System

Abstract

An object of a system according to an embodiment is to reduce a waiting time in a hospital and to reduce stress of a patient.SOLUTION: A system includes a simple determination part, a primary medical examination part, and a continuous support part. The simple determination unit uses the generated AI. The primary diagnosis unit makes an initial diagnosis based on the input information of the patient analyzed by the simple determination unit. The continuous support unit continuously supports the health condition of the patient diagnosed by the primary care unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of long waiting times at hospitals, which increases stress for patients.

[0005] The system according to the embodiment aims to reduce waiting times at hospitals and reduce stress for patients. [Means for solving the problem]

[0006] The system according to the embodiment includes a simplified assessment unit, a primary medical care unit, and an ongoing support unit. The simplified assessment unit uses a generative AI. The primary medical care unit performs an initial diagnosis based on the patient's input information analyzed by the simplified assessment unit. The ongoing support unit provides ongoing support for the patient's health condition diagnosed by the primary medical care unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce waiting times at hospitals and reduce stress for patients. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The system according to an embodiment of the present invention uses a multimodal generation AI called "timefree" to significantly reduce waiting times at hospitals and alleviate patient stress. This system provides the following three services: 1. A simple AI diagnosis service for use at home, 2. A primary care AI service for outpatient visits, and 3. Ongoing support after treatment. This allows the system to reduce congestion at hospitals and improve the efficiency of medical treatment.

[0029] The system according to the embodiment includes a simplified assessment unit, a primary care unit, and a continuous support unit. The simplified assessment unit analyzes patient input information using a generating AI. For example, the generating AI analyzes input information from the patient (e.g., symptoms, body temperature, blood pressure, etc.) and determines whether the patient needs to visit a hospital. The generating AI can also learn the patient's past health data and predict individual health risks. Furthermore, the generating AI can collect the patient's lifestyle data in real time and immediately reflect changes in health status. The primary care unit performs an initial diagnosis based on the patient's input information analyzed by the simplified assessment unit. For example, the generating AI analyzes information such as the patient's symptoms and medical history to perform an initial diagnosis. The generating AI can also analyze the patient's voice input and identify symptoms from the voice. Furthermore, the generating AI can analyze the patient's genetic information and perform a diagnosis taking genetic risk factors into account. The continuous support unit provides ongoing support for the patient's health status diagnosed by the primary care unit. For example, the generating AI analyzes the patient's health data and provides advice and health management suggestions to prevent recurrence. Generative AI can also manage a patient's medication history and provide reminders to encourage adherence. Furthermore, generative AI can also customize a patient's rehabilitation program and monitor their progress. This allows the system to significantly reduce waiting times at hospitals and alleviate patient stress. For example, patients can use a simple AI diagnosis service at home to choose home recuperation when there is no need to go to the hospital. Furthermore, using a primary care AI service during outpatient visits can shorten doctor consultation times and reduce waiting times for other patients. Furthermore, continued support after treatment can prevent recurrence and maintain patients' health.

[0030] The simplified assessment unit can learn from a patient's past health data and predict individual health risks. For example, the simplified assessment unit uses a generative AI to collect a patient's past health data and a machine learning algorithm to predict individual health risks. For example, it evaluates the risk of a specific disease based on past medical records and test results. This allows for more accurate assessment by predicting individual health risks.

[0031] The simplified assessment unit collects the patient's lifestyle data in real time and can immediately reflect any changes in their health condition. The simplified assessment unit, for example, uses a wearable device or smartphone app to collect the patient's lifestyle data in real time. For example, it automatically collects records of food intake, exercise volume, and sleep patterns. This allows for quick responses by reflecting changes in health condition in real time.

[0032] The simplified judgment unit can also be applied to pet health management, allowing you to monitor your pet's health condition. For example, the simplified judgment unit applies the simplified judgment AI service to pet health management, building a system for monitoring your pet's health condition. For example, it can collect your pet's diet, exercise level, and body temperature in real time. This makes it easier to monitor your pet's health condition.

[0033] The simplified judgment unit can be incorporated into a company's employee health management system to predict employees' health risks. For example, the simplified judgment unit incorporates the simplified judgment AI service into a company's employee health management system to build a system that predicts employees' health risks. For example, it analyzes employees' health checkup results and lifestyle habit data. This improves the company's health management by predicting employees' health risks.

[0034] The primary care department can analyze the patient's voice input and identify symptoms from the voice input. The primary care department will build a system in which, for example, generative AI analyzes the patient's voice input and identifies symptoms from the voice. For example, what the patient says can be converted into text using voice recognition technology, and the symptoms analyzed. This will enable rapid diagnosis by identifying symptoms from the voice input.

[0035] The primary care department can analyze a patient's genetic information and make a diagnosis that takes genetic risk factors into account. For example, the primary care department will build a system in which generative AI analyzes a patient's genetic information and makes a diagnosis that takes genetic risk factors into account. For example, the risk of a specific disease will be evaluated based on the results of a genetic test. This will enable more accurate diagnoses by making a diagnosis that takes genetic risk factors into account.

[0036] Primary care departments can be introduced into remote medical facilities to support medical treatment in areas where there is a shortage of doctors. Primary care departments, for example, can introduce primary care AI services to remote medical facilities to build a system that supports medical treatment in areas where there is a shortage of doctors. For example, initial diagnosis of patients can be performed through remote medical consultations. This will improve access to medical care by supporting medical treatment in areas where there is a shortage of doctors.

[0037] The primary care department can be applied to medical support for sports teams and manage the health of athletes. For example, the primary care department can apply the primary care AI service to medical support for sports teams and build a system to manage the health of athletes. For example, athletes' health data can be collected in real time and analyzed by the generation AI. This will improve the performance of sports teams by managing the athletes' health.

[0038] The continuity support unit can manage the patient's medication history and provide reminders to encourage medication compliance. For example, the continuity support unit will build a system in which the generative AI manages the patient's medication history and provides reminders to encourage medication compliance. For example, it will record the time and dosage of medication and send reminders. This will encourage medication compliance and improve treatment effectiveness.

[0039] The continuous support unit can individually customize a patient's rehabilitation program and monitor progress. For example, the continuous support unit will build a system in which generative AI can individually customize a patient's rehabilitation program and monitor progress. For example, it will propose a rehabilitation menu according to the patient's condition. This will enable effective rehabilitation by individually customizing the rehabilitation program and monitoring progress.

[0040] The continuous support unit can be applied to the health management of residents in nursing homes, reducing the burden on nursing staff. For example, the continuous support unit applies continuous support services to the health management of residents in nursing homes, building a system that reduces the burden on nursing staff. For example, resident health data can be collected in real time and analyzed by a generation AI. This will support the health management of residents in nursing homes, reducing the burden on nursing staff.

[0041] The continuous support unit can be integrated into a fitness app to support the user in achieving their health goals. For example, the continuous support unit integrates a continuous support service into a fitness app to build a system that supports the user in achieving their health goals. For example, the system collects the user's exercise data and dietary data in real time and analyzes it using a generation AI. By integrating this into the fitness app, the system supports the user in achieving their health goals.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The simple assessment unit can also analyze a patient's dietary data and provide advice on nutritional balance. For example, it can analyze the dietary information entered by the patient and evaluate whether there are any nutrient deficiencies or excesses. It can also propose a meal plan based on the patient's health condition. It can also continuously monitor dietary records and support long-term nutritional management.

[0044] The simplified assessment unit can also analyze the patient's exercise data and propose an appropriate exercise plan. For example, it can create an individual exercise plan based on the patient's exercise history and current physical fitness level. It can also monitor exercise progress and adjust the plan as needed. It can also evaluate the effectiveness of exercise and provide feedback to the patient.

[0045] The simplified assessment unit can also analyze a patient's sleep data and provide advice to improve sleep quality. For example, it can analyze a patient's sleep patterns and evaluate their sleep quality. It can also suggest improvement measures based on their sleep environment and lifestyle habits. It can also monitor sleep progress and support long-term sleep management.

[0046] The simple assessment unit can also analyze a patient's social activity data and provide advice on strengthening social connections. For example, it can evaluate the frequency and quality of a patient's social activities and suggest activities to reduce feelings of isolation. It can also encourage participation in local support groups and events. It can also monitor the progress of social activities and provide ongoing support.

[0047] The simplified assessment unit can also suggest activities based on the patient's hobbies and interests. For example, it can analyze the patient's hobbies and interests and suggest appropriate activities. It can also monitor the progress of activities and suggest new activities as needed. It can also evaluate the effectiveness of activities and provide feedback to the patient.

[0048] The simple assessment unit can also analyze the patient's environmental data and suggest environments that are optimal for health. For example, it can analyze indoor temperature, humidity, and air quality to suggest environments that are optimal for health. It can also monitor changes in the environment and make adjustments as necessary. It can also evaluate the effects of the environment and provide feedback to the patient.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The simple judgment unit uses the generating AI to analyze the patient's input information. For example, the generating AI analyzes the patient's input information (e.g., symptoms, body temperature, blood pressure, etc.) and determines whether or not the patient needs to go to the hospital. The generating AI can also learn the patient's past health data and predict individual health risks. Furthermore, the generating AI can collect the patient's lifestyle data in real time and immediately reflect changes in their health condition. Step 2: The primary care department makes an initial diagnosis based on the patient's input information analyzed by the simplified judgment department. For example, the generation AI analyzes information such as the patient's symptoms and medical history to make an initial diagnosis. The generation AI can also analyze the patient's voice input and identify symptoms from the voice. Furthermore, the generation AI can analyze the patient's genetic information and make a diagnosis that takes genetic risk factors into account. Step 3: The Continuing Support Department provides ongoing support for the health status of patients diagnosed by the Primary Care Department. For example, the Generative AI analyzes patients' health data and provides advice and health management suggestions to prevent recurrence. The Generative AI can also manage patients' medication history and provide reminders to encourage adherence. Furthermore, the Generative AI can customize patients' rehabilitation programs and monitor their progress.

[0051] (Example 2) The system according to an embodiment of the present invention uses a multimodal generation AI called "timefree" to significantly reduce waiting times at hospitals and alleviate patient stress. This system provides the following three services: 1. A simple AI diagnosis service for use at home, 2. A primary care AI service for outpatient visits, and 3. Ongoing support after treatment. This allows the system to reduce congestion at hospitals and improve the efficiency of medical treatment.

[0052] The system according to the embodiment includes a simplified assessment unit, a primary care unit, and a continuous support unit. The simplified assessment unit analyzes patient input information using a generating AI. For example, the generating AI analyzes input information from the patient (e.g., symptoms, body temperature, blood pressure, etc.) and determines whether the patient needs to visit a hospital. The generating AI can also learn the patient's past health data and predict individual health risks. Furthermore, the generating AI can collect the patient's lifestyle data in real time and immediately reflect changes in health status. The primary care unit performs an initial diagnosis based on the patient's input information analyzed by the simplified assessment unit. For example, the generating AI analyzes information such as the patient's symptoms and medical history to perform an initial diagnosis. The generating AI can also analyze the patient's voice input and identify symptoms from the voice. Furthermore, the generating AI can analyze the patient's genetic information and perform a diagnosis taking genetic risk factors into account. The continuous support unit provides ongoing support for the patient's health status diagnosed by the primary care unit. For example, the generating AI analyzes the patient's health data and provides advice and health management suggestions to prevent recurrence. Generative AI can also manage a patient's medication history and provide reminders to encourage adherence. Furthermore, generative AI can also customize a patient's rehabilitation program and monitor their progress. This allows the system to significantly reduce waiting times at hospitals and alleviate patient stress. For example, patients can use a simple AI diagnosis service at home to choose home recuperation when there is no need to go to the hospital. Furthermore, using a primary care AI service during outpatient visits can shorten doctor consultation times and reduce waiting times for other patients. Furthermore, continued support after treatment can prevent recurrence and maintain patients' health.

[0053] The simplified assessment unit can learn from a patient's past health data and predict individual health risks. For example, the simplified assessment unit uses a generative AI to collect a patient's past health data and a machine learning algorithm to predict individual health risks. For example, it evaluates the risk of a specific disease based on past medical records and test results. This allows for more accurate assessment by predicting individual health risks.

[0054] The simplified assessment unit collects the patient's lifestyle data in real time and can immediately reflect any changes in their health condition. The simplified assessment unit, for example, uses a wearable device or smartphone app to collect the patient's lifestyle data in real time. For example, it automatically collects records of food intake, exercise volume, and sleep patterns. This allows for quick responses by reflecting changes in health condition in real time.

[0055] The simple judgment unit can use the emotion estimation function to evaluate the patient's stress level and suggest relaxation methods if the stress level is high. The simple judgment unit, for example, uses the emotion estimation function to build a system for evaluating the patient's stress level. For example, it analyzes facial expressions and voice tone to quantify the stress level. This makes it possible to evaluate the patient's stress level and suggest appropriate relaxation methods, thereby reducing stress.

[0056] The simplified judgment unit can also be applied to pet health management, allowing you to monitor your pet's health condition. For example, the simplified judgment unit applies the simplified judgment AI service to pet health management, building a system for monitoring your pet's health condition. For example, it can collect your pet's diet, exercise level, and body temperature in real time. This makes it easier to monitor your pet's health condition.

[0057] The simplified judgment unit can be incorporated into a company's employee health management system to predict employees' health risks. For example, the simplified judgment unit incorporates the simplified judgment AI service into a company's employee health management system to build a system that predicts employees' health risks. For example, it analyzes employees' health checkup results and lifestyle habit data. This improves the company's health management by predicting employees' health risks.

[0058] The simple judgment unit uses the emotion estimation function to analyze the emotions expressed by patients when they enter data, and can provide an interface that elicits positive emotions. For example, the simple judgment unit uses the emotion estimation function to build a system that analyzes the emotions expressed by patients when they enter data in real time. For example, it analyzes facial expressions and tone of voice when entering data and calculates an emotion score. This elicits positive emotions, improving the patient's input experience.

[0059] The primary care department can analyze the patient's voice input and identify symptoms from the voice input. The primary care department will build a system in which, for example, generative AI analyzes the patient's voice input and identifies symptoms from the voice. For example, what the patient says can be converted into text using voice recognition technology, and the symptoms analyzed. This will enable rapid diagnosis by identifying symptoms from the voice input.

[0060] The primary care department can analyze a patient's genetic information and make a diagnosis that takes genetic risk factors into account. For example, the primary care department will build a system in which generative AI analyzes a patient's genetic information and makes a diagnosis that takes genetic risk factors into account. For example, the risk of a specific disease will be evaluated based on the results of a genetic test. This will enable more accurate diagnoses by making a diagnosis that takes genetic risk factors into account.

[0061] The primary care department can use the emotion estimation function to provide counseling to reduce the anxiety and fear of patients. For example, the primary care department can use the emotion estimation function to build a system that provides counseling to reduce the anxiety and fear of patients. For example, the emotional state of patients can be analyzed in real time and appropriate counseling can be provided. This reduces the anxiety and fear of patients, improving the medical experience.

[0062] Primary care departments can be introduced into remote medical facilities to support medical treatment in areas where there is a shortage of doctors. Primary care departments, for example, can introduce primary care AI services to remote medical facilities to build a system that supports medical treatment in areas where there is a shortage of doctors. For example, initial diagnosis of patients can be performed through remote medical consultations. This will improve access to medical care by supporting medical treatment in areas where there is a shortage of doctors.

[0063] The primary care department can be applied to medical support for sports teams and manage the health of athletes. For example, the primary care department can apply the primary care AI service to medical support for sports teams and build a system to manage the health of athletes. For example, athletes' health data can be collected in real time and analyzed by the generation AI. This will improve the performance of sports teams by managing the athletes' health.

[0064] The primary care department can use the emotion estimation function to monitor the emotions of patients during treatment in real time and provide feedback to doctors. The primary care department, for example, uses the emotion estimation function to build a system that monitors the emotions of patients during treatment in real time. For example, it analyzes the patient's facial expression and tone of voice to calculate an emotion score. This allows the emotion of patients during treatment to be monitored and feedback to doctors, improving the quality of medical care.

[0065] The continuity support unit can manage the patient's medication history and provide reminders to encourage medication compliance. For example, the continuity support unit will build a system in which the generative AI manages the patient's medication history and provides reminders to encourage medication compliance. For example, it will record the time and dosage of medication and send reminders. This will encourage medication compliance and improve treatment effectiveness.

[0066] The continuous support unit can individually customize a patient's rehabilitation program and monitor progress. For example, the continuous support unit will build a system in which generative AI can individually customize a patient's rehabilitation program and monitor progress. For example, it will propose a rehabilitation menu according to the patient's condition. This will enable effective rehabilitation by individually customizing the rehabilitation program and monitoring progress.

[0067] The continuous support unit can use the emotion estimation function to send encouraging messages to maintain the patient's motivation. The continuous support unit, for example, uses the emotion estimation function to build a system that sends encouraging messages to maintain the patient's motivation. For example, the continuous support unit analyzes the patient's emotional state and sends encouraging messages at appropriate times. This maintains the patient's motivation and promotes continuation of treatment.

[0068] The continuous support unit can be applied to the health management of residents in nursing homes, reducing the burden on nursing staff. For example, the continuous support unit applies continuous support services to the health management of residents in nursing homes, building a system that reduces the burden on nursing staff. For example, resident health data can be collected in real time and analyzed by a generation AI. This will support the health management of residents in nursing homes, reducing the burden on nursing staff.

[0069] The continuous support unit can be integrated into a fitness app to support the user in achieving their health goals. For example, the continuous support unit integrates a continuous support service into a fitness app to build a system that supports the user in achieving their health goals. For example, the system collects the user's exercise data and dietary data in real time and analyzes it using a generation AI. By integrating this into the fitness app, the system supports the user in achieving their health goals.

[0070] The continuous support unit uses the emotion estimation function to provide health advice according to the patient's emotional state, thereby eliciting positive emotions. The continuous support unit, for example, uses the emotion estimation function to build a system that provides health advice according to the patient's emotional state. For example, the continuous support unit analyzes the patient's emotional state in real time and provides appropriate advice. This makes it possible to provide health advice according to the patient's emotional state, thereby eliciting positive emotions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The simple assessment unit can also analyze a patient's dietary data and provide advice on nutritional balance. For example, it can analyze the dietary information entered by the patient and evaluate whether there are any nutrient deficiencies or excesses. It can also propose a meal plan based on the patient's health condition. It can also continuously monitor dietary records and support long-term nutritional management.

[0073] The simplified assessment unit can also analyze the patient's exercise data and propose an appropriate exercise plan. For example, it can create an individual exercise plan based on the patient's exercise history and current physical fitness level. It can also monitor exercise progress and adjust the plan as needed. It can also evaluate the effectiveness of exercise and provide feedback to the patient.

[0074] The simple judgment unit can use the emotion estimation function to analyze the patient's emotional state and provide stress management advice. For example, it can analyze the patient's emotional state in real time and suggest relaxation methods if the stress level is high. It can also identify the cause of stress and provide specific countermeasures. It can also monitor the progress of stress management and provide ongoing support.

[0075] The simplified assessment unit can also analyze a patient's sleep data and provide advice to improve sleep quality. For example, it can analyze a patient's sleep patterns and evaluate their sleep quality. It can also suggest improvement measures based on their sleep environment and lifestyle habits. It can also monitor sleep progress and support long-term sleep management.

[0076] The simple assessment unit can use the emotion estimation function to analyze the patient's emotional state and provide mental health support. For example, it can analyze the patient's emotional state in real time and provide mental health advice. It can also monitor emotional fluctuations and suggest professional counseling as needed. It can also provide ongoing support for mental health progress.

[0077] The simple assessment unit can also analyze a patient's social activity data and provide advice on strengthening social connections. For example, it can evaluate the frequency and quality of a patient's social activities and suggest activities to reduce feelings of isolation. It can also encourage participation in local support groups and events. It can also monitor the progress of social activities and provide ongoing support.

[0078] The simple assessment unit can use the emotion estimation function to analyze the patient's emotional state and suggest exercises to elicit positive emotions. For example, it can analyze the patient's emotional state in real time and suggest exercises to elicit positive emotions. It can also monitor the progress of the exercises and make adjustments as needed. It can also evaluate the effectiveness of the exercises and provide feedback to the patient.

[0079] The simplified assessment unit can also suggest activities based on the patient's hobbies and interests. For example, it can analyze the patient's hobbies and interests and suggest appropriate activities. It can also monitor the progress of activities and suggest new activities as needed. It can also evaluate the effectiveness of activities and provide feedback to the patient.

[0080] The simple assessment unit can use its emotion estimation function to analyze the patient's emotional state and provide music and videos that match the emotion. For example, it can analyze the patient's emotional state in real time and suggest music and videos that are suitable for relaxation or a change of mood. It can also monitor the effectiveness of the music and videos and adjust them as needed. It can also evaluate the effectiveness of the music and videos and provide feedback to the patient.

[0081] The simple assessment unit can also analyze the patient's environmental data and suggest environments that are optimal for health. For example, it can analyze indoor temperature, humidity, and air quality to suggest environments that are optimal for health. It can also monitor changes in the environment and make adjustments as necessary. It can also evaluate the effects of the environment and provide feedback to the patient.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The simple judgment unit uses the generating AI to analyze the patient's input information. For example, the generating AI analyzes the patient's input information (e.g., symptoms, body temperature, blood pressure, etc.) and determines whether or not the patient needs to go to the hospital. The generating AI can also learn the patient's past health data and predict individual health risks. Furthermore, the generating AI can collect the patient's lifestyle data in real time and immediately reflect changes in their health condition. Step 2: The primary care department makes an initial diagnosis based on the patient's input information analyzed by the simplified judgment department. For example, the generation AI analyzes information such as the patient's symptoms and medical history to make an initial diagnosis. The generation AI can also analyze the patient's voice input and identify symptoms from the voice. Furthermore, the generation AI can analyze the patient's genetic information and make a diagnosis that takes genetic risk factors into account. Step 3: The Continuing Support Department provides ongoing support for the health status of patients diagnosed by the Primary Care Department. For example, the Generative AI analyzes patients' health data and provides advice and health management suggestions to prevent recurrence. The Generative AI can also manage patients' medication history and provide reminders to encourage adherence. Furthermore, the Generative AI can customize patients' rehabilitation programs and monitor their progress.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] 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.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] 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.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] 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.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0143] 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.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A simple judgment unit using generation AI, a primary care unit that performs an initial diagnosis based on the patient's input information analyzed by the simplified judgment unit; a continuous support unit that continuously supports the health condition of the patient diagnosed by the primary medical care unit. A system characterized by:

2. The simplified determination unit Learning from a patient's past health data to predict individual health risks 2. The system of claim 1.

3. The simplified determination unit It can also be used to manage pet health, monitoring the health of pets.

2. The system of claim 1.

4. The primary medical care department Analyzes patient voice input and identifies symptoms from voice input 2. The system of claim 1.

5. The continuous support unit Maintaining patient medication history and providing reminders to encourage adherence 2. The system of claim 1.

6. The simplified determination unit Assessing the patient's stress level and suggesting relaxation techniques if stress levels are high 2. The system of claim 1.

7. The primary medical care department Providing counseling to reduce patient anxiety and fear 2. The system of claim 1.

8. The continuous support unit Providing health advice tailored to the patient's emotional state and eliciting positive emotions 2. The system of claim 1.

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