system

Abstract

The system according to the embodiment aims to monitor the biological information of a worker in real time and evaluate the safety state. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a warning unit, and a countermeasure unit. The collection unit collects biometric information of a worker. The analysis unit analyzes the biometric information collected by the collection unit and evaluates the safety status of the worker. The warning unit detects an abnormality based on the safety status evaluated by the analysis unit and issues a warning. The countermeasure unit takes appropriate countermeasures based on the warning issued by the warning unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately monitor workers' biological information in real time and evaluate their safety status, so there is room for improvement.

[0005] The system according to the embodiment aims to monitor the biological information of a worker in real time and evaluate the safety state. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a warning unit, and a countermeasure unit. The collection unit collects biometric information of a worker. The analysis unit analyzes the biometric information collected by the collection unit and evaluates the safety status of the worker. The warning unit detects an abnormality based on the safety status evaluated by the analysis unit and issues a warning. The countermeasure unit takes appropriate countermeasures based on the warning issued by the warning unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the biological information of the worker in real time and evaluate the safety state. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A safety monitoring system according to an embodiment of the present invention uses an AI-equipped wearable device to monitor a worker's biometric information, such as their heart rate and body temperature, in real time. This safety monitoring system collects biometric information, such as their heart rate and body temperature, in real time, and a generating AI analyzes this data to evaluate the worker's safety status. For example, if the worker's heart rate suddenly increases or their body temperature becomes abnormally high, the generating AI determines this to be an abnormality. If an abnormality is detected, the generating AI immediately issues an alert, such as displaying a warning message on the worker's warning device. It also provides instructions for taking appropriate countermeasures, such as instructing the worker to stop work and take a break or to contact a medical institution. This system ensures worker safety and prevents accidents and health damage during work. It can be used in potentially dangerous work environments, such as construction sites and manufacturing plants. It can also be used to monitor health during long hours of desk work or exercise. Specific types of devices include wristwatch-type and chest-worn wearable devices. Regarding the analysis method for the generative AI, normal and abnormal ranges are set based on the collected data, and a mechanism for issuing an alert when an abnormality is detected is clarified. Possible alert methods include an audio alarm, a vibration alarm, or a displayed warning message. Appropriate countermeasures include halting work, instructing workers to take a break, or contacting a medical institution. In this way, the safety monitoring system can ensure the safety of workers and prevent accidents and health damage during work.

[0029] A safety monitoring system according to an embodiment includes a collection unit, an analysis unit, a warning unit, and a countermeasure unit. The collection unit collects biometric information of a worker. The biometric information of the worker includes, but is not limited to, heart rate, body temperature, and blood pressure. The collection unit collects the biometric information using, for example, a wristwatch-type or chest-worn wearable device. For example, the collection unit monitors heart rate in real time using a wristwatch-type wearable device. The collection unit can also monitor body temperature in real time using a chest-worn wearable device. The collection unit can also monitor blood pressure in real time using a blood pressure monitor. The analysis unit uses a generative AI to analyze the biometric information collected by the collection unit and evaluate the safety status of the worker. For example, the analysis unit sets normal and abnormal ranges based on the collected heart rate data and issues a warning if an abnormality is detected. For example, the analysis unit determines a sudden increase in heart rate as an abnormality and issues a warning. The analysis unit can also determine an abnormality in an abnormally high body temperature and issue a warning. Furthermore, the analysis unit can determine that an abnormality has occurred when blood pressure becomes abnormally high and issue a warning. The warning unit detects an abnormality based on the safety state evaluated by the analysis unit and issues a warning. The warning unit issues a warning to the worker, for example, by issuing an audio alarm. The warning unit can also issue a warning to the worker by issuing a vibration alarm. The warning unit can also issue a warning to the worker by displaying a warning message. The countermeasure unit takes appropriate countermeasures based on the warning issued by the warning unit. The countermeasure unit ensures the safety of the worker, for example, by instructing the worker to stop work. The countermeasure unit can also ensure the safety of the worker by instructing the worker to take a break. The countermeasure unit can also ensure the safety of the worker by urging the worker to contact a medical institution. As a result, the safety monitoring system according to the embodiment can ensure the safety of workers and prevent accidents and health damage during work.

[0030] The collection unit includes a wristwatch-type or chest-worn wearable device. The collection unit, for example, monitors the heart rate in real time using the wristwatch-type wearable device. For example, the collection unit monitors the heart rate in real time using the wristwatch-type wearable device. The collection unit can also monitor body temperature in real time using a chest-worn wearable device. For example, the collection unit monitors body temperature in real time using a chest-worn wearable device. The collection unit can also monitor blood pressure in real time using a sphygmomanometer. For example, the collection unit monitors blood pressure in real time using a sphygmomanometer. As a result, by including the collection unit in a wearable device, it is possible to collect the worker's biological information in real time.

[0031] The analysis unit can set normal and abnormal ranges based on the collected data and issue a warning if an abnormality is detected. The analysis unit can set normal and abnormal ranges based on, for example, collected heart rate data and issue a warning if an abnormality is detected. For example, the analysis unit determines that a sudden increase in heart rate is abnormal and issues a warning. The analysis unit can also determine that an abnormally high body temperature is abnormal and issue a warning. Furthermore, the analysis unit can also determine that an abnormally high blood pressure is abnormal and issue a warning. In this way, by the analysis unit setting normal and abnormal ranges, a warning can be issued immediately if an abnormality is detected.

[0032] The warning unit can issue an audio alarm, a vibration alarm, or display a warning message. The warning unit, for example, issues a warning to the worker by issuing an audio alarm. For example, the warning unit issues a warning to the worker by issuing an audio alarm. The warning unit can also issue a warning to the worker by issuing a vibration alarm. For example, the warning unit issues a warning to the worker by issuing a vibration alarm. The warning unit can also issue a warning to the worker by displaying a warning message. For example, the warning unit issues a warning to the worker by displaying a warning message. As a result, the warning unit is equipped with a variety of warning means, making it possible to issue an appropriate warning when an abnormality is detected.

[0033] The countermeasures department can instruct workers to suspend work, take a break, or contact a medical institution. For example, the countermeasures department ensures the safety of workers by instructing them to suspend work. For example, the countermeasures department ensures the safety of workers by instructing them to suspend work. The countermeasures department can also ensure the safety of workers by instructing them to take a break. For example, the countermeasures department ensures the safety of workers by instructing them to take a break. Furthermore, the countermeasures department can also ensure the safety of workers by encouraging them to contact a medical institution. For example, the countermeasures department ensures the safety of workers by encouraging them to contact a medical institution. In this way, the countermeasures department can take appropriate measures to ensure the safety of workers and prevent accidents and health damage.

[0034] The collection unit can analyze the worker's past biometric information history and select the optimal collection method. For example, if the collection unit determines from past data that the worker's heart rate tends to increase during a specific time period, the collection unit increases the collection frequency during that time period. For example, if the collection unit determines from past data that the worker's heart rate tends to increase during a specific time period, the collection unit increases the collection frequency during that time period. Furthermore, if the collection unit determines from past data that the worker's body temperature tends to increase during a specific task, the collection unit can also increase the collection frequency during that task. For example, if the collection unit determines from past data that the worker's body temperature tends to increase during a specific task, the collection unit can also increase the collection frequency during that task. Furthermore, if the collection unit determines from past data that the worker is prone to abnormalities in a specific environment, the collection unit can also increase the collection frequency in that environment. For example, if the collection unit determines from past data that the worker is prone to abnormalities in a specific environment, the collection unit increases the collection frequency in that environment. In this way, by analyzing past biometric information history, the optimal collection method can be selected and the accuracy of the data can be improved.

[0035] When collecting biometric information, the collection unit can perform filtering based on the worker's current work content and environment. For example, when the worker is working in a high-temperature environment, the collection unit prioritizes collecting body temperature data. For example, when the worker is working in a high-temperature environment, the collection unit prioritizes collecting body temperature data. The collection unit can also prioritize collecting heart rate data when the worker is performing heavy work. For example, when the worker is performing heavy work, the collection unit prioritizes collecting heart rate data. Furthermore, the collection unit can also prioritize collecting respiratory rate data when the worker is performing static work. For example, when the worker is performing static work, the collection unit prioritizes collecting respiratory rate data. In this way, by filtering based on the work content and environment, necessary data can be collected preferentially.

[0036] When collecting biometric information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the worker. For example, when the worker is working at high altitude, the collection unit prioritizes collecting oxygen saturation data. For example, when the worker is working at high altitude, the collection unit prioritizes collecting oxygen saturation data. The collection unit can also prioritize collecting body temperature and blood pressure data when the worker is working in a cold region. For example, when the worker is working in a cold region, the collection unit prioritizes collecting body temperature and blood pressure data. Furthermore, the collection unit can also prioritize collecting heart rate and respiratory rate data when the worker is working in an urban area. For example, when the worker is working in an urban area, the collection unit prioritizes collecting heart rate and respiratory rate data. In this way, highly relevant information can be prioritized by taking into account the geographical location information.

[0037] The collection unit can analyze the worker's social media activity and collect related information when collecting biometric information. For example, if the worker posts on social media that he or she is feeling stressed, the collection unit prioritizes collecting heart rate and body temperature data. For example, if the worker posts on social media that he or she is feeling stressed, the collection unit prioritizes collecting heart rate and body temperature data. The collection unit can also prioritize collecting respiratory rate and blood pressure data if the worker posts on social media that he or she is feeling relaxed. For example, if the worker posts on social media that he or she is feeling relaxed, the collection unit can also prioritize collecting heart rate variability and electrodermal activity data if the worker posts on social media that he or she is feeling excited. For example, if the worker posts on social media that he or she is feeling excited, the collection unit prioritizes collecting heart rate variability and electrodermal activity data. This makes it possible to collect related information by analyzing social media activity and improve data accuracy.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the biological information. For example, if the heart rate is abnormally high, the analysis unit performs a detailed analysis to identify the cause of the abnormality. For example, if the heart rate is abnormally high, the analysis unit performs a detailed analysis to identify the cause of the abnormality. Furthermore, if the body temperature is within a normal range, the analysis unit can perform a simple analysis to reduce the data load. For example, if the body temperature is within a normal range, the analysis unit can perform a simple analysis to reduce the data load. Furthermore, if the respiratory rate is abnormally low, the analysis unit can perform a detailed analysis to identify the cause of the abnormality. For example, if the respiratory rate is abnormally low, the analysis unit can perform a detailed analysis to identify the cause of the abnormality. In this way, by adjusting the level of detail of the analysis based on the importance of the biological information, it is possible to analyze important information in detail while reducing the data load.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of biological information. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a temperature fluctuation analysis algorithm to body temperature data. For example, the analysis unit applies a temperature fluctuation analysis algorithm to body temperature data. The analysis unit can also apply a breathing pattern analysis algorithm to respiratory rate data. For example, the analysis unit applies a breathing pattern analysis algorithm to respiratory rate data. In this way, by applying different analysis algorithms depending on the category of biological information, the accuracy of the data can be improved.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time when the biometric information was collected. For example, the analysis unit prioritizes analyzing the most recent data to understand the real-time status. For example, the analysis unit prioritizes analyzing the most recent data to understand the real-time status. The analysis unit can also refer to past data to understand long-term trends. For example, the analysis unit refers to past data to understand long-term trends. Furthermore, the analysis unit can also prioritize analyzing data from a specific time period to identify abnormalities in that time period. For example, the analysis unit prioritizes analyzing data from a specific time period to identify abnormalities in that time period. In this way, by determining the priority of analysis based on the time when the biometric information was collected, it becomes easier to understand the real-time status.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the correlation of the biological information. For example, if heart rate data and body temperature data are correlated, the analysis unit performs the analysis simultaneously. For example, if heart rate data and body temperature data are correlated, the analysis unit performs the analysis simultaneously. Furthermore, the analysis unit can also perform the analysis simultaneously if respiratory rate data and blood pressure data are correlated. For example, if respiratory rate data and blood pressure data are correlated, the analysis unit performs the analysis simultaneously. Furthermore, the analysis unit can also perform the analysis simultaneously if heart rate variability data and electrodermal activity data are correlated. For example, if heart rate variability data and electrodermal activity data are correlated, the analysis unit performs the analysis simultaneously. This allows for efficient analysis by adjusting the order of analysis based on the correlation of the biological information.

[0042] The warning unit can adjust the level of detail of the warning based on the severity of the abnormality when issuing a warning. For example, the warning unit displays a detailed warning message when a serious abnormality is detected. For example, the warning unit displays a detailed warning message when a serious abnormality is detected. The warning unit can also display a simple warning message when a minor abnormality is detected. For example, the warning unit displays a simple warning message when a minor abnormality is detected. Furthermore, the warning unit can also display a warning message with an appropriate level of detail when a medium abnormality is detected. For example, the warning unit displays a warning message with an appropriate level of detail when a medium abnormality is detected. In this way, by adjusting the level of detail of the warning based on the severity of the abnormality, it is possible to issue an appropriate warning.

[0043] When issuing a warning, the warning unit can apply different warning means depending on the category of the abnormality. For example, the warning unit issues an audio alarm when an abnormality in the heart rate is detected. For example, the warning unit issues an audio alarm when an abnormality in the heart rate is detected. The warning unit can also issue a vibration alarm when an abnormality in the body temperature is detected. For example, the warning unit issues a vibration alarm when an abnormality in the body temperature is detected. Furthermore, the warning unit can also display a warning message when an abnormality in the respiratory rate is detected. For example, the warning unit displays a warning message when an abnormality in the respiratory rate is detected. In this way, by applying an appropriate warning means depending on the category of the abnormality, it is possible to issue an effective warning.

[0044] When issuing a warning, the warning unit can determine the priority of the warning based on the time when the abnormality occurred. For example, the warning unit prioritizes warning about the most recent abnormality and encourages a real-time response. For example, the warning unit prioritizes warning about the most recent abnormality and encourages a real-time response. The warning unit can also refer to past abnormalities and encourage a long-term response. For example, the warning unit refers to past abnormalities and encourages a long-term response. Furthermore, the warning unit can also prioritize warning about abnormalities in a specific time period and encourage a response during that time period. For example, the warning unit prioritizes warning about abnormalities in a specific time period and encourages a response during that time period. In this way, by determining the priority of the warning based on the time when the abnormality occurred, it is possible to encourage a real-time response.

[0045] When issuing a warning, the warning unit can adjust the order of warnings based on the correlation between the abnormalities. For example, the warning unit issues warnings simultaneously when abnormalities in heart rate and body temperature are correlated. For example, the warning unit issues warnings simultaneously when abnormalities in heart rate and body temperature are correlated. The warning unit can also issue warnings simultaneously when abnormalities in respiratory rate and blood pressure are correlated. For example, the warning unit issues warnings simultaneously when abnormalities in respiratory rate and blood pressure are correlated. Furthermore, the warning unit can also issue warnings simultaneously when abnormalities in heart rate variability and electrodermal activity are correlated. For example, the warning unit issues warnings simultaneously when abnormalities in heart rate variability and electrodermal activity are correlated. In this way, by adjusting the order of warnings based on the correlation between abnormalities, efficient warnings can be issued.

[0046] When taking countermeasures, the countermeasure department can select the optimal countermeasure method by analyzing the worker's past countermeasure history. For example, the countermeasure department prioritizes proposing countermeasure methods that have been effective in the past. For example, the countermeasure department prioritizes proposing countermeasure methods that have been effective in the past. The countermeasure department can also exclude countermeasure methods that have been ineffective in the past and propose new countermeasure methods. For example, the countermeasure department can exclude countermeasure methods that have been ineffective in the past and propose new countermeasure methods. Furthermore, the countermeasure department can also select a countermeasure method that is effective for a specific situation from the past countermeasure history. For example, the countermeasure department selects a countermeasure method that is effective for a specific situation from the past countermeasure history. In this way, by analyzing the past countermeasure history, the optimal countermeasure method can be selected and effective countermeasures can be taken.

[0047] When taking countermeasures, the countermeasure unit can customize the countermeasure measures based on the worker's current work situation. For example, if the worker is performing heavy work, the countermeasure unit suggests taking a break. For example, if the worker is performing heavy work, the countermeasure unit suggests taking a break. Furthermore, if the worker is performing desk work, the countermeasure unit can also suggest stretching. For example, if the worker is performing desk work, the countermeasure unit suggests stretching. Furthermore, if the worker is working in a high-temperature environment, the countermeasure unit can also suggest hydration. For example, if the worker is working in a high-temperature environment, the countermeasure unit suggests hydration. In this way, by customizing the countermeasure measures based on the current work situation, more appropriate countermeasures can be taken.

[0048] When taking countermeasures, the countermeasure unit can select the optimal countermeasure method by taking into consideration the geographical location information of the worker. For example, if the worker is working at high altitude, the countermeasure unit suggests oxygen supplementation. For example, if the worker is working at high altitude, the countermeasure unit suggests oxygen supplementation. Furthermore, the countermeasure unit can also suggest a hot drink if the worker is working in a cold region. For example, if the worker is working in a cold region, the countermeasure unit suggests a hot drink. Furthermore, the countermeasure unit can also suggest a break in a quiet place if the worker is working in an urban area. For example, if the worker is working in an urban area, the countermeasure unit suggests a break in a quiet place. In this way, by taking into consideration the geographical location information, the optimal countermeasure method can be selected and effective countermeasures can be taken.

[0049] When taking countermeasures, the countermeasure department can analyze the social media activity of the worker and suggest countermeasure measures. For example, if the worker posts on social media that he or she is feeling stressed, the countermeasure department can suggest countermeasures to help him or her relax. For example, if the worker posts on social media that he or she is feeling stressed, the countermeasure department can suggest countermeasures to help him or her relax. Furthermore, if the worker posts on social media that he or she is feeling relaxed, the countermeasure department can also suggest normal countermeasures. For example, if the worker posts on social media that he or she is feeling relaxed, the countermeasure department can suggest normal countermeasures. Furthermore, if the worker posts on social media that he or she is feeling excited, the countermeasure department can also suggest countermeasures to help him or her stay calm. For example, if the worker posts on social media that he or she is feeling excited, the countermeasure department can suggest countermeasures to help him or her stay calm. In this way, by analyzing social media activity, relevant countermeasures can be suggested and effective countermeasures can be taken.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The collection unit can collect not only the worker's biometric information but also information about the surrounding environment. For example, the collection unit can monitor the temperature, humidity, and noise level around the worker in real time. The collection unit can also obtain the worker's location information and understand the work situation in a specific area. Furthermore, the collection unit can collect the worker's motion data and evaluate the workload. This allows the collection unit to collect not only biometric information but also environmental information, enabling more comprehensive safety monitoring.

[0052] The analysis unit can combine and analyze the collected biometric information and environmental information. For example, the analysis unit may consider high ambient temperature when a worker's heart rate is high and assess the risk of heatstroke. The analysis unit may also consider high humidity when a worker's body temperature is high and assess the risk of dehydration. Furthermore, the analysis unit may consider high noise levels when a worker's heart rate is high and identify the cause of stress. This allows the analysis unit to combine and analyze biometric information and environmental information, enabling more accurate safety assessments.

[0053] When collecting the worker's biometric information, the collection unit can also record the worker's diet and fluid intake status. For example, the collection unit can record the time and content of the worker's meals and evaluate the balance with energy expenditure. The collection unit can also record the amount and frequency of the worker's fluid intake and evaluate the risk of dehydration. Furthermore, the collection unit can record whether the worker is consuming specific nutrients and evaluate nutritional balance. This allows the collection unit to record the worker's diet and fluid intake status, enabling more comprehensive health management.

[0054] When collecting the worker's biometric information, the collection unit can also record the worker's sleep status. For example, the collection unit can record the duration and quality of the worker's sleep to evaluate the worker's fatigue level. The collection unit can also record the worker's sleeping position to evaluate the worker's physical strain. Furthermore, the collection unit can record the environment in which the worker sleeps to evaluate the worker's sleep quality. In this way, the collection unit can record the worker's sleep status as well, enabling more comprehensive health management.

[0055] The analysis unit can combine and analyze the collected biometric information with the worker's past health data. For example, the analysis unit can compare the worker's past heart rate data with the current data and evaluate any abnormal trends. The analysis unit can also compare the worker's past body temperature data with the current data and evaluate any abnormal trends. Furthermore, the analysis unit can compare the worker's past blood pressure data with the current data and evaluate any abnormal trends. This allows the analysis unit to combine and analyze past health data, enabling more accurate safety assessments.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection unit collects the worker's biometric information. The worker's biometric information includes heart rate, body temperature, blood pressure, etc. The collection unit collects the biometric information using a wristwatch-type or chest-worn wearable device. For example, the heart rate can be monitored in real time using a wristwatch-type wearable device, and the body temperature can be monitored in real time using a chest-worn wearable device. Blood pressure can also be monitored in real time using a blood pressure monitor. Step 2: The analysis unit uses the generation AI to analyze the biometric information collected by the collection unit and evaluate the worker's safety status. For example, it sets normal and abnormal ranges based on the collected heart rate data and issues a warning if an abnormality is detected. If the heart rate rises suddenly, the body temperature becomes abnormally high, or the blood pressure becomes abnormal, it will determine this as an abnormality and issue a warning. Step 3: The warning unit detects an abnormality based on the safety state evaluated by the analysis unit and issues a warning. For example, the warning can be communicated to the worker by issuing an audio alarm or vibration alarm. It can also be communicated to the worker by displaying a warning message. Step 4: The countermeasures department takes appropriate measures based on the warning issued by the warning department. For example, the countermeasures department can ensure the safety of the workers by instructing them to stop work. It can also ensure the safety of the workers by instructing them to take a break. It can also ensure the safety of the workers by encouraging them to contact a medical institution.

[0058] (Example 2) A safety monitoring system according to an embodiment of the present invention uses an AI-equipped wearable device to monitor a worker's biometric information, such as their heart rate and body temperature, in real time. This safety monitoring system collects biometric information, such as their heart rate and body temperature, in real time, and a generating AI analyzes this data to evaluate the worker's safety status. For example, if the worker's heart rate suddenly increases or their body temperature becomes abnormally high, the generating AI determines this to be an abnormality. If an abnormality is detected, the generating AI immediately issues an alert, such as displaying a warning message on the worker's warning device. It also provides instructions for taking appropriate countermeasures, such as instructing the worker to stop work and take a break or to contact a medical institution. This system ensures worker safety and prevents accidents and health damage during work. It can be used in potentially dangerous work environments, such as construction sites and manufacturing plants. It can also be used to monitor health during long hours of desk work or exercise. Specific types of devices include wristwatch-type and chest-worn wearable devices. Regarding the analysis method for the generative AI, normal and abnormal ranges are set based on the collected data, and a mechanism for issuing an alert when an abnormality is detected is clarified. Possible alert methods include an audio alarm, a vibration alarm, or a displayed warning message. Appropriate countermeasures include halting work, instructing workers to take a break, or contacting a medical institution. In this way, the safety monitoring system can ensure the safety of workers and prevent accidents and health damage during work.

[0059] A safety monitoring system according to an embodiment includes a collection unit, an analysis unit, a warning unit, and a countermeasure unit. The collection unit collects biometric information of a worker. The biometric information of the worker includes, but is not limited to, heart rate, body temperature, and blood pressure. The collection unit collects the biometric information using, for example, a wristwatch-type or chest-worn wearable device. For example, the collection unit monitors heart rate in real time using a wristwatch-type wearable device. The collection unit can also monitor body temperature in real time using a chest-worn wearable device. The collection unit can also monitor blood pressure in real time using a blood pressure monitor. The analysis unit uses a generative AI to analyze the biometric information collected by the collection unit and evaluate the safety status of the worker. For example, the analysis unit sets normal and abnormal ranges based on the collected heart rate data and issues a warning if an abnormality is detected. For example, the analysis unit determines a sudden increase in heart rate as an abnormality and issues a warning. The analysis unit can also determine an abnormality in an abnormally high body temperature and issue a warning. Furthermore, the analysis unit can determine that an abnormality has occurred when blood pressure becomes abnormally high and issue a warning. The warning unit detects an abnormality based on the safety state evaluated by the analysis unit and issues a warning. The warning unit issues a warning to the worker, for example, by issuing an audio alarm. The warning unit can also issue a warning to the worker by issuing a vibration alarm. The warning unit can also issue a warning to the worker by displaying a warning message. The countermeasure unit takes appropriate countermeasures based on the warning issued by the warning unit. The countermeasure unit ensures the safety of the worker, for example, by instructing the worker to stop work. The countermeasure unit can also ensure the safety of the worker by instructing the worker to take a break. The countermeasure unit can also ensure the safety of the worker by urging the worker to contact a medical institution. As a result, the safety monitoring system according to the embodiment can ensure the safety of workers and prevent accidents and health damage during work.

[0060] The collection unit includes a wristwatch-type or chest-worn wearable device. The collection unit, for example, monitors the heart rate in real time using the wristwatch-type wearable device. For example, the collection unit monitors the heart rate in real time using the wristwatch-type wearable device. The collection unit can also monitor body temperature in real time using a chest-worn wearable device. For example, the collection unit monitors body temperature in real time using a chest-worn wearable device. The collection unit can also monitor blood pressure in real time using a sphygmomanometer. For example, the collection unit monitors blood pressure in real time using a sphygmomanometer. As a result, by including the collection unit in a wearable device, it is possible to collect the worker's biological information in real time.

[0061] The analysis unit can set normal and abnormal ranges based on the collected data and issue a warning if an abnormality is detected. The analysis unit can set normal and abnormal ranges based on, for example, collected heart rate data and issue a warning if an abnormality is detected. For example, the analysis unit determines that a sudden increase in heart rate is abnormal and issues a warning. The analysis unit can also determine that an abnormally high body temperature is abnormal and issue a warning. Furthermore, the analysis unit can also determine that an abnormally high blood pressure is abnormal and issue a warning. In this way, by the analysis unit setting normal and abnormal ranges, a warning can be issued immediately if an abnormality is detected.

[0062] The warning unit can issue an audio alarm, a vibration alarm, or display a warning message. The warning unit, for example, issues a warning to the worker by issuing an audio alarm. For example, the warning unit issues a warning to the worker by issuing an audio alarm. The warning unit can also issue a warning to the worker by issuing a vibration alarm. For example, the warning unit issues a warning to the worker by issuing a vibration alarm. The warning unit can also issue a warning to the worker by displaying a warning message. For example, the warning unit issues a warning to the worker by displaying a warning message. As a result, the warning unit is equipped with a variety of warning means, making it possible to issue an appropriate warning when an abnormality is detected.

[0063] The countermeasures department can instruct workers to suspend work, take a break, or contact a medical institution. For example, the countermeasures department ensures the safety of workers by instructing them to suspend work. For example, the countermeasures department ensures the safety of workers by instructing them to suspend work. The countermeasures department can also ensure the safety of workers by instructing them to take a break. For example, the countermeasures department ensures the safety of workers by instructing them to take a break. Furthermore, the countermeasures department can also ensure the safety of workers by encouraging them to contact a medical institution. For example, the countermeasures department ensures the safety of workers by encouraging them to contact a medical institution. In this way, the countermeasures department can take appropriate measures to ensure the safety of workers and prevent accidents and health damage.

[0064] The collection unit can estimate the worker's emotions and adjust the frequency of collection of biometric information based on the estimated worker's emotions. For example, when the worker is feeling stressed, the collection unit increases the collection frequency to closely monitor fluctuations in heart rate and body temperature. For example, when the worker is feeling stressed, the collection unit increases the collection frequency to closely monitor fluctuations in heart rate and body temperature. The collection unit can also reduce the collection frequency to reduce the data load when the worker is relaxed. For example, when the worker is relaxed, the collection unit reduces the collection frequency to reduce the data load. Furthermore, when the worker is excited, the collection unit can set the collection frequency to a medium level so as not to miss any sudden fluctuations. For example, when the worker is excited, the collection unit sets the collection frequency to a medium level so as not to miss any sudden fluctuations. This allows more appropriate data collection by adjusting the collection frequency of biometric information according to the worker's emotions.

[0065] The collection unit can analyze the worker's past biometric information history and select the optimal collection method. For example, if the collection unit determines from past data that the worker's heart rate tends to increase during a specific time period, the collection unit increases the collection frequency during that time period. For example, if the collection unit determines from past data that the worker's heart rate tends to increase during a specific time period, the collection unit increases the collection frequency during that time period. Furthermore, if the collection unit determines from past data that the worker's body temperature tends to increase during a specific task, the collection unit can also increase the collection frequency during that task. For example, if the collection unit determines from past data that the worker's body temperature tends to increase during a specific task, the collection unit can also increase the collection frequency during that task. Furthermore, if the collection unit determines from past data that the worker is prone to abnormalities in a specific environment, the collection unit can also increase the collection frequency in that environment. For example, if the collection unit determines from past data that the worker is prone to abnormalities in a specific environment, the collection unit increases the collection frequency in that environment. In this way, by analyzing past biometric information history, the optimal collection method can be selected and the accuracy of the data can be improved.

[0066] When collecting biometric information, the collection unit can perform filtering based on the worker's current work content and environment. For example, when the worker is working in a high-temperature environment, the collection unit prioritizes collecting body temperature data. For example, when the worker is working in a high-temperature environment, the collection unit prioritizes collecting body temperature data. The collection unit can also prioritize collecting heart rate data when the worker is performing heavy work. For example, when the worker is performing heavy work, the collection unit prioritizes collecting heart rate data. Furthermore, the collection unit can also prioritize collecting respiratory rate data when the worker is performing static work. For example, when the worker is performing static work, the collection unit prioritizes collecting respiratory rate data. In this way, by filtering based on the work content and environment, necessary data can be collected preferentially.

[0067] The collection unit can estimate the worker's emotion and determine the priority of the biometric information to be collected based on the estimated worker's emotion. For example, when the worker is feeling stressed, the collection unit prioritizes collecting heart rate and body temperature data. For example, when the worker is feeling stressed, the collection unit prioritizes collecting heart rate and body temperature data. The collection unit can also prioritize collecting respiratory rate and blood pressure data when the worker is relaxed. For example, when the worker is relaxed, the collection unit prioritizes collecting respiratory rate and blood pressure data. Furthermore, when the worker is excited, the collection unit can also prioritize collecting heart rate variability and electrodermal activity data. For example, when the worker is excited, the collection unit prioritizes collecting heart rate variability and electrodermal activity data. In this way, by determining the priority of the biometric information to be collected according to the worker's emotion, important data can be collected preferentially.

[0068] When collecting biometric information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the worker. For example, when the worker is working at high altitude, the collection unit prioritizes collecting oxygen saturation data. For example, when the worker is working at high altitude, the collection unit prioritizes collecting oxygen saturation data. The collection unit can also prioritize collecting body temperature and blood pressure data when the worker is working in a cold region. For example, when the worker is working in a cold region, the collection unit prioritizes collecting body temperature and blood pressure data. Furthermore, the collection unit can also prioritize collecting heart rate and respiratory rate data when the worker is working in an urban area. For example, when the worker is working in an urban area, the collection unit prioritizes collecting heart rate and respiratory rate data. In this way, highly relevant information can be prioritized by taking into account the geographical location information.

[0069] The collection unit can analyze the worker's social media activity and collect related information when collecting biometric information. For example, if the worker posts on social media that he or she is feeling stressed, the collection unit prioritizes collecting heart rate and body temperature data. For example, if the worker posts on social media that he or she is feeling stressed, the collection unit prioritizes collecting heart rate and body temperature data. The collection unit can also prioritize collecting respiratory rate and blood pressure data if the worker posts on social media that he or she is feeling relaxed. For example, if the worker posts on social media that he or she is feeling relaxed, the collection unit can also prioritize collecting heart rate variability and electrodermal activity data if the worker posts on social media that he or she is feeling excited. For example, if the worker posts on social media that he or she is feeling excited, the collection unit prioritizes collecting heart rate variability and electrodermal activity data. This makes it possible to collect related information by analyzing social media activity and improve data accuracy.

[0070] The analysis unit can estimate the worker's emotions and adjust the analysis algorithm based on the estimated worker's emotions. For example, if the worker is feeling stressed, the generation AI adjusts the analysis algorithm to place emphasis on heart rate and body temperature data. For example, if the worker is feeling stressed, the generation AI adjusts the analysis algorithm to place emphasis on heart rate and body temperature data. In addition, if the worker is relaxed, the generation AI can adjust the analysis algorithm to place emphasis on respiratory rate and blood pressure data. For example, if the worker is relaxed, the generation AI adjusts the analysis algorithm to place emphasis on respiratory rate and blood pressure data. In addition, if the worker is excited, the generation AI can adjust the analysis algorithm to place emphasis on heart rate variability and electrodermal activity data. For example, if the worker is excited, the generation AI adjusts the analysis algorithm to place emphasis on heart rate variability and electrodermal activity data. This allows for more appropriate analysis by adjusting the analysis algorithm according to the worker's emotions.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the biological information. For example, if the heart rate is abnormally high, the analysis unit performs a detailed analysis to identify the cause of the abnormality. For example, if the heart rate is abnormally high, the analysis unit performs a detailed analysis to identify the cause of the abnormality. Furthermore, if the body temperature is within a normal range, the analysis unit can perform a simple analysis to reduce the data load. For example, if the body temperature is within a normal range, the analysis unit can perform a simple analysis to reduce the data load. Furthermore, if the respiratory rate is abnormally low, the analysis unit can perform a detailed analysis to identify the cause of the abnormality. For example, if the respiratory rate is abnormally low, the analysis unit can perform a detailed analysis to identify the cause of the abnormality. In this way, by adjusting the level of detail of the analysis based on the importance of the biological information, it is possible to analyze important information in detail while reducing the data load.

[0072] During analysis, the analysis unit can apply different analysis algorithms depending on the category of biological information. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a temperature fluctuation analysis algorithm to body temperature data. For example, the analysis unit applies a temperature fluctuation analysis algorithm to body temperature data. The analysis unit can also apply a breathing pattern analysis algorithm to respiratory rate data. For example, the analysis unit applies a breathing pattern analysis algorithm to respiratory rate data. In this way, by applying different analysis algorithms depending on the category of biological information, the accuracy of the data can be improved.

[0073] The analysis unit can estimate the worker's emotions and determine the priority of analysis based on the estimated worker's emotions. For example, if the worker is feeling stressed, the analysis unit prioritizes analyzing the heart rate and body temperature data. For example, if the worker is feeling stressed, the analysis unit prioritizes analyzing the heart rate and body temperature data. The analysis unit can also prioritize analyzing the respiratory rate and blood pressure data if the worker is relaxed. For example, if the worker is relaxed, the analysis unit prioritizes analyzing the respiratory rate and blood pressure data. Furthermore, the analysis unit can also prioritize analyzing the heart rate variability and electrodermal activity data if the worker is excited. For example, if the worker is excited, the analysis unit prioritizes analyzing the heart rate variability and electrodermal activity data. In this way, by determining the priority of analysis according to the worker's emotions, important data can be analyzed preferentially.

[0074] During analysis, the analysis unit can determine the priority of analysis based on the time when the biometric information was collected. For example, the analysis unit prioritizes analyzing the most recent data to understand the real-time status. For example, the analysis unit prioritizes analyzing the most recent data to understand the real-time status. The analysis unit can also refer to past data to understand long-term trends. For example, the analysis unit refers to past data to understand long-term trends. Furthermore, the analysis unit can also prioritize analyzing data from a specific time period to identify abnormalities in that time period. For example, the analysis unit prioritizes analyzing data from a specific time period to identify abnormalities in that time period. In this way, by determining the priority of analysis based on the time when the biometric information was collected, it becomes easier to understand the real-time status.

[0075] During analysis, the analysis unit can adjust the order of analysis based on the correlation of the biological information. For example, if heart rate data and body temperature data are correlated, the analysis unit performs the analysis simultaneously. For example, if heart rate data and body temperature data are correlated, the analysis unit performs the analysis simultaneously. Furthermore, the analysis unit can also perform the analysis simultaneously if respiratory rate data and blood pressure data are correlated. For example, if respiratory rate data and blood pressure data are correlated, the analysis unit performs the analysis simultaneously. Furthermore, the analysis unit can also perform the analysis simultaneously if heart rate variability data and electrodermal activity data are correlated. For example, if heart rate variability data and electrodermal activity data are correlated, the analysis unit performs the analysis simultaneously. This allows for efficient analysis by adjusting the order of analysis based on the correlation of the biological information.

[0076] The warning unit can estimate the emotion of the worker and adjust the manner in which the warning is expressed based on the estimated emotion of the worker. For example, if the worker is feeling stressed, the warning unit issues a warning in a calm voice. For example, if the worker is feeling stressed, the warning unit issues a warning in a calm voice. The warning unit can also issue a warning in a normal voice if the worker is relaxed. For example, if the worker is relaxed, the warning unit issues a warning in a normal voice. Furthermore, the warning unit can also issue a warning in an emphasized voice if the worker is excited. For example, if the worker is excited, the warning unit issues a warning in an emphasized voice. In this way, by adjusting the manner in which the warning is expressed according to the emotion of the worker, a more effective warning can be issued.

[0077] The warning unit can adjust the level of detail of the warning based on the severity of the abnormality when issuing a warning. For example, the warning unit displays a detailed warning message when a serious abnormality is detected. For example, the warning unit displays a detailed warning message when a serious abnormality is detected. The warning unit can also display a simple warning message when a minor abnormality is detected. For example, the warning unit displays a simple warning message when a minor abnormality is detected. Furthermore, the warning unit can also display a warning message with an appropriate level of detail when a medium abnormality is detected. For example, the warning unit displays a warning message with an appropriate level of detail when a medium abnormality is detected. In this way, by adjusting the level of detail of the warning based on the severity of the abnormality, it is possible to issue an appropriate warning.

[0078] When issuing a warning, the warning unit can apply different warning means depending on the category of the abnormality. For example, the warning unit issues an audio alarm when an abnormality in the heart rate is detected. For example, the warning unit issues an audio alarm when an abnormality in the heart rate is detected. The warning unit can also issue a vibration alarm when an abnormality in the body temperature is detected. For example, the warning unit issues a vibration alarm when an abnormality in the body temperature is detected. Furthermore, the warning unit can also display a warning message when an abnormality in the respiratory rate is detected. For example, the warning unit displays a warning message when an abnormality in the respiratory rate is detected. In this way, by applying an appropriate warning means depending on the category of the abnormality, it is possible to issue an effective warning.

[0079] The warning unit can estimate the emotion of the worker and determine the priority of warnings based on the estimated emotion of the worker. For example, if the worker is feeling stressed, the warning unit prioritizes warnings about abnormalities in heart rate and body temperature. For example, if the worker is feeling stressed, the warning unit prioritizes warnings about abnormalities in heart rate and body temperature. The warning unit can also prioritize warnings about abnormalities in respiratory rate and blood pressure when the worker is relaxed. For example, if the worker is relaxed, the warning unit prioritizes warnings about abnormalities in respiratory rate and blood pressure. Furthermore, the warning unit can also prioritize warnings about abnormalities in heart rate variability and electrodermal activity when the worker is excited. For example, if the worker is excited, the warning unit prioritizes warnings about abnormalities in heart rate variability and electrodermal activity. In this way, by determining the priority of warnings according to the emotion of the worker, it is possible to prioritize warnings about important abnormalities.

[0080] When issuing a warning, the warning unit can determine the priority of the warning based on the time when the abnormality occurred. For example, the warning unit prioritizes warning about the most recent abnormality and encourages a real-time response. For example, the warning unit prioritizes warning about the most recent abnormality and encourages a real-time response. The warning unit can also refer to past abnormalities and encourage a long-term response. For example, the warning unit refers to past abnormalities and encourages a long-term response. Furthermore, the warning unit can also prioritize warning about abnormalities in a specific time period and encourage a response during that time period. For example, the warning unit prioritizes warning about abnormalities in a specific time period and encourages a response during that time period. In this way, by determining the priority of the warning based on the time when the abnormality occurred, it is possible to encourage a real-time response.

[0081] When issuing a warning, the warning unit can adjust the order of warnings based on the correlation between the abnormalities. For example, the warning unit issues warnings simultaneously when abnormalities in heart rate and body temperature are correlated. For example, the warning unit issues warnings simultaneously when abnormalities in heart rate and body temperature are correlated. The warning unit can also issue warnings simultaneously when abnormalities in respiratory rate and blood pressure are correlated. For example, the warning unit issues warnings simultaneously when abnormalities in respiratory rate and blood pressure are correlated. Furthermore, the warning unit can also issue warnings simultaneously when abnormalities in heart rate variability and electrodermal activity are correlated. For example, the warning unit issues warnings simultaneously when abnormalities in heart rate variability and electrodermal activity are correlated. In this way, by adjusting the order of warnings based on the correlation between abnormalities, efficient warnings can be issued.

[0082] The countermeasure unit can estimate the emotion of the worker and adjust the method of countermeasures based on the estimated emotion of the worker. For example, if the worker is feeling stressed, the countermeasure unit suggests a break to relax. For example, if the worker is feeling stressed, the countermeasure unit suggests a break to relax. Furthermore, if the worker is relaxed, the countermeasure unit can also suggest a normal break. For example, if the worker is relaxed, the countermeasure unit suggests a normal break. Furthermore, if the worker is excited, the countermeasure unit can also suggest a break to calm down. For example, if the worker is excited, the countermeasure unit suggests a break to calm down. In this way, by adjusting the method of countermeasures according to the emotion of the worker, more effective countermeasures can be taken.

[0083] When taking countermeasures, the countermeasure department can select the optimal countermeasure method by analyzing the worker's past countermeasure history. For example, the countermeasure department prioritizes proposing countermeasure methods that have been effective in the past. For example, the countermeasure department prioritizes proposing countermeasure methods that have been effective in the past. The countermeasure department can also exclude countermeasure methods that have been ineffective in the past and propose new countermeasure methods. For example, the countermeasure department can exclude countermeasure methods that have been ineffective in the past and propose new countermeasure methods. Furthermore, the countermeasure department can also select a countermeasure method that is effective for a specific situation from the past countermeasure history. For example, the countermeasure department selects a countermeasure method that is effective for a specific situation from the past countermeasure history. In this way, by analyzing the past countermeasure history, the optimal countermeasure method can be selected and effective countermeasures can be taken.

[0084] When taking countermeasures, the countermeasure unit can customize the countermeasure measures based on the worker's current work situation. For example, if the worker is performing heavy work, the countermeasure unit suggests taking a break. For example, if the worker is performing heavy work, the countermeasure unit suggests taking a break. Furthermore, if the worker is performing desk work, the countermeasure unit can also suggest stretching. For example, if the worker is performing desk work, the countermeasure unit suggests stretching. Furthermore, if the worker is working in a high-temperature environment, the countermeasure unit can also suggest hydration. For example, if the worker is working in a high-temperature environment, the countermeasure unit suggests hydration. In this way, by customizing the countermeasure measures based on the current work situation, more appropriate countermeasures can be taken.

[0085] The countermeasure unit can estimate the emotion of the worker and determine the priority of countermeasures based on the estimated emotion of the worker. For example, if the worker is feeling stressed, the countermeasure unit preferentially suggests a countermeasure for relaxation. For example, if the worker is feeling stressed, the countermeasure unit preferentially suggests a countermeasure for relaxation. Furthermore, if the worker is relaxed, the countermeasure unit can preferentially suggest a normal countermeasure. For example, if the worker is relaxed, the countermeasure unit preferentially suggests a normal countermeasure. Furthermore, if the worker is excited, the countermeasure unit can preferentially suggest a countermeasure for calming down. For example, if the worker is excited, the countermeasure unit preferentially suggests a countermeasure for calming down. In this way, by determining the priority of countermeasures according to the emotion of the worker, important countermeasures can be taken preferentially.

[0086] When taking countermeasures, the countermeasure unit can select the optimal countermeasure method by taking into consideration the geographical location information of the worker. For example, if the worker is working at high altitude, the countermeasure unit suggests oxygen supplementation. For example, if the worker is working at high altitude, the countermeasure unit suggests oxygen supplementation. Furthermore, the countermeasure unit can also suggest a hot drink if the worker is working in a cold region. For example, if the worker is working in a cold region, the countermeasure unit suggests a hot drink. Furthermore, the countermeasure unit can also suggest a break in a quiet place if the worker is working in an urban area. For example, if the worker is working in an urban area, the countermeasure unit suggests a break in a quiet place. In this way, by taking into consideration the geographical location information, the optimal countermeasure method can be selected and effective countermeasures can be taken.

[0087] When taking countermeasures, the countermeasure department can analyze the social media activity of the worker and suggest countermeasure measures. For example, if the worker posts on social media that he or she is feeling stressed, the countermeasure department can suggest countermeasures to help him or her relax. For example, if the worker posts on social media that he or she is feeling stressed, the countermeasure department can suggest countermeasures to help him or her relax. Furthermore, if the worker posts on social media that he or she is feeling relaxed, the countermeasure department can also suggest normal countermeasures. For example, if the worker posts on social media that he or she is feeling relaxed, the countermeasure department can suggest normal countermeasures. Furthermore, if the worker posts on social media that he or she is feeling excited, the countermeasure department can also suggest countermeasures to help him or her stay calm. For example, if the worker posts on social media that he or she is feeling excited, the countermeasure department can suggest countermeasures to help him or her stay calm. In this way, by analyzing social media activity, relevant countermeasures can be suggested and effective countermeasures can be taken. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, warning unit, and countermeasure unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit monitors the heart rate and body temperature in real time using a wristwatch-type or chest-worn wearable device of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected biometric information using a generative AI to evaluate the worker's safety status. The warning unit is realized by the control unit 46A of the smart device 14 and issues a warning to the worker by audio alarm, vibration alarm, display of a warning message, etc. The countermeasure unit is realized by the specific processing unit 290 of the data processing device 12 and takes appropriate measures, such as interrupting work, instructing the worker to take a break, or contacting a medical institution. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, warning unit, and countermeasure unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit monitors the heart rate and body temperature in real time using a wristwatch-type or chest-worn wearable device of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected biometric information using a generative AI to evaluate the worker's safety status. The warning unit is realized by the control unit 46A of the smart glasses 214 and issues a warning to the worker by audio alarm, vibration alarm, display of a warning message, etc. The countermeasure unit is realized by the specific processing unit 290 of the data processing device 12 and takes appropriate measures, such as interrupting work, instructing the worker to take a break, or contacting a medical institution. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, warning unit, and countermeasure unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit monitors the heart rate and body temperature in real time using a wristwatch-type or chest-worn wearable device of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected biometric information using a generative AI to evaluate the worker's safety status. The warning unit is realized by the control unit 46A of the headset terminal 314 and issues a warning to the worker by audio alarm, vibration alarm, display of a warning message, etc. The countermeasure unit is realized by the specific processing unit 290 of the data processing device 12 and takes appropriate measures, such as interrupting work, instructing the worker to take a break, or contacting a medical institution. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, warning unit, and countermeasure unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit monitors the heart rate and body temperature in real time using a wristwatch-type or chest-worn wearable device of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected biometric information using a generative AI to evaluate the worker's safety status. The warning unit is realized by the control unit 46A of the robot 414 and issues a warning to the worker by using an audio alarm, a vibration alarm, or a displayed warning message. The countermeasure unit is realized by the specific processing unit 290 of the data processing device 12 and takes appropriate measures, such as suspending work, instructing the worker to take a break, or contacting a medical institution.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The collection unit can collect not only the worker's biometric information but also information about the surrounding environment. For example, the collection unit can monitor the temperature, humidity, and noise level around the worker in real time. The collection unit can also obtain the worker's location information and understand the work situation in a specific area. Furthermore, the collection unit can collect the worker's motion data and evaluate the workload. This allows the collection unit to collect not only biometric information but also environmental information, enabling more comprehensive safety monitoring.

[0090] The analysis unit can combine and analyze the collected biometric information and environmental information. For example, the analysis unit may consider high ambient temperature when a worker's heart rate is high and assess the risk of heatstroke. The analysis unit may also consider high humidity when a worker's body temperature is high and assess the risk of dehydration. Furthermore, the analysis unit may consider high noise levels when a worker's heart rate is high and identify the cause of stress. This allows the analysis unit to combine and analyze biometric information and environmental information, enabling more accurate safety assessments.

[0091] The warning unit can estimate the worker's emotions and adjust the timing of the warning based on the estimated worker's emotions. For example, if the worker is feeling stressed, the warning unit can delay the warning so as not to disrupt the worker's concentration. Also, if the worker is relaxed, the warning unit can accelerate the warning to encourage a prompt response. Furthermore, if the worker is excited, the warning unit can emphasize the warning to attract the worker's attention. In this way, by adjusting the timing of the warning according to the worker's emotions, more effective warnings can be issued.

[0092] The countermeasure unit can estimate the worker's emotions and customize the content of the countermeasure based on the estimated worker's emotions. For example, if the worker is feeling stressed, the countermeasure unit can provide relaxing music. If the worker is relaxed, the countermeasure unit can also suggest light exercise. Furthermore, if the worker is excited, the countermeasure unit can issue instructions to encourage deep breathing. In this way, by customizing the content of the countermeasure according to the worker's emotions, more effective countermeasures can be taken.

[0093] When collecting the worker's biometric information, the collection unit can also record the worker's diet and fluid intake status. For example, the collection unit can record the time and content of the worker's meals and evaluate the balance with energy expenditure. The collection unit can also record the amount and frequency of the worker's fluid intake and evaluate the risk of dehydration. Furthermore, the collection unit can record whether the worker is consuming specific nutrients and evaluate nutritional balance. This allows the collection unit to record the worker's diet and fluid intake status, enabling more comprehensive health management.

[0094] The analysis unit can estimate the worker's emotions and adjust the accuracy of the analysis based on the estimated worker's emotions. For example, if the worker is feeling stressed, the analysis unit can analyze the heart rate and body temperature data in detail. If the worker is relaxed, the analysis unit can also analyze the respiratory rate and blood pressure data in detail. Furthermore, if the worker is excited, the analysis unit can also analyze the heart rate variability and electrodermal activity data in detail. This allows for more appropriate analysis by adjusting the accuracy of the analysis according to the worker's emotions.

[0095] When collecting the worker's biometric information, the collection unit can also record the worker's sleep status. For example, the collection unit can record the duration and quality of the worker's sleep to evaluate the worker's fatigue level. The collection unit can also record the worker's sleeping position to evaluate the worker's physical strain. Furthermore, the collection unit can record the environment in which the worker sleeps to evaluate the worker's sleep quality. In this way, the collection unit can record the worker's sleep status as well, enabling more comprehensive health management.

[0096] The analysis unit can combine and analyze the collected biometric information with the worker's past health data. For example, the analysis unit can compare the worker's past heart rate data with the current data and evaluate any abnormal trends. The analysis unit can also compare the worker's past body temperature data with the current data and evaluate any abnormal trends. Furthermore, the analysis unit can compare the worker's past blood pressure data with the current data and evaluate any abnormal trends. This allows the analysis unit to combine and analyze past health data, enabling more accurate safety assessments.

[0097] The warning unit can estimate the worker's emotions and customize the content of the warning based on the estimated worker's emotions. For example, if the worker is feeling stressed, the warning unit issues a warning including advice to relax. If the worker is relaxed, the warning unit can also issue a normal warning message. Furthermore, if the worker is excited, the warning unit can also issue a warning including advice to stay calm. In this way, by customizing the content of the warning according to the worker's emotions, it is possible to issue more effective warnings.

[0098] The countermeasure unit can estimate the worker's emotions and adjust the frequency of countermeasures based on the estimated worker's emotions. For example, if the worker is feeling stressed, the countermeasure unit can suggest frequent breaks. If the worker is relaxed, the countermeasure unit can also suggest breaks at a normal frequency. Furthermore, if the worker is excited, the countermeasure unit can also suggest breaks at a frequency that allows the worker to calm down. In this way, by adjusting the frequency of countermeasures according to the worker's emotions, more effective countermeasures can be taken.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The collection unit collects the worker's biometric information. The worker's biometric information includes heart rate, body temperature, blood pressure, etc. The collection unit collects the biometric information using a wristwatch-type or chest-worn wearable device. For example, the heart rate can be monitored in real time using a wristwatch-type wearable device, and the body temperature can be monitored in real time using a chest-worn wearable device. Blood pressure can also be monitored in real time using a blood pressure monitor. Step 2: The analysis unit uses the generation AI to analyze the biometric information collected by the collection unit and evaluate the worker's safety status. For example, it sets normal and abnormal ranges based on the collected heart rate data and issues a warning if an abnormality is detected. If the heart rate rises suddenly, the body temperature becomes abnormally high, or the blood pressure becomes abnormal, it will determine this as an abnormality and issue a warning. Step 3: The warning unit detects an abnormality based on the safety state evaluated by the analysis unit and issues a warning. For example, the warning can be communicated to the worker by issuing an audio alarm or vibration alarm. It can also be communicated to the worker by displaying a warning message. Step 4: The countermeasures department takes appropriate measures based on the warning issued by the warning department. For example, the countermeasures department can ensure the safety of the workers by instructing them to stop work. It can also ensure the safety of the workers by instructing them to take a break. It can also ensure the safety of the workers by encouraging them to contact a medical institution.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] 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).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] 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.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] 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).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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, in order to avoid confusion and to 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 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 collection unit for collecting biometric information of a worker; an analysis unit that analyzes the biological information collected by the collection unit and evaluates the safety state of the worker; a warning unit that detects an abnormality based on the safety state evaluated by the analysis unit and issues a warning; a countermeasure unit that takes appropriate measures based on the warning issued by the warning unit. A system characterized by:

2. The collecting unit Equipped with a wristwatch-type or chest-mounted wearable device 2. The system of claim 1.

3. The analysis unit Based on the collected data, normal and abnormal ranges are set, and an alert is issued if an abnormality is detected.

2. The system of claim 1.

4. The warning unit Sound or vibration alarm, display warning message 2. The system of claim 1.

5. The countermeasure unit Instruct workers to stop work, take a break, or contact a medical institution 2. The system of claim 1.

6. The collecting unit The emotion of the worker is estimated, and the frequency of collection of biometric information is adjusted based on the estimated emotion of the worker.

2. The system of claim 1.

7. The collecting unit Analyze the worker's past biometric information history and select the optimal collection method 2. The system of claim 1.

8. The collecting unit When collecting biometric information, filtering is performed based on the worker's current task and environment.

2. The system of claim 1.

9. The collecting unit Estimate the worker's emotions and determine the priority of the biometric information to be collected based on the estimated emotions.

2. The system of claim 1.

10. The collecting unit When collecting biometric information, the geographic location of the worker is taken into consideration to prioritize the collection of the most relevant information.

2. The system of claim 1.

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