System

Abstract

To share the response histories and necessary response information of patients among nurses and prevent duplications or missed responses.SOLUTION: A system according to an embodiment comprises a recognition unit, a response history acquisition unit, a response information acquisition unit, a determination unit, and a notification unit. The recognition unit recognizes a patient ID. The response history acquisition unit acquires a response history for a patient having the patient ID recognized by the recognition unit. The response information acquisition unit acquires response information indicating responses needed for the patient. The determination unit determines response-done matters and response-missed matters on the basis of the response history acquired by the response history acquisition unit and the response information acquired by the response information acquisition unit. The notification unit notifies a nurse about the result of determination by the determination unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, patient treatment histories and necessary treatment information are not shared among nurses, which can lead to duplication or failure to respond.

[0005] The system according to the embodiment aims to enable nurses to share a patient's response history and necessary response information, thereby preventing duplication or failure to respond. [Means for solving the problem]

[0006] The system according to the embodiment includes a recognition unit, a response history acquisition unit, a response information acquisition unit, a judgment unit, and a notification unit. The recognition unit recognizes a patient ID. The response history acquisition unit acquires a response history for a patient having a patient ID recognized by the recognition unit. The response information acquisition unit acquires response information indicating the response required for the patient. The judgment unit judges which items have been addressed and which items have not been addressed based on the response history acquired by the response history acquisition unit and the response information acquired by the response information acquisition unit. The notification unit notifies the nurse of the judgment result by the judgment unit. Effect of the Invention

[0007] The system according to the embodiment allows nurses to share a patient's response history and necessary response information, preventing duplication or failure to respond. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The system according to the embodiment of the present invention is a gadget worn like a wristband, and issues handover matters and points of caution according to the work and the patient. This system automatically recognizes the patient ID using wireless communication (e.g., Bluetooth (registered trademark)), and acquires the treatment history for the patient based on the recognized patient ID. Furthermore, the necessary measures for that day are input in advance, and based on the treatment history and the treatment information, it is judged whether the matters have been treated or not. The judgment result is notified to the nurse. This allows information to be shared even if the nurse changes, and duplication is prevented. For example, when a nurse approaches a patient, the wristband automatically recognizes the patient ID. This recognized patient ID is transmitted to the treatment history acquisition unit. Next, the treatment history acquisition unit acquires the treatment history for the patient based on the recognized patient ID. For example, past medical records and treatment history are included. This treatment history is transmitted to the treatment information acquisition unit. Furthermore, the treatment information acquisition unit inputs the necessary measures for that day in advance, and based on the treatment history and the treatment information, it is judged whether the matters have been treated or not. For example, the matters that have not been treated include treatments and points of caution that have not yet been performed. This judgment result is transmitted to the notification unit. The notification unit notifies the nurse of the judgment result. For example, the wristband display displays the dealt with items and the undealt with items in different forms. This allows the nurse to judge on the spot whether the items have been dealt with or not. The device also includes a patient information acquisition unit that acquires patient information indicating matters to be handed over and points to be noted regarding the patient, and the notification unit notifies the nurse of the patient information acquired by the patient information acquisition unit. This allows the nurse to grasp important information regarding the patient. Furthermore, the device also includes a function of recognizing the nurse ID, which is the identification information of the nurse, and notifying the judgment result if the nurse ID is a specific nurse ID. This allows the information to be notified only to a specific nurse. Finally, the device includes a vital sign acquisition unit that acquires vital sign information of the patient, and judges whether the items have been dealt with or not based on the vital sign information in addition to the response history and response information. This allows appropriate response according to the patient's condition.This allows the gadget system, which can be worn like a wristband, to consistently perform everything from recognizing the patient ID to obtaining response history and response information, determining whether a response has been received or not, and sending notifications.

[0029] The wristband system according to the embodiment includes a recognition unit, a treatment history acquisition unit, a treatment information acquisition unit, a determination unit, and a notification unit. The recognition unit recognizes a patient ID. The recognition unit automatically recognizes the patient ID using, for example, wireless communication (for example, Bluetooth). For example, when a nurse approaches a patient, the wristband automatically recognizes the patient ID. The recognized patient ID is transmitted to the treatment history acquisition unit. The treatment history acquisition unit acquires a treatment history for the patient based on the recognized patient ID. The treatment history includes, for example, past medical records and treatment history. The treatment history acquisition unit transmits the treatment history to the treatment information acquisition unit. The treatment information acquisition unit inputs the measures required for that day in advance, and determines which items have been treated and which items have not been treated based on the treatment history and the treatment information. For example, the untreated items include treatments and points of attention that have not yet been performed. The determination unit determines which items have been treated and which items have not been treated based on the treatment history acquired by the treatment history acquisition unit and the treatment information acquired by the treatment information acquisition unit. The determination result is transmitted to the notification unit. The notification unit notifies the nurse of the judgment result. For example, the wristband display displays the dealt with items and the undealt with items in different forms. This allows the nurse to judge on the spot whether the item has been dealt with or not. The notification unit includes a patient information acquisition unit that acquires patient information indicating matters to be handed over and points to be noted regarding the patient, and notifies the nurse of the patient information acquired by the patient information acquisition unit. This allows the nurse to grasp important information regarding the patient. The notification unit also recognizes the nurse ID, which is the identification information of the nurse, and has a function of notifying the judgment result if the nurse ID is a specific nurse ID. This allows the information to be notified only to a specific nurse. Furthermore, the notification unit includes a vital sign acquisition unit that acquires vital sign information of the patient, and the judgment unit judges whether the item has been dealt with or not based on the vital sign information in addition to the response history and response information. This allows appropriate response according to the patient's condition. This allows the wristband system to consistently perform operations from recognizing the patient ID to acquiring the response history, acquiring the response information, judging whether the item has been dealt with or not, and notifying.

[0030] The recognition unit recognizes the patient ID. The recognition unit automatically recognizes the patient ID using, for example, wireless communication (e.g., Bluetooth). Specifically, when a nurse approaches a patient, the wristband automatically recognizes the patient ID. This recognition process is realized by mutual communication between a Bluetooth module built into the wristband and a Bluetooth module built into the patient's ID tag. When the wristband recognizes the patient ID, it immediately transmits the information to the response history acquisition unit. This saves the nurse the trouble of manually inputting the patient ID, and allows the patient information to be acquired quickly and accurately. Furthermore, the recognition unit has an algorithm for accurately identifying the ID of each patient even when multiple patients are in the same area. For example, the recognition unit can use signal strength and timestamps to preferentially recognize the ID of the closest patient. This allows the recognition unit to accurately recognize the patient ID even in a crowded environment and transmit it to the response history acquisition unit.

[0031] The response history acquisition unit acquires the response history for the patient based on the recognized patient ID. The response history includes, for example, past medical records and treatment history. Specifically, the response history acquisition unit accesses the hospital's electronic medical record system (EHR) and acquires the medical records and treatment history of the corresponding patient. This includes past consultations, prescribed medications, details of tests and treatments performed, and the like. The response history acquisition unit quickly acquires this information and transmits it to the response information acquisition unit. Furthermore, the response history acquisition unit can also acquire important medical information such as the patient's allergy information and medical history. This allows the nurse to grasp the patient's overall medical history and take appropriate action. The response history acquisition unit can update data in real time and provide the latest information. For example, when a new medical record is added, the response history acquisition unit immediately acquires the information and transmits it to the response information acquisition unit. This allows the response history acquisition unit to always provide a response history based on the latest information and support the nurse's work.

[0032] The correspondence information acquisition unit inputs the necessary measures for that day in advance, and judges which items have been dealt with and which have not been dealt with based on the response history and the response information. Specifically, the correspondence information acquisition unit acquires the planned procedures and precautions for that day input by the doctor or nurse, and compares them with the response history. For example, the unaddressed items include procedures and precautions that have not yet been performed. Based on this information, the correspondence information acquisition unit lists the handled and unaddressed items, and transmits them to the judgment unit. Furthermore, the correspondence information acquisition unit can identify high-priority items in consideration of the patient's condition and vital sign information. For example, it prioritizes the list of procedures with high urgency and matters requiring attention, and notifies the nurse. This allows the correspondence information acquisition unit to provide support for nurses to perform their work efficiently. The correspondence information acquisition unit updates data in real time and can make judgments based on the latest information. For example, when a new procedure is added, the correspondence information acquisition unit immediately acquires the information and transmits it to the judgment unit. This allows the correspondence information acquisition unit to always provide correspondence information based on the latest information, and support the work of nurses.

[0033] The determination unit determines whether an item has been dealt with or not yet dealt with based on the response history acquired by the response history acquisition unit and the response information acquired by the response information acquisition unit. Specifically, the determination unit collates the response history with the response information to identify an item that has not been dealt with. For example, the determination unit compares the past medical records and treatment history with the scheduled treatment for that day, and lists the treatments that have not yet been performed and points to be noted. Based on this information, the determination unit lists the items that have been dealt with and the items that have not yet been dealt with, and transmits them to the notification unit. Furthermore, the determination unit can identify items with high priority in consideration of the vital sign information of the patient. For example, it prioritizes the list of highly urgent treatments and items that require attention, and notifies the nurse. This allows the determination unit to provide support for nurses to perform their work efficiently. The determination unit can update data in real time and make a decision based on the latest information. For example, when a new treatment is added, the determination unit immediately acquires the information and transmits it to the notification unit. This allows the determination unit to always provide a decision based on the latest information and support the work of nurses.

[0034] The notification unit notifies the nurse of the judgment result. Specifically, the wristband display displays the dealt with items and the undealt with items in different ways. For example, the dealt with items are displayed in green and the undealt with items in red, so that the nurse can grasp the situation at a glance. Furthermore, the notification unit includes a patient information acquisition unit that acquires patient information indicating matters to be handed over and points to be noted regarding the patient, and notifies the nurse of the patient information acquired by the patient information acquisition unit. This allows the nurse to grasp important information regarding the patient. The notification unit also recognizes the nurse ID, which is the identification information of the nurse, and has a function of notifying the judgment result if the nurse ID is a specific nurse ID. This makes it possible to notify information only to a specific nurse. Furthermore, the notification unit includes a vital sign acquisition unit that acquires vital sign information of the patient, and the judgment unit judges the dealt with items and the undealt with items based on the vital sign information in addition to the response history and response information. This makes it possible to respond appropriately according to the patient's condition. The notification unit includes various notification means such as voice notification and vibration notification, so that the nurse can respond immediately. For example, for matters of high urgency, the notification unit alerts the nurse by using voice or vibration notifications, thereby supporting the nurse to respond quickly and appropriately.

[0035] The wristband system includes a patient information acquisition unit that acquires patient information indicating matters to be passed on or precautions to be taken regarding the patient. The patient information acquisition unit acquires matters to be passed on or precautions to be taken regarding the patient. For example, the information includes the patient's medical history, allergy information, precautions, and the like. The patient information acquisition unit acquires this information and transmits it to the notification unit. This allows the nurse to grasp important information regarding the patient. Some or all of the above-mentioned processing in the patient information acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the patient information acquisition unit inputs the patient's medical history data into AI, which can analyze the medical history data and extract important information. This allows important information regarding the patient to be acquired and notified to the nurse.

[0036] The notification unit can notify the nurse of the patient information acquired by the patient information acquisition unit. For example, the patient information includes the patient's medical history, allergy information, and precautions. By notifying the nurse of this information, the notification unit can allow the nurse to grasp important information about the patient. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the patient information acquired from the patient information acquisition unit to AI, which can analyze the information and determine the content to be notified to the nurse. In this way, by notifying the nurse of the patient information, the nurse can grasp important information about the patient.

[0037] The recognition unit can recognize the patient ID using wireless communication. The recognition unit recognizes the patient ID using wireless communication (e.g., Bluetooth). For example, when a nurse approaches a patient, the wristband automatically recognizes the patient ID. The recognized patient ID is transmitted to the response history acquisition unit. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input wireless communication data to AI, which analyzes the data to recognize the patient ID. This makes it possible to automatically recognize the patient ID using wireless communication.

[0038] The notification unit may include a display unit that displays dealt with and undealt with in different ways. For example, dealt with and undealt with can be displayed in different colors on the wristband display. Also, icons and text can be used to visually distinguish between the dealt with and undealt with. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input dealt with and undealt with to AI, and the AI ​​may determine the display method. This allows dealt with and undealt with to be visually distinguished from each other.

[0039] The recognition unit recognizes the nurse ID, which is the identification information of the nurse, and the notification unit can notify the judgment result when it is a specific nurse ID. For example, when a nurse wears a wristband, the wristband automatically recognizes the nurse ID. The recognized nurse ID is transmitted to the notification unit. When the nurse ID recognized by the recognition unit is a specific nurse ID, the notification unit notifies the judgment result by the judgment unit. This makes it possible to notify information only to a specific nurse. A part or all of the above-mentioned processing in the recognition unit and the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit can input the nurse ID data to the AI, and the AI ​​can analyze the data and recognize the nurse ID. The notification unit can input the nurse ID acquired from the recognition unit to the AI, and the AI ​​can determine whether it is a specific nurse ID. This makes it possible to notify information only to a specific nurse.

[0040] The wristband system includes a vital sign acquisition unit that acquires vital sign information of a patient, and the judgment unit can judge whether an item has been dealt with or not based on the vital sign information in addition to the response history and the response information. The vital sign acquisition unit acquires vital sign information of the patient. For example, the vital sign information includes heart rate, blood pressure, body temperature, etc. The vital sign acquisition unit acquires this information and transmits it to the judgment unit. The judgment unit judges whether an item has been dealt with or not based on the vital sign information in addition to the response history and the response information. For example, if the vital sign information of the patient is within a normal range, it is judged to have been dealt with, and if there is an abnormality, it is judged to have not been dealt with. A part or all of the above-mentioned processing in the vital sign acquisition unit and the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the vital sign acquisition unit inputs the vital sign data of the patient to AI, and the AI ​​can analyze the data and detect abnormalities. This enables more appropriate response based on the vital sign information of the patient.

[0041] The recognition unit can track the patient's movements and position information in real time and select the optimal recognition timing. For example, if the patient is moving, recognition is waited until the patient stops moving. Also, recognition can be performed automatically when the patient reaches a specific position. Furthermore, if the patient is in bed, recognition can be performed based on the position information of the bed. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the patient's position data to the generation AI, which can analyze the data and select the optimal recognition timing. This makes it possible to recognize the patient ID at the optimal timing based on the patient's movements and position information.

[0042] The recognition unit can improve the recognition accuracy by referring to the past recognition history of the patient during recognition. For example, the recognition accuracy can be improved based on the patient ID recognized in the past. Also, the recognition accuracy can be improved by learning the pattern of erroneous recognition from the past recognition history. Furthermore, a specific patient ID can be preferentially recognized based on the past recognition history. A part or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit can input the past recognition history data to the generation AI, which can analyze the data and improve the recognition accuracy. As a result, the recognition accuracy is improved by referring to the past recognition history.

[0043] The recognition unit can improve the recognition accuracy by taking into account the environmental information of the patient during recognition. The recognition unit improves the recognition accuracy by taking into account the environmental information of the patient (e.g., room temperature and lighting) during recognition. For example, when the room temperature is high, the recognition accuracy can be adjusted. Also, when the lighting is dark, correction can be made to improve the recognition accuracy. Furthermore, when the environmental sound is loud, noise cancellation can be performed to improve the recognition accuracy. Some or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit inputs environmental information data to a generation AI, which can analyze the data to improve the recognition accuracy. Thereby, the recognition accuracy is improved by taking into account the environmental information of the patient.

[0044] During recognition, the recognition unit can analyze the patient's social media activity and obtain relevant information. For example, information about a recent health condition can be obtained from the patient's social media activity. Current location information can also be obtained from the patient's social media activity. Furthermore, information about a specific event can be obtained from the patient's social media activity. Some or all of the above-mentioned processing in the recognition unit can be performed, for example, using AI or can be performed without using AI. For example, the recognition unit can input social media data to a generation AI, which can analyze the data to obtain relevant information. This allows the patient's social media activity to be analyzed to obtain relevant information.

[0045] When acquiring the response history, the response history acquisition unit can analyze the patient's past response history and select the optimal acquisition method. For example, the most relevant history can be preferentially acquired from the past response history. In addition, the past response history can be analyzed and the acquisition method can be optimized. Furthermore, a specific response history can be preferentially acquired based on the past response history. A part or all of the above-mentioned processing in the response history acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the response history acquisition unit inputs past response history data to a generation AI, which can analyze the data and select the optimal acquisition method. As a result, the optimal acquisition method can be selected by analyzing the patient's past response history.

[0046] The response history acquisition unit can perform filtering based on the patient's current health condition when acquiring the response history. For example, if the patient's current health condition is poor, important response history can be preferentially acquired. Also, if the patient's current health condition is good, all response history can be acquired. Furthermore, specific response history can be filtered based on the patient's current health condition. A part or all of the above-mentioned processing in the response history acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the response history acquisition unit can input the patient's health condition data to the generation AI, and the generation AI can analyze the data and perform filtering. This allows important response history to be preferentially acquired based on the patient's current health condition.

[0047] When acquiring the response history, the response history acquisition unit can preferentially acquire a history with high relevance in consideration of the geographical location information of the patient. For example, when the patient is in a specific hospital, the response history at the hospital can be preferentially acquired. Also, when the patient is at home, the response history at home can be preferentially acquired. Furthermore, when the patient is on the move, the response history at the destination can be preferentially acquired. A part or all of the above-mentioned processing in the response history acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the response history acquisition unit inputs the geographical location data of the patient to the generation AI, and the generation AI can analyze the data and preferentially acquire a history with high relevance. As a result, by considering the geographical location information of the patient, a history with high relevance can be preferentially acquired.

[0048] The response history acquisition unit can analyze the social media activity of the patient when acquiring the response history, and acquire related history. For example, a history related to a recent health condition can be acquired from the social media activity of the patient. In addition, a history related to current location information can be acquired from the social media activity of the patient. Furthermore, a history related to a specific event can be acquired from the social media activity of the patient. A part or all of the above-mentioned processing in the response history acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the response history acquisition unit can input social media data to the generation AI, and the generation AI can analyze the data and acquire related history. In this way, the related history can be acquired by analyzing the social media activity of the patient.

[0049] When acquiring the corresponding information, the corresponding information acquisition unit can analyze the patient's past corresponding information and select the optimal acquisition method. For example, the most relevant information can be preferentially acquired from the past corresponding information. In addition, the past corresponding information can be analyzed to optimize the acquisition method. Furthermore, specific corresponding information can be preferentially acquired based on the past corresponding information. A part or all of the above-mentioned processing in the corresponding information acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the corresponding information acquisition unit inputs past corresponding information data to a generating AI, and the generating AI can analyze the data and select the optimal acquisition method. As a result, the optimal acquisition method can be selected by analyzing the patient's past corresponding information.

[0050] The corresponding information acquisition unit can perform filtering based on the patient's current health condition when acquiring the corresponding information. For example, if the patient's current health condition is poor, important corresponding information can be preferentially acquired. Also, if the patient's current health condition is good, all corresponding information can be acquired. Furthermore, specific corresponding information can be filtered based on the patient's current health condition. A part or all of the above-mentioned processing in the corresponding information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the corresponding information acquisition unit can input the patient's health condition data to the generation AI, and the generation AI can analyze the data and perform filtering. This allows important corresponding information to be preferentially acquired based on the patient's current health condition.

[0051] When acquiring the corresponding information, the corresponding information acquisition unit can preferentially acquire information with high relevance in consideration of the geographical location information of the patient. For example, when the patient is in a specific hospital, the corresponding information at the hospital can be preferentially acquired. Furthermore, when the patient is at home, the corresponding information at home can be preferentially acquired. Furthermore, when the patient is on the move, the corresponding information at the move destination can be preferentially acquired. A part or all of the above-mentioned processing in the corresponding information acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the corresponding information acquisition unit inputs the geographical location data of the patient to the generation AI, and the generation AI can analyze the data and preferentially acquire information with high relevance. As a result, by considering the geographical location information of the patient, it is possible to preferentially acquire information with high relevance.

[0052] The corresponding information acquisition unit can analyze the social media activity of the patient when acquiring the corresponding information, and acquire related information. For example, information on a recent health condition can be acquired from the social media activity of the patient. Also, information related to current location information can be acquired from the social media activity of the patient. Furthermore, information on a specific event can be acquired from the social media activity of the patient. A part or all of the above-mentioned processing in the corresponding information acquisition unit can be performed, for example, using AI, or can be performed without using AI. For example, the corresponding information acquisition unit can input social media data to the generation AI, which can analyze the data and acquire related information. In this way, related information can be acquired by analyzing the social media activity of the patient.

[0053] The judgment unit can improve the judgment accuracy by referring to the patient's past response history and response information at the time of judgment. For example, it can determine whether the current response is appropriate based on the past response history. It can also determine whether the current response is appropriate by referring to the past response information. Furthermore, it can improve the judgment accuracy by combining the past response history and the response information. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit inputs past response history data and response information data to the generation AI, and the generation AI analyzes the data to improve the judgment accuracy. As a result, the judgment accuracy is improved by referring to the patient's past response history and response information.

[0054] The judgment unit can customize the judgment criteria based on the patient's current health condition at the time of judgment. For example, if the patient's current health condition is poor, the judgment criteria can be relaxed. Also, if the patient's current health condition is good, the judgment criteria can be tightened. Furthermore, the judgment criteria can be customized based on the patient's current health condition. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the patient's health condition data into the generation AI, and the generation AI can analyze the data and customize the judgment criteria. This allows the judgment criteria to be customized based on the patient's current health condition.

[0055] The judgment unit can adjust the judgment criteria by taking into account the geographical location information of the patient when making a judgment. For example, if the patient is in a specific hospital, the judgment can be made based on the criteria of the hospital. Also, if the patient is at home, the judgment can be made based on the criteria of the home. Furthermore, if the patient is on the move, the judgment can be made based on the criteria of the destination. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the geographical location data of the patient to the generation AI, which can analyze the data and adjust the judgment criteria. In this way, the judgment criteria can be adjusted by taking into account the geographical location information of the patient.

[0056] At the time of judgment, the judgment unit can analyze the social media activity of the patient and reflect related information in the judgment. For example, information on the recent health condition can be reflected in the judgment from the social media activity of the patient. In addition, current location information can be reflected in the judgment from the social media activity of the patient. Furthermore, information on a specific event can be reflected in the judgment from the social media activity of the patient. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit inputs social media data to the generation AI, and the generation AI analyzes the data and reflects related information in the judgment. In this way, by analyzing the social media activity of the patient, related information can be reflected in the judgment.

[0057] The notification unit can customize the notification contents by referring to the patient's past response history and response information at the time of notification. For example, the current notification contents can be customized based on the past response history. In addition, the current notification contents can be customized by referring to the past response information. Furthermore, the notification contents can be customized by combining the past response history and the response information. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the past response history data and the response information data to the generation AI, and the generation AI can analyze the data and customize the notification contents. This allows the notification contents to be customized by referring to the patient's past response history and response information.

[0058] The notification unit can adjust the notification contents based on the patient's current health condition at the time of notification. For example, if the patient's current health condition is poor, important notification contents can be preferentially displayed. Also, if the patient's current health condition is good, all notification contents can be displayed. Furthermore, the notification contents can be adjusted based on the patient's current health condition. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the patient's health condition data to the generation AI, which can analyze the data and adjust the notification contents. This allows important notification contents to be preferentially displayed based on the patient's current health condition.

[0059] The notification unit can adjust the notification contents in consideration of the geographical location information of the patient at the time of notification. For example, when the patient is in a specific hospital, the notification contents at the hospital can be preferentially displayed. Furthermore, when the patient is at home, the notification contents at home can be preferentially displayed. Furthermore, when the patient is on the move, the notification contents at the move destination can be preferentially displayed. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the geographical location data of the patient to the generation AI, and the generation AI can analyze the data and adjust the notification contents. In this way, the notification contents can be adjusted by considering the geographical location information of the patient.

[0060] At the time of notification, the notification unit can analyze the social media activity of the patient and notify the patient of relevant information. For example, information on a recent health condition can be notified from the social media activity of the patient. In addition, information related to current location information can be notified from the social media activity of the patient. Furthermore, information on a specific event can be notified from the social media activity of the patient. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input social media data to a generation AI, which can analyze the data and notify the patient of relevant information. In this way, the patient's social media activity can be analyzed to notify the patient of relevant information.

[0061] When acquiring patient information, the patient information acquisition unit can analyze the patient's past information and select the optimal acquisition method. For example, the most relevant information can be preferentially acquired from the past patient information. In addition, the past patient information can be analyzed to optimize the acquisition method. Furthermore, specific information can be preferentially acquired based on the past patient information. A part or all of the above-mentioned processing in the patient information acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the patient information acquisition unit can input past patient information data to the generation AI, which can analyze the data and select the optimal acquisition method. In this way, the optimal acquisition method can be selected by analyzing the patient's past information.

[0062] When acquiring patient information, the patient information acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the patient. For example, if the patient is in a specific hospital, information at the hospital can be preferentially acquired. Also, if the patient is at home, information at home can be preferentially acquired. Furthermore, if the patient is on the move, information at the move destination can be preferentially acquired. A part or all of the above-mentioned processing in the patient information acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the patient information acquisition unit inputs the geographical location data of the patient to the generation AI, and the generation AI can analyze the data and preferentially acquire highly relevant information. As a result, highly relevant information can be preferentially acquired by considering the geographical location information of the patient.

[0063] The display unit can customize the display contents by referring to the patient's past response history and response information when displaying. For example, the current display contents can be customized based on the past response history. Also, the current display contents can be customized by referring to the past response information. Furthermore, the display contents can be customized by combining the past response history and the response information. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the past response history data and the response information data to the generation AI, and the generation AI can analyze the data and customize the display contents. This allows the display contents to be customized by referring to the patient's past response history and response information.

[0064] The display unit can adjust the display contents taking into consideration the geographical location information of the patient when displaying. For example, when the patient is in a specific hospital, the display contents at the hospital can be preferentially displayed. Also, when the patient is at home, the display contents at home can be preferentially displayed. Furthermore, when the patient is on the move, the display contents at the move destination can be preferentially displayed. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the geographical location data of the patient to the generation AI, which can analyze the data and adjust the display contents. This allows the display contents to be adjusted by taking into consideration the geographical location information of the patient.

[0065] When acquiring vital signs, the vital sign acquisition unit can analyze the patient's past vital sign history and select the optimal acquisition method. For example, based on the past vital sign history, the most relevant vital sign can be preferentially acquired. In addition, the past vital sign history can be analyzed and the acquisition method can be optimized. Furthermore, based on the past vital sign history, a specific vital sign can be preferentially acquired. A part or all of the above-mentioned processing in the vital sign acquisition unit can be performed, for example, using AI or can be performed without using AI. For example, the vital sign acquisition unit can input past vital sign history data to a generation AI, which can analyze the data and select the optimal acquisition method. Thereby, the optimal acquisition method can be selected by analyzing the patient's past vital sign history.

[0066] When acquiring vital signs, the vital sign acquisition unit can preferentially acquire highly relevant vital signs by taking into account the geographical location information of the patient. For example, if the patient is in a specific hospital, the vital signs at the hospital can be preferentially acquired. Also, if the patient is at home, the vital signs at home can be preferentially acquired. Furthermore, if the patient is on the move, the vital signs at the move destination can be preferentially acquired. A part or all of the above-mentioned processing in the vital sign acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the vital sign acquisition unit inputs the geographical location data of the patient to the generation AI, and the generation AI analyzes the data to preferentially acquire highly relevant vital signs. As a result, highly relevant vital signs can be preferentially acquired by taking into account the geographical location information of the patient.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The wristband system also includes a location information sharing unit, which allows nurses to share their location information with each other in real time. For example, when a nurse is near a patient, that location information is notified to other nurses. Also, when a specific nurse is responding to an emergency, that nurse's location information can be shared preferentially. Furthermore, the location information sharing unit can select the most appropriate nurse based on the nurse's location information and notify them. This strengthens cooperation between nurses and enables quicker responses.

[0069] The wristband system further includes an environmental sensor unit that can obtain information about the patient's surroundings. For example, information such as room temperature, humidity, and illuminance can be obtained to evaluate the patient's comfort. The environmental sensor unit can also notify a nurse if it detects abnormal environmental conditions. For example, an alert can be issued if the room temperature is too high or the humidity is too low. This provides information to maintain a comfortable environment for the patient.

[0070] The wristband system also has a learning function that can learn from nurses' operation histories and patients' response histories to improve the system's accuracy. For example, it can learn nurses' operation patterns from past operation histories and predict and suggest the next operation. It can also learn patients' response histories and predict and suggest the next response. This improves the system's usability and reduces the burden on nurses.

[0071] The wristband system further includes an energy management unit that can optimize the battery consumption of the wristband. For example, it can adjust the battery consumption based on the frequency of use and environmental conditions. The energy management unit can also learn the usage status of the wristband and suggest the optimal energy management method. This extends the battery life of the wristband and enables long-term use.

[0072] The process flow of the first embodiment will be briefly described below.

[0073] Step 1: The recognition unit recognizes the patient ID. For example, the patient ID is automatically recognized using wireless communication (e.g., Bluetooth). When the nurse approaches the patient, the wristband automatically recognizes the patient ID. The recognized patient ID is sent to the treatment history acquisition unit. Step 2: The treatment history acquisition unit acquires the treatment history for the patient based on the recognized patient ID. The treatment history includes, for example, past medical records and treatment history. The acquired treatment history is sent to the treatment information acquisition unit. Step 3: The response information acquisition unit inputs the measures required for that day in advance, and acquires information for determining which items have been addressed and which have not been addressed, based on the response history and response information. Step 4: The determination unit determines whether an item has been dealt with or not yet dealt with based on the response history acquired by the response history acquisition unit and the response information acquired by the response information acquisition unit. For example, the items that have not been dealt with include measures that have not yet been taken or points of attention. Step 5: The notification unit notifies the nurse of the result of the determination made by the determination unit. For example, the wristband display shows dealt with items and undealt with items in different ways. This allows the nurse to determine on the spot whether an item has been dealt with or not.

[0074] (Example 2) The system according to the embodiment of the present invention is a gadget worn like a wristband, and issues handover matters and points of caution according to the work and the patient. This system automatically recognizes the patient ID using wireless communication (e.g., Bluetooth), and acquires the treatment history for the patient based on the recognized patient ID. Furthermore, the system inputs the measures required for that day in advance, and judges which matters have been dealt with and which matters have not been dealt with based on the treatment history and the treatment information. The result of this judgment is notified to the nurse. This allows information to be shared even if the nurse changes, and prevents duplication. For example, when a nurse approaches a patient, the wristband automatically recognizes the patient ID. This recognized patient ID is transmitted to the treatment history acquisition unit. Next, the treatment history acquisition unit acquires the treatment history for the patient based on the recognized patient ID. For example, past medical records and treatment history are included. This treatment history is transmitted to the treatment information acquisition unit. Furthermore, the treatment information acquisition unit inputs the measures required for that day in advance, and judges which matters have been dealt with and which matters have not been dealt with based on the treatment history and the treatment information. For example, the unaddressed matters include treatments and points of caution that have not yet been performed. The result of this judgment is transmitted to the notification unit. The notification unit notifies the nurse of the judgment result. For example, the wristband display displays the dealt with items and the undealt with items in different forms. This allows the nurse to judge on the spot whether the items have been dealt with or not. The device also includes a patient information acquisition unit that acquires patient information indicating matters to be handed over and points to be noted regarding the patient, and the notification unit notifies the nurse of the patient information acquired by the patient information acquisition unit. This allows the nurse to grasp important information regarding the patient. Furthermore, the device also includes a function of recognizing the nurse ID, which is the identification information of the nurse, and notifying the judgment result if the nurse ID is a specific nurse ID. This allows the information to be notified only to a specific nurse. Finally, the device includes a vital sign acquisition unit that acquires vital sign information of the patient, and judges whether the items have been dealt with or not based on the vital sign information in addition to the response history and response information. This allows appropriate response according to the patient's condition.This allows the gadget system, which can be worn like a wristband, to consistently perform everything from recognizing the patient ID to obtaining response history and response information, determining whether a response has been received or not, and sending notifications.

[0075] The wristband system according to the embodiment includes a recognition unit, a treatment history acquisition unit, a treatment information acquisition unit, a determination unit, and a notification unit. The recognition unit recognizes a patient ID. The recognition unit automatically recognizes the patient ID using, for example, wireless communication (for example, Bluetooth). For example, when a nurse approaches a patient, the wristband automatically recognizes the patient ID. The recognized patient ID is transmitted to the treatment history acquisition unit. The treatment history acquisition unit acquires a treatment history for the patient based on the recognized patient ID. The treatment history includes, for example, past medical records and treatment history. The treatment history acquisition unit transmits the treatment history to the treatment information acquisition unit. The treatment information acquisition unit inputs the measures required for that day in advance, and determines which items have been treated and which items have not been treated based on the treatment history and the treatment information. For example, the untreated items include treatments and points of attention that have not yet been performed. The determination unit determines which items have been treated and which items have not been treated based on the treatment history acquired by the treatment history acquisition unit and the treatment information acquired by the treatment information acquisition unit. The determination result is transmitted to the notification unit. The notification unit notifies the nurse of the judgment result. For example, the wristband display displays the dealt with items and the undealt with items in different forms. This allows the nurse to judge on the spot whether the item has been dealt with or not. The notification unit includes a patient information acquisition unit that acquires patient information indicating matters to be handed over and points to be noted regarding the patient, and notifies the nurse of the patient information acquired by the patient information acquisition unit. This allows the nurse to grasp important information regarding the patient. The notification unit also recognizes the nurse ID, which is the identification information of the nurse, and has a function of notifying the judgment result if the nurse ID is a specific nurse ID. This allows the information to be notified only to a specific nurse. Furthermore, the notification unit includes a vital sign acquisition unit that acquires vital sign information of the patient, and the judgment unit judges whether the item has been dealt with or not based on the vital sign information in addition to the response history and response information. This allows appropriate response according to the patient's condition. This allows the wristband system to consistently perform operations from recognizing the patient ID to acquiring the response history, acquiring the response information, judging whether the item has been dealt with or not, and notifying.

[0076] The recognition unit recognizes the patient ID. The recognition unit automatically recognizes the patient ID using, for example, wireless communication (e.g., Bluetooth). Specifically, when a nurse approaches a patient, the wristband automatically recognizes the patient ID. This recognition process is realized by mutual communication between a Bluetooth module built into the wristband and a Bluetooth module built into the patient's ID tag. When the wristband recognizes the patient ID, it immediately transmits the information to the response history acquisition unit. This saves the nurse the trouble of manually inputting the patient ID, and allows the patient information to be acquired quickly and accurately. Furthermore, the recognition unit has an algorithm for accurately identifying the ID of each patient even when multiple patients are in the same area. For example, the recognition unit can use signal strength and timestamps to preferentially recognize the ID of the closest patient. This allows the recognition unit to accurately recognize the patient ID even in a crowded environment and transmit it to the response history acquisition unit.

[0077] The response history acquisition unit acquires the response history for the patient based on the recognized patient ID. The response history includes, for example, past medical records and treatment history. Specifically, the response history acquisition unit accesses the hospital's electronic medical record system (EHR) and acquires the medical records and treatment history of the corresponding patient. This includes past consultations, prescribed medications, details of tests and treatments performed, and the like. The response history acquisition unit quickly acquires this information and transmits it to the response information acquisition unit. Furthermore, the response history acquisition unit can also acquire important medical information such as the patient's allergy information and medical history. This allows the nurse to grasp the patient's overall medical history and take appropriate action. The response history acquisition unit can update data in real time and provide the latest information. For example, when a new medical record is added, the response history acquisition unit immediately acquires the information and transmits it to the response information acquisition unit. This allows the response history acquisition unit to always provide a response history based on the latest information and support the nurse's work.

[0078] The correspondence information acquisition unit inputs the necessary measures for that day in advance, and judges which items have been dealt with and which have not been dealt with based on the response history and the response information. Specifically, the correspondence information acquisition unit acquires the planned procedures and precautions for that day input by the doctor or nurse, and compares them with the response history. For example, the unaddressed items include procedures and precautions that have not yet been performed. Based on this information, the correspondence information acquisition unit lists the handled and unaddressed items, and transmits them to the judgment unit. Furthermore, the correspondence information acquisition unit can identify high-priority items in consideration of the patient's condition and vital sign information. For example, it prioritizes the list of procedures with high urgency and matters requiring attention, and notifies the nurse. This allows the correspondence information acquisition unit to provide support for nurses to perform their work efficiently. The correspondence information acquisition unit updates data in real time and can make judgments based on the latest information. For example, when a new procedure is added, the correspondence information acquisition unit immediately acquires the information and transmits it to the judgment unit. This allows the correspondence information acquisition unit to always provide correspondence information based on the latest information, and support the work of nurses.

[0079] The determination unit determines whether an item has been dealt with or not yet dealt with based on the response history acquired by the response history acquisition unit and the response information acquired by the response information acquisition unit. Specifically, the determination unit collates the response history with the response information to identify an item that has not been dealt with. For example, the determination unit compares the past medical records and treatment history with the scheduled treatment for that day, and lists the treatments that have not yet been performed and points to be noted. Based on this information, the determination unit lists the items that have been dealt with and the items that have not yet been dealt with, and transmits them to the notification unit. Furthermore, the determination unit can identify items with high priority in consideration of the vital sign information of the patient. For example, it prioritizes the list of highly urgent treatments and items that require attention, and notifies the nurse. This allows the determination unit to provide support for nurses to perform their work efficiently. The determination unit can update data in real time and make a decision based on the latest information. For example, when a new treatment is added, the determination unit immediately acquires the information and transmits it to the notification unit. This allows the determination unit to always provide a decision based on the latest information and support the work of nurses.

[0080] The notification unit notifies the nurse of the judgment result. Specifically, the wristband display displays the dealt with items and the undealt with items in different ways. For example, the dealt with items are displayed in green and the undealt with items in red, so that the nurse can grasp the situation at a glance. Furthermore, the notification unit includes a patient information acquisition unit that acquires patient information indicating matters to be handed over and points to be noted regarding the patient, and notifies the nurse of the patient information acquired by the patient information acquisition unit. This allows the nurse to grasp important information regarding the patient. The notification unit also recognizes the nurse ID, which is the identification information of the nurse, and has a function of notifying the judgment result if the nurse ID is a specific nurse ID. This makes it possible to notify information only to a specific nurse. Furthermore, the notification unit includes a vital sign acquisition unit that acquires vital sign information of the patient, and the judgment unit judges the dealt with items and the undealt with items based on the vital sign information in addition to the response history and response information. This makes it possible to respond appropriately according to the patient's condition. The notification unit includes various notification means such as voice notification and vibration notification, so that the nurse can respond immediately. For example, for matters of high urgency, the notification unit alerts the nurse by using voice or vibration notifications, thereby supporting the nurse to respond quickly and appropriately.

[0081] The wristband system includes a patient information acquisition unit that acquires patient information indicating matters to be passed on or precautions to be taken regarding the patient. The patient information acquisition unit acquires matters to be passed on or precautions to be taken regarding the patient. For example, the information includes the patient's medical history, allergy information, precautions, and the like. The patient information acquisition unit acquires this information and transmits it to the notification unit. This allows the nurse to grasp important information regarding the patient. Some or all of the above-mentioned processing in the patient information acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the patient information acquisition unit inputs the patient's medical history data into AI, which can analyze the medical history data and extract important information. This allows important information regarding the patient to be acquired and notified to the nurse.

[0082] The notification unit can notify the nurse of the patient information acquired by the patient information acquisition unit. For example, the patient information includes the patient's medical history, allergy information, and precautions. By notifying the nurse of this information, the notification unit can allow the nurse to grasp important information about the patient. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the patient information acquired from the patient information acquisition unit to AI, which can analyze the information and determine the content to be notified to the nurse. In this way, by notifying the nurse of the patient information, the nurse can grasp important information about the patient.

[0083] The recognition unit can recognize the patient ID using wireless communication. The recognition unit recognizes the patient ID using wireless communication (e.g., Bluetooth). For example, when a nurse approaches a patient, the wristband automatically recognizes the patient ID. The recognized patient ID is transmitted to the response history acquisition unit. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input wireless communication data to AI, which analyzes the data to recognize the patient ID. This makes it possible to automatically recognize the patient ID using wireless communication.

[0084] The notification unit may include a display unit that displays dealt with and undealt with in different ways. For example, dealt with and undealt with can be displayed in different colors on the wristband display. Also, icons and text can be used to visually distinguish between the dealt with and undealt with. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input dealt with and undealt with to AI, and the AI ​​may determine the display method. This allows dealt with and undealt with to be visually distinguished from each other.

[0085] The recognition unit recognizes the nurse ID, which is the identification information of the nurse, and the notification unit can notify the judgment result when it is a specific nurse ID. For example, when a nurse wears a wristband, the wristband automatically recognizes the nurse ID. The recognized nurse ID is transmitted to the notification unit. When the nurse ID recognized by the recognition unit is a specific nurse ID, the notification unit notifies the judgment result by the judgment unit. This makes it possible to notify information only to a specific nurse. A part or all of the above-mentioned processing in the recognition unit and the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit can input the nurse ID data to the AI, and the AI ​​can analyze the data and recognize the nurse ID. The notification unit can input the nurse ID acquired from the recognition unit to the AI, and the AI ​​can determine whether it is a specific nurse ID. This makes it possible to notify information only to a specific nurse.

[0086] The wristband system includes a vital sign acquisition unit that acquires vital sign information of a patient, and the judgment unit can judge whether an item has been dealt with or not based on the vital sign information in addition to the response history and the response information. The vital sign acquisition unit acquires vital sign information of the patient. For example, the vital sign information includes heart rate, blood pressure, body temperature, etc. The vital sign acquisition unit acquires this information and transmits it to the judgment unit. The judgment unit judges whether an item has been dealt with or not based on the vital sign information in addition to the response history and the response information. For example, if the vital sign information of the patient is within a normal range, it is judged to have been dealt with, and if there is an abnormality, it is judged to have not been dealt with. A part or all of the above-mentioned processing in the vital sign acquisition unit and the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the vital sign acquisition unit inputs the vital sign data of the patient to AI, and the AI ​​can analyze the data and detect abnormalities. This enables more appropriate response based on the vital sign information of the patient.

[0087] The recognition unit can estimate the user's emotion and adjust the recognition timing of the patient ID based on the estimated user's emotion. For example, if the user is stressed, the recognition timing is delayed to wait until the user relaxes. Also, if the user is relaxed, the patient ID can be recognized immediately to smoothly proceed with the work. Furthermore, if the user is in a hurry, the recognition timing is advanced to quickly recognize the patient ID. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recognition unit inputs the user's facial expression data to the generation AI, which can estimate the emotion and adjust the recognition timing. Thereby, the recognition accuracy is improved by adjusting the recognition timing according to the user's emotion.

[0088] The recognition unit can track the patient's movements and position information in real time and select the optimal recognition timing. For example, if the patient is moving, recognition is waited until the patient stops moving. Also, recognition can be performed automatically when the patient reaches a specific position. Furthermore, if the patient is in bed, recognition can be performed based on the position information of the bed. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the patient's position data to the generation AI, which can analyze the data and select the optimal recognition timing. This makes it possible to recognize the patient ID at the optimal timing based on the patient's movements and position information.

[0089] The recognition unit can improve the recognition accuracy by referring to the past recognition history of the patient during recognition. For example, the recognition accuracy can be improved based on the patient ID recognized in the past. Also, the recognition accuracy can be improved by learning the pattern of erroneous recognition from the past recognition history. Furthermore, a specific patient ID can be preferentially recognized based on the past recognition history. A part or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit can input the past recognition history data to the generation AI, which can analyze the data and improve the recognition accuracy. As a result, the recognition accuracy is improved by referring to the past recognition history.

[0090] The recognition unit can estimate the user's emotion and determine the priority order of patient IDs to be recognized based on the estimated user's emotion. For example, when the user is stressed, the less important patient IDs are postponed. Also, when the user is relaxed, the more important patient IDs can be preferentially recognized. Furthermore, when the user is in a hurry, the most important patient IDs can be recognized first. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recognition unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the recognition unit can input the user's facial expression data to the generation AI and determine the priority order of patient IDs to be recognized by the generation AI by estimating the emotion. As a result, important patient IDs can be preferentially recognized by determining the priority order of patient IDs according to the user's emotion.

[0091] The recognition unit can improve the recognition accuracy by taking into account the environmental information of the patient during recognition. The recognition unit improves the recognition accuracy by taking into account the environmental information of the patient (e.g., room temperature and lighting) during recognition. For example, when the room temperature is high, the recognition accuracy can be adjusted. Also, when the lighting is dark, correction can be made to improve the recognition accuracy. Furthermore, when the environmental sound is loud, noise cancellation can be performed to improve the recognition accuracy. Some or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit inputs environmental information data to a generation AI, which can analyze the data to improve the recognition accuracy. Thereby, the recognition accuracy is improved by taking into account the environmental information of the patient.

[0092] During recognition, the recognition unit can analyze the patient's social media activity and obtain relevant information. For example, information about a recent health condition can be obtained from the patient's social media activity. Current location information can also be obtained from the patient's social media activity. Furthermore, information about a specific event can be obtained from the patient's social media activity. Some or all of the above-mentioned processing in the recognition unit can be performed, for example, using AI or can be performed without using AI. For example, the recognition unit can input social media data to a generation AI, which can analyze the data to obtain relevant information. This allows the patient's social media activity to be analyzed to obtain relevant information.

[0093] The response history acquisition unit can estimate the user's emotion and adjust the timing of acquiring the response history based on the estimated emotion of the user. For example, when the user is stressed, the acquisition timing is delayed and the user is waited for to relax. Also, when the user is relaxed, the response history can be acquired immediately. Furthermore, when the user is in a hurry, the acquisition timing is advanced and the response history can be acquired quickly. The estimation of the emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response history acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response history acquisition unit inputs the user's facial expression data to the generation AI, and the generation AI can estimate the emotion and adjust the acquisition timing. Thereby, the acquisition accuracy is improved by adjusting the acquisition timing of the response history according to the emotion of the user.

[0094] When acquiring the response history, the response history acquisition unit can analyze the patient's past response history and select the optimal acquisition method. For example, the most relevant history can be preferentially acquired from the past response history. In addition, the past response history can be analyzed and the acquisition method can be optimized. Furthermore, a specific response history can be preferentially acquired based on the past response history. A part or all of the above-mentioned processing in the response history acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the response history acquisition unit inputs past response history data to a generation AI, which can analyze the data and select the optimal acquisition method. As a result, the optimal acquisition method can be selected by analyzing the patient's past response history.

[0095] The response history acquisition unit can perform filtering based on the patient's current health condition when acquiring the response history. For example, if the patient's current health condition is poor, important response history can be preferentially acquired. Also, if the patient's current health condition is good, all response history can be acquired. Furthermore, specific response history can be filtered based on the patient's current health condition. A part or all of the above-mentioned processing in the response history acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the response history acquisition unit can input the patient's health condition data to the generation AI, and the generation AI can analyze the data and perform filtering. This allows important response history to be preferentially acquired based on the patient's current health condition.

[0096] The response history acquisition unit can estimate the user's emotion and determine the priority of the response history to be acquired based on the estimated user's emotion. For example, when the user is stressed, the response history with low importance is postponed. Also, when the user is relaxed, the response history with high importance can be acquired preferentially. Furthermore, when the user is in a hurry, the most important response history can be acquired first. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response history acquisition unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the response history acquisition unit can input the user's facial expression data to the generation AI and determine the priority of the response history to be acquired by the generation AI by estimating the emotion. Thereby, by determining the priority of the response history according to the user's emotion, important response history can be acquired preferentially.

[0097] When acquiring the response history, the response history acquisition unit can preferentially acquire a history with high relevance in consideration of the geographical location information of the patient. For example, when the patient is in a specific hospital, the response history at the hospital can be preferentially acquired. Also, when the patient is at home, the response history at home can be preferentially acquired. Furthermore, when the patient is on the move, the response history at the destination can be preferentially acquired. A part or all of the above-mentioned processing in the response history acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the response history acquisition unit inputs the geographical location data of the patient to the generation AI, and the generation AI can analyze the data and preferentially acquire a history with high relevance. As a result, by considering the geographical location information of the patient, a history with high relevance can be preferentially acquired.

[0098] The response history acquisition unit can analyze the social media activity of the patient when acquiring the response history, and acquire related history. For example, a history related to a recent health condition can be acquired from the social media activity of the patient. In addition, a history related to current location information can be acquired from the social media activity of the patient. Furthermore, a history related to a specific event can be acquired from the social media activity of the patient. A part or all of the above-mentioned processing in the response history acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the response history acquisition unit can input social media data to the generation AI, and the generation AI can analyze the data and acquire related history. In this way, the related history can be acquired by analyzing the social media activity of the patient.

[0099] The corresponding information acquisition unit can estimate the user's emotion and adjust the timing of acquiring the corresponding information based on the estimated emotion of the user. For example, when the user is stressed, the acquisition timing is delayed and the user is waited for to relax. Also, when the user is relaxed, the corresponding information can be acquired immediately. Furthermore, when the user is in a hurry, the acquisition timing is advanced and the corresponding information can be acquired quickly. The estimation of the emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the corresponding information acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the corresponding information acquisition unit inputs the user's facial expression data to the generation AI, and the generation AI can estimate the emotion and adjust the acquisition timing. Thereby, the acquisition accuracy is improved by adjusting the acquisition timing of the corresponding information according to the emotion of the user.

[0100] When acquiring the corresponding information, the corresponding information acquisition unit can analyze the patient's past corresponding information and select the optimal acquisition method. For example, the most relevant information can be preferentially acquired from the past corresponding information. In addition, the past corresponding information can be analyzed to optimize the acquisition method. Furthermore, specific corresponding information can be preferentially acquired based on the past corresponding information. A part or all of the above-mentioned processing in the corresponding information acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the corresponding information acquisition unit inputs past corresponding information data to a generating AI, and the generating AI can analyze the data and select the optimal acquisition method. As a result, the optimal acquisition method can be selected by analyzing the patient's past corresponding information.

[0101] The corresponding information acquisition unit can perform filtering based on the patient's current health condition when acquiring the corresponding information. For example, if the patient's current health condition is poor, important corresponding information can be preferentially acquired. Also, if the patient's current health condition is good, all corresponding information can be acquired. Furthermore, specific corresponding information can be filtered based on the patient's current health condition. A part or all of the above-mentioned processing in the corresponding information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the corresponding information acquisition unit can input the patient's health condition data to the generation AI, and the generation AI can analyze the data and perform filtering. This allows important corresponding information to be preferentially acquired based on the patient's current health condition.

[0102] The corresponding information acquisition unit can estimate the user's emotion and determine the priority of the corresponding information to be acquired based on the estimated user's emotion. For example, when the user is stressed, corresponding information with a low importance is postponed. Also, when the user is relaxed, corresponding information with a high importance can be acquired preferentially. Furthermore, when the user is in a hurry, the most important corresponding information can be acquired first. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the corresponding information acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the corresponding information acquisition unit can input the user's facial expression data to the generation AI and determine the priority of the corresponding information to be acquired by the generation AI by estimating the emotion. Thereby, important corresponding information can be preferentially acquired by determining the priority of the corresponding information according to the user's emotion.

[0103] When acquiring the corresponding information, the corresponding information acquisition unit can preferentially acquire information with high relevance in consideration of the geographical location information of the patient. For example, when the patient is in a specific hospital, the corresponding information at the hospital can be preferentially acquired. Furthermore, when the patient is at home, the corresponding information at home can be preferentially acquired. Furthermore, when the patient is on the move, the corresponding information at the move destination can be preferentially acquired. A part or all of the above-mentioned processing in the corresponding information acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the corresponding information acquisition unit inputs the geographical location data of the patient to the generation AI, and the generation AI can analyze the data and preferentially acquire information with high relevance. As a result, by considering the geographical location information of the patient, it is possible to preferentially acquire information with high relevance.

[0104] The corresponding information acquisition unit can analyze the social media activity of the patient when acquiring the corresponding information, and acquire related information. For example, information on a recent health condition can be acquired from the social media activity of the patient. Also, information related to current location information can be acquired from the social media activity of the patient. Furthermore, information on a specific event can be acquired from the social media activity of the patient. A part or all of the above-mentioned processing in the corresponding information acquisition unit can be performed, for example, using AI, or can be performed without using AI. For example, the corresponding information acquisition unit can input social media data to the generation AI, which can analyze the data and acquire related information. In this way, related information can be acquired by analyzing the social media activity of the patient.

[0105] The judgment unit can estimate the user's emotion and adjust the judgment criteria based on the estimated user's emotion. For example, if the user is stressed, the judgment criteria can be relaxed. Also, if the user is relaxed, the judgment criteria can be stricter. Furthermore, if the user is in a hurry, the criteria can be simplified to make a quick judgment. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the judgment unit inputs the user's facial expression data into the generation AI, and the generation AI can estimate the emotion and adjust the judgment criteria. Thereby, the judgment accuracy is improved by adjusting the judgment criteria according to the user's emotion.

[0106] The judgment unit can improve the judgment accuracy by referring to the patient's past response history and response information at the time of judgment. For example, it can determine whether the current response is appropriate based on the past response history. It can also determine whether the current response is appropriate by referring to the past response information. Furthermore, it can improve the judgment accuracy by combining the past response history and the response information. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit inputs past response history data and response information data to the generation AI, and the generation AI analyzes the data to improve the judgment accuracy. As a result, the judgment accuracy is improved by referring to the patient's past response history and response information.

[0107] The judgment unit can customize the judgment criteria based on the patient's current health condition at the time of judgment. For example, if the patient's current health condition is poor, the judgment criteria can be relaxed. Also, if the patient's current health condition is good, the judgment criteria can be tightened. Furthermore, the judgment criteria can be customized based on the patient's current health condition. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the patient's health condition data into the generation AI, and the generation AI can analyze the data and customize the judgment criteria. This allows the judgment criteria to be customized based on the patient's current health condition.

[0108] The judgment unit can estimate the user's emotion and adjust the display method of the judgment result based on the estimated user's emotion. For example, if the user is nervous, a simple and highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit inputs the user's facial expression data into the generation AI, which can estimate the emotion and adjust the display method. Thereby, the visibility is improved by adjusting the display method of the judgment result according to the user's emotion.

[0109] The judgment unit can adjust the judgment criteria by taking into account the geographical location information of the patient when making a judgment. For example, if the patient is in a specific hospital, the judgment can be made based on the criteria of the hospital. Also, if the patient is at home, the judgment can be made based on the criteria of the home. Furthermore, if the patient is on the move, the judgment can be made based on the criteria of the destination. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the geographical location data of the patient to the generation AI, which can analyze the data and adjust the judgment criteria. In this way, the judgment criteria can be adjusted by taking into account the geographical location information of the patient.

[0110] At the time of judgment, the judgment unit can analyze the social media activity of the patient and reflect related information in the judgment. For example, information on the recent health condition can be reflected in the judgment from the social media activity of the patient. In addition, current location information can be reflected in the judgment from the social media activity of the patient. Furthermore, information on a specific event can be reflected in the judgment from the social media activity of the patient. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit inputs social media data to the generation AI, and the generation AI analyzes the data and reflects related information in the judgment. In this way, by analyzing the social media activity of the patient, related information can be reflected in the judgment.

[0111] The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated user's emotion. The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated user's emotion. For example, when the user is stressed, the notification timing is delayed and the notification can be made immediately when the user is relaxed. Furthermore, when the user is in a hurry, the notification timing is advanced and the notification can be made quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's facial expression data to the generation AI, and the generation AI can estimate the emotion and adjust the timing of the notification. This improves the effectiveness of the notification by adjusting the timing of the notification according to the user's emotion.

[0112] The notification unit can customize the notification contents by referring to the patient's past response history and response information at the time of notification. For example, the current notification contents can be customized based on the past response history. In addition, the current notification contents can be customized by referring to the past response information. Furthermore, the notification contents can be customized by combining the past response history and the response information. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the past response history data and the response information data to the generation AI, and the generation AI can analyze the data and customize the notification contents. This allows the notification contents to be customized by referring to the patient's past response history and response information.

[0113] The notification unit can adjust the notification contents based on the patient's current health condition at the time of notification. For example, if the patient's current health condition is poor, important notification contents can be preferentially displayed. Also, if the patient's current health condition is good, all notification contents can be displayed. Furthermore, the notification contents can be adjusted based on the patient's current health condition. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the patient's health condition data to the generation AI, which can analyze the data and adjust the notification contents. This allows important notification contents to be preferentially displayed based on the patient's current health condition.

[0114] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. For example, when the user is stressed, notifications with low importance are postponed. Also, when the user is relaxed, notifications with high importance can be displayed preferentially. Furthermore, when the user is in a hurry, the most important notification can be displayed first. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's facial expression data into the generation AI, and the generation AI can estimate the emotion and determine the priority of notifications. As a result, important notifications can be displayed preferentially by determining the priority of notifications according to the user's emotion.

[0115] The notification unit can adjust the notification contents in consideration of the geographical location information of the patient at the time of notification. For example, when the patient is in a specific hospital, the notification contents at the hospital can be preferentially displayed. Furthermore, when the patient is at home, the notification contents at home can be preferentially displayed. Furthermore, when the patient is on the move, the notification contents at the move destination can be preferentially displayed. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the geographical location data of the patient to the generation AI, and the generation AI can analyze the data and adjust the notification contents. In this way, the notification contents can be adjusted by considering the geographical location information of the patient.

[0116] At the time of notification, the notification unit can analyze the social media activity of the patient and notify the patient of relevant information. For example, information on a recent health condition can be notified from the social media activity of the patient. In addition, information related to current location information can be notified from the social media activity of the patient. Furthermore, information on a specific event can be notified from the social media activity of the patient. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input social media data to a generation AI, which can analyze the data and notify the patient of relevant information. In this way, the patient's social media activity can be analyzed to notify the patient of relevant information.

[0117] The patient information acquisition unit can estimate the user's emotion and adjust the timing of acquiring the patient information based on the estimated user's emotion. The patient information acquisition unit can estimate the user's emotion and adjust the timing of acquiring the patient information based on the estimated user's emotion. For example, when the user is stressed, the acquisition timing is delayed and the user is waited for to relax. Also, when the user is relaxed, the patient information can be acquired immediately. Furthermore, when the user is in a hurry, the acquisition timing is advanced and the patient information can be acquired quickly. The estimation of the emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the patient information acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the patient information acquisition unit can input the user's facial expression data to the generation AI, and the generation AI can estimate the emotion and adjust the acquisition timing. Thereby, the acquisition accuracy is improved by adjusting the acquisition timing of the patient information according to the user's emotion.

[0118] When acquiring patient information, the patient information acquisition unit can analyze the patient's past information and select the optimal acquisition method. For example, the most relevant information can be preferentially acquired from the past patient information. In addition, the past patient information can be analyzed to optimize the acquisition method. Furthermore, specific information can be preferentially acquired based on the past patient information. A part or all of the above-mentioned processing in the patient information acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the patient information acquisition unit can input past patient information data to the generation AI, which can analyze the data and select the optimal acquisition method. In this way, the optimal acquisition method can be selected by analyzing the patient's past information.

[0119] The patient information acquisition unit can estimate the user's emotion and determine the priority of the patient information to be acquired based on the estimated emotion of the user. For example, when the user is stressed, information of low importance is postponed. Also, when the user is relaxed, information of high importance can be acquired preferentially. Furthermore, when the user is in a hurry, the most important information can be acquired first. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the patient information acquisition unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the patient information acquisition unit can input the user's facial expression data to the generation AI, and the generation AI can estimate the emotion and determine the priority of the patient information to be acquired. As a result, important information can be preferentially acquired by determining the priority of the patient information according to the user's emotion.

[0120] When acquiring patient information, the patient information acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the patient. For example, if the patient is in a specific hospital, information at the hospital can be preferentially acquired. Also, if the patient is at home, information at home can be preferentially acquired. Furthermore, if the patient is on the move, information at the move destination can be preferentially acquired. A part or all of the above-mentioned processing in the patient information acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the patient information acquisition unit inputs the geographical location data of the patient to the generation AI, and the generation AI can analyze the data and preferentially acquire highly relevant information. As a result, highly relevant information can be preferentially acquired by considering the geographical location information of the patient.

[0121] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, when the user is nervous, an interface with calm colors is provided to reduce visual stress. When the user is having fun, an interface with bright colors is provided to make input work enjoyable. Furthermore, when the user is tired, a simple and highly visible interface is provided to facilitate input work. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the display unit inputs the user's facial expression data into the generation AI, which can estimate the emotion and adjust the display method. This improves visibility by adjusting the display method according to the user's emotion.

[0122] The display unit can customize the display contents by referring to the patient's past response history and response information when displaying. For example, the current display contents can be customized based on the past response history. Also, the current display contents can be customized by referring to the past response information. Furthermore, the display contents can be customized by combining the past response history and the response information. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the past response history data and the response information data to the generation AI, and the generation AI can analyze the data and customize the display contents. This allows the display contents to be customized by referring to the patient's past response history and response information.

[0123] The display unit can estimate the user's emotion and determine the display priority based on the estimated user's emotion. For example, when the user is stressed, display of less importance is postponed. Also, when the user is relaxed, display of more importance can be prioritized. Furthermore, when the user is in a hurry, display of the most important item can be performed first. Estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit inputs the user's facial expression data into the generation AI, and the generation AI can estimate the emotion and determine the display priority. As a result, important display can be prioritized by determining the display priority according to the user's emotion.

[0124] The display unit can adjust the display contents taking into consideration the geographical location information of the patient when displaying. For example, when the patient is in a specific hospital, the display contents at the hospital can be preferentially displayed. Also, when the patient is at home, the display contents at home can be preferentially displayed. Furthermore, when the patient is on the move, the display contents at the move destination can be preferentially displayed. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the geographical location data of the patient to the generation AI, which can analyze the data and adjust the display contents. This allows the display contents to be adjusted by taking into consideration the geographical location information of the patient.

[0125] The vital sign acquisition unit can estimate the user's emotion and adjust the timing of acquiring the vital sign based on the estimated emotion of the user. For example, when the user is stressed, the timing of acquisition is delayed and the user is waited for to relax. Also, when the user is relaxed, the vital sign can be acquired immediately. Furthermore, when the user is in a hurry, the timing of acquisition is advanced and the vital sign can be acquired quickly. The estimation of the emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the vital sign acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the vital sign acquisition unit inputs the user's facial expression data to the generation AI, and the generation AI can estimate the emotion and adjust the acquisition timing. Thereby, the acquisition accuracy is improved by adjusting the timing of acquiring the vital sign according to the emotion of the user.

[0126] When acquiring vital signs, the vital sign acquisition unit can analyze the patient's past vital sign history and select the optimal acquisition method. For example, based on the past vital sign history, the most relevant vital sign can be preferentially acquired. In addition, the past vital sign history can be analyzed and the acquisition method can be optimized. Furthermore, based on the past vital sign history, a specific vital sign can be preferentially acquired. A part or all of the above-mentioned processing in the vital sign acquisition unit can be performed, for example, using AI or can be performed without using AI. For example, the vital sign acquisition unit can input past vital sign history data to a generation AI, which can analyze the data and select the optimal acquisition method. Thereby, the optimal acquisition method can be selected by analyzing the patient's past vital sign history.

[0127] The vital sign acquisition unit can estimate the user's emotion and determine the priority of vital signs to be acquired based on the estimated user's emotion. For example, when the user is stressed, the vital signs with low importance are postponed. Also, when the user is relaxed, the vital signs with high importance can be acquired preferentially. Furthermore, when the user is in a hurry, the most important vital signs can be acquired first. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the vital sign acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the vital sign acquisition unit can input the user's facial expression data to the generation AI, and the generation AI can estimate the emotion and determine the priority of vital signs to be acquired. Thereby, by determining the priority of vital signs according to the user's emotion, important vital signs can be acquired preferentially.

[0128] When acquiring vital signs, the vital sign acquisition unit can preferentially acquire highly relevant vital signs by taking into account the geographical location information of the patient. For example, if the patient is in a specific hospital, the vital signs at the hospital can be preferentially acquired. Also, if the patient is at home, the vital signs at home can be preferentially acquired. Furthermore, if the patient is on the move, the vital signs at the move destination can be preferentially acquired. A part or all of the above-mentioned processing in the vital sign acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the vital sign acquisition unit inputs the geographical location data of the patient to the generation AI, and the generation AI analyzes the data to preferentially acquire highly relevant vital signs. As a result, highly relevant vital signs can be preferentially acquired by taking into account the geographical location information of the patient.

[0129] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0130] The wristband system also has a voice recognition unit, which can recognize patient IDs and obtain response history by voice commands. For example, if a nurse issues the voice command "recognize patient ID," the voice recognition unit recognizes the command and starts recognizing the patient ID. Also, if a nurse issues the voice command "obtain response history," the voice recognition unit recognizes the command and starts obtaining the response history. Furthermore, the voice recognition unit can estimate the emotion of the nurse from the tone and speed of her voice and adjust the processing of the voice command based on the estimated emotion. This allows nurses to operate the system by voice without using their hands, improving work efficiency.

[0131] The wristband system also includes a location information sharing unit, which allows nurses to share their location information with each other in real time. For example, when a nurse is near a patient, that location information is notified to other nurses. Also, when a specific nurse is responding to an emergency, that nurse's location information can be shared preferentially. Furthermore, the location information sharing unit can select the most appropriate nurse based on the nurse's location information and notify them. This strengthens cooperation between nurses and enables quicker responses.

[0132] The wristband system further includes an environmental sensor unit that can obtain information about the patient's surroundings. For example, information such as room temperature, humidity, and illuminance can be obtained to evaluate the patient's comfort. The environmental sensor unit can also notify a nurse if it detects abnormal environmental conditions. For example, an alert can be issued if the room temperature is too high or the humidity is too low. This provides information to maintain a comfortable environment for the patient.

[0133] The wristband system further includes a health advice unit that can provide individualized advice based on the patient's health condition. For example, it can provide appropriate exercise and diet advice based on the patient's vital sign information and treatment history. The health advice unit can also estimate the patient's emotions and adjust the content and timing of advice based on the estimated emotions. This allows for more effective health management of the patient.

[0134] The wristband system also has a learning function that can learn from nurses' operation histories and patients' response histories to improve the system's accuracy. For example, it can learn nurses' operation patterns from past operation histories and predict and suggest the next operation. It can also learn patients' response histories and predict and suggest the next response. This improves the system's usability and reduces the burden on nurses.

[0135] The wristband system also has a reminder function that can provide regular reminders to nurses, such as reminding them to periodically measure vital signs or administer medicine. The reminder function can also estimate the nurse's emotions and adjust the content and timing of the reminders based on the estimated emotions, helping nurses remember to perform important tasks.

[0136] The wristband system also includes an emergency response unit that can automatically initiate a response in the event of an emergency. For example, if an abnormality is detected in the patient's vital signs, the emergency response unit can automatically issue an alert and notify the nearest nurse. The emergency response unit can also estimate the patient's emotions and adjust the response method based on the estimated emotions. This allows for a quick and appropriate response in the event of an emergency.

[0137] The wristband system also includes a data analysis unit that can analyze patient data to understand trends in health conditions. For example, it can analyze past vital sign data and treatment history to predict changes in health conditions. The data analysis unit can also estimate the nurse's emotions and adjust the way the data is displayed based on the estimated emotions, making it easier for nurses to intuitively understand the data.

[0138] The wristband system further includes a communication unit that can support communication between nurses and between nurses and patients. For example, nurses can send and receive messages to each other through the wristband. Patients can also send messages to nurses through the wristband. Furthermore, the communication unit can estimate the emotions of nurses and patients and adjust the content and timing of messages based on the estimated emotions. This allows for smooth communication.

[0139] The wristband system further includes an energy management unit that can optimize the battery consumption of the wristband. For example, it can adjust the battery consumption based on the frequency of use and environmental conditions. The energy management unit can also learn the usage status of the wristband and suggest the optimal energy management method. This extends the battery life of the wristband and enables long-term use.

[0140] The process flow of the second embodiment will be briefly described below.

[0141] Step 1: The recognition unit recognizes the patient ID. For example, the patient ID is automatically recognized using wireless communication (e.g., Bluetooth). When the nurse approaches the patient, the wristband automatically recognizes the patient ID. The recognized patient ID is sent to the treatment history acquisition unit. Step 2: The treatment history acquisition unit acquires the treatment history for the patient based on the recognized patient ID. The treatment history includes, for example, past medical records and treatment history. The acquired treatment history is sent to the treatment information acquisition unit. Step 3: The response information acquisition unit inputs the measures required for that day in advance, and acquires information for determining which items have been addressed and which have not been addressed, based on the response history and response information. Step 4: The determination unit determines whether an item has been dealt with or not yet dealt with based on the response history acquired by the response history acquisition unit and the response information acquired by the response information acquisition unit. For example, the items that have not been dealt with include measures that have not yet been taken or points of attention. Step 5: The notification unit notifies the nurse of the result of the determination made by the determination unit. For example, the wristband display shows dealt with items and undealt with items in different ways. This allows the nurse to determine on the spot whether an item has been dealt with or not.

[0142] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0144] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] For example, the recognition unit is realized by the processor 46 of the smart device 14, and automatically recognizes the patient ID using wireless communication (e.g., Bluetooth). The action history acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires the action history based on the recognized patient ID. The action information acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and inputs the measures required for that day, and judges which items have been dealt with and which items have not been dealt with based on the action history and the action information. The judgment unit is realized by the specific processing unit 290 of the data processing device 12, and transmits the judgment result to the notification unit. The notification unit is realized by the control unit 46A of the smart device 14, and displays the dealt with items and the undealt with items on the display of the wristband. The patient information acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires the handover items and notes regarding the patient, and transmits them to the notification unit. The vital sign acquisition unit is realized by the processor 46 of the smart device 14, and acquires the vital sign information of the patient. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12, and adjusts the recognition timing based on the emotion of the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0146] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0147] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0148] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0153] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0161] For example, the recognition unit is realized by the processor 46 of the smart glasses 214, and automatically recognizes the patient ID using wireless communication (e.g., Bluetooth). The action history acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires the action history based on the recognized patient ID. The action information acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and inputs the necessary measures for that day, and judges the actioned items and the unactioned items based on the action history and the action information. The judgment unit is realized by the specific processing unit 290 of the data processing device 12, and transmits the judgment result to the notification unit. The notification unit is realized by the control unit 46A of the smart glasses 214, and displays the actioned items and the unactioned items on the display of the smart glasses. The patient information acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires the handover items and the notes regarding the patient, and transmits them to the notification unit. The vital sign acquisition unit is realized by the processor 46 of the smart glasses 214, and acquires the vital sign information of the patient. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12, and adjusts the recognition timing based on the emotion of the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0162] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0163] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0164] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0165] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0166] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0168] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0169] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0170] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0171] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0172] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0173] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0174] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0175] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0176] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0177] For example, the recognition unit is realized by the processor 46 of the headset type terminal 314, and automatically recognizes the patient ID using wireless communication (e.g., Bluetooth). The action history acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires the action history based on the recognized patient ID. The action information acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and inputs the measures required for that day, and judges which items have been dealt with and which items have not been dealt with based on the action history and the action information. The judgment unit is realized by the specific processing unit 290 of the data processing device 12, and transmits the judgment result to the notification unit. The notification unit is realized by the control unit 46A of the headset type terminal 314, and displays the dealt with items and the undealt with items on the display of the headset. The patient information acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires the handover items and notes regarding the patient, and transmits them to the notification unit. The vital sign acquisition unit is realized by the processor 46 of the headset type terminal 314, and acquires the vital sign information of the patient. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12, and adjusts the recognition timing based on the emotion of the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0178] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0179] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0180] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0181] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0182] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0183] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0184] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0185] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0186] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0187] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0188] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0189] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0190] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0191] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0192] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0193] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0194] For example, the recognition unit is realized by the processor 46 of the robot 414, and automatically recognizes the patient ID using wireless communication (e.g., Bluetooth). The action history acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires the action history based on the recognized patient ID. The action information acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and inputs the measures required for that day, and judges which items have been dealt with and which items have not been dealt with based on the action history and the action information. The judgment unit is realized by the specific processing unit 290 of the data processing device 12, and transmits the judgment result to the notification unit. The notification unit is realized by the control unit 46A of the robot 414, and displays the dealt with items and the undealt with items on the display of the robot. The patient information acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires the handover items and notes regarding the patient, and transmits them to the notification unit. The vital sign acquisition unit is realized by the processor 46 of the robot 414, and acquires the vital sign information of the patient. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12, and adjusts the recognition timing based on the emotion of the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0195] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0196] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0197] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0198] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0199] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0200] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0201] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0202] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0203] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0204] In addition, 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 upon request from the data processing device 12.

[0205] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0206] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0207] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0208] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0209] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0210] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0211] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0212] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0213] (Appendix 1) a recognition unit for recognizing a patient ID; a treatment history acquisition unit that acquires a treatment history for a patient having a patient ID recognized by the recognition unit; a treatment information acquisition unit that acquires treatment information indicating a treatment required for the patient; a determination unit that determines whether an item has been dealt with or not, based on the response history acquired by the response history acquisition unit and the response information acquired by the response information acquisition unit; A notification unit that notifies a nurse of the result of the determination by the determination unit. A system characterized by: (Appendix 2) A patient information acquisition unit is provided for acquiring patient information indicating matters to be passed on or precautions to be taken regarding the patient. 2. The system of claim 1, (Appendix 3) The notification unit is The patient information acquired by the patient information acquisition unit is notified to the nurse. 3. The system of claim 2, (Appendix 4) The recognition unit is Recognizing the patient ID using wireless communication 2. The system of claim 1, (Appendix 5) The notification unit is A display unit is provided that displays dealt with and undealt with in different ways. 2. The system of claim 1, (Appendix 6) The recognition unit is Recognizing a nurse ID, which is identification information of the nurse, The notification unit is If the nurse ID recognized by the recognition unit is a specific nurse ID, the result of the determination by the determination unit is notified. 2. The system of claim 1, (Appendix 7) A vital sign acquisition unit is provided for acquiring vital sign information of a patient, The determination unit is Based on the response history, the response information, and the vital sign information, a determination is made as to whether a problem has been dealt with or not. 2. The system of claim 1, (Appendix 8) The recognition unit is The system estimates the user's emotions and adjusts the timing of patient ID recognition based on the estimated user emotions. 2. The system of claim 1, (Appendix 9) The recognition unit is Tracking patient movements and location in real time to determine the optimal timing for recognition 2. The system of claim 1, (Appendix 10) The recognition unit is During recognition, the accuracy of recognition is improved by referring to the patient's past recognition history. 2. The system of claim 1, (Appendix 11) The recognition unit is Estimate the user's emotion and determine the priority of patient IDs to be recognized based on the estimated user's emotion. 2. The system of claim 1, (Appendix 12) The recognition unit is Improve recognition accuracy by taking into account the patient's environmental information during recognition 2. The system of claim 1, (Appendix 13) The recognition unit is Upon recognition, analyze the patient's social media activity to obtain relevant information 2. The system of claim 1, (Appendix 14) The response history acquisition unit A method for predicting a user's emotions and adjusting the timing of acquiring a response history based on the user's emotions 2. The system of claim 1, (Appendix 15) The response history acquisition unit When acquiring a patient's treatment history, the system analyzes the patient's past treatment history and selects the optimal acquisition method. 2. The system of claim 1, (Appendix 16) The response history acquisition unit Filtering the patient's history based on their current health status 2. The system of claim 1, (Appendix 17) The response history acquisition unit A user's emotion is estimated, and a priority order of response histories to be acquired is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 18) The response history acquisition unit When acquiring treatment history, the system prioritizes acquiring history that is highly relevant by taking into account the geographic location information of the patient. 2. The system of claim 1, (Appendix 19) The response history acquisition unit When obtaining correspondence history, analyze the patient's social media activity and obtain related history. 2. The system of claim 1, (Appendix 20) The correspondence information acquisition unit To estimate a user's emotion and adjust the timing of acquiring corresponding information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 21) The correspondence information acquisition unit When acquiring correspondence information, the system analyzes the patient's past correspondence information and selects the optimal acquisition method. 2. The system of claim 1, (Appendix 22) The correspondence information acquisition unit Filtering response information based on the patient's current health status 2. The system of claim 1, (Appendix 23) The correspondence information acquisition unit A user's emotion is estimated, and a priority order of corresponding information to be acquired is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 24) The correspondence information acquisition unit When obtaining corresponding information, the geographic location of the patient is taken into consideration to prioritize obtaining information that is highly relevant 2. The system of claim 1, (Appendix 25) The correspondence information acquisition unit When corresponding information is obtained, the patient's social media activity is analyzed to obtain relevant information. 2. The system of claim 1, (Appendix 26) The determination unit is Inferring a user's emotions and adjusting decision criteria based on the estimated user's emotions 2. The system of claim 1, (Appendix 27) The determination unit is When making a decision, improve the accuracy of the decision by referring to the patient's past treatment history and treatment information. 2. The system of claim 1, (Appendix 28) The determination unit is At the time of decision making, customize decision criteria based on the patient's current health status 2. The system of claim 1, (Appendix 29) The determination unit is Inferring a user's emotions and adjusting the way in which judgment results are displayed based on the estimated user's emotions 2. The system of claim 1, (Appendix 30) The determination unit is When making decisions, adjust the decision criteria to take into account the patient's geographic location. 2. The system of claim 1, (Appendix 31) The determination unit is When making a decision, analyze the patient's social media activity and incorporate relevant information into the decision. 2. The system of claim 1, (Appendix 32) The notification unit is Infer user emotions and adjust notification timing based on the estimated user emotions 2. The system of claim 1, (Appendix 33) The notification unit is When notifying patients, customize the notification content by referencing their past treatment history and treatment information 2. The system of claim 1, (Appendix 34) The notification unit is At the time of notification, tailor the notification content based on the patient's current health status 2. The system of claim 1, (Appendix 35) The notification unit is Infer user emotions and prioritize notifications based on the inferred user emotions 2. The system of claim 1, (Appendix 36) The notification unit is When sending notifications, the notification content is tailored to the patient's geographic location. 2. The system of claim 1, (Appendix 37) The notification unit is At the time of notification, analyze the patient's social media activity and notify them of relevant information 2. The system of claim 1, (Appendix 38) The patient information acquisition unit To estimate a user's emotion and adjust the timing of acquiring patient information based on the estimated user's emotion. 3. The system of claim 2, (Appendix 39) The patient information acquisition unit When acquiring patient information, analyze the patient's past information and select the optimal acquisition method. 3. The system of claim 2, (Appendix 40) The patient information acquisition unit A user's emotion is estimated, and a priority order of patient information to be acquired is determined based on the estimated user's emotion. 3. The system of claim 2, (Appendix 41) The patient information acquisition unit When retrieving patient information, the system takes into account the patient's geographic location to prioritize retrieving the most relevant information 3. The system of claim 2, (Appendix 42) The display unit is Estimate user's emotions and adjust the display method based on the estimated user's emotions 2. The system of claim 1, (Appendix 43) The display unit is When viewing, customize the display by referencing the patient's past treatment history and treatment information 2. The system of claim 1, (Appendix 44) The display unit is Estimate the user's emotions and determine the display priority based on the estimated user's emotions 2. The system of claim 1, (Appendix 45) The display unit is When displayed, the content is adjusted to take into account the patient's geographic location 2. The system of claim 1, (Appendix 46) The vital sign acquisition unit is A method for estimating a user's emotions and adjusting the timing of acquiring vital signs based on the estimated user's emotions 2. The system of claim 1, (Appendix 47) The vital sign acquisition unit is When acquiring vital signs, the system analyzes the patient's past vital sign history and selects the optimal acquisition method. 2. The system of claim 1, (Appendix 48) The vital sign acquisition unit is Estimating a user's emotion and determining the priority of vital signs to be acquired based on the estimated user's emotion 2. The system of claim 1, (Appendix 49) The vital sign acquisition unit is When acquiring vital signs, the system takes into account the geographic location of the patient to prioritize acquisition of the most relevant vital signs. 2. The system of claim 1, [Explanation of symbols]

[0214] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a recognition unit for recognizing a patient ID; a treatment history acquisition unit that acquires a treatment history for a patient having a patient ID recognized by the recognition unit; a treatment information acquisition unit that acquires treatment information indicating a treatment required for the patient; a determination unit that determines whether an item has been dealt with or not, based on the response history acquired by the response history acquisition unit and the response information acquired by the response information acquisition unit; A notification unit that notifies a nurse of the result of the determination by the determination unit. A system characterized by:

2. A patient information acquisition unit is provided for acquiring patient information indicating matters to be passed on or precautions to be taken regarding the patient.

2. The system of claim 1 .

3. The notification unit is The patient information acquired by the patient information acquisition unit is notified to the nurse.

3. The system of claim 2.

4. The recognition unit is Recognizing the patient ID using wireless communication 2. The system of claim 1 .

5. The notification unit is A display unit is provided that displays dealt with and undealt with in different ways.

2. The system of claim 1 .

6. The recognition unit is Recognizing a nurse ID, which is identification information of the nurse, The notification unit is If the nurse ID recognized by the recognition unit is a specific nurse ID, the result of the determination by the determination unit is notified.

2. The system of claim 1 .

7. A vital sign acquisition unit is provided for acquiring vital sign information of a patient, The determination unit is Based on the response history, the response information, and the vital sign information, a determination is made as to whether a problem has been dealt with or not.

2. The system of claim 1 .

8. The recognition unit is The system estimates the user's emotions and adjusts the timing of patient ID recognition based on the estimated user emotions.

2. The system of claim 1 .

Images