system

Abstract

The system according to this embodiment aims to propose optimal treatment methods and lifestyle improvements based on the patient's symptoms and lifestyle. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data on the patient's symptoms and lifestyle. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes optimal treatment methods and lifestyle improvement measures based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently carried out to propose an optimal treatment method or lifestyle guidance based on the symptoms and lifestyle habits of patients, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal treatment method and improvement measures for lifestyle habits based on the symptoms and lifestyle habits of patients.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data on the symptoms and lifestyle habits of patients. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an optimal treatment method and improvement measures for lifestyle habits based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose optimal treatment methods and lifestyle improvements based on the patient's symptoms and lifestyle. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that proposes optimal treatment methods and lifestyle guidance based on the symptoms and lifestyle habits of individual patients. This AI agent system collects data on patients' symptoms and lifestyle habits, and the AI ​​analyzes it to propose optimal treatment methods and lifestyle improvement measures, thereby facilitating communication between medical staff at clinics and patients and improving the quality of treatment. For example, when a patient visits a clinic, the AI ​​agent system collects information through questionnaires and interviews. For example, it collects information such as the patient's diet, exercise habits, sleep patterns, and medical history. This allows for an understanding of the patient's lifestyle habits and health status. Next, the AI ​​agent system analyzes the collected data. Based on the collected data, the AI ​​analyzes the patient's symptoms and lifestyle habits in detail. For example, it analyzes how diet and exercise habits affect the patient's health status. This allows for a comprehensive evaluation of the patient's health status. Furthermore, based on the analysis results, the AI ​​agent system proposes optimal treatment methods and lifestyle improvement measures. For example, by proposing specific dietary restrictions or exercise programs, the patient's health status can be improved. It is also possible to provide advice on stress management and improving sleep quality as lifestyle improvement measures. This proposal aims to make it easier for clinic medical staff to communicate with patients. Medical staff can then communicate with patients and develop specific treatment plans based on treatment methods and lifestyle improvements suggested by AI. This will reduce the burden on medical staff and improve the quality of care for patients. For example, even if doctors and nurses are busy and cannot dedicate enough time, the AI ​​can automatically collect and organize information from patients and suggest optimal treatment methods and lifestyle improvements, thereby reducing the burden on medical staff. Furthermore, even when it is difficult to propose the optimal treatment plan for each patient, the AI ​​can make suggestions based on analysis results, improving the quality of care for patients. In this way, by using an AI agent, it is possible to provide optimal medical care to each individual patient and reduce the burden on the medical field. This will improve the overall quality and efficiency of medical care.This allows the AI ​​agent system to suggest optimal treatment methods and lifestyle guidance based on the patient's symptoms and lifestyle.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data on the patient's symptoms and lifestyle. For example, the data collection unit collects information through questionnaires and interviews when the patient visits a clinic. For example, the data collection unit collects information such as the patient's diet, exercise habits, sleep patterns, and medical history. This allows the data collection unit to understand the patient's lifestyle and health condition. Some or all of the above-described processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the patient's diet and exercise habits into the AI, which can then collect the data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the patient's symptoms and lifestyle in detail based on the collected data. For example, the analysis unit analyzes how diet and exercise habits affect the patient's health condition. This allows the analysis unit to comprehensively evaluate the patient's health condition. Some or all of the above-described processing in the analysis unit may be performed using AI or not. For example, the analysis unit inputs the collected data into the AI, which then analyzes the data. The suggestion unit proposes optimal treatment methods and lifestyle improvements based on the analysis results obtained by the analysis unit. The suggestion unit proposes, for example, specific dietary restrictions or exercise programs. For example, by proposing specific dietary restrictions or exercise programs, the suggestion unit can improve the patient's health condition. The suggestion unit can also provide advice on stress management and improving sleep quality as lifestyle improvements. The suggestion unit can also propose specific treatment plans to facilitate communication between medical staff and patients. Some or all of the above-described processes in the suggestion unit may be performed using or without AI. For example, the suggestion unit inputs the analysis results obtained by the analysis unit into the AI, which then proposes optimal treatment methods and lifestyle improvements. As a result, the AI ​​agent system according to the embodiment can propose optimal treatment methods and lifestyle guidance based on the patient's symptoms and lifestyle.

[0030] The data collection unit collects data on patients' symptoms and lifestyles. Specifically, it collects information through questionnaires and interviews when patients visit the clinic. For example, the data collection unit collects detailed information on patients' diets, exercise habits, sleep patterns, and medical history. This allows the data collection unit to comprehensively understand patients' lifestyles and health status. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on patients' diets and exercise habits into the AI, which can then collect the data. The AI ​​can analyze the contents of questionnaires and interviews using natural language processing technology and extract important information. Furthermore, the AI ​​can also collect data from patients' smartphones and wearable devices. This makes it possible to monitor patients' daily activities and health status in real time. For example, meals can be recorded through a smartphone app, or data such as heart rate and steps can be obtained from wearable devices. This allows the data collection unit to more accurately understand patients' health status and collect data according to individual needs. Furthermore, with the patient's consent, the data collection unit can also collect data from electronic medical records and past medical records. This allows for a better understanding of the patient's long-term health status and treatment history, enabling more accurate data collection. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, it analyzes the patient's symptoms and lifestyle in detail based on the collected data. For example, the analysis unit analyzes how diet and exercise habits affect the patient's health. This allows the analysis unit to comprehensively evaluate the patient's health. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into the AI, which can then analyze the data. The AI ​​can use machine learning algorithms to extract patterns and trends from the collected data and gain insights into the patient's health. For example, the AI ​​can analyze the relationship between diet and blood glucose fluctuations to evaluate the impact of a particular diet on blood glucose levels. The AI ​​can also analyze exercise habits and heart rate data to evaluate the impact of exercise on the cardiovascular system. Furthermore, the AI ​​can analyze the patient's sleep patterns to evaluate the impact of sleep quality on health. This allows the analysis unit to comprehensively evaluate the patient's health and perform analyses tailored to individual needs. The analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past medical data, it can predict the risk of developing specific symptoms or diseases and propose early preventive measures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The proposal unit proposes optimal treatment methods and lifestyle improvements based on the analysis results obtained by the analysis unit. Specifically, it proposes specific dietary restrictions and exercise programs. For example, the proposal unit can improve the patient's health by proposing specific dietary restrictions and exercise programs. The proposal unit can also provide advice on stress management and improving sleep quality as lifestyle improvements. The proposal unit can also propose specific treatment plans to facilitate communication between medical staff and patients. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the analysis results obtained by the analysis unit into the AI, which can then propose optimal treatment methods and lifestyle improvements. The AI ​​uses an algorithm to propose optimal treatment methods and lifestyle improvements based on the patient's individual data. For example, the AI ​​can analyze the patient's diet, exercise habits, and sleep patterns to propose optimal diet plans and exercise programs. The AI ​​can also evaluate the patient's stress level and sleep quality and provide advice on stress management and improving sleep quality. Furthermore, the proposal unit can propose specific treatment plans to facilitate communication between medical staff and patients. For example, the suggestion department can provide guidelines for medical staff to give specific advice to patients based on their symptoms and lifestyle. This allows the suggestion department to make concrete suggestions to improve patients' health and facilitates smooth communication between medical staff and patients. Furthermore, the suggestion department can collect patient feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can collect patient responses and results to suggested treatments and lifestyle improvements and revise the suggestions accordingly. This allows the suggestion department to always provide highly accurate suggestions based on the latest information and optimally manage patients' health.

[0033] The data collection unit can collect data such as the patient's diet, exercise habits, sleep patterns, and medical history. For example, the data collection unit collects information through questionnaires and interviews when the patient visits the clinic. For example, the data collection unit collects information such as the patient's diet, exercise habits, sleep patterns, and medical history. This allows the data collection unit to understand the patient's lifestyle and health status. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the patient's diet and exercise habits into the AI, which can then collect the data. This allows the data collection unit to understand the patient's lifestyle and health status.

[0034] The analysis unit can analyze the patient's symptoms and lifestyle in detail based on the collected data. For example, the analysis unit can analyze how diet and exercise habits affect the patient's health. This allows the analysis unit to comprehensively evaluate the patient's health. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into AI, which can then analyze the data. This allows for a comprehensive evaluation of the patient's health.

[0035] The suggestion unit can propose specific dietary restrictions and exercise programs based on the analysis results. For example, the suggestion unit can improve the patient's health by proposing specific dietary restrictions and exercise programs. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the analysis results into AI, and the AI ​​can propose specific dietary restrictions and exercise programs. This makes it possible to make concrete suggestions to improve the patient's health.

[0036] The suggestion unit can provide advice for stress management and improving sleep quality. For example, by providing advice for stress management and improving sleep quality, the suggestion unit can provide advice that helps improve the patient's lifestyle. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input advice for stress management and improving sleep quality into an AI, which can then provide advice. This allows for the provision of advice that helps improve the patient's lifestyle.

[0037] The proposal unit can propose specific treatment plans to facilitate communication between medical staff and patients. For example, by proposing specific treatment plans to facilitate communication between medical staff and patients, the proposal unit can reduce the burden on medical staff and improve the quality of treatment for patients. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input a specific treatment plan into AI, and the AI ​​can propose a treatment plan. This can reduce the burden on medical staff and improve the quality of treatment for patients.

[0038] The data collection unit can analyze the patient's past medical history and select the optimal data collection method. For example, the data collection unit prioritizes collecting frequently collected data items from the patient's past medical history. For example, the data collection unit focuses on collecting data related to specific symptoms based on the patient's past medical history. For example, the data collection unit analyzes the patient's past medical history and adjusts the frequency of data collection. This allows the data collection unit to select the optimal data collection method based on the patient's past medical history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's medical history data into a generating AI, which can then select the optimal data collection method.

[0039] The data collection unit can filter data based on the patient's current health status and living environment during data collection. For example, the data collection unit can prioritize the collection of important data items based on the patient's current health status. For example, the data collection unit can select and collect highly relevant data according to the patient's living environment. For example, the data collection unit can exclude unnecessary data based on the patient's health status and living environment. This allows the data collection unit to prioritize the collection of important data based on the patient's current health status and living environment. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input patient health status and living environment data into a generating AI, which can then perform filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location information during data collection. For example, the data collection unit can collect data related to region-specific health risks based on the patient's place of residence. For example, the data collection unit can collect data related to health risks during commuting based on the patient's commute route. For example, the data collection unit can collect data related to health risks at travel destinations based on the patient's travel history. This allows the data collection unit to prioritize the collection of highly relevant data based on the patient's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's geographical location information data into a generating AI, which can then prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, the data collection unit can collect health-related information from the patient's social media posts. For example, the data collection unit can collect lifestyle-related data from the patient's social media activity. For example, the data collection unit can collect health risk-related data from the patient's social media friendships. In this way, the data collection unit can collect relevant data based on the patient's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's social media data into a generating AI, and the generating AI can collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the severity of the patient's symptoms. For example, if the patient's symptoms are mild, the analysis unit provides a concise analysis result. For example, if the patient's symptoms are moderate, the analysis unit provides a detailed analysis result. For example, if the patient's symptoms are severe, the analysis unit provides a very detailed analysis result. This allows the analysis unit to provide analysis results with an appropriate level of detail according to the severity of the patient's symptoms. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input patient symptom data into a generating AI, which can then adjust the level of detail of the analysis based on the severity of the symptoms.

[0043] The analysis unit can apply different analysis algorithms depending on the patient's lifestyle category during analysis. For example, the analysis unit can apply a diet-related analysis algorithm based on the patient's eating habits. For example, the analysis unit can apply an exercise-related analysis algorithm based on the patient's exercise habits. For example, the analysis unit can apply a sleep-related analysis algorithm based on the patient's sleep habits. This allows the analysis unit to provide more accurate analysis results by applying an analysis algorithm tailored to the patient's lifestyle. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input patient lifestyle data into a generating AI, and the generating AI can apply different analysis algorithms.

[0044] The analysis unit can improve the accuracy of its analysis based on the patient's medical history. For example, the analysis unit improves the accuracy of its analysis based on the patient's past medical history. For example, the analysis unit focuses on analyzing data related to specific symptoms from the patient's past medical history. For example, the analysis unit analyzes the patient's past medical history and optimizes the analysis algorithm. This allows the analysis unit to improve the accuracy of its analysis based on the patient's medical history. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input patient medical history data into a generating AI, which can then improve the accuracy of its analysis.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the patient during the analysis process. For example, the analysis unit performs its analysis by referring to the latest research papers related to the patient's symptoms. For example, the analysis unit performs its analysis by referring to literature related to the patient's lifestyle. For example, the analysis unit performs its analysis by referring to literature related to the patient's medical history. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the patient. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input patient-related literature data into a generating AI, which can then improve the accuracy of the analysis.

[0046] The suggestion unit can adjust the level of detail of its suggestions based on the severity of the patient's symptoms. For example, if the patient's symptoms are mild, the suggestion unit provides a concise suggestion. If the patient's symptoms are moderate, the suggestion unit provides a detailed suggestion. If the patient's symptoms are severe, the suggestion unit provides a very detailed suggestion. This allows the suggestion unit to provide suggestions with an appropriate level of detail depending on the severity of the patient's symptoms. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input patient symptom data into a generating AI, which can then adjust the level of detail of its suggestions based on the severity of the symptoms.

[0047] The suggestion unit can apply different suggestion algorithms depending on the patient's lifestyle category when making suggestions. For example, the suggestion unit can apply a suggestion algorithm related to diet based on the patient's eating habits. For example, the suggestion unit can apply a suggestion algorithm related to exercise based on the patient's exercise habits. For example, the suggestion unit can apply a suggestion algorithm related to sleep based on the patient's sleep habits. In this way, the suggestion unit can provide more appropriate suggestions by applying a suggestion algorithm that is appropriate for the patient's lifestyle. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input patient lifestyle data into a generating AI, and the generating AI can apply different suggestion algorithms.

[0048] The suggestion unit can improve the accuracy of its suggestions based on the patient's medical history. For example, the suggestion unit improves the accuracy of its suggestions based on the patient's past medical history. For example, the suggestion unit makes suggestions related to specific symptoms based on the patient's past medical history. For example, the suggestion unit analyzes the patient's past medical history and optimizes the suggestion algorithm. This allows the suggestion unit to improve the accuracy of its suggestions based on the patient's medical history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input patient medical history data into a generating AI, which can then improve the accuracy of its suggestions.

[0049] The suggestion unit can improve the accuracy of its suggestions by referring to relevant patient literature during the suggestion process. For example, the suggestion unit may refer to the latest research papers related to the patient's symptoms. For example, the suggestion unit may refer to literature related to the patient's lifestyle. For example, the suggestion unit may refer to literature related to the patient's medical history. In this way, the suggestion unit can improve the accuracy of its suggestions by referring to relevant patient literature. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit may input patient-related literature data into a generating AI, which can then improve the accuracy of its suggestions.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can prioritize the collection of data related to region-specific health risks, taking into account the patient's geographical location. For example, it can collect data related to region-specific infectious disease risks based on the patient's place of residence. It can collect data related to health risks during commuting based on the patient's commute route. It can collect data related to health risks at travel destinations based on the patient's travel history. This allows the data collection unit to prioritize the collection of highly relevant data based on the patient's geographical location.

[0052] The suggestion unit can improve the accuracy of its suggestions based on the patient's medical history. For example, it can make suggestions related to specific symptoms based on the patient's past medical history. By analyzing the patient's past medical history, the suggestion unit can optimize its suggestion algorithm. As a result, the suggestion unit can provide more accurate and effective suggestions based on the patient's medical history.

[0053] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the patient. For example, it can perform analysis by referring to the latest research papers related to the patient's symptoms. It can perform analysis by referring to literature related to the patient's lifestyle. It can perform analysis by referring to literature related to the patient's medical history. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the patient.

[0054] The data collection unit can analyze patients' social media activity and collect relevant data. For example, it can collect health-related information from patients' social media posts. It can collect lifestyle-related data from patients' social media activity. It can collect health risk-related data from patients' social media friendships. In this way, the data collection unit can collect relevant data based on patients' social media activity.

[0055] The suggestion unit can apply different suggestion algorithms depending on the patient's lifestyle category. For example, it can apply a suggestion algorithm related to diet based on the patient's eating habits, an exercise-related suggestion algorithm based on the patient's exercise habits, and a sleep-related suggestion algorithm based on the patient's sleep habits. This allows the suggestion unit to provide more appropriate suggestions by applying a suggestion algorithm tailored to the patient's lifestyle.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects data on the patient's symptoms and lifestyle. For example, when a patient visits the clinic, information is collected through questionnaires and interviews. The data collection unit collects information such as the patient's diet, exercise habits, sleep patterns, and medical history, allowing it to understand the patient's lifestyle and health status. The processing in the data collection unit may or may not be performed using AI. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, based on the collected data, it analyzes the patient's symptoms and lifestyle in detail, and analyzes how diet and exercise habits affect the patient's health. This allows for a comprehensive evaluation of the patient's health. The processing in the analysis unit may be performed using AI or not. Step 3: The proposal unit proposes optimal treatment methods and lifestyle improvements based on the analysis results obtained by the analysis unit. For example, it can propose specific dietary restrictions or exercise programs to improve the patient's health. It can also provide advice on stress management and improving sleep quality as lifestyle improvements. The processing in the proposal unit may be performed using AI or not.

[0058] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that proposes optimal treatment methods and lifestyle guidance based on the symptoms and lifestyle habits of individual patients. This AI agent system collects data on patients' symptoms and lifestyle habits, and the AI ​​analyzes it to propose optimal treatment methods and lifestyle improvement measures, thereby facilitating communication between medical staff at clinics and patients and improving the quality of treatment. For example, when a patient visits a clinic, the AI ​​agent system collects information through questionnaires and interviews. For example, it collects information such as the patient's diet, exercise habits, sleep patterns, and medical history. This allows for an understanding of the patient's lifestyle habits and health status. Next, the AI ​​agent system analyzes the collected data. Based on the collected data, the AI ​​analyzes the patient's symptoms and lifestyle habits in detail. For example, it analyzes how diet and exercise habits affect the patient's health status. This allows for a comprehensive evaluation of the patient's health status. Furthermore, based on the analysis results, the AI ​​agent system proposes optimal treatment methods and lifestyle improvement measures. For example, by proposing specific dietary restrictions or exercise programs, the patient's health status can be improved. It is also possible to provide advice on stress management and improving sleep quality as lifestyle improvement measures. This proposal aims to make it easier for clinic medical staff to communicate with patients. Medical staff can then communicate with patients and develop specific treatment plans based on treatment methods and lifestyle improvements suggested by AI. This will reduce the burden on medical staff and improve the quality of care for patients. For example, even if doctors and nurses are busy and cannot dedicate enough time, the AI ​​can automatically collect and organize information from patients and suggest optimal treatment methods and lifestyle improvements, thereby reducing the burden on medical staff. Furthermore, even when it is difficult to propose the optimal treatment plan for each patient, the AI ​​can make suggestions based on analysis results, improving the quality of care for patients. In this way, by using an AI agent, it is possible to provide optimal medical care to each individual patient and reduce the burden on the medical field. This will improve the overall quality and efficiency of medical care.This allows the AI ​​agent system to suggest optimal treatment methods and lifestyle guidance based on the patient's symptoms and lifestyle.

[0059] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data on the patient's symptoms and lifestyle. For example, the data collection unit collects information through questionnaires and interviews when the patient visits a clinic. For example, the data collection unit collects information such as the patient's diet, exercise habits, sleep patterns, and medical history. This allows the data collection unit to understand the patient's lifestyle and health condition. Some or all of the above-described processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the patient's diet and exercise habits into the AI, which can then collect the data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the patient's symptoms and lifestyle in detail based on the collected data. For example, the analysis unit analyzes how diet and exercise habits affect the patient's health condition. This allows the analysis unit to comprehensively evaluate the patient's health condition. Some or all of the above-described processing in the analysis unit may be performed using AI or not. For example, the analysis unit inputs the collected data into the AI, which then analyzes the data. The suggestion unit proposes optimal treatment methods and lifestyle improvements based on the analysis results obtained by the analysis unit. The suggestion unit proposes, for example, specific dietary restrictions or exercise programs. For example, by proposing specific dietary restrictions or exercise programs, the suggestion unit can improve the patient's health condition. The suggestion unit can also provide advice on stress management and improving sleep quality as lifestyle improvements. The suggestion unit can also propose specific treatment plans to facilitate communication between medical staff and patients. Some or all of the above-described processes in the suggestion unit may be performed using or without AI. For example, the suggestion unit inputs the analysis results obtained by the analysis unit into the AI, which then proposes optimal treatment methods and lifestyle improvements. As a result, the AI ​​agent system according to the embodiment can propose optimal treatment methods and lifestyle guidance based on the patient's symptoms and lifestyle.

[0060] The data collection unit collects data on patients' symptoms and lifestyles. Specifically, it collects information through questionnaires and interviews when patients visit the clinic. For example, the data collection unit collects detailed information on patients' diets, exercise habits, sleep patterns, and medical history. This allows the data collection unit to comprehensively understand patients' lifestyles and health status. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on patients' diets and exercise habits into the AI, which can then collect the data. The AI ​​can analyze the contents of questionnaires and interviews using natural language processing technology and extract important information. Furthermore, the AI ​​can also collect data from patients' smartphones and wearable devices. This makes it possible to monitor patients' daily activities and health status in real time. For example, meals can be recorded through a smartphone app, or data such as heart rate and steps can be obtained from wearable devices. This allows the data collection unit to more accurately understand patients' health status and collect data according to individual needs. Furthermore, with the patient's consent, the data collection unit can also collect data from electronic medical records and past medical records. This allows for a better understanding of the patient's long-term health status and treatment history, enabling more accurate data collection. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall system performance.

[0061] The analysis unit analyzes the data collected by the data collection unit. Specifically, it analyzes the patient's symptoms and lifestyle in detail based on the collected data. For example, the analysis unit analyzes how diet and exercise habits affect the patient's health. This allows the analysis unit to comprehensively evaluate the patient's health. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into the AI, which can then analyze the data. The AI ​​can use machine learning algorithms to extract patterns and trends from the collected data and gain insights into the patient's health. For example, the AI ​​can analyze the relationship between diet and blood glucose fluctuations to evaluate the impact of a particular diet on blood glucose levels. The AI ​​can also analyze exercise habits and heart rate data to evaluate the impact of exercise on the cardiovascular system. Furthermore, the AI ​​can analyze the patient's sleep patterns to evaluate the impact of sleep quality on health. This allows the analysis unit to comprehensively evaluate the patient's health and perform analyses tailored to individual needs. The analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past medical data, it can predict the risk of developing specific symptoms or diseases and propose early preventive measures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0062] The proposal unit proposes optimal treatment methods and lifestyle improvements based on the analysis results obtained by the analysis unit. Specifically, it proposes specific dietary restrictions and exercise programs. For example, the proposal unit can improve the patient's health by proposing specific dietary restrictions and exercise programs. The proposal unit can also provide advice on stress management and improving sleep quality as lifestyle improvements. The proposal unit can also propose specific treatment plans to facilitate communication between medical staff and patients. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the analysis results obtained by the analysis unit into the AI, which can then propose optimal treatment methods and lifestyle improvements. The AI ​​uses an algorithm to propose optimal treatment methods and lifestyle improvements based on the patient's individual data. For example, the AI ​​can analyze the patient's diet, exercise habits, and sleep patterns to propose optimal diet plans and exercise programs. The AI ​​can also evaluate the patient's stress level and sleep quality and provide advice on stress management and improving sleep quality. Furthermore, the proposal unit can propose specific treatment plans to facilitate communication between medical staff and patients. For example, the suggestion department can provide guidelines for medical staff to give specific advice to patients based on their symptoms and lifestyle. This allows the suggestion department to make concrete suggestions to improve patients' health and facilitates smooth communication between medical staff and patients. Furthermore, the suggestion department can collect patient feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can collect patient responses and results to suggested treatments and lifestyle improvements and revise the suggestions accordingly. This allows the suggestion department to always provide highly accurate suggestions based on the latest information and optimally manage patients' health.

[0063] The data collection unit can collect data such as the patient's diet, exercise habits, sleep patterns, and medical history. For example, the data collection unit collects information through questionnaires and interviews when the patient visits the clinic. For example, the data collection unit collects information such as the patient's diet, exercise habits, sleep patterns, and medical history. This allows the data collection unit to understand the patient's lifestyle and health status. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the patient's diet and exercise habits into the AI, which can then collect the data. This allows the data collection unit to understand the patient's lifestyle and health status.

[0064] The analysis unit can analyze the patient's symptoms and lifestyle in detail based on the collected data. For example, the analysis unit can analyze how diet and exercise habits affect the patient's health. This allows the analysis unit to comprehensively evaluate the patient's health. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into AI, which can then analyze the data. This allows for a comprehensive evaluation of the patient's health.

[0065] The suggestion unit can propose specific dietary restrictions and exercise programs based on the analysis results. For example, the suggestion unit can improve the patient's health by proposing specific dietary restrictions and exercise programs. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the analysis results into AI, and the AI ​​can propose specific dietary restrictions and exercise programs. This makes it possible to make concrete suggestions to improve the patient's health.

[0066] The suggestion unit can provide advice for stress management and improving sleep quality. For example, by providing advice for stress management and improving sleep quality, the suggestion unit can provide advice that helps improve the patient's lifestyle. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input advice for stress management and improving sleep quality into an AI, which can then provide advice. This allows for the provision of advice that helps improve the patient's lifestyle.

[0067] The proposal unit can propose specific treatment plans to facilitate communication between medical staff and patients. For example, by proposing specific treatment plans to facilitate communication between medical staff and patients, the proposal unit can reduce the burden on medical staff and improve the quality of treatment for patients. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input a specific treatment plan into AI, and the AI ​​can propose a treatment plan. This can reduce the burden on medical staff and improve the quality of treatment for patients.

[0068] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the patient is relaxed, the data collection unit will provide a detailed questionnaire and collect more information. If the patient is stressed, for example, the data collection unit will prioritize concise questions and collect necessary information quickly. If the patient is in a hurry, for example, the data collection unit will use voice input to quickly collect information. This allows the data collection unit to collect more appropriate data by adjusting the timing of data collection according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input patient emotion data into a generative AI, which can estimate emotions and adjust the timing of data collection.

[0069] The data collection unit can analyze the patient's past medical history and select the optimal data collection method. For example, the data collection unit prioritizes collecting frequently collected data items from the patient's past medical history. For example, the data collection unit focuses on collecting data related to specific symptoms based on the patient's past medical history. For example, the data collection unit analyzes the patient's past medical history and adjusts the frequency of data collection. This allows the data collection unit to select the optimal data collection method based on the patient's past medical history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's medical history data into a generating AI, which can then select the optimal data collection method.

[0070] The data collection unit can filter data based on the patient's current health status and living environment during data collection. For example, the data collection unit can prioritize the collection of important data items based on the patient's current health status. For example, the data collection unit can select and collect highly relevant data according to the patient's living environment. For example, the data collection unit can exclude unnecessary data based on the patient's health status and living environment. This allows the data collection unit to prioritize the collection of important data based on the patient's current health status and living environment. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input patient health status and living environment data into a generating AI, which can then perform filtering.

[0071] The data collection unit can estimate the patient's emotions and prioritize the data to collect based on the estimated emotions. For example, if the patient is relaxed, the data collection unit prioritizes collecting detailed data. If the patient is stressed, the data collection unit prioritizes collecting important data items. If the patient is in a hurry, the data collection unit prioritizes collecting the most necessary data. This allows the data collection unit to collect more appropriate data by prioritizing the data to collect according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input patient emotion data into a generative AI, which can estimate the emotions and determine the priority of the data to collect.

[0072] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location information during data collection. For example, the data collection unit can collect data related to region-specific health risks based on the patient's place of residence. For example, the data collection unit can collect data related to health risks during commuting based on the patient's commute route. For example, the data collection unit can collect data related to health risks at travel destinations based on the patient's travel history. This allows the data collection unit to prioritize the collection of highly relevant data based on the patient's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's geographical location information data into a generating AI, which can then prioritize the collection of highly relevant data.

[0073] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, the data collection unit can collect health-related information from the patient's social media posts. For example, the data collection unit can collect lifestyle-related data from the patient's social media activity. For example, the data collection unit can collect health risk-related data from the patient's social media friendships. In this way, the data collection unit can collect relevant data based on the patient's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's social media data into a generating AI, and the generating AI can collect relevant data.

[0074] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the patient is relaxed, the analysis unit provides detailed analysis results. For example, if the patient is stressed, the analysis unit provides concise analysis results. For example, if the patient is in a hurry, the analysis unit provides concise analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input patient emotion data into a generative AI, which can estimate emotions and adjust the presentation of the analysis.

[0075] The analysis unit can adjust the level of detail of the analysis based on the severity of the patient's symptoms. For example, if the patient's symptoms are mild, the analysis unit provides a concise analysis result. For example, if the patient's symptoms are moderate, the analysis unit provides a detailed analysis result. For example, if the patient's symptoms are severe, the analysis unit provides a very detailed analysis result. This allows the analysis unit to provide analysis results with an appropriate level of detail according to the severity of the patient's symptoms. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input patient symptom data into a generating AI, which can then adjust the level of detail of the analysis based on the severity of the symptoms.

[0076] The analysis unit can apply different analysis algorithms depending on the patient's lifestyle category during analysis. For example, the analysis unit can apply a diet-related analysis algorithm based on the patient's eating habits. For example, the analysis unit can apply an exercise-related analysis algorithm based on the patient's exercise habits. For example, the analysis unit can apply a sleep-related analysis algorithm based on the patient's sleep habits. This allows the analysis unit to provide more accurate analysis results by applying an analysis algorithm tailored to the patient's lifestyle. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input patient lifestyle data into a generating AI, and the generating AI can apply different analysis algorithms.

[0077] The analysis unit can estimate the patient's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the patient is relaxed, the analysis unit will prioritize detailed analysis. If the patient is stressed, the analysis unit will prioritize important analysis. If the patient is in a hurry, the analysis unit will prioritize rapid analysis. This allows the analysis unit to provide more appropriate analysis results by determining the priority of analysis according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input patient emotion data into a generative AI, which can estimate emotions and determine the priority of the analysis.

[0078] The analysis unit can improve the accuracy of its analysis based on the patient's medical history. For example, the analysis unit improves the accuracy of its analysis based on the patient's past medical history. For example, the analysis unit focuses on analyzing data related to specific symptoms from the patient's past medical history. For example, the analysis unit analyzes the patient's past medical history and optimizes the analysis algorithm. This allows the analysis unit to improve the accuracy of its analysis based on the patient's medical history. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input patient medical history data into a generating AI, which can then improve the accuracy of its analysis.

[0079] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the patient during the analysis process. For example, the analysis unit performs its analysis by referring to the latest research papers related to the patient's symptoms. For example, the analysis unit performs its analysis by referring to literature related to the patient's lifestyle. For example, the analysis unit performs its analysis by referring to literature related to the patient's medical history. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the patient. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input patient-related literature data into a generating AI, which can then improve the accuracy of the analysis.

[0080] The suggestion unit can estimate the patient's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the patient is relaxed, the suggestion unit will provide detailed suggestions. If the patient is stressed, the suggestion unit will provide concise suggestions. If the patient is in a hurry, the suggestion unit will provide concise suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way it presents them according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input patient emotion data into a generative AI, which can estimate the emotions and adjust the way it presents its suggestions.

[0081] The suggestion unit can adjust the level of detail of its suggestions based on the severity of the patient's symptoms. For example, if the patient's symptoms are mild, the suggestion unit provides a concise suggestion. If the patient's symptoms are moderate, the suggestion unit provides a detailed suggestion. If the patient's symptoms are severe, the suggestion unit provides a very detailed suggestion. This allows the suggestion unit to provide suggestions with an appropriate level of detail depending on the severity of the patient's symptoms. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input patient symptom data into a generating AI, which can then adjust the level of detail of its suggestions based on the severity of the symptoms.

[0082] The suggestion unit can apply different suggestion algorithms depending on the patient's lifestyle category when making suggestions. For example, the suggestion unit can apply a suggestion algorithm related to diet based on the patient's eating habits. For example, the suggestion unit can apply a suggestion algorithm related to exercise based on the patient's exercise habits. For example, the suggestion unit can apply a suggestion algorithm related to sleep based on the patient's sleep habits. In this way, the suggestion unit can provide more appropriate suggestions by applying a suggestion algorithm that is appropriate for the patient's lifestyle. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input patient lifestyle data into a generating AI, and the generating AI can apply different suggestion algorithms.

[0083] The suggestion unit can estimate the patient's emotions and prioritize suggestions based on those emotions. For example, if the patient is relaxed, the suggestion unit will prioritize detailed suggestions. If the patient is stressed, the suggestion unit will prioritize important suggestions. If the patient is in a hurry, the suggestion unit will prioritize quick suggestions. This allows the suggestion unit to provide more appropriate suggestions by prioritizing suggestions according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input patient emotion data into a generative AI, which can estimate emotions and determine the priority of suggestions.

[0084] The suggestion unit can improve the accuracy of its suggestions based on the patient's medical history. For example, the suggestion unit improves the accuracy of its suggestions based on the patient's past medical history. For example, the suggestion unit makes suggestions related to specific symptoms based on the patient's past medical history. For example, the suggestion unit analyzes the patient's past medical history and optimizes the suggestion algorithm. This allows the suggestion unit to improve the accuracy of its suggestions based on the patient's medical history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input patient medical history data into a generating AI, which can then improve the accuracy of its suggestions.

[0085] The suggestion unit can improve the accuracy of its suggestions by referring to relevant patient literature during the suggestion process. For example, the suggestion unit may refer to the latest research papers related to the patient's symptoms. For example, the suggestion unit may refer to literature related to the patient's lifestyle. For example, the suggestion unit may refer to literature related to the patient's medical history. In this way, the suggestion unit can improve the accuracy of its suggestions by referring to relevant patient literature. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit may input patient-related literature data into a generating AI, which can then improve the accuracy of its suggestions.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The suggestion department can estimate the patient's emotions and adjust the suggested treatments and lifestyle improvements based on those estimates. For example, if the patient is relaxed, it can suggest detailed treatments and lifestyle improvements. If the patient is stressed, it can prioritize concise and easy-to-implement suggestions. If the patient is in a hurry, it can quickly provide the most important suggestions. In this way, the suggestion department can provide more appropriate treatments and lifestyle improvements by adjusting the content and method of suggestions according to the patient's emotions.

[0088] The data collection unit can prioritize the collection of data related to region-specific health risks, taking into account the patient's geographical location. For example, it can collect data related to region-specific infectious disease risks based on the patient's place of residence. It can collect data related to health risks during commuting based on the patient's commute route. It can collect data related to health risks at travel destinations based on the patient's travel history. This allows the data collection unit to prioritize the collection of highly relevant data based on the patient's geographical location.

[0089] The analysis unit can estimate the patient's emotions and prioritize analyses based on those emotions. For example, if the patient is relaxed, detailed analysis can be prioritized. If the patient is stressed, important analyses can be prioritized. If the patient is in a hurry, rapid analysis can be prioritized. This allows the analysis unit to provide more appropriate analysis results by prioritizing analyses according to the patient's emotions.

[0090] The suggestion unit can improve the accuracy of its suggestions based on the patient's medical history. For example, it can make suggestions related to specific symptoms based on the patient's past medical history. By analyzing the patient's past medical history, the suggestion unit can optimize its suggestion algorithm. As a result, the suggestion unit can provide more accurate and effective suggestions based on the patient's medical history.

[0091] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on those emotions. For example, if the patient is relaxed, a detailed questionnaire can be provided to collect more information. If the patient is stressed, concise questions can be prioritized to collect necessary information quickly. If the patient is in a hurry, voice input can be used to quickly collect information. This allows the data collection unit to adjust the timing of data collection according to the patient's emotions, enabling more appropriate data collection.

[0092] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the patient. For example, it can perform analysis by referring to the latest research papers related to the patient's symptoms. It can perform analysis by referring to literature related to the patient's lifestyle. It can perform analysis by referring to literature related to the patient's medical history. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the patient.

[0093] The suggestion function can estimate the patient's emotions and adjust the way it presents suggestions based on those emotions. For example, if the patient is relaxed, it can provide detailed suggestions. If the patient is stressed, it can provide concise suggestions. If the patient is in a hurry, it can provide suggestions that get straight to the point. In this way, the suggestion function can provide more appropriate suggestions by adjusting the way it presents suggestions according to the patient's emotions.

[0094] The data collection unit can analyze patients' social media activity and collect relevant data. For example, it can collect health-related information from patients' social media posts. It can collect lifestyle-related data from patients' social media activity. It can collect health risk-related data from patients' social media friendships. In this way, the data collection unit can collect relevant data based on patients' social media activity.

[0095] The suggestion unit can apply different suggestion algorithms depending on the patient's lifestyle category. For example, it can apply a suggestion algorithm related to diet based on the patient's eating habits, an exercise-related suggestion algorithm based on the patient's exercise habits, and a sleep-related suggestion algorithm based on the patient's sleep habits. This allows the suggestion unit to provide more appropriate suggestions by applying a suggestion algorithm tailored to the patient's lifestyle.

[0096] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis based on those emotions. For example, if the patient is relaxed, it can provide detailed analysis results. If the patient is stressed, it can provide concise analysis results. If the patient is in a hurry, it can provide concise analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the patient's emotions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects data on the patient's symptoms and lifestyle. For example, when a patient visits the clinic, information is collected through questionnaires and interviews. The data collection unit collects information such as the patient's diet, exercise habits, sleep patterns, and medical history, allowing it to understand the patient's lifestyle and health status. The processing in the data collection unit may or may not be performed using AI. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, based on the collected data, it analyzes the patient's symptoms and lifestyle in detail, and analyzes how diet and exercise habits affect the patient's health. This allows for a comprehensive evaluation of the patient's health. The processing in the analysis unit may be performed using AI or not. Step 3: The proposal unit proposes optimal treatment methods and lifestyle improvements based on the analysis results obtained by the analysis unit. For example, it can propose specific dietary restrictions or exercise programs to improve the patient's health. It can also provide advice on stress management and improving sleep quality as lifestyle improvements. The processing in the proposal unit may be performed using AI or not.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14, and collects information through questionnaires and interviews when a patient visits the clinic. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and can analyze the patient's symptoms and lifestyle in detail based on the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, and can propose optimal treatment methods and lifestyle improvement measures based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements, including the collection unit, analysis unit, and proposal unit described above, can be implemented in at least one of the smart glasses 2214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 2214, and can collect information through questionnaires and interviews when a patient visits the clinic. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and can analyze the patient's symptoms and lifestyle in detail based on the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, and can propose the optimal treatment method and lifestyle improvement measures based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements, including the collection unit, analysis unit, and proposal unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314, and can collect information through questionnaires and interviews when a patient visits the clinic. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and can analyze the patient's symptoms and lifestyle in detail based on the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, and can propose the optimal treatment method and lifestyle improvement measures based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements, including the collection unit, analysis unit, and proposal unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and can collect information through questionnaires and interviews when a patient visits the clinic. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and can analyze the patient's symptoms and lifestyle in detail based on the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and can propose optimal treatment methods and lifestyle improvement measures based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A data collection unit that collects data on patients' symptoms and lifestyles, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a proposal unit that proposes optimal treatment methods and lifestyle improvement measures based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on patients' dietary habits, exercise routines, sleep patterns, and medical history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, we conduct a detailed analysis of the patient's symptoms and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we propose specific dietary restrictions and exercise programs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We provide advice on stress management and improving sleep quality. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose specific treatment plans to facilitate communication between medical staff and patients. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the patient's past medical history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the patient's current health status and living environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze patients' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the severity of the patient's symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the patient's lifestyle category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the patient's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the patient's medical history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant patient literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, The system estimates the patient's emotions and adjusts the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the severity of the patient's symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the patient's lifestyle category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, The system estimates the patient's emotions and prioritizes proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, improve the accuracy of the suggestions based on the patient's medical history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, refer to relevant patient literature to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data on patients' symptoms and lifestyles, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a proposal unit that proposes optimal treatment methods and lifestyle improvement measures based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on patients' dietary habits, exercise routines, sleep patterns, and medical history. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we conduct a detailed analysis of the patient's symptoms and lifestyle. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we propose specific dietary restrictions and exercise programs. The system according to feature 1.

5. The aforementioned proposal section is, We provide advice on stress management and improving sleep quality. The system according to feature 1.

6. The aforementioned proposal section is, We propose specific treatment plans to facilitate communication between medical staff and patients. The system according to feature 1.

7. The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the patient's past medical history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on the patient's current health status and living environment. The system according to feature 1.

10. The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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