system

Abstract

The system according to this embodiment aims to reduce the workload of nurses and automate and streamline patient monitoring, emergency response, and record keeping. [Solution] The system according to the embodiment comprises a reception unit, a response unit, a recording unit, a monitoring unit, and an alert unit. The reception unit receives questions from nurses. The response unit analyzes past patient data based on the questions received by the reception unit and provides answers. The recording unit documents the nurse's oral statements in real time. The monitoring unit monitors the patient's vital signs and detects abnormalities. The alert unit notifies the patient of the abnormalities detected by the monitoring unit by voice.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the physical and mental burden on nurses during night work, emergency response, and recording work is large, and there is room for efficiency improvement.

[0005] The system according to the embodiment aims to reduce the workload of nurses and automate and improve the efficiency of patient monitoring, emergency response, and recording.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a response unit, a recording unit, a monitoring unit, and an alert unit. The reception unit receives questions from nurses. The response unit analyzes past patient data based on the questions received by the reception unit and provides answers. The recording unit documents the nurse's oral statements in real time. The monitoring unit monitors the patient's vital signs and detects abnormalities. The alert unit notifies the patient of the abnormalities detected by the monitoring unit by voice. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the workload of nurses and automate and streamline patient monitoring, emergency response, and record keeping. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​care partner according to an embodiment of the present invention is a system designed to alleviate the physical and mental burden of nurses' work, such as night shifts. This AI care partner aims to reduce the burden on nurses and decrease the number of night shifts by automating and streamlining patient monitoring, emergency response, and record-keeping tasks. For example, when a nurse asks a question such as, "What points should I pay particular attention to when caring for patient A at night?", the generating AI analyzes past patient data (such as electronic medical records and vital data) and provides a specific answer. The AI ​​care partner also documents the nurse's verbal statements in real time and organizes them into an appropriate format. The generating AI also automatically standardizes terminology and grammar. Furthermore, when nurses are learning how to use IV pumps or new medical devices, the generating AI visualizes the operating procedures and automatically generates and provides illustrations and flowcharts. Based on data (vital signs, medication records, etc.), the generating AI automatically generates graphs and dashboards, making it possible to visually understand changes in the patient's condition. The AI ​​Care Partner uses AI to generate voice descriptions of patient abnormalities, enabling nurses to respond immediately without needing to check a screen. For new nurses, the AI ​​Care Partner provides voice guides on topics such as "proper IV line replacement procedures" and "precautions for nighttime rounds." These features reduce the burden on nurses during nighttime work and support more efficient work performance. In short, the AI ​​Care Partner reduces the workload on nurses and improves the efficiency of nighttime work.

[0029] The AI ​​care partner according to this embodiment comprises a reception unit, a response unit, a recording unit, a monitoring unit, and an alert unit. The reception unit receives questions from nurses. The reception unit can receive questions in, for example, text or voice format. The response unit uses a generation AI to analyze past patient data based on the questions received by the reception unit and provides answers. The response unit uses, for example, a generation AI to analyze past medical records and vital sign data and provide specific answers. The recording unit uses a generation AI to document the nurse's oral statements in real time and organize them into an appropriate format. The recording unit uses, for example, a generation AI to convert the nurse's oral statements into text data and arrange the document layout and format. The monitoring unit monitors the patient's vital signs in real time and detects abnormalities. The monitoring unit monitors vital signs such as heart rate, blood pressure, and body temperature and detects abnormalities. The alert unit uses a generation AI to notify the monitoring unit of abnormalities detected by the monitoring unit via voice. The alert unit, for example, provides an audio explanation of the situation when the generating AI detects an anomaly, enabling nurses to respond immediately without having to check the screen. As a result, the AI ​​care partner according to this embodiment can reduce the burden on nurses and improve the efficiency of nighttime work.

[0030] The reception desk receives questions from nurses. The reception desk can accept questions in various formats, such as text or voice. Specifically, nurses can send text or voice messages using dedicated terminals or smartphones. In the case of text, nurses simply type their questions using a keyboard and press the send button to send them to the reception desk. In the case of voice, nurses speak their questions into a microphone, and speech recognition technology converts the questions into text, which is then sent to the reception desk. The reception desk receives these questions and forwards them to the response department for appropriate processing. Furthermore, the reception desk can prioritize questions based on their content, allowing for the rapid processing of urgent questions. For example, questions regarding sudden changes in a patient's condition are given high priority and immediately forwarded to the response department. On the other hand, questions regarding routine care are processed with normal priority. This enables the reception desk to efficiently receive and appropriately process a diverse range of questions from nurses.

[0031] The response unit uses generative AI to analyze past patient data based on questions received by the reception unit and provide answers. For example, the response unit uses generative AI to analyze past medical records and vital sign data to provide specific answers. The generative AI uses natural language processing technology to understand the intent of the question and search for relevant data. For example, if a nurse asks, "What was patient A's blood pressure last night?", the generative AI searches for patient A's blood pressure data for the past 24 hours and generates an appropriate answer. Furthermore, the generative AI can also provide advice on the patient's condition and recommended care plans based on past medical records and vital sign data. For example, the generative AI analyzes the patient's past data and suggests how to deal with the appearance of specific symptoms. This allows the response unit to support nurses in quickly and accurately understanding the patient's condition and providing appropriate care.

[0032] The recording unit uses generative AI to document nurses' dictations in real time and organize them into an appropriate format. For example, the generative AI converts nurses' dictations into text data and adjusts the document layout and format. Specifically, when a nurse dictates a patient's condition and treatment details into a microphone, the generative AI converts the audio into text and creates a document in an appropriate format. The generative AI automatically adjusts the document layout and format, placing necessary items in the correct positions. For example, it automatically organizes items such as the patient's name, date and time of consultation, vital signs, and treatment details, creating a document in an easy-to-read format. Furthermore, the generative AI can also check the content of the document and correct typos, grammatical errors, and inappropriate expressions. As a result, the recording unit enables nurses to quickly and accurately record patient conditions and create documents in an appropriate format.

[0033] The monitoring unit monitors the patient's vital signs in real time and detects abnormalities. For example, the monitoring unit monitors vital signs such as heart rate, blood pressure, and body temperature, and detects abnormalities. Specifically, sensors attached to the patient measure vital signs in real time and transmit the data to the monitoring unit. The monitoring unit analyzes this data and detects values ​​that fall outside the normal range. For example, if the heart rate suddenly increases or blood pressure drops abnormally, the monitoring unit immediately detects the abnormality and notifies the alert unit. Furthermore, the monitoring unit can learn patterns of abnormalities based on past data and predict future abnormalities. As a result, the monitoring unit can monitor the patient's condition in real time and detect abnormalities early.

[0034] The alert unit uses a generation AI to notify nurses of abnormalities detected by the monitoring unit via voice. For example, when the generation AI detects an abnormality, the alert unit explains the situation via voice, enabling nurses to respond immediately without checking the screen. Specifically, when the generation AI detects an abnormality, the alert unit uses speech synthesis technology to notify nurses of the nature of the abnormality via voice. For example, a voice notification such as, "Patient B's heart rate has increased rapidly. Please check immediately," may be given. This allows nurses to immediately understand the nature of the abnormality and respond quickly without checking the screen. Furthermore, the alert unit can also suggest appropriate response methods depending on the nature of the abnormality. For example, if a rapid increase in heart rate is detected, the alert unit will provide specific instructions via voice, such as, "Start oxygen administration." In this way, the alert unit supports nurses in responding quickly and appropriately, ensuring patient safety.

[0035] The response unit can analyze past patient data using generative AI and provide specific answers. For example, the response unit's generative AI can analyze past medical records and vital sign data to provide specific answers. Furthermore, the response unit's generative AI can use natural language processing technology to generate appropriate answers to nurses' questions. For instance, in response to the question, "What are the points to pay particular attention to when caring for patient A at night?", the response unit's generative AI can analyze past patient data and provide specific points to note. Thus, by using generative AI, it is possible to analyze past patient data and provide specific answers.

[0036] The recording unit can use generative AI to document nurses' dictations and organize them into an appropriate format. For example, the generative AI can convert nurses' dictations into text data and adjust the document layout and format. The generative AI can also automatically standardize terminology and grammar. For instance, the generative AI can document nurses' dictations in real time and organize them into an appropriate format. In this way, by using generative AI, nurses' dictations can be documented and organized into an appropriate format.

[0037] The monitoring unit can monitor the patient's vital signs in real time and detect abnormalities. For example, it monitors vital signs such as heart rate, blood pressure, and body temperature, and detects abnormalities. Furthermore, the monitoring unit can use AI to analyze fluctuations in vital signs in real time and detect abnormalities early. For instance, the AI ​​in the monitoring unit can detect a sudden change in heart rate and notify the system of the abnormality. This allows for real-time monitoring of the patient's vital signs and the detection of abnormalities.

[0038] The alert unit can notify of abnormalities via voice using a generating AI. For example, when the generating AI detects an abnormality, the alert unit will explain the situation via voice, allowing nurses to respond immediately without having to check the screen. The alert unit can also provide appropriate voice notifications depending on the type and urgency of the abnormality. For example, if the generating AI detects a sudden change in heart rate, the alert unit will notify via voice, "Patient A's heart rate is changing rapidly." In this way, abnormalities can be notified via voice using the generating AI.

[0039] The reception desk can analyze the nurse's past question history when receiving a question and select the most suitable reception method. For example, the reception desk can automatically display as suggestions questions that the nurse has frequently asked in the past. The reception desk can also prioritize suggesting question methods (voice, text, etc.) that the nurse has used in the past. Furthermore, the reception desk can predict and suggest questions that will be asked at a specific time based on the nurse's past question history. In this way, the optimal question reception method can be selected by analyzing past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without the use of AI.

[0040] The reception desk can filter questions based on the nurse's current workload and areas of interest. For example, it can prioritize questions related to patients the nurse is currently caring for. It can also filter and display relevant questions based on the nurse's areas of interest. Furthermore, it can suggest appropriate questions considering the nurse's current workload. This allows for more appropriate question reception by filtering questions based on current workload and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0041] The reception desk can prioritize receiving questions that are highly relevant, taking into account the nurse's geographical location. For example, it can prioritize questions related to the ward the nurse is currently in. The reception desk can also filter and display relevant questions based on the nurse's geographical location. Furthermore, if the nurse is in a specific location, the reception desk can prioritize questions related to that location. In this way, by considering geographical location, highly relevant questions can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or without the use of AI.

[0042] The reception desk can analyze the nurse's social media activity when receiving questions and accept relevant questions. For example, the reception desk can prioritize questions related to topics the nurse has shown interest in on social media. The reception desk can also filter and display relevant questions based on the nurse's social media activity. Furthermore, the reception desk can accept relevant questions based on information the nurse has shared on social media. In this way, relevant questions can be accepted by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0043] The response unit can adjust the level of detail in its response based on the importance of the patient data. For example, it can provide a detailed response based on important patient data. It can also provide a concise response based on general patient data. Furthermore, it can provide a rapid response based on urgent patient data. By adjusting the level of detail in the response based on the importance of the patient data, it is possible to provide a more appropriate response.

[0044] The response unit can apply different response algorithms depending on the category of patient data when responding. For example, the response unit can apply an appropriate response algorithm based on vital sign data. It can also apply an appropriate response algorithm based on medication record data. Furthermore, it can apply an appropriate response algorithm based on the patient's medical history data. This allows for the provision of more appropriate answers by applying different response algorithms depending on the category of patient data.

[0045] The response unit can prioritize responses based on when patient data was submitted. For example, it can prioritize responses based on the most recent patient data. It can also postpone responses based on older patient data. Furthermore, it can provide responses quickly based on patient data with high urgency. By prioritizing responses based on when patient data was submitted, it is possible to provide more appropriate responses.

[0046] The response unit can adjust the order of responses based on the relevance of patient data during the response process. For example, the response unit can prioritize responses based on highly relevant patient data. It can also postpone responses based on less relevant patient data. Furthermore, the response unit can provide responses quickly based on urgent patient data. By adjusting the order of responses based on the relevance of patient data, it is possible to provide more appropriate responses.

[0047] The recording unit can adjust the level of detail in the record based on the importance of the nurse's verbal statements. For example, the recording unit can provide detailed records based on important verbal statements. It can also provide concise records based on general verbal statements. Furthermore, it can provide rapid records based on urgent verbal statements. By adjusting the level of detail in the record based on the importance of the verbal statements, it is possible to provide more appropriate records.

[0048] The recording unit can apply different recording algorithms depending on the category of the nurse's verbal statements during recording. For example, the recording unit can apply an appropriate recording algorithm based on vital sign data. It can also apply an appropriate recording algorithm based on medication record data. Furthermore, it can apply an appropriate recording algorithm based on the patient's medical history data. This allows for the provision of more appropriate records by applying different recording algorithms depending on the category of the verbal statements.

[0049] The records department can prioritize records based on when the nurse's oral statements were submitted. For example, the records department can prioritize providing records based on the most recent oral statements. It can also postpone providing records based on older oral statements. Furthermore, it can provide records quickly based on urgent oral statements. This allows for the provision of more appropriate records by prioritizing them based on when the oral statements were submitted.

[0050] The recording unit can adjust the order of records based on the relevance of the nurse's verbal statements during recording. For example, the recording unit can prioritize providing records based on highly relevant verbal statements. It can also postpone providing records based on less relevant verbal statements. Furthermore, the recording unit can provide records quickly based on highly urgent verbal statements. By adjusting the order of records based on the relevance of the verbal statements, more appropriate records can be provided.

[0051] The monitoring unit can improve the accuracy of monitoring by referring to the patient's past vital sign data during monitoring. For example, the monitoring unit can adjust the monitoring criteria based on the patient's past vital sign data. Furthermore, the monitoring unit can also detect abnormalities early by referring to the patient's past vital sign data. In addition, the monitoring unit can improve the accuracy of monitoring by analyzing the patient's past vital sign data. This means that the accuracy of monitoring can be improved by referring to past vital sign data.

[0052] The monitoring unit can perform monitoring while considering the patient's attribute information. For example, the monitoring unit can adjust the monitoring criteria based on the patient's age and gender. It can also determine the frequency of monitoring by considering the patient's medical history. Furthermore, the monitoring unit can adjust the monitoring method based on the patient's lifestyle. This allows for more appropriate monitoring by considering the patient's attribute information.

[0053] The monitoring unit can perform monitoring while considering the geographical distribution of patients. For example, if patients are concentrated in a particular area, the monitoring unit will prioritize monitoring patients in that area. The monitoring unit can also adjust the frequency of monitoring based on the geographical distribution of patients. Furthermore, if patients are on the move, the monitoring unit can update the geographical distribution in real time and perform monitoring. This allows for more appropriate monitoring by considering the geographical distribution of patients.

[0054] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature on the patient during monitoring. For example, the monitoring unit can refer to literature related to the patient's medical condition and adjust the monitoring criteria. Furthermore, the monitoring unit can improve the monitoring method based on literature related to the patient's treatment. In addition, the monitoring unit can analyze literature related to the patient's case to improve the accuracy of monitoring. Thus, by referring to relevant literature, the accuracy of monitoring can be improved.

[0055] The alert unit can adjust the level of detail in alert notifications based on the importance of the patient's abnormal data. For example, it can provide detailed alert notifications based on important abnormal data. It can also provide concise alert notifications based on general abnormal data. Furthermore, it can provide rapid alert notifications based on highly urgent abnormal data. By adjusting the level of detail in notifications based on the importance of the abnormal data, more appropriate alert notifications can be provided.

[0056] The alert unit can apply different notification algorithms depending on the category of abnormal patient data when an alert is issued. For example, the alert unit can apply an appropriate notification algorithm based on vital sign data. It can also apply an appropriate notification algorithm based on medication record data. Furthermore, it can apply an appropriate notification algorithm based on the patient's medical history data. This allows for more appropriate alert notifications by applying different notification algorithms depending on the category of abnormal data.

[0057] The alert unit can prioritize notifications based on when abnormal patient data is submitted. For example, it can prioritize alert notifications based on the most recent abnormal data. It can also postpone alert notifications based on older abnormal data. Furthermore, it can provide alert notifications quickly based on highly urgent abnormal data. By prioritizing notifications based on when abnormal data is submitted, more appropriate alert notifications can be provided.

[0058] The alert unit can adjust the order of notifications based on the relevance of patient abnormal data when an alert is issued. For example, the alert unit can prioritize alert notifications based on highly relevant abnormal data. It can also postpone alert notifications based on less relevant abnormal data. Furthermore, it can provide alert notifications quickly based on highly urgent abnormal data. By adjusting the order of notifications based on the relevance of abnormal data, more appropriate alert notifications can be provided.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception desk can analyze a nurse's past question history when receiving a question and select the most suitable reception method. For example, it can automatically display frequently asked questions as suggestions. It can also prioritize suggesting question methods (voice, text, etc.) that the nurse has used in the past. Furthermore, it can predict and suggest questions that will be asked at specific times based on the nurse's past question history. In this way, the optimal question reception method can be selected by analyzing past question history.

[0061] The reception desk can filter questions based on the nurse's current workload and areas of interest. For example, it can prioritize questions related to patients the nurse is currently caring for. It can also filter and display relevant questions based on the nurse's areas of interest. Furthermore, it can suggest appropriate questions considering the nurse's current workload. This allows for more appropriate question reception by filtering questions based on current workload and areas of interest.

[0062] The response unit can adjust the level of detail in its response based on the importance of the patient data. For example, it can provide a detailed response based on important patient data, a concise response based on general patient data, and a rapid response based on urgent patient data. By adjusting the level of detail in the response based on the importance of the patient data, it can provide a more appropriate response.

[0063] The monitoring unit can improve the accuracy of monitoring by referring to the patient's past vital sign data during monitoring. For example, it can adjust the monitoring criteria based on the patient's past vital sign data. It can also detect abnormalities early by referring to the patient's past vital sign data. Furthermore, it can analyze the patient's past vital sign data to improve the accuracy of monitoring. In this way, the accuracy of monitoring can be improved by referring to past vital sign data.

[0064] The alerting unit can adjust the level of detail in alerts based on the importance of the patient's abnormal data. For example, it can provide detailed alerts based on important abnormal data, concise alerts based on general abnormal data, and rapid alerts based on highly urgent abnormal data. By adjusting the level of detail in alerts based on the importance of the abnormal data, more appropriate alerts can be provided.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives questions from nurses. The reception desk can accept questions in various formats, such as text or audio. Step 2: The response unit uses a generation AI to analyze past patient data based on the questions received by the reception unit and provides answers. For example, the response unit uses the generation AI to analyze past medical records and vital sign data to provide specific answers. Step 3: The recording unit uses a generation AI to document the nurse's dictation in real time and organize it into an appropriate format. For example, the generation AI converts the nurse's dictation into text data and adjusts the document's layout and format. Step 4: The monitoring unit monitors the patient's vital signs in real time and detects abnormalities. The monitoring unit monitors vital signs such as heart rate, blood pressure, and body temperature, and detects abnormalities. Step 5: The alert unit uses a generation AI to notify users of anomalies detected by the monitoring unit via voice. For example, when the generation AI detects an anomaly, the alert unit will explain the situation via voice, allowing nurses to respond immediately without having to check the screen.

[0067] (Example of form 2) The AI ​​care partner according to an embodiment of the present invention is a system designed to alleviate the physical and mental burden of nurses' work, such as night shifts. This AI care partner aims to reduce the burden on nurses and decrease the number of night shifts by automating and streamlining patient monitoring, emergency response, and record-keeping tasks. For example, when a nurse asks a question such as, "What points should I pay particular attention to when caring for patient A at night?", the generating AI analyzes past patient data (such as electronic medical records and vital data) and provides a specific answer. The AI ​​care partner also documents the nurse's verbal statements in real time and organizes them into an appropriate format. The generating AI also automatically standardizes terminology and grammar. Furthermore, when nurses are learning how to use IV pumps or new medical devices, the generating AI visualizes the operating procedures and automatically generates and provides illustrations and flowcharts. Based on data (vital signs, medication records, etc.), the generating AI automatically generates graphs and dashboards, making it possible to visually understand changes in the patient's condition. The AI ​​Care Partner uses AI to generate voice descriptions of patient abnormalities, enabling nurses to respond immediately without needing to check a screen. For new nurses, the AI ​​Care Partner provides voice guides on topics such as "proper IV line replacement procedures" and "precautions for nighttime rounds." These features reduce the burden on nurses during nighttime work and support more efficient work performance. In short, the AI ​​Care Partner reduces the workload on nurses and improves the efficiency of nighttime work.

[0068] The AI ​​care partner according to this embodiment comprises a reception unit, a response unit, a recording unit, a monitoring unit, and an alert unit. The reception unit receives questions from nurses. The reception unit can receive questions in, for example, text or voice format. The response unit uses a generation AI to analyze past patient data based on the questions received by the reception unit and provides answers. The response unit uses, for example, a generation AI to analyze past medical records and vital sign data and provide specific answers. The recording unit uses a generation AI to document the nurse's oral statements in real time and organize them into an appropriate format. The recording unit uses, for example, a generation AI to convert the nurse's oral statements into text data and arrange the document layout and format. The monitoring unit monitors the patient's vital signs in real time and detects abnormalities. The monitoring unit monitors vital signs such as heart rate, blood pressure, and body temperature and detects abnormalities. The alert unit uses a generation AI to notify the monitoring unit of abnormalities detected by the monitoring unit via voice. The alert unit, for example, provides an audio explanation of the situation when the generating AI detects an anomaly, enabling nurses to respond immediately without having to check the screen. As a result, the AI ​​care partner according to this embodiment can reduce the burden on nurses and improve the efficiency of nighttime work.

[0069] The reception desk receives questions from nurses. The reception desk can accept questions in various formats, such as text or voice. Specifically, nurses can send text or voice messages using dedicated terminals or smartphones. In the case of text, nurses simply type their questions using a keyboard and press the send button to send them to the reception desk. In the case of voice, nurses speak their questions into a microphone, and speech recognition technology converts the questions into text, which is then sent to the reception desk. The reception desk receives these questions and forwards them to the response department for appropriate processing. Furthermore, the reception desk can prioritize questions based on their content, allowing for the rapid processing of urgent questions. For example, questions regarding sudden changes in a patient's condition are given high priority and immediately forwarded to the response department. On the other hand, questions regarding routine care are processed with normal priority. This enables the reception desk to efficiently receive and appropriately process a diverse range of questions from nurses.

[0070] The response unit uses generative AI to analyze past patient data based on questions received by the reception unit and provide answers. For example, the response unit uses generative AI to analyze past medical records and vital sign data to provide specific answers. The generative AI uses natural language processing technology to understand the intent of the question and search for relevant data. For example, if a nurse asks, "What was patient A's blood pressure last night?", the generative AI searches for patient A's blood pressure data for the past 24 hours and generates an appropriate answer. Furthermore, the generative AI can also provide advice on the patient's condition and recommended care plans based on past medical records and vital sign data. For example, the generative AI analyzes the patient's past data and suggests how to deal with the appearance of specific symptoms. This allows the response unit to support nurses in quickly and accurately understanding the patient's condition and providing appropriate care.

[0071] The recording unit uses generative AI to document nurses' dictations in real time and organize them into an appropriate format. For example, the generative AI converts nurses' dictations into text data and adjusts the document layout and format. Specifically, when a nurse dictates a patient's condition and treatment details into a microphone, the generative AI converts the audio into text and creates a document in an appropriate format. The generative AI automatically adjusts the document layout and format, placing necessary items in the correct positions. For example, it automatically organizes items such as the patient's name, date and time of consultation, vital signs, and treatment details, creating a document in an easy-to-read format. Furthermore, the generative AI can also check the content of the document and correct typos, grammatical errors, and inappropriate expressions. As a result, the recording unit enables nurses to quickly and accurately record patient conditions and create documents in an appropriate format.

[0072] The monitoring unit monitors the patient's vital signs in real time and detects abnormalities. For example, the monitoring unit monitors vital signs such as heart rate, blood pressure, and body temperature, and detects abnormalities. Specifically, sensors attached to the patient measure vital signs in real time and transmit the data to the monitoring unit. The monitoring unit analyzes this data and detects values ​​that fall outside the normal range. For example, if the heart rate suddenly increases or blood pressure drops abnormally, the monitoring unit immediately detects the abnormality and notifies the alert unit. Furthermore, the monitoring unit can learn patterns of abnormalities based on past data and predict future abnormalities. As a result, the monitoring unit can monitor the patient's condition in real time and detect abnormalities early.

[0073] The alert unit uses a generation AI to notify nurses of abnormalities detected by the monitoring unit via voice. For example, when the generation AI detects an abnormality, the alert unit explains the situation via voice, enabling nurses to respond immediately without checking the screen. Specifically, when the generation AI detects an abnormality, the alert unit uses speech synthesis technology to notify nurses of the nature of the abnormality via voice. For example, a voice notification such as, "Patient B's heart rate has increased rapidly. Please check immediately," may be given. This allows nurses to immediately understand the nature of the abnormality and respond quickly without checking the screen. Furthermore, the alert unit can also suggest appropriate response methods depending on the nature of the abnormality. For example, if a rapid increase in heart rate is detected, the alert unit will provide specific instructions via voice, such as, "Start oxygen administration." In this way, the alert unit supports nurses in responding quickly and appropriately, ensuring patient safety.

[0074] The response unit can analyze past patient data using generative AI and provide specific answers. For example, the response unit's generative AI can analyze past medical records and vital sign data to provide specific answers. Furthermore, the response unit's generative AI can use natural language processing technology to generate appropriate answers to nurses' questions. For instance, in response to the question, "What are the points to pay particular attention to when caring for patient A at night?", the response unit's generative AI can analyze past patient data and provide specific points to note. Thus, by using generative AI, it is possible to analyze past patient data and provide specific answers.

[0075] The recording unit can use generative AI to document nurses' dictations and organize them into an appropriate format. For example, the generative AI can convert nurses' dictations into text data and adjust the document layout and format. The generative AI can also automatically standardize terminology and grammar. For instance, the generative AI can document nurses' dictations in real time and organize them into an appropriate format. In this way, by using generative AI, nurses' dictations can be documented and organized into an appropriate format.

[0076] The monitoring unit can monitor the patient's vital signs in real time and detect abnormalities. For example, it monitors vital signs such as heart rate, blood pressure, and body temperature, and detects abnormalities. Furthermore, the monitoring unit can use AI to analyze fluctuations in vital signs in real time and detect abnormalities early. For instance, the AI ​​in the monitoring unit can detect a sudden change in heart rate and notify the system of the abnormality. This allows for real-time monitoring of the patient's vital signs and the detection of abnormalities.

[0077] The alert unit can notify of abnormalities via voice using a generating AI. For example, when the generating AI detects an abnormality, the alert unit will explain the situation via voice, allowing nurses to respond immediately without having to check the screen. The alert unit can also provide appropriate voice notifications depending on the type and urgency of the abnormality. For example, if the generating AI detects a sudden change in heart rate, the alert unit will notify via voice, "Patient A's heart rate is changing rapidly." In this way, abnormalities can be notified via voice using the generating AI.

[0078] The reception system can estimate the nurse's emotions and adjust the way questions are answered based on the estimated emotions. For example, if the nurse is stressed, the reception system can provide a simple interface and minimize the steps required to input questions. If the nurse is relaxed, the reception system can also provide detailed question options and suggest customizable questioning methods. Furthermore, if the nurse is in a hurry, the reception system can prioritize voice input to allow for quick questioning. This allows for more appropriate questioning by adjusting the questioning method according to the nurse's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The reception desk can analyze the nurse's past question history when receiving a question and select the most suitable reception method. For example, the reception desk can automatically display as suggestions questions that the nurse has frequently asked in the past. The reception desk can also prioritize suggesting question methods (voice, text, etc.) that the nurse has used in the past. Furthermore, the reception desk can predict and suggest questions that will be asked at a specific time based on the nurse's past question history. In this way, the optimal question reception method can be selected by analyzing past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without the use of AI.

[0080] The reception desk can filter questions based on the nurse's current workload and areas of interest. For example, it can prioritize questions related to patients the nurse is currently caring for. It can also filter and display relevant questions based on the nurse's areas of interest. Furthermore, it can suggest appropriate questions considering the nurse's current workload. This allows for more appropriate question reception by filtering questions based on current workload and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0081] The reception desk can estimate the nurse's emotions and prioritize the questions to be answered based on the estimated emotions. For example, if the nurse is stressed, the reception desk will prioritize important questions. If the nurse is relaxed, the reception desk may also prioritize detailed questions. Furthermore, if the nurse is in a hurry, the reception desk may prioritize urgent questions. This allows for more appropriate question answering by prioritizing questions according to the nurse's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The reception desk can prioritize receiving questions that are highly relevant, taking into account the nurse's geographical location. For example, it can prioritize questions related to the ward the nurse is currently in. The reception desk can also filter and display relevant questions based on the nurse's geographical location. Furthermore, if the nurse is in a specific location, the reception desk can prioritize questions related to that location. In this way, by considering geographical location, highly relevant questions can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or without the use of AI.

[0083] The reception desk can analyze the nurse's social media activity when receiving questions and accept relevant questions. For example, the reception desk can prioritize questions related to topics the nurse has shown interest in on social media. The reception desk can also filter and display relevant questions based on the nurse's social media activity. Furthermore, the reception desk can accept relevant questions based on information the nurse has shared on social media. In this way, relevant questions can be accepted by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0084] The response unit can estimate the nurse's emotions and adjust the way it expresses its response based on the estimated emotions. For example, if the nurse is stressed, the response unit will provide a simple and clear response. If the nurse is relaxed, the response unit can also provide a response that includes a detailed explanation. Furthermore, if the nurse is in a hurry, the response unit can provide a concise and rapid response. This allows for the provision of more appropriate responses by adjusting the way the response is expressed according to the nurse's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The response unit can adjust the level of detail in its response based on the importance of the patient data. For example, it can provide a detailed response based on important patient data. It can also provide a concise response based on general patient data. Furthermore, it can provide a rapid response based on urgent patient data. By adjusting the level of detail in the response based on the importance of the patient data, it is possible to provide a more appropriate response.

[0086] The response unit can apply different response algorithms depending on the category of patient data when responding. For example, the response unit can apply an appropriate response algorithm based on vital sign data. It can also apply an appropriate response algorithm based on medication record data. Furthermore, it can apply an appropriate response algorithm based on the patient's medical history data. This allows for the provision of more appropriate answers by applying different response algorithms depending on the category of patient data.

[0087] The response unit can estimate the nurse's emotions and adjust the length of its response based on the estimated emotions. For example, if the nurse is stressed, the response unit will provide a short, concise response. If the nurse is relaxed, the response unit can provide a longer response with more detailed explanations. Furthermore, if the nurse is in a hurry, the response unit can provide a quick and concise response. By adjusting the length of the response according to the nurse's emotions, a more appropriate response can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The response unit can prioritize responses based on when patient data was submitted. For example, it can prioritize responses based on the most recent patient data. It can also postpone responses based on older patient data. Furthermore, it can provide responses quickly based on patient data with high urgency. By prioritizing responses based on when patient data was submitted, it is possible to provide more appropriate responses.

[0089] The response unit can adjust the order of responses based on the relevance of patient data during the response process. For example, the response unit can prioritize responses based on highly relevant patient data. It can also postpone responses based on less relevant patient data. Furthermore, the response unit can provide responses quickly based on urgent patient data. By adjusting the order of responses based on the relevance of patient data, it is possible to provide more appropriate responses.

[0090] The recording unit can estimate the nurse's emotions and adjust the way the record is presented based on the estimated emotions. For example, if the nurse is stressed, the recording unit can provide a simple and clear way of presenting the information. If the nurse is relaxed, the recording unit can also provide a way of presenting the information that includes detailed explanations. Furthermore, if the nurse is in a hurry, the recording unit can provide a concise and rapid way of presenting the information. This allows for the provision of more appropriate records by adjusting the way the record is presented according to the nurse's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The recording unit can adjust the level of detail in the record based on the importance of the nurse's verbal statements. For example, the recording unit can provide detailed records based on important verbal statements. It can also provide concise records based on general verbal statements. Furthermore, it can provide rapid records based on urgent verbal statements. By adjusting the level of detail in the record based on the importance of the verbal statements, it is possible to provide more appropriate records.

[0092] The recording unit can apply different recording algorithms depending on the category of the nurse's verbal statements during recording. For example, the recording unit can apply an appropriate recording algorithm based on vital sign data. It can also apply an appropriate recording algorithm based on medication record data. Furthermore, it can apply an appropriate recording algorithm based on the patient's medical history data. This allows for the provision of more appropriate records by applying different recording algorithms depending on the category of the verbal statements.

[0093] The recording unit can estimate the nurse's emotions and adjust the length of the record based on the estimated emotions. For example, if the nurse is stressed, the recording unit will provide a short, concise record. If the nurse is relaxed, the recording unit can provide a longer record with more detailed explanations. Furthermore, if the nurse is in a hurry, the recording unit can provide a quick and concise record. This allows for the provision of more appropriate records by adjusting the length of the record according to the nurse's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The records department can prioritize records based on when the nurse's oral statements were submitted. For example, the records department can prioritize providing records based on the most recent oral statements. It can also postpone providing records based on older oral statements. Furthermore, it can provide records quickly based on urgent oral statements. This allows for the provision of more appropriate records by prioritizing them based on when the oral statements were submitted.

[0095] The recording unit can adjust the order of records based on the relevance of the nurse's verbal statements during recording. For example, the recording unit can prioritize providing records based on highly relevant verbal statements. It can also postpone providing records based on less relevant verbal statements. Furthermore, the recording unit can provide records quickly based on highly urgent verbal statements. By adjusting the order of records based on the relevance of the verbal statements, more appropriate records can be provided.

[0096] The monitoring unit can estimate the patient's emotions and adjust the monitoring criteria based on the estimated emotions. For example, if the patient is feeling anxious, the monitoring unit will monitor more frequently. Conversely, if the patient is relaxed, the monitoring unit can reduce the frequency of monitoring. Furthermore, if the patient is agitated, the monitoring unit can tighten the monitoring criteria. This allows for more appropriate monitoring by adjusting the monitoring criteria according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The monitoring unit can improve the accuracy of monitoring by referring to the patient's past vital sign data during monitoring. For example, the monitoring unit can adjust the monitoring criteria based on the patient's past vital sign data. Furthermore, the monitoring unit can also detect abnormalities early by referring to the patient's past vital sign data. In addition, the monitoring unit can improve the accuracy of monitoring by analyzing the patient's past vital sign data. This means that the accuracy of monitoring can be improved by referring to past vital sign data.

[0098] The monitoring unit can perform monitoring while considering the patient's attribute information. For example, the monitoring unit can adjust the monitoring criteria based on the patient's age and gender. It can also determine the frequency of monitoring by considering the patient's medical history. Furthermore, the monitoring unit can adjust the monitoring method based on the patient's lifestyle. This allows for more appropriate monitoring by considering the patient's attribute information.

[0099] The monitoring unit can estimate the patient's emotions and adjust the order in which monitoring results are displayed based on the estimated emotions. For example, if the patient is feeling anxious, the monitoring unit can prioritize displaying important monitoring results. It can also display detailed monitoring results if the patient is relaxed. Furthermore, if the patient is agitated, the monitoring unit can prioritize displaying urgent monitoring results. This allows for the provision of more appropriate monitoring results by adjusting the order in which monitoring results are displayed according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The monitoring unit can perform monitoring while considering the geographical distribution of patients. For example, if patients are concentrated in a particular area, the monitoring unit will prioritize monitoring patients in that area. The monitoring unit can also adjust the frequency of monitoring based on the geographical distribution of patients. Furthermore, if patients are on the move, the monitoring unit can update the geographical distribution in real time and perform monitoring. This allows for more appropriate monitoring by considering the geographical distribution of patients.

[0101] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature on the patient during monitoring. For example, the monitoring unit can refer to literature related to the patient's medical condition and adjust the monitoring criteria. Furthermore, the monitoring unit can improve the monitoring method based on literature related to the patient's treatment. In addition, the monitoring unit can analyze literature related to the patient's case to improve the accuracy of monitoring. Thus, by referring to relevant literature, the accuracy of monitoring can be improved.

[0102] The alert unit can estimate the nurse's emotions and adjust the notification method of alerts based on the estimated emotions. For example, if the nurse is stressed, the alert unit will provide a simple and clear alert notification. If the nurse is relaxed, the alert unit can also provide an alert notification with a detailed explanation. Furthermore, if the nurse is in a hurry, the alert unit can provide a concise and rapid alert notification. This allows for more appropriate alert notifications by adjusting the notification method according to the nurse's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The alert unit can adjust the level of detail in alert notifications based on the importance of the patient's abnormal data. For example, it can provide detailed alert notifications based on important abnormal data. It can also provide concise alert notifications based on general abnormal data. Furthermore, it can provide rapid alert notifications based on highly urgent abnormal data. By adjusting the level of detail in notifications based on the importance of the abnormal data, more appropriate alert notifications can be provided.

[0104] The alert unit can apply different notification algorithms depending on the category of abnormal patient data when an alert is issued. For example, the alert unit can apply an appropriate notification algorithm based on vital sign data. It can also apply an appropriate notification algorithm based on medication record data. Furthermore, it can apply an appropriate notification algorithm based on the patient's medical history data. This allows for more appropriate alert notifications by applying different notification algorithms depending on the category of abnormal data.

[0105] The alert unit can estimate the nurse's emotions and adjust the length of the alert notification based on the estimated emotions. For example, if the nurse is stressed, the alert unit will provide a short, to-the-point alert notification. If the nurse is relaxed, the alert unit can provide a longer alert notification with a more detailed explanation. Furthermore, if the nurse is in a hurry, the alert unit can provide a quick and concise alert notification. By adjusting the length of the alert notification according to the nurse's emotions, more appropriate alert notifications can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The alert unit can prioritize notifications based on when abnormal patient data is submitted. For example, it can prioritize alert notifications based on the most recent abnormal data. It can also postpone alert notifications based on older abnormal data. Furthermore, it can provide alert notifications quickly based on highly urgent abnormal data. By prioritizing notifications based on when abnormal data is submitted, more appropriate alert notifications can be provided.

[0107] The alert unit can adjust the order of notifications based on the relevance of patient abnormal data when an alert is issued. For example, the alert unit can prioritize alert notifications based on highly relevant abnormal data. It can also postpone alert notifications based on less relevant abnormal data. Furthermore, it can provide alert notifications quickly based on highly urgent abnormal data. By adjusting the order of notifications based on the relevance of abnormal data, more appropriate alert notifications can be provided.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The reception system can estimate the nurse's emotions and adjust the way questions are answered based on that estimation. For example, if the nurse is stressed, it can provide a simple interface and minimize the steps required to input questions. If the nurse is relaxed, it can offer detailed question options and suggest customizable questioning methods. Furthermore, if the nurse is in a hurry, it can prioritize voice input to allow for quick questioning. This allows for more appropriate questioning by adjusting the questioning method according to the nurse's emotions.

[0110] The response unit can estimate the nurse's emotions and adjust the way it expresses its response based on those emotions. For example, if the nurse is stressed, it will provide a simple and clear response. If the nurse is relaxed, it can provide a response that includes detailed explanations. Furthermore, if the nurse is in a hurry, it can provide a concise and quick response. In this way, by adjusting the way the response is expressed according to the nurse's emotions, it can provide a more appropriate response.

[0111] The recording unit can estimate the nurse's emotions and adjust the way the record is presented based on those estimates. For example, if the nurse is stressed, it can provide a simple and clear way of expressing the situation. If the nurse is relaxed, it can provide a way of expressing the situation that includes detailed explanations. Furthermore, if the nurse is in a hurry, it can provide a concise and rapid way of expressing the situation. By adjusting the way the record is presented according to the nurse's emotions, it is possible to provide more appropriate records.

[0112] The monitoring unit can estimate the patient's emotions and adjust the monitoring criteria based on those estimates. For example, if the patient is feeling anxious, monitoring can be performed more frequently. Conversely, if the patient is relaxed, the frequency of monitoring can be reduced. Furthermore, if the patient is agitated, the monitoring criteria can be made stricter. This allows for more appropriate monitoring by adjusting the monitoring criteria according to the patient's emotions.

[0113] The alert system can estimate the nurse's emotions and adjust the alert notification method based on those emotions. For example, if the nurse is stressed, it can provide a simple and clear alert notification. If the nurse is relaxed, it can provide an alert notification with a detailed explanation. Furthermore, if the nurse is in a hurry, it can provide a concise and rapid alert notification. This allows for more appropriate alert notifications by adjusting the notification method according to the nurse's emotions.

[0114] The reception desk can analyze a nurse's past question history when receiving a question and select the most suitable reception method. For example, it can automatically display frequently asked questions as suggestions. It can also prioritize suggesting question methods (voice, text, etc.) that the nurse has used in the past. Furthermore, it can predict and suggest questions that will be asked at specific times based on the nurse's past question history. In this way, the optimal question reception method can be selected by analyzing past question history.

[0115] The reception desk can filter questions based on the nurse's current workload and areas of interest. For example, it can prioritize questions related to patients the nurse is currently caring for. It can also filter and display relevant questions based on the nurse's areas of interest. Furthermore, it can suggest appropriate questions considering the nurse's current workload. This allows for more appropriate question reception by filtering questions based on current workload and areas of interest.

[0116] The response unit can adjust the level of detail in its response based on the importance of the patient data. For example, it can provide a detailed response based on important patient data, a concise response based on general patient data, and a rapid response based on urgent patient data. By adjusting the level of detail in the response based on the importance of the patient data, it can provide a more appropriate response.

[0117] The monitoring unit can improve the accuracy of monitoring by referring to the patient's past vital sign data during monitoring. For example, it can adjust the monitoring criteria based on the patient's past vital sign data. It can also detect abnormalities early by referring to the patient's past vital sign data. Furthermore, it can analyze the patient's past vital sign data to improve the accuracy of monitoring. In this way, the accuracy of monitoring can be improved by referring to past vital sign data.

[0118] The alerting unit can adjust the level of detail in alerts based on the importance of the patient's abnormal data. For example, it can provide detailed alerts based on important abnormal data, concise alerts based on general abnormal data, and rapid alerts based on highly urgent abnormal data. By adjusting the level of detail in alerts based on the importance of the abnormal data, more appropriate alerts can be provided.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk receives questions from nurses. The reception desk can accept questions in various formats, such as text or audio. Step 2: The response unit uses a generation AI to analyze past patient data based on the questions received by the reception unit and provides answers. For example, the response unit uses the generation AI to analyze past medical records and vital sign data to provide specific answers. Step 3: The recording unit uses a generation AI to document the nurse's dictation in real time and organize it into an appropriate format. For example, the generation AI converts the nurse's dictation into text data and adjusts the document's layout and format. Step 4: The monitoring unit monitors the patient's vital signs in real time and detects abnormalities. The monitoring unit monitors vital signs such as heart rate, blood pressure, and body temperature, and detects abnormalities. Step 5: The alert unit uses a generation AI to notify users of anomalies detected by the monitoring unit via voice. For example, when the generation AI detects an anomaly, the alert unit will explain the situation via voice, allowing nurses to respond immediately without having to check the screen.

[0121] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0124] Each of the multiple elements described above, including the reception unit, response unit, recording unit, monitoring unit, and alert unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which receives questions from nurses in text or voice format. The response unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, where a generating AI analyzes past medical records and vital sign data to provide specific answers. The recording unit is implemented by, for example, the control unit 46A of the smart device 14, which documents the nurse's oral statements in real time and organizes them in an appropriate format. The monitoring unit monitors the patient's vital signs in real time using, for example, the camera 42 and sensors of the smart device 14 and detects abnormalities. The alert unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, where a generating AI explains the situation aloud when an abnormality is detected, allowing nurses to respond immediately without checking the screen. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 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.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the reception unit, response unit, recording unit, monitoring unit, and alert unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which receives questions from nurses in voice format. The response unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where a generating AI analyzes past medical records and vital sign data to provide specific answers. The recording unit is implemented, for example, by the control unit 46A of the smart glasses 214, which documents the nurse's dictation in real time and organizes it into an appropriate format. The monitoring unit monitors the patient's vital signs in real time using the camera 42 and sensors of the smart glasses 214 and detects abnormalities. The alert unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where a generating AI explains the situation aloud when an abnormality is detected, enabling the nurse to respond immediately without checking the screen. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the reception unit, response unit, recording unit, monitoring unit, and alert unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives questions from nurses in voice format. The response unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, where a generating AI analyzes past medical records and vital sign data to provide specific answers. The recording unit is implemented by, for example, the control unit 46A of the headset terminal 314, which documents the nurse's dictation in real time and organizes it into an appropriate format. The monitoring unit monitors the patient's vital signs in real time using, for example, the camera 42 and sensors of the headset terminal 314 and detects abnormalities. The alert unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, where a generating AI explains the situation aloud when an abnormality is detected, enabling the nurse to respond immediately without checking the screen. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the reception unit, response unit, recording unit, monitoring unit, and alert unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, which receives questions from nurses in voice format. The response unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, where a generating AI analyzes past medical records and vital sign data to provide specific answers. The recording unit is implemented by, for example, the control unit 46A of the robot 414, which documents the nurse's dictation in real time and organizes it into an appropriate format. The monitoring unit monitors the patient's vital signs in real time using the camera 42 and sensors of the robot 414 and detects abnormalities. The alert unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, where a generating AI explains the situation aloud when an abnormality is detected, allowing nurses to respond immediately without checking a screen. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0174] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) The reception desk accepts questions from nurses, A response unit analyzes past patient data based on questions received by the reception unit and provides answers, A recording unit that documents nurses' oral statements in real time, A monitoring unit that monitors the patient's vital signs and detects abnormalities, The system includes an alert unit that provides voice notification of any abnormalities detected by the monitoring unit. A system characterized by the following features. (Note 2) The response unit is The system incorporates a method to analyze past patient data using generated AI and provide specific answers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned recording unit is The system includes a method for documenting nurses' dictations using generation AI and organizing them into an appropriate format. The system described in Appendix 1, characterized by the features described herein. (Note 4) The monitoring unit, It includes a method for monitoring patients' vital signs in real time and detecting abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The alert unit is, It includes a method for notifying users of anomalies via voice using generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system includes a method for estimating the nurse's emotions and adjusting the way questions are answered based on the estimated emotions of the nurse. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system includes a mechanism to analyze the nurse's past question history when receiving questions and select the most appropriate method of handling them. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system includes a method for filtering questions based on the nurse's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It includes a method for estimating the nurse's emotions and determining the priority of questions to ask based on the estimated emotions of the nurse. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system includes a mechanism to prioritize questions based on the nurse's geographical location when receiving inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving questions, the system should analyze the nurses' social media activity and implement a method for receiving relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The response unit is The system includes a method for estimating the nurse's emotions and adjusting the way responses are expressed based on the estimated emotions of the nurse. The system described in Appendix 1, characterized by the features described herein. (Note 13) The response unit is The system includes a method for adjusting the level of detail in responses based on the importance of patient data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The response unit is The system includes a method for applying different response algorithms depending on the category of patient data during the response process. The system described in Appendix 1, characterized by the features described herein. (Note 15) The response unit is It includes a method for estimating the nurse's emotions and adjusting the length of the response based on the estimated emotions of the nurse. The system described in Appendix 1, characterized by the features described herein. (Note 16) The response unit is The system includes a method for prioritizing responses based on when patient data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The response unit is The system includes a method for adjusting the order of responses based on the relevance of patient data during the response process. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recording unit is The system includes a method for estimating a nurse's emotions and adjusting the way records are presented based on the estimated emotions of the nurse. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recording unit is The system includes a method for adjusting the level of detail in the record based on the importance of the nurse's verbal statements. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recording unit is The system includes a method for applying different recording algorithms depending on the category of the nurse's dictated content during recording. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recording unit is It includes a method for estimating the nurse's emotions and adjusting the length of the recording based on the estimated emotions of the nurse. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recording unit is The system includes a method for determining the priority of records based on when the nurse's oral statements were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recording unit is The system includes a method for adjusting the order of recordings based on the relevance of the nurse's verbal statements during the recording process. The system described in Appendix 1, characterized by the features described herein. (Note 24) The monitoring unit, It includes a method for estimating the patient's emotions and adjusting monitoring criteria based on the estimated patient emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, The system includes a method for improving monitoring accuracy based on the patient's past vital sign data during monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, The system includes a method for performing monitoring based on patient attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The monitoring unit, The system includes a method for estimating a patient's emotions and adjusting the order in which monitoring results are displayed based on the estimated patient emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The monitoring unit, The system includes a method for monitoring patients based on their geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 29) The monitoring unit, The system includes a method for improving the accuracy of monitoring based on relevant patient literature during monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 30) The alert unit is, It includes a method for estimating the nurse's emotions and adjusting the alert notification method based on the estimated nurse's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The alert unit is, The system includes a method for adjusting the level of detail of alert notifications based on the importance of the patient's abnormal data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The alert unit is, The system includes a method for applying different notification algorithms depending on the category of abnormal patient data when an alert is sent. The system described in Appendix 1, characterized by the features described herein. (Note 33) The alert unit is, It includes a method to estimate the nurse's emotions and adjust the length of the alert notification based on the estimated nurse's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The alert unit is, The system includes a method for determining the priority of alert notifications based on when abnormal patient data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 35) The alert unit is, The system includes a method for adjusting the order of alert notifications based on the relevance of patient abnormality data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk accepts questions from nurses, A response unit analyzes past patient data based on questions received by the reception unit and provides answers, A recording unit that documents nurses' oral statements in real time, A monitoring unit that monitors the patient's vital signs and detects abnormalities, The system includes an alert unit that provides voice notification of any abnormalities detected by the monitoring unit. A system characterized by the following features.

2. The response unit is It features a method that uses generation AI to analyze past patient data and provide specific answers. The system according to feature 1.

3. The aforementioned recording unit is It includes a method for documenting nurses' oral statements using generation AI and organizing them into an appropriate format. The system according to feature 1.

4. The monitoring unit, It includes a method for monitoring patients' vital signs in real time and detecting abnormalities. The system according to feature 1.

5. The alert unit is, It includes a method for notifying abnormalities via voice using generated AI. The system according to feature 1.

6. The aforementioned reception unit is The system includes a method for estimating the nurse's emotions and adjusting the way questions are answered based on the estimated emotions of the nurse. The system according to feature 1.

7. The aforementioned reception unit is The system includes a mechanism to analyze the nurse's past question history when receiving questions and select the most appropriate method of handling them. The system according to feature 1.

8. The aforementioned reception unit is The system includes a method for filtering questions based on the nurse's current work situation and areas of interest. The system according to feature 1.

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