System

Abstract

To provide a system capable of easily determining the health status of an individual.SOLUTION: A system in an embodiment includes an imaging unit, an analysis unit, and an output unit. The imaging unit picks up an image of the face of an individual. The analysis unit analyzes the image of the face picked up by the imaging unit and determines the health state. The output unit outputs the health state determined by the analysis unit in a text.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] Conventional techniques do not adequately provide a means for easily determining an individual's health condition, and there is room for improvement.

[0005] The system according to the embodiment aims to easily determine the health condition of an individual. [Means for solving the problem]

[0006] The system according to the embodiment includes an image capturing unit, an analysis unit, and an output unit. The image capturing unit captures an image of an individual's face. The analysis unit analyzes the face image captured by the image capturing unit to determine the health condition. The output unit outputs the health condition determined by the analysis unit in text form. Effect of the Invention

[0007] The system according to the embodiment can easily determine the health condition of an individual. [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 health condition determination system according to the embodiment of the present invention is a system that links a generation AI and a camera of a device. This system photographs an individual's face, analyzes the face image based on learned medical data, and outputs the health condition in text. For example, an individual's face is photographed with a camera, and a generation AI analyzes the face image to determine the health condition, such as having a fever or muscle weakness. This system is useful not only for personal use but also for maintaining the health of home care patients, since even patients who have weakened muscles and have difficulty moving their bodies as they wish can simply turn their face to the camera. For example, a face can be photographed using a smartphone or tablet camera. The generation AI analyzes facial features based on learned medical data and determines the health condition. For example, by analyzing facial color, facial expression, muscle movement, etc., it can determine whether or not the user has a fever or muscle weakness. The analysis result is output in text. For example, messages such as "Your body temperature is high" and "Your muscle strength is weakened" are displayed. This text output allows the user to easily understand their health condition. This system is particularly useful for home care patients. Since patients with reduced muscle strength have difficulty moving their bodies, being able to check their health condition by simply pointing their face at the camera is a major advantage. Even if family members are unable to move their bodies, they can easily check their health condition by using this system. Furthermore, this system is also suitable for personal use. For example, you can easily check your health condition when you want to check it on a daily basis or when you feel unwell. This makes it possible to discover abnormalities early and take appropriate measures. As a result, the health condition determination system can easily grasp an individual's health condition.

[0029] The health condition determination system according to the embodiment includes a photographing unit, an analysis unit, and an output unit. The photographing unit photographs the face of an individual. The face of an individual includes, for example, age, sex, and facial expression, but is not limited to such examples. The photographing unit can photograph the face using, for example, a camera of a smartphone or a tablet. The analysis unit uses a generation AI to analyze the face image photographed by the photographing unit and determine the health condition. The generation AI analyzes facial features based on learned medical data and determines the health condition. For example, by analyzing facial color, facial expression, muscle movement, etc., it can determine whether there is a fever or whether muscle strength is weakened. The generation AI uses a text generation AI (for example, LLM) to analyze the face image and determine the health condition. The generation AI can also use a multimodal generation AI to analyze the face image and determine the health condition. For example, the generation AI detects a change in facial color and determines the rise in body temperature and blood circulation. The output unit outputs the health condition determined by the analysis unit in text. The output unit displays messages such as, for example, "Your body temperature is high" and "Your muscle strength is weakened." This allows the user to easily grasp his / her health condition. The output unit can also display the analysis results as voice or graphically. For example, the analysis results are output as voice using voice synthesis technology. The output unit can also visually display the analysis results using graphs or charts. This makes it easier for the user to understand the analysis results. In this way, the health condition determination system according to the embodiment allows the user to easily grasp the health condition of an individual.

[0030] The photographing unit photographs the face of an individual. The face of an individual includes, for example, age, sex, and facial expression, but is not limited to such examples. The photographing unit can photograph the face using, for example, a camera of a smartphone or tablet. Specifically, the camera of a smartphone or tablet has high resolution and can capture minute features of the face. This makes it possible to obtain detailed data such as the color of the face, facial expression, and muscle movement. Furthermore, the photographing unit can photograph not only the face from the front, but also from the side and diagonally, and can collect more multifaceted data. This makes it possible to capture three-dimensional features and minute changes of the face, and enables the analysis unit to determine the health condition with high accuracy. In addition, the photographing unit has a correction function for minimizing the influence of lighting conditions and background, and can perform stable photographing even in various environments. For example, the facial features can be clearly captured even in dark places or backlit situations. Furthermore, the photographing unit has a continuous photographing function and can obtain multiple images in a short period of time. This makes it possible to capture changes in facial expressions and muscle movements in real time, and provide more dynamic data. This enables the imaging unit to collect detailed data on an individual's face with high accuracy and provide basic data for the analysis unit to assess the health condition.

[0031] The analysis unit uses the generation AI to analyze the facial image captured by the photographing unit and determine the health condition. The generation AI analyzes facial features based on the learned medical data and determines the health condition. Specifically, the generation AI can determine whether there is a fever or whether muscle strength is weakened by analyzing the color of the face, facial expression, muscle movement, etc. For example, it detects changes in the color of the face and determines the rise in body temperature and blood circulation state. The generation AI uses a text generation AI (e.g., LLM) to analyze the facial image and determine the health condition. The generation AI can also analyze the facial image and determine the health condition using a multimodal generation AI. Specifically, the generation AI detects changes in the color of the face and determines the rise in body temperature and blood circulation state. Furthermore, the generation AI can analyze subtle changes in facial expressions and muscle movements and determine the degree of stress and fatigue. For example, it can detect signs of stress and fatigue by analyzing muscle tension around the eyes and the degree of drooping of the corners of the mouth. Furthermore, the generation AI can track changes in health condition by comparing with past data and support long-term health management. For example, by comparing past and current facial images, the analysis unit can detect changes in physical condition or signs of improvement. This allows the analysis unit to determine an individual's health condition with high accuracy and provide appropriate health management advice to the user.

[0032] The output unit outputs the health condition determined by the analysis unit in text. Specifically, the output unit displays messages such as "Your body temperature is high" and "Your muscle strength is declining." This allows the user to easily grasp his or her health condition. The output unit can also display the analysis results in voice or graphically. For example, the analysis results are output in voice using voice synthesis technology. The output unit can also visually display the analysis results using graphs and charts. Specifically, a graph showing changes in body temperature and a chart showing declines in muscle strength can be displayed. This makes it easier for the user to understand the analysis results. Furthermore, the output unit can provide specific advice and recommended actions to the user based on the analysis results. For example, advice such as "Drink lots of fluids" and "Try to exercise moderately" can be displayed. This allows the user to take appropriate actions according to his or her health condition. In addition, the output unit can link the analysis results with other devices and applications. For example, the output unit can link with a smart watch or fitness tracker to centrally manage health condition data. This allows the user to perform more detailed health management through multiple devices. This enables the output unit to provide the analysis results to the user in an easily understandable manner, thereby assisting in health management.

[0033] The analysis unit can analyze facial features based on the learned medical data and determine the health condition. The learned medical data includes, for example, medical history data, diagnostic data, etc., but is not limited to such examples. The analysis unit can analyze facial features based on, for example, medical history data and determine the health condition. The analysis unit can also analyze facial features based on diagnostic data and determine the health condition. For example, the analysis unit uses medical history data to analyze facial color, facial expression, muscle movement, etc., and determines the health condition. The diagnostic data is data collected at a medical institution, and the analysis unit analyzes facial features based on this and determines the health condition. As a result, the analysis based on the learned medical data improves the accuracy of determining the health condition. 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 determine the health condition using an AI model that inputs learned medical data, analyzes facial features, and determines the health condition.

[0034] The output unit can display the analysis result by voice or graphically. Methods for displaying the analysis result by voice or graphically include, but are not limited to, voice synthesis technology, graphs, charts, and the like. The output unit outputs the analysis result by voice, for example, using voice synthesis technology. The output unit can also visually display the analysis result by using graphs or charts. For example, the output unit displays the analysis result as a bar graph or pie chart. Voice synthesis technology is a technology for converting text data into voice, and the output unit uses this to output the analysis result by voice. Graphs and charts are means for visually displaying the analysis result, and the output unit uses this to visually display the analysis result. This makes it easier for the user to understand the results by displaying the analysis result by voice or graphically. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can display the analysis result by using an AI model that inputs the analysis result and displays it by voice or graphically.

[0035] The system includes a security unit that encrypts data or uses data after obtaining user consent. Data encryption includes, but is not limited to, encryption methods such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). The security unit encrypts data using, for example, AES. The security unit can also encrypt data using RSA. AES is a symmetric key encryption method that provides high speed and secure encryption. RSA is a public key encryption method that provides high security. Methods for obtaining user consent include, but are not limited to, the format of a consent form and the timing of obtaining consent. The security unit obtains user consent using, for example, a consent form. The security unit can also adjust the timing of obtaining consent and obtain user consent at an appropriate timing. This improves data security by encrypting data or using data after obtaining user consent. A part or all of the above-mentioned processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security department can select a method for encrypting data and encrypt the data using an AI model that encrypts the data.

[0036] The system includes an application unit that is useful for maintaining the health of a home care recipient or for general health management. The health maintenance of a home care recipient includes, for example, regular health checks, a reminder function, and the like, but is not limited to such examples. The application unit includes, for example, a function for performing regular health checks. The application unit also includes a reminder function and can notify a user to perform a regular health check. The general health management includes, for example, daily health records, a feedback function, and the like, but is not limited to such examples. The application unit includes, for example, a function for performing daily health records. The application unit also includes a feedback function and can provide the user with feedback on the health condition. This is useful for home care recipients and general health management. A part or all of the above-mentioned processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit can perform a health check using an AI model that sets a schedule for a health check and sends a reminder.

[0037] The system can take a picture of a face using a smartphone or tablet camera. The smartphone or tablet camera includes, but is not limited to, features such as resolution and frame rate. The system, for example, takes a picture of a face using a smartphone camera. The system can also take a picture of a face using a tablet camera. Resolution is an index indicating the fineness of an image captured by a camera, and a high-resolution camera can capture a more detailed image. Frame rate is an index indicating the number of frames a camera captures per second, and a high-frame-rate camera can capture smoother videos. This allows the system to be used without requiring special equipment by using a smartphone or tablet camera. Some or all of the above-mentioned processing in the system may be performed, for example, using AI, or may be performed without using AI. For example, the system can capture a face using an AI model that adjusts the settings of the smartphone or tablet camera to provide optimal shooting conditions.

[0038] The photographing unit can add a function of automatically adjusting the angle and expression of the user's face when photographing. For example, when the user's face is at an angle to the camera, the photographing unit automatically adjusts the angle of the face to face the front. In addition, when it is difficult for the user to smile, the photographing unit can also issue instructions to allow the AI ​​to analyze the expression and bring out a natural smile. Furthermore, when the user's face is dark, the photographing unit can automatically adjust the lighting conditions to brighten the face. As a result, the optimal photographing conditions can be provided by automatically adjusting the angle and expression of the user's face. The adjustment of the angle and expression of the face is realized, for example, by using face recognition technology or motion tracking technology. Face recognition technology is a technology that detects the position and angle of the face captured by the camera and makes appropriate adjustments. Motion tracking technology is a technology that tracks the movement of the face in real time and makes appropriate adjustments. 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 input the angle and expression data of the face to the generation AI and cause the generation AI to execute the adjustments.

[0039] The photographing unit can set appropriate photographing conditions by referring to the user's past photographing data when photographing. The photographing unit automatically applies optimal settings based on, for example, the photographing conditions (brightness, angle, etc.) that the user has previously preferred. The photographing unit can also select and apply conditions that make the user's health look the best from the user's past photographing data. Furthermore, the photographing unit can also analyze the user's past photographing data and propose optimal background and lighting conditions. As a result, optimal photographing conditions can be provided by referring to the user's past photographing data. The past photographing data includes, for example, the photographing date and time, the photographing conditions, etc., but is not limited to such examples. The photographing unit sets optimal photographing conditions according to the user's preferences and health condition based on the past photographing data. As a result, the user can always photograph under optimal conditions. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the past photographing data to the generation AI and have the generation AI select the optimal photographing conditions.

[0040] The photographing unit can add a function to automatically adjust the user's ambient light and background when photographing. For example, when the user's surroundings are dark, the photographing unit automatically adjusts the exposure of the camera to brighten it. In addition, the photographing unit can automatically apply a function to blur the background when the user's background is cluttered. Furthermore, when the user's ambient light fluctuates, the photographing unit can automatically adjust the white balance of the camera to keep the color constant. This makes it possible to provide optimal photographing conditions by automatically adjusting the user's ambient light and background. The adjustment of the ambient light and background is realized, for example, using a lighting adjustment technique or a background blurring technique. The lighting adjustment technique is a technique that detects the state of the ambient light captured by the camera and makes appropriate adjustments. The background blurring technique is a technique that blurs the background captured by the camera to highlight the subject. 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 input ambient light and background data to the generation AI and cause the generation AI to perform the adjustments.

[0041] The photographing unit can analyze the user's social media activity at the time of photographing, and can photograph related images preferentially. For example, the photographing unit analyzes facial expressions and poses that the user often uses on social media, and preferentially photographs images that match them. The photographing unit can also analyze backgrounds and places that the user often shares on social media, and set photographing conditions that match them. Furthermore, the photographing unit can preferentially photograph images related to a specific event or theme from the user's social media activity. This makes it possible to preferentially photograph related images by analyzing the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to such examples. The photographing unit sets optimal photographing conditions that match the user's preferences and interests based on the social media activity. This allows the user to always take photographs under optimal conditions. 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 input social media activity data to the generation AI and have the generation AI select the optimal photographing conditions.

[0042] The analysis unit detects subtle changes in facial expression during analysis, and can determine the health condition in more detail. The analysis unit, for example, detects subtle changes in facial expression and determines a stress level. The analysis unit can also analyze the movement of facial muscles and determine a decrease in muscle strength. Furthermore, the analysis unit can detect changes in facial color and determine an increase in body temperature and a blood circulation state. This makes it possible to determine the health condition in more detail by detecting subtle changes in facial expression. Subtle changes in facial expression include, for example, eyebrow movements and changes in the corners of the mouth, but are not limited to such examples. The analysis unit analyzes the user's health condition in detail based on the subtle changes in facial expression. This makes it possible to grasp the user's health condition more accurately. 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 input data on subtle changes in facial expression to the generation AI and cause the generation AI to execute a health condition determination.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past health data during the analysis. The analysis unit, for example, more accurately determines the current health condition based on the user's past health data. The analysis unit can also predict a specific health risk from the user's past health data and reflect it in the analysis result. Furthermore, the analysis unit can also issue an alert when an abnormal value is detected by referring to the user's past health data. As a result, the analysis accuracy is improved by referring to the user's past health data. The past health data includes, for example, diagnosis results, health records, etc., but is not limited to such examples. The analysis unit analyzes the user's health condition in detail based on the past health data. As a result, the user's health condition can be grasped more accurately. 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 input the past health data to the generation AI and cause the generation AI to execute improvement of the analysis accuracy.

[0044] The analysis unit can improve the accuracy of the analysis by referring to the lifestyle data of the user during the analysis. The analysis unit, for example, refers to the diet and exercise data of the user to more accurately determine the health condition. The analysis unit can also refer to the sleep data of the user to determine the fatigue level and stress level. Furthermore, the analysis unit can predict a specific health risk based on the lifestyle data of the user and reflect it in the analysis result. As a result, the analysis accuracy is improved by referring to the lifestyle data of the user. The lifestyle data includes, for example, a meal record, an exercise record, and the like, but is not limited to such examples. The analysis unit analyzes the health condition of the user in detail based on the lifestyle data. As a result, the health condition of the user can be grasped more accurately. 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 input the lifestyle data to the generation AI and cause the generation AI to execute the improvement of the analysis accuracy.

[0045] The analysis unit can adjust the analysis result based on the geographical location information of the user during the analysis. For example, when the user is at high altitude, the analysis unit adjusts the analysis result taking into account the decrease in oxygen concentration. In addition, when the user is in an urban area, the analysis unit can also adjust the analysis result taking into account the influence of air pollution. Furthermore, when the user is in a cold area, the analysis unit can also adjust the analysis result taking into account the fluctuation of body temperature. As a result, by taking into account the geographical location information of the user, a more accurate analysis result can be provided. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. The analysis unit analyzes the user's health condition in detail based on the geographical location information. As a result, the user's health condition can be grasped more accurately. 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 input the geographical location information to the generation AI and cause the generation AI to adjust the analysis result.

[0046] The output unit can adjust the output contents by referring to the user's past health data at the time of output. The output unit outputs text that describes the current health condition in detail based on the user's past health data, for example. The output unit can also preferentially output information on a specific health risk from the user's past health data. Furthermore, the output unit can also refer to the user's past health data and output an alert when an abnormal value is detected. This makes it possible to customize the output contents by referring to the user's past health data. The past health data includes, for example, diagnosis results, health records, etc., but is not limited to such examples. The output unit explains the user's health condition in detail based on the past health data. This allows the user to grasp his or her health condition more accurately. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can input the past health data to the generation AI and cause the generation AI to adjust the output contents.

[0047] The output unit can set the output timing based on the user's current activity status at the time of output. For example, when the user is relaxed, the output unit provides detailed text output. In addition, when the user is nervous, the output unit can also provide concise text output. Furthermore, when the user is in a hurry, the output unit can also provide text output with the main points. As a result, by adjusting the output timing based on the user's current activity status, it is possible to provide the result at the optimal timing. The output timing is set, for example, based on the user's schedule and activity status. The user's schedule includes, for example, plans and tasks, but is not limited to such examples. The output unit provides the result at the optimal timing based on the user's schedule. As a result, the user can receive the result at the optimal timing. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can input the current activity status data to the generation AI and cause the generation AI to execute the setting of the output timing.

[0048] The output unit can select an appropriate output format based on the device information of the user at the time of output. For example, when the user is using a smartphone, the output unit provides an output format that matches the screen size. In addition, when the user is using a tablet, the output unit can also provide an output format optimized for a large screen. Furthermore, when the user is using a smartwatch, the output unit can also provide an output format that is concise and highly visible. This makes it possible to provide an optimal output format by taking into account the device information of the user. The device information includes, for example, the type of device, the OS version, and the like, but is not limited to such examples. The output unit selects an output format that is optimal for the user's device based on the device information. This allows the user to always receive the result in the optimal format. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can input the device information to the generation AI and cause the generation AI to select the output format.

[0049] The output unit can analyze the user's social media activity at the time of output and provide related information. The output unit outputs related analysis results based on, for example, health information that the user often shares on social media. The output unit can also preferentially output information related to a specific health theme from the user's social media activity. Furthermore, the output unit can analyze the user's social media activity and output health information of the user's greatest interest. This makes it possible to provide related information by analyzing the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to such examples. The output unit provides optimal information tailored to the user's interest based on the social media activity. This allows the user to always receive optimal information. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can input social media activity data to a generation AI and cause the generation AI to provide related information.

[0050] When encrypting data, the security unit can select an appropriate encryption method by referring to the user's past security history. The security unit, for example, selects an optimal encryption method based on the encryption method used by the user in the past. The security unit can also preferentially apply a specific encryption method from the user's past security history. Furthermore, the security unit can analyze the user's past security history and select the safest encryption method. As a result, the optimal encryption method can be provided by referring to the user's past security history. The security history includes, for example, past security incidents, security settings, etc., but is not limited to such examples. The security unit encrypts the user's data in an optimal manner based on the security history. As a result, the user's data is always protected in an optimal manner. A part or all of the above-mentioned processing in the security unit may be performed, for example, using AI, or may be performed without using AI. For example, the security unit can input security history data to the generation AI and cause the generation AI to select an encryption method.

[0051] The security unit can select an appropriate encryption method based on the geographical location information of the user when encrypting data. For example, when the user is in a high-risk area, the security unit applies a strong encryption method. Also, when the user is in a low-risk area, the security unit can apply a normal encryption method. Furthermore, the security unit can select an optimal encryption method based on the geographical location information of the user. As a result, it is possible to provide an optimal encryption method by taking into account the geographical location information of the user. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. The security unit encrypts the user's data in an optimal manner based on the geographical location information. As a result, the user's data is always protected in an optimal manner. A part or all of the above-mentioned processing in the security unit may be performed, for example, using AI, or may be performed without using AI. For example, the security unit can input geographical location information data to the generation AI and cause the generation AI to select an encryption method.

[0052] The application unit can select an appropriate application method by referring to the user's past health data at the time of application. The application unit, for example, selects an optimal application method based on the user's past health data. The application unit can also select an application method corresponding to a specific health risk from the user's past health data. Furthermore, the application unit can also select the most effective application method by referring to the user's past health data. As a result, the optimal application method can be provided by referring to the user's past health data. The past health data includes, for example, diagnosis results, health records, etc., but is not limited to such examples. The application unit selects an application method optimal for the user's health condition based on the past health data. As a result, the user can always manage his / her health in an optimal manner. A part or all of the above-mentioned processing in the application unit may be performed, for example, using AI, or may be performed without using AI. For example, the application unit can input past health data to the generation AI and cause the generation AI to select an application method.

[0053] The application unit can select an appropriate application method based on the geographical location information of the user at the time of application. For example, when the user is at high altitude, the application unit provides an application method that takes into account the decrease in oxygen concentration. In addition, when the user is in an urban area, the application unit can also provide an application method that takes into account the influence of air pollution. Furthermore, when the user is in a cold area, the application unit can also provide an application method that takes into account the fluctuation of body temperature. As a result, it is possible to provide an optimal application method by taking into account the geographical location information of the user. The geographical location information includes, for example, GPS data, address information, etc., but is not limited to such examples. The application unit selects an application method that is optimal for the health condition of the user based on the geographical location information. As a result, the user can always manage his / her health in an optimal manner. A part or all of the above-mentioned processing in the application unit may be performed, for example, using AI, or may be performed without using AI. For example, the application unit can input geographical location information data to the generation AI and cause the generation AI to select an application method.

[0054] When using the camera, the camera of the smartphone or tablet can select appropriate settings by referring to the user's past shooting data. The camera of the smartphone or tablet can automatically apply optimal settings based on, for example, shooting conditions (brightness, angle, etc.) that the user has previously preferred. The camera of the smartphone or tablet can also select and apply conditions that make the user's health look the best from the user's past shooting data. Furthermore, the camera of the smartphone or tablet can also analyze the user's past shooting data and suggest optimal background and lighting conditions. This makes it possible to provide optimal settings by referring to the user's past shooting data. The past shooting data includes, for example, shooting date and time, shooting conditions, etc., but is not limited to such examples. The camera of the smartphone or tablet selects optimal settings according to the user's preferences and health condition based on the past shooting data. This allows the user to always take photos under optimal conditions. A part or all of the above-mentioned processing in the camera of the smartphone or tablet may be performed, for example, using AI, or may be performed without using AI. For example, the camera of the smartphone or tablet can input past shooting data to the generation AI and have the generation AI select the optimal settings.

[0055] The camera of the smartphone or tablet can select appropriate settings based on the device information of the user when using the camera. For example, when the user is using a smartphone, the camera of the smartphone or tablet can provide settings that match the screen size. In addition, when the user is using a tablet, the camera of the smartphone or tablet can also provide settings that are optimized for a large screen. In addition, when the user is using a smartwatch, the camera of the smartphone or tablet can also provide simple and highly visible settings. This makes it possible to provide optimal settings by taking into account the device information of the user. The device information includes, for example, the type of device, the OS version, and the like, but is not limited to such examples. The camera of the smartphone or tablet selects optimal settings for the user's device based on the device information. This allows the user to always take pictures under optimal conditions. Part or all of the above-mentioned processing in the camera of the smartphone or tablet may be performed, for example, using AI, or may be performed without using AI. For example, the camera of the smartphone or tablet can input device information to the generation AI and have the generation AI select the settings.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The analysis unit can refer to the user's lifestyle data and reflect it in the determination of the health condition. For example, it can analyze changes in nutritional state and physical strength based on the user's diet and exercise data. It can also refer to the user's sleep data and determine the user's fatigue level and stress level. Furthermore, it can predict specific health risks based on the user's lifestyle data and reflect them in the analysis results. In this way, by referring to the user's lifestyle data, the accuracy of the analysis can be improved, making it possible to determine the health condition more accurately.

[0058] The analysis unit can refer to the geographical location information of the user and reflect it in the judgment of the health condition. For example, if the user is at high altitude, the analysis result can be adjusted taking into account the decrease in oxygen concentration. Also, if the user is in an urban area, the analysis result can be adjusted taking into account the effects of air pollution. Furthermore, if the user is in a cold area, the analysis result can be adjusted taking into account the fluctuation of body temperature. In this way, by taking into account the geographical location information of the user, more accurate analysis results can be provided.

[0059] The output unit can adjust the output contents by referring to the user's past health data. For example, the output unit can output text that describes the current health condition in detail based on the user's past health data. The output unit can also preferentially output information on specific health risks from the user's past health data. Furthermore, the output unit can also refer to the user's past health data and output an alert if an abnormal value is detected. This allows the output contents to be customized by referring to the user's past health data.

[0060] The output unit can select an appropriate output format based on the device information of the user. For example, if the user is using a smartphone, an output format that matches the screen size can be provided. Also, if the user is using a tablet, an output format optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, an output format that is concise and highly visible can be provided. In this way, the optimal output format can be provided by taking into account the device information of the user.

[0061] The security unit can select an appropriate encryption method by referring to the user's past security history. For example, the optimal encryption method is selected based on encryption methods used by the user in the past. Also, a specific encryption method can be applied preferentially based on the user's past security history. Furthermore, the user's past security history can be analyzed to select the most secure encryption method. In this way, the optimal encryption method can be provided by referring to the user's past security history.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The photographing unit photographs the face of an individual. The face of an individual includes, but is not limited to, for example, age, gender, and facial expression. The photographing unit can photograph the face using, for example, a camera of a smartphone or tablet. Step 2: The analysis unit uses the generation AI to analyze the facial image captured by the photographing unit and determine the health condition. The generation AI analyzes facial features based on the learned medical data and determines the health condition. For example, by analyzing facial color, facial expressions, muscle movements, etc., it can determine whether there is a fever or whether muscle strength is weakened. The generation AI uses a text generation AI (e.g., LLM) to analyze the facial image and determine the health condition. The generation AI can also use a multimodal generation AI to analyze the facial image and determine the health condition. For example, the generation AI detects changes in facial color and determines an increase in body temperature and blood circulation. Step 3: The output unit outputs the health condition determined by the analysis unit in text. For example, the output unit displays messages such as "Your body temperature is high" or "Your muscle strength is declining." This allows the user to easily understand his or her health condition. The output unit can also display the analysis results as voice or graphically. For example, the analysis results are output as voice using voice synthesis technology. The output unit can also visually display the analysis results using graphs and charts. This allows the user to more easily understand the analysis results.

[0064] (Example 2) The health condition determination system according to the embodiment of the present invention is a system that links a generation AI and a camera of a device. This system photographs an individual's face, analyzes the face image based on learned medical data, and outputs the health condition in text. For example, an individual's face is photographed with a camera, and a generation AI analyzes the face image to determine the health condition, such as having a fever or muscle weakness. This system is useful not only for personal use but also for maintaining the health of home care patients, since even patients who have weakened muscles and have difficulty moving their bodies as they wish can simply turn their face to the camera. For example, a face can be photographed using a smartphone or tablet camera. The generation AI analyzes facial features based on learned medical data and determines the health condition. For example, by analyzing facial color, facial expression, muscle movement, etc., it can determine whether or not the user has a fever or muscle weakness. The analysis result is output in text. For example, messages such as "Your body temperature is high" and "Your muscle strength is weakened" are displayed. This text output allows the user to easily understand their health condition. This system is particularly useful for home care patients. Since patients with reduced muscle strength have difficulty moving their bodies, being able to check their health condition by simply pointing their face at the camera is a major advantage. Even if family members are unable to move their bodies, they can easily check their health condition by using this system. Furthermore, this system is also suitable for personal use. For example, you can easily check your health condition when you want to check it on a daily basis or when you feel unwell. This makes it possible to discover abnormalities early and take appropriate measures. As a result, the health condition determination system can easily grasp an individual's health condition.

[0065] The health condition determination system according to the embodiment includes a photographing unit, an analysis unit, and an output unit. The photographing unit photographs the face of an individual. The face of an individual includes, for example, age, sex, and facial expression, but is not limited to such examples. The photographing unit can photograph the face using, for example, a camera of a smartphone or a tablet. The analysis unit uses a generation AI to analyze the face image photographed by the photographing unit and determine the health condition. The generation AI analyzes facial features based on learned medical data and determines the health condition. For example, by analyzing facial color, facial expression, muscle movement, etc., it can determine whether there is a fever or whether muscle strength is weakened. The generation AI uses a text generation AI (for example, LLM) to analyze the face image and determine the health condition. The generation AI can also use a multimodal generation AI to analyze the face image and determine the health condition. For example, the generation AI detects a change in facial color and determines the rise in body temperature and blood circulation. The output unit outputs the health condition determined by the analysis unit in text. The output unit displays messages such as, for example, "Your body temperature is high" and "Your muscle strength is weakened." This allows the user to easily grasp his / her health condition. The output unit can also display the analysis results as voice or graphically. For example, the analysis results are output as voice using voice synthesis technology. The output unit can also visually display the analysis results using graphs or charts. This makes it easier for the user to understand the analysis results. In this way, the health condition determination system according to the embodiment allows the user to easily grasp the health condition of an individual.

[0066] The photographing unit photographs the face of an individual. The face of an individual includes, for example, age, sex, and facial expression, but is not limited to such examples. The photographing unit can photograph the face using, for example, a camera of a smartphone or tablet. Specifically, the camera of a smartphone or tablet has high resolution and can capture minute features of the face. This makes it possible to obtain detailed data such as the color of the face, facial expression, and muscle movement. Furthermore, the photographing unit can photograph not only the face from the front, but also from the side and diagonally, and can collect more multifaceted data. This makes it possible to capture three-dimensional features and minute changes of the face, and enables the analysis unit to determine the health condition with high accuracy. In addition, the photographing unit has a correction function for minimizing the influence of lighting conditions and background, and can perform stable photographing even in various environments. For example, the facial features can be clearly captured even in dark places or backlit situations. Furthermore, the photographing unit has a continuous photographing function and can obtain multiple images in a short period of time. This makes it possible to capture changes in facial expressions and muscle movements in real time, and provide more dynamic data. This enables the imaging unit to collect detailed data on an individual's face with high accuracy and provide basic data for the analysis unit to assess the health condition.

[0067] The analysis unit uses the generation AI to analyze the facial image captured by the photographing unit and determine the health condition. The generation AI analyzes facial features based on the learned medical data and determines the health condition. Specifically, the generation AI can determine whether there is a fever or whether muscle strength is weakened by analyzing the color of the face, facial expression, muscle movement, etc. For example, it detects changes in the color of the face and determines the rise in body temperature and blood circulation state. The generation AI uses a text generation AI (e.g., LLM) to analyze the facial image and determine the health condition. The generation AI can also analyze the facial image and determine the health condition using a multimodal generation AI. Specifically, the generation AI detects changes in the color of the face and determines the rise in body temperature and blood circulation state. Furthermore, the generation AI can analyze subtle changes in facial expressions and muscle movements and determine the degree of stress and fatigue. For example, it can detect signs of stress and fatigue by analyzing muscle tension around the eyes and the degree of drooping of the corners of the mouth. Furthermore, the generation AI can track changes in health condition by comparing with past data and support long-term health management. For example, by comparing past and current facial images, the analysis unit can detect changes in physical condition or signs of improvement. This allows the analysis unit to determine an individual's health condition with high accuracy and provide appropriate health management advice to the user.

[0068] The output unit outputs the health condition determined by the analysis unit in text. Specifically, the output unit displays messages such as "Your body temperature is high" and "Your muscle strength is declining." This allows the user to easily grasp his or her health condition. The output unit can also display the analysis results in voice or graphically. For example, the analysis results are output in voice using voice synthesis technology. The output unit can also visually display the analysis results using graphs and charts. Specifically, a graph showing changes in body temperature and a chart showing declines in muscle strength can be displayed. This makes it easier for the user to understand the analysis results. Furthermore, the output unit can provide specific advice and recommended actions to the user based on the analysis results. For example, advice such as "Drink lots of fluids" and "Try to exercise moderately" can be displayed. This allows the user to take appropriate actions according to his or her health condition. In addition, the output unit can link the analysis results with other devices and applications. For example, the output unit can link with a smart watch or fitness tracker to centrally manage health condition data. This allows the user to perform more detailed health management through multiple devices. This enables the output unit to provide the analysis results to the user in an easily understandable manner, thereby assisting in health management.

[0069] The analysis unit can analyze facial features based on the learned medical data and determine the health condition. The learned medical data includes, for example, medical history data, diagnostic data, etc., but is not limited to such examples. The analysis unit can analyze facial features based on, for example, medical history data and determine the health condition. The analysis unit can also analyze facial features based on diagnostic data and determine the health condition. For example, the analysis unit uses medical history data to analyze facial color, facial expression, muscle movement, etc., and determines the health condition. The diagnostic data is data collected at a medical institution, and the analysis unit analyzes facial features based on this and determines the health condition. As a result, the analysis based on the learned medical data improves the accuracy of determining the health condition. 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 determine the health condition using an AI model that inputs learned medical data, analyzes facial features, and determines the health condition.

[0070] The output unit can display the analysis result by voice or graphically. Methods for displaying the analysis result by voice or graphically include, but are not limited to, voice synthesis technology, graphs, charts, and the like. The output unit outputs the analysis result by voice, for example, using voice synthesis technology. The output unit can also visually display the analysis result by using graphs or charts. For example, the output unit displays the analysis result as a bar graph or pie chart. Voice synthesis technology is a technology for converting text data into voice, and the output unit uses this to output the analysis result by voice. Graphs and charts are means for visually displaying the analysis result, and the output unit uses this to visually display the analysis result. This makes it easier for the user to understand the results by displaying the analysis result by voice or graphically. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can display the analysis result by using an AI model that inputs the analysis result and displays it by voice or graphically.

[0071] The system includes a security unit that encrypts data or uses data after obtaining user consent. Data encryption includes, but is not limited to, encryption methods such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). The security unit encrypts data using, for example, AES. The security unit can also encrypt data using RSA. AES is a symmetric key encryption method that provides high speed and secure encryption. RSA is a public key encryption method that provides high security. Methods for obtaining user consent include, but are not limited to, the format of a consent form and the timing of obtaining consent. The security unit obtains user consent using, for example, a consent form. The security unit can also adjust the timing of obtaining consent and obtain user consent at an appropriate timing. This improves data security by encrypting data or using data after obtaining user consent. A part or all of the above-mentioned processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security department can select a method for encrypting data and encrypt the data using an AI model that encrypts the data.

[0072] The system includes an application unit that is useful for maintaining the health of a home care recipient or for general health management. The health maintenance of a home care recipient includes, for example, regular health checks, a reminder function, and the like, but is not limited to such examples. The application unit includes, for example, a function for performing regular health checks. The application unit also includes a reminder function and can notify a user to perform a regular health check. The general health management includes, for example, daily health records, a feedback function, and the like, but is not limited to such examples. The application unit includes, for example, a function for performing daily health records. The application unit also includes a feedback function and can provide the user with feedback on the health condition. This is useful for home care recipients and general health management. A part or all of the above-mentioned processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit can perform a health check using an AI model that sets a schedule for a health check and sends a reminder.

[0073] The system can take a picture of a face using a smartphone or tablet camera. The smartphone or tablet camera includes, but is not limited to, features such as resolution and frame rate. The system, for example, takes a picture of a face using a smartphone camera. The system can also take a picture of a face using a tablet camera. Resolution is an index indicating the fineness of an image captured by a camera, and a high-resolution camera can capture a more detailed image. Frame rate is an index indicating the number of frames a camera captures per second, and a high-frame-rate camera can capture smoother videos. This allows the system to be used without requiring special equipment by using a smartphone or tablet camera. Some or all of the above-mentioned processing in the system may be performed, for example, using AI, or may be performed without using AI. For example, the system can capture a face using an AI model that adjusts the settings of the smartphone or tablet camera to provide optimal shooting conditions.

[0074] The photographing unit can estimate the user's emotion and adjust the timing of photographing based on the estimated emotion of the user. For example, when the user is relaxed, the photographing unit delays the timing of photographing in order to capture a natural facial expression. In addition, when the user is nervous, the photographing unit can also take a photograph early and complete the photographing before the nervousness appears in the facial expression. Furthermore, when the user is in a hurry, the photographing unit can take a photograph quickly so as not to waste the user's time. In this way, a more natural facial expression can be captured by adjusting the timing of photographing based on 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 input the user's facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0075] The photographing unit can add a function of automatically adjusting the angle and expression of the user's face when photographing. For example, when the user's face is at an angle to the camera, the photographing unit automatically adjusts the angle of the face to face the front. In addition, when it is difficult for the user to smile, the photographing unit can also issue instructions to allow the AI ​​to analyze the expression and bring out a natural smile. Furthermore, when the user's face is dark, the photographing unit can automatically adjust the lighting conditions to brighten the face. As a result, the optimal photographing conditions can be provided by automatically adjusting the angle and expression of the user's face. The adjustment of the angle and expression of the face is realized, for example, by using face recognition technology or motion tracking technology. Face recognition technology is a technology that detects the position and angle of the face captured by the camera and makes appropriate adjustments. Motion tracking technology is a technology that tracks the movement of the face in real time and makes appropriate adjustments. 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 input the angle and expression data of the face to the generation AI and cause the generation AI to execute the adjustments.

[0076] The photographing unit can set appropriate photographing conditions by referring to the user's past photographing data when photographing. The photographing unit automatically applies optimal settings based on, for example, the photographing conditions (brightness, angle, etc.) that the user has previously preferred. The photographing unit can also select and apply conditions that make the user's health look the best from the user's past photographing data. Furthermore, the photographing unit can also analyze the user's past photographing data and propose optimal background and lighting conditions. As a result, optimal photographing conditions can be provided by referring to the user's past photographing data. The past photographing data includes, for example, the photographing date and time, the photographing conditions, etc., but is not limited to such examples. The photographing unit sets optimal photographing conditions according to the user's preferences and health condition based on the past photographing data. As a result, the user can always photograph under optimal conditions. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the past photographing data to the generation AI and have the generation AI select the optimal photographing conditions.

[0077] The photographing unit can estimate the user's emotion and determine the priority of the images to be photographed based on the estimated emotion of the user. For example, when the user is relaxed, the photographing unit preferentially photographs an image of a natural facial expression. Also, when the user is nervous, the photographing unit can preferentially photograph an image before the tension appears in the facial expression. Furthermore, when the user is in a hurry, the photographing unit can quickly photograph and preferentially save the most important image. In this way, the most important image can be photographed preferentially by determining the priority of the images to be photographed based on 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 input the user's facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0078] The photographing unit can add a function to automatically adjust the user's ambient light and background when photographing. For example, when the user's surroundings are dark, the photographing unit automatically adjusts the exposure of the camera to brighten it. In addition, the photographing unit can automatically apply a function to blur the background when the user's background is cluttered. Furthermore, when the user's ambient light fluctuates, the photographing unit can automatically adjust the white balance of the camera to keep the color constant. This makes it possible to provide optimal photographing conditions by automatically adjusting the user's ambient light and background. The adjustment of the ambient light and background is realized, for example, using a lighting adjustment technique or a background blurring technique. The lighting adjustment technique is a technique that detects the state of the ambient light captured by the camera and makes appropriate adjustments. The background blurring technique is a technique that blurs the background captured by the camera to highlight the subject. 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 input ambient light and background data to the generation AI and cause the generation AI to perform the adjustments.

[0079] The photographing unit can analyze the user's social media activity at the time of photographing, and can photograph related images preferentially. For example, the photographing unit analyzes facial expressions and poses that the user often uses on social media, and preferentially photographs images that match them. The photographing unit can also analyze backgrounds and places that the user often shares on social media, and set photographing conditions that match them. Furthermore, the photographing unit can preferentially photograph images related to a specific event or theme from the user's social media activity. This makes it possible to preferentially photograph related images by analyzing the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to such examples. The photographing unit sets optimal photographing conditions that match the user's preferences and interests based on the social media activity. This allows the user to always take photographs under optimal conditions. 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 input social media activity data to the generation AI and have the generation AI select the optimal photographing conditions.

[0080] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit performs a detailed analysis to more accurately determine the health condition. In addition, when the user is tense, the analysis unit can also perform a simple analysis and quickly output the result. Furthermore, when the user is in a hurry, the analysis unit can also perform an analysis focusing on the most important health indicators. This makes it possible to provide a more accurate analysis result by adjusting the analysis algorithm based on 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0081] The analysis unit detects subtle changes in facial expression during analysis, and can determine the health condition in more detail. The analysis unit, for example, detects subtle changes in facial expression and determines a stress level. The analysis unit can also analyze the movement of facial muscles and determine a decrease in muscle strength. Furthermore, the analysis unit can detect changes in facial color and determine an increase in body temperature and a blood circulation state. This makes it possible to determine the health condition in more detail by detecting subtle changes in facial expression. Subtle changes in facial expression include, for example, eyebrow movements and changes in the corners of the mouth, but are not limited to such examples. The analysis unit analyzes the user's health condition in detail based on the subtle changes in facial expression. This makes it possible to grasp the user's health condition more accurately. 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 input data on subtle changes in facial expression to the generation AI and cause the generation AI to execute a health condition determination.

[0082] The analysis unit can improve the accuracy of the analysis by referring to the user's past health data during the analysis. The analysis unit, for example, more accurately determines the current health condition based on the user's past health data. The analysis unit can also predict a specific health risk from the user's past health data and reflect it in the analysis result. Furthermore, the analysis unit can also issue an alert when an abnormal value is detected by referring to the user's past health data. As a result, the analysis accuracy is improved by referring to the user's past health data. The past health data includes, for example, diagnosis results, health records, etc., but is not limited to such examples. The analysis unit analyzes the user's health condition in detail based on the past health data. As a result, the user's health condition can be grasped more accurately. 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 input the past health data to the generation AI and cause the generation AI to execute improvement of the analysis accuracy.

[0083] The analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit preferentially displays detailed analysis results. In addition, when the user is nervous, the analysis unit can also preferentially display simple analysis results. Furthermore, when the user is in a hurry, the analysis unit can also preferentially display the most important analysis results. In this way, by determining the priority of the analysis results based on the user's emotion, the most important analysis results can be preferentially displayed. 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 input the user's emotion data to the generation AI and have the generation AI determine the priority of the analysis results.

[0084] The analysis unit can improve the accuracy of the analysis by referring to the lifestyle data of the user during the analysis. The analysis unit, for example, refers to the diet and exercise data of the user to more accurately determine the health condition. The analysis unit can also refer to the sleep data of the user to determine the fatigue level and stress level. Furthermore, the analysis unit can predict a specific health risk based on the lifestyle data of the user and reflect it in the analysis result. As a result, the analysis accuracy is improved by referring to the lifestyle data of the user. The lifestyle data includes, for example, a meal record, an exercise record, and the like, but is not limited to such examples. The analysis unit analyzes the health condition of the user in detail based on the lifestyle data. As a result, the health condition of the user can be grasped more accurately. 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 input the lifestyle data to the generation AI and cause the generation AI to execute the improvement of the analysis accuracy.

[0085] The analysis unit can adjust the analysis result based on the geographical location information of the user during the analysis. For example, when the user is at high altitude, the analysis unit adjusts the analysis result taking into account the decrease in oxygen concentration. In addition, when the user is in an urban area, the analysis unit can also adjust the analysis result taking into account the influence of air pollution. Furthermore, when the user is in a cold area, the analysis unit can also adjust the analysis result taking into account the fluctuation of body temperature. As a result, by taking into account the geographical location information of the user, a more accurate analysis result can be provided. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. The analysis unit analyzes the user's health condition in detail based on the geographical location information. As a result, the user's health condition can be grasped more accurately. 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 input the geographical location information to the generation AI and cause the generation AI to adjust the analysis result.

[0086] The output unit can estimate the user's emotion and adjust the output format based on the estimated user's emotion. For example, the output unit provides a detailed text output when the user is relaxed. The output unit can also provide a concise text output when the user is nervous. Furthermore, the output unit can also provide a text output with the main points when the user is in a hurry. This allows the output format to be adjusted based on the user's emotion, thereby providing a result in an optimal format for the user. 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 output unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the output unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the output format.

[0087] The output unit can adjust the output contents by referring to the user's past health data at the time of output. The output unit outputs text that describes the current health condition in detail based on the user's past health data, for example. The output unit can also preferentially output information on a specific health risk from the user's past health data. Furthermore, the output unit can also refer to the user's past health data and output an alert when an abnormal value is detected. This makes it possible to customize the output contents by referring to the user's past health data. The past health data includes, for example, diagnosis results, health records, etc., but is not limited to such examples. The output unit explains the user's health condition in detail based on the past health data. This allows the user to grasp his or her health condition more accurately. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can input the past health data to the generation AI and cause the generation AI to adjust the output contents.

[0088] The output unit can set the output timing based on the user's current activity status at the time of output. For example, when the user is relaxed, the output unit provides detailed text output. In addition, when the user is nervous, the output unit can also provide concise text output. Furthermore, when the user is in a hurry, the output unit can also provide text output with the main points. As a result, by adjusting the output timing based on the user's current activity status, it is possible to provide the result at the optimal timing. The output timing is set, for example, based on the user's schedule and activity status. The user's schedule includes, for example, plans and tasks, but is not limited to such examples. The output unit provides the result at the optimal timing based on the user's schedule. As a result, the user can receive the result at the optimal timing. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can input the current activity status data to the generation AI and cause the generation AI to execute the setting of the output timing.

[0089] The output unit can estimate the user's emotion and determine the priority of the output contents based on the estimated user's emotion. For example, when the user is relaxed, the output unit preferentially displays detailed analysis results. In addition, when the user is nervous, the output unit can also preferentially display simple analysis results. Furthermore, when the user is in a hurry, the output unit can also preferentially display the most important analysis results. In this way, by determining the priority of the output contents based on the user's emotion, the most important information can be provided preferentially. 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 output unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the output unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of the output contents.

[0090] The output unit can select an appropriate output format based on the device information of the user at the time of output. For example, when the user is using a smartphone, the output unit provides an output format that matches the screen size. In addition, when the user is using a tablet, the output unit can also provide an output format optimized for a large screen. Furthermore, when the user is using a smartwatch, the output unit can also provide an output format that is concise and highly visible. This makes it possible to provide an optimal output format by taking into account the device information of the user. The device information includes, for example, the type of device, the OS version, and the like, but is not limited to such examples. The output unit selects an output format that is optimal for the user's device based on the device information. This allows the user to always receive the result in the optimal format. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can input the device information to the generation AI and cause the generation AI to select the output format.

[0091] The output unit can analyze the user's social media activity at the time of output and provide related information. The output unit outputs related analysis results based on, for example, health information that the user often shares on social media. The output unit can also preferentially output information related to a specific health theme from the user's social media activity. Furthermore, the output unit can analyze the user's social media activity and output health information of the user's greatest interest. This makes it possible to provide related information by analyzing the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to such examples. The output unit provides optimal information tailored to the user's interest based on the social media activity. This allows the user to always receive optimal information. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can input social media activity data to a generation AI and cause the generation AI to provide related information.

[0092] The security unit can estimate the user's emotion and adjust the security level based on the estimated user's emotion. For example, when the user is relaxed, the security unit applies a normal security level. Also, when the user is nervous, the security unit can increase the security level and strengthen the protection of data. Furthermore, when the user is in a hurry, the security unit can adjust the security level to allow quick access. In this way, appropriate security can be provided by adjusting the security level based on 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 security unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the security unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the security level.

[0093] When encrypting data, the security unit can select an appropriate encryption method by referring to the user's past security history. The security unit, for example, selects an optimal encryption method based on the encryption method used by the user in the past. The security unit can also preferentially apply a specific encryption method from the user's past security history. Furthermore, the security unit can analyze the user's past security history and select the safest encryption method. As a result, the optimal encryption method can be provided by referring to the user's past security history. The security history includes, for example, past security incidents, security settings, etc., but is not limited to such examples. The security unit encrypts the user's data in an optimal manner based on the security history. As a result, the user's data is always protected in an optimal manner. A part or all of the above-mentioned processing in the security unit may be performed, for example, using AI, or may be performed without using AI. For example, the security unit can input security history data to the generation AI and cause the generation AI to select an encryption method.

[0094] The security unit can estimate the user's emotions and obtain consent for data use based on the estimated user's emotions. For example, when the user is relaxed, the security unit provides a detailed explanation and obtains consent. Also, when the user is nervous, the security unit can provide a concise explanation and obtain consent quickly. Furthermore, when the user is in a hurry, the security unit can provide an explanation that covers the main points and obtain consent. In this way, appropriate consent can be obtained by obtaining consent for data use based on 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 security unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the security unit can input the user's emotion data into the generation AI and cause the generation AI to execute the consent acquisition process.

[0095] The security unit can select an appropriate encryption method based on the geographical location information of the user when encrypting data. For example, when the user is in a high-risk area, the security unit applies a strong encryption method. Also, when the user is in a low-risk area, the security unit can apply a normal encryption method. Furthermore, the security unit can select an optimal encryption method based on the geographical location information of the user. As a result, it is possible to provide an optimal encryption method by taking into account the geographical location information of the user. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. The security unit encrypts the user's data in an optimal manner based on the geographical location information. As a result, the user's data is always protected in an optimal manner. A part or all of the above-mentioned processing in the security unit may be performed, for example, using AI, or may be performed without using AI. For example, the security unit can input geographical location information data to the generation AI and cause the generation AI to select an encryption method.

[0096] The application unit can estimate the user's emotion and adjust the application method based on the estimated user's emotion. For example, the application unit provides a detailed application method when the user is relaxed. Furthermore, the application unit can also provide a concise application method when the user is nervous. Furthermore, the application unit can provide an application method that can be quickly executed when the user is in a hurry. This allows the application method to be adjusted based on the user's emotion to be provided as an optimal application method. The emotion estimation is realized using an emotion estimation function, for example, using 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 application unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the application unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the application method.

[0097] The application unit can select an appropriate application method by referring to the user's past health data at the time of application. The application unit, for example, selects an optimal application method based on the user's past health data. The application unit can also select an application method corresponding to a specific health risk from the user's past health data. Furthermore, the application unit can also select the most effective application method by referring to the user's past health data. As a result, the optimal application method can be provided by referring to the user's past health data. The past health data includes, for example, diagnosis results, health records, etc., but is not limited to such examples. The application unit selects an application method optimal for the user's health condition based on the past health data. As a result, the user can always manage his / her health in an optimal manner. A part or all of the above-mentioned processing in the application unit may be performed, for example, using AI, or may be performed without using AI. For example, the application unit can input past health data to the generation AI and cause the generation AI to select an application method.

[0098] The application unit can estimate the user's emotion and determine the priority of the application based on the estimated user's emotion. For example, when the user is relaxed, the application unit can provide a detailed application method preferentially. In addition, when the user is nervous, the application unit can also provide a concise application method preferentially. In addition, when the user is in a hurry, the application unit can also provide the most important application method preferentially. In this way, the most important application method can be provided preferentially by determining the priority of the application based on 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 application unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the application unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of the application.

[0099] The application unit can select an appropriate application method based on the geographical location information of the user at the time of application. For example, when the user is at high altitude, the application unit provides an application method that takes into account the decrease in oxygen concentration. In addition, when the user is in an urban area, the application unit can also provide an application method that takes into account the influence of air pollution. Furthermore, when the user is in a cold area, the application unit can also provide an application method that takes into account the fluctuation of body temperature. As a result, it is possible to provide an optimal application method by taking into account the geographical location information of the user. The geographical location information includes, for example, GPS data, address information, etc., but is not limited to such examples. The application unit selects an application method that is optimal for the health condition of the user based on the geographical location information. As a result, the user can always manage his / her health in an optimal manner. A part or all of the above-mentioned processing in the application unit may be performed, for example, using AI, or may be performed without using AI. For example, the application unit can input geographical location information data to the generation AI and cause the generation AI to select an application method.

[0100] The camera of the smartphone or tablet can estimate the user's emotion and adjust the camera settings based on the estimated user's emotion. For example, when the user is relaxed, the camera of the smartphone or tablet can adjust the camera settings to capture a natural facial expression. In addition, when the user is nervous, the camera of the smartphone or tablet can take a picture early and complete the picture before the nervousness appears in the facial expression. Furthermore, when the user is in a hurry, the camera of the smartphone or tablet can take a picture quickly so as not to waste the user's time. In this way, the optimal shooting conditions can be provided by adjusting the camera settings based on 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 camera of the smartphone or tablet may be performed using, for example, an AI, or may be performed without using an AI. For example, the camera of the smartphone or tablet can input the user's emotion data into the generation AI and cause the generation AI to adjust the camera settings.

[0101] When using the camera, the camera of the smartphone or tablet can select appropriate settings by referring to the user's past shooting data. The camera of the smartphone or tablet can automatically apply optimal settings based on, for example, shooting conditions (brightness, angle, etc.) that the user has previously preferred. The camera of the smartphone or tablet can also select and apply conditions that make the user's health look the best from the user's past shooting data. Furthermore, the camera of the smartphone or tablet can also analyze the user's past shooting data and suggest optimal background and lighting conditions. This makes it possible to provide optimal settings by referring to the user's past shooting data. The past shooting data includes, for example, shooting date and time, shooting conditions, etc., but is not limited to such examples. The camera of the smartphone or tablet selects optimal settings according to the user's preferences and health condition based on the past shooting data. This allows the user to always take photos under optimal conditions. A part or all of the above-mentioned processing in the camera of the smartphone or tablet may be performed, for example, using AI, or may be performed without using AI. For example, the camera of the smartphone or tablet can input past shooting data to the generation AI and have the generation AI select the optimal settings.

[0102] The camera of the smartphone or tablet can estimate the user's emotion and adjust the frequency of camera use based on the estimated user's emotion. For example, when the user is relaxed, the camera of the smartphone or tablet can periodically check the health condition using the camera. Furthermore, when the user is nervous, the camera of the smartphone or tablet can also use the camera with the minimum necessary frequency. Furthermore, when the user is in a hurry, the camera of the smartphone or tablet can quickly check the health condition using the camera. In this way, the health condition can be checked with the optimal frequency by adjusting the frequency of camera use based on 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 camera of the smartphone or tablet may be performed using, for example, an AI, or may be performed without using an AI. For example, the camera of the smartphone or tablet can input the user's emotion data to the generation AI and cause the generation AI to adjust the frequency of camera use.

[0103] The camera of the smartphone or tablet can select appropriate settings based on the device information of the user when using the camera. For example, when the user is using a smartphone, the camera of the smartphone or tablet can provide settings that match the screen size. In addition, when the user is using a tablet, the camera of the smartphone or tablet can also provide settings that are optimized for a large screen. In addition, when the user is using a smartwatch, the camera of the smartphone or tablet can also provide simple and highly visible settings. This makes it possible to provide optimal settings by taking into account the device information of the user. The device information includes, for example, the type of device, the OS version, and the like, but is not limited to such examples. The camera of the smartphone or tablet selects optimal settings for the user's device based on the device information. This allows the user to always take pictures under optimal conditions. Part or all of the above-mentioned processing in the camera of the smartphone or tablet may be performed, for example, using AI, or may be performed without using AI. For example, the camera of the smartphone or tablet can input device information to the generation AI and have the generation AI select the settings.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The analysis unit can refer to the user's lifestyle data and reflect it in the determination of the health condition. For example, it can analyze changes in nutritional state and physical strength based on the user's diet and exercise data. It can also refer to the user's sleep data and determine the user's fatigue level and stress level. Furthermore, it can predict specific health risks based on the user's lifestyle data and reflect them in the analysis results. In this way, by referring to the user's lifestyle data, the accuracy of the analysis can be improved, making it possible to determine the health condition more accurately.

[0106] The analysis unit can refer to the geographical location information of the user and reflect it in the judgment of the health condition. For example, if the user is at high altitude, the analysis result can be adjusted taking into account the decrease in oxygen concentration. Also, if the user is in an urban area, the analysis result can be adjusted taking into account the effects of air pollution. Furthermore, if the user is in a cold area, the analysis result can be adjusted taking into account the fluctuation of body temperature. In this way, by taking into account the geographical location information of the user, more accurate analysis results can be provided.

[0107] The output unit can adjust the output contents by referring to the user's past health data. For example, the output unit can output text that describes the current health condition in detail based on the user's past health data. The output unit can also preferentially output information on specific health risks from the user's past health data. Furthermore, the output unit can also refer to the user's past health data and output an alert if an abnormal value is detected. This allows the output contents to be customized by referring to the user's past health data.

[0108] The output unit can select an appropriate output format based on the device information of the user. For example, if the user is using a smartphone, an output format that matches the screen size can be provided. Also, if the user is using a tablet, an output format optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, an output format that is concise and highly visible can be provided. In this way, the optimal output format can be provided by taking into account the device information of the user.

[0109] The security unit can select an appropriate encryption method by referring to the user's past security history. For example, the optimal encryption method is selected based on encryption methods used by the user in the past. Also, a specific encryption method can be applied preferentially based on the user's past security history. Furthermore, the user's past security history can be analyzed to select the most secure encryption method. In this way, the optimal encryption method can be provided by referring to the user's past security history.

[0110] The photographing unit can estimate the user's emotion and adjust the timing of photographing based on the estimated emotion of the user. For example, when the user is relaxed, the timing of photographing is delayed to capture a natural facial expression. Also, when the user is nervous, photographing can be performed early and completed before the nervousness shows in the facial expression. Furthermore, when the user is in a hurry, photographing can be performed quickly so as not to waste the user's time. In this way, by adjusting the timing of photographing based on the user's emotion, a more natural facial expression can be captured.

[0111] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be performed to more accurately determine the health condition. Also, if the user is nervous, a simple analysis can be performed to quickly output the results. Furthermore, if the user is in a hurry, an analysis focusing on the most important health indicators can be performed. In this way, by adjusting the analysis algorithm based on the user's emotions, more accurate analysis results can be provided.

[0112] The output unit can estimate the user's emotion and adjust the output format based on the estimated user's emotion. For example, if the user is relaxed, detailed text output can be provided. If the user is nervous, concise text output can be provided. Furthermore, if the user is in a hurry, text output that focuses on the main points can be provided. In this way, by adjusting the output format based on the user's emotion, it is possible to provide results in a format that is optimal for the user.

[0113] The security unit can estimate the user's emotions and adjust the security level based on the estimated user's emotions. For example, when the user is relaxed, a normal security level can be applied. Also, when the user is nervous, the security level can be increased to strengthen data protection. Furthermore, when the user is in a hurry, the security level can be adjusted to allow quick access. In this way, appropriate security can be provided by adjusting the security level based on the user's emotions.

[0114] The application unit can estimate the user's emotion and adjust the application method based on the estimated user's emotion. For example, if the user is relaxed, a detailed application method can be provided. Also, if the user is nervous, a concise application method can be provided. Furthermore, if the user is in a hurry, a quickly executable application method can be provided. In this way, the application method can be adjusted based on the user's emotion, thereby providing an optimal application method.

[0115] The process flow of the second embodiment will be briefly described below.

[0116] Step 1: The photographing unit photographs the face of an individual. The face of an individual includes, but is not limited to, for example, age, gender, and facial expression. The photographing unit can photograph the face using, for example, a camera of a smartphone or tablet. Step 2: The analysis unit uses the generation AI to analyze the facial image captured by the photographing unit and determine the health condition. The generation AI analyzes facial features based on the learned medical data and determines the health condition. For example, by analyzing facial color, facial expressions, muscle movements, etc., it can determine whether there is a fever or whether muscle strength is weakened. The generation AI uses a text generation AI (e.g., LLM) to analyze the facial image and determine the health condition. The generation AI can also use a multimodal generation AI to analyze the facial image and determine the health condition. For example, the generation AI detects changes in facial color and determines an increase in body temperature and blood circulation. Step 3: The output unit outputs the health condition determined by the analysis unit in text. For example, the output unit displays messages such as "Your body temperature is high" or "Your muscle strength is declining." This allows the user to easily understand his or her health condition. The output unit can also display the analysis results as voice or graphically. For example, the analysis results are output as voice using voice synthesis technology. The output unit can also visually display the analysis results using graphs and charts. This allows the user to more easily understand the analysis results.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, output unit, security unit, and application 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 photographs an individual's face 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 image using a generating AI to determine the health condition. The output unit outputs the analysis result in text or voice using the display 40A or speaker 40B of the smart device 14. The security unit is realized by the specific processing unit 290 of the data processing device 12, and encrypts data using AES or RSA. The application unit is realized by the control unit 46A of the smart device 14, and provides periodic health check and reminder functions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, output unit, security unit, and application 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 photographs an individual's face 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 image using a generation AI to determine the health condition. The output unit outputs the analysis result as voice using the speaker 240 of the smart glasses 214. The security unit is realized by the specific processing unit 290 of the data processing device 12, and encrypts data using AES or RSA. The application unit is realized by the control unit 46A of the smart glasses 214, and provides periodic health checks and reminder functions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, output unit, security unit, and application 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 photographs an individual's face 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 image using a generation AI to determine the health condition. The output unit outputs the analysis result in text or voice using the display 343 or speaker 240 of the headset type terminal 314. The security unit is realized by the specific processing unit 290 of the data processing device 12, and encrypts data using AES or RSA. The application unit is realized by the control unit 46A of the headset type terminal 314, and provides periodic health check and reminder functions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, output unit, security unit, and application unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit photographs an individual's face 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 image using a generation AI to determine the health condition. The output unit outputs the analysis result by voice using the speaker 240 of the robot 414. The security unit is realized by the specific processing unit 290 of the data processing device 12, and encrypts data using AES or RSA. The application unit is realized by the control unit 46A of the robot 414, and provides periodic health checks and reminder functions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Appendix 1) A photography department that takes pictures of individuals' faces; an analysis unit that analyzes the facial image captured by the imaging unit and determines a health condition; An output unit that outputs the health condition determined by the analysis unit in text form. A system characterized by: (Appendix 2) The analysis unit is Analyze facial features based on learned medical data to determine health status 2. The system of claim 1, (Appendix 3) The output unit is Display analysis results audibly or graphically 2. The system of claim 1, (Appendix 4) Equipped with a security department that encrypts data or uses data only with the user's consent 2. The system of claim 1, (Appendix 5) Equipped with application parts that are useful for maintaining the health of home care recipients and for general health management 2. The system of claim 1, (Appendix 6) Take a picture of your face using your smartphone or tablet camera 2. The system of claim 1, (Appendix 7) The imaging unit includes: Estimating a user's emotions and adjusting the timing of photographing based on the estimated user's emotions 2. The system of claim 1, (Appendix 8) The imaging unit includes: Add a function to automatically adjust the angle or expression of the user's face when taking a photo. 2. The system of claim 1, (Appendix 9) The imaging unit includes: When taking a photo, the system references the user's past shooting data to set appropriate shooting conditions. 2. The system of claim 1, (Appendix 10) The imaging unit includes: Estimate the user's emotions and prioritize the images to be taken based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The imaging unit includes: Add the ability to automatically adjust for the user's ambient light or background when taking a 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 prioritizes taking relevant images. 2. The system of claim 1, (Appendix 13) The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, subtle changes in facial expressions are detected to determine the health condition more specifically. 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, the user's past health data is referenced to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and prioritize analysis results based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, the accuracy of the analysis is improved by referring to the user's lifestyle data. 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust analysis results based on the user's geographic location 2. The system of claim 1, (Appendix 19) The output unit is Estimate user emotions and adjust output format based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The output unit is At the time of output, the output content is adjusted by referring to the user's past health data. 2. The system of claim 1, (Appendix 21) The output unit is When printing, set the printing timing based on the user's current activity 2. The system of claim 1, (Appendix 22) The output unit is Estimate the user's emotions and prioritize the output contents based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The output unit is When outputting, select the appropriate output format based on the user's device information 2. The system of claim 1, (Appendix 24) The output unit is Upon output, analyze the user's social media activity and provide relevant information 2. The system of claim 1, (Appendix 25) The security unit includes: Estimate user emotions and adjust security levels based on the estimated user emotions 2. The system of claim 1, (Appendix 26) The security unit includes: When encrypting data, select the appropriate encryption method by referring to the user's past security history 2. The system of claim 1, (Appendix 27) The security unit includes: Estimate user emotions and obtain consent for data usage based on the estimated user emotions 2. The system of claim 1, (Appendix 28) The security unit includes: When encrypting data, choose the appropriate encryption method based on the user's geographic location 2. The system of claim 1, (Appendix 29) The application unit includes: Inferring a user's emotions and adjusting an application method based on the estimated user's emotions 2. The system of claim 1, (Appendix 30) The application unit includes: When applying the technology, the system selects the appropriate application method by referring to the user's past health data. 2. The system of claim 1, (Appendix 31) The application unit includes: Estimate user emotions and prioritize applications based on the estimated user emotions. 2. The system of claim 1, (Appendix 32) The application unit includes: At the time of application, the appropriate application method is selected based on the user's geographic location information. 2. The system of claim 1, (Appendix 33) Smartphone and tablet cameras Estimating user emotions and adjusting camera settings based on the estimated user emotions 2. The system of claim 1, (Appendix 34) Smartphone and tablet cameras When using the camera, the system selects appropriate settings by referencing the user's past shooting data. 2. The system of claim 1, (Appendix 35) Smartphone and tablet cameras To estimate a user's emotion and adjust the frequency of camera usage based on the estimated user's emotion. 2. The system of claim 1, (Appendix 36) Smartphone and tablet cameras When using the camera, select the appropriate settings based on the user's device information 2. The system of claim 1, [Explanation of symbols]

[0189] 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 photography department that takes pictures of individuals' faces; an analysis unit that analyzes the facial image captured by the imaging unit and determines a health condition; An output unit that outputs the health condition determined by the analysis unit in text form. A system characterized by:

2. The analysis unit is Analyze facial features based on learned medical data to determine health status 2. The system of claim 1 .

3. The output unit is Display analysis results audibly or graphically 2. The system of claim 1 .

4. Equipped with a security department that encrypts data or uses data only with the user's consent 2. The system of claim 1 .

5. Equipped with application parts that are useful for maintaining the health of home care recipients and for general health management 2. The system of claim 1 .

6. Take a picture of your face using your smartphone or tablet camera 2. The system of claim 1 .

7. The imaging unit includes: Estimating a user's emotions and adjusting the timing of photographing based on the estimated user's emotions 2. The system of claim 1 .

8. The imaging unit includes: Add a function to automatically adjust the angle or expression of the user's face when taking a photo.

2. The system of claim 1 .

Images