system

Abstract

The system according to this embodiment aims to reduce the burden on healthcare professionals and improve the quality of medical care. [Solution] The system according to the embodiment comprises a diagnostic support unit, an administrative automation unit, and a delivery automation unit. The diagnostic support unit analyzes medical data and supports diagnosis. The administrative automation unit automates administrative tasks. The delivery automation unit automates delivery operations.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a shortage of medical staff and a large burden of administrative work, and there is room for improvement in improving the quality and efficiency of medical care.

[0005] The system according to the embodiment aims to reduce the burden on medical staff and improve the quality of medical care.

Means for Solving the Problems

[0006] The system according to the embodiment includes a diagnostic support unit, an administrative automation unit, and a delivery automation unit. The diagnostic support unit analyzes medical data and supports diagnosis. The administrative automation unit automates administrative work. The delivery automation unit automates delivery operations.

Effects of the Invention

[0007] The system according to this embodiment can reduce the burden on healthcare professionals and improve the quality of medical care. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The medical support system according to an embodiment of the present invention is a system that contributes to solving the shortage of medical personnel by introducing an AI agent and a meal delivery robot. This medical support system comprises a diagnostic support unit that analyzes medical data and supports diagnosis, an administrative automation unit that automates administrative tasks, and a delivery automation unit that automates delivery operations. The medical support system aims to improve the quality of medical care by analyzing medical data and supporting diagnosis. For example, the medical support system can detect lesions that doctors might overlook by having the AI ​​agent analyze medical images and detect abnormalities. Next, the medical support system reduces the burden on medical personnel by automating administrative tasks. For example, the medical support system allows doctors and nurses to spend more time with patients by having the AI ​​agent automatically input medical records and manage appointments. Furthermore, the medical support system automates delivery operations through the cooperation of the AI ​​agent and the meal delivery robot. For example, the medical support system reduces the opportunities for medical personnel to have direct contact with patients and reduces the risk of infection by having the meal delivery robot deliver meals to patients' rooms. This reduces the burden on medical personnel and lowers the risk of hospital-acquired infections. Thus, medical support systems are expected to improve the quality of medical care, automate administrative tasks, and reduce the burden on healthcare professionals, thereby contributing to solving the shortage of healthcare personnel. In this way, medical support systems can reduce the burden on healthcare professionals and improve the quality of medical care.

[0029] The medical support system according to this embodiment comprises a diagnostic support unit, an administrative automation unit, and a delivery automation unit. The diagnostic support unit analyzes medical data and supports diagnosis. The diagnostic support unit, for example, analyzes medical images and detects abnormalities. The diagnostic support unit can, for example, use an AI agent to analyze medical images and detect abnormalities. The diagnostic support unit can, for example, use deep learning technology to analyze medical images. The diagnostic support unit can, for example, use a convolutional neural network (CNN) to analyze medical images. The administrative automation unit automates administrative tasks. The administrative automation unit, for example, automatically inputs medical records. The administrative automation unit can, for example, use an AI agent to automatically input medical records. The administrative automation unit can, for example, use speech recognition technology to automatically input medical records. The administrative automation unit can, for example, use OCR technology to automatically input medical records. The delivery automation unit automates delivery operations. The delivery automation unit, for example, transports pharmaceuticals, meals, and medical supplies. The automated delivery unit can, for example, have an AI agent and a food delivery robot work together to transport medicines, meals, and medical supplies. The automated delivery unit can, for example, use a food delivery robot to deliver meals to patients' rooms. The automated delivery unit can, for example, use a food delivery robot to transport medicines. The automated delivery unit can, for example, use a food delivery robot to transport medical supplies. As a result, the medical support system according to this embodiment can perform medical data analysis, automate administrative tasks, and automate delivery operations.

[0030] The Diagnostic Support Department analyzes medical data to support diagnosis. Specifically, it uses deep learning technology to analyze medical images and detect abnormalities. Among deep learning technologies, convolutional neural networks (CNNs) are particularly effective. CNNs demonstrate excellent performance in image recognition and can detect subtle abnormalities in medical images with high accuracy. For example, using MRI and CT scan images as input, the CNN learns abnormal patterns in the images and identifies the location of tumors and lesions. Furthermore, the Diagnostic Support Department uses an AI agent to present diagnostic results to physicians. Based on the analysis results, the AI ​​agent provides information such as the type and progression of the abnormality and recommended treatment methods. This allows physicians to make quick and accurate diagnoses and appropriately determine the treatment plan for patients. In addition, the Diagnostic Support Department further improves the accuracy of diagnoses by referring to past diagnostic data and case databases and comparing them with similar cases. As a result, the Diagnostic Support Department can significantly streamline diagnostic work in medical settings and reduce the burden on physicians.

[0031] The Administrative Automation Department automates administrative tasks. Specifically, it uses an AI agent to automatically input medical records. The AI ​​agent utilizes speech recognition and OCR technologies to achieve automatic input of medical records. For example, it uses speech recognition technology to transcribe what a doctor says during a consultation into text in real time and input it as a medical record. It can also digitize handwritten medical notes and paper charts using OCR technology and automatically import them into the electronic medical record system. This eliminates the need for manual record entry for doctors and medical staff, allowing them to concentrate on patient care. Furthermore, the Administrative Automation Department can automate not only medical record input but also various other administrative tasks such as schedule management, patient appointment management, and insurance claim document creation. The AI ​​agent efficiently processes these tasks, reduces errors, and improves the accuracy of operations. As a result, the Administrative Automation Department can significantly streamline administrative tasks in the medical field and reduce the burden on medical staff.

[0032] The Delivery Automation Department automates delivery operations. Specifically, AI agents and food delivery robots work together to transport medications, meals, and medical supplies. The food delivery robots can autonomously move through rooms and floors within the hospital and deliver items to designated locations. For example, when delivering medication to a patient's room, the AI ​​agent instructs the food delivery robot on the type and quantity of medication and the delivery destination, and the robot calculates the optimal route and moves accordingly. In meal delivery, the food delivery robots deliver appropriate meals according to the content of the meal and the patient's dietary restrictions. Furthermore, in the transport of medical supplies, priority routes are set to ensure the fastest possible delivery of urgent items. As a result, the Delivery Automation Department can streamline logistics within the hospital and reduce the burden on medical staff. In addition, the food delivery robots are equipped with functions to detect and avoid obstacles and to automatically use elevators, allowing them to move smoothly even in the complex environment of the hospital. In this way, the Delivery Automation Department can significantly streamline logistics operations in the medical field and contribute to improving services for patients.

[0033] The diagnostic support unit includes an image analysis unit that analyzes medical images and detects abnormalities. The image analysis unit analyzes medical images such as X-ray images, CT scan images, and MRI images. The image analysis unit can analyze medical images and detect abnormalities using, for example, an AI agent. The image analysis unit can analyze medical images and detect abnormalities using, for example, deep learning technology. The image analysis unit can analyze medical images and detect abnormalities using, for example, a convolutional neural network (CNN). This enables the analysis and detection of abnormalities in medical images.

[0034] The administrative automation unit includes a record entry unit that automatically inputs medical records. The record entry unit can, for example, use an AI agent to automatically input medical records. The record entry unit can, for example, use speech recognition technology to automatically input medical records. The record entry unit can, for example, use OCR technology to automatically input medical records. The record entry unit can, for example, have an AI agent recognize a doctor's voice and automatically input medical records. This enables the automatic input of medical records.

[0035] The automated delivery unit includes a transport unit that handles the delivery of pharmaceuticals, meals, and medical supplies. The transport unit, for example, uses an AI agent and a food delivery robot in conjunction to deliver pharmaceuticals, meals, and medical supplies. For example, the transport unit can use a food delivery robot to deliver meals to patients' rooms. For example, the transport unit can use a food delivery robot to deliver pharmaceuticals. For example, the transport unit can use a food delivery robot to deliver medical supplies. This automates the delivery of pharmaceuticals, meals, and medical supplies.

[0036] The automated delivery unit performs delivery operations in cooperation with meal delivery robots. For example, the automated delivery unit uses AI agents and meal delivery robots to transport medications, meals, and medical supplies. For example, the automated delivery unit can use meal delivery robots to deliver meals to patients' rooms. For example, the automated delivery unit can use meal delivery robots to transport medications. For example, the automated delivery unit can use meal delivery robots to transport medical supplies. This enables the automation of delivery operations through cooperation with meal delivery robots.

[0037] The diagnostic support unit learns from vast amounts of medical data to assist physicians in their diagnoses. For example, the diagnostic support unit can use AI agents to learn from vast amounts of medical data and assist physicians in their diagnoses. For example, the diagnostic support unit can use deep learning technology to learn from vast amounts of medical data and assist physicians in their diagnoses. For example, the diagnostic support unit can use convolutional neural networks (CNNs) to learn from vast amounts of medical data and assist physicians in their diagnoses. This enables physicians to provide diagnostic support.

[0038] The Diagnostic Support Department analyzes patients' past diagnostic history to improve diagnostic accuracy. For example, it makes a diagnosis by comparing the patient's past diagnostic results with their current symptoms. For example, it considers the patient's past treatment history to propose the most suitable treatment method. For example, it analyzes the patient's past test data to help detect abnormalities early. This makes it possible to improve diagnostic accuracy based on past diagnostic history.

[0039] The Diagnostic Support Department makes diagnoses while considering the patient's lifestyle data. For example, it analyzes the patient's diet and exercise habits to assess the risk of lifestyle-related diseases. For example, it diagnoses sleep disorders based on the patient's sleep data. For example, it diagnoses mental health by considering the patient's stress level. This makes it possible to make diagnoses that take lifestyle data into account.

[0040] The diagnostic support department makes diagnoses while considering the patient's geographical location. For example, the diagnostic support department considers the infectious disease risk in the area where the patient lives. For example, the diagnostic support department assesses the health risks during commuting based on the patient's commute route. For example, the diagnostic support department assesses the risk of infectious diseases by considering the patient's travel history. This makes it possible to make diagnoses that take geographical location into account.

[0041] The Diagnostic Support Department analyzes patients' social media activity to aid in diagnosis. For example, it assesses stress levels from patients' social media posts. For example, it understands patients' lifestyle habits from their social media activity to aid in diagnosis. For example, it analyzes patients' health-related posts on social media and incorporates this into the diagnosis. This makes it possible to make diagnoses based on social media activity.

[0042] The Office Automation Department analyzes past office work data and selects the optimal automation method. For example, the Office Automation Department automates frequently performed tasks based on past office work data. For example, the Office Automation Department analyzes past office work data and automates tasks that are prone to errors. For example, the Office Automation Department proposes efficient workflows based on past office work data. This enables the optimization of office work based on historical data.

[0043] The administrative automation department adjusts the timing of administrative tasks based on the schedules of healthcare workers. For example, the administrative automation department optimizes the timing of administrative tasks by considering the schedules of healthcare workers. For example, the administrative automation department automates administrative tasks to coincide with the break times of healthcare workers. For example, the administrative automation department determines the priority of administrative tasks based on the working hours of healthcare workers. This makes it possible to adjust the timing of administrative tasks based on schedules.

[0044] The automated administrative department performs administrative tasks while taking into account the geographical location of healthcare professionals. For example, if a healthcare professional is in the hospital, the automated administrative department prioritizes administrative tasks. For example, if a healthcare professional is out of the hospital, the automated administrative department postpones administrative tasks. For example, if a healthcare professional is at home, the automated administrative department performs administrative tasks remotely. This makes it possible to perform administrative tasks while taking geographical location into consideration.

[0045] The Administrative Automation Department analyzes the social media activities of healthcare professionals and uses the findings to improve administrative tasks. For example, the Department assesses stress levels based on healthcare professionals' social media posts and reduces the burden of administrative work. For example, the Department suggests ways to improve work efficiency based on healthcare professionals' social media activities. For example, the Department analyzes health-related posts on social media by healthcare professionals and incorporates this information into administrative tasks. This enables the optimization of administrative tasks based on social media activity.

[0046] The delivery automation department analyzes past delivery data and selects the optimal delivery route. For example, it optimizes frequently used delivery routes based on past delivery data. It also improves delivery routes prone to errors by analyzing past delivery data. Furthermore, it proposes efficient delivery routes based on past delivery data. This enables the optimization of delivery routes based on historical data.

[0047] The automated delivery unit adjusts delivery timing based on the schedules of healthcare workers. For example, the automated delivery unit optimizes delivery timing by considering the schedules of healthcare workers. For example, the automated delivery unit automates deliveries to coincide with healthcare workers' break times. For example, the automated delivery unit determines delivery priorities based on the working hours of healthcare workers. This enables delivery timing adjustments based on schedules.

[0048] The automated delivery system takes into account the geographical location of healthcare workers when making deliveries. For example, if a healthcare worker is inside the hospital, the automated delivery system prioritizes deliveries. For example, if a healthcare worker is out of the hospital, the automated delivery system postpones deliveries. For example, if a healthcare worker is at home, the automated delivery system performs deliveries remotely. This enables deliveries that take geographical location into consideration.

[0049] The Delivery Automation Department analyzes the social media activity of healthcare professionals and uses this information to improve delivery operations. For example, the Delivery Automation Department assesses stress levels from healthcare professionals' social media posts to reduce the burden on delivery staff. For example, the Delivery Automation Department proposes ways to improve work efficiency based on the social media activity of healthcare professionals. For example, the Delivery Automation Department analyzes health-related posts on social media by healthcare professionals and incorporates this information into delivery operations. This enables the optimization of delivery operations based on social media activity.

[0050] The image analysis unit analyzes past image data to improve the accuracy of the analysis. For example, the image analysis unit detects anomalies by comparing current images with past image data. For example, the image analysis unit analyzes past image data to learn patterns of specific lesions. For example, the image analysis unit optimizes the analysis algorithm based on past image data. This makes it possible to improve the accuracy of image analysis based on past data.

[0051] The image analysis unit performs image analysis while considering the patient's lifestyle data. For example, the image analysis unit analyzes the patient's diet and exercise habits to assess the risk of lifestyle-related diseases. For example, the image analysis unit diagnoses sleep disorders based on the patient's sleep data. For example, the image analysis unit diagnoses mental health by considering the patient's stress level. This makes it possible to perform image analysis while considering lifestyle data.

[0052] The image analysis unit performs image analysis while considering the patient's geographical location. For example, the image analysis unit considers the infection risk in the patient's residential area. For example, the image analysis unit assesses health risks during commuting based on the patient's commute route. For example, the image analysis unit assesses infection risk by considering the patient's travel history. This enables image analysis that takes geographical location into account.

[0053] The image analysis department analyzes patients' social media activity and uses this information for image analysis. For example, the image analysis department assesses stress levels from patients' social media posts. For example, the image analysis department understands patients' lifestyle habits from their social media activity and uses this information for image analysis. For example, the image analysis department analyzes patients' health-related posts on social media and incorporates this into image analysis. This makes image analysis based on social media activity possible.

[0054] The data entry unit analyzes past recorded data and selects the optimal input method. For example, the data entry unit automates frequently performed input tasks based on past recorded data. For example, the data entry unit analyzes past recorded data and automates input tasks prone to errors. For example, the data entry unit proposes an efficient input flow based on past recorded data. This enables the optimization of data entry based on past data.

[0055] The record entry unit adjusts the timing of record entry based on the healthcare worker's schedule. For example, the record entry unit optimizes the timing of record entry by considering the healthcare worker's schedule. For example, the record entry unit automates record entry to coincide with the healthcare worker's break times. For example, the record entry unit determines the priority of record entry based on the healthcare worker's working hours. This enables the timing of record entry to be adjusted based on the schedule.

[0056] The record entry unit performs record entry while considering the geographical location information of healthcare workers. For example, if a healthcare worker is inside the hospital, the record entry unit prioritizes record entry. For example, if a healthcare worker is out of the hospital, the record entry unit postpones record entry. For example, if a healthcare worker is at home, the record entry unit performs record entry remotely. This makes it possible to perform record entry while considering geographical location information.

[0057] The data entry unit analyzes the social media activities of healthcare professionals and uses this information to improve data entry. For example, the unit assesses stress levels based on healthcare professionals' social media posts, reducing the burden of data entry. For example, the unit suggests ways to improve work efficiency based on healthcare professionals' social media activities. For example, the unit analyzes health-related posts on healthcare professionals' social media and incorporates this information into data entry. This enables the optimization of data entry based on social media activity.

[0058] The transport unit analyzes past transport data and selects the optimal transport route. For example, the transport unit optimizes frequently used transport routes based on past transport data. For example, the transport unit analyzes past transport data and improves transport routes with a high error rate. For example, the transport unit proposes efficient transport routes based on past transport data. This enables the optimization of transport routes based on past data.

[0059] The transport unit adjusts the timing of transport based on the schedules of healthcare workers. For example, the transport unit optimizes the timing of transport by considering the schedules of healthcare workers. For example, the transport unit automates transport to coincide with the break times of healthcare workers. For example, the transport unit determines the priority of transport based on the working hours of healthcare workers. This enables the adjustment of transport timing based on schedules.

[0060] The transport unit takes into account the geographical location of healthcare workers when transporting them. For example, if a healthcare worker is inside the hospital, the transport unit will prioritize transporting them. For example, if a healthcare worker is out of the hospital, the transport unit will postpone transporting them. For example, if a healthcare worker is at home, the transport unit will perform the transport remotely. This makes it possible to transport patients while taking geographical location into consideration.

[0061] The Transportation Department analyzes the social media activities of healthcare workers and uses this information to improve transportation operations. For example, the Transportation Department assesses stress levels based on healthcare workers' social media posts to reduce the burden on transportation operations. For example, the Transportation Department proposes ways to improve work efficiency based on healthcare workers' social media activities. For example, the Transportation Department analyzes health-related posts on social media by healthcare workers and incorporates this information into transportation operations. This makes it possible to optimize transportation operations based on social media activity.

[0062] The serving robot analyzes past serving data and selects the optimal serving method. For example, the serving robot optimizes frequently used serving methods based on past serving data. For example, the serving robot analyzes past serving data and improves serving methods that frequently result in errors. For example, the serving robot proposes efficient serving methods based on past serving data. This makes it possible to optimize serving methods based on past data.

[0063] The meal delivery robot customizes the meal contents based on the patient's dietary history. For example, it can provide meals that the patient prefers based on their past eating history. For example, it can provide appropriate meals considering the patient's dietary restrictions. For example, it can provide safe meals based on the patient's allergy information. This makes it possible to customize meal contents based on dietary history.

[0064] The meal delivery robot takes into account the patient's geographical location when delivering meals. For example, the meal delivery robot considers the location of the patient's room and selects the optimal delivery route. For example, the meal delivery robot determines the most efficient delivery order based on the layout of the patient's room. For example, the meal delivery robot optimizes its movement based on the location information of the patient's room. This makes it possible to deliver meals while considering geographical location.

[0065] The meal delivery robot analyzes patients' social media activity and customizes the contents of their meals. For example, the robot can understand patients' food preferences from their social media posts and customize the contents of their meals. For example, the robot can optimize the timing of meals based on patients' social media activity. For example, the robot can analyze patients' health-related posts on social media and reflect this in the contents of their meals. This makes it possible to customize meal contents based on social media activity.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The Diagnostic Support Department analyzes patients' past diagnostic history to improve diagnostic accuracy. For example, it makes a diagnosis by comparing the patient's past diagnostic results with their current symptoms. For example, it considers the patient's past treatment history to propose the most suitable treatment method. For example, it analyzes the patient's past test data to help detect abnormalities early. This makes it possible to improve diagnostic accuracy based on past diagnostic history.

[0068] The Diagnostic Support Department makes diagnoses while considering the patient's lifestyle data. For example, it analyzes the patient's diet and exercise habits to assess the risk of lifestyle-related diseases. For example, it diagnoses sleep disorders based on the patient's sleep data. For example, it diagnoses mental health by considering the patient's stress level. This makes it possible to make diagnoses that take lifestyle data into account.

[0069] The diagnostic support department makes diagnoses while considering the patient's geographical location. For example, the diagnostic support department considers the infectious disease risk in the area where the patient lives. For example, the diagnostic support department assesses the health risks during commuting based on the patient's commute route. For example, the diagnostic support department assesses the risk of infectious diseases by considering the patient's travel history. This makes it possible to make diagnoses that take geographical location into account.

[0070] The Office Automation Department analyzes past office work data and selects the optimal automation method. For example, the Office Automation Department automates frequently performed tasks based on past office work data. For example, the Office Automation Department analyzes past office work data and automates tasks that are prone to errors. For example, the Office Automation Department proposes efficient workflows based on past office work data. This enables the optimization of office work based on historical data.

[0071] The administrative automation department adjusts the timing of administrative tasks based on the schedules of healthcare workers. For example, the administrative automation department optimizes the timing of administrative tasks by considering the schedules of healthcare workers. For example, the administrative automation department automates administrative tasks to coincide with the break times of healthcare workers. For example, the administrative automation department determines the priority of administrative tasks based on the working hours of healthcare workers. This makes it possible to adjust the timing of administrative tasks based on schedules.

[0072] The automated administrative department performs administrative tasks while taking into account the geographical location of healthcare professionals. For example, if a healthcare professional is in the hospital, the automated administrative department prioritizes administrative tasks. For example, if a healthcare professional is out of the hospital, the automated administrative department postpones administrative tasks. For example, if a healthcare professional is at home, the automated administrative department performs administrative tasks remotely. This makes it possible to perform administrative tasks while taking geographical location into consideration.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The diagnostic support unit analyzes medical data and supports diagnosis. For example, AI agents, deep learning technologies, and convolutional neural networks (CNNs) can be used to analyze medical images and detect abnormalities. Step 2: The administrative automation department automates administrative tasks. For example, AI agents, speech recognition technology, and OCR technology can be used to automatically input medical records. Step 3: The delivery automation department automates delivery operations. For example, to transport medications, meals, and medical supplies, AI agents and delivery robots work together to deliver meals to patients' rooms or transport medications and medical supplies.

[0075] (Example of form 2) The medical support system according to an embodiment of the present invention is a system that contributes to solving the shortage of medical personnel by introducing an AI agent and a meal delivery robot. This medical support system comprises a diagnostic support unit that analyzes medical data and supports diagnosis, an administrative automation unit that automates administrative tasks, and a delivery automation unit that automates delivery operations. The medical support system aims to improve the quality of medical care by analyzing medical data and supporting diagnosis. For example, the medical support system can detect lesions that doctors might overlook by having the AI ​​agent analyze medical images and detect abnormalities. Next, the medical support system reduces the burden on medical personnel by automating administrative tasks. For example, the medical support system allows doctors and nurses to spend more time with patients by having the AI ​​agent automatically input medical records and manage appointments. Furthermore, the medical support system automates delivery operations through the cooperation of the AI ​​agent and the meal delivery robot. For example, the medical support system reduces the opportunities for medical personnel to have direct contact with patients and reduces the risk of infection by having the meal delivery robot deliver meals to patients' rooms. This reduces the burden on medical personnel and lowers the risk of hospital-acquired infections. Thus, medical support systems are expected to improve the quality of medical care, automate administrative tasks, and reduce the burden on healthcare professionals, thereby contributing to solving the shortage of healthcare personnel. In this way, medical support systems can reduce the burden on healthcare professionals and improve the quality of medical care.

[0076] The medical support system according to this embodiment comprises a diagnostic support unit, an administrative automation unit, and a delivery automation unit. The diagnostic support unit analyzes medical data and supports diagnosis. The diagnostic support unit, for example, analyzes medical images and detects abnormalities. The diagnostic support unit can, for example, use an AI agent to analyze medical images and detect abnormalities. The diagnostic support unit can, for example, use deep learning technology to analyze medical images. The diagnostic support unit can, for example, use a convolutional neural network (CNN) to analyze medical images. The administrative automation unit automates administrative tasks. The administrative automation unit, for example, automatically inputs medical records. The administrative automation unit can, for example, use an AI agent to automatically input medical records. The administrative automation unit can, for example, use speech recognition technology to automatically input medical records. The administrative automation unit can, for example, use OCR technology to automatically input medical records. The delivery automation unit automates delivery operations. The delivery automation unit, for example, transports pharmaceuticals, meals, and medical supplies. The automated delivery unit can, for example, have an AI agent and a food delivery robot work together to transport medicines, meals, and medical supplies. The automated delivery unit can, for example, use a food delivery robot to deliver meals to patients' rooms. The automated delivery unit can, for example, use a food delivery robot to transport medicines. The automated delivery unit can, for example, use a food delivery robot to transport medical supplies. As a result, the medical support system according to this embodiment can perform medical data analysis, automate administrative tasks, and automate delivery operations.

[0077] The Diagnostic Support Department analyzes medical data to support diagnosis. Specifically, it uses deep learning technology to analyze medical images and detect abnormalities. Among deep learning technologies, convolutional neural networks (CNNs) are particularly effective. CNNs demonstrate excellent performance in image recognition and can detect subtle abnormalities in medical images with high accuracy. For example, using MRI and CT scan images as input, the CNN learns abnormal patterns in the images and identifies the location of tumors and lesions. Furthermore, the Diagnostic Support Department uses an AI agent to present diagnostic results to physicians. Based on the analysis results, the AI ​​agent provides information such as the type and progression of the abnormality and recommended treatment methods. This allows physicians to make quick and accurate diagnoses and appropriately determine the treatment plan for patients. In addition, the Diagnostic Support Department further improves the accuracy of diagnoses by referring to past diagnostic data and case databases and comparing them with similar cases. As a result, the Diagnostic Support Department can significantly streamline diagnostic work in medical settings and reduce the burden on physicians.

[0078] The Administrative Automation Department automates administrative tasks. Specifically, it uses an AI agent to automatically input medical records. The AI ​​agent utilizes speech recognition and OCR technologies to achieve automatic input of medical records. For example, it uses speech recognition technology to transcribe what a doctor says during a consultation into text in real time and input it as a medical record. It can also digitize handwritten medical notes and paper charts using OCR technology and automatically import them into the electronic medical record system. This eliminates the need for manual record entry for doctors and medical staff, allowing them to concentrate on patient care. Furthermore, the Administrative Automation Department can automate not only medical record input but also various other administrative tasks such as schedule management, patient appointment management, and insurance claim document creation. The AI ​​agent efficiently processes these tasks, reduces errors, and improves the accuracy of operations. As a result, the Administrative Automation Department can significantly streamline administrative tasks in the medical field and reduce the burden on medical staff.

[0079] The Delivery Automation Department automates delivery operations. Specifically, AI agents and food delivery robots work together to transport medications, meals, and medical supplies. The food delivery robots can autonomously move through rooms and floors within the hospital and deliver items to designated locations. For example, when delivering medication to a patient's room, the AI ​​agent instructs the food delivery robot on the type and quantity of medication and the delivery destination, and the robot calculates the optimal route and moves accordingly. In meal delivery, the food delivery robots deliver appropriate meals according to the content of the meal and the patient's dietary restrictions. Furthermore, in the transport of medical supplies, priority routes are set to ensure the fastest possible delivery of urgent items. As a result, the Delivery Automation Department can streamline logistics within the hospital and reduce the burden on medical staff. In addition, the food delivery robots are equipped with functions to detect and avoid obstacles and to automatically use elevators, allowing them to move smoothly even in the complex environment of the hospital. In this way, the Delivery Automation Department can significantly streamline logistics operations in the medical field and contribute to improving services for patients.

[0080] The diagnostic support unit includes an image analysis unit that analyzes medical images and detects abnormalities. The image analysis unit analyzes medical images such as X-ray images, CT scan images, and MRI images. The image analysis unit can analyze medical images and detect abnormalities using, for example, an AI agent. The image analysis unit can analyze medical images and detect abnormalities using, for example, deep learning technology. The image analysis unit can analyze medical images and detect abnormalities using, for example, a convolutional neural network (CNN). This enables the analysis and detection of abnormalities in medical images.

[0081] The administrative automation unit includes a record entry unit that automatically inputs medical records. The record entry unit can, for example, use an AI agent to automatically input medical records. The record entry unit can, for example, use speech recognition technology to automatically input medical records. The record entry unit can, for example, use OCR technology to automatically input medical records. The record entry unit can, for example, have an AI agent recognize a doctor's voice and automatically input medical records. This enables the automatic input of medical records.

[0082] The automated delivery unit includes a transport unit that handles the delivery of pharmaceuticals, meals, and medical supplies. The transport unit, for example, uses an AI agent and a food delivery robot in conjunction to deliver pharmaceuticals, meals, and medical supplies. For example, the transport unit can use a food delivery robot to deliver meals to patients' rooms. For example, the transport unit can use a food delivery robot to deliver pharmaceuticals. For example, the transport unit can use a food delivery robot to deliver medical supplies. This automates the delivery of pharmaceuticals, meals, and medical supplies.

[0083] The automated delivery unit performs delivery operations in cooperation with meal delivery robots. For example, the automated delivery unit uses AI agents and meal delivery robots to transport medications, meals, and medical supplies. For example, the automated delivery unit can use meal delivery robots to deliver meals to patients' rooms. For example, the automated delivery unit can use meal delivery robots to transport medications. For example, the automated delivery unit can use meal delivery robots to transport medical supplies. This enables the automation of delivery operations through cooperation with meal delivery robots.

[0084] The diagnostic support unit learns from vast amounts of medical data to assist physicians in their diagnoses. For example, the diagnostic support unit can use AI agents to learn from vast amounts of medical data and assist physicians in their diagnoses. For example, the diagnostic support unit can use deep learning technology to learn from vast amounts of medical data and assist physicians in their diagnoses. For example, the diagnostic support unit can use convolutional neural networks (CNNs) to learn from vast amounts of medical data and assist physicians in their diagnoses. This enables physicians to provide diagnostic support.

[0085] The diagnostic support unit estimates the patient's emotions and adjusts how the diagnostic results are communicated based on those estimates. For example, if the patient is feeling anxious, the diagnostic support unit will communicate the results gently and carefully. If the patient is relaxed, the diagnostic support unit will provide the results with detailed explanations. If the patient is in a hurry, the diagnostic support unit will provide the results concisely and to the point. This makes it possible to communicate diagnostic results in a way that is appropriate to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The Diagnostic Support Department analyzes patients' past diagnostic history to improve diagnostic accuracy. For example, it makes a diagnosis by comparing the patient's past diagnostic results with their current symptoms. For example, it considers the patient's past treatment history to propose the most suitable treatment method. For example, it analyzes the patient's past test data to help detect abnormalities early. This makes it possible to improve diagnostic accuracy based on past diagnostic history.

[0087] The Diagnostic Support Department makes diagnoses while considering the patient's lifestyle data. For example, it analyzes the patient's diet and exercise habits to assess the risk of lifestyle-related diseases. For example, it diagnoses sleep disorders based on the patient's sleep data. For example, it diagnoses mental health by considering the patient's stress level. This makes it possible to make diagnoses that take lifestyle data into account.

[0088] The diagnostic support unit estimates the patient's emotions and determines diagnostic priorities based on the estimated emotions. For example, if the patient is experiencing strong anxiety, the diagnostic support unit will prioritize the diagnosis. For example, if the patient is relaxed, the diagnostic support unit will prioritize other patients with higher urgency. For example, if the patient is in a hurry, the diagnostic support unit will perform a diagnosis quickly. This makes it possible to determine diagnostic priorities according to the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The diagnostic support department makes diagnoses while considering the patient's geographical location. For example, the diagnostic support department considers the infectious disease risk in the area where the patient lives. For example, the diagnostic support department assesses the health risks during commuting based on the patient's commute route. For example, the diagnostic support department assesses the risk of infectious diseases by considering the patient's travel history. This makes it possible to make diagnoses that take geographical location into account.

[0090] The Diagnostic Support Department analyzes patients' social media activity to aid in diagnosis. For example, it assesses stress levels from patients' social media posts. For example, it understands patients' lifestyle habits from their social media activity to aid in diagnosis. For example, it analyzes patients' health-related posts on social media and incorporates this into the diagnosis. This makes it possible to make diagnoses based on social media activity.

[0091] The administrative automation department estimates the emotions of healthcare workers and adjusts methods to reduce the burden of administrative tasks based on the estimated emotions. For example, if a healthcare worker is stressed, the administrative automation department prioritizes the automation of administrative tasks. For example, if a healthcare worker is relaxed, the administrative automation department reduces manual verification tasks. For example, if a healthcare worker is tired, the administrative automation department provides a simple interface and streamlines tasks. This makes it possible to reduce the burden of administrative tasks in accordance with the emotions of healthcare workers. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The Office Automation Department analyzes past office work data and selects the optimal automation method. For example, the Office Automation Department automates frequently performed tasks based on past office work data. For example, the Office Automation Department analyzes past office work data and automates tasks that are prone to errors. For example, the Office Automation Department proposes efficient workflows based on past office work data. This enables the optimization of office work based on historical data.

[0093] The administrative automation department adjusts the timing of administrative tasks based on the schedules of healthcare workers. For example, the administrative automation department optimizes the timing of administrative tasks by considering the schedules of healthcare workers. For example, the administrative automation department automates administrative tasks to coincide with the break times of healthcare workers. For example, the administrative automation department determines the priority of administrative tasks based on the working hours of healthcare workers. This makes it possible to adjust the timing of administrative tasks based on schedules.

[0094] The administrative automation unit estimates the emotions of healthcare workers and prioritizes administrative tasks based on the estimated emotions. For example, if a healthcare worker is stressed, the administrative automation unit prioritizes important administrative tasks. For example, if a healthcare worker is relaxed, the administrative automation unit prioritizes routine administrative tasks. For example, if a healthcare worker is tired, the administrative automation unit prioritizes simple administrative tasks. This makes it possible to prioritize administrative tasks according to the emotions of healthcare workers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The automated administrative department performs administrative tasks while taking into account the geographical location of healthcare professionals. For example, if a healthcare professional is in the hospital, the automated administrative department prioritizes administrative tasks. For example, if a healthcare professional is out of the hospital, the automated administrative department postpones administrative tasks. For example, if a healthcare professional is at home, the automated administrative department performs administrative tasks remotely. This makes it possible to perform administrative tasks while taking geographical location into consideration.

[0096] The Administrative Automation Department analyzes the social media activities of healthcare professionals and uses the findings to improve administrative tasks. For example, the Department assesses stress levels based on healthcare professionals' social media posts and reduces the burden of administrative work. For example, the Department suggests ways to improve work efficiency based on healthcare professionals' social media activities. For example, the Department analyzes health-related posts on social media by healthcare professionals and incorporates this information into administrative tasks. This enables the optimization of administrative tasks based on social media activity.

[0097] The delivery automation unit estimates the emotions of healthcare workers and adjusts methods to reduce the burden of delivery operations based on the estimated emotions. For example, if a healthcare worker is stressed, the delivery automation unit prioritizes automating delivery operations. For example, if a healthcare worker is relaxed, the delivery automation unit reduces manual verification tasks. For example, if a healthcare worker is tired, the delivery automation unit provides a simple interface to streamline operations. This makes it possible to reduce the burden of delivery operations in accordance with the emotions of healthcare workers. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The delivery automation department analyzes past delivery data and selects the optimal delivery route. For example, it optimizes frequently used delivery routes based on past delivery data. It also improves delivery routes prone to errors by analyzing past delivery data. Furthermore, it proposes efficient delivery routes based on past delivery data. This enables the optimization of delivery routes based on historical data.

[0099] The automated delivery unit adjusts delivery timing based on the schedules of healthcare workers. For example, the automated delivery unit optimizes delivery timing by considering the schedules of healthcare workers. For example, the automated delivery unit automates deliveries to coincide with healthcare workers' break times. For example, the automated delivery unit determines delivery priorities based on the working hours of healthcare workers. This enables delivery timing adjustments based on schedules.

[0100] The automated delivery unit estimates the emotions of healthcare workers and prioritizes delivery tasks based on the estimated emotions. For example, if a healthcare worker is stressed, the automated delivery unit prioritizes important delivery tasks. For example, if a healthcare worker is relaxed, the automated delivery unit prioritizes routine delivery tasks. For example, if a healthcare worker is tired, the automated delivery unit prioritizes simple delivery tasks. This makes it possible to prioritize delivery tasks according to the emotions of healthcare workers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The automated delivery system takes into account the geographical location of healthcare workers when making deliveries. For example, if a healthcare worker is inside the hospital, the automated delivery system prioritizes deliveries. For example, if a healthcare worker is out of the hospital, the automated delivery system postpones deliveries. For example, if a healthcare worker is at home, the automated delivery system performs deliveries remotely. This enables deliveries that take geographical location into consideration.

[0102] The Delivery Automation Department analyzes the social media activity of healthcare professionals and uses this information to improve delivery operations. For example, the Delivery Automation Department assesses stress levels from healthcare professionals' social media posts to reduce the burden on delivery staff. For example, the Delivery Automation Department proposes ways to improve work efficiency based on the social media activity of healthcare professionals. For example, the Delivery Automation Department analyzes health-related posts on social media by healthcare professionals and incorporates this information into delivery operations. This enables the optimization of delivery operations based on social media activity.

[0103] The image analysis unit estimates the patient's emotions and adjusts how the image analysis results are communicated based on the estimated emotions. For example, if the patient is feeling anxious, the image analysis unit will communicate the image analysis results gently and carefully. For example, if the patient is relaxed, the image analysis unit will provide image analysis results that include detailed explanations. For example, if the patient is in a hurry, the image analysis unit will provide image analysis results that are concise and to the point. This makes it possible to communicate image analysis results in a way that is appropriate to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The image analysis unit analyzes past image data to improve the accuracy of the analysis. For example, the image analysis unit detects anomalies by comparing current images with past image data. For example, the image analysis unit analyzes past image data to learn patterns of specific lesions. For example, the image analysis unit optimizes the analysis algorithm based on past image data. This makes it possible to improve the accuracy of image analysis based on past data.

[0105] The image analysis unit performs image analysis while considering the patient's lifestyle data. For example, the image analysis unit analyzes the patient's diet and exercise habits to assess the risk of lifestyle-related diseases. For example, the image analysis unit diagnoses sleep disorders based on the patient's sleep data. For example, the image analysis unit diagnoses mental health by considering the patient's stress level. This makes it possible to perform image analysis while considering lifestyle data.

[0106] The image analysis unit estimates the patient's emotions and determines the priority of image analysis based on the estimated emotions. For example, if the patient is experiencing strong anxiety, the image analysis unit will prioritize the analysis. For example, if the patient is relaxed, the image analysis unit will prioritize other patients with higher urgency. For example, if the patient is in a hurry, the image analysis unit will perform the analysis quickly. This makes it possible to determine the priority of image analysis according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The image analysis unit performs image analysis while considering the patient's geographical location. For example, the image analysis unit considers the infection risk in the patient's residential area. For example, the image analysis unit assesses health risks during commuting based on the patient's commute route. For example, the image analysis unit assesses infection risk by considering the patient's travel history. This enables image analysis that takes geographical location into account.

[0108] The image analysis department analyzes patients' social media activity and uses this information for image analysis. For example, the image analysis department assesses stress levels from patients' social media posts. For example, the image analysis department understands patients' lifestyle habits from their social media activity and uses this information for image analysis. For example, the image analysis department analyzes patients' health-related posts on social media and incorporates this into image analysis. This makes image analysis based on social media activity possible.

[0109] The data entry unit estimates the emotions of healthcare workers and adjusts methods to reduce the burden of data entry based on the estimated emotions. For example, if a healthcare worker is stressed, the data entry unit prioritizes automating data entry. For example, if a healthcare worker is relaxed, the data entry unit reduces manual verification tasks. For example, if a healthcare worker is tired, the data entry unit provides a simple interface to streamline the work. This makes it possible to reduce the burden of data entry in accordance with the emotions of healthcare workers. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The data entry unit analyzes past recorded data and selects the optimal input method. For example, the data entry unit automates frequently performed input tasks based on past recorded data. For example, the data entry unit analyzes past recorded data and automates input tasks prone to errors. For example, the data entry unit proposes an efficient input flow based on past recorded data. This enables the optimization of data entry based on past data.

[0111] The record entry unit adjusts the timing of record entry based on the healthcare worker's schedule. For example, the record entry unit optimizes the timing of record entry by considering the healthcare worker's schedule. For example, the record entry unit automates record entry to coincide with the healthcare worker's break times. For example, the record entry unit determines the priority of record entry based on the healthcare worker's working hours. This enables the timing of record entry to be adjusted based on the schedule.

[0112] The recording input unit estimates the emotions of healthcare workers and determines the priority of recording input based on the estimated emotions. For example, if a healthcare worker is stressed, the recording input unit prioritizes important recording input. For example, if a healthcare worker is relaxed, the recording input unit prioritizes normal recording input. For example, if a healthcare worker is tired, the recording input unit prioritizes simple recording input. This makes it possible to determine the priority of recording input according to the emotions of healthcare workers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The record entry unit performs record entry while considering the geographical location information of healthcare workers. For example, if a healthcare worker is inside the hospital, the record entry unit prioritizes record entry. For example, if a healthcare worker is out of the hospital, the record entry unit postpones record entry. For example, if a healthcare worker is at home, the record entry unit performs record entry remotely. This makes it possible to perform record entry while considering geographical location information.

[0114] The data entry unit analyzes the social media activities of healthcare professionals and uses this information to improve data entry. For example, the unit assesses stress levels based on healthcare professionals' social media posts, reducing the burden of data entry. For example, the unit suggests ways to improve work efficiency based on healthcare professionals' social media activities. For example, the unit analyzes health-related posts on healthcare professionals' social media and incorporates this information into data entry. This enables the optimization of data entry based on social media activity.

[0115] The transport unit estimates the emotions of healthcare workers and adjusts methods to reduce the burden of transport operations based on the estimated emotions. For example, if a healthcare worker is stressed, the transport unit prioritizes automating transport operations. For example, if a healthcare worker is relaxed, the transport unit reduces manual verification tasks. For example, if a healthcare worker is tired, the transport unit provides a simple interface to streamline operations. This makes it possible to reduce the burden of transport operations in accordance with the emotions of healthcare workers. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The transport unit analyzes past transport data and selects the optimal transport route. For example, the transport unit optimizes frequently used transport routes based on past transport data. For example, the transport unit analyzes past transport data and improves transport routes with a high error rate. For example, the transport unit proposes efficient transport routes based on past transport data. This enables the optimization of transport routes based on past data.

[0117] The transport unit adjusts the timing of transport based on the schedules of healthcare workers. For example, the transport unit optimizes the timing of transport by considering the schedules of healthcare workers. For example, the transport unit automates transport to coincide with the break times of healthcare workers. For example, the transport unit determines the priority of transport based on the working hours of healthcare workers. This enables the adjustment of transport timing based on schedules.

[0118] The transport unit estimates the emotions of healthcare workers and determines the priority of transport tasks based on the estimated emotions. For example, if a healthcare worker is stressed, the transport unit prioritizes important transport tasks. For example, if a healthcare worker is relaxed, the transport unit prioritizes routine transport tasks. For example, if a healthcare worker is tired, the transport unit prioritizes simple transport tasks. This makes it possible to determine the priority of transport tasks according to the emotions of healthcare workers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The transport unit takes into account the geographical location of healthcare workers when transporting them. For example, if a healthcare worker is inside the hospital, the transport unit will prioritize transporting them. For example, if a healthcare worker is out of the hospital, the transport unit will postpone transporting them. For example, if a healthcare worker is at home, the transport unit will perform the transport remotely. This makes it possible to transport patients while taking geographical location into consideration.

[0120] The Transportation Department analyzes the social media activities of healthcare workers and uses this information to improve transportation operations. For example, the Transportation Department assesses stress levels based on healthcare workers' social media posts to reduce the burden on transportation operations. For example, the Transportation Department proposes ways to improve work efficiency based on healthcare workers' social media activities. For example, the Transportation Department analyzes health-related posts on social media by healthcare workers and incorporates this information into transportation operations. This makes it possible to optimize transportation operations based on social media activity.

[0121] The meal delivery robot estimates the patient's emotions and adjusts its delivery method based on the estimated emotions. For example, if the patient is feeling anxious, the robot will deliver the meal gently and carefully. If the patient is relaxed, the robot will deliver the meal with detailed explanations. If the patient is in a hurry, the robot will deliver the meal quickly and concisely. This allows for adjustments to the delivery method according to the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0122] The serving robot analyzes past serving data and selects the optimal serving method. For example, the serving robot optimizes frequently used serving methods based on past serving data. For example, the serving robot analyzes past serving data and improves serving methods that frequently result in errors. For example, the serving robot proposes efficient serving methods based on past serving data. This makes it possible to optimize serving methods based on past data.

[0123] The meal delivery robot customizes the meal contents based on the patient's dietary history. For example, it can provide meals that the patient prefers based on their past eating history. For example, it can provide appropriate meals considering the patient's dietary restrictions. For example, it can provide safe meals based on the patient's allergy information. This makes it possible to customize meal contents based on dietary history.

[0124] The meal delivery robot estimates the patient's emotions and determines the priority of meal delivery based on the estimated emotions. For example, if a patient is experiencing strong anxiety, the robot will prioritize serving that patient. If a patient is relaxed, the robot will prioritize other patients with more urgent needs. If a patient is in a hurry, the robot will deliver the meal quickly. This makes it possible to determine the priority of meal delivery according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0125] The meal delivery robot takes into account the patient's geographical location when delivering meals. For example, the meal delivery robot considers the location of the patient's room and selects the optimal delivery route. For example, the meal delivery robot determines the most efficient delivery order based on the layout of the patient's room. For example, the meal delivery robot optimizes its movement based on the location information of the patient's room. This makes it possible to deliver meals while considering geographical location.

[0126] The meal delivery robot analyzes patients' social media activity and customizes the contents of their meals. For example, the robot can understand patients' food preferences from their social media posts and customize the contents of their meals. For example, the robot can optimize the timing of meals based on patients' social media activity. For example, the robot can analyze patients' health-related posts on social media and reflect this in the contents of their meals. This makes it possible to customize meal contents based on social media activity.

[0127] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0128] The diagnostic support unit estimates the patient's emotions and adjusts how the diagnostic results are communicated based on those estimated emotions. For example, if the patient is feeling anxious, the diagnostic support unit will communicate the results gently and carefully. If the patient is relaxed, the diagnostic support unit will provide the results with detailed explanations. If the patient is in a hurry, the diagnostic support unit will provide the results concisely and to the point. This makes it possible to communicate diagnostic results in a way that is appropriate to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0129] The Diagnostic Support Department analyzes patients' past diagnostic history to improve diagnostic accuracy. For example, it makes a diagnosis by comparing the patient's past diagnostic results with their current symptoms. For example, it considers the patient's past treatment history to propose the most suitable treatment method. For example, it analyzes the patient's past test data to help detect abnormalities early. This makes it possible to improve diagnostic accuracy based on past diagnostic history.

[0130] The Diagnostic Support Department makes diagnoses while considering the patient's lifestyle data. For example, it analyzes the patient's diet and exercise habits to assess the risk of lifestyle-related diseases. For example, it diagnoses sleep disorders based on the patient's sleep data. For example, it diagnoses mental health by considering the patient's stress level. This makes it possible to make diagnoses that take lifestyle data into account.

[0131] The diagnostic support unit estimates the patient's emotions and determines diagnostic priorities based on the estimated emotions. For example, if the patient is experiencing strong anxiety, the diagnostic support unit will prioritize the diagnosis. For example, if the patient is relaxed, the diagnostic support unit will prioritize other patients with higher urgency. For example, if the patient is in a hurry, the diagnostic support unit will perform a diagnosis quickly. This makes it possible to determine diagnostic priorities according to the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0132] The diagnostic support department makes diagnoses while considering the patient's geographical location. For example, the diagnostic support department considers the infectious disease risk in the area where the patient lives. For example, the diagnostic support department assesses the health risks during commuting based on the patient's commute route. For example, the diagnostic support department assesses the risk of infectious diseases by considering the patient's travel history. This makes it possible to make diagnoses that take geographical location into account.

[0133] The administrative automation department estimates the emotions of healthcare workers and adjusts methods to reduce the burden of administrative tasks based on the estimated emotions. For example, if a healthcare worker is stressed, the administrative automation department prioritizes the automation of administrative tasks. For example, if a healthcare worker is relaxed, the administrative automation department reduces manual verification tasks. For example, if a healthcare worker is tired, the administrative automation department provides a simple interface and streamlines tasks. This makes it possible to reduce the burden of administrative tasks in accordance with the emotions of healthcare workers. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0134] The Office Automation Department analyzes past office work data and selects the optimal automation method. For example, the Office Automation Department automates frequently performed tasks based on past office work data. For example, the Office Automation Department analyzes past office work data and automates tasks that are prone to errors. For example, the Office Automation Department proposes efficient workflows based on past office work data. This enables the optimization of office work based on historical data.

[0135] The administrative automation department adjusts the timing of administrative tasks based on the schedules of healthcare workers. For example, the administrative automation department optimizes the timing of administrative tasks by considering the schedules of healthcare workers. For example, the administrative automation department automates administrative tasks to coincide with the break times of healthcare workers. For example, the administrative automation department determines the priority of administrative tasks based on the working hours of healthcare workers. This makes it possible to adjust the timing of administrative tasks based on schedules.

[0136] The administrative automation unit estimates the emotions of healthcare workers and prioritizes administrative tasks based on the estimated emotions. For example, if a healthcare worker is stressed, the administrative automation unit prioritizes important administrative tasks. For example, if a healthcare worker is relaxed, the administrative automation unit prioritizes routine administrative tasks. For example, if a healthcare worker is tired, the administrative automation unit prioritizes simple administrative tasks. This makes it possible to prioritize administrative tasks according to the emotions of healthcare workers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0137] The automated administrative department performs administrative tasks while taking into account the geographical location of healthcare professionals. For example, if a healthcare professional is in the hospital, the automated administrative department prioritizes administrative tasks. For example, if a healthcare professional is out of the hospital, the automated administrative department postpones administrative tasks. For example, if a healthcare professional is at home, the automated administrative department performs administrative tasks remotely. This makes it possible to perform administrative tasks while taking geographical location into consideration.

[0138] The following briefly describes the processing flow for example form 2.

[0139] Step 1: The diagnostic support unit analyzes medical data and supports diagnosis. For example, AI agents, deep learning technologies, and convolutional neural networks (CNNs) can be used to analyze medical images and detect abnormalities. Step 2: The administrative automation department automates administrative tasks. For example, AI agents, speech recognition technology, and OCR technology can be used to automatically input medical records. Step 3: The delivery automation department automates delivery operations. For example, to transport medications, meals, and medical supplies, AI agents and delivery robots work together to deliver meals to patients' rooms or transport medications and medical supplies.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0142] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the diagnostic support unit, the office automation unit, and the delivery automation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the diagnostic support unit analyzes medical data using the control unit 46A of the smart device 14 to support diagnosis. The office automation unit automates office work using the control unit 46A of the smart device 14. Furthermore, the delivery automation unit automates delivery operations using the control unit 46A of the smart device 14 and a food delivery robot. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0144] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0145] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the diagnostic support unit, the office automation unit, and the delivery automation unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the diagnostic support unit analyzes medical data using the control unit 46A of the smart glasses 214 to support diagnosis. The office automation unit automates office work using the control unit 46A of the smart glasses 214. Furthermore, the delivery automation unit automates delivery operations using the control unit 46A of the smart glasses 214 and a food delivery robot. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0160] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0161] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the diagnostic support unit, the office automation unit, and the delivery automation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the diagnostic support unit analyzes medical data using the control unit 46A of the headset terminal 314 to support diagnosis. The office automation unit automates office work using the control unit 46A of the headset terminal 314. Furthermore, the delivery automation unit automates delivery operations using the control unit 46A of the headset terminal 314 and a food delivery robot. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0176] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0177] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0178] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0179] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0180] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0181] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0182] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0183] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0184] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0185] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0186] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0187] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0188] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0189] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0190] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0191] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0192] Each of the multiple elements described above, including the diagnostic support unit, the office automation unit, and the delivery automation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the diagnostic support unit analyzes medical data using the control unit 46A of the robot 414 to support diagnosis. The office automation unit automates office work using the control unit 46A of the robot 414. Furthermore, the delivery automation unit automates delivery operations using the control unit 46A of the robot 414 and a food delivery robot. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0193] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0194] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0195] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0196] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0197] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0198] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0199] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0200] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0201] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0202] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0203] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0204] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0205] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0206] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0207] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0208] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0209] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0210] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0211] (Note 1) The Diagnostic Support Department analyzes medical data and supports diagnosis, The Office Automation Department automates administrative tasks, It includes a delivery automation unit that automates delivery operations. A system characterized by the following features. (Note 2) The aforementioned diagnostic support unit, It includes an image analysis unit that analyzes medical images and detects abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned business automation unit, It is equipped with a record entry unit that automatically inputs medical records. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned delivery automation unit is It is equipped with a transport unit for transporting medicines, meals, and medical supplies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned delivery automation unit is Delivery operations are carried out in conjunction with food delivery robots. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned diagnostic support unit, It learns from vast amounts of medical data and supports doctors' diagnoses. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned diagnostic support unit, The system estimates the patient's emotions and adjusts how the diagnosis is communicated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned diagnostic support unit, Analyzing a patient's past diagnostic history improves the accuracy of their diagnosis. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned diagnostic support unit, Diagnosis is made considering the patient's lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned diagnostic support unit, The system estimates the patient's emotions and determines diagnostic priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned diagnostic support unit, Diagnosis is made considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned diagnostic support unit, Analyzing patients' social media activity to aid in diagnosis The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned business automation unit, Estimate the emotions of healthcare workers and adjust methods to reduce their administrative workload based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned business automation unit, Analyze past administrative work data to select the optimal automation method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned business automation unit, Adjusting the timing of administrative tasks based on the schedules of healthcare workers. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned business automation unit, Estimate the emotions of healthcare workers and prioritize administrative tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned business automation unit, Perform administrative tasks while taking into account the geographical location information of healthcare workers. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned business automation unit, Analyzing the social media activity of healthcare professionals to help with administrative tasks. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned delivery automation unit is Estimate the emotions of healthcare workers and adjust methods to reduce the burden of delivery work based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned delivery automation unit is Analyze past delivery data to select the optimal delivery route. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned delivery automation unit is We will adjust the delivery timing based on the schedules of healthcare workers. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned delivery automation unit is The system estimates the emotions of healthcare workers and prioritizes delivery tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned delivery automation unit is Delivery will be carried out taking into account the geographical location of healthcare workers. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned delivery automation unit is Analyzing the social media activity of healthcare workers to improve delivery operations. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned image analysis unit, The system estimates the patient's emotions and adjusts how the image analysis results are communicated based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned image analysis unit, Analyzing past image data improves the accuracy of the analysis. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned image analysis unit, Image analysis is performed while considering the patient's lifestyle data. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned image analysis unit, The system estimates the patient's emotions and prioritizes image analysis based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned image analysis unit, Image analysis is performed while taking into account the patient's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned image analysis unit, Analyzing patients' social media activity to aid in image analysis. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned recording input unit is Estimate the emotions of healthcare workers and adjust methods to reduce the burden of record entry based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned recording input unit is Analyze past recorded data and select the optimal input method. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned recording input unit is Adjust the timing of record entry based on the healthcare worker's schedule. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned recording input unit is The system estimates the emotions of healthcare workers and determines the priority of record entry based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned recording input unit is Record entry should take into account the geographical location information of healthcare professionals. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned recording input unit is Analyze the social media activity of healthcare professionals to aid in record-keeping. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned transport unit is Estimate the emotions of healthcare workers and adjust methods to reduce the burden of transport operations based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned transport unit is Analyze past transport data to select the optimal transport route. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned transport unit is The timing of transport will be adjusted based on the schedules of medical personnel. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned transport unit is The system estimates the emotions of healthcare workers and prioritizes transport operations based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned transport unit is Transportation will be carried out taking into account the geographical location of medical personnel. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned transport unit is Analyzing the social media activity of healthcare workers to improve transportation operations. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned serving robot, The system estimates the patient's emotions and adjusts the meal service method based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 44) The aforementioned serving robot, We analyze past serving data to select the optimal serving method. The system described in Appendix 5, characterized by the features described herein. (Note 45) The aforementioned serving robot, Customize meal contents based on the patient's dietary history. The system described in Appendix 5, characterized by the features described herein. (Note 46) The aforementioned serving robot, The system estimates the patient's emotions and determines the priority of meal service based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 47) The aforementioned serving robot, Meal delivery is carried out taking into account the patient's geographical location. The system described in Appendix 5, characterized by the features described herein. (Note 48) The aforementioned serving robot, Analyze patients' social media activity to customize meal contents. The system described in Appendix 5, characterized by the features described herein. [Explanation of symbols]

[0212] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Diagnostic Support Department analyzes medical data and supports diagnosis, The Office Automation Department automates administrative tasks, It includes a delivery automation unit that automates delivery operations. A system characterized by the following features.

2. The aforementioned diagnostic support unit, It includes an image analysis unit that analyzes medical images and detects abnormalities. The system according to feature 1.

3. The aforementioned business automation unit, It is equipped with a record entry unit that automatically inputs medical records. The system according to feature 1.

4. The aforementioned delivery automation unit is It is equipped with a transport unit for transporting medicines, meals, and medical supplies. The system according to feature 1.

5. The aforementioned delivery automation unit is Delivery operations are carried out in conjunction with food delivery robots. The system according to feature 1.

6. The aforementioned diagnostic support unit, It learns from vast amounts of medical data and supports doctors' diagnoses. The system according to feature 1.

7. The aforementioned diagnostic support unit, The system estimates the patient's emotions and adjusts how the diagnosis is communicated based on those estimated emotions. The system according to feature 1.

8. The aforementioned diagnostic support unit, Analyzing a patient's past diagnostic history improves the accuracy of their diagnosis. The system according to feature 1.

9. The aforementioned diagnostic support unit, Diagnosis is made considering the patient's lifestyle data. The system according to feature 1.

10. The aforementioned diagnostic support unit, The system estimates the patient's emotions and determines diagnostic priorities based on those estimated emotions. The system according to feature 1.

Images