System

Abstract

To provide a system that estimates the emotions of a patient and provides an appropriate response accordingly.SOLUTION: A system according to an embodiment comprises an acquisition unit, an estimation unit, a determination unit, and a response unit. The acquisition unit acquires emotion estimation information for estimating the emotions of a patient. The estimation unit estimates the emotions of the patient on the basis of the emotion estimation information acquired by the acquisition unit. The determination unit determines a response according to the emotions of the patient as estimated by the estimation unit. The response unit gives the response determined by the determination unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] Conventional techniques do not adequately estimate the patient's emotions and respond accordingly, so there is room for improvement.

[0005] The system according to the embodiment aims to estimate the patient's emotions and take appropriate measures accordingly. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an estimation unit, a decision unit, and a response unit. The acquisition unit acquires emotion estimation information for estimating an emotion of a patient. The estimation unit estimates an emotion of the patient based on the emotion estimation information acquired by the acquisition unit. The decision unit decides a response according to the emotion of the patient estimated by the estimation unit. The response unit executes the response decided by the decision unit. Effect of the Invention

[0007] The system according to the embodiment can estimate the patient's emotions and respond appropriately accordingly. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A medical support system according to an embodiment of the present invention is a system that utilizes an agent having an emotion engine as a medical support robot. This medical support system reduces the stress and anxiety of a patient by reading the patient's emotions and taking appropriate measures. In addition, the medical support system also supports the patient's health management and medical procedures in cooperation with medical staff. For example, the medical support system includes an acquisition unit that acquires emotion estimation information for estimating the patient's emotions. This acquisition unit acquires one or more pieces of information among the patient's facial expression, movement, voice, heart rate, blood pressure, pupil response, brain wave, body temperature, breathing, and posted content as emotion estimation information. For example, emotions can be read from the patient's facial expression and voice. Next, the medical support system includes an estimation unit that estimates the patient's emotions based on the emotion estimation information acquired by the acquisition unit. This estimation unit includes AI processing and accurately estimates the patient's emotions. For example, it estimates whether the patient is sad, happy, or angry. Furthermore, the medical support system includes a decision unit that determines a response according to the estimated patient's emotions. This decision unit determines a dialogue or treatment mode according to the patient's emotions. For example, the system determines how to say comforting words when the patient is sad, how to share joy with the patient when the patient is happy, and how to provide a topic to calm the patient when the patient is angry. Finally, the medical support system includes a response unit that executes the response determined by the determination unit. This response unit communicates with the patient and treats the patient in a manner that corresponds to the patient's emotions. For example, the system uses comforting words when the patient is sad, shares joy with the patient when the patient is happy, and provides a topic to calm the patient when the patient is angry. The response unit can also notify the medical staff of the determined response. This makes it possible to respond appropriately according to the patient's emotions, thereby reducing the patient's stress and anxiety. Furthermore, by cooperating with the medical staff, the system can also support the patient's health management and medical procedures. This allows the medical support system to respond appropriately according to the patient's emotions.

[0029] The medical support system according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and a corresponding unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a patient. The emotion estimation information includes, for example, facial expression data, voice data, and biosignal data, but is not limited to such examples. For example, the acquisition unit photographs the facial expression of the patient with a camera and acquires the emotion estimation information using a facial expression recognition technique. The acquisition unit can also record the voice of the patient and acquire the emotion estimation information using a voice analysis technique. Furthermore, the acquisition unit can measure the heart rate and blood pressure of the patient and acquire the biosignal data as the emotion estimation information. For example, the acquisition unit photographs the facial expression of the patient with a camera and acquires facial expression data such as a smiling face or an angry face using a facial expression recognition technique. The acquisition unit can also record the voice of the patient and acquire voice data such as a tone of voice and a way of speaking using a voice analysis technique. Furthermore, the acquisition unit can measure the heart rate and blood pressure of the patient and acquire biosignal data such as fluctuations in the heart rate and blood pressure. The estimation unit estimates the emotion of the patient based on the emotion estimation information acquired by the acquisition unit. The estimation unit includes AI processing and accurately estimates the patient's emotions. For example, the estimation unit uses a machine learning model to analyze the acquired facial expression data, voice data, and biosignal data to estimate the patient's emotions. For example, the estimation unit estimates the emotion of joy when the patient is smiling, and estimates the emotion of anger when the patient has an angry face. The estimation unit can also analyze the patient's tone of voice and speaking style to estimate emotions such as joy, sadness, and anger. Furthermore, the estimation unit can analyze the fluctuations in the patient's heart rate and blood pressure to estimate emotions such as relaxation and stress. The determination unit determines a response according to the patient's emotions estimated by the estimation unit. The determination unit determines a dialogue or treatment mode according to the patient's emotions. For example, the determination unit determines a mode of comforting words when the patient is sad, a mode of rejoicing together with the patient when the patient is happy, and a mode of providing a topic to calm the patient when the patient is angry. For example, the determination unit determines a mode of comforting words such as "Are you okay?" when the patient is sad, and a mode of rejoicing together with the patient when the patient is happy, such as "That's wonderful!" when the patient is happy.In addition, the determination unit can also determine a manner of providing a topic to calm the patient, such as "please calm down" when the patient is angry. The response unit executes the response determined by the determination unit. The response unit communicates with the patient and provides treatment in a manner according to the patient's emotions. For example, the response unit provides words of comfort when the patient is sad, rejoices with the patient when the patient is happy, and provides a topic to calm the patient when the patient is angry. The response unit can also notify the medical staff of the determined response. For example, the response unit provides words of comfort such as "Are you okay?" when the patient is sad, and rejoices with the patient when the patient is happy, such as "That's wonderful!" when the patient is happy. In addition, the response unit can provide a topic to calm the patient, such as "please calm down" when the patient is angry. This allows the medical support system according to the embodiment to perform an appropriate response according to the patient's emotions. Part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input emotion data of the patient to a generation AI, and cause the generation AI to generate an appropriate response.

[0030] The acquisition unit acquires emotion estimation information for estimating the emotion of the patient. The emotion estimation information includes, but is not limited to, facial expression data, voice data, and biosignal data. For example, the acquisition unit photographs the facial expression of the patient with a camera and acquires the emotion estimation information using a facial expression recognition technology. Specifically, the camera photographs the patient's face with high resolution and extracts facial feature points. This makes it possible to acquire detailed facial expression data such as a smile, an angry face, and a surprised face. The acquisition unit can also record the patient's voice and acquire emotion estimation information using a voice analysis technology. A high-sensitivity microphone is used to acquire the voice data, and the tone, pitch, speed, intonation, and the like of the patient's voice are analyzed in detail. Furthermore, the acquisition unit can measure the patient's heart rate and blood pressure, and acquire biosignal data as emotion estimation information. Fluctuations in heart rate and blood pressure are important indicators of the patient's stress level and relaxation state. These data are acquired in real time using a wearable device or a medical sensor. For example, the acquisition unit photographs the patient's facial expression with a camera and acquires facial expression data such as a smile and an angry face using a facial expression recognition technology. The acquisition unit can also record the patient's voice and acquire voice data such as tone of voice and speaking style using voice analysis technology. Furthermore, the acquisition unit can measure the patient's heart rate and blood pressure and acquire biosignal data such as fluctuations in heart rate and blood pressure. This allows the acquisition unit to collect various emotion estimation information and provide basic data for accurately estimating the patient's emotion.

[0031] The estimation unit estimates the patient's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit includes AI processing and accurately estimates the patient's emotion. Specifically, the estimation unit uses a machine learning model to analyze the acquired facial expression data, voice data, and biosignal data to estimate the patient's emotion. For example, the estimation unit uses an image recognition model using deep learning to estimate emotions such as a smile, an angry face, and a surprised face with high accuracy from the facial expression data. In addition, for voice data, natural language processing technology can be used to analyze the tone of voice and speaking style to estimate emotions such as joy, sadness, and anger. Furthermore, the estimation unit can analyze the fluctuations in the patient's heart rate and blood pressure to estimate emotions such as relaxation and stress. As a result, the estimation unit can comprehensively analyze the collected various data and evaluate the patient's emotion from multiple angles. For example, the estimation unit estimates the emotion of joy when the patient is smiling, and estimates the emotion of anger when the patient is angry. In addition, the estimation unit can analyze the tone of voice and speaking style of the patient to estimate emotions such as joy, sadness, and anger. This enables the estimation unit to accurately estimate the patient's emotions and provide important information for determining the next response.

[0032] The determination unit determines a response according to the patient's emotion estimated by the estimation unit. The determination unit determines a dialogue or treatment mode according to the patient's emotion. For example, the determination unit determines a mode of speaking comforting words when the patient is sad, a mode of rejoicing together with the patient when the patient is happy, and a mode of providing a topic to calm the patient when the patient is angry. Specifically, the determination unit determines a mode of speaking comforting words such as "Are you okay?" when the patient is sad, and a mode of rejoicing together with the patient such as "That's wonderful!" when the patient is happy. The determination unit can also determine a mode of providing a topic to calm the patient such as "Please calm down" when the patient is angry. Furthermore, the determination unit can determine a treatment mode according to the patient's emotion. For example, when the patient is feeling stressed, it is possible to provide music with a relaxing effect or to issue an instruction to encourage deep breathing. Also, when the patient is relaxed, it is possible to issue an instruction to smoothly proceed with the treatment. In this way, the determination unit can quickly determine an optimal response according to the patient's emotion and provide a specific action to improve the patient's condition.

[0033] The responding unit executes the response determined by the determining unit. The responding unit communicates with the patient and treats the patient in a manner according to the patient's emotions. For example, if the patient is sad, the responding unit speaks words of comfort, if the patient is happy, the responding unit rejoices with the patient, and if the patient is angry, the responding unit provides a topic to calm the patient. The responding unit can also notify the medical staff of the determined response. Specifically, if the patient is sad, the responding unit speaks words of comfort such as "Are you okay?", if the patient is happy, the responding unit speaks words of joy such as "That's wonderful!", if the patient is angry. The responding unit can also provide a topic to calm the patient such as "Please calm down". The responding unit can also execute the treatment mode instructed by the determining unit. For example, if the patient is stressed, the responding unit issues an instruction to play music with a relaxing effect and encourage deep breathing. If the patient is relaxed, the responding unit can issue an instruction to smoothly proceed with the treatment. This allows the responding unit to take an appropriate response according to the patient's emotions. A part or all of the above-mentioned processing in the responding unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the patient's emotional data into the generation AI, which can then generate an appropriate response. The generation AI then proposes optimal dialogue content and treatment methods based on the patient's emotional data, and the response unit then executes them. This enables the response unit to quickly and effectively implement the optimal response according to the patient's emotions.

[0034] The determination unit determines a dialogue in a manner corresponding to the patient's emotion as a response corresponding to the patient's emotion, and the response unit can conduct a dialogue with the patient in a manner corresponding to the patient's emotion. The determination unit determines, for example, a manner of speaking comforting words when the patient is sad, a manner of rejoicing together with the patient when the patient is happy, and a manner of providing a topic to calm the patient when the patient is angry. For example, the determination unit determines a manner of speaking comforting words such as "Are you OK?" when the patient is sad, and a manner of rejoicing together with the patient such as "That's wonderful!" when the patient is happy. The determination unit can also determine a manner of providing a topic to calm the patient such as "Please calm down" when the patient is angry. The response unit executes the response determined by the determination unit. The response unit conducts a dialogue with the patient in a manner corresponding to the patient's emotion. For example, the response unit provides comforting words such as "Are you OK?" when the patient is sad, and provides words to rejoice together with the patient such as "That's wonderful!" when the patient is happy. The response unit can also provide a topic to calm the patient such as "Please calm down" when the patient is angry. In this way, the stress and anxiety of the patient can be reduced by conducting a dialogue in accordance with the patient's emotion. A part or all of the above-mentioned processes in the decision unit and the corresponding unit may be performed, for example, using AI or may be performed without using AI. For example, the decision unit can input the patient's emotion data to the generation AI and cause the generation AI to generate an appropriate dialogue mode. Also, the corresponding unit can execute the dialogue mode generated by the generation AI.

[0035] The determination unit determines a treatment mode according to the patient's emotion as a response according to the patient's emotion, and the response unit can perform treatment for the patient in a mode according to the patient's emotion. The determination unit determines, for example, a mode of speaking comforting words when the patient is sad, a mode of rejoicing together with the patient when the patient is happy, and a mode of providing a topic to calm the patient when the patient is angry. For example, the determination unit determines a mode of speaking comforting words such as "Are you OK?" when the patient is sad, and a mode of rejoicing together with the patient such as "That's wonderful!" when the patient is happy. The determination unit can also determine a mode of providing a topic to calm the patient such as "Please calm down" when the patient is angry. The response unit executes the response determined by the determination unit. The response unit performs treatment for the patient in a mode according to the patient's emotion. For example, the response unit uses comforting words such as "Are you OK?" when the patient is sad, and uses words to rejoice together with the patient such as "That's wonderful!" when the patient is happy. The response unit can also provide a topic to calm the patient such as "Please calm down" when the patient is angry. This improves the health management of the patient by performing treatment according to the patient's emotion. A part or all of the above-mentioned processing in the decision unit and the corresponding unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can input the patient's emotion data to the generating AI and have the generating AI generate an appropriate treatment mode. Also, the corresponding unit can execute the treatment mode generated by the generating AI.

[0036] The response unit can notify the medical staff of the content of the response determined by the determination unit. The notification method includes, for example, an alert, a message, a voice notification, and the like, but is not limited to such examples. For example, the response unit notifies the medical staff of the content of the response determined by the determination unit as an alert. Furthermore, the response unit can also notify the medical staff of the content of the response determined by the determination unit as a voice notification. This strengthens cooperation with the medical staff and improves the health management of the patient. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can notify the medical staff of the notification content generated by the generation AI.

[0037] The decision unit can determine a response according to the patient's emotions and the attributes of the patient. The attributes of the patient include, but are not limited to, for example, age, sex, medical history, and the like. For example, the decision unit determines a gentle response when the patient is elderly, and determines an active response when the patient is young. In addition, the decision unit can determine a response suitable for men when the patient is male, and can determine a response suitable for women when the patient is female. Furthermore, the decision unit can determine a response suitable for a specific medical condition based on the patient's medical history. For example, the decision unit determines a response to stabilize blood pressure when the patient has a history of high blood pressure, and determines a response to manage blood sugar levels when the patient has a history of diabetes. This makes it possible to respond according to the individual situation of the patient. Part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can input the patient's emotion data and attribute data to the generation AI, and cause the generation AI to generate an appropriate response.

[0038] The medical support robot can function as a medical support robot including an acquisition unit, an estimation unit, a determination unit, and a response unit. The medical support robot has, for example, a moving function, an interaction function, a data collection function, and the like. The moving function includes, for example, a moving means using wheels or caterpillars, but is not limited to such examples. The interaction function includes, for example, an interaction means using voice recognition technology or voice synthesis technology, but is not limited to such examples. The data collection function includes, for example, a data collection means using a camera or a sensor, but is not limited to such examples. For example, the medical support robot moves in the hospital using wheels and arrives at the patient. In addition, the medical support robot can recognize the patient's voice using voice recognition technology and can communicate with the patient using voice synthesis technology. Furthermore, the medical support robot can take a picture of the patient's facial expression using a camera and measure the patient's heart rate and blood pressure using a sensor. This enables the medical support robot to respond according to the patient's emotions. Part or all of the above-mentioned processing in the medical support robot may be performed using, for example, AI, or may be performed without using AI. For example, the medical support robot can execute the dialogue content and treatment content generated by the generation AI.

[0039] The acquisition unit can acquire one or more pieces of information among the patient's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content as emotion estimation information. For example, the acquisition unit captures the patient's facial expression with a camera and acquires emotion estimation information using facial expression recognition technology. The acquisition unit can also record the patient's voice and acquire emotion estimation information using voice analysis technology. Furthermore, the acquisition unit can measure the patient's heart rate and blood pressure and acquire biosignal data as emotion estimation information. For example, the acquisition unit captures the patient's facial expression with a camera and acquires facial expression data such as a smile or an angry face using facial expression recognition technology. The acquisition unit can also record the patient's voice and acquire voice data such as tone of voice and speaking style using voice analysis technology. Furthermore, the acquisition unit can measure the patient's heart rate and blood pressure and acquire biosignal data such as fluctuations in heart rate and blood pressure. This makes it possible to estimate the patient's emotion based on various information. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire emotion estimating information generated by a generation AI.

[0040] The acquisition unit can estimate the emotion of the patient and adjust the timing of acquiring the emotion estimation information based on the estimated emotion of the patient. For example, when the patient is relaxed, the acquisition unit periodically acquires the emotion estimation information. Furthermore, when the patient is stressed, the acquisition unit can also frequently acquire the emotion estimation information. Furthermore, when the emotion of the patient suddenly changes, the acquisition unit can immediately acquire the emotion estimation information. For example, when the patient is relaxed, the acquisition unit acquires the emotion estimation information every hour. Furthermore, when the patient is stressed, the acquisition unit can also acquire the emotion estimation information every 10 minutes. Furthermore, when the emotion of the patient suddenly changes, the acquisition unit can immediately acquire the emotion estimation information. This makes it possible to acquire the emotion estimation information at an appropriate timing according to the emotion of the patient. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can adjust the timing of acquiring the emotion estimation information generated by the generation AI.

[0041] The acquisition unit can analyze the patient's past emotion data and select the acquisition method. The acquisition unit acquires emotion estimation information, for example, during a time period when the patient was relaxed in the past. The acquisition unit can also acquire emotion estimation information during a time period when the patient was stressed in the past. Furthermore, the acquisition unit can acquire emotion estimation information from the patient's past emotion data according to a specific event or situation. For example, the acquisition unit acquires emotion estimation information every hour during a time period when the patient was relaxed in the past. The acquisition unit can also acquire emotion estimation information every 10 minutes during a time period when the patient was stressed in the past. Furthermore, the acquisition unit can acquire emotion estimation information from the patient's past emotion data according to a specific event or situation. This makes it possible to select an optimal emotion estimation information acquisition method based on the past emotion data. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can analyze past emotion data generated by the generation AI and select an optimal acquisition method.

[0042] The acquisition unit can perform filtering based on the patient's current health condition and living situation when acquiring the emotion estimation information. For example, when the patient is in good health, the acquisition unit acquires standard emotion estimation information. Also, when the patient is in poor health, the acquisition unit can acquire detailed emotion estimation information. Furthermore, the acquisition unit can acquire appropriate emotion estimation information according to the living situation of the patient. For example, when the patient is in good health, the acquisition unit acquires facial expression data and voice data as standard emotion estimation information. Also, when the patient is in poor health, the acquisition unit can acquire heart rate data and blood pressure data as detailed emotion estimation information. Furthermore, the acquisition unit can acquire appropriate emotion estimation information according to the living situation of the patient. Thereby, it is possible to acquire appropriate emotion estimation information according to the patient's health condition and living situation. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can filter the emotion estimation information based on the health condition and living situation generated by the generation AI.

[0043] The acquisition unit can estimate the patient's emotion and determine the priority of emotion estimation information to be acquired based on the estimated emotion of the patient. For example, when the patient is relaxed, the acquisition unit preferentially acquires facial expression and voice information. In addition, when the patient is stressed, the acquisition unit can also preferentially acquire heart rate and blood pressure information. Furthermore, when the patient's emotion suddenly changes, the acquisition unit can also preferentially acquire pupil response and brain wave information. This makes it possible to determine the emotion estimation information to be preferentially acquired according to the emotion of the patient. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can determine the priority of emotion estimation information generated by the generation AI.

[0044] When acquiring emotion estimation information, the acquisition unit can preferentially acquire highly relevant information based on the geographical location information of the patient. For example, when the patient is in a hospital, the acquisition unit preferentially acquires information on heart rate and blood pressure. In addition, when the patient is at home, the acquisition unit can also preferentially acquire information on facial expressions and voice. Furthermore, when the patient is out, the acquisition unit can also preferentially acquire information on movement and breathing. This makes it possible to acquire emotion estimation information that is highly relevant based on the geographical location information of the patient. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can preferentially acquire information that is highly relevant based on the geographical location information generated by the generation AI.

[0045] The acquisition unit can analyze the social media activity of the patient when acquiring the emotion estimation information and acquire related information. For example, when the patient posts positively on social media, the acquisition unit preferentially acquires facial expression and voice information. In addition, when the patient posts negatively on social media, the acquisition unit can also preferentially acquire heart rate and blood pressure information. Furthermore, when the patient is frequently active on social media, the acquisition unit can also preferentially acquire pupil response and brain wave information. This makes it possible to acquire related emotion estimation information based on the social media activity of the patient. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can analyze the social media activity generated by the generation AI and acquire related information.

[0046] The estimation unit can estimate the patient's emotion and adjust the emotion estimation algorithm based on the estimated emotion of the patient. For example, when the patient is relaxed, the estimation unit gently adjusts the emotion estimation algorithm. Also, when the patient is stressed, the estimation unit can sensitively adjust the emotion estimation algorithm. Furthermore, when the patient's emotion suddenly changes, the estimation unit can quickly adjust the emotion estimation algorithm. As a result, the estimation accuracy is improved by adjusting the emotion estimation algorithm according to the patient's emotion. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can adjust the emotion estimation algorithm generated by the generation AI.

[0047] The estimation unit can adjust the level of detail of the estimation based on the importance of the emotion estimation information at the time of estimation. The estimation unit performs detailed emotion estimation based on information of high importance (heart rate, blood pressure, etc.), for example. The estimation unit can also perform simple emotion estimation based on information of low importance (facial expression, voice, etc.). Furthermore, the estimation unit can perform emotion estimation with a medium level of detail based on information of medium importance (movement, breathing, etc.). Thereby, the estimation accuracy is improved by adjusting the level of detail of the estimation according to the importance of the emotion estimation information. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can adjust the level of detail of the estimation based on the importance of the emotion estimation information generated by the generation AI.

[0048] The estimation unit can apply different estimation algorithms depending on the category of the emotion estimation information at the time of estimation. For example, the estimation unit applies a facial expression recognition algorithm to the facial expression information. The estimation unit can also apply a voice emotion recognition algorithm to the voice information. Furthermore, the estimation unit can also apply a physiological emotion recognition algorithm to the heart rate information. Thereby, by applying an appropriate estimation algorithm depending on the category of the emotion estimation information, the estimation accuracy is improved. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can apply different estimation algorithms depending on the category of the emotion estimation information generated by the generation AI.

[0049] The estimation unit can estimate the patient's emotions and adjust the display method of the estimation result based on the estimated patient's emotions. For example, when the patient is relaxed, the estimation unit displays a detailed estimation result. In addition, when the patient is stressed, the estimation unit can also display a concise estimation result. Furthermore, when the patient's emotions suddenly change, the estimation unit can also immediately display the estimation result. This makes it possible to provide appropriate information by adjusting the display method of the estimation result according to the patient's emotions. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI or may be performed without using AI. For example, the estimation unit can adjust the display method of the estimation result generated by the generation AI.

[0050] The estimation unit can determine the priority of estimation based on the acquisition time of the emotion estimation information at the time of estimation. For example, the estimation unit performs estimation by preferentially using the latest emotion estimation information. The estimation unit can also perform estimation by referring to past emotion estimation information. Furthermore, the estimation unit can also perform estimation by preferentially using emotion estimation information acquired in a specific time period. Thereby, by determining the priority of estimation based on the acquisition time of the emotion estimation information, the estimation accuracy is improved. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can determine the priority of estimation based on the acquisition time of the emotion estimation information generated by the generation AI.

[0051] The estimation unit can adjust the order of estimation based on the relevance of the emotion estimation information at the time of estimation. For example, the estimation unit performs estimation by preferentially using information with high relevance. The estimation unit can also perform estimation by using information with medium relevance next. Furthermore, the estimation unit can also perform estimation by using information with low relevance last. Thereby, by adjusting the order of estimation based on the relevance of the emotion estimation information, the estimation accuracy is improved. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can adjust the order of estimation based on the relevance of the emotion estimation information generated by the generation AI.

[0052] The determination unit can estimate the patient's emotion and adjust the response determination method based on the estimated patient's emotion. For example, when the patient is relaxed, the determination unit determines a gentle response. In addition, when the patient is stressed, the determination unit can also determine a quick response. Furthermore, when the patient's emotion suddenly changes, the determination unit can immediately determine the response. This makes it possible to respond appropriately by adjusting the response determination method according to the patient's emotion. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can adjust the response determination method based on emotion estimation information generated by the generation AI.

[0053] At the time of making a decision, the decision unit can select the optimal response method by analyzing the patient's past response history. The decision unit, for example, refers to the response method when the patient was relaxed in the past. The decision unit can also refer to the response method when the patient was stressed in the past. Furthermore, the decision unit can select the optimal response method according to a specific situation from the patient's past response history. As a result, the accuracy of the response is improved by selecting the optimal response method based on the patient's past response history. A part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can analyze the past response history generated by the generation AI and select the optimal response method.

[0054] The decision unit can customize the response measures based on the patient's current health condition at the time of decision. For example, the decision unit determines a standard response when the patient is in good health. The decision unit can also determine a special response when the patient is in poor health. Furthermore, the decision unit can customize an appropriate response measure according to the patient's health condition. This makes it possible to provide an appropriate response measure according to the patient's health condition. A part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can customize the response measures based on the health condition generated by the generation AI.

[0055] The determination unit can estimate the patient's emotion and determine the priority of the response based on the estimated emotion of the patient. For example, when the patient is relaxed, the determination unit determines the response with normal priority. In addition, when the patient is stressed, the determination unit can also determine the response with priority. Furthermore, when the patient's emotion suddenly changes, the determination unit can immediately determine the response. This enables a quick and appropriate response by determining the priority of the response according to the emotion of the patient. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can determine the priority of the response based on the emotion estimation information generated by the generation AI.

[0056] The decision unit can select the optimal response method based on the geographical location information of the patient at the time of decision. For example, when the patient is in a hospital, the decision unit can determine a quick response. In addition, when the patient is at home, the decision unit can also determine a remote response. Furthermore, when the patient is out, the decision unit can also select an appropriate response method. This makes it possible to respond appropriately by selecting the optimal response method based on the geographical location information of the patient. Part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can select the optimal response method based on the geographical location information generated by the generation AI.

[0057] At the time of decision, the decision unit can analyze the social media activity of the patient and suggest a means of response. For example, if the patient posts positively on social media, the decision unit can suggest a gentle response. In addition, if the patient posts negatively on social media, the decision unit can also suggest a quick response. Furthermore, if the patient is frequently active on social media, the decision unit can also suggest an appropriate response. This makes it possible to suggest an appropriate response based on the patient's social media activity. Some or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can analyze the social media activity generated by the generation AI and suggest an appropriate response.

[0058] The response unit can estimate the patient's emotion and adjust the method of executing the response based on the estimated emotion of the patient. For example, when the patient is relaxed, the response unit executes a gentle response. In addition, when the patient is stressed, the response unit can also execute a quick response. Furthermore, when the patient's emotion suddenly changes, the response unit can also execute an immediate response. This allows an appropriate response by adjusting the method of executing the response according to the emotion of the patient. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can adjust the method of executing the response based on the emotion estimation information generated by the generation AI.

[0059] The response unit can provide the optimal response by referring to the patient's past response history when responding. For example, the response unit refers to the response method when the patient was relaxed in the past. The response unit can also refer to the response method when the patient was stressed in the past. Furthermore, the response unit can provide the optimal response according to a specific situation from the patient's past response history. This improves the accuracy of the response by providing the optimal response based on the patient's past response history. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can provide the optimal response by referring to the past response history generated by the generation AI.

[0060] The response unit can customize the response means based on the patient's current health condition when responding. For example, the response unit provides a standard response when the patient is in good health. The response unit can also provide a special response when the patient is in poor health. Furthermore, the response unit can customize an appropriate response means according to the patient's health condition. This makes it possible to provide an appropriate response means according to the patient's health condition. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI or may be performed without using AI. For example, the response unit can customize the response means based on the health condition generated by the generation AI.

[0061] The response unit can estimate the patient's emotion and determine the priority of the response based on the estimated emotion of the patient. For example, when the patient is relaxed, the response unit executes the response with normal priority. In addition, when the patient is stressed, the response unit can also execute the response with priority. Furthermore, when the patient's emotion suddenly changes, the response unit can also execute the response immediately. This enables a quick and appropriate response by determining the priority of the response according to the emotion of the patient. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can determine the priority of the response based on the emotion estimation information generated by the generation AI.

[0062] The response unit can provide an optimal response based on the geographical location information of the patient when responding. For example, when the patient is in a hospital, the response unit provides a quick response. In addition, when the patient is at home, the response unit can also provide a remote response. Furthermore, when the patient is out, the response unit can also provide an appropriate response method. This makes it possible to provide an appropriate response by providing an optimal response based on the geographical location information of the patient. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can provide an optimal response based on the geographical location information generated by the generation AI.

[0063] The response unit can analyze the patient's social media activity and suggest a response measure when responding. For example, the response unit can provide a gentle response when the patient posts positively on social media. The response unit can also provide a quick response when the patient posts negatively on social media. Furthermore, the response unit can provide an appropriate response measure when the patient is frequently active on social media. This makes it possible to suggest an appropriate response measure based on the patient's social media activity. Some or all of the above-mentioned processing in the response unit may be performed, for example, using AI or may be performed without using AI. For example, the response unit can analyze the social media activity generated by the generation AI and suggest an appropriate response measure.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The medical support system may further include a health management unit that collects the patient's health data and performs long-term health management. The health management unit periodically collects data such as the patient's blood pressure, heart rate, and body temperature, and monitors the patient's health condition. The health management unit may also provide health advice based on the collected data. Furthermore, the health management unit may share the patient's health data with medical staff and provide appropriate medical support. This improves the patient's health management and enables appropriate medical support.

[0066] The medical support system may further include an exercise suggestion unit that collects exercise data of the patient and proposes an exercise program. The exercise suggestion unit collects data such as the number of steps taken by the patient, exercise time, and calories burned, and proposes an appropriate exercise program. The exercise suggestion unit may also adjust the exercise intensity according to the patient's health condition. Furthermore, the exercise suggestion unit may share the patient's exercise data with medical staff and provide appropriate exercise support. This improves the patient's exercise habits and promotes health management.

[0067] The medical support system may further include a sleep management unit that collects the patient's sleep data and provides advice for improving the quality of sleep. The sleep management unit collects data such as the patient's sleep time, sleep depth, and number of turns in sleep, and evaluates the quality of sleep. The sleep management unit may also provide advice for improving the quality of sleep based on the collected data. Furthermore, the sleep management unit may share the patient's sleep data with medical staff and provide appropriate sleep support. This improves the patient's quality of sleep and promotes health management.

[0068] The medical support system may further include a nutrition management unit that collects dietary data of the patient and evaluates the nutritional balance. The nutrition management unit collects data such as the patient's dietary content, calorie intake, and nutrient balance, and evaluates the nutritional state. The nutrition management unit may also provide advice for improving nutritional balance based on the collected data. Furthermore, the nutrition management unit may share the patient's dietary data with medical staff and provide appropriate nutritional support. This improves the patient's nutritional state and promotes health management.

[0069] The medical support system may further include a medication management unit that collects medication data of patients and manages medication. The medication management unit collects data such as the patient's medication schedule, dosage, medication history, and the like, and manages medication. The medication management unit may also provide medication reminders based on the collected data. Furthermore, the medication management unit may share the patient's medication data with medical staff and provide appropriate medication support. This improves medication management of patients and enables appropriate medical support.

[0070] The process flow of the first embodiment will be briefly described below.

[0071] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a patient. The emotion estimation information includes facial expression data, voice data, and biosignal data. For example, the acquisition unit captures the facial expression of the patient with a camera and acquires the emotion estimation information using facial expression recognition technology. It is also possible to record the patient's voice and acquire the emotion estimation information using voice analysis technology. Furthermore, it is also possible to measure the patient's heart rate and blood pressure and acquire biosignal data as emotion estimation information. Step 2: The estimation unit estimates the patient's emotions based on the emotion estimation information acquired by the acquisition unit. The estimation unit includes AI processing and uses a machine learning model to analyze facial expression data, voice data, and biosignal data to accurately estimate the patient's emotions. For example, if the patient is smiling, the emotion of joy is estimated, and if the patient has an angry face, the emotion of anger is estimated. In addition, the tone of voice and speaking style can be analyzed to estimate emotions such as joy, sadness, and anger. Furthermore, the fluctuations in heart rate and blood pressure can be analyzed to estimate emotions such as relaxation and stress. Step 3: The determination unit determines a response according to the patient's emotion estimated by the estimation unit. The determination unit determines a dialogue or treatment mode according to the patient's emotion. For example, it determines a mode of speaking comforting words when the patient is sad, a mode of sharing joy with the patient when the patient is happy, and a mode of providing a topic to calm the patient when the patient is angry. Step 4: The response unit executes the response decided by the decision unit. The response unit communicates with the patient and provides treatment in a manner that corresponds to the patient's emotions. For example, if the patient is sad, the response unit will offer comforting words; if the patient is happy, the response unit will share in the patient's joy; if the patient is angry, the response unit will provide a topic of conversation to calm the patient. The response unit can also notify medical staff of the content of the response that has been decided.

[0072] (Example 2) A medical support system according to an embodiment of the present invention is a system that utilizes an agent having an emotion engine as a medical support robot. This medical support system reduces the stress and anxiety of a patient by reading the patient's emotions and taking appropriate measures. In addition, the medical support system also supports the patient's health management and medical procedures in cooperation with medical staff. For example, the medical support system includes an acquisition unit that acquires emotion estimation information for estimating the patient's emotions. This acquisition unit acquires one or more pieces of information among the patient's facial expression, movement, voice, heart rate, blood pressure, pupil response, brain wave, body temperature, breathing, and posted content as emotion estimation information. For example, emotions can be read from the patient's facial expression and voice. Next, the medical support system includes an estimation unit that estimates the patient's emotions based on the emotion estimation information acquired by the acquisition unit. This estimation unit includes AI processing and accurately estimates the patient's emotions. For example, it estimates whether the patient is sad, happy, or angry. Furthermore, the medical support system includes a decision unit that determines a response according to the estimated patient's emotions. This decision unit determines a dialogue or treatment mode according to the patient's emotions. For example, the system determines how to say comforting words when the patient is sad, how to share joy with the patient when the patient is happy, and how to provide a topic to calm the patient when the patient is angry. Finally, the medical support system includes a response unit that executes the response determined by the determination unit. This response unit communicates with the patient and treats the patient in a manner that corresponds to the patient's emotions. For example, the system uses comforting words when the patient is sad, shares joy with the patient when the patient is happy, and provides a topic to calm the patient when the patient is angry. The response unit can also notify the medical staff of the determined response. This makes it possible to respond appropriately according to the patient's emotions, thereby reducing the patient's stress and anxiety. Furthermore, by cooperating with the medical staff, the system can also support the patient's health management and medical procedures. This allows the medical support system to respond appropriately according to the patient's emotions.

[0073] The medical support system according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and a corresponding unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a patient. The emotion estimation information includes, for example, facial expression data, voice data, and biosignal data, but is not limited to such examples. For example, the acquisition unit photographs the facial expression of the patient with a camera and acquires the emotion estimation information using a facial expression recognition technique. The acquisition unit can also record the voice of the patient and acquire the emotion estimation information using a voice analysis technique. Furthermore, the acquisition unit can measure the heart rate and blood pressure of the patient and acquire the biosignal data as the emotion estimation information. For example, the acquisition unit photographs the facial expression of the patient with a camera and acquires facial expression data such as a smiling face or an angry face using a facial expression recognition technique. The acquisition unit can also record the voice of the patient and acquire voice data such as a tone of voice and a way of speaking using a voice analysis technique. Furthermore, the acquisition unit can measure the heart rate and blood pressure of the patient and acquire biosignal data such as fluctuations in the heart rate and blood pressure. The estimation unit estimates the emotion of the patient based on the emotion estimation information acquired by the acquisition unit. The estimation unit includes AI processing and accurately estimates the patient's emotions. For example, the estimation unit uses a machine learning model to analyze the acquired facial expression data, voice data, and biosignal data to estimate the patient's emotions. For example, the estimation unit estimates the emotion of joy when the patient is smiling, and estimates the emotion of anger when the patient has an angry face. The estimation unit can also analyze the patient's tone of voice and speaking style to estimate emotions such as joy, sadness, and anger. Furthermore, the estimation unit can analyze the fluctuations in the patient's heart rate and blood pressure to estimate emotions such as relaxation and stress. The determination unit determines a response according to the patient's emotions estimated by the estimation unit. The determination unit determines a dialogue or treatment mode according to the patient's emotions. For example, the determination unit determines a mode of comforting words when the patient is sad, a mode of rejoicing together with the patient when the patient is happy, and a mode of providing a topic to calm the patient when the patient is angry. For example, the determination unit determines a mode of comforting words such as "Are you okay?" when the patient is sad, and a mode of rejoicing together with the patient when the patient is happy, such as "That's wonderful!" when the patient is happy.In addition, the determination unit can also determine a manner of providing a topic to calm the patient, such as "please calm down" when the patient is angry. The response unit executes the response determined by the determination unit. The response unit communicates with the patient and provides treatment in a manner according to the patient's emotions. For example, the response unit provides words of comfort when the patient is sad, rejoices with the patient when the patient is happy, and provides a topic to calm the patient when the patient is angry. The response unit can also notify the medical staff of the determined response. For example, the response unit provides words of comfort such as "Are you okay?" when the patient is sad, and rejoices with the patient when the patient is happy, such as "That's wonderful!" when the patient is happy. In addition, the response unit can provide a topic to calm the patient, such as "please calm down" when the patient is angry. This allows the medical support system according to the embodiment to perform an appropriate response according to the patient's emotions. Part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input emotion data of the patient to a generation AI, and cause the generation AI to generate an appropriate response.

[0074] The acquisition unit acquires emotion estimation information for estimating the emotion of the patient. The emotion estimation information includes, but is not limited to, facial expression data, voice data, and biosignal data. For example, the acquisition unit photographs the facial expression of the patient with a camera and acquires the emotion estimation information using a facial expression recognition technology. Specifically, the camera photographs the patient's face with high resolution and extracts facial feature points. This makes it possible to acquire detailed facial expression data such as a smile, an angry face, and a surprised face. The acquisition unit can also record the patient's voice and acquire emotion estimation information using a voice analysis technology. A high-sensitivity microphone is used to acquire the voice data, and the tone, pitch, speed, intonation, and the like of the patient's voice are analyzed in detail. Furthermore, the acquisition unit can measure the patient's heart rate and blood pressure, and acquire biosignal data as emotion estimation information. Fluctuations in heart rate and blood pressure are important indicators of the patient's stress level and relaxation state. These data are acquired in real time using a wearable device or a medical sensor. For example, the acquisition unit photographs the patient's facial expression with a camera and acquires facial expression data such as a smile and an angry face using a facial expression recognition technology. The acquisition unit can also record the patient's voice and acquire voice data such as tone of voice and speaking style using voice analysis technology. Furthermore, the acquisition unit can measure the patient's heart rate and blood pressure and acquire biosignal data such as fluctuations in heart rate and blood pressure. This allows the acquisition unit to collect various emotion estimation information and provide basic data for accurately estimating the patient's emotion.

[0075] The estimation unit estimates the patient's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit includes AI processing and accurately estimates the patient's emotion. Specifically, the estimation unit uses a machine learning model to analyze the acquired facial expression data, voice data, and biosignal data to estimate the patient's emotion. For example, the estimation unit uses an image recognition model using deep learning to estimate emotions such as a smile, an angry face, and a surprised face with high accuracy from the facial expression data. In addition, for voice data, natural language processing technology can be used to analyze the tone of voice and speaking style to estimate emotions such as joy, sadness, and anger. Furthermore, the estimation unit can analyze the fluctuations in the patient's heart rate and blood pressure to estimate emotions such as relaxation and stress. As a result, the estimation unit can comprehensively analyze the collected various data and evaluate the patient's emotion from multiple angles. For example, the estimation unit estimates the emotion of joy when the patient is smiling, and estimates the emotion of anger when the patient is angry. In addition, the estimation unit can analyze the tone of voice and speaking style of the patient to estimate emotions such as joy, sadness, and anger. Furthermore, the estimation unit can analyze the fluctuations in the patient's heart rate and blood pressure to estimate emotions such as relaxation, stress, etc. This allows the estimation unit to accurately estimate the patient's emotions and provide important information for determining the next response.

[0076] The determination unit determines a response according to the patient's emotion estimated by the estimation unit. The determination unit determines a dialogue or treatment mode according to the patient's emotion. For example, the determination unit determines a mode of speaking comforting words when the patient is sad, a mode of rejoicing together with the patient when the patient is happy, and a mode of providing a topic to calm the patient when the patient is angry. Specifically, the determination unit determines a mode of speaking comforting words such as "Are you okay?" when the patient is sad, and a mode of rejoicing together with the patient such as "That's wonderful!" when the patient is happy. The determination unit can also determine a mode of providing a topic to calm the patient such as "Please calm down" when the patient is angry. Furthermore, the determination unit can determine a treatment mode according to the patient's emotion. For example, when the patient is feeling stressed, it is possible to provide music with a relaxing effect or to issue an instruction to encourage deep breathing. Also, when the patient is relaxed, it is possible to issue an instruction to smoothly proceed with the treatment. In this way, the determination unit can quickly determine an optimal response according to the patient's emotion and provide a specific action to improve the patient's condition.

[0077] The responding unit executes the response determined by the determining unit. The responding unit communicates with the patient and treats the patient in a manner according to the patient's emotions. For example, if the patient is sad, the responding unit speaks words of comfort, if the patient is happy, the responding unit rejoices with the patient, and if the patient is angry, the responding unit provides a topic to calm the patient. The responding unit can also notify the medical staff of the determined response. Specifically, if the patient is sad, the responding unit speaks words of comfort such as "Are you okay?", if the patient is happy, the responding unit speaks words of joy such as "That's wonderful!", if the patient is angry. The responding unit can also provide a topic to calm the patient such as "Please calm down". The responding unit can also execute the treatment mode instructed by the determining unit. For example, if the patient is stressed, the responding unit issues an instruction to play music with a relaxing effect and encourage deep breathing. If the patient is relaxed, the responding unit can issue an instruction to smoothly proceed with the treatment. This allows the responding unit to take an appropriate response according to the patient's emotions. A part or all of the above-mentioned processing in the responding unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the patient's emotional data into the generation AI, which can then generate an appropriate response. The generation AI then proposes optimal dialogue content and treatment methods based on the patient's emotional data, and the response unit then executes them. This enables the response unit to quickly and effectively implement the optimal response according to the patient's emotions.

[0078] The determination unit determines a dialogue in a manner corresponding to the patient's emotion as a response corresponding to the patient's emotion, and the response unit can conduct a dialogue with the patient in a manner corresponding to the patient's emotion. The determination unit determines, for example, a manner of speaking comforting words when the patient is sad, a manner of rejoicing together with the patient when the patient is happy, and a manner of providing a topic to calm the patient when the patient is angry. For example, the determination unit determines a manner of speaking comforting words such as "Are you OK?" when the patient is sad, and a manner of rejoicing together with the patient such as "That's wonderful!" when the patient is happy. The determination unit can also determine a manner of providing a topic to calm the patient such as "Please calm down" when the patient is angry. The response unit executes the response determined by the determination unit. The response unit conducts a dialogue with the patient in a manner corresponding to the patient's emotion. For example, the response unit provides comforting words such as "Are you OK?" when the patient is sad, and provides words to rejoice together with the patient such as "That's wonderful!" when the patient is happy. The response unit can also provide a topic to calm the patient such as "Please calm down" when the patient is angry. In this way, the stress and anxiety of the patient can be reduced by conducting a dialogue in accordance with the patient's emotion. A part or all of the above-mentioned processes in the decision unit and the corresponding unit may be performed, for example, using AI or may be performed without using AI. For example, the decision unit can input the patient's emotion data to the generation AI and cause the generation AI to generate an appropriate dialogue mode. Also, the corresponding unit can execute the dialogue mode generated by the generation AI.

[0079] The determination unit determines a treatment mode according to the patient's emotion as a response according to the patient's emotion, and the response unit can perform treatment for the patient in a mode according to the patient's emotion. The determination unit determines, for example, a mode of speaking comforting words when the patient is sad, a mode of rejoicing together with the patient when the patient is happy, and a mode of providing a topic to calm the patient when the patient is angry. For example, the determination unit determines a mode of speaking comforting words such as "Are you OK?" when the patient is sad, and a mode of rejoicing together with the patient such as "That's wonderful!" when the patient is happy. The determination unit can also determine a mode of providing a topic to calm the patient such as "Please calm down" when the patient is angry. The response unit executes the response determined by the determination unit. The response unit performs treatment for the patient in a mode according to the patient's emotion. For example, the response unit uses comforting words such as "Are you OK?" when the patient is sad, and uses words to rejoice together with the patient such as "That's wonderful!" when the patient is happy. The response unit can also provide a topic to calm the patient such as "Please calm down" when the patient is angry. This improves the health management of the patient by performing treatment according to the patient's emotion. A part or all of the above-mentioned processing in the decision unit and the corresponding unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can input the patient's emotion data to the generating AI and have the generating AI generate an appropriate treatment mode. Also, the corresponding unit can execute the treatment mode generated by the generating AI.

[0080] The response unit can notify the medical staff of the content of the response determined by the determination unit. The notification method includes, for example, an alert, a message, a voice notification, and the like, but is not limited to such examples. For example, the response unit notifies the medical staff of the content of the response determined by the determination unit as an alert. The response unit can also notify the medical staff of the content of the response determined by the determination unit as a message. Furthermore, the response unit can also notify the medical staff of the content of the response determined by the determination unit as a voice notification. This strengthens cooperation with the medical staff and improves the health management of the patient. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can notify the medical staff of the notification content generated by the generation AI.

[0081] The decision unit can determine a response according to the patient's emotions and the attributes of the patient. The attributes of the patient include, but are not limited to, for example, age, sex, medical history, and the like. For example, the decision unit determines a gentle response when the patient is elderly, and determines an active response when the patient is young. In addition, the decision unit can determine a response suitable for men when the patient is male, and can determine a response suitable for women when the patient is female. Furthermore, the decision unit can determine a response suitable for a specific medical condition based on the patient's medical history. For example, the decision unit determines a response to stabilize blood pressure when the patient has a history of high blood pressure, and determines a response to manage blood sugar levels when the patient has a history of diabetes. This makes it possible to respond according to the individual situation of the patient. Part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can input the patient's emotion data and attribute data to the generation AI, and cause the generation AI to generate an appropriate response.

[0082] The medical support robot can function as a medical support robot including an acquisition unit, an estimation unit, a determination unit, and a response unit. The medical support robot has, for example, a moving function, an interaction function, a data collection function, and the like. The moving function includes, for example, a moving means using wheels or caterpillars, but is not limited to such examples. The interaction function includes, for example, an interaction means using voice recognition technology or voice synthesis technology, but is not limited to such examples. The data collection function includes, for example, a data collection means using a camera or a sensor, but is not limited to such examples. For example, the medical support robot moves in the hospital using wheels and arrives at the patient. In addition, the medical support robot can recognize the patient's voice using voice recognition technology and can communicate with the patient using voice synthesis technology. Furthermore, the medical support robot can take a picture of the patient's facial expression using a camera and measure the patient's heart rate and blood pressure using a sensor. This enables the medical support robot to respond according to the patient's emotions. Part or all of the above-mentioned processing in the medical support robot may be performed using, for example, AI, or may be performed without using AI. For example, the medical support robot can execute the dialogue content and treatment content generated by the generation AI.

[0083] The acquisition unit can acquire one or more pieces of information among the patient's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content as emotion estimation information. For example, the acquisition unit captures the patient's facial expression with a camera and acquires emotion estimation information using facial expression recognition technology. The acquisition unit can also record the patient's voice and acquire emotion estimation information using voice analysis technology. Furthermore, the acquisition unit can measure the patient's heart rate and blood pressure and acquire biosignal data as emotion estimation information. For example, the acquisition unit captures the patient's facial expression with a camera and acquires facial expression data such as a smile or an angry face using facial expression recognition technology. The acquisition unit can also record the patient's voice and acquire voice data such as tone of voice and speaking style using voice analysis technology. Furthermore, the acquisition unit can measure the patient's heart rate and blood pressure and acquire biosignal data such as fluctuations in heart rate and blood pressure. This makes it possible to estimate the patient's emotion based on various information. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire emotion estimating information generated by a generation AI.

[0084] The acquisition unit can estimate the emotion of the patient and adjust the timing of acquiring the emotion estimation information based on the estimated emotion of the patient. For example, when the patient is relaxed, the acquisition unit periodically acquires the emotion estimation information. Furthermore, when the patient is stressed, the acquisition unit can also frequently acquire the emotion estimation information. Furthermore, when the emotion of the patient suddenly changes, the acquisition unit can immediately acquire the emotion estimation information. For example, when the patient is relaxed, the acquisition unit acquires the emotion estimation information every hour. Furthermore, when the patient is stressed, the acquisition unit can also acquire the emotion estimation information every 10 minutes. Furthermore, when the emotion of the patient suddenly changes, the acquisition unit can immediately acquire the emotion estimation information. This makes it possible to acquire the emotion estimation information at an appropriate timing according to the emotion of the patient. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can adjust the timing of acquiring the emotion estimation information generated by the generation AI.

[0085] The acquisition unit can analyze the patient's past emotion data and select the acquisition method. The acquisition unit acquires emotion estimation information, for example, during a time period when the patient was relaxed in the past. The acquisition unit can also acquire emotion estimation information during a time period when the patient was stressed in the past. Furthermore, the acquisition unit can acquire emotion estimation information from the patient's past emotion data according to a specific event or situation. For example, the acquisition unit acquires emotion estimation information every hour during a time period when the patient was relaxed in the past. The acquisition unit can also acquire emotion estimation information every 10 minutes during a time period when the patient was stressed in the past. Furthermore, the acquisition unit can acquire emotion estimation information from the patient's past emotion data according to a specific event or situation. This makes it possible to select an optimal emotion estimation information acquisition method based on the past emotion data. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can analyze past emotion data generated by the generation AI and select an optimal acquisition method.

[0086] The acquisition unit can perform filtering based on the patient's current health condition and living situation when acquiring the emotion estimation information. For example, when the patient is in good health, the acquisition unit acquires standard emotion estimation information. Also, when the patient is in poor health, the acquisition unit can acquire detailed emotion estimation information. Furthermore, the acquisition unit can acquire appropriate emotion estimation information according to the living situation of the patient. For example, when the patient is in good health, the acquisition unit acquires facial expression data and voice data as standard emotion estimation information. Also, when the patient is in poor health, the acquisition unit can acquire heart rate data and blood pressure data as detailed emotion estimation information. This makes it possible to acquire appropriate emotion estimation information according to the patient's health condition and living situation. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can filter the emotion estimation information based on the health condition and living situation generated by the generation AI.

[0087] The acquisition unit can estimate the patient's emotion and determine the priority of emotion estimation information to be acquired based on the estimated emotion of the patient. For example, when the patient is relaxed, the acquisition unit preferentially acquires facial expression and voice information. In addition, when the patient is stressed, the acquisition unit can also preferentially acquire heart rate and blood pressure information. Furthermore, when the patient's emotion suddenly changes, the acquisition unit can also preferentially acquire pupil response and brain wave information. This makes it possible to determine the emotion estimation information to be preferentially acquired according to the emotion of the patient. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can determine the priority of emotion estimation information generated by the generation AI.

[0088] When acquiring emotion estimation information, the acquisition unit can preferentially acquire highly relevant information based on the geographical location information of the patient. For example, when the patient is in a hospital, the acquisition unit preferentially acquires information on heart rate and blood pressure. In addition, when the patient is at home, the acquisition unit can also preferentially acquire information on facial expressions and voice. Furthermore, when the patient is out, the acquisition unit can also preferentially acquire information on movement and breathing. This makes it possible to acquire emotion estimation information that is highly relevant based on the geographical location information of the patient. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can preferentially acquire information that is highly relevant based on the geographical location information generated by the generation AI.

[0089] The acquisition unit can analyze the social media activity of the patient when acquiring the emotion estimation information and acquire related information. For example, when the patient posts positively on social media, the acquisition unit preferentially acquires facial expression and voice information. In addition, when the patient posts negatively on social media, the acquisition unit can also preferentially acquire heart rate and blood pressure information. Furthermore, when the patient is frequently active on social media, the acquisition unit can also preferentially acquire pupil response and brain wave information. This makes it possible to acquire related emotion estimation information based on the social media activity of the patient. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can analyze the social media activity generated by the generation AI and acquire related information.

[0090] The estimation unit can estimate the patient's emotion and adjust the emotion estimation algorithm based on the estimated emotion of the patient. For example, when the patient is relaxed, the estimation unit gently adjusts the emotion estimation algorithm. Also, when the patient is stressed, the estimation unit can sensitively adjust the emotion estimation algorithm. Furthermore, when the patient's emotion suddenly changes, the estimation unit can quickly adjust the emotion estimation algorithm. As a result, the estimation accuracy is improved by adjusting the emotion estimation algorithm according to the patient's emotion. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can adjust the emotion estimation algorithm generated by the generation AI.

[0091] The estimation unit can adjust the level of detail of the estimation based on the importance of the emotion estimation information at the time of estimation. The estimation unit performs detailed emotion estimation based on information of high importance (heart rate, blood pressure, etc.), for example. The estimation unit can also perform simple emotion estimation based on information of low importance (facial expression, voice, etc.). Furthermore, the estimation unit can perform emotion estimation with a medium level of detail based on information of medium importance (movement, breathing, etc.). Thereby, the estimation accuracy is improved by adjusting the level of detail of the estimation according to the importance of the emotion estimation information. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can adjust the level of detail of the estimation based on the importance of the emotion estimation information generated by the generation AI.

[0092] The estimation unit can apply different estimation algorithms depending on the category of the emotion estimation information at the time of estimation. For example, the estimation unit applies a facial expression recognition algorithm to the facial expression information. The estimation unit can also apply a voice emotion recognition algorithm to the voice information. Furthermore, the estimation unit can also apply a physiological emotion recognition algorithm to the heart rate information. Thereby, by applying an appropriate estimation algorithm depending on the category of the emotion estimation information, the estimation accuracy is improved. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can apply different estimation algorithms depending on the category of the emotion estimation information generated by the generation AI.

[0093] The estimation unit can estimate the patient's emotions and adjust the display method of the estimation result based on the estimated patient's emotions. For example, when the patient is relaxed, the estimation unit displays a detailed estimation result. In addition, when the patient is stressed, the estimation unit can also display a concise estimation result. Furthermore, when the patient's emotions suddenly change, the estimation unit can also immediately display the estimation result. This makes it possible to provide appropriate information by adjusting the display method of the estimation result according to the patient's emotions. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI or may be performed without using AI. For example, the estimation unit can adjust the display method of the estimation result generated by the generation AI.

[0094] The estimation unit can determine the priority of estimation based on the acquisition time of the emotion estimation information at the time of estimation. For example, the estimation unit performs estimation by preferentially using the latest emotion estimation information. The estimation unit can also perform estimation by referring to past emotion estimation information. Furthermore, the estimation unit can also perform estimation by preferentially using emotion estimation information acquired in a specific time period. Thereby, by determining the priority of estimation based on the acquisition time of the emotion estimation information, the estimation accuracy is improved. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can determine the priority of estimation based on the acquisition time of the emotion estimation information generated by the generation AI.

[0095] The estimation unit can adjust the order of estimation based on the relevance of the emotion estimation information at the time of estimation. For example, the estimation unit performs estimation by preferentially using information with high relevance. The estimation unit can also perform estimation by using information with medium relevance next. Furthermore, the estimation unit can also perform estimation by using information with low relevance last. Thereby, by adjusting the order of estimation based on the relevance of the emotion estimation information, the estimation accuracy is improved. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can adjust the order of estimation based on the relevance of the emotion estimation information generated by the generation AI.

[0096] The determination unit can estimate the patient's emotion and adjust the response determination method based on the estimated patient's emotion. For example, when the patient is relaxed, the determination unit determines a gentle response. In addition, when the patient is stressed, the determination unit can also determine a quick response. Furthermore, when the patient's emotion suddenly changes, the determination unit can immediately determine the response. This makes it possible to respond appropriately by adjusting the response determination method according to the patient's emotion. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can adjust the response determination method based on emotion estimation information generated by the generation AI.

[0097] At the time of making a decision, the decision unit can select the optimal response method by analyzing the patient's past response history. The decision unit, for example, refers to the response method when the patient was relaxed in the past. The decision unit can also refer to the response method when the patient was stressed in the past. Furthermore, the decision unit can select the optimal response method according to a specific situation from the patient's past response history. As a result, the accuracy of the response is improved by selecting the optimal response method based on the patient's past response history. A part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can analyze the past response history generated by the generation AI and select the optimal response method.

[0098] The decision unit can customize the response measures based on the patient's current health condition at the time of decision. For example, the decision unit determines a standard response when the patient is in good health. The decision unit can also determine a special response when the patient is in poor health. Furthermore, the decision unit can customize an appropriate response measure according to the patient's health condition. This makes it possible to provide an appropriate response measure according to the patient's health condition. A part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can customize the response measures based on the health condition generated by the generation AI.

[0099] The determination unit can estimate the patient's emotion and determine the priority of the response based on the estimated emotion of the patient. For example, when the patient is relaxed, the determination unit determines the response with normal priority. In addition, when the patient is stressed, the determination unit can also determine the response with priority. Furthermore, when the patient's emotion suddenly changes, the determination unit can immediately determine the response. This enables a quick and appropriate response by determining the priority of the response according to the emotion of the patient. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can determine the priority of the response based on the emotion estimation information generated by the generation AI.

[0100] The decision unit can select the optimal response method based on the geographical location information of the patient at the time of decision. For example, when the patient is in a hospital, the decision unit can determine a quick response. In addition, when the patient is at home, the decision unit can also determine a remote response. Furthermore, when the patient is out, the decision unit can also select an appropriate response method. This makes it possible to respond appropriately by selecting the optimal response method based on the geographical location information of the patient. Part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can select the optimal response method based on the geographical location information generated by the generation AI.

[0101] At the time of decision, the decision unit can analyze the social media activity of the patient and suggest a means of response. For example, if the patient posts positively on social media, the decision unit can suggest a gentle response. In addition, if the patient posts negatively on social media, the decision unit can also suggest a quick response. Furthermore, if the patient is frequently active on social media, the decision unit can also suggest an appropriate response. This makes it possible to suggest an appropriate response based on the patient's social media activity. Some or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can analyze the social media activity generated by the generation AI and suggest an appropriate response.

[0102] The response unit can estimate the patient's emotion and adjust the method of executing the response based on the estimated emotion of the patient. For example, when the patient is relaxed, the response unit executes a gentle response. In addition, when the patient is stressed, the response unit can also execute a quick response. Furthermore, when the patient's emotion suddenly changes, the response unit can also execute an immediate response. This allows an appropriate response by adjusting the method of executing the response according to the emotion of the patient. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can adjust the method of executing the response based on the emotion estimation information generated by the generation AI.

[0103] The response unit can provide the optimal response by referring to the patient's past response history when responding. For example, the response unit refers to the response method when the patient was relaxed in the past. The response unit can also refer to the response method when the patient was stressed in the past. Furthermore, the response unit can provide the optimal response according to a specific situation from the patient's past response history. This improves the accuracy of the response by providing the optimal response based on the patient's past response history. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can provide the optimal response by referring to the past response history generated by the generation AI.

[0104] The response unit can customize the response means based on the patient's current health condition when responding. For example, the response unit provides a standard response when the patient is in good health. The response unit can also provide a special response when the patient is in poor health. Furthermore, the response unit can customize an appropriate response means according to the patient's health condition. This makes it possible to provide an appropriate response means according to the patient's health condition. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI or may be performed without using AI. For example, the response unit can customize the response means based on the health condition generated by the generation AI.

[0105] The response unit can estimate the patient's emotion and determine the priority of the response based on the estimated emotion of the patient. For example, when the patient is relaxed, the response unit executes the response with normal priority. In addition, when the patient is stressed, the response unit can also execute the response with priority. Furthermore, when the patient's emotion suddenly changes, the response unit can also execute the response immediately. This enables a quick and appropriate response by determining the priority of the response according to the emotion of the patient. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can determine the priority of the response based on the emotion estimation information generated by the generation AI.

[0106] The response unit can provide an optimal response based on the geographical location information of the patient when responding. For example, when the patient is in a hospital, the response unit provides a quick response. In addition, when the patient is at home, the response unit can also provide a remote response. Furthermore, when the patient is out, the response unit can also provide an appropriate response method. This makes it possible to provide an appropriate response by providing an optimal response based on the geographical location information of the patient. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can provide an optimal response based on the geographical location information generated by the generation AI.

[0107] The response unit can analyze the patient's social media activity and suggest a response measure when responding. For example, the response unit can provide a gentle response when the patient posts positively on social media. The response unit can also provide a quick response when the patient posts negatively on social media. Furthermore, the response unit can provide an appropriate response measure when the patient is frequently active on social media. This makes it possible to suggest an appropriate response measure based on the patient's social media activity. Some or all of the above-mentioned processing in the response unit may be performed, for example, using AI or may be performed without using AI. For example, the response unit can analyze the social media activity generated by the generation AI and suggest an appropriate response measure.

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

[0109] The medical support system may further include a music providing unit that selects and plays music based on the patient's emotions. For example, when the patient is relaxed, the music providing unit selects and plays calm music. Also, when the patient is feeling stressed, the music providing unit can select and play music that has a relaxing effect. Furthermore, when the patient is happy, the music providing unit can select and play cheerful music. In this way, by providing music according to the patient's emotions, the patient's stress and anxiety can be reduced.

[0110] The medical support system may further include a lighting adjustment unit that adjusts the color and brightness of the lighting based on the patient's emotions. For example, the lighting adjustment unit provides gentle warm-colored lighting when the patient is relaxed. Also, when the patient is feeling stressed, it may provide soft lighting with a relaxing effect. Furthermore, it may provide bright lighting when the patient is happy. In this way, it is possible to reduce the patient's stress and anxiety by providing a lighting environment that corresponds to the patient's emotions.

[0111] The medical support system may further include an aroma providing unit that selects and provides an aroma based on the patient's emotions. For example, when the patient is relaxed, the aroma providing unit provides an aroma with a relaxing effect, such as lavender. When the patient is stressed, the aroma providing unit may provide an aroma with a stress reducing effect, such as chamomile. When the patient is happy, the aroma providing unit may provide a bright citrus-scented aroma. In this way, by providing an aroma according to the patient's emotions, the patient's stress and anxiety can be reduced.

[0112] The medical support system can further include a VR providing unit that provides virtual reality (VR) content based on the patient's emotions. For example, when the patient is relaxed, the VR providing unit provides natural scenery and calming images. Also, when the patient is stressed, it can provide images that have a relaxing effect. Furthermore, when the patient is happy, it can provide enjoyable images. In this way, by providing VR content according to the patient's emotions, it is possible to reduce the patient's stress and anxiety.

[0113] The medical support system may further include a meal suggestion unit that suggests a meal menu based on the patient's emotions. For example, the meal suggestion unit may suggest a light meal if the patient is relaxed. Also, if the patient is feeling stressed, the meal suggestion unit may suggest a meal that uses ingredients that have a relaxing effect. Furthermore, if the patient is happy, the meal suggestion unit may suggest a pleasant meal. In this way, by providing a meal that corresponds to the patient's emotions, the patient's stress and anxiety can be reduced.

[0114] The medical support system may further include a health management unit that collects the patient's health data and performs long-term health management. The health management unit periodically collects data such as the patient's blood pressure, heart rate, and body temperature, and monitors the patient's health condition. The health management unit may also provide health advice based on the collected data. Furthermore, the health management unit may share the patient's health data with medical staff and provide appropriate medical support. This improves the patient's health management and enables appropriate medical support.

[0115] The medical support system may further include an exercise suggestion unit that collects exercise data of the patient and proposes an exercise program. The exercise suggestion unit collects data such as the number of steps taken by the patient, exercise time, and calories burned, and proposes an appropriate exercise program. The exercise suggestion unit may also adjust the exercise intensity according to the patient's health condition. Furthermore, the exercise suggestion unit may share the patient's exercise data with medical staff and provide appropriate exercise support. This improves the patient's exercise habits and promotes health management.

[0116] The medical support system may further include a sleep management unit that collects the patient's sleep data and provides advice for improving the quality of sleep. The sleep management unit collects data such as the patient's sleep time, sleep depth, and number of turns in sleep, and evaluates the quality of sleep. The sleep management unit may also provide advice for improving the quality of sleep based on the collected data. Furthermore, the sleep management unit may share the patient's sleep data with medical staff and provide appropriate sleep support. This improves the patient's quality of sleep and promotes health management.

[0117] The medical support system may further include a nutrition management unit that collects dietary data of the patient and evaluates the nutritional balance. The nutrition management unit collects data such as the patient's dietary content, calorie intake, and nutrient balance, and evaluates the nutritional state. The nutrition management unit may also provide advice for improving nutritional balance based on the collected data. Furthermore, the nutrition management unit may share the patient's dietary data with medical staff and provide appropriate nutritional support. This improves the patient's nutritional state and promotes health management.

[0118] The medical support system may further include a medication management unit that collects medication data of patients and manages medication. The medication management unit collects data such as the patient's medication schedule, dosage, medication history, and the like, and manages medication. The medication management unit may also provide medication reminders based on the collected data. Furthermore, the medication management unit may share the patient's medication data with medical staff and provide appropriate medication support. This improves medication management of patients and enables appropriate medical support.

[0119] The process flow of the second embodiment will be briefly described below.

[0120] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a patient. The emotion estimation information includes facial expression data, voice data, and biosignal data. For example, the acquisition unit captures the facial expression of the patient with a camera and acquires the emotion estimation information using facial expression recognition technology. It is also possible to record the patient's voice and acquire the emotion estimation information using voice analysis technology. Furthermore, it is also possible to measure the patient's heart rate and blood pressure and acquire biosignal data as emotion estimation information. Step 2: The estimation unit estimates the patient's emotions based on the emotion estimation information acquired by the acquisition unit. The estimation unit includes AI processing and uses a machine learning model to analyze facial expression data, voice data, and biosignal data to accurately estimate the patient's emotions. For example, if the patient is smiling, the emotion of joy is estimated, and if the patient has an angry face, the emotion of anger is estimated. In addition, the tone of voice and speaking style can be analyzed to estimate emotions such as joy, sadness, and anger. Furthermore, the fluctuations in heart rate and blood pressure can be analyzed to estimate emotions such as relaxation and stress. Step 3: The determination unit determines a response according to the patient's emotion estimated by the estimation unit. The determination unit determines a dialogue or treatment mode according to the patient's emotion. For example, it determines a mode of speaking comforting words when the patient is sad, a mode of sharing joy with the patient when the patient is happy, and a mode of providing a topic to calm the patient when the patient is angry. Step 4: The response unit executes the response decided by the decision unit. The response unit communicates with the patient and provides treatment in a manner that corresponds to the patient's emotions. For example, if the patient is sad, the response unit will offer comforting words; if the patient is happy, the response unit will share in the patient's joy; if the patient is angry, the response unit will provide a topic of conversation to calm the patient. The response unit can also notify medical staff of the content of the response that has been decided.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0123] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] For example, the acquisition unit can acquire the facial expression and voice of the patient using the camera 42 and microphone 38B of the smart device 14, and acquire emotion estimation information by the specific processing unit 290 of the data processing device 12. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the emotion of the patient based on the acquired emotion estimation information. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines a response according to the estimated emotion. The response unit is realized, for example, by the control unit 46A of the smart device 14, and executes the determined response to interact with the patient and perform treatment. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

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

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0132] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] For example, the acquisition unit can acquire the facial expression and voice of the patient using the camera 42 and microphone 238 of the smart glasses 214, and acquire emotion estimation information by the specific processing unit 290 of the data processing device 12. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the emotion of the patient based on the acquired emotion estimation information. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines a response according to the estimated emotion. The response unit is realized, for example, by the control unit 46A of the smart glasses 214, and executes the determined response to interact with the patient and perform treatment. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

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

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

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

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

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

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] For example, the acquisition unit can acquire the facial expression and voice of the patient using the camera 42 and microphone 238 of the headset type terminal 314, and acquire emotion estimation information by the specific processing unit 290 of the data processing device 12. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the emotion of the patient based on the acquired emotion estimation information. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines a response according to the estimated emotion. The response unit is realized, for example, by the control unit 46A of the headset type terminal 314, and executes the determined response to communicate with the patient and perform treatment. The correspondence relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

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

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

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

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0168] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] For example, the acquisition unit can acquire the facial expression and voice of the patient using the camera 42 and microphone 238 of the robot 414, and acquire emotion estimation information by the specific processing unit 290 of the data processing device 12. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the emotion of the patient based on the acquired emotion estimation information. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines a response according to the estimated emotion. The response unit is realized, for example, by the control unit 46A of the robot 414, and executes the determined response, and conducts a dialogue with and treatment of the patient. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0175] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0177] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0179] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

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

[0183] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0184] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0185] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0186] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0187] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0188] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0190] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0192] (Appendix 1) An acquisition unit that acquires emotion estimation information for estimating an emotion of a patient; an estimation unit that estimates an emotion of a patient based on the emotion estimation information acquired by the acquisition unit; a determination unit that determines a response according to the emotion of the patient estimated by the estimation unit; A response unit that executes the response determined by the determination unit. A system characterized by: (Appendix 2) The determination unit: determining a dialogue in a manner corresponding to the patient's emotion as a response corresponding to the patient's emotion; The corresponding part is To communicate with a patient in a manner that corresponds to the patient's emotions 2. The system of claim 1, (Appendix 3) The determination unit: determining a treatment mode according to the patient's feelings as a response according to the patient's feelings; The corresponding part is Treat patients in a way that responds to their emotions 2. The system of claim 1, (Appendix 4) The corresponding part is The medical staff is notified of the response decided by the decision unit. 2. The system of claim 1, (Appendix 5) The determination unit: Decide on a response based on the patient's emotions and attributes 2. The system of claim 1, (Appendix 6) Functions as a medical support robot including an acquisition unit, an estimation unit, a decision unit, and a response unit 2. The system of claim 1, (Appendix 7) The acquisition unit: One or more pieces of information among the patient's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, respiration, and posted content are acquired as emotion estimation information. 2. The system of claim 1, (Appendix 8) The acquisition unit: To estimate a patient's emotion and adjust timing of acquiring emotion estimation information based on the estimated emotion of the patient. 2. The system of claim 1, (Appendix 9) The acquisition unit: Analyze the patient's past emotional data and select the method of acquisition 2. The system of claim 1, (Appendix 10) The acquisition unit: When acquiring information for emotion estimation, filtering is performed based on the patient's current health condition and living situation. 2. The system of claim 1, (Appendix 11) The acquisition unit is The patient's emotion is estimated, and a priority order of emotion estimation information to be acquired is determined based on the estimated patient's emotion. 2. The system of claim 1, (Appendix 12) The acquisition unit is When acquiring information for emotion estimation, the system prioritizes acquiring information that is highly relevant based on the patient's geographic location information. 2. The system of claim 1, (Appendix 13) The acquisition unit is When obtaining emotion estimation information, the patient's social media activity is analyzed to obtain related information. 2. The system of claim 1, (Appendix 14) The estimation unit is Estimating a patient's emotion and adjusting an emotion estimation algorithm based on the estimated patient's emotion. 2. The system of claim 1, (Appendix 15) The estimation unit is During estimation, the level of detail of the estimation is adjusted based on the importance of the emotion estimation information. 2. The system of claim 1, (Appendix 16) The estimation unit is During estimation, different estimation algorithms are applied depending on the category of emotion estimation information. 2. The system of claim 1, (Appendix 17) The estimation unit is Estimate the patient's emotions and adjust the way the estimation results are displayed based on the estimated patient emotions 2. The system of claim 1, (Appendix 18) The estimation unit is During estimation, the priority of estimation is determined based on the time when emotion estimation information was acquired. 2. The system of claim 1, (Appendix 19) The estimation unit is During estimation, the order of estimation is adjusted based on the relevance of emotion estimation information. 2. The system of claim 1, (Appendix 20) The determination unit is Inferring a patient's emotions and adjusting response decisions based on the inferred patient emotions 2. The system of claim 1, (Appendix 21) The determination unit is When making a decision, the patient's past treatment history is analyzed to select the most appropriate treatment method. 2. The system of claim 1, (Appendix 22) The determination unit is When making a decision, tailor the response measures based on the patient's current health status 2. The system of claim 1, (Appendix 23) The determination unit is Estimate the patient's emotions and prioritize responses based on the estimated patient's emotions 2. The system of claim 1, (Appendix 24) The determination unit is When making a decision, use the patient's geographic location to determine the best course of action 2. The system of claim 1, (Appendix 25) The determination unit is Analyze patients' social media activity to suggest action measures at decision time 2. The system of claim 1, (Appendix 26) The corresponding portion is Inferring patient emotions and adjusting response execution based on the inferred patient emotions 2. The system of claim 1, (Appendix 27) The corresponding portion is When responding, provide the most appropriate response by referring to the patient's past treatment history 2. The system of claim 1, (Appendix 28) The corresponding portion is When responding, tailor the response based on the patient's current health status 2. The system of claim 1, (Appendix 29) The corresponding portion is Estimate the patient's emotions and prioritize responses based on the estimated patient's emotions 2. The system of claim 1, (Appendix 30) The corresponding portion is At the time of response, provide optimal response based on the patient's geographic location 2. The system of claim 1, (Appendix 31) The corresponding portion is When responding, analyze the patient's social media activity to suggest action options 2. The system of claim 1, [Explanation of symbols]

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

Claims

1. An acquisition unit that acquires emotion estimation information for estimating an emotion of a patient; an estimation unit that estimates an emotion of a patient based on the emotion estimation information acquired by the acquisition unit; a determination unit that determines a response according to the emotion of the patient estimated by the estimation unit; a response unit that executes the response determined by the determination unit. A system characterized by:

2. The determination unit is determining a dialogue in a manner corresponding to the patient's emotion as a response corresponding to the patient's emotion; The corresponding portion is To communicate with a patient in a manner that corresponds to the patient's emotions 2. The system of claim 1 .

3. The determination unit is determining a treatment mode according to the patient's feelings as a response according to the patient's feelings; The corresponding portion is Treat patients in a way that responds to their emotions 2. The system of claim 1 .

4. The corresponding portion is The medical staff is notified of the response determined by the determination unit.

2. The system of claim 1 .

5. The determination unit is Decide on a response based on the patient's emotions and attributes 2. The system of claim 1 .

6. The acquisition unit, the estimation unit, the determination unit, and the corresponding unit function as a medical support robot.

2. The system of claim 1 .

7. The acquisition unit is One or more pieces of information among the patient's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, respiration, and posted content are acquired as emotion estimation information.

2. The system of claim 1 .

8. The acquisition unit is To estimate a patient's emotion and adjust timing of acquiring emotion estimation information based on the estimated emotion of the patient.

2. The system of claim 1 .

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