System

Abstract

An object of a system according to an embodiment is to efficiently collect and analyze health data of a patient and to appropriately notify a nurse.SOLUTION: A system includes a health data collection unit, an analysis unit, a notification unit, and an interaction unit. The health data collection unit collects health data of a patient. The analysis unit analyzes the health data collected by the health data collection unit. The notification part notifies a nurse of a result analyzed by the analysis part. The interaction unit supports an interaction between a nurse and a patient.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not efficiently collect and analyze patient health data and adequately notify nurses, so there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect and analyze patient health data and notify nurses appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes a health data collection unit, an analysis unit, a notification unit, and a dialogue unit. The health data collection unit collects health data of patients. The analysis unit analyzes the health data collected by the health data collection unit. The notification unit notifies the nurse of the results of the analysis by the analysis unit. The dialogue unit supports dialogue between the nurse and the patient. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and analyze patient health data and appropriately notify nurses. [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 medical care support system according to an embodiment of the present invention automatically collects and analyzes patient health data, notifies nurses, and allows nurses to provide medical care through direct dialogue with patients. This allows the medical care support system to continuously monitor patient health data and provide personalized care from nurses.

[0029] The medical care support system according to the embodiment includes a health data collection unit, an analysis unit, a notification unit, and a dialogue unit. The health data collection unit collects health data from a patient. For example, it monitors vital signs such as heart rate, blood pressure, body temperature, and blood glucose level. The health data collection unit can also collect data naturally in daily life using a wearable device. The health data collection unit can also collect data by incorporating the patient's past medical history and genetic information. For example, it can continuously monitor the patient's heart rate and notify a nurse if an abnormality is detected. The analysis unit analyzes the health data collected by the health data collection unit. For example, it can analyze the data using statistical analysis or machine learning algorithms to predict the patient's health condition. The analysis unit can also analyze the patient's emotional state using an emotion estimation function to evaluate the impact of stress and anxiety on health. The notification unit notifies the nurse of the results of the analysis by the analysis unit. For example, it can send an alert or message to notify the nurse of an abnormality. The notification unit can also store the monitoring data in the cloud so that the patient can check their health condition at any time. The dialogue unit supports dialogue between nurses and patients. For example, the generation AI analyzes the content of the conversation in real time and suggests appropriate advice and questions. The dialogue unit can also record the care provided by the nurse and share it with other medical staff. As a result, the medical care support system according to the embodiment can provide appropriate medical care by collecting and analyzing patient health data and notifying the nurse. For example, because the patient's health condition is constantly monitored, any abnormalities can be dealt with promptly. Furthermore, direct care by the nurse provides patients with a sense of security.

[0030] The health data collection unit can monitor a patient's vital signs and notify a nurse if an abnormality is detected. The health data collection unit continuously monitors a patient's vital signs, such as heart rate, blood pressure, body temperature, and blood sugar level. For example, if the heart rate suddenly increases or blood pressure shows an abnormal value, the health data collection unit notifies the nurse. In addition, the health data collection unit can issue an alert if an abnormality is detected, prompting the nurse to take prompt action. This allows for early response by quickly detecting abnormalities in vital signs and notifying the nurse.

[0031] The health data collection unit incorporates a patient's past medical history and genetic information to make more accurate predictions of their health condition. For example, the health data collection unit obtains a patient's past medical history from an electronic medical record, and AI analyzes that data to use it to predict their health condition. For example, it predicts future health risks based on past medical history and treatment history. The health data collection unit also incorporates a patient's genetic information to make health condition predictions that take genetic risk factors into account. For example, it predicts the risk of a specific disease based on gene mutations and family history. In this way, by utilizing past medical history and genetic information, it becomes possible to make more accurate predictions of health condition.

[0032] The health data collection unit can utilize a wearable device to enable patients to provide data naturally in their daily lives. For example, the health data collection unit continuously collects vital signs such as heart rate, blood pressure, and body temperature using a wearable device worn by the patient on a daily basis. This allows the patient to provide data without any special operations. The wearable device can also collect data such as the patient's activity level and sleep patterns. For example, a smartwatch or fitness tracker can be used to collect data naturally in daily life. Furthermore, the wearable device can collect the patient's location information, enabling rapid response in emergencies. This allows the patient to provide data naturally by utilizing the wearable device.

[0033] The health data collection unit can build a platform for sharing data between different medical institutions and comprehensively evaluating a patient's health status. The health data collection unit, for example, builds a platform for sharing patient health data between different medical institutions and performs comprehensive health evaluations. For example, it integrates electronic medical record systems to achieve centralized data management. The platform can also implement data access control and security measures to protect patient privacy. Furthermore, the platform allows experts from different medical institutions to collaborate to evaluate a patient's health status and propose optimal treatment plans. This makes it possible to share data between different medical institutions and perform comprehensive health evaluations.

[0034] In the dialogue unit, when a nurse converses with a patient, the generation AI analyzes the conversation content in real time and suggests appropriate advice and questions. For example, when a nurse converses with a patient, the generation AI analyzes the conversation content in real time and suggests appropriate advice and questions. For example, it automatically generates questions based on the patient's symptoms. The generation AI can also provide appropriate advice based on the patient's health condition. For example, it can provide advice on diet and exercise. Furthermore, the generation AI can analyze the patient's emotional state and give advice to provide psychological support. This allows the generation AI to suggest appropriate advice and questions when a nurse converses with a patient, improving the quality of the dialogue.

[0035] The dialogue unit collects patient feedback to evaluate the effectiveness of the care provided by nurses, and the generation AI can analyze the data and suggest areas for improvement. For example, the dialogue unit collects patient feedback to evaluate the effectiveness of the care provided by nurses, and the generation AI analyzes the data and suggests areas for improvement. For example, it evaluates patient satisfaction and the degree of improvement in symptoms. The generation AI can also make specific suggestions for improving care based on the feedback data. For example, it makes suggestions for adjusting the method or timing of care. Furthermore, the dialogue unit can share the feedback data with other medical staff to improve the quality of care across the entire team. This makes it possible to evaluate the effectiveness of the care provided by nurses and suggest areas for improvement, thereby improving the quality of care.

[0036] The dialogue unit records the content of care provided by nurses and shares it with other medical staff, thereby improving the quality of care across the entire team. For example, the dialogue unit builds a system that records the detailed content of care provided by nurses and shares it with other medical staff. For example, the care content is entered into an electronic medical record and the information is shared across the entire team. In addition, when recording the care content, the dialogue unit uses a generative AI to automatically summarize it, allowing for efficient information sharing. Furthermore, the dialogue unit can analyze the care content and suggest areas for improvement. In this way, by recording the care content and sharing it with other medical staff, the quality of care across the entire team can be improved.

[0037] The dialogue unit can integrate video call and chat functions so that nurses can provide care to patients in remote locations. The dialogue unit, for example, builds a system that integrates video call and chat functions so that nurses can provide care to patients in remote locations. For example, online medical consultations and remote counseling can be performed. The dialogue unit can also use the video call and chat functions to monitor patients' health conditions in real time and provide necessary care. Furthermore, the dialogue unit can also provide an interface to facilitate communication with patients in remote locations. As a result, care can be provided to patients in remote locations by integrating the video call and chat functions.

[0038] The analysis unit can analyze monitoring data and develop algorithms to understand long-term trends in a patient's health condition. For example, the analysis unit collects monitoring data over a long period of time and uses AI to analyze the data to develop algorithms that understand health trends. For example, it analyzes long-term fluctuations in heart rate and blood pressure. The analysis unit can also adjust the patient's health management plan based on the results of trend analysis. For example, it can provide advice on diet and exercise. Furthermore, the analysis unit can notify nurses of the results of trend analysis and provide information to provide appropriate care. This enables more effective health management by analyzing monitoring data and understanding long-term trends in health conditions.

[0039] The analysis unit allows the generation AI to automatically update the health management plan based on the monitoring data and notify the nurse. The analysis unit, for example, builds a system in which the generation AI automatically updates the health management plan based on the monitoring data and notifies the nurse. For example, the plan is adjusted according to fluctuations in vital signs. The generation AI can also propose an optimal health management plan based on the patient's lifestyle and health condition. For example, it can provide advice on diet and exercise. Furthermore, the generation AI can notify the nurse of the results of the health management plan updates and provide information to provide appropriate care. This allows the health management plan to be automatically updated based on the monitoring data and notified to the nurse, enabling more appropriate care to be provided.

[0040] The notification unit can store monitoring data in the cloud, allowing patients to check their health status at any time. The notification unit, for example, stores monitoring data in the cloud and builds a system that allows patients to check their health status at any time. For example, the data can be accessed through a smartphone app. The notification unit can also share the data stored in the cloud with nurses and medical staff to conduct comprehensive health assessments. Furthermore, the notification unit can use the generative AI to propose health management plans based on the data stored in the cloud. This makes self-management easier by storing monitoring data in the cloud and allowing patients to check their health status at any time.

[0041] The notification unit can integrate the monitoring data with other medical data to perform a comprehensive health assessment. The notification unit, for example, can integrate the monitoring data with other medical data to build a system that performs a comprehensive health assessment. For example, it can integrate test results and diagnostic information to evaluate health status. The notification unit can also use the generative AI to propose a health management plan based on the integrated data. For example, it can provide advice on diet and exercise. Furthermore, the notification unit can share the integrated data with nurses and medical staff to perform a comprehensive health assessment. This makes it possible to perform a comprehensive health assessment by integrating the monitoring data with other medical data.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The medical care support system can further include an environmental data collection unit that collects data on the patient's living environment. For example, it can monitor the temperature, humidity, noise level, etc. of the patient's living environment to identify factors that affect their health condition. The environmental data collection unit can also record the patient's lifestyle habits (diet, exercise, sleep patterns) and analyze them in conjunction with health data. Furthermore, the environmental data collection unit can make suggestions for improving the patient's living environment. For example, it can suggest appropriate temperature and humidity settings and noise reduction measures. This allows for a comprehensive evaluation of the patient's living environment and contributes to improving their health condition.

[0044] The medical care support system can further include a data sharing unit that anonymizes patient health data and shares it with research institutions. For example, with the patient's consent, the health data can be anonymized and provided to the research institution. The data sharing unit can also receive feedback from the research institution and use it to improve the system. Furthermore, the data sharing unit can notify patients of the progress and results of research, raising their awareness of health management. This makes it possible to utilize patient health data to advance medical research and also to help improve the system.

[0045] The health data collection unit may further include a dietary data collection unit that collects dietary data of the patient. For example, the contents and calories of the meals eaten by the patient may be recorded and the data may be integrated with the health data for analysis. The dietary data collection unit may also analyze the patient's dietary patterns and make suggestions for improving nutritional balance. Furthermore, the dietary data collection unit may record allergy information related to the patient's meals and make suggestions for reducing the risk of allergic reactions. In this way, the collection and analysis of the patient's dietary data can contribute to improving the patient's health.

[0046] The medical care support system can further include an exercise data collection unit that collects patient exercise data. For example, the system records the type and duration of exercise performed by the patient, as well as the calories burned, and integrates this data with health data for analysis. The exercise data collection unit can also analyze the patient's exercise patterns and propose an appropriate exercise plan. Furthermore, the exercise data collection unit can provide feedback to increase the patient's motivation to exercise. This allows the system to collect and analyze patient exercise data and contribute to improving their health.

[0047] The medical care support system may further include a preventive medical department that provides a preventive medical plan based on the patient's health data. For example, the preventive medical department may analyze the patient's health data and predict future health risks. The preventive medical department may also suggest appropriate preventive measures based on the predicted risks. Furthermore, the preventive medical department may notify the patient of schedules for regular health checks and examinations. This allows the provision of a preventive medical plan based on the patient's health data, thereby reducing health risks.

[0048] The medical care support system may further include a personalized care unit that provides a personalized care plan based on the patient's health data. For example, the personalized care unit may analyze the patient's health data and propose an optimal care plan for each individual patient. The personalized care unit may also customize the care plan based on the patient's lifestyle and preferences. Furthermore, the personalized care unit may collect patient feedback and continuously improve the care plan. This allows the system to provide a personalized care plan based on the patient's health data and achieve optimal care for each individual patient.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The health data collection unit collects the patient's health data. For example, it monitors vital signs such as heart rate, blood pressure, body temperature, and blood sugar level. The health data collection unit can also collect data naturally in daily life using wearable devices. Furthermore, the health data collection unit can also collect data by incorporating the patient's past medical history and genetic information. Step 2: The analysis unit analyzes the health data collected by the health data collection unit. For example, it may analyze the data using statistical analysis or machine learning algorithms to predict the patient's health status. The analysis unit may also use an emotion estimation function to analyze the patient's emotional state and evaluate the impact of stress and anxiety on health. Step 3: The notification unit notifies the nurse of the results of the analysis by the analysis unit. For example, it can send an alert or message to notify the nurse of an abnormality. The notification unit can also store the monitoring data in the cloud so that the patient can check their health condition at any time. Step 4: The dialogue unit supports the dialogue between the nurse and the patient. For example, the generative AI analyzes the conversation in real time and suggests appropriate advice or questions. The dialogue unit can also record the care provided by the nurse and share it with other medical staff.

[0051] (Example 2) The medical care support system according to an embodiment of the present invention automatically collects and analyzes patient health data, notifies nurses, and allows nurses to provide medical care through direct dialogue with patients. This allows the medical care support system to continuously monitor patient health data and provide personalized care from nurses.

[0052] The medical care support system according to the embodiment includes a health data collection unit, an analysis unit, a notification unit, and a dialogue unit. The health data collection unit collects health data from a patient. For example, it monitors vital signs such as heart rate, blood pressure, body temperature, and blood glucose level. The health data collection unit can also collect data naturally in daily life using a wearable device. The health data collection unit can also collect data by incorporating the patient's past medical history and genetic information. For example, it can continuously monitor the patient's heart rate and notify a nurse if an abnormality is detected. The analysis unit analyzes the health data collected by the health data collection unit. For example, it can analyze the data using statistical analysis or machine learning algorithms to predict the patient's health condition. The analysis unit can also analyze the patient's emotional state using an emotion estimation function to evaluate the impact of stress and anxiety on health. The notification unit notifies the nurse of the results of the analysis by the analysis unit. For example, it can send an alert or message to notify the nurse of an abnormality. The notification unit can also store the monitoring data in the cloud so that the patient can check their health condition at any time. The dialogue unit supports dialogue between nurses and patients. For example, the generation AI analyzes the content of the conversation in real time and suggests appropriate advice and questions. The dialogue unit can also record the care provided by the nurse and share it with other medical staff. As a result, the medical care support system according to the embodiment can provide appropriate medical care by collecting and analyzing patient health data and notifying the nurse. For example, because the patient's health condition is constantly monitored, any abnormalities can be dealt with promptly. Furthermore, direct care by the nurse provides patients with a sense of security.

[0053] The health data collection unit can monitor a patient's vital signs and notify a nurse if an abnormality is detected. The health data collection unit continuously monitors a patient's vital signs, such as heart rate, blood pressure, body temperature, and blood sugar level. For example, if the heart rate suddenly increases or blood pressure shows an abnormal value, the health data collection unit notifies the nurse. In addition, the health data collection unit can issue an alert if an abnormality is detected, prompting the nurse to take prompt action. This allows for early response by quickly detecting abnormalities in vital signs and notifying the nurse.

[0054] The health data collection unit incorporates a patient's past medical history and genetic information to make more accurate predictions of their health condition. For example, the health data collection unit obtains a patient's past medical history from an electronic medical record, and AI analyzes that data to use it to predict their health condition. For example, it predicts future health risks based on past medical history and treatment history. The health data collection unit also incorporates a patient's genetic information to make health condition predictions that take genetic risk factors into account. For example, it predicts the risk of a specific disease based on gene mutations and family history. In this way, by utilizing past medical history and genetic information, it becomes possible to make more accurate predictions of health condition.

[0055] The health data collection unit can use the emotion estimation function to analyze the patient's emotional state along with health data to evaluate the impact of stress and anxiety on health. The health data collection unit, for example, uses the emotion estimation function to analyze the patient's facial expressions and voice to collect emotional states in real time. The AI ​​integrates this data with the health data to evaluate the impact of stress and anxiety on health. For example, facial expression recognition technology can be used to analyze the patient's facial expressions and quantify the emotional state. Voice analysis technology can also be used to analyze the tone and speed of the patient's voice to evaluate the emotional state. Furthermore, the emotion estimation function can collect the patient's biometric data (heart rate and electrodermal activity) and analyze the emotional state. This makes it possible to evaluate the impact of stress and anxiety on health by analyzing the emotional state.

[0056] The health data collection unit can utilize a wearable device to enable patients to provide data naturally in their daily lives. For example, the health data collection unit continuously collects vital signs such as heart rate, blood pressure, and body temperature using a wearable device worn by the patient on a daily basis. This allows the patient to provide data without any special operations. The wearable device can also collect data such as the patient's activity level and sleep patterns. For example, a smartwatch or fitness tracker can be used to collect data naturally in daily life. Furthermore, the wearable device can collect the patient's location information, enabling rapid response in emergencies. This allows the patient to provide data naturally by utilizing the wearable device.

[0057] The health data collection unit can build a platform for sharing data between different medical institutions and comprehensively evaluating a patient's health status. The health data collection unit, for example, builds a platform for sharing patient health data between different medical institutions and performs comprehensive health evaluations. For example, it integrates electronic medical record systems to achieve centralized data management. The platform can also implement data access control and security measures to protect patient privacy. Furthermore, the platform allows experts from different medical institutions to collaborate to evaluate a patient's health status and propose optimal treatment plans. This makes it possible to share data between different medical institutions and perform comprehensive health evaluations.

[0058] The health data collection unit can use the emotion estimation function to develop an interface for reducing the discomfort and stress felt by patients during data collection. For example, the health data collection unit can use the emotion estimation function to detect in real time the discomfort and stress felt by patients during data collection and adjust the interface accordingly. For example, the interface can provide relaxing music or videos. The interface can also adjust the timing and method of data collection according to the patient's emotional state. For example, if the patient is feeling stressed, the interface can temporarily suspend data collection and provide time for relaxation. Furthermore, the interface can collect patient feedback and make continuous improvements. This can reduce the burden on patients by reducing discomfort and stress during data collection.

[0059] In the dialogue unit, when a nurse converses with a patient, the generation AI analyzes the conversation content in real time and suggests appropriate advice and questions. For example, when a nurse converses with a patient, the generation AI analyzes the conversation content in real time and suggests appropriate advice and questions. For example, it automatically generates questions based on the patient's symptoms. The generation AI can also provide appropriate advice based on the patient's health condition. For example, it can provide advice on diet and exercise. Furthermore, the generation AI can analyze the patient's emotional state and give advice to provide psychological support. This allows the generation AI to suggest appropriate advice and questions when a nurse converses with a patient, improving the quality of the dialogue.

[0060] The dialogue unit collects patient feedback to evaluate the effectiveness of the care provided by nurses, and the generation AI can analyze the data and suggest areas for improvement. For example, the dialogue unit collects patient feedback to evaluate the effectiveness of the care provided by nurses, and the generation AI analyzes the data and suggests areas for improvement. For example, it evaluates patient satisfaction and the degree of improvement in symptoms. The generation AI can also make specific suggestions for improving care based on the feedback data. For example, it makes suggestions for adjusting the method or timing of care. Furthermore, the dialogue unit can share the feedback data with other medical staff to improve the quality of care across the entire team. This makes it possible to evaluate the effectiveness of the care provided by nurses and suggest areas for improvement, thereby improving the quality of care.

[0061] The dialogue unit uses the emotion estimation function to enable nurses to understand the emotional state of patients and provide enhanced psychological support. For example, the dialogue unit uses the emotion estimation function to enable nurses to understand the emotional state of patients in real time and provide enhanced psychological support. For example, if a patient is feeling anxious, the emotion estimation function can suggest relaxation methods. The emotion estimation function can also analyze the patient's facial expressions and voice to quantify the emotional state. For example, facial expression recognition technology can be used to analyze the patient's facial expressions and evaluate the emotional state. Furthermore, the emotion estimation function can collect the patient's biometric data (heart rate and electrodermal activity) and analyze the emotional state. This allows nurses to understand the patient's emotional state and provide enhanced psychological support, thereby increasing the patient's sense of security.

[0062] The dialogue unit records the content of care provided by nurses and shares it with other medical staff, thereby improving the quality of care across the entire team. For example, the dialogue unit builds a system that records the detailed content of care provided by nurses and shares it with other medical staff. For example, the care content is entered into an electronic medical record and the information is shared across the entire team. In addition, when recording the care content, the dialogue unit uses a generative AI to automatically summarize it, allowing for efficient information sharing. Furthermore, the dialogue unit can analyze the care content and suggest areas for improvement. In this way, by recording the care content and sharing it with other medical staff, the quality of care across the entire team can be improved.

[0063] The dialogue unit can integrate video call and chat functions so that nurses can provide care to patients in remote locations. The dialogue unit, for example, builds a system that integrates video call and chat functions so that nurses can provide care to patients in remote locations. For example, online medical consultations and remote counseling can be performed. The dialogue unit can also use the video call and chat functions to monitor patients' health conditions in real time and provide necessary care. Furthermore, the dialogue unit can also provide an interface to facilitate communication with patients in remote locations. As a result, care can be provided to patients in remote locations by integrating the video call and chat functions.

[0064] The dialogue unit can use the emotion estimation function to develop a training program for nurses to provide care that is responsive to patients' emotions. For example, the dialogue unit uses the emotion estimation function to develop a training program for nurses to provide care that is responsive to patients' emotions. For example, training is conducted to learn how to respond according to emotional states. The training program can also be provided through simulation training or online courses. Furthermore, the training program can be continuously improved based on feedback provided by the generative AI. As a result, the quality of care can be improved by developing a training program for nurses to provide care that is responsive to patients' emotions.

[0065] The analysis unit can analyze monitoring data and develop algorithms to understand long-term trends in a patient's health condition. For example, the analysis unit collects monitoring data over a long period of time and uses AI to analyze the data to develop algorithms that understand health trends. For example, it analyzes long-term fluctuations in heart rate and blood pressure. The analysis unit can also adjust the patient's health management plan based on the results of trend analysis. For example, it can provide advice on diet and exercise. Furthermore, the analysis unit can notify nurses of the results of trend analysis and provide information to provide appropriate care. This enables more effective health management by analyzing monitoring data and understanding long-term trends in health conditions.

[0066] The analysis unit allows the generation AI to automatically update the health management plan based on the monitoring data and notify the nurse. The analysis unit, for example, builds a system in which the generation AI automatically updates the health management plan based on the monitoring data and notifies the nurse. For example, the plan is adjusted according to fluctuations in vital signs. The generation AI can also propose an optimal health management plan based on the patient's lifestyle and health condition. For example, it can provide advice on diet and exercise. Furthermore, the generation AI can notify the nurse of the results of the health management plan updates and provide information to provide appropriate care. This allows the health management plan to be automatically updated based on the monitoring data and notified to the nurse, enabling more appropriate care to be provided.

[0067] The analysis unit can use the emotion estimation function to monitor changes in the patient's emotional state and identify when psychological support is needed. For example, the analysis unit can use the emotion estimation function to monitor changes in the patient's emotional state in real time and identify when psychological support is needed. For example, counseling can be provided during periods of high stress levels. The emotion estimation function can also analyze the patient's facial expressions and voice to quantify the emotional state. For example, facial expression recognition technology can be used to analyze the patient's facial expressions and evaluate the emotional state. Furthermore, the emotion estimation function can collect the patient's biometric data (heart rate and electrodermal activity) and analyze the emotional state. This makes it possible to monitor changes in the emotional state and identify when psychological support is needed, thereby providing appropriate support.

[0068] The notification unit can store monitoring data in the cloud, allowing patients to check their health status at any time. The notification unit, for example, stores monitoring data in the cloud and builds a system that allows patients to check their health status at any time. For example, the data can be accessed through a smartphone app. The notification unit can also share the data stored in the cloud with nurses and medical staff to conduct comprehensive health assessments. Furthermore, the notification unit can use the generative AI to propose health management plans based on the data stored in the cloud. This makes self-management easier by storing monitoring data in the cloud and allowing patients to check their health status at any time.

[0069] The notification unit can integrate the monitoring data with other medical data to perform a comprehensive health assessment. The notification unit, for example, can integrate the monitoring data with other medical data to build a system that performs a comprehensive health assessment. For example, it can integrate test results and diagnostic information to evaluate health status. The notification unit can also use the generative AI to propose a health management plan based on the integrated data. For example, it can provide advice on diet and exercise. Furthermore, the notification unit can share the integrated data with nurses and medical staff to perform a comprehensive health assessment. This makes it possible to perform a comprehensive health assessment by integrating the monitoring data with other medical data.

[0070] The notification unit can use the emotion estimation function to evaluate the psychological impact that feedback based on the monitoring data has on the patient and optimize the feedback method. The notification unit, for example, uses the emotion estimation function to evaluate the psychological impact that feedback based on the monitoring data has on the patient. For example, the notification unit adjusts the feedback if the feedback content causes stress. The notification unit can also monitor the patient's emotional state in real time to provide appropriate feedback in order to optimize the feedback method. For example, it can emphasize positive feedback to increase the patient's motivation. Furthermore, the notification unit can continuously evaluate the effectiveness of the feedback and make improvements. As a result, the psychological impact that feedback has on the patient and the feedback method can be optimized, thereby reducing the patient's stress.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The medical care support system can further include an environmental data collection unit that collects data on the patient's living environment. For example, it can monitor the temperature, humidity, noise level, etc. of the patient's living environment to identify factors that affect their health condition. The environmental data collection unit can also record the patient's lifestyle habits (diet, exercise, sleep patterns) and analyze them in conjunction with health data. Furthermore, the environmental data collection unit can make suggestions for improving the patient's living environment. For example, it can suggest appropriate temperature and humidity settings and noise reduction measures. This allows for a comprehensive evaluation of the patient's living environment and contributes to improving their health condition.

[0073] The health data collection unit can further include a social data collection unit for evaluating the patient's social connections. For example, the health data collection unit can monitor the patient's use of social media and messaging apps to detect signs of social isolation and stress. The social data collection unit can also record the frequency of communication with the patient's friends and family to evaluate the level of social support. Furthermore, the social data collection unit can collect data on the community activities and hobbies in which the patient participates and make suggestions to support strengthening social connections. This makes it possible to evaluate the patient's social connections and comprehensively understand their impact on health status.

[0074] The medical care support system can further include a data sharing unit that anonymizes patient health data and shares it with research institutions. For example, with the patient's consent, the health data can be anonymized and provided to the research institution. The data sharing unit can also receive feedback from the research institution and use it to improve the system. Furthermore, the data sharing unit can notify patients of the progress and results of research, raising their awareness of health management. This makes it possible to utilize patient health data to advance medical research and also to help improve the system.

[0075] The health data collection unit may further include a dietary data collection unit that collects dietary data of the patient. For example, the contents and calories of the meals eaten by the patient may be recorded and the data may be integrated with the health data for analysis. The dietary data collection unit may also analyze the patient's dietary patterns and make suggestions for improving nutritional balance. Furthermore, the dietary data collection unit may record allergy information related to the patient's meals and make suggestions for reducing the risk of allergic reactions. In this way, the collection and analysis of the patient's dietary data can contribute to improving the patient's health.

[0076] The medical care support system can further include an exercise data collection unit that collects patient exercise data. For example, the system records the type and duration of exercise performed by the patient, as well as the calories burned, and integrates this data with health data for analysis. The exercise data collection unit can also analyze the patient's exercise patterns and propose an appropriate exercise plan. Furthermore, the exercise data collection unit can provide feedback to increase the patient's motivation to exercise. This allows the system to collect and analyze patient exercise data and contribute to improving their health.

[0077] The dialogue unit may further include an emotion monitoring unit that monitors the patient's emotional state in real time and provides care according to the emotion. For example, the emotion monitoring unit may analyze the patient's facial expressions and voice to quantify the emotional state. The emotion monitoring unit may also provide advice on relaxation techniques and stress reduction according to the patient's emotional state. Furthermore, the emotion monitoring unit may record the patient's emotional state and analyze long-term changes in emotion. This allows the patient's emotional state to be grasped in real time and appropriate care to be provided.

[0078] The medical care support system may further include a preventive medical department that provides a preventive medical plan based on the patient's health data. For example, the preventive medical department may analyze the patient's health data and predict future health risks. The preventive medical department may also suggest appropriate preventive measures based on the predicted risks. Furthermore, the preventive medical department may notify the patient of schedules for regular health checks and examinations. This allows the provision of a preventive medical plan based on the patient's health data, thereby reducing health risks.

[0079] The dialogue unit may further include an entertainment unit that analyzes the patient's emotional state and provides music and images according to the emotion. For example, if the patient is feeling stressed, the entertainment unit may play relaxing music. The entertainment unit may also display images that have a relaxing effect according to the patient's emotional state. Furthermore, the entertainment unit may collect patient feedback and continuously improve the content it provides. This makes it possible to provide entertainment according to the patient's emotional state and contribute to stress reduction.

[0080] The medical care support system may further include a personalized care unit that provides a personalized care plan based on the patient's health data. For example, the personalized care unit may analyze the patient's health data and propose an optimal care plan for each individual patient. The personalized care unit may also customize the care plan based on the patient's lifestyle and preferences. Furthermore, the personalized care unit may collect patient feedback and continuously improve the care plan. This allows the system to provide a personalized care plan based on the patient's health data and achieve optimal care for each individual patient.

[0081] The dialogue unit may further include a psychological support unit that analyzes the patient's emotional state and provides psychological support according to the emotion. For example, counseling may be provided if the patient is feeling anxious. The psychological support unit may also provide advice on relaxation techniques and stress reduction according to the patient's emotional state. Furthermore, the psychological support unit may record the patient's emotional state and analyze long-term changes in emotion. This allows the patient's emotional state to be grasped in real time and appropriate psychological support to be provided.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The health data collection unit collects the patient's health data. For example, it monitors vital signs such as heart rate, blood pressure, body temperature, and blood sugar level. The health data collection unit can also collect data naturally in daily life using wearable devices. Furthermore, the health data collection unit can also collect data by incorporating the patient's past medical history and genetic information. Step 2: The analysis unit analyzes the health data collected by the health data collection unit. For example, it may analyze the data using statistical analysis or machine learning algorithms to predict the patient's health status. The analysis unit may also use an emotion estimation function to analyze the patient's emotional state and evaluate the impact of stress and anxiety on health. Step 3: The notification unit notifies the nurse of the results of the analysis by the analysis unit. For example, it can send an alert or message to notify the nurse of an abnormality. The notification unit can also store the monitoring data in the cloud so that the patient can check their health condition at any time. Step 4: The dialogue unit supports the dialogue between the nurse and the patient. For example, the generative AI analyzes the conversation in real time and suggests appropriate advice or questions. The dialogue unit can also record the care provided by the nurse and share it with other medical staff.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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. Note that 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 health data collection unit that collects health data of patients; an analysis unit that analyzes the health data collected by the health data collection unit; a notification unit that notifies a nurse of the results of the analysis performed by the analysis unit; a dialogue unit that supports dialogue between the nurse and the patient; A system characterized by:

2. The health data collection unit: Using an emotion estimation function, the patient's emotional state is analyzed along with the health data to assess the impact of stress or anxiety on health.

2. The system of claim 1.

3. The health data collection unit: Utilizing wearable devices to enable patients to provide data naturally in their daily lives The system of claim 1 .

4. The dialogue unit As the nurse interacts with the patient, generative AI analyzes the conversation in real time and suggests appropriate advice or questions.

2. The system of claim 1.

5. The analysis unit Developing algorithms to analyze the monitoring data and identify long-term trends in the patient's health. The system of claim 1 .

6. The dialogue unit Using the emotion estimation function, the nurse can grasp the emotional state of the patient and provide enhanced psychological support. The system of claim 1 .

7. The analysis unit Using emotion estimation capabilities to monitor changes in the patient's emotional state and identify when psychological support is needed. The system of claim 1 .

8. The notification unit Using an emotion estimation function, the psychological impact of feedback based on monitoring data on the patient is evaluated, and the feedback method is optimized. The system of claim 1 .

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