System

Abstract

An object of a system according to an embodiment is to quickly and accurately determine the priority order of nurse calls and reduce the burden on nurses.SOLUTION: A system includes a nurse call reception part, a priority determination part, and a notification part. The nurse call-receiving unit is loaded with the generated AI. The priority determination part analyzes the content of the nurse call received by the nurse call reception part and determines the priority. The notification unit notifies the nurse based on the priority determined by the priority determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to quickly and accurately determine the priority of nurse calls, which can increase the burden on nurses.

[0005] The system according to the embodiment aims to quickly and accurately determine the priority of nurse calls and reduce the burden on nurses. [Means for solving the problem]

[0006] The system according to the embodiment includes a nurse call receiving unit, a priority determination unit, and a notification unit. The nurse call receiving unit is equipped with a generation AI. The priority determination unit analyzes the content of the nurse call received by the nurse call receiving unit and determines the priority. The notification unit notifies the nurse based on the priority determined by the priority determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately determine the priority of nurse calls, thereby reducing the burden on 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 non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In the nurse call system according to an embodiment of the present invention, when a patient makes a nurse call, the AI ​​automatically responds, analyzes the content of the call, determines the priority, and notifies the nurse. This reduces the burden on nurses and improves patient satisfaction.

[0029] The nurse call system according to the embodiment includes a nurse call receiving unit, a priority determination unit, and a notification unit. The nurse call receiving unit is equipped with a generation AI and automatically responds when a patient makes a nurse call. For example, the generation AI uses speech recognition technology to understand what the patient is saying and generate an appropriate response. The generation AI can also analyze the patient's tone and speed of voice to estimate their emotional state and adjust the response accordingly. The priority determination unit analyzes the content of the nurse call received by the nurse call receiving unit and determines the priority. For example, the generation AI analyzes the patient's message and determines the priority based on its urgency and importance. The notification unit notifies the nurse based on the priority determined by the priority determination unit. For example, the generation AI may notify the nurse in the form of, "Patient A is complaining of pain. Please prioritize this." This reduces the burden on nurses and improves patient satisfaction. For example, the generation AI can automatically respond to simple requests such as "I'd like some water," reducing the nurse's workload.

[0030] The nurse call receiver can generate the optimal response based on the patient's past nurse call history and individual response history. For example, the nurse call receiver stores the patient's past nurse call history in a database, and the generation AI references that history to generate the optimal response. For example, a patient who previously complained of severe pain could be responsive with a response such as, "How was the pain last time?" The generation AI also analyzes the patient's past nurse call history and customizes the response based on the individual response history. For example, a patient who frequently requests water could be responsive with a response such as, "I'll bring you some water." A system can also be constructed in which the generation AI references the patient's past nurse call history to generate the optimal response. For example, a patient who previously complained of anxiety could be responsive with a response such as, "How was your anxiety last time?" This makes it possible to provide individual responses based on the patient's past history, thereby improving patient satisfaction.

[0031] The nurse call receiver monitors a patient's health data in real time and can automatically make a nurse call if an abnormality is detected. For example, a system will be developed in which a generating AI monitors a patient's heart rate and blood pressure in real time and automatically makes a nurse call if an abnormality is detected. For example, if the heart rate suddenly rises, the system will respond by saying, "Your heart rate is high. We will respond immediately." We will also develop an algorithm for monitoring health data in real time, and the generating AI will automatically make a nurse call if it detects an abnormality. For example, if blood pressure suddenly drops, the system will respond by saying, "Your blood pressure is low. We will respond immediately." We will also develop a system in which the generating AI monitors a patient's health data and automatically makes a nurse call if an abnormality is detected. For example, if oxygen saturation drops, the system will respond by saying, "Your oxygen saturation is low. We will respond immediately." This will enable a rapid response by monitoring a patient's health data in real time and automatically making a nurse call if an abnormality is detected.

[0032] The priority determination unit can determine the level of urgency not only from the content of what the patient says, but also from the tone or speed of their voice. For example, the priority determination unit will build a system in which the generation AI analyzes the tone and speed of the patient's voice along with the content of what the patient says to determine the level of urgency. For example, if the voice is fast and high, it will be determined to be a high level of urgency and prompt a prompt response. In addition, an algorithm will be developed to analyze the tone and speed of the patient's voice to determine the level of urgency. For example, if the voice is trembling, it will be determined to be feeling anxious or scared and the priority will be set high. In addition, a system will be developed in which the generation AI analyzes the content of what the patient says and the tone and speed of the voice to determine the level of urgency. For example, if the voice is low and slow, it will be determined to be a low level of urgency and put off until later. This will enable more appropriate prioritization by determining the level of urgency from the content of what the patient says and the tone and speed of the voice.

[0033] The priority determination unit can set a high priority if certain symptoms appear based on the patient's past medical history. For example, the priority determination unit will build a system in which the generating AI stores the patient's past medical history in a database and sets a high priority if certain symptoms appear. For example, if a patient with a history of heart disease complains of chest pain, the priority will be set high. We will also develop an algorithm that references the patient's past medical history and sets a high priority if certain symptoms appear. For example, if a patient with a history of asthma complains of shortness of breath, the priority will be set high. We will also develop a system in which the generating AI analyzes the patient's past medical history and sets a high priority if certain symptoms appear. For example, if a patient with a history of diabetes complains of hypoglycemia, the priority will be set high. This will enable more appropriate responses by setting priorities based on the patient's past medical history.

[0034] The priority determination unit can analyze the nurse call situations of other patients in real time and dynamically adjust priorities based on the overall situation. For example, the priority determination unit will build a system in which the generation AI analyzes the nurse call situations of other patients in real time and dynamically adjusts priorities based on the overall situation. For example, if multiple emergency calls occur simultaneously, the most urgent call will be prioritized. We will also develop an algorithm that analyzes the nurse call situations of other patients in real time and dynamically adjusts priorities. For example, if the total number of calls is high, calls with lower urgency will be postponed. We will also develop a system in which the generation AI analyzes the nurse call situations of other patients in real time and dynamically adjusts priorities based on the overall situation. For example, if calls are concentrated during a specific time period, the priority for that time period will be adjusted. This will enable a more appropriate response by analyzing the nurse call situations of other patients in real time and dynamically adjusting priorities based on the overall situation.

[0035] The priority determination unit can collect patient location information and send a notification to the nearest nurse. For example, the priority determination unit will build a system in which the generating AI obtains the patient's location information in real time and prioritizes notifying the nurse closest to the toilet. For example, if a patient says they need to go to the toilet, the nearest nurse will be notified. In addition, an algorithm will be developed in which the generating AI obtains the patient's location information and determines the priority based on that information. For example, if the patient is away from their bed, the priority will be set high. In addition, a system will be developed in which the generating AI obtains the patient's location information in real time and notifies the nearest nurse. For example, if the patient is far from the nurse's station, the priority will be set high. This will enable a quick response by obtaining the patient's location information and notifying the nearest nurse.

[0036] The notification unit can monitor the work status of nurses in real time and send notifications to the nurse who is most available to respond. For example, the notification unit will build a system in which the generation AI grasps the current work status of nurses in real time and sends notifications to the nurse who is most available to respond. For example, notifications will be sent preferentially to nurses with a light current workload. In addition, an algorithm will be developed to grasp the work status of nurses in real time, and the generation AI will use that information to send notifications to the most suitable nurse. For example, notifications will not be sent to nurses on break. In addition, a system will be developed in which the generation AI grasps the current work status of nurses in real time and sends notifications to the nurse who is most available to respond. For example, a notification will be sent to a nurse who has just finished a specific task. This will enable efficient response by grasping the work status of nurses in real time and notifying the nurse who is most available to respond.

[0037] The notification unit can adjust the content of notifications to suit the nurse's specialty and provide optimal instructions. For example, the notification unit will build a system in which the generation AI stores the nurse's specialty in a database and customizes the content of notifications. For example, notifications about children will be sent preferentially to a pediatric nurse. An algorithm will also be developed to customize the content of notifications to suit the nurse's specialty, and the generation AI will provide optimal instructions based on that information. For example, heart-related notifications will be sent to a nurse who specializes in cardiology. A system will also be developed in which the generation AI will understand the nurse's specialty and customize the content of notifications. For example, notifications about patients who need psychological support will be sent to a psychiatric nurse. In this way, by customizing the content of notifications to suit the nurse's specialty, more appropriate instructions can be provided.

[0038] The notification unit can send notifications to nurses' smartwatches or smartphones, encouraging them to take immediate action. For example, the notification unit will build a system in which the generation AI sends notifications to nurses' smartwatches or smartphones. For example, in the event of an emergency nurse call, the smartwatch will be notified by vibration. In addition, an algorithm will be developed to send notifications to smartwatches or smartphones, and the generation AI will use that information to encourage immediate action. For example, it will send a push notification to the smartphone. In addition, a system will be developed in which the generation AI sends notifications to nurses' smartwatches or smartphones, encouraging immediate action. For example, it will send an audio notification to the smartwatch. This will enable immediate action by sending notifications to nurses' smartwatches or smartphones.

[0039] The notification unit can collect nurses' location information and send a notification to the nearest nurse. For example, the notification unit will build a system in which the generation AI obtains nurses' location information in real time and sends a notification to the nearest nurse. For example, it will send a notification to the nurse closest to the patient's bedside. In addition, an algorithm will be developed in which the generation AI obtains nurses' location information and sends a notification to the most appropriate nurse based on that information. For example, it will send a notification to the nurse closest to the nurse's station. In addition, a system will be developed in which the generation AI obtains nurses' location information in real time and sends a notification to the nearest nurse. For example, it will send a notification to the nurse closest to a specific hospital room. This will enable a rapid response by obtaining nurses' location information and notifying the nearest nurse.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The nurse call receiver not only analyzes the tone and speed of the patient's voice, but can also adjust the response based on the patient's health data. For example, the generating AI can monitor the patient's heart rate and blood pressure in real time, and if an abnormality is detected, it can respond with, "Your heart rate is high, we will respond immediately." If blood pressure drops suddenly, it can respond with, "Your blood pressure is low, we will respond immediately." Furthermore, if oxygen saturation drops, it can respond with, "Your oxygen saturation is low, we will respond immediately." This allows for a rapid response by adjusting the response based on the patient's health data.

[0042] The nurse call receiver not only adjusts the response content based on the patient's past nurse call history, but can also customize the response content based on the patient's communication history with family and friends. For example, if the generation AI references the patient's conversation history with family and friends and the patient says, "I want to see my family," it can respond with, "Would you like us to contact your family?". If the patient says, "I want to call a friend," it can also respond with, "Would you like us to call a friend?". Furthermore, if the patient says, "I want to see my grandchildren," it can respond with, "Would you like to video call your grandchildren?". In this way, patient satisfaction can be improved by customizing the response content based on the patient's communication history with family and friends.

[0043] The nurse call receiver not only monitors the patient's health data in real time, but can also analyze the patient's sleep patterns and adjust the response accordingly. For example, if the generating AI monitors the patient's sleep patterns and detects a lack of sleep, it can respond with something like, "Have you been sleeping well lately?". If an abnormality is detected during sleep, it can respond with something like, "There was something unusual during your sleep. Are you feeling okay?". Furthermore, if the quality of sleep is poor, it can respond with something like, "Your sleep quality is poor. Is there anything I can help you do to relax?". This allows for more appropriate responses to be tailored based on the patient's sleep patterns.

[0044] The priority determination unit not only analyzes the content, tone, and speed of the patient's speech, but can also determine the level of urgency based on the patient's activity level. For example, if the generation AI monitors the patient's activity level and detects sudden activity, it will set the priority high by asking, "You've moved suddenly. Are you okay?". If the activity level is low, it can also adjust the priority by asking, "You've been less active lately. Are you feeling okay?". Furthermore, if the activity level is higher than usual, it can also adjust the priority by asking, "You're very active. Is there something I can help you with?". This allows for more appropriate prioritization by determining the level of urgency based on the patient's activity level.

[0045] The priority determination unit not only sets priorities based on the patient's past medical history, but can also adjust priorities based on the patient's lifestyle data. For example, the generative AI monitors the patient's diet and exercise data, and if the patient's eating habits are irregular, it sets the priority high by asking, "You've been eating irregularly lately. Are you feeling okay?". Also, if a lack of exercise is detected, it can adjust the priority by asking, "You haven't been exercising much lately. Are you feeling okay?". Furthermore, if the patient's lifestyle habits are unhealthy, it can adjust the priority by asking, "Your lifestyle habits are unhealthy. Are you having any problems?". This allows for more appropriate responses by setting priorities based on the patient's lifestyle data.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The nurse call receiver is equipped with a generation AI and automatically responds when a patient calls. The generation AI uses voice recognition technology to understand what the patient is saying and generate an appropriate response. The generation AI can also analyze the tone and speed of the patient's voice to estimate their emotional state and adjust the response accordingly. Step 2: The priority determination unit analyzes the content of the nurse call received by the nurse call receiving unit and determines the priority. The generation AI analyzes the content of the call from the patient and determines the priority based on its urgency and importance. Step 3: The notification unit notifies the nurse based on the priority determined by the priority determination unit. The generation AI notifies the nurse in the form of, "Patient A is complaining of pain. Please treat him as a priority."

[0048] (Example 2) In the nurse call system according to an embodiment of the present invention, when a patient makes a nurse call, the AI ​​automatically responds, analyzes the content of the call, determines the priority, and notifies the nurse. This reduces the burden on nurses and improves patient satisfaction.

[0049] The nurse call system according to the embodiment includes a nurse call receiving unit, a priority determination unit, and a notification unit. The nurse call receiving unit is equipped with a generation AI and automatically responds when a patient makes a nurse call. For example, the generation AI uses speech recognition technology to understand what the patient is saying and generate an appropriate response. The generation AI can also analyze the patient's tone and speed of voice to estimate their emotional state and adjust the response accordingly. The priority determination unit analyzes the content of the nurse call received by the nurse call receiving unit and determines the priority. For example, the generation AI analyzes the patient's message and determines the priority based on its urgency and importance. The notification unit notifies the nurse based on the priority determined by the priority determination unit. For example, the generation AI may notify the nurse in the form of, "Patient A is complaining of pain. Please prioritize this." This reduces the burden on nurses and improves patient satisfaction. For example, the generation AI can automatically respond to simple requests such as "I'd like some water," reducing the nurse's workload.

[0050] The nurse call receiver can analyze the tone or speed of the patient's voice, infer their emotional state, and adjust the response accordingly. For example, the generation AI in the nurse call receiver analyzes the patient's voice tone and speed in real time to infer their emotional state. For example, if the patient's voice is trembling, it can be determined that they are feeling anxious or scared, and the response can be adjusted to something that conveys reassurance, such as "Are you okay? We'll respond right away." The generation AI will also develop an algorithm that analyzes the patient's voice tone and speed to infer their emotional state. For example, if the voice is fast and high-pitched, it can be determined that there is a high level of urgency, and a response that encourages a prompt response can be generated. The generation AI will also build a system that analyzes the patient's voice tone and speed to infer their emotional state. For example, if the voice is low and slow, it can be determined that the patient is relaxed, and the response can be adjusted to something like "What's wrong? Please speak slowly." This enables responses that correspond to the patient's emotional state, increasing their sense of security.

[0051] The nurse call receiver can generate the optimal response based on the patient's past nurse call history and individual response history. For example, the nurse call receiver stores the patient's past nurse call history in a database, and the generation AI references that history to generate the optimal response. For example, a patient who previously complained of severe pain could be responsive with a response such as, "How was the pain last time?" The generation AI also analyzes the patient's past nurse call history and customizes the response based on the individual response history. For example, a patient who frequently requests water could be responsive with a response such as, "I'll bring you some water." A system can also be constructed in which the generation AI references the patient's past nurse call history to generate the optimal response. For example, a patient who previously complained of anxiety could be responsive with a response such as, "How was your anxiety last time?" This makes it possible to provide individual responses based on the patient's past history, thereby improving patient satisfaction.

[0052] The nurse call receiver can analyze the patient's emotions and respond in a way that is sensitive to their emotions. For example, the generation AI in the nurse call receiver analyzes the tone and speed of the patient's voice to estimate their emotional state and adjust the response accordingly. For example, if the patient sounds anxious, the response might be something like, "You sound anxious. What's wrong?" An algorithm is also developed to estimate the patient's emotions, and the generation AI generates a response based on the results. For example, if the patient sounds sad, the response might be something like, "You sound sad. Is there something I can help you with?" A system is also being developed in which the generation AI estimates the patient's emotions and responds in a way that is sensitive to their emotions. For example, if the patient sounds angry, the response might be something like, "You sound angry. What's wrong?" This makes it possible to respond in a way that is sensitive to the patient's emotions, thereby increasing their sense of security.

[0053] The nurse call receiver can recognize the patient's face, infer their emotions from their facial expressions, and adjust the response accordingly. For example, a system will be built in which a generation AI recognizes the patient's face and infers their emotions from their facial expressions. For example, if the patient is smiling, the system will respond with, "You look well. What's wrong?" Furthermore, using facial recognition technology, the generation AI will analyze the patient's facial expressions and infer their emotional state. For example, if the patient is frowning, the system will respond with, "You look worried. Is there something I can help you with?" Furthermore, an algorithm will be developed in which the generation AI recognizes the patient's face, infers their emotions from their facial expressions, and adjusts the response accordingly. For example, if the patient is crying, the system will respond with, "You look like you're crying. What's wrong?" This will enable a more appropriate response by inferring emotions from the patient's facial expressions and adjusting the response accordingly.

[0054] The nurse call receiver monitors a patient's health data in real time and can automatically make a nurse call if an abnormality is detected. For example, a system will be developed in which a generating AI monitors a patient's heart rate and blood pressure in real time and automatically makes a nurse call if an abnormality is detected. For example, if the heart rate suddenly rises, the system will respond by saying, "Your heart rate is high. We will respond immediately." We will also develop an algorithm for monitoring health data in real time, and the generating AI will automatically make a nurse call if it detects an abnormality. For example, if blood pressure suddenly drops, the system will respond by saying, "Your blood pressure is low. We will respond immediately." We will also develop a system in which the generating AI monitors a patient's health data and automatically makes a nurse call if an abnormality is detected. For example, if oxygen saturation drops, the system will respond by saying, "Your oxygen saturation is low. We will respond immediately." This will enable a rapid response by monitoring a patient's health data in real time and automatically making a nurse call if an abnormality is detected.

[0055] The nurse call receiver can analyze the patient's emotions and provide advice based on those emotions. For example, the generation AI in the nurse call receiver analyzes the tone and speed of the patient's voice to estimate their emotional state and provide advice. For example, if the patient sounds anxious, the AI ​​may provide advice such as, "Try taking a deep breath to relax." We will also develop an algorithm to estimate the patient's emotions, and the generation AI will provide advice based on the results. For example, if the patient sounds nervous, the AI ​​may provide advice such as, "Try taking a short break to relax." We will also build a system in which the generation AI estimates the patient's emotions and provides advice based on those emotions. For example, if the patient sounds angry, the AI ​​may provide advice such as, "Try taking a deep breath to relax." This allows the AI ​​to provide advice based on the patient's emotions, thereby increasing their sense of security.

[0056] The priority determination unit can determine the level of urgency not only from the content of what the patient says, but also from the tone or speed of their voice. For example, the priority determination unit will build a system in which the generation AI analyzes the tone and speed of the patient's voice along with the content of what the patient says to determine the level of urgency. For example, if the voice is fast and high, it will be determined to be a high level of urgency and prompt a prompt response. In addition, an algorithm will be developed to analyze the tone and speed of the patient's voice to determine the level of urgency. For example, if the voice is trembling, it will be determined to be feeling anxious or scared and the priority will be set high. In addition, a system will be developed in which the generation AI analyzes the content of what the patient says and the tone and speed of the voice to determine the level of urgency. For example, if the voice is low and slow, it will be determined to be a low level of urgency and put off until later. This will enable more appropriate prioritization by determining the level of urgency from the content of what the patient says and the tone and speed of the voice.

[0057] The priority determination unit can set a high priority if certain symptoms appear based on the patient's past medical history. For example, the priority determination unit will build a system in which the generating AI stores the patient's past medical history in a database and sets a high priority if certain symptoms appear. For example, if a patient with a history of heart disease complains of chest pain, the priority will be set high. We will also develop an algorithm that references the patient's past medical history and sets a high priority if certain symptoms appear. For example, if a patient with a history of asthma complains of shortness of breath, the priority will be set high. We will also develop a system in which the generating AI analyzes the patient's past medical history and sets a high priority if certain symptoms appear. For example, if a patient with a history of diabetes complains of hypoglycemia, the priority will be set high. This will enable more appropriate responses by setting priorities based on the patient's past medical history.

[0058] The priority determination unit can analyze the patient's emotions and determine priorities taking into account the intensity of the emotions. For example, the priority determination unit will build a system in which the generation AI analyzes the tone and speed of the patient's voice to estimate their emotional state and determine priorities. For example, if the patient says, "I'm in severe pain," and their voice is trembling, the priority will be set high. We will also develop an algorithm to estimate the patient's emotions, and the generation AI will determine priorities based on the results. For example, if the patient says, "I'm in severe pain," and their voice is fast and high, the priority will be set high. We will also develop a system in which the generation AI estimates the patient's emotions and determines priorities taking into account the intensity of their emotions. For example, if the patient says, "I'm in severe pain," and their voice is low and slow, the priority will be set low. This will enable more appropriate responses by determining priorities taking into account the intensity of the patient's emotions.

[0059] The priority determination unit can analyze the nurse call situations of other patients in real time and dynamically adjust priorities based on the overall situation. For example, the priority determination unit will build a system in which the generation AI analyzes the nurse call situations of other patients in real time and dynamically adjusts priorities based on the overall situation. For example, if multiple emergency calls occur simultaneously, the most urgent call will be prioritized. We will also develop an algorithm that analyzes the nurse call situations of other patients in real time and dynamically adjusts priorities. For example, if the total number of calls is high, calls with lower urgency will be postponed. We will also develop a system in which the generation AI analyzes the nurse call situations of other patients in real time and dynamically adjusts priorities based on the overall situation. For example, if calls are concentrated during a specific time period, the priority for that time period will be adjusted. This will enable a more appropriate response by analyzing the nurse call situations of other patients in real time and dynamically adjusting priorities based on the overall situation.

[0060] The priority determination unit can collect patient location information and send a notification to the nearest nurse. For example, the priority determination unit will build a system in which the generating AI obtains the patient's location information in real time and prioritizes notifying the nurse closest to the toilet. For example, if a patient says they need to go to the toilet, the nearest nurse will be notified. In addition, an algorithm will be developed in which the generating AI obtains the patient's location information and determines the priority based on that information. For example, if the patient is away from their bed, the priority will be set high. In addition, a system will be developed in which the generating AI obtains the patient's location information in real time and notifies the nearest nurse. For example, if the patient is far from the nurse's station, the priority will be set high. This will enable a quick response by obtaining the patient's location information and notifying the nearest nurse.

[0061] The priority determination unit can analyze patients' emotions and prioritize patients who need psychological support. For example, the priority determination unit will build a system in which the generation AI analyzes the tone and speed of a patient's voice to estimate their emotional state and determine priorities. For example, if it determines that a patient has high anxiety, it will prioritize patients who need psychological support. In addition, an algorithm will be developed to estimate a patient's emotions, and the generation AI will determine priorities based on the results. For example, if it determines that a patient has high anxiety, it will prioritize patients who need psychological support. In addition, a system will be developed in which the generation AI estimates a patient's emotions and determines priorities taking the intensity of the emotions into consideration. For example, if it determines that a patient has high anxiety, it will prioritize patients who need psychological support. This will enable more appropriate treatment by estimating patients' emotions and prioritizing patients who need psychological support.

[0062] The notification unit can monitor the work status of nurses in real time and send notifications to the nurse who is most available to respond. For example, the notification unit will build a system in which the generation AI grasps the current work status of nurses in real time and sends notifications to the nurse who is most available to respond. For example, notifications will be sent preferentially to nurses with a light current workload. In addition, an algorithm will be developed to grasp the work status of nurses in real time, and the generation AI will use that information to send notifications to the most suitable nurse. For example, notifications will not be sent to nurses on break. In addition, a system will be developed in which the generation AI grasps the current work status of nurses in real time and sends notifications to the nurse who is most available to respond. For example, a notification will be sent to a nurse who has just finished a specific task. This will enable efficient response by grasping the work status of nurses in real time and notifying the nurse who is most available to respond.

[0063] The notification unit can adjust the content of notifications to suit the nurse's specialty and provide optimal instructions. For example, the notification unit will build a system in which the generation AI stores the nurse's specialty in a database and customizes the content of notifications. For example, notifications about children will be sent preferentially to a pediatric nurse. An algorithm will also be developed to customize the content of notifications to suit the nurse's specialty, and the generation AI will provide optimal instructions based on that information. For example, heart-related notifications will be sent to a nurse who specializes in cardiology. A system will also be developed in which the generation AI will understand the nurse's specialty and customize the content of notifications. For example, notifications about patients who need psychological support will be sent to a psychiatric nurse. In this way, by customizing the content of notifications to suit the nurse's specialty, more appropriate instructions can be provided.

[0064] The notification unit can analyze the nurse's emotions and distribute notifications taking into account the intensity of the emotion. For example, the notification unit will build a system in which the generation AI analyzes the tone and speed of a nurse's voice to estimate their emotional state and distribute notifications accordingly. For example, if it determines that they are "tired," it will distribute notifications to other nurses. In addition, an algorithm will be developed to estimate a nurse's emotions, and the generation AI will distribute notifications based on the results. For example, if it determines that they are "tired," it will distribute notifications to other nurses. In addition, a system will be developed in which the generation AI estimates a nurse's emotions and distributes notifications taking into account the intensity of the emotion. For example, if it determines that they are "tired," it will distribute notifications to other nurses. In this way, by distributing notifications taking into account the intensity of the nurse's emotion, it will reduce the burden on nurses and enable more efficient responses.

[0065] The notification unit can send notifications to nurses' smartwatches or smartphones, encouraging them to take immediate action. For example, the notification unit will build a system in which the generation AI sends notifications to nurses' smartwatches or smartphones. For example, in the event of an emergency nurse call, the smartwatch will be notified by vibration. In addition, an algorithm will be developed to send notifications to smartwatches or smartphones, and the generation AI will use that information to encourage immediate action. For example, it will send a push notification to the smartphone. In addition, a system will be developed in which the generation AI sends notifications to nurses' smartwatches or smartphones, encouraging immediate action. For example, it will send an audio notification to the smartwatch. This will enable immediate action by sending notifications to nurses' smartwatches or smartphones.

[0066] The notification unit can collect nurses' location information and send a notification to the nearest nurse. For example, the notification unit will build a system in which the generation AI obtains nurses' location information in real time and sends a notification to the nearest nurse. For example, it will send a notification to the nurse closest to the patient's bedside. In addition, an algorithm will be developed in which the generation AI obtains nurses' location information and sends a notification to the most appropriate nurse based on that information. For example, it will send a notification to the nurse closest to the nurse's station. In addition, a system will be developed in which the generation AI obtains nurses' location information in real time and sends a notification to the nearest nurse. For example, it will send a notification to the nurse closest to a specific hospital room. This will enable a rapid response by obtaining nurses' location information and notifying the nearest nurse.

[0067] The notification unit can analyze the nurse's emotions and provide advice based on those emotions. For example, the notification unit will build a system in which the generation AI analyzes the tone and speed of a nurse's voice to estimate their emotional state and provide advice. For example, if it determines that they are "high stress," it will give advice such as "take a short break." We will also develop an algorithm to estimate a nurse's emotions, and the generation AI will provide advice based on that result. For example, if it determines that they are "high stress," it will give advice such as "take a deep breath and relax." We will also develop a system in which the generation AI estimates a nurse's emotions and provides advice taking into account the intensity of the emotion. For example, if it determines that they are "high stress," it will give advice such as "take a short walk to refresh yourself." By providing advice based on the nurse's emotions, it will be possible to reduce their stress and respond more efficiently.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The nurse call receiver not only analyzes the tone and speed of the patient's voice, but can also detect the patient's physical movements and adjust the response accordingly. For example, the generation AI can monitor the patient's movements with a camera, and if the movements are sudden, it can determine that there is a high level of urgency and prompt a prompt response. If the patient is trying to get up from the bed, it can respond with, "Do you need help?". Furthermore, if the patient is waving, it can respond with, "Is there something I can help you with?" This allows for a more appropriate response by analyzing the patient's movements and adjusting the response accordingly.

[0070] The nurse call receiver not only analyzes the tone and speed of the patient's voice, but can also adjust the response based on the patient's health data. For example, the generating AI can monitor the patient's heart rate and blood pressure in real time, and if an abnormality is detected, it can respond with, "Your heart rate is high, we will respond immediately." If blood pressure drops suddenly, it can respond with, "Your blood pressure is low, we will respond immediately." Furthermore, if oxygen saturation drops, it can respond with, "Your oxygen saturation is low, we will respond immediately." This allows for a rapid response by adjusting the response based on the patient's health data.

[0071] The nurse call receiver not only adjusts the response content based on the patient's past nurse call history, but can also customize the response content based on the patient's communication history with family and friends. For example, if the generation AI references the patient's conversation history with family and friends and the patient says, "I want to see my family," it can respond with, "Would you like us to contact your family?". If the patient says, "I want to call a friend," it can also respond with, "Would you like us to call a friend?". Furthermore, if the patient says, "I want to see my grandchildren," it can respond with, "Would you like to video call your grandchildren?". In this way, patient satisfaction can be improved by customizing the response content based on the patient's communication history with family and friends.

[0072] The nurse call receiver not only analyzes the patient's emotions, but can also tailor the response based on the patient's hobbies and interests. For example, if the generation AI stores the patient's hobbies and interests in a database and the patient complains of being bored, it can respond by saying, "Would you like me to play your favorite music?" If the patient says, "I want to read a book," it can respond by saying, "Would you like me to read your favorite book?" If the patient says, "I want to watch a movie," it can also respond by saying, "Would you like me to play your favorite movie?" This makes it possible to improve patient satisfaction by tailoring the response based on the patient's hobbies and interests.

[0073] The nurse call receiver not only recognizes the patient's face and infers emotions from their facial expressions, but can also detect the patient's body temperature and sweating level and adjust the response accordingly. For example, the generation AI can monitor the patient's body temperature and, if it is high, respond with, "Your temperature is high. Would you like to cool it down?" It can also detect sweating and, if abnormal sweating is observed, respond with, "You are sweating a lot. Are you feeling okay?" If the body temperature is low, it can respond with, "Your temperature is low. Would you like to warm it up?" This allows for more appropriate responses by adjusting the response based on the patient's body temperature and sweating level.

[0074] The nurse call receiver not only monitors the patient's health data in real time, but can also analyze the patient's sleep patterns and adjust the response accordingly. For example, if the generating AI monitors the patient's sleep patterns and detects a lack of sleep, it can respond with something like, "Have you been sleeping well lately?". If an abnormality is detected during sleep, it can respond with something like, "There was something unusual during your sleep. Are you feeling okay?". Furthermore, if the quality of sleep is poor, it can respond with something like, "Your sleep quality is poor. Is there anything I can help you do to relax?". This allows for more appropriate responses to be tailored based on the patient's sleep patterns.

[0075] The nurse call receiver not only analyzes the patient's emotions and provides advice based on those emotions, but can also monitor the patient's stress level and provide advice based on that. For example, the generative AI can monitor the patient's stress level and, if stress is high, advise them to "take a deep breath to relax." If stress levels are medium, it can also advise them to "take a short break." Furthermore, if stress levels are low, it can advise them to "try to relax." This allows the patient's sense of security to be increased by providing advice based on their stress level.

[0076] The priority determination unit not only analyzes the content, tone, and speed of the patient's speech, but can also determine the level of urgency based on the patient's activity level. For example, if the generation AI monitors the patient's activity level and detects sudden activity, it will set the priority high by asking, "You've moved suddenly. Are you okay?". If the activity level is low, it can also adjust the priority by asking, "You've been less active lately. Are you feeling okay?". Furthermore, if the activity level is higher than usual, it can also adjust the priority by asking, "You're very active. Is there something I can help you with?". This allows for more appropriate prioritization by determining the level of urgency based on the patient's activity level.

[0077] The priority determination unit not only sets priorities based on the patient's past medical history, but can also adjust priorities based on the patient's lifestyle data. For example, the generative AI monitors the patient's diet and exercise data, and if the patient's eating habits are irregular, it sets the priority high by asking, "You've been eating irregularly lately. Are you feeling okay?". Also, if a lack of exercise is detected, it can adjust the priority by asking, "You haven't been exercising much lately. Are you feeling okay?". Furthermore, if the patient's lifestyle habits are unhealthy, it can adjust the priority by asking, "Your lifestyle habits are unhealthy. Are you having any problems?". This allows for more appropriate responses by setting priorities based on the patient's lifestyle data.

[0078] The priority determination unit not only analyzes the patient's emotions and determines priorities based on the intensity of those emotions, but can also adjust priorities based on the patient's social support situation. For example, the generation AI monitors the frequency of contact with the patient's family and friends, and if there is little contact, it sets a high priority by asking, "Have you been in contact with your family and friends recently?". It can also adjust priorities if there is a lack of social support by asking, "You are lacking in social support. Is there anything I can help you with?". Furthermore, if there is insufficient social support, it can adjust priorities by asking, "You are not receiving enough social support. Is there anything I can help you with?". This allows for more appropriate responses by setting priorities based on the patient's social support situation.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The nurse call receiver is equipped with a generation AI and automatically responds when a patient calls. The generation AI uses voice recognition technology to understand what the patient is saying and generate an appropriate response. The generation AI can also analyze the tone and speed of the patient's voice to estimate their emotional state and adjust the response accordingly. Step 2: The priority determination unit analyzes the content of the nurse call received by the nurse call receiving unit and determines the priority. The generation AI analyzes the content of the call from the patient and determines the priority based on its urgency and importance. Step 3: The notification unit notifies the nurse based on the priority determined by the priority determination unit. The generation AI notifies the nurse in the form of, "Patient A is complaining of pain. Please treat him as a priority."

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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 AI 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 AI 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] 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 AI 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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 nurse call receiver equipped with a generation AI, a priority determination unit that analyzes the content of the nurse call received by the nurse call receiving unit and determines the priority; a notification unit that notifies a nurse based on the priority determined by the priority determination unit. A system characterized by:

2. The nurse call receiving unit Analyze the patient's tone or rate of voice to estimate their emotional state and tailor responses accordingly 2. The system of claim 1.

3. The nurse call receiving unit Monitors patient health data in real time and automatically initiates a nurse call if an abnormality is detected 2. The system of claim 1.

4. The priority determination unit Determine the urgency of a patient's voice based not only on what they say, but also on the tone or speed of their voice.

2. The system of claim 1.

5. The priority determination unit Analyze the nurse call situations of other patients in real time and dynamically adjust the priority based on the overall situation.

2. The system of claim 1.

6. The notification unit Monitor the nurse's work status in real time and send the notification to the nurse who is most available 2. The system of claim 1.

7. The notification unit Analyze the nurse's emotions and distribute the notifications taking into consideration the intensity of the emotions.

2. The system of claim 1.

8. The priority determination unit Analyze the patient's emotions and determine the priority taking into account the intensity of the emotions.

2. The system of claim 1.

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