system

Abstract

The system according to this embodiment aims to quickly suggest the most suitable treatment method, hospital, and doctor based on an individual's genetic data and health checkup information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a selection unit, and a provision unit. The collection unit collects an individual's genetic data and health checkup information. The analysis unit analyzes the data collected by the collection unit to analyze the patient's constitution and medical history. The proposal unit proposes a treatment method based on the data analyzed by the analysis unit. The selection unit selects the most suitable hospital and doctor based on the treatment method proposed by the proposal unit. The provision unit provides information about the hospital and doctor selected by the selection unit.

Description

Technical Field

[0004] ,

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[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 the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it takes time to select a treatment method suitable for a patient and a hospital / doctor, and there is a risk that appropriate treatment may not be performed during that time.

[0005] The system according to the embodiment aims to quickly propose an optimal treatment method and a hospital / doctor based on an individual's genetic data and health diagnosis information.<000002,9>

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a selection unit, and a provision unit. The collection unit collects personal genetic data and health checkup information. The analysis unit analyzes the data collected by the collection unit to analyze the patient's constitution and medical history. The proposal unit proposes a treatment method based on the data analyzed by the analysis unit. The selection unit selects the most suitable hospital and doctor based on the treatment method proposed by the proposal unit. The provision unit provides information about the hospital and doctor selected by the selection unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly suggest the most suitable treatment method, hospital, and doctor based on an individual's genetic data and health checkup information. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 system according to an embodiment of the present invention is a system that utilizes AI to combine personal data such as an individual's genetic data and health checkup information with information on hospitals, doctors, diseases, and treatment methods, providing individually optimized services for early detection, hospital and doctor selection, and second opinions on treatment methods. This system collects personal data such as an individual's genetic data and health checkup information, and the AI ​​analyzes it to propose individually optimized treatment methods. Furthermore, based on the proposed treatment methods, it selects the most suitable hospital and doctor and provides a second opinion on treatment methods. Through this system, patients can receive the most appropriate treatment quickly, potentially saving lives. For example, personal data such as an individual's genetic data and health checkup information is collected. In this process, the challenges of data collection are solved by introducing a system that links with medical insurance and reduces insurance premiums for providing information. For example, if a patient provides their genetic data and health checkup information, their insurance premium is discounted by a certain percentage. Next, the collected data is analyzed by AI. Based on the genetic data and health checkup information, the AI ​​analyzes the patient's constitution and medical history and proposes the most suitable treatment method. For example, it may be found that a specific anticancer drug is effective for a patient with a specific genetic mutation. In this way, individually optimized treatment methods can be proposed. Furthermore, based on the proposed treatment method, the system selects the most suitable hospital and doctor. The AI ​​consults a database of hospitals and doctors to suggest the best fit for the patient. For example, it can select hospitals and doctors with a strong track record in specific treatment methods. Finally, it provides a second opinion on treatment methods. The AI ​​provides the latest information on the proposed treatment methods to help patients make informed decisions. For example, by providing information on the latest research findings and clinical trials, patients can choose the best treatment option. This system allows patients to receive the most appropriate treatment quickly, potentially saving lives. In addition, by integrating with health insurance, the system overcomes data collection challenges and allows patients to receive premium discounts. For example, by providing their genetic data and health checkup information, patients can receive a certain percentage discount on their insurance premiums. In this way, patients can receive the best treatment while reducing their financial burden.This allows AI-powered systems to provide individually optimized services for early detection, hospital and doctor selection, and second opinions on treatment methods.

[0029] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a selection unit, and a provision unit. The collection unit collects an individual's genetic data and health checkup information. For example, the collection unit can use a genetic testing kit to obtain genetic data. The collection unit can also collect data from hospitals and clinics to obtain health checkup information. For example, the collection unit analyzes genetic data provided by the patient and detects specific genetic markers. The collection unit can also collect the patient's blood test results and imaging diagnostic results based on the health checkup information. Furthermore, the collection unit can be linked with medical insurance and introduce a system where insurance premiums are reduced by providing information. For example, the collection unit can introduce a system where insurance premiums are discounted by a certain percentage if the patient provides their genetic data and health checkup information. The analysis unit analyzes the data collected by the collection unit and analyzes the patient's constitution and medical history. For example, the analysis unit can detect specific genetic mutations based on genetic data. The analysis unit can also analyze the patient's blood test results and imaging diagnostic results based on the health checkup information. For example, the analysis unit suggests that a specific anticancer drug would be effective for a patient with a particular gene mutation. The analysis unit can analyze the patient's constitution and medical history in detail based on genetic data and health checkup information. The proposal unit proposes a treatment method based on the data analyzed by the analysis unit. For example, the proposal unit can suggest that a specific anticancer drug would be effective for a patient with a particular gene mutation. The proposal unit can also propose the optimal treatment method based on the patient's constitution and medical history. For example, the proposal unit suggests that a specific anticancer drug would be effective for a patient with a particular gene mutation. The selection unit selects the most suitable hospital and doctor based on the treatment method proposed by the proposal unit. For example, the selection unit can refer to a database of hospitals and doctors and propose the most suitable hospital and doctor for the patient. The selection unit can also select hospitals and doctors with a strong track record in a particular treatment method. For example, the selection unit selects hospitals and doctors with a strong track record in a particular treatment method. The provision unit provides information about the hospitals and doctors selected by the selection unit. For example, the provision unit can provide the latest information on the proposed treatment method.The service provider can also provide patients with the latest research findings and clinical trial information to help them choose a treatment method. For example, the service provider can provide the latest research findings and clinical trial information. This allows the system according to the embodiment to provide individually optimized services for early detection, hospital and doctor selection, and second opinions on treatment methods.

[0030] The data collection unit collects individual genetic data and health checkup information. For example, to obtain genetic data, the unit can use genetic testing kits. These kits collect samples such as saliva or blood, which are then sent to a specialized laboratory for analysis. The analysis results are sent to the data collection unit in digital format and stored in a database. The data collection unit can also collect data from hospitals and clinics to obtain health checkup information. For example, it can obtain patients' blood test results and imaging diagnostic results through electronic medical record systems. This requires patient consent, and data privacy is strictly protected. Furthermore, the data collection unit can collaborate with health insurance companies to introduce a system where insurance premiums are reduced for providing information. For example, the data collection unit could introduce a system where patients receive a certain percentage discount on their insurance premiums for providing their genetic data and health checkup information. This would incentivize patients to proactively provide their health information, allowing the data collection unit to collect more data. The collected data is stored in a secure cloud environment and managed so that the analysis unit and proposal unit can access it. This allows the data collection unit to efficiently and securely collect individual genetic data and health checkup information, enriching the overall system database.

[0031] The analysis unit analyzes data collected by the data collection unit to analyze the patient's constitution and medical history. For example, the analysis unit can detect specific gene mutations based on genetic data. Next-generation sequencing technology and AI-based data analysis methods are used for analyzing genetic data. AI analyzes vast amounts of genetic data quickly and accurately to identify specific gene mutations and disease risks. The analysis unit can also analyze the patient's blood test results and imaging diagnostic results based on health checkup information. For example, it can use AI to detect abnormal values ​​in blood test results and identify the presence or absence of lesions from imaging diagnostic results. This allows the analysis unit to analyze the patient's constitution and medical history in detail and provide a foundation for personalized medicine. Furthermore, the analysis unit can also predict the patient's health risks by utilizing past data and statistical information. For example, it can predict future disease risks based on past health checkup data and propose early preventive measures. This allows the analysis unit to comprehensively evaluate the patient's health status and provide information to propose optimal treatment methods and preventive measures.

[0032] The Proposal Department proposes treatment methods based on data analyzed by the Analysis Department. For example, the Proposal Department can suggest that a specific anticancer drug is effective for patients with a particular gene mutation. The Proposal Department uses AI to evaluate the analysis results and identify the optimal treatment method. The AI ​​learns from past treatment data and clinical trial results to propose the treatment method best suited to the individual patient's situation. The Proposal Department can also propose the optimal treatment method based on the patient's constitution and medical history. For example, it can propose a treatment method with fewer side effects by considering the patient's allergy information and past treatment history. Furthermore, the Proposal Department constantly updates its information on the latest medical technologies and treatments, enabling it to propose cutting-edge treatment methods. This allows the Proposal Department to provide patients with the optimal treatment method and maximize treatment effectiveness.

[0033] The selection unit selects the most suitable hospital and physician based on the treatment method proposed by the suggestion unit. For example, the selection unit can refer to a database of hospitals and physicians to suggest the best fit for the patient. The selection unit can also select hospitals and physicians with a strong track record in specific treatment methods. For instance, it can select hospitals and physicians with a high success rate in a particular anticancer drug treatment. The selection unit makes its optimal selection based on hospital and physician evaluation data and patient feedback. Furthermore, the selection unit can consider factors such as the patient's place of residence and desired treatment duration when selecting the most suitable hospital and physician. This allows the selection unit to suggest the most suitable healthcare provider to the patient, thereby improving the quality of treatment.

[0034] The provision department provides information about hospitals and doctors selected by the selection department. For example, the provision department can provide the latest information on proposed treatment methods. The provision department can also provide information on the latest research findings and clinical trials to help patients choose a treatment method. This allows patients to obtain sufficient information about treatment methods and healthcare providers, enabling them to make informed treatment choices. Furthermore, the provision department also provides information on the procedures and necessary documents for patients receiving treatment. This allows patients to prepare for a smooth treatment process. The provision department also responds promptly to patients' questions and concerns, providing an environment where they can receive treatment with peace of mind. In this way, the provision department can provide comprehensive information to patients and improve the quality of treatment.

[0035] The data collection unit can collaborate with health insurance companies to implement a system where insurance premiums are reduced in exchange for information provided. For example, the unit could implement a system where patients receive a certain percentage discount on their insurance premiums if they provide their genetic data or health checkup information. The data collection unit can enter into data sharing agreements with health insurance companies to securely manage the data provided by patients. For example, the unit could collaborate with health insurance companies to set data sharing methods and premium discount criteria. The data collection unit can also anonymize the data provided by patients and take measures to protect their privacy. For example, the unit could use data anonymization technology to protect patients' personal information. This solves the challenges of data collection and allows patients to receive premium discounts. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit could input the data provided by patients into a generating AI and have the generating AI perform data analysis and management.

[0036] The analysis unit can analyze a patient's constitution and medical history based on genetic data and health checkup information. For example, the analysis unit can detect specific gene mutations based on genetic data. The analysis unit can also analyze a patient's blood test results and imaging diagnostic results based on health checkup information. For example, the analysis unit can suggest that a specific anticancer drug is effective for a patient with a specific gene mutation. The analysis unit can analyze a patient's constitution and medical history in detail based on genetic data and health checkup information. This allows the analysis unit to suggest the optimal treatment method by analyzing the patient's constitution and medical history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input genetic data and health checkup information into a generating AI and have the generating AI perform the data analysis.

[0037] The suggestion unit can suggest that a specific anticancer drug is effective for patients with a specific gene mutation. For example, the suggestion unit can suggest that a specific anticancer drug is effective for patients with a specific gene mutation. The suggestion unit can also suggest the optimal treatment method based on the patient's constitution and medical history. For example, the suggestion unit can suggest that a specific anticancer drug is effective for patients with a specific gene mutation. This allows the suggestion unit to suggest the optimal treatment method for patients with a specific gene mutation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input genetic data and health checkup information into a generating AI and have the generating AI suggest the optimal treatment method.

[0038] The selection unit can refer to a database of hospitals and doctors and suggest the most suitable hospital and doctor for the patient. For example, the selection unit can refer to a database of hospitals and doctors and suggest the most suitable hospital and doctor for the patient. The selection unit can also select hospitals and doctors with a strong track record in specific treatment methods. This allows the selection unit to suggest the most suitable hospital and doctor for the patient. Some or all of the above-described processes in the selection unit may be performed using AI, or not. For example, the selection unit can input a database of hospitals and doctors into a generating AI and have the generating AI select the most suitable hospital and doctor.

[0039] The information provider can provide the latest information on proposed treatment methods. For example, the information provider can provide the latest research results and clinical trial information on proposed treatment methods. The information provider can also provide the latest information to help patients choose a treatment method. For example, the information provider can provide the latest research results and clinical trial information, which can help patients choose a treatment method. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the latest research results and clinical trial information into a generating AI and have the generating AI perform the information provision.

[0040] The data collection unit can analyze a patient's past medical history and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on the patient's past treatment and examination history. The data collection unit can also determine whether a specific test is necessary based on the patient's past medical history. For example, the data collection unit can determine whether a specific test is necessary based on the patient's past medical history. The data collection unit can also analyze a patient's past medical history and adjust the frequency of data collection. For example, the data collection unit analyzes a patient's past medical history and adjusts the frequency of data collection. This allows the optimal data collection method to be selected 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, for example, or without AI. For example, the data collection unit can input the patient's past medical history into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter data based on the patient's current health status and lifestyle. For example, the data collection unit can collect only the necessary data based on the patient's current health status. The data collection unit can also customize the content of data collection by considering the patient's lifestyle. For example, the data collection unit can customize the content of data collection by considering the patient's lifestyle. The data collection unit can also determine the priority of data collection based on the patient's health status and lifestyle. For example, the data collection unit can determine the priority of data collection based on the patient's health status and lifestyle. This allows the collection to collect only the necessary data based on the patient's current health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the patient's current health status and lifestyle into a generating AI and have the generating AI perform the filtering.

[0042] 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. The data collection unit can also prioritize the collection of data from nearby medical institutions based on the patient's geographical location information. For example, the data collection unit can prioritize the collection of data from nearby medical institutions based on the patient's geographical location information. The data collection unit can also collect data related to environmental factors by considering the patient's geographical location information. For example, the data collection unit can collect data related to environmental factors by considering the patient's geographical location information. This allows for the priority collection of highly relevant data by considering the patient's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze patients' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze health-related posts from patients' social media activity and collect relevant data. The data collection unit can also estimate lifestyle habits and stress levels based on patients' social media activity and reflect this in data collection. For example, the data collection unit can estimate lifestyle habits and stress levels based on patients' social media activity and reflect this in data collection. The data collection unit can also analyze patients' social media activity and collect information related to health risks. For example, the data collection unit can analyze patients' social media activity and collect information related to health risks. This allows for more appropriate data collection by analyzing patients' social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on patients' social media activity into a generating AI and have the generating AI collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the patient's important health indicators during the analysis. For example, if the patient's important health indicators show abnormal values, the analysis unit will perform a detailed analysis. The analysis unit can also provide a simplified analysis result if the patient's health indicators are within the normal range. For example, the analysis unit will provide a simplified analysis result if the patient's health indicators are within the normal range. The analysis unit can also perform an analysis that focuses on specific items based on the patient's health indicators. For example, the analysis unit will perform an analysis that focuses on specific items based on the patient's health indicators. This allows the level of detail of the analysis to be adjusted based on the patient's important health indicators. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient health indicator data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the patient's medical history during analysis. For example, the analysis unit can apply an analysis algorithm specialized for a particular disease based on the patient's medical history. The analysis unit can also use multiple analysis algorithms in combination, taking the patient's medical history into consideration. For example, the analysis unit can use multiple analysis algorithms in combination, taking the patient's medical history into consideration. The analysis unit can also adjust the parameters of the analysis algorithm according to the patient's medical history. For example, the analysis unit adjusts the parameters of the analysis algorithm according to the patient's medical history. This allows the optimal analysis algorithm to be applied according to the patient's medical history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient medical history data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0046] The analysis unit can determine the priority of analyses based on when the patient's medical data was submitted. For example, the analysis unit will prioritize analyses of patients whose medical data has been recently submitted. The analysis unit can also adjust the order of analyses based on when the patient's medical data was submitted. For example, the analysis unit adjusts the order of analyses based on when the patient's medical data was submitted. The analysis unit can also adjust the analysis schedule taking into account when the patient's medical data was submitted. For example, the analysis unit adjusts the analysis schedule taking into account when the patient's medical data was submitted. This allows the analysis unit to determine the priority of analyses based on when the patient's medical data was submitted. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the patient's medical data submission date into a generating AI and have the generating AI determine the priority of analyses.

[0047] The analysis unit can adjust the order of analysis based on patient relevance during the analysis. For example, the analysis unit prioritizes the analysis of data with high patient relevance. The analysis unit can also adjust the order of analysis based on patient relevance. For example, the analysis unit adjusts the order of analysis based on patient relevance. The analysis unit can also adjust the analysis schedule considering patient relevance. For example, the analysis unit adjusts the analysis schedule considering patient relevance. This allows the order of analysis to be adjusted based on patient relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The proposal unit can adjust the level of detail of its proposals based on the importance of the treatment methods. For example, the proposal unit can provide detailed proposals for highly important treatment methods. For example, the proposal unit can provide simplified proposals for less important treatment methods. The proposal unit can also adjust the content of its proposals based on the importance of the treatment methods. For example, the proposal unit adjusts the content of its proposals based on the importance of the treatment methods. This allows the level of detail of the proposals to be adjusted based on the importance of the treatment methods. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input treatment method importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0049] The proposal unit can apply different proposal algorithms depending on the category of treatment method when making a proposal. For example, the proposal unit can apply a specific proposal algorithm depending on the category of treatment method. The proposal unit can also use a combination of different proposal algorithms for multiple categories of treatment method. For example, the proposal unit can use a combination of different proposal algorithms for multiple categories of treatment method. The proposal unit can also adjust the parameters of the proposal algorithm based on the category of treatment method. For example, the proposal unit adjusts the parameters of the proposal algorithm based on the category of treatment method. This allows the optimal proposal algorithm to be applied depending on the category of treatment method. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input treatment method category data into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0050] The proposal department can determine the priority of proposals based on the submission timing of treatment methods at the time of proposal submission. For example, the proposal department may prioritize proposals with more recent submission times. The proposal department can also adjust the order of proposals based on submission timing. For example, the proposal department may adjust the order of proposals based on submission timing. The proposal department can also adjust the schedule of proposals taking submission timing into consideration. For example, the proposal department may adjust the schedule of proposals taking submission timing into consideration. This allows the proposal department to determine the priority of proposals based on the submission timing of treatment methods. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department may input data on the submission timing of treatment methods into a generating AI and have the generating AI perform the determination of proposal priority.

[0051] The proposal unit can adjust the order of proposals based on the relevance of the treatment methods when making proposals. For example, the proposal unit prioritizes proposing treatment methods that are highly relevant. The proposal unit can also adjust the order of proposals based on relevance. For example, the proposal unit adjusts the order of proposals based on relevance. The proposal unit can also adjust the schedule of proposals considering relevance. For example, the proposal unit adjusts the schedule of proposals considering relevance. This allows the order of proposals to be adjusted based on the relevance of the treatment methods. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input relevance data of treatment methods into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0052] The selection unit can improve the accuracy of its selections by considering the performance data of hospitals and doctors. For example, the selection unit can make the optimal selection based on the past treatment performance of hospitals and doctors. The selection unit can also improve the accuracy of its selections by considering the success rate of hospitals and doctors and patient satisfaction. For example, the selection unit can improve the accuracy of its selections by considering the success rate of hospitals and doctors and patient satisfaction. The selection unit can also make the optimal selection by considering the specialty and years of experience of hospitals and doctors. For example, the selection unit can make the optimal selection by considering the specialty and years of experience of hospitals and doctors. This allows for improved selection accuracy by considering the performance data of hospitals and doctors. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the performance data of hospitals and doctors into a generating AI and have the generating AI perform the improvement of selection accuracy.

[0053] The selection unit can select the most suitable hospital and doctor based on the patient's medical history and treatment method. For example, the selection unit can select a hospital and doctor specializing in a particular disease based on the patient's medical history. The selection unit can also select the most suitable hospital and doctor based on the patient's treatment method. For example, the selection unit can select the most suitable hospital and doctor based on the patient's treatment method. The selection unit can also improve the accuracy of its selection by considering the patient's medical history and treatment method. For example, the selection unit can improve the accuracy of its selection by considering the patient's medical history and treatment method. This allows the selection of the most suitable hospital and doctor based on the patient's medical history and treatment method. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input data on the patient's medical history and treatment method into a generating AI and have the generating AI perform the selection of the most suitable hospital and doctor.

[0054] The selection unit can make selections while considering the geographical distribution of hospitals and doctors. For example, the selection unit can prioritize hospitals and doctors that are close to the patient's place of residence. The selection unit can also make the optimal selection by considering the geographical distribution of hospitals and doctors. For example, the selection unit can make the optimal selection by considering the geographical distribution of hospitals and doctors. The selection unit can also improve the accuracy of the selection by considering the patient's range of movement. For example, the selection unit can improve the accuracy of the selection by considering the patient's range of movement. This allows for the optimal selection to be made while considering the geographical distribution of hospitals and doctors. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input geographical distribution data of hospitals and doctors into a generating AI and have the generating AI perform the selection.

[0055] The selection unit can improve the accuracy of its selections by referring to relevant literature on hospitals and physicians during the selection process. For example, the selection unit can improve the accuracy of its selections by referring to relevant literature on hospitals and physicians. The selection unit can also make the optimal selection by considering the research results and publications of hospitals and physicians. For example, the selection unit can make the optimal selection by considering the research results and publications of hospitals and physicians. The selection unit can also improve the reliability of its selections based on relevant literature on hospitals and physicians. For example, the selection unit can improve the reliability of its selections based on relevant literature on hospitals and physicians. This allows for improved selection accuracy by referring to relevant literature on hospitals and physicians. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input relevant literature data on hospitals and physicians into a generating AI and have the generating AI perform the selection.

[0056] The information provider can provide optimal information by referring to the patient's past treatment history when providing information. For example, the information provider can provide relevant information based on the patient's past treatment history. The information provider can also provide optimal information by referring to the patient's treatment history. For example, the information provider can provide optimal information by referring to the patient's treatment history. The information provider can also adjust the content of the information provided by considering the patient's past treatment history. For example, the information provider can adjust the content of the information provided by considering the patient's past treatment history. This allows the information provider to provide optimal information by referring to the patient's past treatment history. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input the patient's past treatment history data into a generating AI and have the generating AI perform the provision of optimal information.

[0057] The information provider can customize the content of the information based on the patient's current health status when providing information. For example, the information provider can provide optimal information based on the patient's current health status. The information provider can also customize the content of the information provided according to the patient's health status. For example, the information provider can customize the content of the information provided according to the patient's health status. The information provider can also determine the priority of information provision considering the patient's current health status. For example, the information provider can determine the priority of information provision considering the patient's current health status. This allows the information content to be customized based on the patient's current health status. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the patient's current health status data into a generating AI and have the generating AI perform the customization of the information content.

[0058] The information provider can provide optimal information by considering the patient's geographical location when providing information. For example, the provider can provide information related to region-specific health risks based on the patient's place of residence. The provider can also provide information about nearby medical institutions based on the patient's geographical location. For example, the provider can provide information about nearby medical institutions based on the patient's geographical location. The provider can also provide information related to environmental factors by considering the patient's geographical location. For example, the provider can provide information related to environmental factors by considering the patient's geographical location. This allows the provider to provide optimal information by considering the patient's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the patient's geographical location data into a generating AI and have the generating AI perform the task of providing optimal information.

[0059] The information provider can analyze the patient's social media activity and provide relevant information when providing information. For example, the provider can analyze health-related posts from the patient's social media activity and provide relevant information. The provider can also estimate lifestyle habits and stress levels based on the patient's social media activity and reflect this in the information provided. For example, the provider can estimate lifestyle habits and stress levels based on the patient's social media activity and reflect this in the information provided. The provider can also analyze the patient's social media activity and provide information related to health risks. For example, the provider can analyze the patient's social media activity and provide information related to health risks. This allows the provider to analyze the patient's social media activity and provide relevant information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the patient's social media activity data into a generating AI and have the generating AI perform the provision of relevant information.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit collects lifestyle data from patients, and the analysis unit can evaluate the patients' health risks based on this data. For example, the data collection unit collects data on patients' diet, exercise, and sleep patterns. The analysis unit analyzes this data and evaluates the impact of patients' lifestyles on their health. Furthermore, the suggestion unit can make suggestions for improving patients' lifestyles based on the analysis results. For example, the suggestion unit recommends dietary improvements and exercise to patients. This allows patients to review their lifestyles and reduce their health risks.

[0062] The analysis unit can perform analyses by combining patient genetic data and environmental data. For example, the analysis unit can combine patient genetic data with environmental data of the patient's place of residence to evaluate the impact of environmental factors on health. Furthermore, the analysis unit can also combine patient genetic data with workplace environmental data to evaluate the impact of the workplace environment on health. For example, the analysis unit can combine patient genetic data with workplace environmental data to evaluate the impact of the workplace environment on health. This allows for a more detailed health risk assessment by combining patient genetic data and environmental data in the analysis.

[0063] The data collection unit can prioritize data collection by considering the patient's geographical location. For example, it can prioritize the collection of data related to region-specific health risks based on the patient's place of residence. The data collection unit can also prioritize the collection of data from nearby healthcare facilities based on the patient's geographical location. For example, it can prioritize the collection of data from nearby healthcare facilities based on the patient's geographical location. Furthermore, the data collection unit can also collect data related to environmental factors by considering the patient's geographical location. For example, it can collect data related to environmental factors by considering the patient's geographical location. This allows for the priority collection of highly relevant data by considering the patient's geographical location.

[0064] The analysis unit can analyze a patient's past medical history and select the optimal analysis algorithm. For example, it can apply an analysis algorithm specialized for a particular disease based on the patient's past treatment history. The analysis unit can also combine and use multiple analysis algorithms, taking into account the patient's past medical history. For example, the analysis unit can combine and use multiple analysis algorithms, taking into account the patient's past medical history. Furthermore, the analysis unit can adjust the parameters of the analysis algorithm according to the patient's past medical history. For example, the analysis unit adjusts the parameters of the analysis algorithm according to the patient's past medical history. This allows the system to select the optimal analysis algorithm based on the patient's past medical history.

[0065] The proposal department can adjust the level of detail in its proposals based on the importance of the treatment methods. For example, it can provide detailed proposals for highly important treatment methods. It can also provide simplified proposals for less important treatment methods. Furthermore, the proposal department can adjust the content of its proposals based on the importance of the treatment methods. This allows the level of detail in the proposals to be adjusted based on the importance of the treatment methods.

[0066] The selection unit can improve the accuracy of its selections by considering the performance data of hospitals and doctors. For example, it can make the optimal selection based on the past treatment results of hospitals and doctors. The selection unit can also improve the accuracy of its selections by considering the success rate of hospitals and doctors and patient satisfaction. For example, the selection unit can improve the accuracy of its selections by considering the success rate of hospitals and doctors and patient satisfaction. Furthermore, the selection unit can make the optimal selection by considering the specialty and years of experience of hospitals and doctors. For example, the selection unit can make the optimal selection by considering the specialty and years of experience of hospitals and doctors. This allows for improved selection accuracy by considering the performance data of hospitals and doctors.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit collects individual genetic data and health checkup information. The collection unit can acquire genetic data using genetic testing kits and collect health checkup information from hospitals and clinics. For example, it can analyze genetic data provided by patients to detect specific genetic markers. It can also collect patients' blood test results and imaging diagnostic results based on health checkup information. Furthermore, it can be linked with health insurance companies to introduce a system where insurance premiums are reduced in exchange for information provided. Step 2: The analysis unit analyzes the data collected by the data collection unit to analyze the patient's constitution and medical history. Based on the genetic data, the analysis unit can detect specific gene mutations and analyze blood test results and imaging diagnostic results based on health checkup information. For example, it can suggest that a specific anticancer drug would be effective for a patient with a specific gene mutation. Step 3: The proposal unit proposes treatment methods based on the data analyzed by the analysis unit. The proposal unit can suggest that a specific anticancer drug is effective for patients with a specific gene mutation and propose the optimal treatment method based on the patient's constitution and medical history. Step 4: The selection unit selects the most suitable hospital and doctor based on the treatment method proposed by the proposal unit. The selection unit can refer to a database of hospitals and doctors and select hospitals and doctors with a strong track record in specific treatment methods. Step 5: The providing department provides information about the hospitals and doctors selected by the selection department. The providing department can provide the latest information on proposed treatment methods, as well as the latest research results and clinical trial information.

[0069] (Example of form 2) The system according to an embodiment of the present invention is a system that utilizes AI to combine personal data such as an individual's genetic data and health checkup information with information on hospitals, doctors, diseases, and treatment methods, providing individually optimized services for early detection, hospital and doctor selection, and second opinions on treatment methods. This system collects personal data such as an individual's genetic data and health checkup information, and the AI ​​analyzes it to propose individually optimized treatment methods. Furthermore, based on the proposed treatment methods, it selects the most suitable hospital and doctor and provides a second opinion on treatment methods. Through this system, patients can receive the most appropriate treatment quickly, potentially saving lives. For example, personal data such as an individual's genetic data and health checkup information is collected. In this process, the challenges of data collection are solved by introducing a system that links with medical insurance and reduces insurance premiums for providing information. For example, if a patient provides their genetic data and health checkup information, their insurance premium is discounted by a certain percentage. Next, the collected data is analyzed by AI. Based on the genetic data and health checkup information, the AI ​​analyzes the patient's constitution and medical history and proposes the most suitable treatment method. For example, it may be found that a specific anticancer drug is effective for a patient with a specific genetic mutation. In this way, individually optimized treatment methods can be proposed. Furthermore, based on the proposed treatment method, the system selects the most suitable hospital and doctor. The AI ​​consults a database of hospitals and doctors to suggest the best fit for the patient. For example, it can select hospitals and doctors with a strong track record in specific treatment methods. Finally, it provides a second opinion on treatment methods. The AI ​​provides the latest information on the proposed treatment methods to help patients make informed decisions. For example, by providing information on the latest research findings and clinical trials, patients can choose the best treatment option. This system allows patients to receive the most appropriate treatment quickly, potentially saving lives. In addition, by integrating with health insurance, the system overcomes data collection challenges and allows patients to receive premium discounts. For example, by providing their genetic data and health checkup information, patients can receive a certain percentage discount on their insurance premiums. In this way, patients can receive the best treatment while reducing their financial burden.This allows AI-powered systems to provide individually optimized services for early detection, hospital and doctor selection, and second opinions on treatment methods.

[0070] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a selection unit, and a provision unit. The collection unit collects an individual's genetic data and health checkup information. For example, the collection unit can use a genetic testing kit to obtain genetic data. The collection unit can also collect data from hospitals and clinics to obtain health checkup information. For example, the collection unit analyzes genetic data provided by the patient and detects specific genetic markers. The collection unit can also collect the patient's blood test results and imaging diagnostic results based on the health checkup information. Furthermore, the collection unit can be linked with medical insurance and introduce a system where insurance premiums are reduced by providing information. For example, the collection unit can introduce a system where insurance premiums are discounted by a certain percentage if the patient provides their genetic data and health checkup information. The analysis unit analyzes the data collected by the collection unit and analyzes the patient's constitution and medical history. For example, the analysis unit can detect specific genetic mutations based on genetic data. The analysis unit can also analyze the patient's blood test results and imaging diagnostic results based on the health checkup information. For example, the analysis unit suggests that a specific anticancer drug would be effective for a patient with a particular gene mutation. The analysis unit can analyze the patient's constitution and medical history in detail based on genetic data and health checkup information. The proposal unit proposes a treatment method based on the data analyzed by the analysis unit. For example, the proposal unit can suggest that a specific anticancer drug would be effective for a patient with a particular gene mutation. The proposal unit can also propose the optimal treatment method based on the patient's constitution and medical history. For example, the proposal unit suggests that a specific anticancer drug would be effective for a patient with a particular gene mutation. The selection unit selects the most suitable hospital and doctor based on the treatment method proposed by the proposal unit. For example, the selection unit can refer to a database of hospitals and doctors and propose the most suitable hospital and doctor for the patient. The selection unit can also select hospitals and doctors with a strong track record in a particular treatment method. For example, the selection unit selects hospitals and doctors with a strong track record in a particular treatment method. The provision unit provides information about the hospitals and doctors selected by the selection unit. For example, the provision unit can provide the latest information on the proposed treatment method.The service provider can also provide patients with the latest research findings and clinical trial information to help them choose a treatment method. For example, the service provider can provide the latest research findings and clinical trial information. This allows the system according to the embodiment to provide individually optimized services for early detection, hospital and doctor selection, and second opinions on treatment methods.

[0071] The data collection unit collects individual genetic data and health checkup information. For example, to obtain genetic data, the unit can use genetic testing kits. These kits collect samples such as saliva or blood, which are then sent to a specialized laboratory for analysis. The analysis results are sent to the data collection unit in digital format and stored in a database. The data collection unit can also collect data from hospitals and clinics to obtain health checkup information. For example, it can obtain patients' blood test results and imaging diagnostic results through electronic medical record systems. This requires patient consent, and data privacy is strictly protected. Furthermore, the data collection unit can collaborate with health insurance companies to introduce a system where insurance premiums are reduced for providing information. For example, the data collection unit could introduce a system where patients receive a certain percentage discount on their insurance premiums for providing their genetic data and health checkup information. This would incentivize patients to proactively provide their health information, allowing the data collection unit to collect more data. The collected data is stored in a secure cloud environment and managed so that the analysis unit and proposal unit can access it. This allows the data collection unit to efficiently and securely collect individual genetic data and health checkup information, enriching the overall system database.

[0072] The analysis unit analyzes data collected by the data collection unit to analyze the patient's constitution and medical history. For example, the analysis unit can detect specific gene mutations based on genetic data. Next-generation sequencing technology and AI-based data analysis methods are used for analyzing genetic data. AI analyzes vast amounts of genetic data quickly and accurately to identify specific gene mutations and disease risks. The analysis unit can also analyze the patient's blood test results and imaging diagnostic results based on health checkup information. For example, it can use AI to detect abnormal values ​​in blood test results and identify the presence or absence of lesions from imaging diagnostic results. This allows the analysis unit to analyze the patient's constitution and medical history in detail and provide a foundation for personalized medicine. Furthermore, the analysis unit can also predict the patient's health risks by utilizing past data and statistical information. For example, it can predict future disease risks based on past health checkup data and propose early preventive measures. This allows the analysis unit to comprehensively evaluate the patient's health status and provide information to propose optimal treatment methods and preventive measures.

[0073] The Proposal Department proposes treatment methods based on data analyzed by the Analysis Department. For example, the Proposal Department can suggest that a specific anticancer drug is effective for patients with a particular gene mutation. The Proposal Department uses AI to evaluate the analysis results and identify the optimal treatment method. The AI ​​learns from past treatment data and clinical trial results to propose the treatment method best suited to the individual patient's situation. The Proposal Department can also propose the optimal treatment method based on the patient's constitution and medical history. For example, it can propose a treatment method with fewer side effects by considering the patient's allergy information and past treatment history. Furthermore, the Proposal Department constantly updates its information on the latest medical technologies and treatments, enabling it to propose cutting-edge treatment methods. This allows the Proposal Department to provide patients with the optimal treatment method and maximize treatment effectiveness.

[0074] The selection unit selects the most suitable hospital and physician based on the treatment method proposed by the suggestion unit. For example, the selection unit can refer to a database of hospitals and physicians to suggest the best fit for the patient. The selection unit can also select hospitals and physicians with a strong track record in specific treatment methods. For instance, it can select hospitals and physicians with a high success rate in a particular anticancer drug treatment. The selection unit makes its optimal selection based on hospital and physician evaluation data and patient feedback. Furthermore, the selection unit can consider factors such as the patient's place of residence and desired treatment duration when selecting the most suitable hospital and physician. This allows the selection unit to suggest the most suitable healthcare provider to the patient, thereby improving the quality of treatment.

[0075] The provision department provides information about hospitals and doctors selected by the selection department. For example, the provision department can provide the latest information on proposed treatment methods. The provision department can also provide information on the latest research findings and clinical trials to help patients choose a treatment method. This allows patients to obtain sufficient information about treatment methods and healthcare providers, enabling them to make informed treatment choices. Furthermore, the provision department also provides information on the procedures and necessary documents for patients receiving treatment. This allows patients to prepare for a smooth treatment process. The provision department also responds promptly to patients' questions and concerns, providing an environment where they can receive treatment with peace of mind. In this way, the provision department can provide comprehensive information to patients and improve the quality of treatment.

[0076] The data collection unit can collaborate with health insurance companies to implement a system where insurance premiums are reduced in exchange for information provided. For example, the unit could implement a system where patients receive a certain percentage discount on their insurance premiums if they provide their genetic data or health checkup information. The data collection unit can enter into data sharing agreements with health insurance companies to securely manage the data provided by patients. For example, the unit could collaborate with health insurance companies to set data sharing methods and premium discount criteria. The data collection unit can also anonymize the data provided by patients and take measures to protect their privacy. For example, the unit could use data anonymization technology to protect patients' personal information. This solves the challenges of data collection and allows patients to receive premium discounts. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit could input the data provided by patients into a generating AI and have the generating AI perform data analysis and management.

[0077] The analysis unit can analyze a patient's constitution and medical history based on genetic data and health checkup information. For example, the analysis unit can detect specific gene mutations based on genetic data. The analysis unit can also analyze a patient's blood test results and imaging diagnostic results based on health checkup information. For example, the analysis unit can suggest that a specific anticancer drug is effective for a patient with a specific gene mutation. The analysis unit can analyze a patient's constitution and medical history in detail based on genetic data and health checkup information. This allows the analysis unit to suggest the optimal treatment method by analyzing the patient's constitution and medical history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input genetic data and health checkup information into a generating AI and have the generating AI perform the data analysis.

[0078] The suggestion unit can suggest that a specific anticancer drug is effective for patients with a specific gene mutation. For example, the suggestion unit can suggest that a specific anticancer drug is effective for patients with a specific gene mutation. The suggestion unit can also suggest the optimal treatment method based on the patient's constitution and medical history. For example, the suggestion unit can suggest that a specific anticancer drug is effective for patients with a specific gene mutation. This allows the suggestion unit to suggest the optimal treatment method for patients with a specific gene mutation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input genetic data and health checkup information into a generating AI and have the generating AI suggest the optimal treatment method.

[0079] The selection unit can refer to a database of hospitals and doctors and suggest the most suitable hospital and doctor for the patient. For example, the selection unit can refer to a database of hospitals and doctors and suggest the most suitable hospital and doctor for the patient. The selection unit can also select hospitals and doctors with a strong track record in specific treatment methods. This allows the selection unit to suggest the most suitable hospital and doctor for the patient. Some or all of the above-described processes in the selection unit may be performed using AI, or not. For example, the selection unit can input a database of hospitals and doctors into a generating AI and have the generating AI select the most suitable hospital and doctor.

[0080] The information provider can provide the latest information on proposed treatment methods. For example, the information provider can provide the latest research results and clinical trial information on proposed treatment methods. The information provider can also provide the latest information to help patients choose a treatment method. For example, the information provider can provide the latest research results and clinical trial information, which can help patients choose a treatment method. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the latest research results and clinical trial information into a generating AI and have the generating AI perform the information provision.

[0081] 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 stressed, the data collection unit may delay data collection until the patient is relaxed. If the patient is relaxed, the data collection unit may also start data collection immediately. For example, if the patient is relaxed, the data collection unit may start data collection immediately. If the patient is anxious, the data collection unit may also provide explanations before data collection to reassure them. For example, if the patient is anxious, the data collection unit may provide explanations before data collection to reassure them. By adjusting the timing of data collection according to the patient's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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, for example, or not using AI. For example, the data collection unit may input the patient's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The data collection unit can analyze a patient's past medical history and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on the patient's past treatment and examination history. The data collection unit can also determine whether a specific test is necessary based on the patient's past medical history. For example, the data collection unit can determine whether a specific test is necessary based on the patient's past medical history. The data collection unit can also analyze a patient's past medical history and adjust the frequency of data collection. For example, the data collection unit analyzes a patient's past medical history and adjusts the frequency of data collection. This allows the optimal data collection method to be selected 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, for example, or without AI. For example, the data collection unit can input the patient's past medical history into a generating AI and have the generating AI select the optimal data collection method.

[0083] The data collection unit can filter data based on the patient's current health status and lifestyle. For example, the data collection unit can collect only the necessary data based on the patient's current health status. The data collection unit can also customize the content of data collection by considering the patient's lifestyle. For example, the data collection unit can customize the content of data collection by considering the patient's lifestyle. The data collection unit can also determine the priority of data collection based on the patient's health status and lifestyle. For example, the data collection unit can determine the priority of data collection based on the patient's health status and lifestyle. This allows the collection to collect only the necessary data based on the patient's current health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the patient's current health status and lifestyle into a generating AI and have the generating AI perform the filtering.

[0084] The data collection unit can estimate the patient's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the patient is stressed, the data collection unit will prioritize collecting high-priority data. If the patient is relaxed, the data collection unit can also collect detailed data. For example, if the patient is relaxed, the data collection unit will collect detailed data. If the patient is anxious, the data collection unit can start by collecting simple data to provide reassurance. For example, if the patient is anxious, the data collection unit will start by collecting simple data to provide reassurance. This allows for more appropriate data collection by prioritizing the data to be collected according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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, for example, or not using AI. For example, the data collection unit can input the patient's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] 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. The data collection unit can also prioritize the collection of data from nearby medical institutions based on the patient's geographical location information. For example, the data collection unit can prioritize the collection of data from nearby medical institutions based on the patient's geographical location information. The data collection unit can also collect data related to environmental factors by considering the patient's geographical location information. For example, the data collection unit can collect data related to environmental factors by considering the patient's geographical location information. This allows for the priority collection of highly relevant data by considering the patient's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0086] The data collection unit can analyze patients' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze health-related posts from patients' social media activity and collect relevant data. The data collection unit can also estimate lifestyle habits and stress levels based on patients' social media activity and reflect this in data collection. For example, the data collection unit can estimate lifestyle habits and stress levels based on patients' social media activity and reflect this in data collection. The data collection unit can also analyze patients' social media activity and collect information related to health risks. For example, the data collection unit can analyze patients' social media activity and collect information related to health risks. This allows for more appropriate data collection by analyzing patients' social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on patients' social media activity into a generating AI and have the generating AI collect relevant data.

[0087] 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 stressed, the analysis unit provides a simple and easy-to-understand analysis result. If the patient is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the patient is relaxed, the analysis unit provides a detailed analysis result. If the patient is anxious, the analysis unit can also use positive language to provide reassurance. For example, if the patient is anxious, the analysis unit uses positive language to provide reassurance. By adjusting the presentation of the analysis according to the patient's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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, for example, or without AI. For example, the analysis unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The analysis unit can adjust the level of detail of the analysis based on the patient's important health indicators during the analysis. For example, if the patient's important health indicators show abnormal values, the analysis unit will perform a detailed analysis. The analysis unit can also provide a simplified analysis result if the patient's health indicators are within the normal range. For example, the analysis unit will provide a simplified analysis result if the patient's health indicators are within the normal range. The analysis unit can also perform an analysis that focuses on specific items based on the patient's health indicators. For example, the analysis unit will perform an analysis that focuses on specific items based on the patient's health indicators. This allows the level of detail of the analysis to be adjusted based on the patient's important health indicators. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient health indicator data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the patient's medical history during analysis. For example, the analysis unit can apply an analysis algorithm specialized for a particular disease based on the patient's medical history. The analysis unit can also use multiple analysis algorithms in combination, taking the patient's medical history into consideration. For example, the analysis unit can use multiple analysis algorithms in combination, taking the patient's medical history into consideration. The analysis unit can also adjust the parameters of the analysis algorithm according to the patient's medical history. For example, the analysis unit adjusts the parameters of the analysis algorithm according to the patient's medical history. This allows the optimal analysis algorithm to be applied according to the patient's medical history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient medical history data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0090] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the patient is stressed, the analysis unit can provide a short, concise analysis. If the patient is relaxed, the analysis unit can also provide a detailed analysis. For example, if the patient is relaxed, the analysis unit can provide a detailed analysis. If the patient is anxious, the analysis unit can use positive language to provide reassurance. For example, if the patient is anxious, the analysis unit can use positive language to provide reassurance. By adjusting the length of the analysis according to the patient's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, 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, for example, or without AI. For example, the analysis unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can determine the priority of analyses based on when the patient's medical data was submitted. For example, the analysis unit will prioritize analyses of patients whose medical data has been recently submitted. The analysis unit can also adjust the order of analyses based on when the patient's medical data was submitted. For example, the analysis unit adjusts the order of analyses based on when the patient's medical data was submitted. The analysis unit can also adjust the analysis schedule taking into account when the patient's medical data was submitted. For example, the analysis unit adjusts the analysis schedule taking into account when the patient's medical data was submitted. This allows the analysis unit to determine the priority of analyses based on when the patient's medical data was submitted. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the patient's medical data submission date into a generating AI and have the generating AI determine the priority of analyses.

[0092] The analysis unit can adjust the order of analysis based on patient relevance during the analysis. For example, the analysis unit prioritizes the analysis of data with high patient relevance. The analysis unit can also adjust the order of analysis based on patient relevance. For example, the analysis unit adjusts the order of analysis based on patient relevance. The analysis unit can also adjust the analysis schedule considering patient relevance. For example, the analysis unit adjusts the analysis schedule considering patient relevance. This allows the order of analysis to be adjusted based on patient relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0093] 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 stressed, the suggestion unit will present simple and easy-to-understand suggestions. If the patient is relaxed, the suggestion unit can also present more detailed suggestions. For example, if the patient is relaxed, the suggestion unit will present more detailed suggestions. If the patient is anxious, the suggestion unit can also use positive language to provide reassurance. For example, if the patient is anxious, the suggestion unit will use positive language to provide reassurance. This allows for more appropriate suggestions to be presented by adjusting the way the suggestions are presented 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 using AI. For example, the suggestion unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The proposal unit can adjust the level of detail of its proposals based on the importance of the treatment methods. For example, the proposal unit can provide detailed proposals for highly important treatment methods. For example, the proposal unit can provide simplified proposals for less important treatment methods. The proposal unit can also adjust the content of its proposals based on the importance of the treatment methods. For example, the proposal unit adjusts the content of its proposals based on the importance of the treatment methods. This allows the level of detail of the proposals to be adjusted based on the importance of the treatment methods. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input treatment method importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0095] The proposal unit can apply different proposal algorithms depending on the category of treatment method when making a proposal. For example, the proposal unit can apply a specific proposal algorithm depending on the category of treatment method. The proposal unit can also use a combination of different proposal algorithms for multiple categories of treatment method. For example, the proposal unit can use a combination of different proposal algorithms for multiple categories of treatment method. The proposal unit can also adjust the parameters of the proposal algorithm based on the category of treatment method. For example, the proposal unit adjusts the parameters of the proposal algorithm based on the category of treatment method. This allows the optimal proposal algorithm to be applied depending on the category of treatment method. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input treatment method category data into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0096] The suggestion unit can estimate the patient's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the patient is stressed, the suggestion unit will make a short, concise suggestion. If the patient is relaxed, the suggestion unit can also make a detailed suggestion. For example, if the patient is relaxed, the suggestion unit will make a detailed suggestion. If the patient is anxious, the suggestion unit can also use positive language to provide reassurance. For example, if the patient is anxious, the suggestion unit will use positive language to provide reassurance. This allows for more appropriate suggestions to be made by adjusting the length of the suggestion according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 processing described above in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The proposal department can determine the priority of proposals based on the submission timing of treatment methods at the time of proposal submission. For example, the proposal department may prioritize proposals with more recent submission times. The proposal department can also adjust the order of proposals based on submission timing. For example, the proposal department may adjust the order of proposals based on submission timing. The proposal department can also adjust the schedule of proposals taking submission timing into consideration. For example, the proposal department may adjust the schedule of proposals taking submission timing into consideration. This allows the proposal department to determine the priority of proposals based on the submission timing of treatment methods. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department may input data on the submission timing of treatment methods into a generating AI and have the generating AI perform the determination of proposal priority.

[0098] The proposal unit can adjust the order of proposals based on the relevance of the treatment methods when making proposals. For example, the proposal unit prioritizes proposing treatment methods that are highly relevant. The proposal unit can also adjust the order of proposals based on relevance. For example, the proposal unit adjusts the order of proposals based on relevance. The proposal unit can also adjust the schedule of proposals considering relevance. For example, the proposal unit adjusts the schedule of proposals considering relevance. This allows the order of proposals to be adjusted based on the relevance of the treatment methods. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input relevance data of treatment methods into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0099] The selection unit can estimate the patient's emotions and adjust the criteria for selecting hospitals and doctors based on the estimated emotions. For example, if the patient is stressed, the selection unit will select hospitals and doctors that provide a relaxing environment. If the patient is relaxed, the selection unit can also select hospitals and doctors that provide detailed information. For example, if the patient is relaxed, the selection unit can select hospitals and doctors that provide detailed information. If the patient is anxious, the selection unit can also select reliable hospitals and doctors to provide a sense of security. For example, if the patient is anxious, the selection unit can select reliable hospitals and doctors to provide a sense of security. This allows for more appropriate selections by adjusting the criteria for selecting hospitals and doctors according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input patient emotion data into a generating AI and have the generating AI perform emotion estimation.

[0100] The selection unit can improve the accuracy of its selections by considering the performance data of hospitals and doctors. For example, the selection unit can make the optimal selection based on the past treatment performance of hospitals and doctors. The selection unit can also improve the accuracy of its selections by considering the success rate of hospitals and doctors and patient satisfaction. For example, the selection unit can improve the accuracy of its selections by considering the success rate of hospitals and doctors and patient satisfaction. The selection unit can also make the optimal selection by considering the specialty and years of experience of hospitals and doctors. For example, the selection unit can make the optimal selection by considering the specialty and years of experience of hospitals and doctors. This allows for improved selection accuracy by considering the performance data of hospitals and doctors. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the performance data of hospitals and doctors into a generating AI and have the generating AI perform the improvement of selection accuracy.

[0101] The selection unit can select the most suitable hospital and doctor based on the patient's medical history and treatment method. For example, the selection unit can select a hospital and doctor specializing in a particular disease based on the patient's medical history. The selection unit can also select the most suitable hospital and doctor based on the patient's treatment method. For example, the selection unit can select the most suitable hospital and doctor based on the patient's treatment method. The selection unit can also improve the accuracy of its selection by considering the patient's medical history and treatment method. For example, the selection unit can improve the accuracy of its selection by considering the patient's medical history and treatment method. This allows the selection of the most suitable hospital and doctor based on the patient's medical history and treatment method. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input data on the patient's medical history and treatment method into a generating AI and have the generating AI perform the selection of the most suitable hospital and doctor.

[0102] The selection unit can estimate the patient's emotions and adjust the display method of the selection results based on the estimated emotions. For example, if the patient is stressed, the selection unit provides a simple and easy-to-understand display method. If the patient is relaxed, the selection unit can also provide a display method that includes detailed information. For example, if the patient is relaxed, the selection unit can provide a display method that includes detailed information. If the patient is anxious, the selection unit can also use positive expressions to provide reassurance. For example, if the patient is anxious, the selection unit uses positive expressions to provide reassurance. This allows for more appropriate information to be provided by adjusting the display method of the selection results according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The selection unit can make selections while considering the geographical distribution of hospitals and doctors. For example, the selection unit can prioritize hospitals and doctors that are close to the patient's place of residence. The selection unit can also make the optimal selection by considering the geographical distribution of hospitals and doctors. For example, the selection unit can make the optimal selection by considering the geographical distribution of hospitals and doctors. The selection unit can also improve the accuracy of the selection by considering the patient's range of movement. For example, the selection unit can improve the accuracy of the selection by considering the patient's range of movement. This allows for the optimal selection to be made while considering the geographical distribution of hospitals and doctors. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input geographical distribution data of hospitals and doctors into a generating AI and have the generating AI perform the selection.

[0104] The selection unit can improve the accuracy of its selections by referring to relevant literature on hospitals and physicians during the selection process. For example, the selection unit can improve the accuracy of its selections by referring to relevant literature on hospitals and physicians. The selection unit can also make the optimal selection by considering the research results and publications of hospitals and physicians. For example, the selection unit can make the optimal selection by considering the research results and publications of hospitals and physicians. The selection unit can also improve the reliability of its selections based on relevant literature on hospitals and physicians. For example, the selection unit can improve the reliability of its selections based on relevant literature on hospitals and physicians. This allows for improved selection accuracy by referring to relevant literature on hospitals and physicians. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input relevant literature data on hospitals and physicians into a generating AI and have the generating AI perform the selection.

[0105] The information provider can estimate the patient's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the patient is stressed, the information provider will provide simple and easy-to-understand information. If the patient is relaxed, the information provider can also provide detailed information. For example, if the patient is relaxed, the information provider will provide detailed information. If the patient is anxious, the information provider can also use positive language to provide reassurance. For example, if the patient is anxious, the information provider will use positive language to provide reassurance. This allows for more appropriate information delivery by adjusting the method of information delivery according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input patient emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The information provider can provide optimal information by referring to the patient's past treatment history when providing information. For example, the information provider can provide relevant information based on the patient's past treatment history. The information provider can also provide optimal information by referring to the patient's treatment history. For example, the information provider can provide optimal information by referring to the patient's treatment history. The information provider can also adjust the content of the information provided by considering the patient's past treatment history. For example, the information provider can adjust the content of the information provided by considering the patient's past treatment history. This allows the information provider to provide optimal information by referring to the patient's past treatment history. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input the patient's past treatment history data into a generating AI and have the generating AI perform the provision of optimal information.

[0107] The information provider can customize the content of the information based on the patient's current health status when providing information. For example, the information provider can provide optimal information based on the patient's current health status. The information provider can also customize the content of the information provided according to the patient's health status. For example, the information provider can customize the content of the information provided according to the patient's health status. The information provider can also determine the priority of information provision considering the patient's current health status. For example, the information provider can determine the priority of information provision considering the patient's current health status. This allows the information content to be customized based on the patient's current health status. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the patient's current health status data into a generating AI and have the generating AI perform the customization of the information content.

[0108] The information provider can estimate the patient's emotions and determine the priority of information provision based on the estimated emotions. For example, if the patient is stressed, the information provider will prioritize providing information of high importance. If the patient is relaxed, the information provider can also provide detailed information. For example, if the patient is relaxed, the information provider will provide detailed information. If the patient is anxious, the information provider can also prioritize providing positive information to reassure them. For example, if the patient is anxious, the information provider will prioritize providing positive information to reassure them. This allows for more appropriate information provision by determining the priority of information provision according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input patient emotion data into a generative AI and have the generative AI perform emotion estimation.

[0109] The information provider can provide optimal information by considering the patient's geographical location when providing information. For example, the provider can provide information related to region-specific health risks based on the patient's place of residence. The provider can also provide information about nearby medical institutions based on the patient's geographical location. For example, the provider can provide information about nearby medical institutions based on the patient's geographical location. The provider can also provide information related to environmental factors by considering the patient's geographical location. For example, the provider can provide information related to environmental factors by considering the patient's geographical location. This allows the provider to provide optimal information by considering the patient's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the patient's geographical location data into a generating AI and have the generating AI perform the task of providing optimal information.

[0110] The information provider can analyze the patient's social media activity and provide relevant information when providing information. For example, the provider can analyze health-related posts from the patient's social media activity and provide relevant information. The provider can also estimate lifestyle habits and stress levels based on the patient's social media activity and reflect this in the information provided. For example, the provider can estimate lifestyle habits and stress levels based on the patient's social media activity and reflect this in the information provided. The provider can also analyze the patient's social media activity and provide information related to health risks. For example, the provider can analyze the patient's social media activity and provide information related to health risks. This allows the provider to analyze the patient's social media activity and provide relevant information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the patient's social media activity data into a generating AI and have the generating AI perform the provision of relevant information.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The data collection unit collects lifestyle data from patients, and the analysis unit can evaluate the patients' health risks based on this data. For example, the data collection unit collects data on patients' diet, exercise, and sleep patterns. The analysis unit analyzes this data and evaluates the impact of patients' lifestyles on their health. Furthermore, the suggestion unit can make suggestions for improving patients' lifestyles based on the analysis results. For example, the suggestion unit recommends dietary improvements and exercise to patients. This allows patients to review their lifestyles and reduce their health risks.

[0113] The data collection unit can estimate the patient's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the patient is stressed, the frequency of data collection can be reduced. If the patient is relaxed, the frequency of data collection can be increased. For example, if the patient is relaxed, the frequency of data collection can be increased. Furthermore, if the patient is feeling anxious, the data collection unit can provide advice on how to relax before data collection. For example, if the data collection unit is feeling anxious, it can provide advice on how to relax. This allows for more appropriate data collection by adjusting the frequency of data collection according to the patient's emotions.

[0114] The analysis unit can perform analyses by combining patient genetic data and environmental data. For example, the analysis unit can combine patient genetic data with environmental data of the patient's place of residence to evaluate the impact of environmental factors on health. Furthermore, the analysis unit can also combine patient genetic data with workplace environmental data to evaluate the impact of the workplace environment on health. For example, the analysis unit can combine patient genetic data with workplace environmental data to evaluate the impact of the workplace environment on health. This allows for a more detailed health risk assessment by combining patient genetic data and environmental data in the analysis.

[0115] The suggestion function can estimate the patient's emotions and adjust the suggested treatment methods based on those emotions. For example, if the patient is stressed, it can suggest a simple and easy-to-understand treatment method. If the patient is relaxed, it can suggest a more detailed treatment method. For example, if the patient is relaxed, it can suggest a more detailed treatment method. Furthermore, if the patient is anxious, the suggestion function can use positive language to provide reassurance. For example, if the suggestion function is anxious, it can use positive language to provide reassurance. This allows for more appropriate suggestions by adjusting the suggested treatment methods according to the patient's emotions.

[0116] The selection unit can estimate the patient's emotions and adjust the criteria for selecting a hospital or doctor based on those estimated emotions. For example, if the patient is stressed, it can select a hospital or doctor that provides a relaxing environment. If the patient is relaxed, it can also select a hospital or doctor that provides detailed information. Furthermore, if the patient is anxious, the selection unit can select a reliable hospital or doctor to provide a sense of security. In this way, by adjusting the criteria for selecting a hospital or doctor according to the patient's emotions, more appropriate choices can be made.

[0117] The information provider can estimate the patient's emotions and adjust the method of information delivery based on those estimates. For example, if the patient is stressed, they can provide simple and easy-to-understand information. If the patient is relaxed, they can provide more detailed information. For example, if the patient is relaxed, they can provide more detailed information. Furthermore, if the patient is anxious, the information provider can use positive language to provide reassurance. For example, if the patient is anxious, the information provider can use positive language to provide reassurance. This allows for more appropriate information delivery by adjusting the method of information delivery according to the patient's emotions.

[0118] The data collection unit can prioritize data collection by considering the patient's geographical location. For example, it can prioritize the collection of data related to region-specific health risks based on the patient's place of residence. The data collection unit can also prioritize the collection of data from nearby healthcare facilities based on the patient's geographical location. For example, it can prioritize the collection of data from nearby healthcare facilities based on the patient's geographical location. Furthermore, the data collection unit can also collect data related to environmental factors by considering the patient's geographical location. For example, it can collect data related to environmental factors by considering the patient's geographical location. This allows for the priority collection of highly relevant data by considering the patient's geographical location.

[0119] The analysis unit can analyze a patient's past medical history and select the optimal analysis algorithm. For example, it can apply an analysis algorithm specialized for a particular disease based on the patient's past treatment history. The analysis unit can also combine and use multiple analysis algorithms, taking into account the patient's past medical history. For example, the analysis unit can combine and use multiple analysis algorithms, taking into account the patient's past medical history. Furthermore, the analysis unit can adjust the parameters of the analysis algorithm according to the patient's past medical history. For example, the analysis unit adjusts the parameters of the analysis algorithm according to the patient's past medical history. This allows the system to select the optimal analysis algorithm based on the patient's past medical history.

[0120] The proposal department can adjust the level of detail in its proposals based on the importance of the treatment methods. For example, it can provide detailed proposals for highly important treatment methods. It can also provide simplified proposals for less important treatment methods. Furthermore, the proposal department can adjust the content of its proposals based on the importance of the treatment methods. This allows the level of detail in the proposals to be adjusted based on the importance of the treatment methods.

[0121] The selection unit can improve the accuracy of its selections by considering the performance data of hospitals and doctors. For example, it can make the optimal selection based on the past treatment results of hospitals and doctors. The selection unit can also improve the accuracy of its selections by considering the success rate of hospitals and doctors and patient satisfaction. For example, the selection unit can improve the accuracy of its selections by considering the success rate of hospitals and doctors and patient satisfaction. Furthermore, the selection unit can make the optimal selection by considering the specialty and years of experience of hospitals and doctors. For example, the selection unit can make the optimal selection by considering the specialty and years of experience of hospitals and doctors. This allows for improved selection accuracy by considering the performance data of hospitals and doctors.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The collection unit collects individual genetic data and health checkup information. The collection unit can acquire genetic data using genetic testing kits and collect health checkup information from hospitals and clinics. For example, it can analyze genetic data provided by patients to detect specific genetic markers. It can also collect patients' blood test results and imaging diagnostic results based on health checkup information. Furthermore, it can be linked with health insurance companies to introduce a system where insurance premiums are reduced in exchange for information provided. Step 2: The analysis unit analyzes the data collected by the data collection unit to analyze the patient's constitution and medical history. Based on the genetic data, the analysis unit can detect specific gene mutations and analyze blood test results and imaging diagnostic results based on health checkup information. For example, it can suggest that a specific anticancer drug would be effective for a patient with a specific gene mutation. Step 3: The proposal unit proposes treatment methods based on the data analyzed by the analysis unit. The proposal unit can suggest that a specific anticancer drug is effective for patients with a specific gene mutation and propose the optimal treatment method based on the patient's constitution and medical history. Step 4: The selection unit selects the most suitable hospital and doctor based on the treatment method proposed by the proposal unit. The selection unit can refer to a database of hospitals and doctors and select hospitals and doctors with a strong track record in specific treatment methods. Step 5: The providing department provides information about the hospitals and doctors selected by the selection department. The providing department can provide the latest information on proposed treatment methods, as well as the latest research results and clinical trial information.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, selection unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects personal genetic data and health checkup information using the camera 42 and microphone 38B of the smart device 14, and manages the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to analyze the patient's constitution and medical history. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes the optimal treatment method based on the analysis results. The selection unit is implemented in the specific processing unit 290 of the data processing unit 12, and selects the optimal hospital and doctor based on the proposed treatment method. The provision unit is implemented in the control unit 46A of the smart device 14, and provides information about the selected hospital and doctor. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, selection unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect personal genetic data and health checkup information, and the control unit 46A manages the data. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to analyze the patient's constitution and medical history. The proposal unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and proposes the optimal treatment method based on the analysis results. The selection unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and selects the optimal hospital and doctor based on the proposed treatment method. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214, and provides information about the selected hospital and doctor. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, selection unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect personal genetic data and health checkup information, and the control unit 46A manages the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and analyze the patient's constitution and medical history. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to propose the optimal treatment method based on the analysis results. The selection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to select the optimal hospital and doctor based on the proposed treatment method. The provision unit is implemented in the control unit 46A of the headset terminal 314, for example, to provide information about the selected hospital and doctor. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, selection unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect personal genetic data and health checkup information, and the control unit 46A manages the data. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to analyze the patient's constitution and medical history. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes the optimal treatment method based on the analysis results. The selection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and selects the optimal hospital and doctor based on the proposed treatment method. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides information about the selected hospital and doctor. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A collection unit that collects personal genetic data and health checkup information, The data collected by the aforementioned collection unit is analyzed by an analysis unit that analyzes the patient's constitution and medical history, A proposal unit proposes a treatment method based on the data analyzed by the aforementioned analysis unit, A selection unit that selects the most suitable hospital and doctor based on the treatment method proposed by the aforementioned proposal unit, The system includes a providing unit that provides information about hospitals and doctors selected by the selection unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We will introduce a system that works in conjunction with health insurance, where providing information will lower insurance premiums. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on genetic data and health checkup information, the system analyzes the patient's constitution and medical history. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose that certain anticancer drugs are effective for patients with specific gene mutations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned selection unit is We refer to hospital and doctor databases to suggest the most suitable hospital and doctor for each patient. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We provide the latest information on proposed treatment methods. 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 During data collection, filtering is performed based on the patient's current health status and lifestyle. 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, adjust the level of detail based on the patient's key health indicators. 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 medical history. 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 adjusts the length of the 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 priority of analyses is determined based on when the patient's medical data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on patient relevance. 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 importance of the treatment method. 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 category of the treatment method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, Estimate the patient's emotions and adjust the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the treatment methods are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, adjust the order of the proposed treatments based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned selection unit is Estimate patients' emotions and adjust hospital and doctor selection criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned selection unit is When making a selection, we improve the accuracy of the selection by considering the performance data of hospitals and doctors. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned selection unit is When making a selection, the most suitable hospital and doctor are chosen based on the patient's medical history and treatment methods. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned selection unit is The system estimates the patient's emotions and adjusts how the selection results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned selection unit is When making a selection, consider the geographical distribution of hospitals and doctors. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned selection unit is When making a selection, we refer to relevant literature from hospitals and doctors to improve the accuracy of the selection. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, Estimate the patient's emotions and adjust the method of information delivery based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing information, we refer to the patient's past treatment history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing information, customize the content of the information based on the patient's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, The system estimates the patient's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing information, we will consider the patient's geographical location to provide the most appropriate information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing information, we analyze the patient's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 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 collection unit that collects personal genetic data and health checkup information, The data collected by the aforementioned collection unit is analyzed by an analysis unit that analyzes the patient's constitution and medical history, A proposal unit proposes a treatment method based on the data analyzed by the aforementioned analysis unit, A selection unit that selects the most suitable hospital and doctor based on the treatment method proposed by the aforementioned proposal unit, The system includes a providing unit that provides information about hospitals and doctors selected by the selection unit. A system characterized by the following features.

2. The aforementioned collection unit is We will introduce a system that works in conjunction with health insurance, where providing information will lower insurance premiums. The system according to feature 1.

3. The aforementioned analysis unit, Based on genetic data and health checkup information, the system analyzes the patient's constitution and medical history. The system according to feature 1.

4. The aforementioned proposal section is, We propose that certain anticancer drugs are effective for patients with specific gene mutations. The system according to feature 1.

5. The aforementioned selection unit is We refer to hospital and doctor databases to suggest the most suitable hospital and doctor for each patient. The system according to feature 1.

6. The aforementioned supply unit is, We provide the latest information on proposed treatment methods. 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.

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