System

Abstract

An object of the system according to the embodiment is to achieve efficiency of work and reduction of working hours in a health center for the elderly or a hospital.SOLUTION: A system includes a sensor, an abnormality detection unit, and an alert transmission unit. The sensor is installed in each room or hospital room. The abnormality detection unit detects an abnormality based on the biological information collected by the sensor. The alert transmission unit transmits an alert based on the abnormality detected by the abnormality detection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] With conventional technology, there was an issue that caregivers at elderly care facilities and hospitals were burdened with a heavy workload at night, and there was a need to improve work efficiency and reduce working hours.

[0005] The system according to the embodiment aims to improve the efficiency of work and reduce working hours in elderly care facilities and hospitals. [Means for solving the problem]

[0006] A system according to an embodiment includes a sensor, an anomaly detection unit, and an alert transmission unit. The sensor is installed in each room or hospital room. The anomaly detection unit detects an anomaly based on biological information collected by the sensor. The alert transmission unit transmits an alert based on the anomaly detected by the anomaly detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve work efficiency and reduce working hours in nursing homes and hospitals. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The anomaly detection system according to an embodiment of the present invention uses sensors to monitor each individual's biological information, such as their movements, body temperature, and respiratory rate, and issues an alert when it detects an abnormal condition. This eliminates the need for caregivers and medical staff to make night rounds, thereby improving work efficiency and reducing working hours.

[0029] An anomaly detection system according to an embodiment includes a sensor, an anomaly detection unit, and an alert transmission unit. The sensor is installed in each room or hospital room and collects biometric information such as an individual's movements, body temperature, and respiratory rate. For example, the sensor is installed around a bed and detects when the individual leaves the bed or abnormal movements. The sensor also constantly monitors the individual's body temperature using a body temperature sensor. The sensor also measures the rhythm and rate of the individual's breathing using a respiratory rate sensor. For example, the sensor is installed around the bed and detects when the individual leaves the bed or abnormal movements. The body temperature sensor may be, for example, a skin-adhered type or a non-contact type. The respiratory rate sensor may be, for example, a chest-worn type or a non-contact type. The anomaly detection unit detects an abnormality based on the biometric information collected by the sensor. For example, the anomaly detection unit detects an abnormality when the body temperature exceeds a normal range or when the respiratory rate suddenly changes. The anomaly detection unit also detects an abnormality when the motion sensor detects abnormal movements. The alert transmission unit transmits an alert based on the abnormality detected by the anomaly detection unit. For example, the alert issuing unit issues an alert by a method such as a voice alert, a vibration alert, a notification message, etc. As a result, the anomaly detection system according to the embodiment eliminates the need for caregivers and medical staff to make night rounds, thereby improving work efficiency and reducing working hours.

[0030] The sensors are installed around the bed and can detect when an individual leaves the bed or abnormal movements. The sensors are installed, for example, around the bed and can detect when an individual leaves the bed or abnormal movements. For example, sensors are installed at the head, foot, or side of the bed. Examples of abnormal movements include falling or long periods of inactivity. This allows for rapid detection of abnormal movements of an individual.

[0031] The sensor can constantly monitor an individual's body temperature using a body temperature sensor. For example, body temperature sensors can be attached to the skin or non-contact types. Constant monitoring can be achieved by measuring every minute and transmitting the data in real time. This allows constant monitoring of an individual's body temperature.

[0032] The sensor can measure the rhythm and frequency of an individual's breathing using a respiratory rate sensor. The sensor measures the rhythm and frequency of an individual's breathing using, for example, a respiratory rate sensor. For example, respiratory rate sensors are available in chest-worn types and non-contact types. Examples of respiratory rhythm and frequency include the number of breaths per minute and the depth of breathing. This allows the rhythm and frequency of an individual's breathing to be measured.

[0033] The abnormality detection unit can issue an alert if the body temperature exceeds the normal range or if the respiratory rate changes suddenly. The abnormality detection unit issues an alert, for example, if the body temperature exceeds the normal range or if the respiratory rate changes suddenly. Normal ranges include, for example, a body temperature of 36.5°C to 37.5°C and a respiratory rate of 12 to 20 breaths per minute. An example of a sudden change is a fluctuation in the respiratory rate of 10 or more breaths per minute. This allows abnormalities in body temperature or respiratory rate to be quickly detected and an alert to be issued.

[0034] The abnormality detection unit can issue an alert when the motion sensor detects abnormal motion. The abnormality detection unit issues an alert, for example, when the motion sensor detects abnormal motion. Examples of motion sensors include infrared sensors and acceleration sensors. Examples of abnormal motion include falling or long periods of inactivity. This makes it possible to quickly detect abnormal motion and issue an alert.

[0035] Instead of patrolling each room at night, the system responds only when an abnormality is detected, thereby significantly reducing the amount of labor required. For example, instead of patrolling each room at night, the system responds only when an abnormality is detected, thereby significantly reducing the amount of labor required. Nighttime hours could be from 10 PM to 6 AM. Patrols could be performed every hour or on a specific route, for example. Labor reductions could include reducing patrol time and reducing the burden on staff. This could significantly reduce the amount of labor required at night.

[0036] The system can reduce working hours by responding only when an alert is issued. The system can reduce working hours by responding only when an alert is issued. Examples of alerts include voice alerts, vibration alerts, and notification messages. Examples of reducing working hours include reducing working hours per day or working hours per week. This can reduce working hours.

[0037] The system stores collected biometric information in a database and can perform long-term analysis. The system stores collected biometric information in a database and can perform long-term analysis. Examples of databases include SQL databases and NoSQL databases. Data storage can be performed, for example, by storing data in real time or daily. Long-term analysis can be performed, for example, by analyzing data over a year or by trend analysis. This makes it possible to understand changes in an individual's health condition through long-term analysis.

[0038] The system can analyze changes in body temperature or respiratory rate over the long term and use this information to detect or prevent illnesses early.The system can analyze changes in body temperature or respiratory rate over the long term and use this information to detect or prevent illnesses early.Examples of early detection of illness include methods to detect specific symptoms before they appear and preventive diagnosis.Examples of prevention include improving lifestyle habits and regular health checks.This can be used to detect or prevent illnesses early.

[0039] An algorithm that dynamically optimizes sensor installation locations can be introduced, making it possible to automatically determine the optimal placement based on the layout of each room or hospital room. To dynamically optimize sensor installation locations, an algorithm is introduced that collects room or hospital room layout information and automatically calculates the optimal sensor placement. For example, the optimal location for motion sensors and body temperature sensors can be determined taking into account the shape of the room and the layout of furniture. Possible installation locations include the center of the room or the head of the bed. Examples of dynamic optimization include machine learning algorithms and real-time adjustments. By optimizing sensor installation locations, the accuracy of anomaly detection can be improved.

[0040] A self-learning function can be added that provides real-time feedback on data detected by sensors, thereby successively improving collection accuracy. A self-learning function can be introduced that provides real-time feedback on biological information collected by sensors, thereby improving collection accuracy. For example, the sensitivity and measurement range of a sensor can be automatically adjusted based on data detected by a body temperature sensor. Examples of real-time feedback include updating data every second and providing real-time notifications. Examples of feedback include real-time feedback of data and alert feedback. Examples of self-learning functions include machine learning algorithms and sequential learning of data. This allows for successive improvements in collection accuracy, thereby improving the accuracy of anomaly detection.

[0041] The system aggregates sensor data in the cloud and shares the data among multiple facilities, enabling extensive data analysis. A system is being built that aggregates data collected by sensors in the cloud and shares the data among multiple facilities. For example, data from each facility can be centrally managed to improve the accuracy of anomaly detection. Examples of clouds include AWS, Google Cloud, and Azure. Examples of data sharing between facilities include multiple facilities within the same corporation or facilities of different corporations. Examples of data sharing include real-time data sharing and periodic data sharing. Examples of extensive data analysis include big data analysis and the application of machine learning algorithms. This enables extensive data analysis by aggregating data in the cloud and sharing it among multiple facilities.

[0042] The anomaly detection unit incorporates a predictive model based on past data and can issue a preventative alert before an abnormality occurs. A predictive model based on past data is incorporated into the abnormality detection algorithm to build a system that issues a preventative alert before an abnormality occurs. For example, past body temperature and respiratory rate data is analyzed to predict signs of an abnormality. Past data includes, for example, data from the past year or data from specific events. Predictive models include, for example, machine learning models and statistical models. One preventative alert method is to detect signs of an abnormality before it occurs and issue a preventative notification. This enables early response by issuing a preventative alert before an abnormality occurs.

[0043] The alert sending unit can diversify the alert sending method and notify in the most appropriate way depending on the situation, such as a voice alert or a vibration alert. The alert sending method is diversified and a voice alert is introduced. For example, if an abnormality is detected, a voice alert is sent to prompt a caregiver or medical staff to take immediate action. Examples of voice alerts include a voice message or a warning sound. Examples of vibration alerts include device vibration or a vibration pattern. Examples of the most appropriate way depending on the situation include a vibration alert at night and a voice alert during the day. This allows for a prompt response by notifying the alert in the most appropriate way depending on the situation.

[0044] The anomaly detection unit can integrate data from multiple sensors to achieve more accurate anomaly detection. A system is constructed that integrates data from multiple sensors to improve the accuracy of detecting abnormal conditions. For example, data from a motion sensor, a body temperature sensor, and a respiratory rate sensor is integrated to detect anomalies. Data integration includes, for example, integrating data from multiple sensors in real time and data preprocessing methods. High-accuracy anomaly detection includes, for example, improving the accuracy of the anomaly detection algorithm and reducing the false detection rate. As a result, integrating data from multiple sensors enables more accurate anomaly detection.

[0045] The alert sending unit can automate the response process after an alert is sent and introduce robots or drones to quickly take the necessary action. The response process after an alert is sent can be automated and a robot can be introduced to build a system for rapid response. For example, if an abnormality is detected, a robot will rush to the site and check the situation. Examples of automated response processes include automatic response procedures after an alert is sent and operation programs for robots and drones. Examples of rapid responses include the time from alert sending to response and response priority. This makes it possible to quickly respond by automating the response process after an alert is sent.

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

[0047] The anomaly detection system further includes a voice recognition unit that can monitor changes in an individual's voice. For example, it can detect changes in the tone or rhythm of the voice to detect anomalies. Examples of voice changes include hoarseness, sudden changes in voice pitch, and slowing of speaking speed. This makes it possible to detect anomalies through changes in voice.

[0048] The anomaly detection system is further equipped with environmental sensors that can monitor the temperature, humidity, and illuminance of a room. For example, an anomaly is detected when the room temperature rises suddenly or the humidity becomes abnormally high. Examples of environmental sensors include temperature sensors, humidity sensors, and illuminance sensors. This makes it possible to detect anomalies through changes in the environment.

[0049] The anomaly detection system also includes a location information acquisition unit, which can grasp an individual's location information in real time. For example, a GPS can be used to identify an individual's current location and detect abnormal movements. Location information can include, for example, when an individual leaves a room or when an individual remains in a specific area for an extended period of time. This makes it possible to detect anomalies through location information.

[0050] The anomaly detection system further includes a meal monitoring unit that can grasp an individual's dietary intake status. For example, it can monitor the amount and frequency of meals and detect abnormalities. Meal monitoring can be done, for example, by analyzing photos of meals or measuring the weight of meals. This makes it possible to detect abnormalities through dietary intake status.

[0051] The anomaly detection system further includes a sleep monitoring unit that can grasp an individual's sleep patterns. For example, the system can monitor the quality and duration of sleep to detect abnormalities. Sleep monitoring can be performed, for example, by analyzing changes in heart rate and breathing rate or by using a motion sensor. This allows abnormalities to be detected through sleep patterns.

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

[0053] Step 1: Sensors are installed in each room or patient room to collect biometric information such as an individual's movements, body temperature, and respiratory rate. For example, sensors are installed around a bed to detect when an individual leaves the bed or any abnormal movements. In addition, a body temperature sensor constantly monitors an individual's body temperature, and a respiratory rate sensor measures the rhythm and rate of an individual's breathing. Body temperature sensors are available in skin-attached and non-contact types, while respiratory rate sensors are available in chest-worn and non-contact types. Step 2: The anomaly detection unit detects anomalies based on the biological information collected by the sensors. For example, anomalies are detected when body temperature exceeds the normal range, when breathing rate changes suddenly, or when the movement sensor detects abnormal movement. Step 3: The alert issuing unit issues an alert based on the abnormality detected by the abnormality detection unit, for example, by audio alert, vibration alert, notification message, or the like.

[0054] (Example 2) The anomaly detection system according to an embodiment of the present invention uses sensors to monitor each individual's biological information, such as their movements, body temperature, and respiratory rate, and issues an alert when it detects an abnormal condition. This eliminates the need for caregivers and medical staff to make night rounds, thereby improving work efficiency and reducing working hours.

[0055] An anomaly detection system according to an embodiment includes a sensor, an anomaly detection unit, and an alert transmission unit. The sensor is installed in each room or hospital room and collects biometric information such as an individual's movements, body temperature, and respiratory rate. For example, the sensor is installed around a bed and detects when the individual leaves the bed or abnormal movements. The sensor also constantly monitors the individual's body temperature using a body temperature sensor. The sensor also measures the rhythm and rate of the individual's breathing using a respiratory rate sensor. For example, the sensor is installed around the bed and detects when the individual leaves the bed or abnormal movements. The body temperature sensor may be, for example, a skin-adhered type or a non-contact type. The respiratory rate sensor may be, for example, a chest-worn type or a non-contact type. The anomaly detection unit detects an abnormality based on the biometric information collected by the sensor. For example, the anomaly detection unit detects an abnormality when the body temperature exceeds a normal range or when the respiratory rate suddenly changes. The anomaly detection unit also detects an abnormality when the motion sensor detects abnormal movements. The alert transmission unit transmits an alert based on the abnormality detected by the anomaly detection unit. For example, the alert issuing unit issues an alert by a method such as a voice alert, a vibration alert, a notification message, etc. As a result, the anomaly detection system according to the embodiment eliminates the need for caregivers and medical staff to make night rounds, thereby improving work efficiency and reducing working hours.

[0056] The sensors are installed around the bed and can detect when an individual leaves the bed or abnormal movements. The sensors are installed, for example, around the bed and can detect when an individual leaves the bed or abnormal movements. For example, sensors are installed at the head, foot, or side of the bed. Examples of abnormal movements include falling or long periods of inactivity. This allows for rapid detection of abnormal movements of an individual.

[0057] The sensor can constantly monitor an individual's body temperature using a body temperature sensor. For example, body temperature sensors can be attached to the skin or non-contact types. Constant monitoring can be achieved by measuring every minute and transmitting the data in real time. This allows constant monitoring of an individual's body temperature.

[0058] The sensor can measure the rhythm and frequency of an individual's breathing using a respiratory rate sensor. The sensor measures the rhythm and frequency of an individual's breathing using, for example, a respiratory rate sensor. For example, respiratory rate sensors are available in chest-worn types and non-contact types. Examples of respiratory rhythm and frequency include the number of breaths per minute and the depth of breathing. This allows the rhythm and frequency of an individual's breathing to be measured.

[0059] The abnormality detection unit can issue an alert if the body temperature exceeds the normal range or if the respiratory rate changes suddenly. The abnormality detection unit issues an alert, for example, if the body temperature exceeds the normal range or if the respiratory rate changes suddenly. Normal ranges include, for example, a body temperature of 36.5°C to 37.5°C and a respiratory rate of 12 to 20 breaths per minute. An example of a sudden change is a fluctuation in the respiratory rate of 10 or more breaths per minute. This allows abnormalities in body temperature or respiratory rate to be quickly detected and an alert to be issued.

[0060] The abnormality detection unit can issue an alert when the motion sensor detects abnormal motion. The abnormality detection unit issues an alert, for example, when the motion sensor detects abnormal motion. Examples of motion sensors include infrared sensors and acceleration sensors. Examples of abnormal motion include falling or long periods of inactivity. This makes it possible to quickly detect abnormal motion and issue an alert.

[0061] Instead of patrolling each room at night, the system responds only when an abnormality is detected, thereby significantly reducing the amount of labor required. For example, instead of patrolling each room at night, the system responds only when an abnormality is detected, thereby significantly reducing the amount of labor required. Nighttime hours could be from 10 PM to 6 AM. Patrols could be performed every hour or on a specific route, for example. Labor reductions could include reducing patrol time and reducing the burden on staff. This could significantly reduce the amount of labor required at night.

[0062] The system can reduce working hours by responding only when an alert is issued. The system can reduce working hours by responding only when an alert is issued. Examples of alerts include voice alerts, vibration alerts, and notification messages. Examples of reducing working hours include reducing working hours per day or working hours per week. This can reduce working hours.

[0063] The system stores collected biometric information in a database and can perform long-term analysis. The system stores collected biometric information in a database and can perform long-term analysis. Examples of databases include SQL databases and NoSQL databases. Data storage can be performed, for example, by storing data in real time or daily. Long-term analysis can be performed, for example, by analyzing data over a year or by trend analysis. This makes it possible to understand changes in an individual's health condition through long-term analysis.

[0064] The system can analyze changes in body temperature or respiratory rate over the long term and use this information to detect or prevent illnesses early.The system can analyze changes in body temperature or respiratory rate over the long term and use this information to detect or prevent illnesses early.Examples of early detection of illness include methods to detect specific symptoms before they appear and preventive diagnosis.Examples of prevention include improving lifestyle habits and regular health checks.This can be used to detect or prevent illnesses early.

[0065] An algorithm that dynamically optimizes sensor installation locations can be introduced, making it possible to automatically determine the optimal placement based on the layout of each room or hospital room. To dynamically optimize sensor installation locations, an algorithm is introduced that collects room or hospital room layout information and automatically calculates the optimal sensor placement. For example, the optimal location for motion sensors and body temperature sensors can be determined taking into account the shape of the room and the layout of furniture. Possible installation locations include the center of the room or the head of the bed. Examples of dynamic optimization include machine learning algorithms and real-time adjustments. By optimizing sensor installation locations, the accuracy of anomaly detection can be improved.

[0066] A self-learning function can be added that provides real-time feedback on data detected by sensors, thereby successively improving collection accuracy. A self-learning function can be introduced that provides real-time feedback on biological information collected by sensors, thereby improving collection accuracy. For example, the sensitivity and measurement range of a sensor can be automatically adjusted based on data detected by a body temperature sensor. Examples of real-time feedback include updating data every second and providing real-time notifications. Examples of feedback include real-time feedback of data and alert feedback. Examples of self-learning functions include machine learning algorithms and sequential learning of data. This allows for successive improvements in collection accuracy, thereby improving the accuracy of anomaly detection.

[0067] An individual's emotional state can be estimated from biometric information collected by sensors, and abnormalities can be detected based on changes in emotions. An algorithm is introduced to estimate an individual's emotional state based on the biometric information collected by sensors. For example, changes in body temperature and respiratory rate can be analyzed to estimate emotional states such as stress and anxiety. Examples of emotional states include stress levels and happiness levels. Examples of emotional changes include fluctuations in stress levels and changes in happiness levels. This allows for more accurate anomaly detection by detecting abnormalities based on changes in emotions.

[0068] The system aggregates sensor data in the cloud and shares the data among multiple facilities, enabling extensive data analysis. A system is being built that aggregates data collected by sensors in the cloud and shares the data among multiple facilities. For example, data from each facility can be centrally managed to improve the accuracy of anomaly detection. Examples of clouds include AWS, Google Cloud, and Azure. Examples of data sharing between facilities include multiple facilities within the same corporation or facilities of different corporations. Examples of data sharing include real-time data sharing and periodic data sharing. Examples of extensive data analysis include big data analysis and the application of machine learning algorithms. This enables extensive data analysis by aggregating data in the cloud and sharing it among multiple facilities.

[0069] The system introduces a wearable device equipped with an emotion estimation function, enabling real-time monitoring of an individual's emotional state. A system for real-time monitoring of an individual's emotional state is constructed by introducing a wearable device equipped with an emotion estimation function. For example, a smartwatch or fitness tracker is used to measure heart rate and body temperature and estimate the emotional state. Emotion estimation functions include, for example, facial expression recognition and voice analysis. Wearable devices include, for example, smartwatches and fitness trackers. Real-time methods include, for example, updating data every second and providing real-time notifications. Monitoring methods include, for example, continuous monitoring and periodic monitoring. This makes it possible to monitor an individual's emotional state in real time using a wearable device equipped with an emotion estimation function.

[0070] The anomaly detection unit incorporates a predictive model based on past data and can issue a preventative alert before an abnormality occurs. A predictive model based on past data is incorporated into the abnormality detection algorithm to build a system that issues a preventative alert before an abnormality occurs. For example, past body temperature and respiratory rate data is analyzed to predict signs of an abnormality. Past data includes, for example, data from the past year or data from specific events. Predictive models include, for example, machine learning models and statistical models. One preventative alert method is to detect signs of an abnormality before it occurs and issue a preventative notification. This enables early response by issuing a preventative alert before an abnormality occurs.

[0071] The alert sending unit can diversify the alert sending method and notify in the most appropriate way depending on the situation, such as a voice alert or a vibration alert. The alert sending method is diversified and a voice alert is introduced. For example, if an abnormality is detected, a voice alert is sent to prompt a caregiver or medical staff to take immediate action. Examples of voice alerts include a voice message or a warning sound. Examples of vibration alerts include device vibration or a vibration pattern. Examples of the most appropriate way depending on the situation include a vibration alert at night and a voice alert during the day. This allows for a prompt response by notifying the alert in the most appropriate way depending on the situation.

[0072] Using the emotion estimation function, it is possible to consider an individual's emotional state when an alert is issued and suggest countermeasures to reduce stress. Using the emotion estimation function, we have built a system that considers an individual's emotional state when an alert is issued. For example, if an alert is issued when the individual is in a high-stress state, suggestions for relaxation can be made. Countermeasures to reduce stress include, for example, suggesting relaxation methods or counseling. By considering an individual's emotional state when an alert is issued, it is possible to suggest countermeasures to reduce stress.

[0073] The anomaly detection unit can integrate data from multiple sensors to achieve more accurate anomaly detection. A system is constructed that integrates data from multiple sensors to improve the accuracy of detecting abnormal conditions. For example, data from a motion sensor, a body temperature sensor, and a respiratory rate sensor is integrated to detect anomalies. Data integration includes, for example, integrating data from multiple sensors in real time and data preprocessing methods. High-accuracy anomaly detection includes, for example, improving the accuracy of the anomaly detection algorithm and reducing the false detection rate. As a result, integrating data from multiple sensors enables more accurate anomaly detection.

[0074] The alert sending unit can automate the response process after an alert is sent and introduce robots or drones to quickly take the necessary action. The response process after an alert is sent can be automated and a robot can be introduced to build a system for rapid response. For example, if an abnormality is detected, a robot will rush to the site and check the situation. Examples of automated response processes include automatic response procedures after an alert is sent and operation programs for robots and drones. Examples of rapid responses include the time from alert sending to response and response priority. This makes it possible to quickly respond by automating the response process after an alert is sent.

[0075] Using the emotion estimation function, it is possible to provide customized responses according to an individual's emotional state when responding to an alert. Using the emotion estimation function, we have built a system that takes an individual's emotional state into account when responding to an alert. For example, if an alert is issued when the individual is under high stress, suggestions for relaxation can be made. Customized responses could include relaxation methods or specific action plans tailored to the individual's emotional state. This allows for more appropriate responses by providing customized responses according to the individual's emotional state when responding to an alert.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The anomaly detection system further includes a voice recognition unit that can monitor changes in an individual's voice. For example, it can detect changes in the tone or rhythm of the voice to detect anomalies. Examples of voice changes include hoarseness, sudden changes in voice pitch, and slowing of speaking speed. This makes it possible to detect anomalies through changes in voice.

[0078] The anomaly detection system is further equipped with environmental sensors that can monitor the temperature, humidity, and illuminance of a room. For example, an anomaly is detected when the room temperature rises suddenly or the humidity becomes abnormally high. Examples of environmental sensors include temperature sensors, humidity sensors, and illuminance sensors. This makes it possible to detect anomalies through changes in the environment.

[0079] The anomaly detection system also includes a location information acquisition unit, which can grasp an individual's location information in real time. For example, a GPS can be used to identify an individual's current location and detect abnormal movements. Location information can include, for example, when an individual leaves a room or when an individual remains in a specific area for an extended period of time. This makes it possible to detect anomalies through location information.

[0080] The anomaly detection system further includes a meal monitoring unit that can grasp an individual's dietary intake status. For example, it can monitor the amount and frequency of meals and detect abnormalities. Meal monitoring can be done, for example, by analyzing photos of meals or measuring the weight of meals. This makes it possible to detect abnormalities through dietary intake status.

[0081] The anomaly detection system further includes a sleep monitoring unit that can grasp an individual's sleep patterns. For example, the system can monitor the quality and duration of sleep to detect abnormalities. Sleep monitoring can be performed, for example, by analyzing changes in heart rate and breathing rate or by using a motion sensor. This allows abnormalities to be detected through sleep patterns.

[0082] The anomaly detection system uses emotion estimation functionality to monitor an individual's emotional state in real time and detect anomalies. For example, it analyzes stress levels and anxiety levels to detect anomalies. Examples of emotional states include an increase in stress levels and sudden changes in anxiety levels. This makes it possible to detect anomalies through emotional states.

[0083] The anomaly detection system can use emotion estimation to prioritize alerts based on an individual's emotional state. For example, if an anomaly is detected during a high-stress state, the system can set the priority of the alert high. Examples of emotional states include stress level and happiness level. This allows for a rapid response by prioritizing alerts based on the individual's emotional state.

[0084] The anomaly detection system can use its emotion estimation function to customize alert content according to an individual's emotional state. For example, if an anomaly is detected when the individual is under high stress, the system can make suggestions for relaxation. Examples of emotional states include stress level and anxiety level. This allows for customizing alert content according to the individual's emotional state, enabling more appropriate responses.

[0085] The anomaly detection system can use the emotion estimation function to identify the cause of an anomaly based on an individual's emotional state. For example, if an anomaly is detected during a high-stress state, it suggests that stress may be the cause. Examples of emotional states include stress level and happiness level. This allows for more accurate anomaly detection by identifying the cause of an anomaly based on the emotional state.

[0086] The anomaly detection system can use its emotion estimation function to suggest preventive measures for anomalies based on an individual's emotional state. For example, if an anomaly is detected when an individual is in a high-stress state, the system can suggest ways to reduce stress. Examples of emotional states include stress level and anxiety level. By suggesting preventive measures for anomalies based on the individual's emotional state, the system can prevent anomalies from occurring in the first place.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: Sensors are installed in each room or patient room to collect biometric information such as an individual's movements, body temperature, and respiratory rate. For example, sensors are installed around a bed to detect when an individual leaves the bed or any abnormal movements. In addition, a body temperature sensor constantly monitors an individual's body temperature, and a respiratory rate sensor measures the rhythm and rate of an individual's breathing. Body temperature sensors are available in skin-attached and non-contact types, while respiratory rate sensors are available in chest-worn and non-contact types. Step 2: The anomaly detection unit detects anomalies based on the biological information collected by the sensors. For example, anomalies are detected when body temperature exceeds the normal range, when breathing rate changes suddenly, or when the movement sensor detects abnormal movement. Step 3: The alert issuing unit issues an alert based on the abnormality detected by the abnormality detection unit, for example, by audio alert, vibration alert, notification message, or the like.

[0089] 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.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] 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.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The 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.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0121] The data processing system 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0137] The data processing system 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0155] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0156] 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. Sensors installed in each room and patient room, an abnormality detection unit that detects an abnormality based on the biological information collected by the sensor; an alert issuing unit that issues an alert based on the abnormality detected by the abnormality detection unit. A system characterized by:

2. The sensor They are placed around the bed and detect when an individual leaves the bed or when there is abnormal movement. The system of claim 1 .

3. The system comprises: Expanding the types of sensors and collecting environmental information such as sound or smell will improve the accuracy of anomaly detection. The system of claim 1 .

4. The abnormality detection unit Incorporates predictive models based on historical data to proactively alert you to anomalies before they occur. The system of claim 1 .

5. The alert sending unit Automate the response process after an alert is issued and deploy robots and drones to expedite the necessary response. The system of claim 1 .

6. The emotional state of the individual is estimated from the biometric information collected by the sensor, and abnormalities are detected based on changes in the individual's emotions. The system of claim 1 .

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