit can also provide a display layout that includes detailed information. This allows the display unit to provide an optimal display layout according to the user's environment and device. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can adjust the layout using an AI model that inputs the user's device data and outputs the display layout.

[0048] The display unit can provide region-specific display content in consideration of the geographical location information of the user when displaying the diagnosis result. The display unit can display an optimal personal color based on, for example, the climate and environment of the region where the user lives. The display unit can also display an optimal bone structure diagnosis based on the culture and trends of the region where the user lives. Furthermore, the display unit can display an optimal face type diagnosis based on the beauty trends of the region where the user lives. This allows the display unit to provide optimal display content based on the geographical location information of the user. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can provide content using an AI model that inputs the geographical location information of the user and outputs display content.

[0049] The display unit can analyze the user's social media activities and provide related display contents when displaying the diagnosis result. For example, the display unit analyzes the tendency of photos frequently posted by the user and displays the optimal personal color based on the results. The display unit can also display the optimal bone structure diagnosis with reference to the style of an influencer the user follows. Furthermore, the display unit can display the optimal face type diagnosis based on the style that has received the most positive response from the user's social media activities. This allows the display unit to provide optimal display contents based on the user's social media activities. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can provide contents using an AI model that inputs the user's social media data and outputs display contents.

[0050] The answering unit can provide an appropriate answer by referring to the user's past question history when answering. The answering unit can provide a related answer based on, for example, the content of a question asked by the user in the past. The answering unit can also select and provide the most appropriate answer from the user's past question history. Furthermore, the answering unit can analyze the user's past question history and provide a consistent answer. This allows the answering unit to provide an optimal answer based on the user's past question history. A part or all of the above-mentioned processing in the answering unit can be performed, for example, using AI or can be performed without using AI. For example, the answering unit can provide an answer using an AI model that inputs the user's past question data and outputs an answer.

[0051] The answering unit can customize the answer content based on the user's current situation and interests when answering. The answering unit can provide a relevant answer based on, for example, a topic in which the user is currently interested. The answering unit can also provide an optimal answer based on the user's current situation (for example, a season or an event). Furthermore, the answering unit can analyze the user's current interests and situation and provide the most appropriate answer. This allows the answering unit to provide an optimal answer based on the user's current situation and interests. A part or all of the above-mentioned processing in the answering unit can be performed, for example, using AI or can be performed without using AI. For example, the answering unit can customize the answer using an AI model that inputs the user's current situation data and outputs an answer.

[0052] When answering, the answering unit can provide a region-specific answer by taking into account the geographical location information of the user. For example, the answering unit can provide an answer regarding the optimal personal color based on the climate and environment of the region where the user lives. The answering unit can also provide an answer regarding the optimal bone structure diagnosis based on the culture and trends of the region where the user lives. Furthermore, the answering unit can provide an answer regarding the optimal face type diagnosis based on the beauty trends of the region where the user lives. This allows the answering unit to provide an optimal answer based on the geographical location information of the user. A part or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can provide an answer using an AI model that inputs the geographical location information of the user and outputs an answer.

[0053] The answering unit can analyze the user's social media activity at the time of answering and provide a related answer. For example, the answering unit can analyze the tendency of photos frequently posted by the user and provide an answer regarding the optimal personal color based on the analysis. The answering unit can also provide an answer regarding the optimal bone structure diagnosis by referring to the style of an influencer the user follows. Furthermore, the answering unit can provide an answer regarding the optimal face type diagnosis based on the style that has received the most positive response from the user's social media activity. This allows the answering unit to provide an optimal answer based on the user's social media activity. A part or all of the above-mentioned processing in the answering unit may be performed, for example, using AI or may be performed without using AI. For example, the answering unit can provide an answer using an AI model that inputs the user's social media data and outputs an answer.

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

[0055] The diagnostic system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit, for example, measures the user's heart rate and blood pressure and monitors the health condition in real time. The health monitoring unit may also analyze the user's health data and provide a diagnostic result based on the health condition. For example, if the user is feeling stressed, advice on how to relax may be provided. Furthermore, the health monitoring unit may adjust the display method of the diagnostic result according to the user's health condition. This allows the diagnostic system to provide an optimal diagnostic result that takes into account the user's health condition.

[0056] The diagnostic system may further include a lifestyle analysis unit that analyzes the user's lifestyle. The lifestyle analysis unit may, for example, analyze the user's diet and exercise habits, and provide a diagnostic result based on the lifestyle. The lifestyle analysis unit may also suggest healthy lifestyle habits based on the user's lifestyle data. For example, if the user is not getting enough exercise, an appropriate exercise plan may be suggested. Furthermore, the lifestyle analysis unit may adjust the display method of the diagnostic result according to the user's lifestyle. This allows the diagnostic system to provide optimal diagnostic results that take into account the user's lifestyle.

[0057] The diagnostic system may further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit may, for example, analyze the user's preferred colors and styles, and provide a diagnostic result based on the user's hobbies. The hobby analysis unit may also suggest related fashion items and outfits based on the user's hobby data. For example, if the user likes a particular color, the hobby analysis unit may suggest an outfit that incorporates that color. Furthermore, the hobby analysis unit may adjust the display method of the diagnostic result according to the user's hobbies. This allows the diagnostic system to provide an optimal diagnostic result that takes into account the user's hobbies.

[0058] The diagnostic system can further improve the accuracy of the analysis by referring to the user's past diagnostic results. The analysis unit, for example, corrects the current analysis result based on the diagnostic results the user has received in the past. The analysis unit can also compare the user's past diagnostic results with the current facial photograph to provide a consistent analysis result. Furthermore, the analysis unit can refer to the user's past diagnostic results and perform an analysis that emphasizes specific features. This allows the analysis unit to improve the accuracy of the analysis based on the user's past diagnostic results.

[0059] The diagnostic system can further provide a region-specific diagnostic result by taking into account the geographical location information of the user. The analysis unit can, for example, suggest an optimal personal color based on the climate and environment of the region where the user lives. The analysis unit can also provide an optimal bone structure diagnosis based on the culture and trends of the region where the user lives. Furthermore, the analysis unit can provide an optimal face type diagnosis based on the beauty trends of the region where the user lives. This allows the analysis unit to provide an optimal diagnostic result based on the geographical location information of the user.

[0060] The diagnostic system can further analyze the user's social media activities and provide relevant diagnostic results. The analysis unit, for example, analyzes the trends of photos frequently posted by the user and suggests the optimal personal color based on the results. The analysis unit can also provide an optimal bone structure diagnosis by referring to the styles of influencers the user follows. Furthermore, the analysis unit can provide an optimal face type diagnosis based on the style that has generated the most positive response from the user's social media activities. This allows the analysis unit to provide optimal diagnostic results based on the user's social media activities.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The photographing unit photographs the user's face. The user can take a face photo using the camera of the smartphone. The photographing unit takes the optimal photograph, taking into consideration the resolution, shooting angle, lighting conditions, etc. of the face photo. For example, the photographing unit automatically sets appropriate lighting conditions and provides appropriate instructions to the user to adjust the shooting angle of the face photo. Step 2: The analysis unit uses AI to analyze the face photo taken by the photography unit and diagnoses personal color, bone structure, and face type. For example, the analysis unit uses deep learning technology to analyze the face photo, diagnoses personal color, diagnoses bone structure using a neural network, and extracts features from the face photo to diagnose face type. Step 3: The display unit displays the diagnosis results obtained by the analysis unit in real time. For example, the display unit displays the diagnosis results within a few seconds in a format suitable for the user's device. A display format optimized for the screen of a smartphone can be provided.

[0063] (Example 2) The diagnostic system according to the embodiment of the present invention is a system that allows a user to easily receive a personal color diagnosis, bone structure diagnosis, and face type diagnosis at home. This diagnostic system can provide accurate diagnosis and advice to customers by using AI face recognition and image processing technology. For example, when a user starts the app and takes a face photo, the AI ​​analyzes the face photo and diagnoses the personal color, bone structure, and face type. The diagnosis results are displayed in real time, and the user can ask questions on the spot. The AI ​​provides answers to the user's questions in real time. This mechanism allows users to receive accurate diagnosis at home without going to a salon. In addition, since it is an AI diagnosis, there is no labor cost, and it is possible to monetize. For example, when a user starts the app and takes a face photo, the AI ​​analyzes the face photo and diagnoses the personal color. Next, a bone structure diagnosis is performed, and finally a face type diagnosis is performed. The diagnosis results are displayed in real time, and the user can ask, "What is my personal color?" on the spot. The AI ​​answers, "Your personal color is a spring type." In this way, the user can easily receive a diagnosis, and the effort and cost of going to a salon can be saved. In addition, the use of AI facial recognition and image processing technology enables accurate diagnosis and increases user satisfaction. This allows the diagnostic system to provide an accurate diagnosis while the user is at home.

[0064] The diagnostic system according to the embodiment includes a photographing unit, an analysis unit, and a display unit. The photographing unit photographs a face photo of a user. When the user photographs a face photo, for example, a smartphone camera can be used. The photographing unit can perform optimal photographing in consideration of the resolution, photographing angle, lighting conditions, and the like of the face photo. For example, the photographing unit can automatically set appropriate lighting conditions to keep the resolution of the face photo high. In addition, the photographing unit can provide appropriate instructions to the user to adjust the photographing angle of the face photo. The analysis unit uses AI to analyze the face photo photographed by the photographing unit and diagnose the personal color, bone structure, and face type. For example, the analysis unit can analyze the face photo using deep learning technology and diagnose the personal color. In addition, the analysis unit can analyze the face photo using a neural network and diagnose the bone structure. Furthermore, the analysis unit can extract features of the face photo and diagnose the face type. The display unit displays the diagnosis result obtained by the analysis unit in real time. For example, the display unit can display the diagnosis result within a few seconds. The display unit can also display the diagnosis results in a format suitable for the user's device. For example, a display format optimized for a smartphone screen can be provided. As a result, the diagnostic system according to the embodiment allows the user to easily receive a diagnosis of personal color, bone structure, and face type while at home. A part or all of the above-mentioned processing in the diagnostic system may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform a diagnosis using an AI model that inputs a face photo and outputs a diagnosis result of personal color, bone structure, and face type. As a result, the diagnostic system allows the user to receive an accurate diagnosis while at home.

[0065] The photographing unit photographs the user's face. When the user photographs the face, for example, the camera of a smartphone can be used. The photographing unit can take the optimal photograph by considering the resolution, shooting angle, lighting conditions, etc. of the face photograph. Specifically, the smartphone's camera app has a function of automatically detecting the face and focusing appropriately. Furthermore, the photographing unit can automatically set appropriate lighting conditions to keep the resolution of the face photograph high. For example, the camera app detects the amount of light in the surroundings and instructs the user to use a flash or use natural light as necessary. The photographing unit can also provide appropriate instructions to the user to adjust the shooting angle of the face photograph. For example, the app displays voice guidance or on-screen instructions such as "Turn your face slightly to the left" or "Adjust the camera to eye level" to the user. This allows the user to easily take the optimal face photograph. Furthermore, the photographing unit also has a function of taking multiple photographs in succession and automatically selecting the most suitable photograph from among them. This allows the user to obtain a high-quality face photograph with a single operation. The photographing unit is designed to enable the user to easily take high-quality face photographs by combining these functions.

[0066] The analysis unit uses AI to analyze the face photo taken by the photographing unit and diagnoses the personal color, bone structure, and face type. Specifically, it can analyze the face photo using deep learning technology and diagnose the personal color. The deep learning model is trained using a large amount of face photo data and its personal color labels, and can identify the face tone and skin tone with high accuracy. The analysis unit can also analyze the face photo using a neural network and diagnose the bone structure. The neural network analyzes the facial contour and bone structure to identify the user's bone structure type. Furthermore, the analysis unit can extract features of the face photo and diagnose the face type. An algorithm is used to diagnose the face type, which analyzes features such as the shape and position of the eyes, the shape of the nose, and the shape of the mouth. This allows the analysis unit to extract multifaceted information from the user's face photo and perform a comprehensive diagnosis. Furthermore, the analysis unit has a verification process to check the accuracy of the results internally before providing the diagnosis result to the user. For example, the same face photo is analyzed using multiple AI models to check whether the results match. In this way, the analysis unit can provide diagnostic results with high reliability and accuracy.

[0067] The display unit displays the diagnosis results obtained by the analysis unit in real time. For example, the display unit can display the diagnosis results within a few seconds. Specifically, the display unit quickly processes the data sent from the analysis unit and displays it in a format suitable for the user's device. For example, a display format optimized for a smartphone screen can be provided. The diagnosis results are displayed on the smartphone screen in a visually easy-to-understand manner. For example, a palette of colors that suit the user best is displayed as a result of a personal color diagnosis. Fashion advice based on the user's bone structure type is displayed as a result of a bone structure diagnosis. Makeup suggestions based on the user's facial features are displayed as a result of a face type diagnosis. Furthermore, the display unit has a function that allows the user to easily share the diagnosis results. For example, a button for sharing the diagnosis results via SNS or email is displayed. The display unit also has a function that allows the user to save the diagnosis results and check them again later. This allows the user to check and utilize the diagnosis results at any time. The display unit is designed with maximum consideration given to the user's convenience, and provides an intuitive and easy-to-use interface.

[0068] The analysis unit can analyze the face photo using AI and diagnose the personal color. The analysis unit can analyze the face photo using, for example, deep learning technology and diagnose the personal color. Deep learning technology can learn a large amount of data and extract features of the face photo. For example, the analysis unit can diagnose the personal color based on information such as the hue, brightness, and saturation of the face photo. The analysis unit can also analyze the face photo using a neural network and diagnose the personal color. The neural network is a model with multiple layers and can hierarchically extract features of the face photo. For example, the analysis unit inputs information such as the hue, brightness, and saturation of the face photo into the neural network and diagnoses the personal color. Furthermore, the analysis unit can develop an algorithm for extracting features of the face photo and diagnosing the personal color. For example, the analysis unit develops an algorithm for diagnosing the personal color based on information such as the hue, brightness, and saturation of the face photo. As a result, the analysis unit can improve the accuracy of the personal color diagnosis by using AI. Some or all of the above-mentioned processes in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may perform a diagnosis using an AI model that inputs a face photo and outputs a diagnosis result of personal color.

[0069] The analysis unit can analyze the facial photo using AI and diagnose the skeleton. The analysis unit can analyze the facial photo using, for example, deep learning technology and diagnose the skeleton. Deep learning technology can learn a large amount of data and extract features of the facial photo. For example, the analysis unit diagnoses the skeleton based on information such as the shape of the face and the structure of the bones. The analysis unit can also analyze the facial photo using a neural network and diagnose the skeleton. A neural network is a model with multiple layers and can hierarchically extract features of the facial photo. For example, the analysis unit inputs information such as the shape of the face and the structure of the bones into the neural network and diagnoses the skeleton. Furthermore, the analysis unit can develop an algorithm for extracting features of the facial photo and diagnosing the skeleton. For example, the analysis unit develops an algorithm for diagnosing the skeleton based on information such as the shape of the face and the structure of the bones. As a result, the analysis unit can improve the accuracy of diagnosis of the skeleton by using AI. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can make a diagnosis using an AI model that takes a facial photo as input and outputs a skeletal diagnosis result.

[0070] The analysis unit can analyze the face photo using AI and diagnose the face type. The analysis unit can analyze the face photo using, for example, deep learning technology and diagnose the face type. Deep learning technology can learn a large amount of data and extract features of the face photo. For example, the analysis unit diagnoses the face type based on information such as the arrangement and balance of the facial parts. The analysis unit can also analyze the face photo using a neural network and diagnose the face type. A neural network is a model with multiple layers and can hierarchically extract features of the face photo. For example, the analysis unit inputs information such as the arrangement and balance of the facial parts into the neural network and diagnoses the face type. Furthermore, the analysis unit can develop an algorithm for extracting features of the face photo and diagnosing the face type. For example, the analysis unit develops an algorithm for diagnosing the face type based on information such as the arrangement and balance of the facial parts. As a result, the analysis unit can improve the accuracy of diagnosing the face type by using AI. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can make a diagnosis using an AI model that takes a facial photo as input and outputs a diagnosis result for the face type.

[0071] The display unit can instantly display the diagnostic result. The display unit, for example, displays the diagnostic result within a few seconds. The display unit can display the diagnostic result in a format suitable for the user's device. For example, a display format optimized for a smartphone screen can be provided. The display unit can also use an appropriate technique to display the diagnostic result in real time. For example, the display unit updates the diagnostic result in real time so that the user can immediately check the result. As a result, the display unit displays the diagnostic result in real time so that the user can immediately check the result. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can display the diagnostic result using an AI model that inputs the diagnostic result and outputs a display format.

[0072] The diagnostic system includes an answering unit that provides an answer to a user's question instantly. The answering unit provides an answer to the user's question in real time, for example. The answering unit can use AI to analyze the user's question and generate an appropriate answer. For example, the answering unit uses natural language processing technology to analyze the user's question and generate an appropriate answer. The answering unit can also use an appropriate technology to provide an answer to the user's question instantly. For example, the answering unit analyzes the user's question in real time and generates an answer instantly. This allows the answering unit to provide an answer to the question in real time. A part or all of the above-mentioned processing in the answering unit may be performed, for example, using AI, or may be performed without using AI. For example, the answering unit can provide an answer using an AI model that receives the user's question as an input and outputs an answer.

[0073] The photographing unit can estimate the user's emotion and adjust the timing of taking a face photo based on the emotion. For example, when the user is relaxed, the photographing unit takes a face photo at a relaxed timing in order to bring out a natural expression. In addition, when the user is tense, the photographing unit can wait until the user relaxes in order to relieve tension before taking a face photo. Furthermore, when the user is in a hurry, the photographing unit can complete the photographing in the shortest time in order to quickly take a face photo. This allows the photographing unit to take a face photo at an optimal timing according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the photographing unit can adjust the shooting timing using an AI model that inputs the user's emotion data and outputs the shooting timing.

[0074] The photographing unit can analyze the user's past photographing history and select the optimal photographing method. The photographing unit automatically selects the optimal photographing setting based on, for example, the photographing angle and lighting that the user has preferred in the past. The photographing unit can also photograph with the same setting based on the setting that was most highly rated among the photos taken by the user in the past. Furthermore, the photographing unit can analyze the timing at which the most natural facial expression can be captured from the user's past photographing history and photograph at that timing. This allows the photographing unit to select the optimal photographing method based on the past photographing history. A part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can select the photographing method using an AI model that inputs the user's past photographing data and outputs the optimal photographing method.

[0075] The photographing unit can automatically set optimal photographing conditions in consideration of the user's current environmental light and background when photographing. For example, when the user photographs indoors, the photographing unit automatically adjusts the exposure and white balance of the camera according to the lighting conditions in the room. In addition, when the user photographs outdoors, the photographing unit can also set optimal photographing settings according to the intensity and direction of natural light. Furthermore, when the user's background is complex, the photographing unit can automatically apply a setting to blur the background and photograph so that the face stands out. This allows the photographing unit to automatically set optimal photographing conditions according to the user's environment. Part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can automatically set photographing conditions using an AI model that inputs the user's environmental data and outputs optimal photographing conditions.

[0076] The photographing unit can estimate the user's emotion and determine the priority of the facial photos to be taken based on the emotion. For example, when the user is relaxed, the photographing unit preferentially uses a facial photo taken at that timing. In addition, when the user is tense, the photographing unit can also preferentially use a facial photo taken after waiting until the user is relieved. Furthermore, when the user is in a hurry, the photographing unit can also preferentially use a facial photo taken quickly. This allows the photographing unit to preferentially use an optimal facial photo according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the photographing unit can determine the priority using an AI model that inputs the user's emotion data and outputs the priority of the facial photos.

[0077] The photographing unit can propose an optimal photographing background in consideration of the geographical location information of the user at the time of photographing. For example, when the user is at a tourist spot, the photographing unit proposes a characteristic background of the place. In addition, when the user is at home, the photographing unit can also propose a simple and calm background. Furthermore, when the user is in an office, the photographing unit can also propose a background with a professional atmosphere. This allows the photographing unit to propose an optimal photographing background based on the geographical location information of the user. Part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can propose a background using an AI model that inputs the geographical location information of the user and outputs an optimal photographing background.

[0078] The photographing unit can analyze the user's social media activities and suggest related photographing poses at the time of photographing. The photographing unit can suggest popular poses based on trends of social media frequently used by the user, for example. The photographing unit can also suggest similar poses based on the poses that received the most positive responses in photos posted by the user in the past. Furthermore, the photographing unit can analyze poses preferred by the user's followers and suggest the poses. This allows the photographing unit to suggest optimal photographing poses based on the user's social media activities. Some or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI, or may be performed without using AI. For example, the photographing unit can suggest poses using an AI model that inputs the user's social media data and outputs optimal photographing poses.

[0079] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the emotion. For example, when the user is relaxed, the analysis unit performs a detailed analysis and provides a highly accurate diagnosis result. In addition, when the user is nervous, the analysis unit can perform a simple analysis and provide a diagnosis result quickly. Furthermore, when the user is in a hurry, the analysis unit can perform an analysis focusing on the most important points and provide a diagnosis result in a short time. This allows the analysis unit to provide optimal analysis accuracy according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can adjust the accuracy using an AI model that inputs the user's emotion data and outputs the analysis accuracy.

[0080] The analysis unit can appropriately adjust the analysis algorithm based on the resolution and quality of the facial photograph during the analysis. For example, when a high-resolution facial photograph is used, the analysis unit performs a detailed analysis to provide a highly accurate diagnosis result. In addition, when a low-resolution facial photograph is used, the analysis unit can perform a simple analysis to quickly provide a diagnosis result. Furthermore, when the quality of the facial photograph is low, the analysis unit can perform the analysis after performing noise removal and correction. This allows the analysis unit to provide an optimal analysis algorithm according to the resolution and quality of the facial photograph. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the algorithm using an AI model that inputs the resolution and quality data of the facial photograph and outputs the analysis algorithm.

[0081] The analysis unit can improve the accuracy of the analysis by referring to the user's past diagnosis results during the analysis. The analysis unit corrects the current analysis result based on, for example, the diagnosis results the user has received in the past. The analysis unit can also compare the user's past diagnosis results with the current face photo to provide a consistent analysis result. Furthermore, the analysis unit can also perform an analysis that emphasizes specific features by referring to the user's past diagnosis results. This allows the analysis unit to improve the accuracy of the analysis based on the user's past diagnosis results. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy by using an AI model that inputs the user's past diagnosis data and outputs the analysis accuracy.

[0082] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the emotion. For example, when the user is relaxed, the analysis unit displays a detailed analysis result. Furthermore, when the user is nervous, the analysis unit can also display a simple analysis result. Furthermore, when the user is in a hurry, the analysis unit can display an analysis result with the main points. This allows the analysis unit to provide an optimal display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can adjust the display method using an AI model that inputs the user's emotion data and outputs a display method.

[0083] During the analysis, the analysis unit can provide a region-specific diagnosis result by taking into account the geographical location information of the user. The analysis unit can, for example, suggest an optimal personal color based on the climate and environment of the region in which the user lives. The analysis unit can also provide an optimal bone structure diagnosis based on the culture and trends of the region in which the user lives. Furthermore, the analysis unit can also provide an optimal face type diagnosis based on the beauty trends of the region in which the user lives. This allows the analysis unit to provide an optimal diagnosis result based on the geographical location information of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can provide a diagnosis result using an AI model that inputs the geographical location information of the user and outputs a diagnosis result.

[0084] During the analysis, the analysis unit can analyze the user's social media activity and provide a related diagnosis result. For example, the analysis unit can analyze the tendency of photos frequently posted by the user and suggest an optimal personal color based on the analysis. The analysis unit can also provide an optimal bone structure diagnosis by referring to the style of an influencer the user follows. Furthermore, the analysis unit can provide an optimal face type diagnosis based on the style that has received the most positive response from the user's social media activity. This allows the analysis unit to provide an optimal diagnosis result based on the user's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can provide a diagnosis result using an AI model that inputs the user's social media data and outputs a diagnosis result.

[0085] The display unit can estimate the user's emotion and adjust the display method of the diagnosis result based on the emotion. For example, when the user is relaxed, the display unit displays a detailed diagnosis result. Furthermore, when the user is nervous, the display unit can also display a simple diagnosis result. Furthermore, when the user is in a hurry, the display unit can display a diagnosis result with the main points. This allows the display unit to provide an optimal display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can adjust the display method using an AI model that inputs the user's emotion data and outputs a display method.

[0086] When displaying the diagnostic result, the display unit can select an appropriate display method by referring to the user's past diagnostic history. For example, the display unit displays the diagnostic result in a similar format based on the display format that the user has previously preferred. The display unit can also select the most understandable display method from the user's past diagnostic history. Furthermore, the display unit can also provide a consistent display method by referring to the user's past diagnostic history. This allows the display unit to provide an optimal display method based on the user's past diagnostic history. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can select a display method using an AI model that inputs the user's past diagnostic data and outputs a display method.

[0087] The display unit can automatically adjust the display layout based on the user's current environment and device when displaying the diagnosis result. For example, when the user is using a smartphone, the display unit provides a display layout that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display layout optimized for a large screen. Furthermore, when the user is using a desktop, the display unit can also provide a display layout that includes detailed information. This allows the display unit to provide an optimal display layout according to the user's environment and device. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can adjust the layout using an AI model that inputs the user's device data and outputs the display layout.

[0088] The display unit can estimate the user's emotion and adjust the display order of the diagnosis results based on the emotion. For example, when the user is relaxed, the display unit displays detailed diagnosis results in an orderly manner. In addition, when the user is nervous, the display unit can display simple diagnosis results first and detailed results later. Furthermore, when the user is in a hurry, the display unit can display the most important diagnosis results first. In this way, the display unit can provide an optimal display order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can adjust the order using an AI model that inputs the user's emotion data and outputs the display order.

[0089] The display unit can provide region-specific display content in consideration of the geographical location information of the user when displaying the diagnosis result. The display unit can display an optimal personal color based on, for example, the climate and environment of the region where the user lives. The display unit can also display an optimal bone structure diagnosis based on the culture and trends of the region where the user lives. Furthermore, the display unit can display an optimal face type diagnosis based on the beauty trends of the region where the user lives. This allows the display unit to provide optimal display content based on the geographical location information of the user. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can provide content using an AI model that inputs the geographical location information of the user and outputs display content.

[0090] The display unit can analyze the user's social media activities and provide related display contents when displaying the diagnosis result. For example, the display unit analyzes the tendency of photos frequently posted by the user and displays the optimal personal color based on the results. The display unit can also display the optimal bone structure diagnosis with reference to the style of an influencer the user follows. Furthermore, the display unit can display the optimal face type diagnosis based on the style that has received the most positive response from the user's social media activities. This allows the display unit to provide optimal display contents based on the user's social media activities. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can provide contents using an AI model that inputs the user's social media data and outputs display contents.

[0091] The answering unit can estimate the user's emotions and adjust the way of expressing the answer based on the emotions. For example, when the user is relaxed, the answering unit provides an answer including a detailed explanation. In addition, when the user is nervous, the answering unit can also provide a concise and easy-to-understand answer. Furthermore, when the user is in a hurry, the answering unit can also provide a quick answer that covers the main points. This allows the answering unit to provide an optimal answer expression according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the answering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the answering unit can adjust the way of expression using an AI model that inputs the user's emotion data and outputs the way of expressing the answer.

[0092] The answering unit can provide an appropriate answer by referring to the user's past question history when answering. The answering unit can provide a related answer based on, for example, the content of a question asked by the user in the past. The answering unit can also select and provide the most appropriate answer from the user's past question history. Furthermore, the answering unit can analyze the user's past question history and provide a consistent answer. This allows the answering unit to provide an optimal answer based on the user's past question history. A part or all of the above-mentioned processing in the answering unit can be performed, for example, using AI or can be performed without using AI. For example, the answering unit can provide an answer using an AI model that inputs the user's past question data and outputs an answer.

[0093] The answering unit can customize the answer content based on the user's current situation and interests when answering. The answering unit can provide a relevant answer based on, for example, a topic in which the user is currently interested. The answering unit can also provide an optimal answer based on the user's current situation (for example, a season or an event). Furthermore, the answering unit can analyze the user's current interests and situation and provide the most appropriate answer. This allows the answering unit to provide an optimal answer based on the user's current situation and interests. A part or all of the above-mentioned processing in the answering unit can be performed, for example, using AI or can be performed without using AI. For example, the answering unit can customize the answer using an AI model that inputs the user's current situation data and outputs an answer.

[0094] The answering unit can estimate the user's emotions and determine the priority of answers based on the emotions. For example, when the user is relaxed, the answering unit can provide a detailed answer preferentially. Also, when the user is nervous, the answering unit can provide a concise answer preferentially. Furthermore, when the user is in a hurry, the answering unit can provide an answer that focuses on the main points preferentially. This allows the answering unit to provide an optimal priority of answers according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the answering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the answering unit can determine the priority using an AI model that inputs the user's emotion data and outputs the priority of answers.

[0095] When answering, the answering unit can provide a region-specific answer by taking into account the geographical location information of the user. For example, the answering unit can provide an answer regarding the optimal personal color based on the climate and environment of the region where the user lives. The answering unit can also provide an answer regarding the optimal bone structure diagnosis based on the culture and trends of the region where the user lives. Furthermore, the answering unit can provide an answer regarding the optimal face type diagnosis based on the beauty trends of the region where the user lives. This allows the answering unit to provide an optimal answer based on the geographical location information of the user. A part or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can provide an answer using an AI model that inputs the geographical location information of the user and outputs an answer.

[0096] The answering section can analyze the user's social media activity at the time of answering and provide a related answer. For example, the answering section can analyze the tendency of photos frequently posted by the user and provide an answer regarding the optimal personal color based on the analysis. The answering section can also provide an answer regarding the optimal bone structure diagnosis by referring to the style of an influencer the user follows. Furthermore, the answering section can provide an answer regarding the optimal face type diagnosis based on the style that has received the most positive response from the user's social media activity. This allows the answering section to provide an optimal answer based on the user's social media activity. A part or all of the above-mentioned processing in the answering section may be performed, for example, using AI or may be performed without using AI. For example, the answering section can provide an answer using an AI model that inputs the user's social media data and outputs an answer.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The diagnostic system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit, for example, measures the user's heart rate and blood pressure and monitors the health condition in real time. The health monitoring unit may also analyze the user's health data and provide a diagnostic result based on the health condition. For example, if the user is feeling stressed, advice on how to relax may be provided. Furthermore, the health monitoring unit may adjust the display method of the diagnostic result according to the user's health condition. This allows the diagnostic system to provide an optimal diagnostic result that takes into account the user's health condition.

[0099] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. For example, if the user is relaxed, a detailed analysis can be performed to provide a highly accurate diagnosis result. If the user is nervous, a simple analysis can be performed to provide a quick diagnosis result. Furthermore, if the user is in a hurry, an analysis focused on the most important points can be performed to provide a diagnosis result in a short time. This allows the analysis unit to provide optimal analysis accuracy according to the user's emotions.

[0100] The diagnostic system may further include a lifestyle analysis unit that analyzes the user's lifestyle. The lifestyle analysis unit may, for example, analyze the user's diet and exercise habits, and provide a diagnostic result based on the lifestyle. The lifestyle analysis unit may also suggest healthy lifestyle habits based on the user's lifestyle data. For example, if the user is not getting enough exercise, an appropriate exercise plan may be suggested. Furthermore, the lifestyle analysis unit may adjust the display method of the diagnostic result according to the user's lifestyle. This allows the diagnostic system to provide optimal diagnostic results that take into account the user's lifestyle.

[0101] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on those emotions. For example, if the user is relaxed, detailed analysis results can be displayed. If the user is nervous, simple analysis results can be displayed. Furthermore, if the user is in a hurry, analysis results that focus on the main points can be displayed. In this way, the analysis unit can provide the optimal display method according to the user's emotions.

[0102] The diagnostic system may further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit may, for example, analyze the user's preferred colors and styles, and provide a diagnostic result based on the user's hobbies. The hobby analysis unit may also suggest related fashion items and outfits based on the user's hobby data. For example, if the user likes a particular color, the hobby analysis unit may suggest an outfit that incorporates that color. Furthermore, the hobby analysis unit may adjust the display method of the diagnostic result according to the user's hobbies. This allows the diagnostic system to provide an optimal diagnostic result that takes into account the user's hobbies.

[0103] The diagnostic system can further estimate the user's emotions and adjust the display order of the diagnostic results based on the emotions. For example, when the user is relaxed, detailed diagnostic results can be displayed in an orderly manner. When the user is nervous, simple diagnostic results can be displayed first, and detailed results can be displayed later. When the user is in a hurry, the most important diagnostic results can be displayed first. This allows the diagnostic system to provide an optimal display order according to the user's emotions.

[0104] The diagnostic system can further improve the accuracy of the analysis by referring to the user's past diagnostic results. The analysis unit, for example, corrects the current analysis result based on the diagnostic results the user has received in the past. The analysis unit can also compare the user's past diagnostic results with the current facial photograph to provide a consistent analysis result. Furthermore, the analysis unit can refer to the user's past diagnostic results and perform an analysis that emphasizes specific features. This allows the analysis unit to improve the accuracy of the analysis based on the user's past diagnostic results.

[0105] The diagnostic system can further estimate the user's emotions and adjust the display method of the diagnostic results based on the emotions. For example, if the user is relaxed, a detailed diagnostic result can be displayed. If the user is nervous, a simple diagnostic result can be displayed. If the user is in a hurry, a diagnostic result that focuses on the main points can be displayed. This allows the diagnostic system to provide an optimal display method according to the user's emotions.

[0106] The diagnostic system can further provide a region-specific diagnostic result by taking into account the geographical location information of the user. The analysis unit can, for example, suggest an optimal personal color based on the climate and environment of the region where the user lives. The analysis unit can also provide an optimal bone structure diagnosis based on the culture and trends of the region where the user lives. Furthermore, the analysis unit can provide an optimal face type diagnosis based on the beauty trends of the region where the user lives. This allows the analysis unit to provide an optimal diagnostic result based on the geographical location information of the user.

[0107] The diagnostic system can further analyze the user's social media activities and provide relevant diagnostic results. The analysis unit, for example, analyzes the trends of photos frequently posted by the user and suggests the optimal personal color based on the results. The analysis unit can also provide an optimal bone structure diagnosis by referring to the styles of influencers the user follows. Furthermore, the analysis unit can provide an optimal face type diagnosis based on the style that has generated the most positive response from the user's social media activities. This allows the analysis unit to provide optimal diagnostic results based on the user's social media activities.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The photographing unit photographs the user's face. The user can take a face photo using the camera of the smartphone. The photographing unit takes the optimal photograph, taking into consideration the resolution, shooting angle, lighting conditions, etc. of the face photo. For example, the photographing unit automatically sets appropriate lighting conditions and provides appropriate instructions to the user to adjust the shooting angle of the face photo. Step 2: The analysis unit uses AI to analyze the face photo taken by the photography unit and diagnoses personal color, bone structure, and face type. For example, the analysis unit uses deep learning technology to analyze the face photo, diagnoses personal color, diagnoses bone structure using a neural network, and extracts features from the face photo to diagnose face type. Step 3: The display unit displays the diagnosis results obtained by the analysis unit in real time. For example, the display unit displays the diagnosis results within a few seconds in a format suitable for the user's device. A display format optimized for the screen of a smartphone can be provided.

[0110] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0111] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, display unit, and answer 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 takes a face photo of the user using the camera 42 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the face photo using AI to diagnose the personal color, bone structure, and face type. The display unit displays the diagnosis result in real time using the display 40A of the smart device 14. The answer unit is realized by the specific processing unit 290 of the data processing device 12, and provides answers to the user's questions in real time. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0117] 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.

[0118] 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 the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] 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 performed in a secure state.

[0121] Fig. 4 shows an example of 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.

[0122] 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.

[0123] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0127] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, display unit, and answer 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 takes a face photo of the user using the camera 42 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the face photo using AI to diagnose the personal color, bone structure, and face type. The display unit displays the diagnosis result in real time using the display of the smart glasses 214. The answer unit is realized by the specific processing unit 290 of the data processing device 12, and provides answers to the user's questions in real time. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0133] 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.

[0134] 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 the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] 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 performed in a secure state.

[0137] 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.

[0138] 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.

[0139] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0141] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] 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 voice indicating a user input for 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.

[0143] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, display unit, and answer 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 takes a face photo of the user using the camera 42 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the face photo using AI to diagnose the personal color, bone structure, and face type. The display unit displays the diagnosis result in real time using the display 343 of the headset type terminal 314. The answer unit is realized by the specific processing unit 290 of the data processing device 12, and provides answers to the user's questions in real time. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0149] 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.

[0150] 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 the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] 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 performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0154] 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.

[0155] 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.

[0156] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] 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 a voice indicating a user input for 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.

[0160] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0162] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, display unit, and answer unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit takes a face photo of the user using the camera 42 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the face photo using AI to diagnose the personal color, bone structure, and face type. The display unit displays the diagnosis result in real time using the display of the robot 414. The answer unit is realized by the specific processing unit 290 of the data processing device 12, and provides answers to the user's questions in real time. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0170] 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.

[0171] 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 Universal Serial Bus (USB) 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.

[0172] In addition, 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 upon request from the data processing device 12.

[0173] 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.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0176] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) A photographing unit for photographing a face photograph of a user; an analysis unit that analyzes the face photograph taken by the photographing unit and diagnoses personal color, bone structure, and face type; A display unit that instantly displays the diagnosis result obtained by the analysis unit. A system characterized by: (Appendix 2) The analysis unit is AI analyzes face photos and diagnoses personal colors 2. The system of claim 1, (Appendix 3) The analysis unit is AI-based facial photo analysis and bone structure diagnosis 2. The system of claim 1, (Appendix 4) The analysis unit is Analyze facial photos using AI and diagnose face type 2. The system of claim 1, (Appendix 5) The display unit is Instant diagnostic results 2. The system of claim 1, (Appendix 6) Equipped with an answering section that provides immediate answers to user questions 2. The system of claim 1, (Appendix 7) The imaging unit includes: Estimate the user's emotions and adjust the timing of taking facial photos based on those emotions 2. The system of claim 1, (Appendix 8) The imaging unit includes: Analyze the user's past shooting history and select the appropriate shooting method 2. The system of claim 1, (Appendix 9) The imaging unit includes: When taking a photo, the system automatically sets appropriate shooting conditions by considering the user's current ambient light and background. 2. The system of claim 1, (Appendix 10) The imaging unit includes: Estimate the user's emotions and prioritize which facial photos to take based on those emotions 2. The system of claim 1, (Appendix 11) The imaging unit includes: When taking a photo, the system considers the user's geographic location to suggest an appropriate background for the photo. 2. The system of claim 1, (Appendix 12) The imaging unit includes: When taking a photo, the app analyzes the user's social media activity and suggests relevant photo poses. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust analysis accuracy based on those emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, the analysis algorithm is adjusted appropriately based on the resolution and quality of the face photo. 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, the accuracy of the analysis is improved by referring to the user's past diagnostic results. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and adjust how analysis results are displayed based on those emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, the geographic location of the user is taken into account to provide location-specific diagnostic results. 2. The system of claim 1, (Appendix 18) The analysis unit is During analytics, analyze your social media activity and provide relevant diagnostic results 2. The system of claim 1, (Appendix 19) The display unit is Inferring user emotions and adjusting how diagnostic results are displayed based on those emotions 2. The system of claim 1, (Appendix 20) The display unit is When displaying diagnostic results, select the appropriate display method by referring to the user's past diagnostic history. 2. The system of claim 1, (Appendix 21) The display unit is When displaying diagnostic results, automatically adjust the display layout based on the user's current environment and device 2. The system of claim 1, (Appendix 22) The display unit is Inferring user emotions and adjusting the display order of diagnostic results based on those emotions 2. The system of claim 1, (Appendix 23) The display unit is When displaying diagnostic results, the system takes into account the user's geographic location to provide region-specific content. 2. The system of claim 1, (Appendix 24) The display unit is When displaying diagnostic results, analyze the user's social media activity to provide relevant display content 2. The system of claim 1, (Appendix 25) The answering section: Infer the user's emotions and tailor the way you phrase your answers based on those emotions 3. The system of claim 2, (Appendix 26) The answering section: When answering a question, the system provides appropriate answers by referring to the user's past question history. 3. The system of claim 2, (Appendix 27) The answering section: When answering, tailor your answers based on your current situation and interests 3. The system of claim 2, (Appendix 28) The answering section: Infer user sentiment and prioritize answers based on that sentiment 3. The system of claim 2, (Appendix 29) The answering section: When answering, it takes into account the user's geographic location to provide localized answers 3. The system of claim 2, (Appendix 30) The answering section: When answering, we analyze your social media activity to provide you with a relevant answer 3. The system of claim 2, [Explanation of symbols]

[0182] 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 photographing unit for photographing a face photograph of a user; an analysis unit that analyzes the face photograph taken by the photographing unit and diagnoses personal color, bone structure, and face type; A display unit that instantly displays the diagnosis result obtained by the analysis unit. A system characterized by:

2. The analysis unit is AI analyzes facial photos and diagnoses personal colors 2. The system of claim 1 .

3. The analysis unit is Analyzing facial photos using AI and diagnosing bone structure 2. The system of claim 1 .

4. The analysis unit is Analyze facial photos using AI and diagnose face type 2. The system of claim 1 .

5. Equipped with an answering section that provides immediate answers to user questions 2. The system of claim 1 .

6. The imaging unit includes: Estimate the user's emotions and adjust the timing of taking facial photos based on those emotions 2. The system of claim 1 .

7. The imaging unit includes: Analyze the user's past shooting history and select the appropriate shooting method 2. The system of claim 1 .

8. The imaging unit includes: When taking a photo, the system automatically sets appropriate shooting conditions by considering the user's current ambient light and background.

2. The system of claim 1 .

Images