System

Abstract

An object of a system according to an embodiment is to shorten a waiting time in a hospital and realize an efficient medical examination.SOLUTION: A system includes a symptom input unit, an analysis unit, a reservation determination unit, and a notification unit. The symptom input unit inputs a symptom of a patient. The analysis unit analyzes the symptom input by the symptom input unit. The reservation determination unit determines an optimal examination date and time based on the symptom information analyzed by the analysis unit. The notification unit notifies the patient of the examination date and time determined by the reservation determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of long waiting times at hospitals, making it difficult for both patients and hospitals to operate efficiently.

[0005] The system according to the embodiment aims to reduce waiting times at hospitals and realize efficient medical examinations. [Means for solving the problem]

[0006] The system according to the embodiment includes a symptom input unit, an analysis unit, an appointment decision unit, and a notification unit. The symptom input unit inputs the patient's symptoms. The analysis unit analyzes the symptoms input by the symptom input unit. The appointment decision unit determines the optimal consultation date and time based on the symptom information analyzed by the analysis unit. The notification unit notifies the patient of the consultation date and time determined by the appointment decision unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce waiting times at hospitals and realize efficient medical examinations. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The assistant system according to the embodiment of the present invention is a system that shortens waiting times at hospitals and improves the efficiency of medical examinations by using a generation AI to analyze patients' symptoms in advance. As a result, the assistant system can shorten waiting times at hospitals and improve the efficiency of medical examinations.

[0029] The assistant system according to the embodiment includes a symptom input unit, an analysis unit, an appointment decision unit, and a notification unit. The symptom input unit inputs a patient's symptoms. For example, the patient inputs specific symptoms such as "I have a headache" or "I can't stop coughing." Images and videos that convey the symptoms can also be input. For example, a photo of a skin rash or audio of a cough can be input. The analysis unit analyzes the symptoms input by the symptom input unit. For example, a generation AI analyzes the symptoms using a text generation AI (e.g., LLM). The generation AI can also analyze images and audio using a multimodal generation AI. The generation AI can also extract and analyze important parts of the symptoms. The appointment decision unit determines the optimal appointment date and time based on the symptom information analyzed by the analysis unit. For example, it can link with a hospital's appointment system to automatically search for available time slots and suggest the optimal date and time for the patient. The notification unit notifies the patient of the appointment date and time determined by the appointment decision unit. For example, the notification can be sent via email or SMS. It can also be sent via a dedicated app. This allows the assistant system to reduce waiting times at hospitals and improve the efficiency of consultations. For example, patients will be able to reduce waiting times at hospitals and use their time more efficiently, and hospitals will be able to operate with fewer staff, optimizing medical resources.

[0030] The analysis unit can analyze the correlation with symptoms based on the patient's past medical history. For example, the analysis unit retrieves the patient's past medical history from a database and inputs it into the generation AI. For example, it analyzes the correlation with current symptoms based on information on previously diagnosed illnesses and prescribed medications. This enables more accurate diagnoses based on past medical history.

[0031] The analysis unit can analyze the impact on symptoms based on the patient's lifestyle and environmental information. The analysis unit inputs, for example, the patient's lifestyle (diet, exercise, sleep, etc.) and analyzes the impact of these factors on symptoms. For example, the analysis unit identifies the cause of symptoms based on the patient's diet and exercise frequency. The analysis unit also inputs the patient's environmental information (living environment, work environment, climate, air quality, etc.) and analyzes the impact of these factors on symptoms. For example, the analysis unit identifies the cause of symptoms based on the humidity and temperature of the living environment. This makes it possible to make a diagnosis that takes lifestyle and environmental information into consideration.

[0032] The analysis unit can present recommended self-care methods based on the patient's symptoms. For example, the generation AI of the analysis unit presents appropriate self-care methods based on the patient's symptoms. For example, for a mild headache, it recommends rest and hydration. The analysis unit also presents appropriate self-care methods based on the patient's symptoms. For example, for mild muscle pain, it recommends stretching and heat therapy. The analysis unit also presents appropriate self-care methods based on the patient's symptoms. For example, for mild stress, it recommends relaxation techniques and meditation. In this way, by presenting self-care methods, it is possible to support the patient's self-management.

[0033] The analysis unit can visualize the results of the preliminary symptom analysis and provide them as infographics. For example, the analysis unit visualizes the results of the preliminary symptom analysis in graphs or charts and provides them in a way that is easy for patients to understand. For example, the progress of symptoms is displayed in chronological order. The analysis unit also provides the results of the preliminary symptom analysis as infographics. For example, the causes of symptoms and countermeasures are visually represented. The analysis unit also visualizes the results of the preliminary symptom analysis and provides them as infographics. For example, the frequency of occurrence and severity of symptoms are displayed in graphs. This visualization makes it easier for patients to understand the analysis results.

[0034] The appointment decision unit can predict and propose the optimal consultation date and time based on the patient's past medical examination history. The appointment decision unit, for example, analyzes the patient's past medical examination history to predict the optimal consultation date and time. For example, it proposes the next consultation date and time based on past medical examination dates and contents. The appointment decision unit also predicts and proposes the optimal consultation date and time based on the patient's past medical examination history. For example, it proposes the next consultation date and time based on past medical examination results and treatment progress. The appointment decision unit also predicts and proposes the optimal consultation date and time based on the patient's past medical examination history. For example, it predicts and proposes the next consultation date and time based on the past medical examination history. This makes it possible to propose the optimal consultation date and time based on the past medical examination history.

[0035] The appointment decision unit can take the patient's life schedule into consideration and propose the optimal consultation date and time. The appointment decision unit, for example, inputs the patient's life schedule (work, school, home schedule, etc.) and proposes the optimal consultation date and time based on that. For example, it takes into consideration work breaks and holidays. The appointment decision unit also takes the patient's life schedule into consideration and proposes the optimal consultation date and time. For example, it takes into consideration home schedules and time for hobbies. The appointment decision unit also takes the patient's life schedule into consideration and proposes the optimal consultation date and time. For example, it takes into consideration work schedules and school schedules. In this way, it is possible to propose an appointment date and time that takes the patient's life schedule into consideration.

[0036] The appointment decision unit is able to propose the optimal consultation date and time by taking into consideration the patient's travel time and traffic conditions. The appointment decision unit, for example, considers the patient's travel time and proposes the optimal consultation date and time. For example, it proposes a time period that avoids commuting time and traffic congestion. The appointment decision unit also considers the patient's travel time and traffic conditions and proposes the optimal consultation date and time. For example, it considers the operating status of public transportation. The appointment decision unit also considers the patient's travel time and traffic conditions and proposes the optimal consultation date and time. For example, it proposes a time period that avoids traffic congestion. In this way, it is possible to propose an appointment date and time that takes into consideration travel time and traffic conditions.

[0037] The appointment decision unit can link the determined appointment date and time with the patient's calendar app and automatically add it to the schedule. The appointment decision unit, for example, links the determined appointment date and time with the patient's calendar app and automatically adds it to the schedule. For example, it links with Google Calendar or Outlook Calendar. The appointment decision unit also links the determined appointment date and time with the patient's calendar app and automatically adds it to the schedule. For example, it links with a smartphone calendar app. The appointment decision unit also links the determined appointment date and time with the patient's calendar app and automatically adds it to the schedule. For example, it links with a desktop calendar app. This makes it possible to automatically add the appointment date and time to the schedule.

[0038] The analysis unit takes into account the patient's lifestyle habits and environmental information during online diagnosis, enabling more accurate diagnosis. For example, during online diagnosis, the analysis unit inputs the patient's lifestyle habits (diet, exercise, sleep, etc.) and analyzes the impact of these factors on symptoms. For example, the analysis unit identifies the cause of symptoms based on the patient's diet and exercise frequency. Furthermore, during online diagnosis, the analysis unit inputs the patient's environmental information (living environment, work environment, climate, air quality, etc.) and analyzes the impact of these factors on symptoms. For example, the analysis unit identifies the cause of symptoms based on the humidity and temperature of the living environment. This enables online diagnosis that takes into account lifestyle habits and environmental information.

[0039] The analysis unit can visualize the results of the online diagnosis and provide them as infographics. For example, the analysis unit visualizes the results of the online diagnosis in graphs or charts and provides them so that the patient can easily understand. For example, the progress of the diagnosis results is displayed in chronological order. The analysis unit also provides the results of the online diagnosis as infographics. For example, the causes of the diagnosis results and countermeasures are visually represented. The analysis unit also visualizes the results of the online diagnosis and provides them as infographics. For example, the frequency of occurrence and severity of the diagnosis results are displayed in graphs. In this way, visualizing the results of the online diagnosis makes it easier for the patient to understand.

[0040] The analysis unit can recommend medications and treatments based on the patient's symptoms during online diagnosis. For example, the analysis unit uses the generating AI to suggest appropriate medications and treatments based on the patient's symptoms. For example, for a mild headache, an over-the-counter painkiller is recommended. The analysis unit also uses the generating AI to suggest appropriate medications and treatments based on the patient's symptoms. For example, for mild muscle pain, stretching or heat therapy is recommended. The analysis unit also uses the generating AI to suggest appropriate medications and treatments based on the patient's symptoms. For example, for mild stress, relaxation techniques or meditation are recommended. This makes it possible to suggest appropriate medications and treatments during online diagnosis.

[0041] The analysis unit can provide more detailed diagnostic information to a doctor based on the patient's past medical history when providing diagnostic information to the doctor. For example, when providing diagnostic information to a doctor, the generation AI provides more detailed diagnostic information based on the patient's past medical history. For example, the analysis unit analyzes the association with current symptoms based on past diagnostic results and treatment progress. Furthermore, when providing diagnostic information to a doctor, the generation AI provides more detailed diagnostic information based on the patient's past medical history. For example, the analysis unit analyzes the association with current symptoms based on past diagnostic results and treatment progress. Furthermore, when providing diagnostic information to a doctor, the generation AI provides more detailed diagnostic information based on the patient's past medical history. For example, the analysis unit analyzes the association with current symptoms based on past diagnostic results and treatment progress. This makes it possible to provide more detailed diagnostic information based on past medical history.

[0042] The analysis unit takes into account the patient's lifestyle and environmental information when providing diagnostic information to a doctor, thereby enabling the provision of more accurate diagnostic information. For example, when providing diagnostic information to a doctor, the generation AI inputs the patient's lifestyle (diet, exercise, sleep, etc.) and analyzes the impact of these factors on symptoms. For example, the cause of symptoms is identified based on dietary content and exercise frequency. Furthermore, when providing diagnostic information to a doctor, the generation AI takes into account the patient's lifestyle and environmental information to provide more accurate diagnostic information. For example, the cause of symptoms is identified based on the humidity and temperature of the living environment. Furthermore, when providing diagnostic information to a doctor, the generation AI takes into account the patient's lifestyle and environmental information to provide more accurate diagnostic information. For example, the cause of symptoms is identified based on the humidity and temperature of the living environment. In this way, diagnostic information that takes into account lifestyle and environmental information can be provided.

[0043] When providing diagnostic information to a doctor, the analysis unit can recommend treatments and medications based on the patient's symptoms. For example, the analysis unit uses the generation AI to suggest appropriate treatments and medications based on the patient's symptoms. For example, for a mild headache, an over-the-counter painkiller may be recommended. The analysis unit also uses the generation AI to suggest appropriate treatments and medications based on the patient's symptoms. For example, for mild muscle pain, the generation AI may recommend stretching or heat therapy. The analysis unit also uses the generation AI to suggest appropriate treatments and medications based on the patient's symptoms. For example, for mild stress, the generation AI may recommend relaxation techniques or meditation. This allows the suggestion of appropriate treatments and medications, thereby improving the accuracy of diagnosis.

[0044] The analysis unit, when providing diagnostic information to a doctor, can improve the accuracy of the diagnosis by referring to relevant medical literature and guidelines based on the patient's symptoms. The analysis unit, for example, causes the generation AI to refer to relevant medical literature and guidelines based on the patient's symptoms to improve the accuracy of the diagnosis. For example, the analysis unit provides diagnostic information based on the latest medical research and treatment guidelines. The analysis unit also causes the generation AI to refer to relevant medical literature and guidelines based on the patient's symptoms to improve the accuracy of the diagnosis. For example, the analysis unit provides diagnostic information based on the latest medical research and treatment guidelines. The analysis unit also causes the generation AI to refer to relevant medical literature and guidelines based on the patient's symptoms to improve the accuracy of the diagnosis. For example, the analysis unit provides diagnostic information based on the latest medical research and treatment guidelines. In this way, by referring to relevant medical literature and guidelines, the accuracy of the diagnosis can be improved.

[0045] In analyzing multimodal information, the analysis unit can make a more accurate diagnosis based on the patient's past medical history. In analyzing multimodal information, for example, the generation AI makes a more accurate diagnosis based on the patient's past medical history. For example, the analysis unit analyzes the association with current symptoms based on past diagnostic results and treatment progress. In analyzing multimodal information, the generation AI makes a more accurate diagnosis based on the patient's past medical history. For example, the analysis unit analyzes the association with current symptoms based on past diagnostic results and treatment progress. In analyzing multimodal information, the generation AI makes a more accurate diagnosis based on the patient's past medical history. For example, the analysis unit analyzes the association with current symptoms based on past diagnostic results and treatment progress. In analyzing multimodal information, the generation AI makes a more accurate diagnosis based on the patient's past medical history. For example, the analysis unit analyzes the association with current symptoms based on past diagnostic results and treatment progress. This enables a more accurate diagnosis based on past medical history.

[0046] The analysis unit takes into account the patient's lifestyle habits and environmental information when analyzing the multimodal information, enabling a more accurate diagnosis. For example, in analyzing the multimodal information, the generation AI inputs the patient's lifestyle habits (diet, exercise, sleep, etc.) and analyzes the impact of these factors on symptoms. For example, the cause of symptoms is identified based on dietary content and exercise frequency. Furthermore, in analyzing the multimodal information, the generation AI takes into account the patient's lifestyle habits and environmental information to enable a more accurate diagnosis. For example, the cause of symptoms is identified based on the humidity and temperature of the living environment. Furthermore, in analyzing the multimodal information, the generation AI takes into account the patient's lifestyle habits and environmental information to enable a more accurate diagnosis. For example, the cause of symptoms is identified based on the humidity and temperature of the living environment. This enables a diagnosis that takes into account lifestyle habits and environmental information.

[0047] The analysis unit can visualize the analysis results of the multimodal information and provide them as infographics. For example, the analysis unit visualizes the analysis results of the multimodal information in graphs or charts and provides them in a way that is easy for patients to understand. For example, the progress of symptoms is displayed in chronological order. The analysis unit also provides the analysis results of the multimodal information as infographics. For example, the causes of symptoms and countermeasures are visually represented. The analysis unit also visualizes the analysis results of the multimodal information and provides them as infographics. For example, the frequency of occurrence and severity of symptoms are displayed in graphs. In this way, visualizing the analysis results makes it easier for patients to understand.

[0048] In analyzing the multimodal information, the analysis unit can recommend self-care methods based on the patient's symptoms. For example, the generation AI in the analysis unit recommends appropriate self-care methods based on the patient's symptoms. For example, for a mild headache, rest and hydration are recommended. The generation AI in the analysis unit also recommends appropriate self-care methods based on the patient's symptoms. For example, for mild muscle pain, stretching and heat therapy are recommended. The generation AI in the analysis unit also recommends appropriate self-care methods based on the patient's symptoms. For example, relaxation techniques and meditation are recommended for mild stress. In this way, self-care methods can be suggested to support patients in self-management.

[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 assistant system can further provide recommended meal plans based on the patient's symptoms. For example, the analysis unit allows the generation AI to present an appropriate meal plan based on the patient's symptoms. For example, for indigestion, it may recommend a meal using easily digestible ingredients. The analysis unit also allows the generation AI to present an appropriate meal plan based on the patient's symptoms. For example, for high blood pressure, it may recommend a low-salt meal. The analysis unit also allows the generation AI to present an appropriate meal plan based on the patient's symptoms. For example, for diabetes, it may recommend a low-carbohydrate meal. In this way, by providing meal plans, it is possible to support the patient's health management.

[0051] The assistant system can further provide recommended exercise plans based on the patient's symptoms. For example, the analysis unit allows the generation AI to present an appropriate exercise plan based on the patient's symptoms. For example, for mild joint pain, exercise that puts less strain on the joints is recommended. The analysis unit also allows the generation AI to present an appropriate exercise plan based on the patient's symptoms. For example, aerobic exercise is recommended for obesity. The analysis unit also allows the generation AI to present an appropriate exercise plan based on the patient's symptoms. For example, yoga or meditation is recommended for stress relief. In this way, by providing an exercise plan, it is possible to support the patient's health management.

[0052] The assistant system can further provide recommended sleep improvement methods based on the patient's symptoms. For example, the analysis unit allows the generating AI to suggest appropriate sleep improvement methods based on the patient's symptoms. For example, for insomnia, the generating AI may recommend relaxation breathing techniques or meditation. The analysis unit also allows the generating AI to suggest appropriate sleep improvement methods based on the patient's symptoms. For example, the generating AI may recommend environmental adjustments (temperature, humidity, lighting, etc.) to improve sleep quality. The analysis unit also allows the generating AI to suggest appropriate sleep improvement methods based on the patient's symptoms. For example, the generating AI may provide advice on improving a pre-bedtime routine. This allows the system to support the patient's health management by providing sleep improvement methods.

[0053] The assistant system can further provide recommended relaxation methods based on the patient's symptoms. For example, the analysis unit allows the generating AI to suggest appropriate relaxation methods based on the patient's symptoms. For example, for mild stress, deep breathing or meditation may be recommended. The analysis unit also allows the generating AI to suggest appropriate relaxation methods based on the patient's symptoms. For example, stretching to relieve muscle tension may be recommended. The analysis unit also allows the generating AI to suggest appropriate relaxation methods based on the patient's symptoms. For example, relaxing music or aromatherapy may be recommended. In this way, the patient's stress can be reduced by providing relaxation methods.

[0054] The assistant system can further provide recommended lifestyle improvement methods based on the patient's symptoms. For example, the analysis unit allows the generating AI to present appropriate lifestyle improvement methods based on the patient's symptoms. For example, for obesity, it would recommend reviewing diet and making exercise a habit. The analysis unit also allows the generating AI to present appropriate lifestyle improvement methods based on the patient's symptoms. For example, for stress, it would recommend relaxation techniques or increasing time for hobbies. The analysis unit also allows the generating AI to present appropriate lifestyle improvement methods based on the patient's symptoms. For example, for lack of sleep, it would recommend establishing a regular lifestyle rhythm. This allows the system to provide lifestyle improvement methods to assist patients in managing their health.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The symptom input unit inputs the patient's symptoms. For example, the patient inputs specific symptoms such as "I have a headache" or "I can't stop coughing." Images or videos that show the symptoms can also be input. For example, a photo of a skin rash or the sound of a cough can be input. Step 2: The analysis unit analyzes the symptoms entered by the symptom input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the symptoms. The generation AI can also use a multimodal generation AI to analyze images and audio. The generation AI can also extract and analyze important parts of the symptoms. Step 3: The appointment decision unit decides the optimal consultation date and time based on the symptom information analyzed by the analysis unit. For example, it may link with a hospital's reservation system, automatically search for available time slots, and suggest the optimal date and time for the patient. Step 4: The notification unit notifies the patient of the appointment date and time determined by the appointment determination unit. For example, the notification may be sent by email or SMS. The notification may also be sent via a dedicated app.

[0057] (Example 2) The assistant system according to the embodiment of the present invention is a system that shortens waiting times at hospitals and improves the efficiency of medical examinations by using a generation AI to analyze patients' symptoms in advance. As a result, the assistant system can shorten waiting times at hospitals and improve the efficiency of medical examinations.

[0058] The assistant system according to the embodiment includes a symptom input unit, an analysis unit, an appointment decision unit, and a notification unit. The symptom input unit inputs a patient's symptoms. For example, the patient inputs specific symptoms such as "I have a headache" or "I can't stop coughing." Images and videos that convey the symptoms can also be input. For example, a photo of a skin rash or audio of a cough can be input. The analysis unit analyzes the symptoms input by the symptom input unit. For example, a generation AI analyzes the symptoms using a text generation AI (e.g., LLM). The generation AI can also analyze images and audio using a multimodal generation AI. The generation AI can also extract and analyze important parts of the symptoms. The appointment decision unit determines the optimal appointment date and time based on the symptom information analyzed by the analysis unit. For example, it can link with a hospital's appointment system to automatically search for available time slots and suggest the optimal date and time for the patient. The notification unit notifies the patient of the appointment date and time determined by the appointment decision unit. For example, the notification can be sent via email or SMS. It can also be sent via a dedicated app. This allows the assistant system to reduce waiting times at hospitals and improve the efficiency of consultations. For example, patients will be able to reduce waiting times at hospitals and use their time more efficiently, and hospitals will be able to operate with fewer staff, optimizing medical resources.

[0059] The analysis unit can analyze the correlation with symptoms based on the patient's past medical history. For example, the analysis unit retrieves the patient's past medical history from a database and inputs it into the generation AI. For example, it analyzes the correlation with current symptoms based on information on previously diagnosed illnesses and prescribed medications. This enables more accurate diagnoses based on past medical history.

[0060] The analysis unit can analyze the impact on symptoms based on the patient's lifestyle and environmental information. The analysis unit inputs, for example, the patient's lifestyle (diet, exercise, sleep, etc.) and analyzes the impact of these factors on symptoms. For example, the analysis unit identifies the cause of symptoms based on the patient's diet and exercise frequency. The analysis unit also inputs the patient's environmental information (living environment, work environment, climate, air quality, etc.) and analyzes the impact of these factors on symptoms. For example, the analysis unit identifies the cause of symptoms based on the humidity and temperature of the living environment. This makes it possible to make a diagnosis that takes lifestyle and environmental information into consideration.

[0061] The analysis unit uses the emotion estimation function to analyze the emotional state of the patient and can make a diagnosis that takes into account the impact of stress and anxiety on symptoms. The analysis unit, for example, analyzes the emotional state of the patient in real time and evaluates the impact of stress and anxiety on symptoms. For example, it analyzes the patient's facial expressions and voice and calculates an emotion score. The analysis unit also uses the emotion estimation function to analyze the emotional state of the patient and makes a diagnosis that takes into account the impact of stress and anxiety on symptoms. For example, it evaluates the impact of stress and anxiety on symptoms based on the emotion score. This makes it possible to make a diagnosis that takes into account the emotional state.

[0062] The analysis unit can present recommended self-care methods based on the patient's symptoms. For example, the generation AI of the analysis unit presents appropriate self-care methods based on the patient's symptoms. For example, for a mild headache, it recommends rest and hydration. The analysis unit also presents appropriate self-care methods based on the patient's symptoms. For example, for mild muscle pain, it recommends stretching and heat therapy. The analysis unit also presents appropriate self-care methods based on the patient's symptoms. For example, for mild stress, it recommends relaxation techniques and meditation. In this way, by presenting self-care methods, it is possible to support the patient's self-management.

[0063] The analysis unit can visualize the results of the preliminary symptom analysis and provide them as infographics. For example, the analysis unit visualizes the results of the preliminary symptom analysis in graphs or charts and provides them in a way that is easy for patients to understand. For example, the progress of symptoms is displayed in chronological order. The analysis unit also provides the results of the preliminary symptom analysis as infographics. For example, the causes of symptoms and countermeasures are visually represented. The analysis unit also visualizes the results of the preliminary symptom analysis and provides them as infographics. For example, the frequency of occurrence and severity of symptoms are displayed in graphs. This visualization makes it easier for patients to understand the analysis results.

[0064] The analysis unit can use the emotion estimation function to analyze the emotions of the patient when they input their symptoms in real time, and provide advice to elicit positive emotions. For example, the analysis unit can use the emotion estimation function to analyze the emotions of the patient when they input their symptoms in real time, and provide advice to elicit positive emotions. For example, an encouraging message can be displayed. The analysis unit can also use the emotion estimation function to analyze the emotions of the patient when they input their symptoms in real time, and provide advice to elicit positive emotions. For example, a breathing technique can be suggested to help them relax. The analysis unit can also use the emotion estimation function to analyze the emotions of the patient when they input their symptoms in real time, and provide advice to elicit positive emotions. For example, a message that encourages positive thinking can be displayed. This can elicit positive emotions, thereby reducing the patient's stress.

[0065] The appointment decision unit can predict and propose the optimal consultation date and time based on the patient's past medical examination history. The appointment decision unit, for example, analyzes the patient's past medical examination history to predict the optimal consultation date and time. For example, it proposes the next consultation date and time based on past medical examination dates and contents. The appointment decision unit also predicts and proposes the optimal consultation date and time based on the patient's past medical examination history. For example, it proposes the next consultation date and time based on past medical examination results and treatment progress. The appointment decision unit also predicts and proposes the optimal consultation date and time based on the patient's past medical examination history. For example, it predicts and proposes the next consultation date and time based on the past medical examination history. This makes it possible to propose the optimal consultation date and time based on the past medical examination history.

[0066] The appointment decision unit can take the patient's life schedule into consideration and propose the optimal consultation date and time. The appointment decision unit, for example, inputs the patient's life schedule (work, school, home schedule, etc.) and proposes the optimal consultation date and time based on that. For example, it takes into consideration work breaks and holidays. The appointment decision unit also takes the patient's life schedule into consideration and proposes the optimal consultation date and time. For example, it takes into consideration home schedules and time for hobbies. The appointment decision unit also takes the patient's life schedule into consideration and proposes the optimal consultation date and time. For example, it takes into consideration work schedules and school schedules. In this way, it is possible to propose an appointment date and time that takes the patient's life schedule into consideration.

[0067] The appointment decision unit can use the emotion estimation function to analyze the emotional state of the patient and propose a less stressful appointment date and time. The appointment decision unit, for example, analyzes the emotional state of the patient in real time and proposes a less stressful appointment date and time. For example, based on the emotion score, it proposes a time period when the patient can relax. The appointment decision unit can also use the emotion estimation function to analyze the emotional state of the patient and propose a less stressful appointment date and time. For example, based on the emotion score, it proposes a less stressful time period. The appointment decision unit can also use the emotion estimation function to analyze the emotional state of the patient and propose a less stressful appointment date and time. For example, based on the emotion score, it proposes a less stressful time period. This makes it possible to propose a less stressful appointment date and time.

[0068] The appointment decision unit is able to propose the optimal consultation date and time by taking into consideration the patient's travel time and traffic conditions. The appointment decision unit, for example, considers the patient's travel time and proposes the optimal consultation date and time. For example, it proposes a time period that avoids commuting time and traffic congestion. The appointment decision unit also considers the patient's travel time and traffic conditions and proposes the optimal consultation date and time. For example, it considers the operating status of public transportation. The appointment decision unit also considers the patient's travel time and traffic conditions and proposes the optimal consultation date and time. For example, it proposes a time period that avoids traffic congestion. In this way, it is possible to propose an appointment date and time that takes into consideration travel time and traffic conditions.

[0069] The appointment decision unit can link the determined appointment date and time with the patient's calendar app and automatically add it to the schedule. The appointment decision unit, for example, links the determined appointment date and time with the patient's calendar app and automatically adds it to the schedule. For example, it links with Google Calendar or Outlook Calendar. The appointment decision unit also links the determined appointment date and time with the patient's calendar app and automatically adds it to the schedule. For example, it links with a smartphone calendar app. The appointment decision unit also links the determined appointment date and time with the patient's calendar app and automatically adds it to the schedule. For example, it links with a desktop calendar app. This makes it possible to automatically add the appointment date and time to the schedule.

[0070] The appointment decision unit can use the emotion estimation function to analyze the emotion of the patient when selecting an appointment date and time in real time, and make suggestions to elicit positive emotions. For example, the appointment decision unit can use the emotion estimation function to analyze the emotion of the patient when selecting an appointment date and time in real time, and make suggestions to elicit positive emotions. For example, an encouraging message can be displayed. Furthermore ... a breathing technique for relaxation can be displayed. Furthermore, the appointment decision unit can use the emotion estimation function to analyze the emotion of the patient when selecting an appointment date and time in real time, and make suggestions to elicit positive emotions. For example, a message that encourages positive thinking can be displayed. This can elicit positive emotions, thereby reducing stress in the patient.

[0071] The analysis unit takes into account the patient's lifestyle habits and environmental information during online diagnosis, enabling more accurate diagnosis. For example, during online diagnosis, the analysis unit inputs the patient's lifestyle habits (diet, exercise, sleep, etc.) and analyzes the impact of these factors on symptoms. For example, the analysis unit identifies the cause of symptoms based on the patient's diet and exercise frequency. Furthermore, during online diagnosis, the analysis unit inputs the patient's environmental information (living environment, work environment, climate, air quality, etc.) and analyzes the impact of these factors on symptoms. For example, the analysis unit identifies the cause of symptoms based on the humidity and temperature of the living environment. This enables online diagnosis that takes into account lifestyle habits and environmental information.

[0072] The analysis unit can use the emotion estimation function to analyze the emotional state of the patient during the online diagnosis and provide advice to reduce stress and anxiety. For example, the analysis unit can use the emotion estimation function to analyze the emotional state of the patient in real time during the online diagnosis and provide advice to reduce stress and anxiety. For example, the analysis unit can suggest breathing techniques to help relax. The analysis unit can also use the emotion estimation function to analyze the emotional state of the patient in real time during the online diagnosis and provide advice to reduce stress and anxiety. For example, the analysis unit can display an encouraging message. The analysis unit can also use the emotion estimation function to analyze the emotional state of the patient in real time during the online diagnosis and provide advice to reduce stress and anxiety. For example, the analysis unit can display a message to encourage positive thinking. This makes it possible to provide advice to reduce stress and anxiety.

[0073] The analysis unit can visualize the results of the online diagnosis and provide them as infographics. For example, the analysis unit visualizes the results of the online diagnosis in graphs or charts and provides them so that the patient can easily understand. For example, the progress of the diagnosis results is displayed in chronological order. The analysis unit also provides the results of the online diagnosis as infographics. For example, the causes of the diagnosis results and countermeasures are visually represented. The analysis unit also visualizes the results of the online diagnosis and provides them as infographics. For example, the frequency of occurrence and severity of the diagnosis results are displayed in graphs. In this way, visualizing the results of the online diagnosis makes it easier for the patient to understand.

[0074] The analysis unit can recommend medications and treatments based on the patient's symptoms during online diagnosis. For example, the analysis unit uses the generating AI to suggest appropriate medications and treatments based on the patient's symptoms. For example, for a mild headache, an over-the-counter painkiller is recommended. The analysis unit also uses the generating AI to suggest appropriate medications and treatments based on the patient's symptoms. For example, for mild muscle pain, stretching or heat therapy is recommended. The analysis unit also uses the generating AI to suggest appropriate medications and treatments based on the patient's symptoms. For example, for mild stress, relaxation techniques or meditation are recommended. This makes it possible to suggest appropriate medications and treatments during online diagnosis.

[0075] The analysis unit can use the emotion estimation function to analyze the patient's emotions in real time during the online diagnosis and make suggestions to elicit positive emotions. For example, the analysis unit can use the emotion estimation function to analyze the patient's emotional state in real time during the online diagnosis and make suggestions to elicit positive emotions. For example, an encouraging message can be displayed. The analysis unit can also use the emotion estimation function to analyze the patient's emotional state in real time during the online diagnosis and make suggestions to elicit positive emotions. For example, a breathing technique can be suggested to help relax. The analysis unit can also use the emotion estimation function to analyze the patient's emotional state in real time during the online diagnosis and make suggestions to elicit positive emotions. For example, a message encouraging positive thinking can be displayed. This can elicit positive emotions and reduce the patient's stress.

[0076] The analysis unit can provide more detailed diagnostic information to a doctor based on the patient's past medical history when providing diagnostic information to the doctor. For example, when providing diagnostic information to a doctor, the generation AI provides more detailed diagnostic information based on the patient's past medical history. For example, the analysis unit analyzes the association with current symptoms based on past diagnostic results and treatment progress. Furthermore, when providing diagnostic information to a doctor, the generation AI provides more detailed diagnostic information based on the patient's past medical history. For example, the analysis unit analyzes the association with current symptoms based on past diagnostic results and treatment progress. Furthermore, when providing diagnostic information to a doctor, the generation AI provides more detailed diagnostic information based on the patient's past medical history. For example, the analysis unit analyzes the association with current symptoms based on past diagnostic results and treatment progress. This makes it possible to provide more detailed diagnostic information based on past medical history.

[0077] The analysis unit takes into account the patient's lifestyle and environmental information when providing diagnostic information to a doctor, thereby enabling the provision of more accurate diagnostic information. For example, when providing diagnostic information to a doctor, the generation AI inputs the patient's lifestyle (diet, exercise, sleep, etc.) and analyzes the impact of these factors on symptoms. For example, the cause of symptoms is identified based on dietary content and exercise frequency. Furthermore, when providing diagnostic information to a doctor, the generation AI takes into account the patient's lifestyle and environmental information to provide more accurate diagnostic information. For example, the cause of symptoms is identified based on the humidity and temperature of the living environment. Furthermore, when providing diagnostic information to a doctor, the generation AI takes into account the patient's lifestyle and environmental information to provide more accurate diagnostic information. For example, the cause of symptoms is identified based on the humidity and temperature of the living environment. In this way, diagnostic information that takes into account lifestyle and environmental information can be provided.

[0078] The analysis unit uses the emotion estimation function to analyze the emotional state of the patient and provide diagnostic information that takes into account the impact of stress and anxiety on symptoms. The analysis unit, for example, analyzes the emotional state of the patient in real time and evaluates the impact of stress and anxiety on symptoms. For example, it analyzes the patient's facial expressions and voice and calculates an emotion score. The analysis unit also uses the emotion estimation function to analyze the emotional state of the patient and provide diagnostic information that takes into account the impact of stress and anxiety on symptoms. For example, it evaluates the impact of stress and anxiety on symptoms based on the emotion score. This makes it possible to provide diagnostic information that takes into account the emotional state.

[0079] When providing diagnostic information to a doctor, the analysis unit can recommend treatments and medications based on the patient's symptoms. For example, the analysis unit uses the generation AI to suggest appropriate treatments and medications based on the patient's symptoms. For example, for a mild headache, an over-the-counter painkiller may be recommended. The analysis unit also uses the generation AI to suggest appropriate treatments and medications based on the patient's symptoms. For example, for mild muscle pain, the generation AI may recommend stretching or heat therapy. The analysis unit also uses the generation AI to suggest appropriate treatments and medications based on the patient's symptoms. For example, for mild stress, the generation AI may recommend relaxation techniques or meditation. This allows the suggestion of appropriate treatments and medications, thereby improving the accuracy of diagnosis.

[0080] The analysis unit, when providing diagnostic information to a doctor, can improve the accuracy of the diagnosis by referring to relevant medical literature and guidelines based on the patient's symptoms. The analysis unit, for example, causes the generation AI to refer to relevant medical literature and guidelines based on the patient's symptoms to improve the accuracy of the diagnosis. For example, the analysis unit provides diagnostic information based on the latest medical research and treatment guidelines. The analysis unit also causes the generation AI to refer to relevant medical literature and guidelines based on the patient's symptoms to improve the accuracy of the diagnosis. For example, the analysis unit provides diagnostic information based on the latest medical research and treatment guidelines. The analysis unit also causes the generation AI to refer to relevant medical literature and guidelines based on the patient's symptoms to improve the accuracy of the diagnosis. For example, the analysis unit provides diagnostic information based on the latest medical research and treatment guidelines. In this way, by referring to relevant medical literature and guidelines, the accuracy of the diagnosis can be improved.

[0081] The analysis unit can use the emotion estimation function to analyze the emotion of the doctor when he receives the diagnostic information in real time and make suggestions to elicit positive emotions. For example, the analysis unit can use the emotion estimation function to analyze the emotion of the doctor when he receives the diagnostic information in real time and make suggestions to elicit positive emotions. For example, an encouraging message can be displayed. Furthermore, the analysis unit can use the emotion estimation function to analyze the emotion of the doctor when he receives the diagnostic information in real time and make suggestions to elicit positive emotions. For example, a breathing technique can be suggested to help him relax. Furthermore, the analysis unit can use the emotion estimation function to analyze the emotion of the doctor when he receives the diagnostic information in real time and make suggestions to elicit positive emotions. For example, a message that encourages positive thinking can be displayed. This can elicit positive emotions and reduce the doctor's stress.

[0082] In analyzing multimodal information, the analysis unit can make a more accurate diagnosis based on the patient's past medical history. In analyzing multimodal information, for example, the generation AI makes a more accurate diagnosis based on the patient's past medical history. For example, the analysis unit analyzes the association with current symptoms based on past diagnostic results and treatment progress. In analyzing multimodal information, the generation AI makes a more accurate diagnosis based on the patient's past medical history. For example, the analysis unit analyzes the association with current symptoms based on past diagnostic results and treatment progress. In analyzing multimodal information, the generation AI makes a more accurate diagnosis based on the patient's past medical history. For example, the analysis unit analyzes the association with current symptoms based on past diagnostic results and treatment progress. In analyzing multimodal information, the generation AI makes a more accurate diagnosis based on the patient's past medical history. For example, the analysis unit analyzes the association with current symptoms based on past diagnostic results and treatment progress. This enables a more accurate diagnosis based on past medical history.

[0083] The analysis unit takes into account the patient's lifestyle habits and environmental information when analyzing the multimodal information, enabling a more accurate diagnosis. For example, in analyzing the multimodal information, the generation AI inputs the patient's lifestyle habits (diet, exercise, sleep, etc.) and analyzes the impact of these factors on symptoms. For example, the cause of symptoms is identified based on dietary content and exercise frequency. Furthermore, in analyzing the multimodal information, the generation AI takes into account the patient's lifestyle habits and environmental information to enable a more accurate diagnosis. For example, the cause of symptoms is identified based on the humidity and temperature of the living environment. Furthermore, in analyzing the multimodal information, the generation AI takes into account the patient's lifestyle habits and environmental information to enable a more accurate diagnosis. For example, the cause of symptoms is identified based on the humidity and temperature of the living environment. This enables a diagnosis that takes into account lifestyle habits and environmental information.

[0084] The analysis unit can use the emotion estimation function to analyze the emotional state of the patient while analyzing the multimodal information and provide advice to reduce stress and anxiety. For example, the analysis unit can use the emotion estimation function to analyze the emotional state of the patient in real time while analyzing the multimodal information and provide advice to reduce stress and anxiety. For example, the analysis unit can suggest breathing techniques to help relax. The analysis unit can also use the emotion estimation function to analyze the emotional state of the patient in real time while analyzing the multimodal information and provide advice to reduce stress and anxiety. For example, the analysis unit can display an encouraging message. The analysis unit can also use the emotion estimation function to analyze the emotional state of the patient in real time while analyzing the multimodal information and provide advice to reduce stress and anxiety. For example, the analysis unit can display a message to encourage positive thinking. This makes it possible to provide advice to reduce stress and anxiety.

[0085] The analysis unit can visualize the analysis results of the multimodal information and provide them as infographics. For example, the analysis unit visualizes the analysis results of the multimodal information in graphs or charts and provides them in a way that is easy for patients to understand. For example, the progress of symptoms is displayed in chronological order. The analysis unit also provides the analysis results of the multimodal information as infographics. For example, the causes of symptoms and countermeasures are visually represented. The analysis unit also visualizes the analysis results of the multimodal information and provides them as infographics. For example, the frequency of occurrence and severity of symptoms are displayed in graphs. In this way, visualizing the analysis results makes it easier for patients to understand.

[0086] In analyzing the multimodal information, the analysis unit can recommend self-care methods based on the patient's symptoms. For example, the generation AI in the analysis unit recommends appropriate self-care methods based on the patient's symptoms. For example, for a mild headache, rest and hydration are recommended. The generation AI in the analysis unit also recommends appropriate self-care methods based on the patient's symptoms. For example, for mild muscle pain, stretching and heat therapy are recommended. The generation AI in the analysis unit also recommends appropriate self-care methods based on the patient's symptoms. For example, relaxation techniques and meditation are recommended for mild stress. In this way, self-care methods can be suggested to support patients in self-management.

[0087] The analysis unit can use the emotion estimation function to analyze the patient's emotion in real time while analyzing the multimodal information and make suggestions to elicit positive emotions. For example, the analysis unit can use the emotion estimation function to analyze the patient's emotional state in real time while analyzing the multimodal information and make suggestions to elicit positive emotions. For example, an encouraging message can be displayed. The analysis unit can also use the emotion estimation function to analyze the patient's emotional state in real time while analyzing the multimodal information and make suggestions to elicit positive emotions. For example, a breathing technique can be suggested to help relax. The analysis unit can also use the emotion estimation function to analyze the patient's emotional state in real time while analyzing the multimodal information and make suggestions to elicit positive emotions. For example, a message encouraging positive thinking can be displayed. This can elicit positive emotions and reduce the patient's stress.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The assistant system can further provide recommended meal plans based on the patient's symptoms. For example, the analysis unit allows the generation AI to present an appropriate meal plan based on the patient's symptoms. For example, for indigestion, it may recommend a meal using easily digestible ingredients. The analysis unit also allows the generation AI to present an appropriate meal plan based on the patient's symptoms. For example, for high blood pressure, it may recommend a low-salt meal. The analysis unit also allows the generation AI to present an appropriate meal plan based on the patient's symptoms. For example, for diabetes, it may recommend a low-carbohydrate meal. In this way, by providing meal plans, it is possible to support the patient's health management.

[0090] The assistant system can further provide recommended exercise plans based on the patient's symptoms. For example, the analysis unit allows the generation AI to present an appropriate exercise plan based on the patient's symptoms. For example, for mild joint pain, exercise that puts less strain on the joints is recommended. The analysis unit also allows the generation AI to present an appropriate exercise plan based on the patient's symptoms. For example, aerobic exercise is recommended for obesity. The analysis unit also allows the generation AI to present an appropriate exercise plan based on the patient's symptoms. For example, yoga or meditation is recommended for stress relief. In this way, by providing an exercise plan, it is possible to support the patient's health management.

[0091] The assistant system can further provide recommended sleep improvement methods based on the patient's symptoms. For example, the analysis unit allows the generating AI to suggest appropriate sleep improvement methods based on the patient's symptoms. For example, for insomnia, the generating AI may recommend relaxation breathing techniques or meditation. The analysis unit also allows the generating AI to suggest appropriate sleep improvement methods based on the patient's symptoms. For example, the generating AI may recommend environmental adjustments (temperature, humidity, lighting, etc.) to improve sleep quality. The analysis unit also allows the generating AI to suggest appropriate sleep improvement methods based on the patient's symptoms. For example, the generating AI may provide advice on improving a pre-bedtime routine. This allows the system to support the patient's health management by providing sleep improvement methods.

[0092] The assistant system can further provide recommended relaxation methods based on the patient's symptoms. For example, the analysis unit allows the generating AI to suggest appropriate relaxation methods based on the patient's symptoms. For example, for mild stress, deep breathing or meditation may be recommended. The analysis unit also allows the generating AI to suggest appropriate relaxation methods based on the patient's symptoms. For example, stretching to relieve muscle tension may be recommended. The analysis unit also allows the generating AI to suggest appropriate relaxation methods based on the patient's symptoms. For example, relaxing music or aromatherapy may be recommended. In this way, the patient's stress can be reduced by providing relaxation methods.

[0093] The assistant system can further provide recommended lifestyle improvement methods based on the patient's symptoms. For example, the analysis unit allows the generating AI to present appropriate lifestyle improvement methods based on the patient's symptoms. For example, for obesity, it would recommend reviewing diet and making exercise a habit. The analysis unit also allows the generating AI to present appropriate lifestyle improvement methods based on the patient's symptoms. For example, for stress, it would recommend relaxation techniques or increasing time for hobbies. The analysis unit also allows the generating AI to present appropriate lifestyle improvement methods based on the patient's symptoms. For example, for lack of sleep, it would recommend establishing a regular lifestyle rhythm. This allows the system to provide lifestyle improvement methods to assist patients in managing their health.

[0094] The assistant system can further analyze the emotional state of the patient and determine the priority of examinations based on the emotion. For example, the analysis unit analyzes the emotional state of the patient in real time and prioritizes examinations for patients with high stress or anxiety. The analysis unit also uses an emotion estimation function to analyze the emotional state of the patient and determine the priority of examinations based on the emotion. For example, based on the emotion score, it prioritizes examinations for patients with high urgency. The analysis unit also uses the emotion estimation function to analyze the emotional state of the patient and determine the priority of examinations based on the emotion. For example, based on the emotion score, it may perform examinations during times when the patient is most relaxed. This makes it possible to determine the priority of examinations taking the emotional state into consideration.

[0095] The assistant system can further analyze the emotional state of the patient and adjust the order of examinations based on the emotion. For example, the analysis unit analyzes the emotional state of the patient in real time and prioritizes examinations of patients with high stress or anxiety. The analysis unit also uses an emotion estimation function to analyze the emotional state of the patient and adjust the order of examinations based on the emotion. For example, based on the emotion score, it prioritizes examinations of patients with high urgency. The analysis unit also uses the emotion estimation function to analyze the emotional state of the patient and adjust the order of examinations based on the emotion. For example, based on the emotion score, it performs examinations during times when the patient is most relaxed. This makes it possible to adjust the order of examinations taking the emotional state into consideration.

[0096] The assistant system can further analyze the patient's emotional state and suggest an examination method based on the emotion. For example, the analysis unit analyzes the patient's emotional state in real time and suggests a relaxing examination method for patients with high stress or anxiety. The analysis unit also uses an emotion estimation function to analyze the patient's emotional state and suggest an examination method based on the emotion. For example, based on the emotion score, it suggests performing the examination in a relaxing environment. The analysis unit also uses the emotion estimation function to analyze the patient's emotional state and suggest an examination method based on the emotion. For example, based on the emotion score, it provides advice on how to relax before the examination. This makes it possible to suggest an examination method that takes the emotional state into consideration.

[0097] The assistant system can further analyze the patient's emotional state and create an examination environment based on the patient's emotions. For example, the analysis unit analyzes the patient's emotional state in real time and provides a relaxing environment for patients with high stress or anxiety. The analysis unit also uses an emotion estimation function to analyze the patient's emotional state and create an examination environment based on the patient's emotions. For example, it provides relaxing music and lighting based on the emotion score. The analysis unit also uses the emotion estimation function to analyze the patient's emotional state and create an examination environment based on the patient's emotions. For example, it provides a relaxing scent based on the emotion score. This makes it possible to create an examination environment that takes the patient's emotional state into consideration.

[0098] The assistant system can further analyze the patient's emotional state and propose an examination approach based on the emotion. For example, the analysis unit analyzes the patient's emotional state in real time and proposes an approach that will help the patient relax for patients with high levels of stress or anxiety. The analysis unit also uses an emotion estimation function to analyze the patient's emotional state and proposes an examination approach based on the emotion. For example, it proposes a communication method that will help the patient relax based on the emotion score. The analysis unit also uses the emotion estimation function to analyze the patient's emotional state and proposes an examination approach based on the emotion. For example, it provides advice on how to relax before the examination based on the emotion score. This makes it possible to propose an examination approach that takes the patient's emotional state into consideration.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The symptom input unit inputs the patient's symptoms. For example, the patient inputs specific symptoms such as "I have a headache" or "I can't stop coughing." Images or videos that show the symptoms can also be input. For example, a photo of a skin rash or the sound of a cough can be input. Step 2: The analysis unit analyzes the symptoms entered by the symptom input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the symptoms. The generation AI can also use a multimodal generation AI to analyze images and audio. The generation AI can also extract and analyze important parts of the symptoms. Step 3: The appointment decision unit decides the optimal consultation date and time based on the symptom information analyzed by the analysis unit. For example, it may link with a hospital's reservation system, automatically search for available time slots, and suggest the optimal date and time for the patient. Step 4: The notification unit notifies the patient of the appointment date and time determined by the appointment determination unit. For example, the notification may be sent by email or SMS. The notification may also be sent via a dedicated app.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a symptom input unit for inputting the symptoms of a patient; an analysis unit that analyzes the symptoms input by the symptom input unit; an appointment determination unit that determines an optimal consultation date and time based on the symptom information analyzed by the analysis unit; a notification unit that notifies the patient of the examination date and time determined by the appointment determination unit. A system characterized by:

2. The analysis unit Analyze the correlation with the above symptoms based on the patient's past medical history The system of claim 1 .

3. The analysis unit Present recommended self-care methods based on the patient's symptoms The system of claim 1 .

4. The reservation determination unit Predict and suggest the optimal consultation date and time based on the patient's past medical history The system of claim 1 .

5. The analysis unit During online diagnosis, the patient's lifestyle and environmental information are taken into account to provide a more accurate diagnosis. The system of claim 1 .

6. The analysis unit Using emotion estimation functionality, the system analyzes the patient's emotional state and provides diagnostic information that takes into account the impact of stress and anxiety on the symptoms. The system of claim 1 .

7. The analysis unit In analyzing multimodal information, more accurate diagnoses can be made based on the patient's medical history. The system of claim 1 .

8. The analysis unit Using emotion estimation functionality, the system analyzes the patient's emotional state and makes a diagnosis that takes into account the impact of stress and anxiety on the symptoms. The system of claim 1 .

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