System

Abstract

An object of the system according to the embodiment is to reduce a waiting time of a hospital and to improve efficiency of diagnosis and treatment of a patient.SOLUTION: A system according to an embodiment includes an initial diagnosis unit, a hospital cooperation unit, and a pharmacy cooperation unit. The initial diagnostic unit performs initial diagnoses of patients using the generated AI. The hospital cooperation unit transmits the diagnosis result generated by the initial diagnosis unit to the hospital and determines whether the patient needs to receive a medical examination. The pharmacy division sends a prescription to the pharmacy based on the diagnosis result sent by the hospital division, and prepares for the patient to receive the medicine.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 increases waiting times at hospitals, causing congestion in waiting rooms, especially for the elderly.

[0005] The system according to the embodiment aims to reduce waiting times at hospitals and improve the efficiency of diagnosis and treatment of patients. [Means for solving the problem]

[0006] The system according to the embodiment includes an initial diagnosis unit, a hospital collaboration unit, and a pharmacy collaboration unit. The initial diagnosis unit performs an initial diagnosis of the patient using a generation AI. The hospital collaboration unit transmits the diagnosis results generated by the initial diagnosis unit to a hospital and determines whether the patient needs to be examined. The pharmacy collaboration unit transmits a prescription to a pharmacy based on the diagnosis results transmitted by the hospital collaboration unit, and prepares for the patient to receive the medication. [Effects of the Invention]

[0007] Systems according to embodiments can reduce hospital waiting times and streamline patient diagnosis and treatment. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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. 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 AI ​​home doctor system according to an embodiment of the present invention is a system that utilizes communication networks and AI to connect hospitals, prescription pharmacies, and patients, improve turnover, provide early diagnosis, and take measures to solve the problem of increasing waiting times at hospitals and crowded waiting rooms, especially for elderly people, due to the declining birthrate and aging population. As a result, the AI ​​home doctor system can shorten waiting times at hospitals and improve convenience for patients.

[0029] The AI ​​home doctor system according to the embodiment includes an initial diagnosis unit, a hospital collaboration unit, and a pharmacy collaboration unit. The initial diagnosis unit uses a generation AI to perform an initial diagnosis on a patient. For example, the patient inputs symptoms using a smartphone or computer, and the generation AI generates a diagnosis based on the prompts. The initial diagnosis unit can also analyze the patient's voice input, estimate emotions and stress levels from the patient's tone and speaking style, and reflect these in the diagnosis. The initial diagnosis unit can also generate more accurate diagnosis results by referencing the patient's past diagnosis history and family medical history. The hospital collaboration unit transmits the diagnosis results generated by the initial diagnosis unit to a hospital and determines whether the patient needs to be examined. For example, the generation AI transmits the diagnosis results to a hospital so that the patient can receive priority medical treatment. The hospital collaboration unit can also analyze hospital schedules and congestion in real time and suggest optimal consultation times. The hospital collaboration unit can also recommend specialists or provide second opinions based on the patient's diagnosis results. The pharmacy collaboration unit sends a prescription to a pharmacy based on the diagnosis results sent by the hospital collaboration unit, and prepares the medication for the patient to receive. For example, the generation AI can send a prescription issued by a doctor to a pharmacy, and have the medication ready before the patient arrives at the pharmacy. The pharmacy collaboration unit can also analyze the contents of the prescription and provide the patient with information about drug side effects and interactions. The pharmacy collaboration unit can also refer to the patient's past medication history to select the most appropriate medication and adjust the dosage. As a result, the AI ​​home doctor system according to the embodiment can shorten waiting times at hospitals and improve patient convenience.

[0030] The initial diagnosis unit can generate more accurate diagnostic results by referring to the patient's past diagnostic history and family medical history. For example, the initial diagnosis unit retrieves the patient's past diagnostic history from a database, and the generation AI analyzes their current symptoms based on that information. For example, if a patient previously diagnosed with asthma complains of a cough, the possibility of an asthma recurrence can be considered. The initial diagnosis unit can also reference family medical history to generate diagnostic results that take genetic factors into account. For example, if there are many family members with diabetes, a diagnostic result that increases the risk of diabetes can be generated. The initial diagnosis unit can also perform an integrated analysis of the patient's past diagnostic history and family medical history to generate highly accurate diagnostic results that take multiple factors into account. This enables highly accurate diagnoses that take past diagnostic history and family medical history into account.

[0031] The initial diagnosis unit can provide patients with lifestyle improvement suggestions and preventive measures based on the results of the initial diagnosis. For example, the initial diagnosis unit uses a generative AI to make specific lifestyle improvement suggestions to patients based on the results of the initial diagnosis. For example, it can provide suggestions such as "reconsidering the balance of your diet" and "increasing exercise." It also suggests preventive measures to patients based on the diagnosis results. For example, it can provide infection prevention measures such as "thoroughly wash your hands" and "wear a mask." It also customizes lifestyle improvement suggestions and preventive measures to suit the individual situation of each patient. For example, for patients who already exercise, it can make suggestions to adjust the type and frequency of exercise. This makes it possible to suggest lifestyle improvements and preventive measures.

[0032] The initial diagnosis unit can automatically provide relevant health information and articles based on the symptoms entered by the patient. For example, when a patient enters their symptoms, the initial diagnosis unit allows the generation AI to automatically provide health information and articles related to those symptoms. For example, if a patient enters "persistent cough," medical articles and preventive measures related to coughs can be displayed. Based on the symptoms, the generation AI also collects relevant articles from reliable medical information sites and provides them to the patient. For example, information can be obtained from official medical institution websites and expert blogs. The generation AI also searches for health information related to the patient's symptoms in real time and provides the latest information. For example, articles about new treatments and medications can be displayed. This makes it possible to automatically provide relevant health information and articles.

[0033] The Hospital Collaboration Department can analyze hospital schedules and congestion in real time and suggest the optimal appointment time. For example, the Hospital Collaboration Department's generation AI can analyze hospital schedules and congestion in real time and suggest the optimal appointment time to the patient. For example, it can suggest time periods when it is least crowded. In addition, by linking with the hospital's reservation system, the generation AI can suggest available times to the patient. For example, it can display time periods when it is easy to make an appointment. The generation AI can also customize and suggest the optimal appointment time taking into account the patient's convenience and the hospital's congestion. For example, it can make suggestions that fit the patient's work and home schedule. This can reduce the patient's waiting time by suggesting the optimal appointment time.

[0034] The Hospital Collaboration Department can recommend specialists and provide second opinions based on the patient's diagnosis results. For example, the Generative AI in the Hospital Collaboration Department analyzes the patient's diagnosis results and recommends specialists as necessary. For example, if heart disease is suspected, a cardiologist can be recommended. The Generative AI can also provide second opinions based on the diagnosis results. For example, if a patient is concerned about their diagnosis results, it can suggest that they seek the opinion of another doctor. The Generative AI can also compare the patient's diagnosis results with a database of specialists and recommend the most suitable specialist. For example, it can introduce a doctor with a strong track record of treating a specific illness. This makes it possible to recommend specialists and provide second opinions.

[0035] The Hospital Collaboration Department can automatically make appointments for remote medical consultations and online consultations through collaboration with hospitals. In the Hospital Collaboration Department, for example, the generation AI collaborates with the hospital's system to automatically make appointments for remote medical consultations and online consultations for patients. For example, it can suggest an appointment for an online consultation when a consultation is necessary. Furthermore, based on the patient's diagnosis results, the generation AI determines whether remote medical consultation is appropriate and automatically makes the appointment. For example, it can suggest remote medical consultation in cases where the symptoms are mild. Furthermore, by collaborating with the hospital's online consultation system, the generation AI suggests the optimal consultation time for the patient and completes the appointment. For example, it can suggest an online consultation time that suits the patient's convenience. This makes it possible to automatically make appointments for remote medical consultations and online consultations.

[0036] The Hospital Collaboration Department can propose the next appointment and follow-up schedule to the patient based on the hospital examination results. For example, the Generative AI in the Hospital Collaboration Department analyzes the hospital examination results and proposes the next appointment to the patient. For example, if regular follow-up is necessary, the Generative AI can present the next appointment date. The Generative AI also proposes a follow-up schedule to the patient based on the examination results. For example, it can set an appointment date to check the progress of treatment. The Generative AI also customizes and proposes an optimal follow-up schedule based on the patient's examination results and past data. For example, it can adjust the appointment date to check the effectiveness of treatment. This makes it possible to propose the next appointment and follow-up schedule.

[0037] The pharmacy collaboration unit can analyze the contents of prescriptions and provide patients with information about drug side effects and interactions. For example, the generation AI in the pharmacy collaboration unit analyzes the contents of prescriptions and provides patients with information about drug side effects. For example, it can display information such as, "This drug may cause drowsiness." The generation AI can also provide information about drug interactions based on the contents of the prescription. For example, it can display information such as, "This drug may be less effective when taken with other drugs." The generation AI can also refer to the patient's past medication history and provide customized information about drug side effects and interactions. For example, it can warn patients about drugs that have caused side effects in the past. This makes it possible to provide information about drug side effects and interactions.

[0038] The pharmacy collaboration unit can refer to the patient's past medication history and select the most appropriate medication and adjust the dosage. In the pharmacy collaboration unit, for example, the generation AI retrieves the patient's past medication history from a database and selects the most appropriate medication. For example, it can prioritize the selection of medications that have been effective in the past. The generation AI also adjusts the dosage based on the patient's medication history. For example, it can suggest reducing the dosage of medications that have caused side effects in the past. It also performs an integrated analysis of the patient's past medication history and current symptoms to select the most appropriate medication and adjust the dosage. For example, it can suggest the optimal dosage when multiple medications are used together. This makes it possible to select the most appropriate medication and adjust the dosage.

[0039] The pharmacy collaboration unit can provide information on drug stock status and the nearest pharmacy through collaboration with prescription pharmacies. For example, the pharmacy collaboration unit uses generation AI to analyze prescription pharmacy stock status in real time and provide patients with information on the nearest pharmacy. For example, it can display information such as "This drug is in stock at a nearby pharmacy." In addition, by collaborating with prescription pharmacy systems, the generation AI can provide patients with drug stock status. For example, it can display information such as "This drug is currently out of stock, but is available at other pharmacies." Furthermore, based on the patient's location information, the generation AI can provide information on the nearest pharmacy. For example, it can display map information such as "The nearest pharmacy is here." This makes it possible to provide information on drug stock status and the nearest pharmacy.

[0040] The pharmacy collaboration department can use videos to explain to patients how to take medication and precautions based on the contents of the prescription. For example, the pharmacy collaboration department uses a generation AI to analyze the contents of a prescription and explain to patients how to take medication and precautions through videos. For example, it can provide a video that says, "Take this medication after a meal." In addition, based on the contents of the prescription, a system can be built in which the generation AI explains how to take medication and precautions through videos. For example, it can provide an animated video showing the steps for taking medication. In addition, to improve patients' understanding, the generation AI can use videos to explain how to take medication and precautions. For example, it can provide a video that includes detailed explanations of the medication's side effects and interactions. This makes it possible to explain how to take medication and precautions through videos.

[0041] The rehabilitation management unit can analyze rehabilitation progress and suggest specific improvements and next steps to the patient. For example, the generative AI in the rehabilitation management unit can analyze rehabilitation progress in real time and suggest specific improvements to the patient. For example, it can make a suggestion such as, "As a next step, we recommend increasing the intensity of your exercise." The generative AI can also suggest next steps to the patient based on rehabilitation progress data. For example, it can make a suggestion such as, "Incorporate a new exercise into your next rehabilitation session." The generative AI can also continuously monitor the patient's rehabilitation progress and customize and suggest specific improvements and next steps. For example, it can provide an individual rehabilitation plan. This makes it possible to analyze rehabilitation progress and suggest specific improvements and next steps.

[0042] The rehabilitation management unit can customize and provide an optimal rehabilitation plan based on the patient's rehabilitation history. In the rehabilitation management unit, for example, the generation AI retrieves the patient's rehabilitation history from a database and customizes and provides an optimal rehabilitation plan. For example, a new plan can be created based on past rehabilitation results. The generation AI can also provide an individual rehabilitation plan based on the patient's rehabilitation history. For example, it can recommend specific exercises and training. The generation AI can also perform an integrated analysis of the patient's rehabilitation history and current progress, and customize and provide an optimal rehabilitation plan. For example, it can suggest specific steps toward achieving rehabilitation goals. This makes it possible to customize and provide an optimal rehabilitation plan.

[0043] The rehabilitation management unit can share rehabilitation progress with family and caregivers, strengthening the support system. For example, the rehabilitation management unit's generating AI can analyze rehabilitation progress in real time and share that information with family and caregivers. For example, it can provide a function such as "notifying rehabilitation progress by email." In addition, based on rehabilitation progress data, the generating AI can suggest specific support methods to family and caregivers. For example, it can indicate "points that require assistance during rehabilitation." Furthermore, the generating AI can continuously monitor the patient's rehabilitation progress and share it with family and caregivers, strengthening the support system. For example, it can suggest "support methods for achieving rehabilitation goals." This makes it possible to share rehabilitation progress with family and caregivers, strengthening the support system.

[0044] The rehabilitation management unit can explain the rehabilitation content using videos and animations, providing them in a format that is easy for patients to understand. For example, the rehabilitation management unit uses a generative AI to explain the rehabilitation content using videos and animations and provide them to patients. For example, it can provide a "video showing the exercise steps." In addition, a system can be built in which the generative AI creates videos and animations based on the rehabilitation content and provides them to patients. For example, it can provide an "animation showing the progress of rehabilitation." In addition, to improve patients' understanding, the generative AI explains the rehabilitation content using videos and animations. For example, it can provide a "video showing the effects of rehabilitation." This makes it possible to explain the rehabilitation content using videos and animations and provide it to patients in a format that is easy for patients to understand.

[0045] The data management unit can analyze patient data and predict health risks and suggest preventive measures. In the data management unit, for example, the generation AI analyzes patient data and predicts health risks. For example, it can provide a prediction such as "high risk of heart disease." The generation AI can also suggest preventive measures based on the patient data. For example, it can make a suggestion such as "recommending regular exercise." The generation AI can also continuously monitor patient data and provide customized health risk predictions and preventive measures. For example, it can provide specific preventive measures such as "suggesting dietary improvements." This makes it possible to predict health risks and suggest preventive measures.

[0046] The data management unit can provide an individually customized health management plan based on patient data. In the data management unit, for example, the generating AI analyzes patient data and provides an individually customized health management plan. For example, it can suggest "specific meal plans and exercise plans." The generating AI also creates an individual health management plan based on the patient data. For example, it can provide "a specific action plan for blood pressure management." The generating AI also continuously monitors patient data and provides an individually customized health management plan. For example, it can suggest "relaxation methods for stress management." This makes it possible to provide an individually customized health management plan.

[0047] The data management unit can anonymize patient data and use it for medical research and statistical analysis. For example, the data management unit allows the generative AI to anonymize patient data and use it for medical research. For example, it can provide data for analyzing the incidence rate of specific diseases and the effectiveness of treatments. It can also anonymize patient data and use it for statistical analysis. For example, it can analyze health conditions and medical service usage by region. It can also build a system where the generative AI anonymizes patient data and uses it for medical research and statistical analysis. For example, it can provide data to researchers while ensuring data security. This makes it possible to anonymize patient data and use it for medical research and statistical analysis.

[0048] The data management unit can monitor the health status of an entire region based on patient data and propose public health measures. In the data management unit, for example, the generative AI analyzes patient data and monitors the health status of an entire region. For example, it can analyze the incidence rate and health risks of specific diseases. The generative AI can also propose public health measures based on patient data. For example, it can suggest "recommending vaccinations" or "implementing health education programs." The generative AI can also continuously monitor the health status of an entire region and propose customized public health measures. For example, it can provide "specific measures to reduce health risks in specific regions." This makes it possible to monitor the health status of an entire region and propose public health measures.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The AI ​​home doctor system can also be equipped with an environmental data collection unit that collects data on the patient's living environment and reflects it in the diagnosis results. For example, data on the patient's living environment and work environment can be collected, and the generation AI can adjust the diagnosis results based on that information. For example, if the living environment is humid, it can take into account the possibility that respiratory symptoms will worsen. Also, if the work environment is stressful, it can produce a diagnosis that increases the risk of stress-related illnesses. This enables highly accurate diagnosis that takes the patient's living environment into account.

[0051] The initial diagnostic unit can collect the patient's dietary data, analyze their nutritional status, and reflect this in the diagnostic results. For example, when a patient inputs their daily dietary information, the generating AI analyzes the data and reflects the risk of nutritional deficiencies or overdose in the diagnostic results. For example, if there is a vitamin D deficiency, the generating AI can take into account the risk of decreased bone density. The generating AI can also make suggestions for improving nutritional balance based on the dietary data. This makes it possible to make a diagnosis that takes the patient's nutritional status into account.

[0052] The initial diagnosis unit can collect the patient's exercise data, analyze their exercise habits, and reflect this in the diagnosis results. For example, when a patient inputs the amount of exercise they do each day, the generating AI analyzes the data and reflects the risk of insufficient or excessive exercise in the diagnosis results. For example, insufficient exercise can result in a diagnosis that indicates an increased risk of cardiovascular disease. The generating AI can also make suggestions for improving exercise habits based on the exercise data. This makes it possible to make a diagnosis that takes into account the patient's exercise habits.

[0053] The initial diagnosis unit collects the patient's sleep data, analyzes their sleep status, and reflects this in the diagnosis results. For example, if the patient inputs their daily sleep duration and quality, the generation AI analyzes the data and reflects the risk of insufficient or excessive sleep in the diagnosis results. For example, insufficient sleep can result in a diagnosis that indicates a higher risk of a weakened immune system. The generation AI can also make suggestions for improving sleep habits based on the sleep data. This makes it possible to make a diagnosis that takes the patient's sleep status into account.

[0054] The Hospital Collaboration Department can recommend specialists and provide second opinions based on the patient's diagnosis results. For example, the generative AI analyzes the patient's diagnosis results and recommends specialists as needed. For example, if heart disease is suspected, a cardiologist can be recommended. The generative AI can also provide second opinions based on the diagnosis results. For example, if a patient is concerned about their diagnosis results, it can suggest that they seek the opinion of another doctor. It can also compare the patient's diagnosis results with a database of specialists and recommend the most suitable specialist. For example, it can introduce a doctor with a strong track record of treating a specific illness. This makes it possible to recommend specialists and provide second opinions.

[0055] The Hospital Collaboration Department can analyze hospital schedules and congestion in real time and suggest optimal appointment times. For example, the generation AI can analyze hospital schedules and congestion in real time and suggest optimal appointment times to patients. For example, it can suggest time periods when it is least crowded. In addition, by linking with the hospital's reservation system, the generation AI can suggest available times to patients. For example, it can display time periods when it is easiest to make an appointment. The generation AI can also customize and suggest optimal appointment times taking into account the patient's convenience and the hospital's congestion. For example, it can make suggestions that fit the patient's work and home schedules. This can reduce patients' waiting times by suggesting optimal appointment times.

[0056] The Hospital Collaboration Department can automatically make appointments for remote medical consultations and online consultations through collaboration with hospitals. For example, the Generative AI can collaborate with the hospital's system to automatically make appointments for remote medical consultations and online consultations for patients. For example, it can suggest an online consultation appointment when a consultation is necessary. Based on the patient's diagnosis results, the Generative AI can also determine whether remote medical consultations are appropriate and automatically make an appointment. For example, it can suggest remote consultations in cases of mild symptoms. The Generative AI can also collaborate with the hospital's online consultation system to suggest the optimal consultation time for the patient and complete the appointment. For example, it can suggest an online consultation time that suits the patient's convenience. This makes it possible to automatically make appointments for remote medical consultations and online consultations.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The initial diagnosis unit uses the generation AI to perform an initial diagnosis on the patient. For example, the patient inputs their symptoms using a smartphone or computer, and the generation AI generates a diagnosis based on the prompts. The generation AI can also analyze the patient's voice input, inferring emotions and stress levels from the tone of voice and speaking style, and reflecting these in the diagnosis. Furthermore, the generation AI can refer to the patient's past diagnosis history and family medical history to generate more accurate diagnosis results. Step 2: The Hospital Collaboration Department sends the diagnosis results generated by the Initial Diagnosis Department to the hospital and determines whether the patient needs to be examined. For example, the generation AI sends the diagnosis results to the hospital so that the patient can receive a medical examination on a priority basis. The Hospital Collaboration Department can also analyze hospital schedules and congestion in real time to suggest the optimal time to examine the patient. Furthermore, the Hospital Collaboration Department can recommend a specialist or provide a second opinion based on the patient's diagnosis results. Step 3: The Pharmacy Collaboration Department sends a prescription to the pharmacy based on the diagnosis results sent by the Hospital Collaboration Department, and prepares the medication for the patient to receive. For example, the generative AI can send a prescription issued by a doctor to a pharmacy, so that the medication is ready before the patient arrives at the pharmacy. The Pharmacy Collaboration Department can also analyze the contents of the prescription and provide patients with information about drug side effects and interactions. Furthermore, the Pharmacy Collaboration Department can refer to the patient's past medication history to select the most appropriate medication and adjust the dosage.

[0059] (Example 2) The AI ​​home doctor system according to an embodiment of the present invention is a system that utilizes communication networks and AI to connect hospitals, prescription pharmacies, and patients, improve turnover, provide early diagnosis, and take measures to solve the problem of increasing waiting times at hospitals and crowded waiting rooms, especially for elderly people, due to the declining birthrate and aging population. As a result, the AI ​​home doctor system can shorten waiting times at hospitals and improve convenience for patients.

[0060] The AI ​​home doctor system according to the embodiment includes an initial diagnosis unit, a hospital collaboration unit, and a pharmacy collaboration unit. The initial diagnosis unit uses a generation AI to perform an initial diagnosis on a patient. For example, the patient inputs symptoms using a smartphone or computer, and the generation AI generates a diagnosis based on the prompts. The initial diagnosis unit can also analyze the patient's voice input, estimate emotions and stress levels from the patient's tone and speaking style, and reflect these in the diagnosis. The initial diagnosis unit can also generate more accurate diagnosis results by referencing the patient's past diagnosis history and family medical history. The hospital collaboration unit transmits the diagnosis results generated by the initial diagnosis unit to a hospital and determines whether the patient needs to be examined. For example, the generation AI transmits the diagnosis results to a hospital so that the patient can receive priority medical treatment. The hospital collaboration unit can also analyze hospital schedules and congestion in real time and suggest optimal consultation times. The hospital collaboration unit can also recommend specialists or provide second opinions based on the patient's diagnosis results. The pharmacy collaboration unit sends a prescription to a pharmacy based on the diagnosis results sent by the hospital collaboration unit, and prepares the medication for the patient to receive. For example, the generation AI can send a prescription issued by a doctor to a pharmacy, and have the medication ready before the patient arrives at the pharmacy. The pharmacy collaboration unit can also analyze the contents of the prescription and provide the patient with information about drug side effects and interactions. The pharmacy collaboration unit can also refer to the patient's past medication history to select the most appropriate medication and adjust the dosage. As a result, the AI ​​home doctor system according to the embodiment can shorten waiting times at hospitals and improve patient convenience.

[0061] The initial diagnosis unit analyzes the patient's voice input and estimates emotions and stress levels from the tone of voice and speaking style, which can be reflected in the diagnosis results. For example, when a patient uses a smartphone or computer to voice-input their symptoms, the generation AI analyzes the voice data and estimates emotions and stress levels from the tone of voice and speaking style. For example, a trembling voice may indicate a high stress level. The generation AI also adjusts the diagnosis results based on the results of the voice input analysis. For example, a high stress level can result in a diagnosis that increases the possibility of a stress-related illness. In addition, by continuously collecting the patient's voice data and comparing it with past data, fluctuations in emotions and stress levels can be monitored and reflected in the diagnosis results. This makes it possible to make a diagnosis that takes the patient's emotions and stress levels into account.

[0062] The initial diagnosis unit can generate more accurate diagnostic results by referring to the patient's past diagnostic history and family medical history. For example, the initial diagnosis unit retrieves the patient's past diagnostic history from a database, and the generation AI analyzes their current symptoms based on that information. For example, if a patient previously diagnosed with asthma complains of a cough, the possibility of an asthma recurrence can be considered. The initial diagnosis unit can also reference family medical history to generate diagnostic results that take genetic factors into account. For example, if there are many family members with diabetes, a diagnostic result that increases the risk of diabetes can be generated. The initial diagnosis unit can also perform an integrated analysis of the patient's past diagnostic history and family medical history to generate highly accurate diagnostic results that take multiple factors into account. This enables highly accurate diagnoses that take past diagnostic history and family medical history into account.

[0063] The initial diagnosis unit uses the emotion estimation function to analyze the patient's emotions regarding the symptoms entered and can provide a diagnosis result that provides emotional reassurance. For example, when a patient enters their symptoms, the initial diagnosis unit uses the emotion estimation function to analyze the emotions. For example, if the patient is feeling anxious, the unit can provide a diagnosis result that alleviates that anxiety. The emotion estimation function can also be used to analyze the patient's emotions regarding the symptoms entered in real time and generate a message that provides reassurance. For example, a message such as "This symptom is common and there is no need to worry" can be displayed. Furthermore, based on the patient's emotional data, the generation AI can customize the diagnosis result to provide emotional reassurance. For example, positive feedback or words of encouragement can be included. This enables a diagnosis that takes the patient's emotions into consideration and provides reassurance.

[0064] The initial diagnosis unit can provide patients with lifestyle improvement suggestions and preventive measures based on the results of the initial diagnosis. For example, the initial diagnosis unit uses a generative AI to make specific lifestyle improvement suggestions to patients based on the results of the initial diagnosis. For example, it can provide suggestions such as "reconsidering the balance of your diet" and "increasing exercise." It also suggests preventive measures to patients based on the diagnosis results. For example, it can provide infection prevention measures such as "thoroughly wash your hands" and "wear a mask." It also customizes lifestyle improvement suggestions and preventive measures to suit the individual situation of each patient. For example, for patients who already exercise, it can make suggestions to adjust the type and frequency of exercise. This makes it possible to suggest lifestyle improvements and preventive measures.

[0065] The initial diagnosis unit can automatically provide relevant health information and articles based on the symptoms entered by the patient. For example, when a patient enters their symptoms, the initial diagnosis unit allows the generation AI to automatically provide health information and articles related to those symptoms. For example, if a patient enters "persistent cough," medical articles and preventive measures related to coughs can be displayed. Based on the symptoms, the generation AI also collects relevant articles from reliable medical information sites and provides them to the patient. For example, information can be obtained from official medical institution websites and expert blogs. The generation AI also searches for health information related to the patient's symptoms in real time and provides the latest information. For example, articles about new treatments and medications can be displayed. This makes it possible to automatically provide relevant health information and articles.

[0066] The initial diagnosis unit uses the emotion estimation function to analyze the emotional reaction of the patient when they receive their diagnosis results and can provide positive feedback. For example, when a patient receives their diagnosis results, the generation AI in the initial diagnosis unit uses the emotion estimation function to analyze their emotional reaction in real time. For example, if the patient is feeling anxious about the diagnosis results, reassuring feedback can be provided. The emotion estimation function can also be used to analyze the emotions the patient felt when they received their diagnosis results and generate positive feedback. For example, a message such as "This symptom is common and there is no need to worry" can be displayed. The generation AI can also customize the positive feedback based on the patient's emotional reaction data. For example, it can include words of encouragement or success stories. This makes it possible to analyze the patient's emotional reaction and provide positive feedback.

[0067] The Hospital Collaboration Department can analyze hospital schedules and congestion in real time and suggest the optimal appointment time. For example, the Hospital Collaboration Department's generation AI can analyze hospital schedules and congestion in real time and suggest the optimal appointment time to the patient. For example, it can suggest time periods when it is least crowded. In addition, by linking with the hospital's reservation system, the generation AI can suggest available times to the patient. For example, it can display time periods when it is easy to make an appointment. The generation AI can also customize and suggest the optimal appointment time taking into account the patient's convenience and the hospital's congestion. For example, it can make suggestions that fit the patient's work and home schedule. This can reduce the patient's waiting time by suggesting the optimal appointment time.

[0068] The Hospital Collaboration Department can recommend specialists and provide second opinions based on the patient's diagnosis results. For example, the Generative AI in the Hospital Collaboration Department analyzes the patient's diagnosis results and recommends specialists as necessary. For example, if heart disease is suspected, a cardiologist can be recommended. The Generative AI can also provide second opinions based on the diagnosis results. For example, if a patient is concerned about their diagnosis results, it can suggest that they seek the opinion of another doctor. The Generative AI can also compare the patient's diagnosis results with a database of specialists and recommend the most suitable specialist. For example, it can introduce a doctor with a strong track record of treating a specific illness. This makes it possible to recommend specialists and provide second opinions.

[0069] The Hospital Collaboration Department can automatically make appointments for remote medical consultations and online consultations through collaboration with hospitals. In the Hospital Collaboration Department, for example, the generation AI collaborates with the hospital's system to automatically make appointments for remote medical consultations and online consultations for patients. For example, it can suggest an appointment for an online consultation when a consultation is necessary. Furthermore, based on the patient's diagnosis results, the generation AI determines whether remote medical consultation is appropriate and automatically makes the appointment. For example, it can suggest remote medical consultation in cases where the symptoms are mild. Furthermore, by collaborating with the hospital's online consultation system, the generation AI suggests the optimal consultation time for the patient and completes the appointment. For example, it can suggest an online consultation time that suits the patient's convenience. This makes it possible to automatically make appointments for remote medical consultations and online consultations.

[0070] The Hospital Collaboration Department can propose the next appointment and follow-up schedule to the patient based on the hospital examination results. For example, the Generative AI in the Hospital Collaboration Department analyzes the hospital examination results and proposes the next appointment to the patient. For example, if regular follow-up is necessary, the Generative AI can present the next appointment date. The Generative AI also proposes a follow-up schedule to the patient based on the examination results. For example, it can set an appointment date to check the progress of treatment. The Generative AI also customizes and proposes an optimal follow-up schedule based on the patient's examination results and past data. For example, it can adjust the appointment date to check the effectiveness of treatment. This makes it possible to propose the next appointment and follow-up schedule.

[0071] The Hospital Collaboration Department can use the emotion estimation function to analyze a patient's emotional reactions after a medical examination at a hospital and suggest improvements for the next examination. For example, after a patient receives a medical examination at a hospital, the generation AI uses the emotion estimation function to analyze the patient's emotional reactions in real time. For example, it can analyze the patient's satisfaction or anxiety with the examination. The emotion estimation function can also be used to analyze the patient's emotions after the examination and suggest improvements for the next examination. For example, it can make suggestions such as "shorten the waiting time" or "provide a detailed explanation of the examination contents." Furthermore, based on the patient's emotional data, the generation AI can suggest customized improvements for the next examination. For example, it can suggest the optimal examination method based on past data. This makes it possible to suggest improvements for the next examination.

[0072] The pharmacy collaboration unit can analyze the contents of prescriptions and provide patients with information about drug side effects and interactions. For example, the generation AI in the pharmacy collaboration unit analyzes the contents of prescriptions and provides patients with information about drug side effects. For example, it can display information such as, "This drug may cause drowsiness." The generation AI can also provide information about drug interactions based on the contents of the prescription. For example, it can display information such as, "This drug may be less effective when taken with other drugs." The generation AI can also refer to the patient's past medication history and provide customized information about drug side effects and interactions. For example, it can warn patients about drugs that have caused side effects in the past. This makes it possible to provide information about drug side effects and interactions.

[0073] The pharmacy collaboration unit can refer to the patient's past medication history and select the most appropriate medication and adjust the dosage. In the pharmacy collaboration unit, for example, the generation AI retrieves the patient's past medication history from a database and selects the most appropriate medication. For example, it can prioritize the selection of medications that have been effective in the past. The generation AI also adjusts the dosage based on the patient's medication history. For example, it can suggest reducing the dosage of medications that have caused side effects in the past. It also performs an integrated analysis of the patient's past medication history and current symptoms to select the most appropriate medication and adjust the dosage. For example, it can suggest the optimal dosage when multiple medications are used together. This makes it possible to select the most appropriate medication and adjust the dosage.

[0074] The pharmacy collaboration unit can use the emotion estimation function to provide support for patients' anxieties and questions when receiving their medication. For example, when a patient receives their medication, the generation AI uses the emotion estimation function to analyze their anxieties and questions and provide support. For example, it can display a message such as, "We will explain in detail about the side effects of this medication." The emotion estimation function can also be used to analyze the patient's emotions in real time when receiving their medication and generate a message that provides reassurance. For example, it can display a message such as, "This medication is safe and is expected to be effective." Furthermore, based on the patient's emotional data, the generation AI can provide customized support for their anxieties and questions. For example, it can suggest the optimal explanation method based on past data. This makes it possible to provide support for patients' anxieties and questions.

[0075] The pharmacy collaboration unit can provide information on drug stock status and the nearest pharmacy through collaboration with prescription pharmacies. For example, the pharmacy collaboration unit uses generation AI to analyze prescription pharmacy stock status in real time and provide patients with information on the nearest pharmacy. For example, it can display information such as "This drug is in stock at a nearby pharmacy." In addition, by collaborating with prescription pharmacy systems, the generation AI can provide patients with drug stock status. For example, it can display information such as "This drug is currently out of stock, but is available at other pharmacies." Furthermore, based on the patient's location information, the generation AI can provide information on the nearest pharmacy. For example, it can display map information such as "The nearest pharmacy is here." This makes it possible to provide information on drug stock status and the nearest pharmacy.

[0076] The pharmacy collaboration department can use videos to explain to patients how to take medication and precautions based on the contents of the prescription. For example, the pharmacy collaboration department uses a generation AI to analyze the contents of a prescription and explain to patients how to take medication and precautions through videos. For example, it can provide a video that says, "Take this medication after a meal." In addition, based on the contents of the prescription, a system can be built in which the generation AI explains how to take medication and precautions through videos. For example, it can provide an animated video showing the steps for taking medication. In addition, to improve patients' understanding, the generation AI can use videos to explain how to take medication and precautions. For example, it can provide a video that includes detailed explanations of the medication's side effects and interactions. This makes it possible to explain how to take medication and precautions through videos.

[0077] The pharmacy collaboration unit uses the emotion estimation function to analyze the emotional reactions of patients after receiving their medication and can provide advice to increase their motivation to take the medication. For example, after a patient receives their medication, the generation AI uses the emotion estimation function to analyze their emotional reactions in real time. For example, it can analyze their anxiety or questions about the medication. The emotion estimation function can also be used to analyze the emotions of patients after receiving their medication and provide advice to increase their motivation to take the medication. For example, it can display a message such as, "This medication is expected to be effective, so please continue taking it." Furthermore, based on the patient's emotional data, the generation AI can provide customized advice to increase their motivation to take the medication. For example, it can suggest optimal words of encouragement based on past data. This makes it possible to provide advice to increase motivation to take the medication.

[0078] The rehabilitation management unit can analyze rehabilitation progress and suggest specific improvements and next steps to the patient. For example, the generative AI in the rehabilitation management unit can analyze rehabilitation progress in real time and suggest specific improvements to the patient. For example, it can make a suggestion such as, "As a next step, we recommend increasing the intensity of your exercise." The generative AI can also suggest next steps to the patient based on rehabilitation progress data. For example, it can make a suggestion such as, "Incorporate a new exercise into your next rehabilitation session." The generative AI can also continuously monitor the patient's rehabilitation progress and customize and suggest specific improvements and next steps. For example, it can provide an individual rehabilitation plan. This makes it possible to analyze rehabilitation progress and suggest specific improvements and next steps.

[0079] The rehabilitation management unit can customize and provide an optimal rehabilitation plan based on the patient's rehabilitation history. In the rehabilitation management unit, for example, the generation AI retrieves the patient's rehabilitation history from a database and customizes and provides an optimal rehabilitation plan. For example, a new plan can be created based on past rehabilitation results. The generation AI can also provide an individual rehabilitation plan based on the patient's rehabilitation history. For example, it can recommend specific exercises and training. The generation AI can also perform an integrated analysis of the patient's rehabilitation history and current progress, and customize and provide an optimal rehabilitation plan. For example, it can suggest specific steps toward achieving rehabilitation goals. This makes it possible to customize and provide an optimal rehabilitation plan.

[0080] The rehabilitation management unit can share rehabilitation progress with family and caregivers, strengthening the support system. For example, the rehabilitation management unit's generating AI can analyze rehabilitation progress in real time and share that information with family and caregivers. For example, it can provide a function such as "notifying rehabilitation progress by email." In addition, based on rehabilitation progress data, the generating AI can suggest specific support methods to family and caregivers. For example, it can indicate "points that require assistance during rehabilitation." Furthermore, the generating AI can continuously monitor the patient's rehabilitation progress and share it with family and caregivers, strengthening the support system. For example, it can suggest "support methods for achieving rehabilitation goals." This makes it possible to share rehabilitation progress with family and caregivers, strengthening the support system.

[0081] The rehabilitation management unit can explain the rehabilitation content using videos and animations, providing them in a format that is easy for patients to understand. For example, the rehabilitation management unit uses a generative AI to explain the rehabilitation content using videos and animations and provide them to patients. For example, it can provide a "video showing the exercise steps." In addition, a system can be built in which the generative AI creates videos and animations based on the rehabilitation content and provides them to patients. For example, it can provide an "animation showing the progress of rehabilitation." In addition, to improve patients' understanding, the generative AI explains the rehabilitation content using videos and animations. For example, it can provide a "video showing the effects of rehabilitation." This makes it possible to explain the rehabilitation content using videos and animations and provide it to patients in a format that is easy for patients to understand.

[0082] The rehabilitation management unit can use the emotion estimation function to analyze the emotional responses of patients during rehabilitation and provide feedback to increase motivation. For example, the rehabilitation management unit can analyze the emotions of patients during rehabilitation in real time, and the generation AI can provide feedback to increase motivation. For example, it can display a "message acknowledging the results of rehabilitation." The emotion estimation function can also be used to analyze the emotions of patients during rehabilitation and provide specific advice to increase motivation. For example, it can provide "feedback that allows patients to feel the progress of rehabilitation." Furthermore, based on the patient's emotion data, the generation AI can provide customized feedback to increase motivation during rehabilitation. For example, it can suggest optimal words of encouragement based on past data. This makes it possible to analyze the emotional responses of patients during rehabilitation and provide feedback to increase motivation.

[0083] The data management unit can analyze patient data and predict health risks and suggest preventive measures. In the data management unit, for example, the generation AI analyzes patient data and predicts health risks. For example, it can provide a prediction such as "high risk of heart disease." The generation AI can also suggest preventive measures based on the patient data. For example, it can make a suggestion such as "recommending regular exercise." The generation AI can also continuously monitor patient data and provide customized health risk predictions and preventive measures. For example, it can provide specific preventive measures such as "suggesting dietary improvements." This makes it possible to predict health risks and suggest preventive measures.

[0084] The data management unit can provide an individually customized health management plan based on patient data. In the data management unit, for example, the generating AI analyzes patient data and provides an individually customized health management plan. For example, it can suggest "specific meal plans and exercise plans." The generating AI also creates an individual health management plan based on the patient data. For example, it can provide "a specific action plan for blood pressure management." The generating AI also continuously monitors patient data and provides an individually customized health management plan. For example, it can suggest "relaxation methods for stress management." This makes it possible to provide an individually customized health management plan.

[0085] The data management unit can use the emotion estimation function to analyze the patient's emotional response to their health condition and promote positive health behavior. For example, the data management unit can analyze the patient's emotional response to their health condition in real time, and the generation AI can provide advice to promote positive health behavior. For example, it can display an "encouraging message to continue eating healthy." The emotion estimation function can also be used to analyze the patient's emotions regarding their health condition and provide specific advice to promote positive health behavior. For example, it can provide "feedback to increase motivation to continue exercising." Furthermore, based on the patient's emotional data, the generation AI can provide customized advice to promote positive health behavior. For example, it can suggest optimal words of encouragement based on past data. This allows for advice to be provided to promote positive health behavior.

[0086] The data management unit can anonymize patient data and use it for medical research and statistical analysis. For example, the data management unit allows the generative AI to anonymize patient data and use it for medical research. For example, it can provide data for analyzing the incidence rate of specific diseases and the effectiveness of treatments. It can also anonymize patient data and use it for statistical analysis. For example, it can analyze health conditions and medical service usage by region. It can also build a system where the generative AI anonymizes patient data and uses it for medical research and statistical analysis. For example, it can provide data to researchers while ensuring data security. This makes it possible to anonymize patient data and use it for medical research and statistical analysis.

[0087] The data management unit can monitor the health status of an entire region based on patient data and propose public health measures. In the data management unit, for example, the generative AI analyzes patient data and monitors the health status of an entire region. For example, it can analyze the incidence rate and health risks of specific diseases. The generative AI can also propose public health measures based on patient data. For example, it can suggest "recommending vaccinations" or "implementing health education programs." The generative AI can also continuously monitor the health status of an entire region and propose customized public health measures. For example, it can provide "specific measures to reduce health risks in specific regions." This makes it possible to monitor the health status of an entire region and propose public health measures.

[0088] The data management unit can use the emotion estimation function to analyze the patient's emotional response to the health management plan based on their data and suggest improvements to the plan. For example, the data management unit can analyze the patient's emotional response to the health management plan in real time, and the generation AI can suggest improvements to the plan. For example, it can make a suggestion such as, "Increase the patient's motivation by adjusting part of the plan." The emotion estimation function can also be used to analyze the patient's emotions regarding the health management plan and suggest specific improvements to the plan. For example, it can make a suggestion such as, "Add a new exercise to the plan." The generation AI can also customize and suggest improvements to the health management plan based on the patient's emotional data. For example, it can suggest the optimal way to adjust the plan based on past data. This makes it possible to analyze the patient's emotional response to the health management plan and suggest improvements to the plan.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The AI ​​home doctor system can also be equipped with an environmental data collection unit that collects data on the patient's living environment and reflects it in the diagnosis results. For example, data on the patient's living environment and work environment can be collected, and the generation AI can adjust the diagnosis results based on that information. For example, if the living environment is humid, it can take into account the possibility that respiratory symptoms will worsen. Also, if the work environment is stressful, it can produce a diagnosis that increases the risk of stress-related illnesses. This enables highly accurate diagnosis that takes the patient's living environment into account.

[0091] The initial diagnostic unit can collect the patient's dietary data, analyze their nutritional status, and reflect this in the diagnostic results. For example, when a patient inputs their daily dietary information, the generating AI analyzes the data and reflects the risk of nutritional deficiencies or overdose in the diagnostic results. For example, if there is a vitamin D deficiency, the generating AI can take into account the risk of decreased bone density. The generating AI can also make suggestions for improving nutritional balance based on the dietary data. This makes it possible to make a diagnosis that takes the patient's nutritional status into account.

[0092] The initial diagnosis unit can collect the patient's exercise data, analyze their exercise habits, and reflect this in the diagnosis results. For example, when a patient inputs the amount of exercise they do each day, the generating AI analyzes the data and reflects the risk of insufficient or excessive exercise in the diagnosis results. For example, insufficient exercise can result in a diagnosis that indicates an increased risk of cardiovascular disease. The generating AI can also make suggestions for improving exercise habits based on the exercise data. This makes it possible to make a diagnosis that takes into account the patient's exercise habits.

[0093] The initial diagnosis unit uses the emotion estimation function to analyze the patient's emotions regarding the symptoms entered and can provide a diagnosis result that provides emotional reassurance. For example, when a patient enters their symptoms, the generation AI uses the emotion estimation function to analyze their emotions. For example, if the patient is feeling anxious, a diagnosis result that alleviates that anxiety can be provided. The emotion estimation function can also be used to analyze the patient's emotions regarding the symptoms entered in real time and generate a message that provides reassurance. For example, a message such as "This symptom is common and there is no need to worry" can be displayed. Furthermore, based on the patient's emotional data, the generation AI can customize the diagnosis result to provide emotional reassurance. For example, positive feedback or words of encouragement can be included. This enables a diagnosis that takes the patient's emotions into consideration and provides reassurance.

[0094] The initial diagnosis unit collects the patient's sleep data, analyzes their sleep status, and reflects this in the diagnosis results. For example, if the patient inputs their daily sleep duration and quality, the generation AI analyzes the data and reflects the risk of insufficient or excessive sleep in the diagnosis results. For example, insufficient sleep can result in a diagnosis that indicates a higher risk of a weakened immune system. The generation AI can also make suggestions for improving sleep habits based on the sleep data. This makes it possible to make a diagnosis that takes the patient's sleep status into account.

[0095] The initial diagnosis unit uses an emotion estimation function to analyze the emotional reaction of the patient when they receive their diagnosis results and provide positive feedback. For example, when a patient receives their diagnosis results, the generation AI uses the emotion estimation function to analyze their emotional reaction in real time. For example, if the patient is feeling anxious about the diagnosis results, it can provide reassuring feedback. The emotion estimation function can also be used to analyze the emotions the patient felt when they received their diagnosis results and generate positive feedback. For example, it can display a message such as, "This symptom is common and there is no need to worry." The generation AI can also customize the positive feedback based on the patient's emotional reaction data. For example, it can include words of encouragement or success stories. This makes it possible to analyze the patient's emotional reaction and provide positive feedback.

[0096] The Hospital Collaboration Department can recommend specialists and provide second opinions based on the patient's diagnosis results. For example, the generative AI analyzes the patient's diagnosis results and recommends specialists as needed. For example, if heart disease is suspected, a cardiologist can be recommended. The generative AI can also provide second opinions based on the diagnosis results. For example, if a patient is concerned about their diagnosis results, it can suggest that they seek the opinion of another doctor. It can also compare the patient's diagnosis results with a database of specialists and recommend the most suitable specialist. For example, it can introduce a doctor with a strong track record of treating a specific illness. This makes it possible to recommend specialists and provide second opinions.

[0097] The Hospital Collaboration Department can analyze hospital schedules and congestion in real time and suggest optimal appointment times. For example, the generation AI can analyze hospital schedules and congestion in real time and suggest optimal appointment times to patients. For example, it can suggest time periods when it is least crowded. In addition, by linking with the hospital's reservation system, the generation AI can suggest available times to patients. For example, it can display time periods when it is easiest to make an appointment. The generation AI can also customize and suggest optimal appointment times taking into account the patient's convenience and the hospital's congestion. For example, it can make suggestions that fit the patient's work and home schedules. This can reduce patients' waiting times by suggesting optimal appointment times.

[0098] The Hospital Collaboration Department can automatically make appointments for remote medical consultations and online consultations through collaboration with hospitals. For example, the Generative AI can collaborate with the hospital's system to automatically make appointments for remote medical consultations and online consultations for patients. For example, it can suggest an online consultation appointment when a consultation is necessary. Based on the patient's diagnosis results, the Generative AI can also determine whether remote medical consultations are appropriate and automatically make an appointment. For example, it can suggest remote consultations in cases of mild symptoms. The Generative AI can also collaborate with the hospital's online consultation system to suggest the optimal consultation time for the patient and complete the appointment. For example, it can suggest an online consultation time that suits the patient's convenience. This makes it possible to automatically make appointments for remote medical consultations and online consultations.

[0099] The Hospital Collaboration Department can use the emotion estimation function to analyze a patient's emotional reactions after a medical examination at the hospital and suggest improvements for the next examination. For example, after a patient receives a medical examination at the hospital, the generation AI uses the emotion estimation function to analyze the patient's emotional reactions in real time. For example, it can analyze their satisfaction or anxiety with the examination. The emotion estimation function can also be used to analyze the patient's emotions after the examination and suggest improvements for the next examination. For example, it can make suggestions such as "shorten the waiting time" or "provide a detailed explanation of the examination contents." Furthermore, based on the patient's emotional data, the generation AI can suggest customized improvements for the next examination. For example, it can suggest the optimal examination method based on past data. This makes it possible to suggest improvements for the next examination.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The initial diagnosis unit uses the generation AI to perform an initial diagnosis on the patient. For example, the patient inputs their symptoms using a smartphone or computer, and the generation AI generates a diagnosis based on the prompts. The generation AI can also analyze the patient's voice input, inferring emotions and stress levels from the tone of voice and speaking style, and reflecting these in the diagnosis. Furthermore, the generation AI can refer to the patient's past diagnosis history and family medical history to generate more accurate diagnosis results. Step 2: The Hospital Collaboration Department sends the diagnosis results generated by the Initial Diagnosis Department to the hospital and determines whether the patient needs to be examined. For example, the generation AI sends the diagnosis results to the hospital so that the patient can receive a medical examination on a priority basis. The Hospital Collaboration Department can also analyze hospital schedules and congestion in real time to suggest the optimal time to examine the patient. Furthermore, the Hospital Collaboration Department can recommend a specialist or provide a second opinion based on the patient's diagnosis results. Step 3: The Pharmacy Collaboration Department sends a prescription to the pharmacy based on the diagnosis results sent by the Hospital Collaboration Department, and prepares the medication for the patient to receive. For example, the generative AI can send a prescription issued by a doctor to a pharmacy, so that the medication is ready before the patient arrives at the pharmacy. The Pharmacy Collaboration Department can also analyze the contents of the prescription and provide patients with information about drug side effects and interactions. Furthermore, the Pharmacy Collaboration Department can refer to the patient's past medication history to select the most appropriate medication and adjust the dosage.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, 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 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 specific processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the robot 414, 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. 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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]

[0169] 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 initial diagnosis unit that uses generative AI to perform an initial diagnosis of patients; a hospital cooperation unit that transmits the diagnosis result generated by the initial diagnosis unit to a hospital and determines whether the patient needs to be examined; a pharmacy collaboration unit that transmits a prescription to a pharmacy based on the diagnosis results transmitted by the hospital collaboration unit and prepares for the patient to receive the medicine. A system characterized by:

2. The initial diagnosis unit Analyze the patient's voice input, estimate emotions and stress levels from the tone of voice and speaking style, and reflect this in the diagnosis results.

2. The system of claim 1.

3. The initial diagnosis unit Generate more accurate diagnostic results by referencing the patient's past diagnostic history and family medical history 2. The system of claim 1.

4. The initial diagnosis unit Analyze the patient's feelings about the symptoms and provide a diagnosis that provides emotional relief 2. The system of claim 1.

5. The initial diagnosis unit Based on the results of the initial diagnosis, provide the patient with lifestyle improvement suggestions and preventative measures.

2. The system of claim 1.

6. The initial diagnosis unit Automatically provide relevant health information and articles based on the patient's input symptoms 2. The system of claim 1.

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