System

Abstract

An object of a system according to an embodiment is to appropriately grasp a physical condition of a subordinate even during remote work.SOLUTION: A system includes a face image acquisition unit, a face color analysis unit, a facial expression analysis unit, and a notification unit. The face image acquisition part acquires a face image photographed by a camera during the WEB conference. The face color analysis unit analyzes a face color from the face image acquired by the face image acquisition unit. The expression analysis unit analyzes an expression on the basis of the face color analyzed by the face color analysis unit. In a case where it is determined that the physical condition is deteriorated on the basis of the analysis result of the face color and the facial expression, the notification unit notifies the user or a manager of the result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for managers to understand the physical condition of their subordinates while they are working remotely.

[0005] The system according to the embodiment aims to properly grasp the physical condition of subordinates even when they are working remotely. [Means for solving the problem]

[0006] The system according to the embodiment includes a facial image acquisition unit, a facial color analysis unit, a facial expression analysis unit, and a notification unit. The facial image acquisition unit acquires a facial image captured by a camera during a web conference. The facial color analysis unit analyzes facial color from the facial image acquired by the facial image acquisition unit. The facial expression analysis unit analyzes facial expression based on the facial color analyzed by the facial color analysis unit. If the notification unit determines that a person is in poor health based on the analysis results of the facial color and facial expression, it notifies the person or a manager of the result. [Effects of the Invention]

[0007] The system according to the embodiment allows a person to properly grasp the physical condition of his or her subordinates even while working remotely. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The health management system according to an embodiment of the present invention uses AI to analyze facial images taken with a camera during a web conference, check whether the employee is in good health based on their facial color and facial expression, and notify the employee and their manager of the results. This makes it easier for managers to understand the health of their subordinates even in a remote work environment, allowing them to take appropriate action.

[0029] A health management system according to an embodiment includes a facial image acquisition unit, a facial color analysis unit, an expression analysis unit, and a notification unit. The facial image acquisition unit acquires facial images captured by a camera during a web conference. For example, a camera built into the web conference system automatically captures facial images of participants. The facial image acquisition unit can also acquire facial images in real time. The facial color analysis unit analyzes facial color from the acquired facial images. For example, the facial color analysis unit detects changes in facial color and identifies abnormal facial colors. The facial color analysis unit can analyze facial color based on RGB values, hue, and saturation. The expression analysis unit analyzes facial expressions based on the facial color analyzed by the facial color analysis unit. For example, the expression analysis unit detects abnormal facial expressions, such as tired expressions or expressions of pain. The expression analysis unit can analyze facial expressions based on the movement of facial feature points and emotional classifications. If the notification unit determines that a participant is in poor health based on the analysis results of facial color and expression, it notifies the participant or an administrator of the result. For example, the notification unit may send a notification via email or a messenger app after a web conference ends. The notification unit may send a notification including the results of a health check and, if necessary, a message recommending that the employee visit a medical institution. This allows the health management system according to the embodiment to make it easier for managers to understand the health of their subordinates even in a remote work environment, enabling them to take appropriate action.

[0030] The facial image acquisition unit can correct for changes in ambient light in real time in addition to facial images captured by a camera during a web conference, thereby obtaining more accurate facial images. For example, the facial image acquisition unit constructs a system that corrects for changes in ambient light in real time when acquiring facial images captured by a camera during a web conference. For example, the camera sensor detects the amount of ambient light and automatically adjusts the exposure and white balance. This allows for more accurate facial images to be obtained by correcting for changes in ambient light.

[0031] The facial image acquisition unit can automatically adjust the direction and angle of the face when acquiring a facial image, and acquire an image from the optimal angle. For example, the facial image acquisition unit constructs a system in which, when acquiring a facial image, a camera automatically detects the direction and angle of the face and acquires an image from the optimal angle. For example, the system uses face recognition technology to identify the position of the face and adjust the angle of the camera. This allows the direction and angle of the face to be automatically adjusted, and an image from the optimal angle to be acquired.

[0032] The facial image acquisition unit simultaneously acquires audio data in addition to facial images captured by a camera during a web conference, and can check the person's physical condition by combining this with audio analysis. For example, the facial image acquisition unit simultaneously acquires audio data along with facial images captured by a camera during a web conference, and builds a system that checks the person's physical condition by combining this with audio analysis. For example, it analyzes changes in voice tone and speaking style. This makes it possible to check the person's physical condition by combining facial images and audio data.

[0033] The facial image acquisition unit acquires facial images not only during web conferences but also during regular check-ins while working remotely, enabling continuous health management. The facial image acquisition unit, for example, builds a system that acquires facial images not only during web conferences but also during regular check-ins while working remotely. For example, a camera automatically captures facial images at regular intervals. This allows facial images to be acquired during regular check-ins while working remotely, enabling continuous health management.

[0034] The facial color analysis unit can analyze not only skin tone but also physiological indices such as blood flow and oxygen saturation when analyzing facial color. For example, the facial color analysis unit constructs a system that analyzes not only skin tone but also physiological indices such as blood flow and oxygen saturation when analyzing facial color. For example, it analyzes facial blood flow patterns and estimates oxygen saturation. This allows for more detailed health checks by analyzing not only skin tone but also physiological indices such as blood flow and oxygen saturation.

[0035] The facial color analysis unit can compare changes in facial color with past data and automatically set a baseline for detecting abnormal values. The facial color analysis unit, for example, builds a system that compares changes in facial color with past data and automatically sets a baseline for detecting abnormal values. For example, it sets a normal range based on past facial color data. This allows for early detection of abnormalities by automatically setting a baseline for detecting abnormal values ​​by comparing with past data.

[0036] The facial color analysis unit combines the analysis of facial color with image analysis of other body parts, thereby enabling a more comprehensive physical condition check. For example, the facial color analysis unit combines the analysis of facial color with image analysis of other body parts (e.g., hands and neck), thereby building a system that enables a more comprehensive physical condition check. For example, it analyzes the blood flow in the hands and the color tone of the neck. In this way, by combining the analysis of facial color with image analysis of other body parts, a more comprehensive physical condition check is possible.

[0037] The facial color analysis unit can integrate the facial color analysis results with the user's diet and sleep data to make suggestions for improving lifestyle habits. The facial color analysis unit, for example, integrates the facial color analysis results with the user's diet and sleep data to build a system that makes suggestions for improving lifestyle habits. For example, it analyzes changes in facial color based on dietary content and sleep duration. In this way, by integrating the facial color analysis results with dietary and sleep data, it becomes possible to make suggestions for improving lifestyle habits.

[0038] The facial expression analysis unit detects subtle changes in facial expressions in analyzing facial expressions, making it possible to grasp a more detailed emotional state. For example, the facial expression analysis unit constructs a system that detects subtle changes in facial expressions (micro-expressions) in analyzing facial expressions. For example, it analyzes the minute movements of facial muscles to grasp an emotional state. In this way, by detecting subtle changes in facial expressions, a more detailed emotional state can be grasped.

[0039] The facial expression analysis unit can compare the results of the facial expression analysis with the user's past facial expression data to identify abnormal changes. The facial expression analysis unit, for example, builds a system that compares the results of the facial expression analysis with the user's past facial expression data to identify abnormal changes. For example, a normal range is set based on the past facial expression data. This makes it possible to identify abnormal changes by comparing the results of the facial expression analysis with the past data.

[0040] The facial expression analysis unit combines facial expression analysis with voice analysis to realize comprehensive emotional analysis that also takes into account changes in voice tone and speaking style. For example, the facial expression analysis unit combines facial expression analysis with voice analysis to build a system that realizes comprehensive emotional analysis that also takes into account changes in voice tone and speaking style. For example, it analyzes changes in voice tone and speaking style. In this way, comprehensive emotional analysis becomes possible by combining facial expression analysis and voice analysis.

[0041] The facial expression analysis unit can integrate the results of the facial expression analysis with the user's work performance data and analyze the relationship between physical condition and performance. The facial expression analysis unit, for example, builds a system that integrates the results of the facial expression analysis with the user's work performance data and analyzes the relationship between physical condition and performance. For example, the physical condition is evaluated based on work efficiency and error rate. In this way, the relationship between physical condition and performance can be analyzed by integrating the results of the facial expression analysis with the work performance data.

[0042] The notification unit can individually customize the notification content when notifying the user of the results of a health check, and provide specific advice based on the user's past health data and current situation. The notification unit, for example, builds a system that individually customizes the notification content when notifying the user of the results of a health check. For example, specific advice is provided based on the user's past health data. This makes it possible to individually customize the notification content when notifying the user of the results of a health check, and provide specific advice based on the user's past health data and current situation.

[0043] The notification unit can optimize the timing of notification of the results of the health check in accordance with the user's schedule and the progress of work. The notification unit, for example, builds a system that optimizes the timing of notification of the results of the health check in accordance with the user's schedule and the progress of work. For example, the notification is sent during breaks or between meetings. This allows the timing of notification of the results of the health check to be optimized in accordance with the user's schedule and the progress of work.

[0044] The notification unit can link the results of the health check with other health management apps and wearable devices to realize comprehensive health management. For example, the notification unit can link the results of the health check with other health management apps and wearable devices to build a system that realizes comprehensive health management. For example, the notification unit can share data with fitness apps and smartwatches. This allows the results of the health check to be linked with other health management apps and wearable devices, enabling comprehensive health management.

[0045] The notification unit shares the notification content with the user's family and medical institutions, thereby building a more extensive support system. The notification unit, for example, shares the notification of the health check results with the user's family and medical institutions, building a system that builds a more extensive support system. For example, the notification unit sends a notification to the user's family and a doctor. In this way, the notification content can be shared with the user's family and medical institutions, thereby building a more extensive support system.

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

[0047] The health management system can also acquire the user's exercise data and combine it with the results of health checks to evaluate the user's overall health. For example, it can acquire exercise data from a smartwatch or fitness tracker and analyze the amount of daily exercise and heart rate. This can evaluate the impact of insufficient or excessive exercise on physical condition and suggest appropriate amounts of exercise. It can also send notifications to the user based on the exercise data, encouraging them to stretch or do some light exercise. This makes it possible to provide more comprehensive support for the user's health management.

[0048] The health management system can also acquire the user's dietary data and combine it with the results of health checks to evaluate the user's overall health condition. For example, it can acquire data from a food recording app or calorie counting app and analyze nutritional balance and calorie intake. This can evaluate the impact of dietary content on health and suggest balanced meals. It can also provide the user with nutritional advice based on the dietary data. This makes it possible to provide more comprehensive support for the user's health management.

[0049] The health management system can also acquire the user's sleep data and combine it with the results of the health check to evaluate the user's overall health. For example, it can acquire sleep data from a smartwatch or sleep tracker and analyze the quality and duration of sleep. This can evaluate the impact of insufficient or excessive sleep on physical condition and suggest appropriate sleep durations. It can also provide the user with advice on relaxation methods and how to improve their sleep environment based on the sleep data. This makes it possible to provide more comprehensive support for the user's health management.

[0050] The health management system can also measure the user's stress level and combine it with the results of the health check to evaluate the user's overall health. For example, it can obtain data from a stress measurement device or app and analyze heart rate variability and electrodermal activity. This can then evaluate the impact of stress on physical condition and suggest stress management methods. It can also provide the user with advice on relaxation methods and stress reduction based on the stress data. This makes it possible to provide more comprehensive support for the user's health management.

[0051] The health management system can also monitor the user's water intake and combine this with the results of health checks to evaluate the user's overall health. For example, it can obtain data from a water intake recording app or smart bottle and analyze daily water intake. This can then evaluate the impact of lack of water on health and suggest appropriate water intake amounts. It can also send notifications to the user based on the water intake data, encouraging them to drink water regularly. This allows for more comprehensive support for the user's health management.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The face image acquisition unit acquires face images taken by a camera during a web conference. For example, a camera built into the web conference system automatically captures the face images of participants and acquires face images in real time. Step 2: The facial color analysis unit analyzes the facial color from the acquired facial image. For example, the facial color analysis unit detects changes in facial color and identifies abnormal facial colors. The facial color can be analyzed based on RGB values, hue, and saturation. Step 3: The facial expression analysis unit analyzes facial expressions based on the facial color analyzed by the facial color analysis unit. For example, the facial expression analysis unit detects unusual facial expressions, such as tiredness or pain. Facial expressions can be analyzed based on the movement of facial features and the classification of emotions. Step 4: If the notification unit determines that a person is in poor health based on the analysis of their facial color and facial expression, it notifies the person or their administrator of the result. For example, after the end of a web conference, a notification can be sent via email or messenger app, including the results of the health check and a message recommending that the person seek medical attention if necessary.

[0054] (Example 2) The health management system according to an embodiment of the present invention uses AI to analyze facial images taken with a camera during a web conference, check whether the employee is in good health based on their facial color and facial expression, and notify the employee and their manager of the results. This makes it easier for managers to understand the health of their subordinates even in a remote work environment, allowing them to take appropriate action.

[0055] A health management system according to an embodiment includes a facial image acquisition unit, a facial color analysis unit, an expression analysis unit, and a notification unit. The facial image acquisition unit acquires facial images captured by a camera during a web conference. For example, a camera built into the web conference system automatically captures facial images of participants. The facial image acquisition unit can also acquire facial images in real time. The facial color analysis unit analyzes facial color from the acquired facial images. For example, the facial color analysis unit detects changes in facial color and identifies abnormal facial colors. The facial color analysis unit can analyze facial color based on RGB values, hue, and saturation. The expression analysis unit analyzes facial expressions based on the facial color analyzed by the facial color analysis unit. For example, the expression analysis unit detects abnormal facial expressions, such as tired expressions or expressions of pain. The expression analysis unit can analyze facial expressions based on the movement of facial feature points and emotional classifications. If the notification unit determines that a participant is in poor health based on the analysis results of facial color and expression, it notifies the participant or an administrator of the result. For example, the notification unit may send a notification via email or a messenger app after a web conference ends. The notification unit may send a notification including the results of a health check and, if necessary, a message recommending that the employee visit a medical institution. This allows the health management system according to the embodiment to make it easier for managers to understand the health of their subordinates even in a remote work environment, enabling them to take appropriate action.

[0056] The facial image acquisition unit can correct for changes in ambient light in real time in addition to facial images captured by a camera during a web conference, thereby obtaining more accurate facial images. For example, the facial image acquisition unit constructs a system that corrects for changes in ambient light in real time when acquiring facial images captured by a camera during a web conference. For example, the camera sensor detects the amount of ambient light and automatically adjusts the exposure and white balance. This allows for more accurate facial images to be obtained by correcting for changes in ambient light.

[0057] The facial image acquisition unit can automatically adjust the direction and angle of the face when acquiring a facial image, and acquire an image from the optimal angle. For example, the facial image acquisition unit constructs a system in which, when acquiring a facial image, a camera automatically detects the direction and angle of the face and acquires an image from the optimal angle. For example, the system uses face recognition technology to identify the position of the face and adjust the angle of the camera. This allows the direction and angle of the face to be automatically adjusted, and an image from the optimal angle to be acquired.

[0058] The facial image acquisition unit can use the emotion estimation function to estimate the emotion of the user when facing the camera in real time and provide feedback to elicit positive emotions. The facial image acquisition unit, for example, uses the emotion estimation function to analyze the emotion of the user when facing the camera in real time and build a system that provides feedback to elicit positive emotions. For example, a message that makes the user smile is displayed. This makes it possible to estimate the emotion of the user in real time and provide feedback to elicit positive emotions.

[0059] The facial image acquisition unit simultaneously acquires audio data in addition to facial images captured by a camera during a web conference, and can check the person's physical condition by combining this with audio analysis. For example, the facial image acquisition unit simultaneously acquires audio data along with facial images captured by a camera during a web conference, and builds a system that checks the person's physical condition by combining this with audio analysis. For example, it analyzes changes in voice tone and speaking style. This makes it possible to check the person's physical condition by combining facial images and audio data.

[0060] The facial image acquisition unit acquires facial images not only during web conferences but also during regular check-ins while working remotely, enabling continuous health management. The facial image acquisition unit, for example, builds a system that acquires facial images not only during web conferences but also during regular check-ins while working remotely. For example, a camera automatically captures facial images at regular intervals. This allows facial images to be acquired during regular check-ins while working remotely, enabling continuous health management.

[0061] The facial image acquisition unit uses the emotion estimation function to estimate the emotion of the user when facing the camera in real time, and can provide advice to relax if a negative emotion is detected. The facial image acquisition unit, for example, uses the emotion estimation function to analyze the emotion of the user when facing the camera in real time, and can provide advice to relax if a negative emotion is detected. For example, a system is constructed in which a message encouraging the user to take a deep breath is displayed. This allows the user's emotion to be estimated in real time, and advice to relax to be provided if a negative emotion is detected.

[0062] The facial color analysis unit can analyze not only skin tone but also physiological indices such as blood flow and oxygen saturation when analyzing facial color. For example, the facial color analysis unit constructs a system that analyzes not only skin tone but also physiological indices such as blood flow and oxygen saturation when analyzing facial color. For example, it analyzes facial blood flow patterns and estimates oxygen saturation. This allows for more detailed health checks by analyzing not only skin tone but also physiological indices such as blood flow and oxygen saturation.

[0063] The facial color analysis unit can compare changes in facial color with past data and automatically set a baseline for detecting abnormal values. The facial color analysis unit, for example, builds a system that compares changes in facial color with past data and automatically sets a baseline for detecting abnormal values. For example, it sets a normal range based on past facial color data. This allows for early detection of abnormalities by automatically setting a baseline for detecting abnormal values ​​by comparing with past data.

[0064] The facial color analysis unit can use the emotion estimation function to analyze whether a change in facial color is due to emotional factors and identify signs of stress or fatigue. The facial color analysis unit, for example, uses the emotion estimation function to analyze whether a change in facial color is due to emotional factors and builds a system that identifies signs of stress or fatigue. For example, the facial color change is associated with an emotion score. This allows for analyzing changes in facial color due to emotional factors and identifying signs of stress or fatigue, enabling appropriate responses.

[0065] The facial color analysis unit combines the analysis of facial color with image analysis of other body parts, thereby enabling a more comprehensive physical condition check. For example, the facial color analysis unit combines the analysis of facial color with image analysis of other body parts (e.g., hands and neck), thereby building a system that enables a more comprehensive physical condition check. For example, it analyzes the blood flow in the hands and the color tone of the neck. In this way, by combining the analysis of facial color with image analysis of other body parts, a more comprehensive physical condition check is possible.

[0066] The facial color analysis unit can integrate the facial color analysis results with the user's diet and sleep data to make suggestions for improving lifestyle habits. The facial color analysis unit, for example, integrates the facial color analysis results with the user's diet and sleep data to build a system that makes suggestions for improving lifestyle habits. For example, it analyzes changes in facial color based on dietary content and sleep duration. In this way, by integrating the facial color analysis results with dietary and sleep data, it becomes possible to make suggestions for improving lifestyle habits.

[0067] The facial color analysis unit can use the emotion estimation function to monitor the user's emotional state in real time when a change in facial color is detected and suggest an appropriate relaxation method. The facial color analysis unit can, for example, use the emotion estimation function to monitor the user's emotional state in real time when a change in facial color is detected and suggest an appropriate relaxation method, thereby building a system. For example, relaxing music can be played. This allows the user's emotional state to be monitored in real time when a change in facial color is detected and suggest an appropriate relaxation method.

[0068] The facial expression analysis unit detects subtle changes in facial expressions in analyzing facial expressions, making it possible to grasp a more detailed emotional state. For example, the facial expression analysis unit constructs a system that detects subtle changes in facial expressions (micro-expressions) in analyzing facial expressions. For example, it analyzes the minute movements of facial muscles to grasp an emotional state. In this way, by detecting subtle changes in facial expressions, a more detailed emotional state can be grasped.

[0069] The facial expression analysis unit can compare the results of the facial expression analysis with the user's past facial expression data to identify abnormal changes. The facial expression analysis unit, for example, builds a system that compares the results of the facial expression analysis with the user's past facial expression data to identify abnormal changes. For example, a normal range is set based on the past facial expression data. This makes it possible to identify abnormal changes by comparing the results of the facial expression analysis with the past data.

[0070] The facial expression analysis unit can use the emotion estimation function to estimate the user's emotional state in real time from the results of the facial expression analysis and provide feedback to elicit positive emotions. The facial expression analysis unit, for example, uses the emotion estimation function to construct a system that estimates the user's emotional state in real time from the results of the facial expression analysis and provides feedback to elicit positive emotions. For example, a message that makes the user smile is displayed. This makes it possible to estimate the user's emotional state in real time from the results of the facial expression analysis and provide feedback to elicit positive emotions.

[0071] The facial expression analysis unit combines facial expression analysis with voice analysis to realize comprehensive emotional analysis that also takes into account changes in voice tone and speaking style. For example, the facial expression analysis unit combines facial expression analysis with voice analysis to build a system that realizes comprehensive emotional analysis that also takes into account changes in voice tone and speaking style. For example, it analyzes changes in voice tone and speaking style. In this way, comprehensive emotional analysis becomes possible by combining facial expression analysis and voice analysis.

[0072] The facial expression analysis unit can integrate the results of the facial expression analysis with the user's work performance data and analyze the relationship between physical condition and performance. The facial expression analysis unit, for example, builds a system that integrates the results of the facial expression analysis with the user's work performance data and analyzes the relationship between physical condition and performance. For example, the physical condition is evaluated based on work efficiency and error rate. In this way, the relationship between physical condition and performance can be analyzed by integrating the results of the facial expression analysis with the work performance data.

[0073] The facial expression analysis unit can use the emotion estimation function to monitor the user's emotional state in real time from the results of the facial expression analysis, and suggest a method for refreshing if a negative emotion is detected. For example, the facial expression analysis unit can use the emotion estimation function to monitor the user's emotional state in real time from the results of the facial expression analysis, and suggest a method for refreshing if a negative emotion is detected. For example, a system can be constructed in which a message encouraging the user to take a short break is displayed. This allows the user's emotional state to be monitored in real time from the results of the facial expression analysis, and a method for refreshing if a negative emotion is detected.

[0074] The notification unit can individually customize the notification content when notifying the user of the results of a health check, and provide specific advice based on the user's past health data and current situation. The notification unit, for example, builds a system that individually customizes the notification content when notifying the user of the results of a health check. For example, specific advice is provided based on the user's past health data. This makes it possible to individually customize the notification content when notifying the user of the results of a health check, and provide specific advice based on the user's past health data and current situation.

[0075] The notification unit can optimize the timing of notification of the results of the health check in accordance with the user's schedule and the progress of work. The notification unit, for example, builds a system that optimizes the timing of notification of the results of the health check in accordance with the user's schedule and the progress of work. For example, the notification is sent during breaks or between meetings. This allows the timing of notification of the results of the health check to be optimized in accordance with the user's schedule and the progress of work.

[0076] The notification unit can use the emotion estimation function to analyze the user's emotional reaction when receiving a notification and improve the notification method to elicit a positive reaction. The notification unit, for example, uses the emotion estimation function to analyze the user's emotional reaction when receiving a notification and builds a system to improve the notification method to elicit a positive reaction. For example, the notification content and timing are adjusted. This makes it possible to analyze the user's emotional reaction when receiving a notification and improve the notification method to elicit a positive reaction.

[0077] The notification unit can link the results of the health check with other health management apps and wearable devices to realize comprehensive health management. For example, the notification unit can link the results of the health check with other health management apps and wearable devices to build a system that realizes comprehensive health management. For example, the notification unit can share data with fitness apps and smartwatches. This allows the results of the health check to be linked with other health management apps and wearable devices, enabling comprehensive health management.

[0078] The notification unit shares the notification content with the user's family and medical institutions, thereby building a more extensive support system. The notification unit, for example, shares the notification of the health check results with the user's family and medical institutions, building a system that builds a more extensive support system. For example, the notification unit sends a notification to the user's family and a doctor. In this way, the notification content can be shared with the user's family and medical institutions, thereby building a more extensive support system.

[0079] The notification unit can use the emotion estimation function to monitor the user's emotional reaction when receiving the notification in real time, and send a follow-up notification if a negative reaction is detected. The notification unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional reaction when receiving the notification in real time, and sends a follow-up notification if a negative reaction is detected. For example, to provide additional advice or support. This makes it possible to monitor the user's emotional reaction when receiving the notification in real time, and send a follow-up notification if a negative reaction is detected.

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

[0081] The health management system can also acquire the user's exercise data and combine it with the results of health checks to evaluate the user's overall health. For example, it can acquire exercise data from a smartwatch or fitness tracker and analyze the amount of daily exercise and heart rate. This can evaluate the impact of insufficient or excessive exercise on physical condition and suggest appropriate amounts of exercise. It can also send notifications to the user based on the exercise data, encouraging them to stretch or do some light exercise. This makes it possible to provide more comprehensive support for the user's health management.

[0082] The health management system can also acquire the user's dietary data and combine it with the results of health checks to evaluate the user's overall health condition. For example, it can acquire data from a food recording app or calorie counting app and analyze nutritional balance and calorie intake. This can evaluate the impact of dietary content on health and suggest balanced meals. It can also provide the user with nutritional advice based on the dietary data. This makes it possible to provide more comprehensive support for the user's health management.

[0083] The health management system can also acquire the user's sleep data and combine it with the results of the health check to evaluate the user's overall health. For example, it can acquire sleep data from a smartwatch or sleep tracker and analyze the quality and duration of sleep. This can evaluate the impact of insufficient or excessive sleep on physical condition and suggest appropriate sleep durations. It can also provide the user with advice on relaxation methods and how to improve their sleep environment based on the sleep data. This makes it possible to provide more comprehensive support for the user's health management.

[0084] The health management system can also measure the user's stress level and combine it with the results of the health check to evaluate the user's overall health. For example, it can obtain data from a stress measurement device or app and analyze heart rate variability and electrodermal activity. This can then evaluate the impact of stress on physical condition and suggest stress management methods. It can also provide the user with advice on relaxation methods and stress reduction based on the stress data. This makes it possible to provide more comprehensive support for the user's health management.

[0085] The health management system can also monitor the user's water intake and combine this with the results of health checks to evaluate the user's overall health. For example, it can obtain data from a water intake recording app or smart bottle and analyze daily water intake. This can then evaluate the impact of lack of water on health and suggest appropriate water intake amounts. It can also send notifications to the user based on the water intake data, encouraging them to drink water regularly. This allows for more comprehensive support for the user's health management.

[0086] The health management system can use the emotion estimation function to monitor the user's emotional state in real time and provide feedback to elicit positive emotions. For example, it can display a message that makes the user smile. It can also play music that helps the user relax. This makes it possible to support the user's mental health by monitoring the user's emotional state in real time and providing feedback to elicit positive emotions.

[0087] The health management system uses the emotion estimation function to monitor the user's emotional state in real time and suggest ways to refresh themselves if negative emotions are detected. For example, it can display a message encouraging deep breathing. It can also send a notification encouraging the user to take a short break. This makes it possible to support the user's mental health by monitoring the user's emotional state in real time and suggesting ways to refresh themselves if negative emotions are detected.

[0088] The health management system uses the emotion estimation function to monitor the user's emotional state in real time and identify signs of stress or fatigue. For example, it can associate changes in facial color with an emotion score. It can also provide advice on creating a relaxing environment for the user. This allows the system to monitor the user's emotional state in real time, identify signs of stress or fatigue, and take appropriate measures.

[0089] The health management system can use the emotion estimation function to monitor the user's emotional state in real time and provide feedback to elicit positive emotions. For example, it can display a message that makes the user smile. It can also play music that helps the user relax. This makes it possible to support the user's mental health by monitoring the user's emotional state in real time and providing feedback to elicit positive emotions.

[0090] The health management system uses the emotion estimation function to monitor the user's emotional state in real time and suggest ways to refresh themselves if negative emotions are detected. For example, it can display a message encouraging deep breathing. It can also send a notification encouraging the user to take a short break. This makes it possible to support the user's mental health by monitoring the user's emotional state in real time and suggesting ways to refresh themselves if negative emotions are detected.

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

[0092] Step 1: The face image acquisition unit acquires face images taken by a camera during a web conference. For example, a camera built into the web conference system automatically captures the face images of participants and acquires face images in real time. Step 2: The facial color analysis unit analyzes the facial color from the acquired facial image. For example, the facial color analysis unit detects changes in facial color and identifies abnormal facial colors. The facial color can be analyzed based on RGB values, hue, and saturation. Step 3: The facial expression analysis unit analyzes facial expressions based on the facial color analyzed by the facial color analysis unit. For example, the facial expression analysis unit detects unusual facial expressions, such as tiredness or pain. Facial expressions can be analyzed based on the movement of facial features and the classification of emotions. Step 4: If the notification unit determines that a person is in poor health based on the analysis of their facial color and facial expression, it notifies the person or their administrator of the result. For example, after the end of a web conference, a notification can be sent via email or messenger app, including the results of the health check and a message recommending that the person seek medical attention if necessary.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0127] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a face image acquisition unit that acquires a face image captured by a camera during a web conference; a facial color analysis unit that analyzes a facial color from the facial image acquired by the facial image acquisition unit; an expression analysis unit that analyzes an expression based on the facial color analyzed by the facial color analysis unit; a notification unit that notifies the person or a manager of the result when it is determined that the person is in poor health based on the analysis results of the facial color and the facial expression. A system characterized by:

2. The face image acquisition unit In addition to the facial image captured by the camera during the web conference, changes in ambient light are corrected in real time to obtain a more accurate facial image. The system of claim 1 .

3. The complexion analysis unit In analyzing the complexion, not only the skin tone but also physiological indices such as blood flow and oxygen saturation are analyzed. The system of claim 1 .

4. The facial expression analysis unit In the facial expression analysis, subtle changes in facial expressions are detected to grasp more detailed emotional states. The system of claim 1 .

5. The notification unit When notifying users of their health check results, the notification content can be individually customized to provide specific advice based on the user's past health data and current situation. The system of claim 1 .

6. The face image acquisition unit Using emotion estimation, the system estimates the user's emotions in real time while facing the camera and provides advice to elicit positive emotions. The system of claim 1 .

Images