system

Abstract

The system according to this embodiment aims to efficiently collect and analyze patients' biological information in real time and provide diagnostic support and treatment suggestions. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a diagnostic support unit, a treatment proposal unit, and a report generation unit. The data collection unit collects the patient's biological information in real time. The analysis unit analyzes the data collected by the data collection unit. The diagnostic support unit supports diagnosis based on the data analyzed by the analysis unit. The treatment proposal unit proposes a treatment plan based on the diagnostic results obtained by the diagnostic support unit. The report generation unit automatically generates a report of the treatment plan proposed by the treatment proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, there is room for improvement because the process of collecting, analyzing, and providing diagnostic support and treatment proposals for patients' biological information in real time is not efficient.

[0005] The system according to the embodiment aims to efficiently collect, analyze, and provide diagnostic support and treatment proposals for patients' biological information in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a diagnostic support unit, a treatment proposal unit, and a report generation unit. The data collection unit collects the patient's biological information in real time. The analysis unit analyzes the data collected by the data collection unit. The diagnostic support unit supports diagnosis based on the data analyzed by the analysis unit. The treatment proposal unit proposes a treatment plan based on the diagnostic results obtained by the diagnostic support unit. The report generation unit automatically generates a report of the treatment plan proposed by the treatment proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can collect and analyze patients' biological information in real time, and efficiently provide diagnostic support and treatment suggestions. [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 a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 reception 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 reception 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) An AI agent-driven medical diagnostic support platform according to an embodiment of the present invention is a system that utilizes generative AI and audio glasses technology to collect, integrate, and analyze medical data in real time. This system collects patient biometric information in real time, the generative AI analyzes the collected data in real time, and visualizes it on a dashboard for healthcare professionals. Furthermore, the generative AI assists in diagnosis based on the patient's past medical history and current biometric information, and proposes an optimal treatment plan. Finally, the generative AI automatically generates detailed reports of the diagnosis results and treatment plan, reducing the workload of healthcare professionals. This platform solves challenges such as data dispersion, delays in diagnosis and treatment planning, information overload, and the effort and cost of current alternatives. For example, an AI agent collects patient biometric information in real time using a built-in generative AI and audio glasses. The generative AI analyzes the collected data in real time and visualizes it on a dashboard for healthcare professionals. The generative AI assists in diagnosis based on the patient's past medical history and current biometric information, and proposes an optimal treatment plan. The generative AI automatically generates detailed reports of the diagnosis results and treatment plan, reducing the workload of healthcare professionals. This allows AI agent-driven medical diagnostic support platforms to reduce the workload of healthcare professionals and improve the accuracy of diagnoses and treatments.

[0029] The AI ​​agent-driven medical diagnostic support platform according to this embodiment comprises a data collection unit, an analysis unit, a diagnostic support unit, a treatment proposal unit, and a report generation unit. The data collection unit collects the patient's biometric information in real time. The patient's biometric information includes, but is not limited to, heart rate, blood pressure, and body temperature. The data collection unit can collect the patient's biometric information in real time, for example, using audio glasses. The audio glasses have built-in sensors for collecting the patient's biometric information and can collect data in real time. The analysis unit uses generative AI to analyze the data collected by the data collection unit in real time. The analysis unit can, for example, analyze the collected data in real time and visualize it on a dashboard for healthcare professionals. The dashboard visually displays the collected data, enabling healthcare professionals to quickly grasp the information. The diagnostic support unit uses generative AI to support diagnosis based on the data analyzed by the analysis unit. The diagnostic support unit can, for example, support diagnosis based on the patient's past medical history and current biometric information. The treatment proposal unit uses generative AI to propose an optimal treatment plan based on the diagnostic results obtained by the diagnostic support unit. The treatment proposal unit can, for example, propose an optimal treatment plan. The report generation unit uses a generation AI to automatically generate a detailed report of the treatment plan proposed by the treatment proposal unit. The report generation unit can, for example, automatically generate a detailed report of the diagnosis results and treatment plan. As a result, the AI ​​agent-driven medical diagnostic support platform according to this embodiment can reduce the workload of medical professionals and improve the accuracy of diagnosis and treatment.

[0030] The data collection unit collects patient biometric information in real time. This biometric information includes, but is not limited to, heart rate, blood pressure, and body temperature. For example, the data collection unit can collect patient biometric information in real time using audio glasses. These audio glasses incorporate sensors for collecting patient biometric information and can collect data in real time. The audio glasses incorporate optical sensors for measuring heart rate, pressure sensors for measuring blood pressure, and temperature sensors for measuring body temperature. These sensors can collect data non-contactually, without direct contact with the patient's skin. Furthermore, the audio glasses can transmit the collected data to the data collection unit in real time using wireless communication technologies such as Bluetooth® or Wi-Fi. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, the data collection unit can store the collected data on a cloud server, making it accessible to the analysis unit and diagnostic support unit. The data collection unit also monitors the accuracy and reliability of the collected data to ensure its quality and can issue alerts if abnormal data is detected. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit uses generative AI to analyze data collected by the data collection unit in real time. For example, the analysis unit can analyze the collected data in real time and visualize it on a dashboard for healthcare professionals. The dashboard visually displays the collected data, allowing healthcare professionals to quickly grasp the information. Specifically, the generative AI analyzes biometric information such as heart rate, blood pressure, and body temperature, and detects anomalies and trends. For example, if the heart rate increases rapidly or blood pressure is abnormally high, the generative AI will detect the anomaly and display an alert on the dashboard. The generative AI can also analyze fluctuations in current data by comparing them with past data to grasp long-term trends. This allows healthcare professionals to monitor the patient's condition in real time and respond quickly. Furthermore, the analysis unit can also predict the patient's health status based on the collected data. For example, based on past data, the generative AI can predict the possibility of a patient's health deteriorating and issue a warning to healthcare professionals in advance. This allows the analysis unit to not only grasp the situation in real time but also to handle future risk management, improving the reliability and safety of the entire system.

[0032] The Diagnostic Support Department uses generative AI to assist in diagnosis based on data analyzed by the Analysis Department. For example, the Diagnostic Support Department can assist in diagnosis based on a patient's past medical history and current biometric information. Specifically, the generative AI analyzes the patient's past medical history and current biometric information to provide useful information for diagnosis. For instance, the generative AI determines whether current symptoms are related to the patient's past medical history. Furthermore, the generative AI evaluates the patient's health status based on current biometric information and provides reference information for diagnosis. This allows the Diagnostic Support Department to support healthcare professionals in making quick and accurate diagnoses. Additionally, the Diagnostic Support Department can continuously improve algorithms to enhance diagnostic accuracy using the generative AI. For example, the generative AI compares past and current diagnostic results to evaluate diagnostic accuracy. The generative AI can also develop new algorithms for diagnosis to improve diagnostic accuracy. This allows the Diagnostic Support Department to always support diagnoses using the latest technology, reducing the workload of healthcare professionals.

[0033] The Treatment Proposal Department uses generative AI to propose the optimal treatment plan based on the diagnostic results obtained by the Diagnostic Support Department. For example, the Treatment Proposal Department can propose the most suitable treatment plan. Specifically, the generative AI selects the most appropriate treatment method for the patient's condition based on the diagnostic results. For instance, the generative AI considers the patient's medical history and current health status to propose treatment methods such as drug therapy, surgery, and rehabilitation. Furthermore, the generative AI can predict the effectiveness of the treatment and formulate the optimal treatment plan. This allows the Treatment Proposal Department to provide support to healthcare professionals in selecting the most appropriate treatment method. In addition, the Treatment Proposal Department can use the generative AI to continuously evaluate the effectiveness of the treatment plan and modify it as needed. For example, the generative AI monitors the progress of the treatment and modifies the treatment plan if the treatment is not as expected. The generative AI can also predict treatment side effects and propose measures to minimize them. This enables the Treatment Proposal Department to consistently provide the optimal treatment plan and support the improvement of the patient's health.

[0034] The report generation unit uses generation AI to automatically generate detailed reports of treatment plans proposed by the treatment proposal unit. For example, the report generation unit can automatically generate detailed reports of diagnostic results and treatment plans. Specifically, the generation AI creates detailed reports based on diagnostic results and treatment plans. These reports include an overview of the diagnostic results, details of the treatment plan, treatment progress, and predicted treatment effects. This allows the report generation unit to support healthcare professionals in quickly understanding information and taking appropriate action. Furthermore, the report generation unit can continuously improve the content of reports using generation AI. For example, the generation AI compares past and current reports to evaluate their content. It can also develop new report creation methods based on the report content, improving report accuracy. This allows the report generation unit to automatically generate detailed reports using the latest technology, reducing the workload of healthcare professionals.

[0035] The data acquisition unit can collect patient biometric information in real time using audio glasses. For example, the data acquisition unit collects patient biometric information in real time using audio glasses. The audio glasses have built-in sensors for collecting patient biometric information and can collect data in real time. For example, the audio glasses can collect biometric information such as heart rate, blood pressure, and body temperature. The audio glasses are worn on the patient's ears and can collect biometric information in real time. Therefore, by using audio glasses, patient biometric information can be collected in real time.

[0036] The analysis unit can analyze the collected data in real time and visualize it on a dashboard for healthcare professionals. For example, the analysis unit can analyze the collected data in real time and visualize it on a dashboard for healthcare professionals. The dashboard visually displays the collected data, allowing healthcare professionals to quickly grasp the information. The dashboard can display biometric information such as heart rate, blood pressure, and body temperature. The dashboard can update the collected data in real time and display the latest information. This allows healthcare professionals to quickly grasp the information by analyzing the collected data in real time and visualizing it on the dashboard.

[0037] The diagnostic support unit can assist in diagnosis based on the patient's past medical history and current biometric information. For example, the diagnostic support unit can assist in diagnosis based on the patient's past medical history and current biometric information. The patient's past medical history includes, but is not limited to, past diagnoses, treatment history, and medical history. Current biometric information includes, but is not limited to, heart rate, blood pressure, and body temperature. The diagnostic support unit can use generative AI to analyze the patient's past medical history and current biometric information to assist in diagnosis. This improves the accuracy of diagnosis by supporting diagnosis based on the patient's past medical history and current biometric information.

[0038] The treatment proposal unit can propose an optimal treatment plan. For example, the treatment proposal unit can propose an optimal treatment plan. This treatment plan may include, but is not limited to, drug therapy, surgery, and rehabilitation. Using generative AI, the treatment proposal unit can propose an optimal treatment plan based on the diagnostic results obtained by the diagnostic support unit. This improves the effectiveness of treatment for patients by proposing an optimal treatment plan.

[0039] The report generation unit can automatically generate detailed reports on diagnostic results and treatment plans. For example, the report generation unit automatically generates detailed reports on diagnostic results and treatment plans. These reports may include, but are not limited to, detailed diagnostic results, treatment plan contents, and treatment progress. The report generation unit can automatically generate detailed reports on diagnostic results and treatment plans using generation AI. This reduces the workload of healthcare professionals by automatically generating detailed reports on diagnostic results and treatment plans.

[0040] The data collection unit can analyze a patient's past biometric data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection time from past collected data. For example, the data collection unit can set the optimal collection interval based on past data collection frequency. For example, the data collection unit can analyze past data collection methods and select the method to obtain the most accurate data. In this way, by analyzing past biometric data collection history, the optimal collection method is selected and the accuracy of the data is improved.

[0041] The data collection unit can filter the collected biometric information based on the patient's current health status and lifestyle. For example, the data collection unit collects only the necessary data based on the patient's current health status. For example, the data collection unit selects the types of data to collect considering the patient's lifestyle. For example, if the patient's health status changes, the data collection unit automatically adjusts the filtering conditions for the collected data. This improves data accuracy by collecting only the necessary data through filtering based on the patient's current health status and lifestyle.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the patient's geographical location when collecting biometric data. For example, if the patient is at high altitude, the unit will prioritize the collection of oxygen saturation. If the patient is in an urban area, the unit will prioritize the collection of ambient noise and air quality. If the patient is exercising, the unit will prioritize the collection of heart rate and calorie consumption. By prioritizing the collection of highly relevant information while considering the patient's geographical location, more accurate data can be collected.

[0043] The data collection unit can analyze the patient's social media activity while collecting biometric data and collect relevant information. For example, if the patient posts about feeling stressed on social media, the data collection unit will prioritize collecting stress levels. For example, if the patient posts about exercise, the data collection unit will prioritize collecting biometric data during exercise. For example, if the patient posts about food, the data collection unit will prioritize collecting post-meal blood glucose levels. By analyzing the patient's social media activity, relevant information is collected, improving the accuracy of the data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the biological information during the analysis. For example, the analysis unit performs a detailed analysis on biological information of high importance. For example, the analysis unit performs a simplified analysis on biological information of low importance. For example, the analysis unit sets the priority of the analysis according to importance. In this way, by adjusting the level of detail of the analysis based on the importance of the biological information, important information can be analyzed in detail.

[0045] The analysis unit can apply different analysis algorithms depending on the category of biological information during analysis. For example, the analysis unit applies a standard analysis algorithm to basic biological information such as heart rate and blood pressure. For advanced biological information such as electroencephalogram (EEG) and heart rate variability, the analysis unit applies a specialized analysis algorithm. For psychological biological information such as stress levels and cortisol levels, the analysis unit applies a psychological analysis algorithm. By applying different analysis algorithms depending on the category of biological information, more accurate analysis results can be obtained.

[0046] The analysis unit can determine the priority of analysis based on the timing of biological data collection during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent biological data. For example, the analysis unit may analyze current biological data while referring to past biological data. For example, the analysis unit may set the priority of analysis according to the timing of biological data collection. This allows for the prioritization of analysis based on the timing of biological data collection, thereby prioritizing the analysis of the most recent information.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the biological information during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant biological information. For example, the analysis unit postpones the analysis of less relevant biological information. For example, the analysis unit sets the order of analysis according to the relevance of the biological information. In this way, by adjusting the order of analysis based on the relevance of the biological information, highly relevant information can be analyzed preferentially.

[0048] The diagnostic support department can improve the accuracy of diagnoses by considering the interrelationships of patients during diagnostic support. For example, the diagnostic support department considers the patient's family history when making a diagnosis. For example, the diagnostic support department considers the patient's living environment when making a diagnosis. For example, the diagnostic support department considers the patient's social background when making a diagnosis. In this way, the accuracy of diagnoses can be improved by considering the interrelationships of patients.

[0049] The diagnostic support department can make diagnoses while considering the patient's attribute information. For example, the diagnostic support department can make diagnoses while considering the patient's age. For example, the diagnostic support department can make diagnoses while considering the patient's gender. For example, the diagnostic support department can make diagnoses while considering the patient's occupation. By considering the patient's attribute information, a more appropriate diagnosis can be made.

[0050] The diagnostic support department can perform diagnoses while considering the geographical distribution of patients. For example, if a patient lives in a high-altitude area, the diagnostic support department will consider the health risks specific to high-altitude areas when performing the diagnosis. For example, if a patient lives in an urban area, the diagnostic support department will consider the health risks specific to urban areas when performing the diagnosis. For example, if a patient lives in a rural area, the diagnostic support department will consider the health risks specific to rural areas when performing the diagnosis. By considering the geographical distribution of patients, more appropriate diagnoses can be made.

[0051] The diagnostic support department can improve the accuracy of its diagnosis by referring to relevant literature on the patient during diagnostic support. For example, the diagnostic support department can refer to the latest research papers related to the patient's symptoms. For example, the diagnostic support department can refer to past literature related to the patient's medical history. For example, the diagnostic support department can refer to guidelines related to the patient's treatment plan. In this way, the accuracy of the diagnosis can be improved by referring to relevant literature on the patient.

[0052] The treatment proposal unit can analyze the patient's past treatment history to select the optimal treatment method when proposing a treatment. For example, the treatment proposal unit can select the most effective treatment method based on the patient's past treatment history. For example, the treatment proposal unit can select a treatment method with fewer side effects based on the patient's past treatment history. For example, the treatment proposal unit can analyze the patient's past treatment history to select the most efficient treatment method. In this way, by analyzing the patient's past treatment history, the optimal treatment method can be selected and the effectiveness of the treatment can be improved.

[0053] The treatment suggestion unit can customize the means of suggesting treatment based on the patient's current health condition. For example, the treatment suggestion unit can suggest the optimal drug therapy based on the patient's current health condition. For example, the treatment suggestion unit can suggest the optimal rehabilitation program based on the patient's current health condition. For example, the treatment suggestion unit can suggest the optimal lifestyle improvement plan based on the patient's current health condition. By customizing the means of suggesting treatment based on the patient's current health condition, more appropriate treatment suggestions can be made.

[0054] The treatment proposal unit can select the most appropriate treatment method when proposing treatment, taking into account the patient's geographical location. For example, if the patient lives at high altitude, the treatment proposal unit will select a treatment method considering the health risks specific to high altitude. For example, if the patient lives in an urban area, the treatment proposal unit will select a treatment method considering the health risks specific to urban areas. For example, if the patient lives in a rural area, the treatment proposal unit will select a treatment method considering the health risks specific to rural areas. In this way, by considering the patient's geographical location, a more appropriate treatment method can be selected.

[0055] The treatment proposal department can analyze a patient's social media activity when proposing treatment and suggest appropriate treatment options. For example, if a patient posts about feeling stressed on social media, the department will suggest stress reduction treatment methods. If a patient posts about exercise, the department will suggest exercise therapy. If a patient posts about food, the department will suggest dietary therapy. By analyzing a patient's social media activity, the department can provide more appropriate treatment suggestions.

[0056] The report generation unit can adjust the level of detail in the report based on the importance of the diagnostic results when generating the report. For example, the report generation unit provides a detailed report for high-importance diagnostic results. For example, the report generation unit provides a concise report for low-importance diagnostic results. For example, the report generation unit sets the priority of the report according to its importance. This allows important information to be reported in detail by adjusting the level of detail in the report based on the importance of the diagnostic results.

[0057] The report generation unit can apply different report generation algorithms depending on the category of the diagnostic result when generating a report. For example, the report generation unit applies a standard report generation algorithm to basic diagnostic results such as heart rate and blood pressure. For advanced diagnostic results such as electroencephalogram (EEG) and heart rate variability, the report generation unit applies a specialized report generation algorithm. For psychological diagnostic results such as stress levels and cortisol levels, the report generation unit applies a psychological report generation algorithm. By applying different report generation algorithms depending on the category of the diagnostic result, a more accurate report can be provided.

[0058] The report generation unit can determine the priority of reports based on when the diagnostic results were submitted. For example, the report generation unit may prioritize reflecting the latest diagnostic results in the report. For example, the report generation unit may reflect the current diagnostic results in the report while referring to past diagnostic results. For example, the report generation unit may set the priority of reports according to when the diagnostic results were submitted. This allows the latest information to be reported preferentially by determining the priority of reports based on when the diagnostic results were submitted.

[0059] The report generation unit can adjust the order of reports based on the relevance of the diagnostic results during report generation. For example, the report generation unit prioritizes reflecting highly relevant diagnostic results in the report. For example, the report generation unit postpones reflecting less relevant diagnostic results in the report. For example, the report generation unit sets the order of reports according to the relevance of the diagnostic results. In this way, by adjusting the order of reports based on the relevance of the diagnostic results, highly relevant information can be prioritized in the reports.

[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 can adjust the types of biometric information collected based on the patient's living environment. For example, if the patient lives in a hot and humid environment, body temperature and sweating can be prioritized for collection. If the patient lives in a cold region, fluctuations in body temperature and blood pressure can be prioritized for collection. Furthermore, if the patient lives in an urban area, data on ambient noise and air quality can be collected, and this data can be correlated with biometric information for analysis. This makes it possible to collect biometric information tailored to the patient's living environment, resulting in more accurate data.

[0062] The diagnostic support department can make diagnoses while considering the patient's social background. For example, if the patient is elderly, the diagnosis can be made considering the specific health risks associated with aging. If the patient is a parent raising children, the diagnosis can be made considering parenting stress and sleep deprivation. Furthermore, if the patient is in a high-stress occupational environment, the diagnosis can be made considering their stress level. In this way, by considering the patient's social background, a more accurate diagnosis can be made.

[0063] The report generation unit can analyze a patient's past report viewing history when generating reports on diagnostic results and treatment plans, and select the most appropriate report format. For example, a patient who previously preferred detailed reports can be provided with a detailed report. Similarly, a patient who previously preferred concise reports can be provided with a concise report. Furthermore, a patient who previously preferred visual reports can be provided with a visually easy-to-understand report. In this way, by analyzing a patient's past report viewing history, the system can select the most appropriate report format and provide reports that are easy for the patient to understand.

[0064] The analysis unit can analyze past analysis results for patients and select the optimal analysis algorithm. For example, it can identify the most effective analysis algorithm from past analysis results. It can also select algorithms to improve the accuracy of the analysis based on past analysis results. Furthermore, it can analyze past analysis results and select the most efficient analysis algorithm. In this way, by analyzing past analysis results for patients, the optimal analysis algorithm can be selected and the accuracy of the analysis can be improved.

[0065] The treatment proposal department can analyze a patient's past treatment history and select the optimal treatment method. For example, it can identify the most effective treatment method from past treatment history. It can also select a treatment method with fewer side effects based on past treatment history. Furthermore, it can analyze past treatment history to select the most efficient treatment method. In this way, by analyzing a patient's past treatment history, the optimal treatment method can be selected, and the effectiveness of the treatment can be improved.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data acquisition unit collects the patient's biometric information in real time. This biometric information includes heart rate, blood pressure, and body temperature. The data acquisition unit can collect the patient's biometric information in real time using audio glasses. The audio glasses have built-in sensors for collecting the patient's biometric information and can collect data in real time. Step 2: The analysis unit uses generational AI to analyze the data collected by the data collection unit in real time. The analysis unit can analyze the collected data in real time and visualize it on a dashboard for healthcare professionals. The dashboard visually displays the collected data, enabling healthcare professionals to quickly grasp the information. Step 3: The diagnostic support unit uses generation AI to assist in diagnosis based on the data analyzed by the analysis unit. The diagnostic support unit can assist in diagnosis based on the patient's past medical history and current biological information. Step 4: The treatment proposal unit uses generation AI to propose the optimal treatment plan based on the diagnostic results obtained by the diagnostic support unit. The treatment proposal unit can propose the optimal treatment plan. Step 5: The report generation unit uses generation AI to automatically generate a detailed report of the treatment plan proposed by the treatment proposal unit. The report generation unit can automatically generate detailed reports of diagnostic results and treatment plans.

[0068] (Example of form 2) An AI agent-driven medical diagnostic support platform according to an embodiment of the present invention is a system that utilizes generative AI and audio glasses technology to collect, integrate, and analyze medical data in real time. This system collects patient biometric information in real time, the generative AI analyzes the collected data in real time, and visualizes it on a dashboard for healthcare professionals. Furthermore, the generative AI assists in diagnosis based on the patient's past medical history and current biometric information, and proposes an optimal treatment plan. Finally, the generative AI automatically generates detailed reports of the diagnosis results and treatment plan, reducing the workload of healthcare professionals. This platform solves challenges such as data dispersion, delays in diagnosis and treatment planning, information overload, and the effort and cost of current alternatives. For example, an AI agent collects patient biometric information in real time using a built-in generative AI and audio glasses. The generative AI analyzes the collected data in real time and visualizes it on a dashboard for healthcare professionals. The generative AI assists in diagnosis based on the patient's past medical history and current biometric information, and proposes an optimal treatment plan. The generative AI automatically generates detailed reports of the diagnosis results and treatment plan, reducing the workload of healthcare professionals. This allows AI agent-driven medical diagnostic support platforms to reduce the workload of healthcare professionals and improve the accuracy of diagnoses and treatments.

[0069] The AI ​​agent-driven medical diagnostic support platform according to this embodiment comprises a data collection unit, an analysis unit, a diagnostic support unit, a treatment proposal unit, and a report generation unit. The data collection unit collects the patient's biometric information in real time. The patient's biometric information includes, but is not limited to, heart rate, blood pressure, and body temperature. The data collection unit can collect the patient's biometric information in real time, for example, using audio glasses. The audio glasses have built-in sensors for collecting the patient's biometric information and can collect data in real time. The analysis unit uses generative AI to analyze the data collected by the data collection unit in real time. The analysis unit can, for example, analyze the collected data in real time and visualize it on a dashboard for healthcare professionals. The dashboard visually displays the collected data, enabling healthcare professionals to quickly grasp the information. The diagnostic support unit uses generative AI to support diagnosis based on the data analyzed by the analysis unit. The diagnostic support unit can, for example, support diagnosis based on the patient's past medical history and current biometric information. The treatment proposal unit uses generative AI to propose an optimal treatment plan based on the diagnostic results obtained by the diagnostic support unit. The treatment proposal unit can, for example, propose an optimal treatment plan. The report generation unit uses a generation AI to automatically generate a detailed report of the treatment plan proposed by the treatment proposal unit. The report generation unit can, for example, automatically generate a detailed report of the diagnosis results and treatment plan. As a result, the AI ​​agent-driven medical diagnostic support platform according to this embodiment can reduce the workload of medical professionals and improve the accuracy of diagnosis and treatment.

[0070] The data collection unit collects patient biometric information in real time. This biometric information includes, but is not limited to, heart rate, blood pressure, and body temperature. For example, the data collection unit can collect patient biometric information in real time using audio glasses. These audio glasses incorporate sensors for collecting patient biometric information and can collect data in real time. These sensors include an optical sensor for measuring heart rate, a pressure sensor for measuring blood pressure, and a temperature sensor for measuring body temperature. These sensors can collect data non-contactually, without direct contact with the patient's skin. Furthermore, the audio glasses can transmit the collected data to the data collection unit in real time using wireless communication technologies such as Bluetooth or Wi-Fi. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, the data collection unit can store the collected data on a cloud server, making it accessible to the analysis unit and the diagnostic support unit. The data collection unit also monitors the accuracy and reliability of the collected data to ensure its quality and can issue alerts if abnormal data is detected. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0071] The analysis unit uses generative AI to analyze data collected by the data collection unit in real time. For example, the analysis unit can analyze the collected data in real time and visualize it on a dashboard for healthcare professionals. The dashboard visually displays the collected data, allowing healthcare professionals to quickly grasp the information. Specifically, the generative AI analyzes biometric information such as heart rate, blood pressure, and body temperature, and detects anomalies and trends. For example, if the heart rate increases rapidly or blood pressure is abnormally high, the generative AI will detect the anomaly and display an alert on the dashboard. The generative AI can also analyze fluctuations in current data by comparing them with past data to grasp long-term trends. This allows healthcare professionals to monitor the patient's condition in real time and respond quickly. Furthermore, the analysis unit can also predict the patient's health status based on the collected data. For example, based on past data, the generative AI can predict the possibility of a patient's health deteriorating and issue a warning to healthcare professionals in advance. This allows the analysis unit to not only grasp the situation in real time but also to handle future risk management, improving the reliability and safety of the entire system.

[0072] The Diagnostic Support Department uses generative AI to assist in diagnosis based on data analyzed by the Analysis Department. For example, the Diagnostic Support Department can assist in diagnosis based on a patient's past medical history and current biometric information. Specifically, the generative AI analyzes the patient's past medical history and current biometric information to provide useful information for diagnosis. For instance, the generative AI determines whether current symptoms are related to the patient's past medical history. Furthermore, the generative AI evaluates the patient's health status based on current biometric information and provides reference information for diagnosis. This allows the Diagnostic Support Department to support healthcare professionals in making quick and accurate diagnoses. Additionally, the Diagnostic Support Department can continuously improve algorithms to enhance diagnostic accuracy using the generative AI. For example, the generative AI compares past and current diagnostic results to evaluate diagnostic accuracy. The generative AI can also develop new algorithms for diagnosis to improve diagnostic accuracy. This allows the Diagnostic Support Department to always support diagnoses using the latest technology, reducing the workload of healthcare professionals.

[0073] The Treatment Proposal Department uses generative AI to propose the optimal treatment plan based on the diagnostic results obtained by the Diagnostic Support Department. For example, the Treatment Proposal Department can propose the most suitable treatment plan. Specifically, the generative AI selects the most appropriate treatment method for the patient's condition based on the diagnostic results. For instance, the generative AI considers the patient's medical history and current health status to propose treatment methods such as drug therapy, surgery, and rehabilitation. Furthermore, the generative AI can predict the effectiveness of the treatment and formulate the optimal treatment plan. This allows the Treatment Proposal Department to provide support to healthcare professionals in selecting the most appropriate treatment method. In addition, the Treatment Proposal Department can use the generative AI to continuously evaluate the effectiveness of the treatment plan and modify it as needed. For example, the generative AI monitors the progress of the treatment and modifies the treatment plan if the treatment is not as expected. The generative AI can also predict treatment side effects and propose measures to minimize them. This enables the Treatment Proposal Department to consistently provide the optimal treatment plan and support the improvement of the patient's health.

[0074] The report generation unit uses generation AI to automatically generate detailed reports of treatment plans proposed by the treatment proposal unit. For example, the report generation unit can automatically generate detailed reports of diagnostic results and treatment plans. Specifically, the generation AI creates detailed reports based on diagnostic results and treatment plans. These reports include an overview of the diagnostic results, details of the treatment plan, treatment progress, and predicted treatment effects. This allows the report generation unit to support healthcare professionals in quickly understanding information and taking appropriate action. Furthermore, the report generation unit can continuously improve the content of reports using generation AI. For example, the generation AI compares past and current reports to evaluate their content. It can also develop new report creation methods based on the report content, improving report accuracy. This allows the report generation unit to automatically generate detailed reports using the latest technology, reducing the workload of healthcare professionals.

[0075] The data acquisition unit can collect patient biometric information in real time using audio glasses. For example, the data acquisition unit collects patient biometric information in real time using audio glasses. The audio glasses have built-in sensors for collecting patient biometric information and can collect data in real time. For example, the audio glasses can collect biometric information such as heart rate, blood pressure, and body temperature. The audio glasses are worn on the patient's ears and can collect biometric information in real time. Therefore, by using audio glasses, patient biometric information can be collected in real time.

[0076] The analysis unit can analyze the collected data in real time and visualize it on a dashboard for healthcare professionals. For example, the analysis unit can analyze the collected data in real time and visualize it on a dashboard for healthcare professionals. The dashboard visually displays the collected data, allowing healthcare professionals to quickly grasp the information. The dashboard can display biometric information such as heart rate, blood pressure, and body temperature. The dashboard can update the collected data in real time and display the latest information. This allows healthcare professionals to quickly grasp the information by analyzing the collected data in real time and visualizing it on the dashboard.

[0077] The diagnostic support unit can assist in diagnosis based on the patient's past medical history and current biometric information. For example, the diagnostic support unit can assist in diagnosis based on the patient's past medical history and current biometric information. The patient's past medical history includes, but is not limited to, past diagnoses, treatment history, and medical history. Current biometric information includes, but is not limited to, heart rate, blood pressure, and body temperature. The diagnostic support unit can use generative AI to analyze the patient's past medical history and current biometric information to assist in diagnosis. This improves the accuracy of diagnosis by supporting diagnosis based on the patient's past medical history and current biometric information.

[0078] The treatment proposal unit can propose an optimal treatment plan. For example, the treatment proposal unit can propose an optimal treatment plan. This treatment plan may include, but is not limited to, drug therapy, surgery, and rehabilitation. Using generative AI, the treatment proposal unit can propose an optimal treatment plan based on the diagnostic results obtained by the diagnostic support unit. This improves the effectiveness of treatment for patients by proposing an optimal treatment plan.

[0079] The report generation unit can automatically generate detailed reports on diagnostic results and treatment plans. For example, the report generation unit automatically generates detailed reports on diagnostic results and treatment plans. These reports may include, but are not limited to, detailed diagnostic results, treatment plan contents, and treatment progress. The report generation unit can automatically generate detailed reports on diagnostic results and treatment plans using generation AI. This reduces the workload of healthcare professionals by automatically generating detailed reports on diagnostic results and treatment plans.

[0080] The data collection unit can estimate the patient's emotions and adjust the timing of biometric data collection based on the estimated emotions. For example, if the patient is relaxed, the data collection unit collects biometric data at regular intervals. If the patient is stressed, for example, the data collection unit reduces the collection frequency to lessen the patient's burden. If the patient is agitated, for example, the data collection unit shortens the collection interval to capture changes in real time. By adjusting the timing of biometric data collection based on the patient's emotions, the burden on the patient is reduced and more accurate data can be collected. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The data collection unit can analyze a patient's past biometric data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection time from past collected data. For example, the data collection unit can set the optimal collection interval based on past data collection frequency. For example, the data collection unit can analyze past data collection methods and select the method to obtain the most accurate data. In this way, by analyzing past biometric data collection history, the optimal collection method is selected and the accuracy of the data is improved.

[0082] The data collection unit can filter the collected biometric information based on the patient's current health status and lifestyle. For example, the data collection unit collects only the necessary data based on the patient's current health status. For example, the data collection unit selects the types of data to collect considering the patient's lifestyle. For example, if the patient's health status changes, the data collection unit automatically adjusts the filtering conditions for the collected data. This improves data accuracy by collecting only the necessary data through filtering based on the patient's current health status and lifestyle.

[0083] The data collection unit can estimate the patient's emotions and prioritize the biometric data to collect based on the estimated emotions. For example, if the patient is relaxed, the data collection unit prioritizes collecting basic biometric data such as heart rate and blood pressure. If the patient is stressed, the data collection unit prioritizes collecting stress levels and cortisol levels. If the patient is agitated, the data collection unit prioritizes collecting electroencephalograms and heart rate variability. This allows for the collection of more important data by prioritizing the biometric data to be collected based on the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The data collection unit can prioritize the collection of highly relevant information by considering the patient's geographical location when collecting biometric data. For example, if the patient is at high altitude, the unit will prioritize the collection of oxygen saturation. If the patient is in an urban area, the unit will prioritize the collection of ambient noise and air quality. If the patient is exercising, the unit will prioritize the collection of heart rate and calorie consumption. By prioritizing the collection of highly relevant information while considering the patient's geographical location, more accurate data can be collected.

[0085] The data collection unit can analyze the patient's social media activity while collecting biometric data and collect relevant information. For example, if the patient posts about feeling stressed on social media, the data collection unit will prioritize collecting stress levels. For example, if the patient posts about exercise, the data collection unit will prioritize collecting biometric data during exercise. For example, if the patient posts about food, the data collection unit will prioritize collecting post-meal blood glucose levels. By analyzing the patient's social media activity, relevant information is collected, improving the accuracy of the data.

[0086] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the patient is relaxed, the analysis unit provides detailed analysis results. For example, if the patient is stressed, the analysis unit provides concise and to-the-point analysis results. For example, if the patient is agitated, the analysis unit provides visually stimulating analysis results. By adjusting the presentation of the analysis based on the patient's emotions, it is possible to provide analysis results that are easy for the patient to understand. 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.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the biological information during the analysis. For example, the analysis unit performs a detailed analysis on biological information of high importance. For example, the analysis unit performs a simplified analysis on biological information of low importance. For example, the analysis unit sets the priority of the analysis according to importance. In this way, by adjusting the level of detail of the analysis based on the importance of the biological information, important information can be analyzed in detail.

[0088] The analysis unit can apply different analysis algorithms depending on the category of biological information during analysis. For example, the analysis unit applies a standard analysis algorithm to basic biological information such as heart rate and blood pressure. For advanced biological information such as electroencephalogram (EEG) and heart rate variability, the analysis unit applies a specialized analysis algorithm. For psychological biological information such as stress levels and cortisol levels, the analysis unit applies a psychological analysis algorithm. By applying different analysis algorithms depending on the category of biological information, more accurate analysis results can be obtained.

[0089] 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 relaxed, the analysis unit will perform a detailed analysis and provide a longer report. For example, if the patient is stressed, the analysis unit will perform a concise analysis and provide a shorter report. For example, if the patient is agitated, the analysis unit will perform a visually stimulating analysis and provide a report of appropriate length. In this way, by adjusting the length of the analysis based on the patient's emotions, it is possible to provide analysis results of an appropriate length for the patient. 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.

[0090] The analysis unit can determine the priority of analysis based on the timing of biological data collection during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent biological data. For example, the analysis unit may analyze current biological data while referring to past biological data. For example, the analysis unit may set the priority of analysis according to the timing of biological data collection. This allows for the prioritization of analysis based on the timing of biological data collection, thereby prioritizing the analysis of the most recent information.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the biological information during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant biological information. For example, the analysis unit postpones the analysis of less relevant biological information. For example, the analysis unit sets the order of analysis according to the relevance of the biological information. In this way, by adjusting the order of analysis based on the relevance of the biological information, highly relevant information can be analyzed preferentially.

[0092] The diagnostic support unit can estimate the patient's emotions and adjust the diagnostic criteria based on the estimated emotions. For example, if the patient is relaxed, the diagnostic support unit applies detailed diagnostic criteria. For example, if the patient is stressed, the diagnostic support unit applies concise diagnostic criteria. For example, if the patient is agitated, the diagnostic support unit applies visually stimulating diagnostic criteria. This allows for a more accurate diagnosis by adjusting the diagnostic criteria based on the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The diagnostic support department can improve the accuracy of diagnoses by considering the interrelationships of patients during diagnostic support. For example, the diagnostic support department considers the patient's family history when making a diagnosis. For example, the diagnostic support department considers the patient's living environment when making a diagnosis. For example, the diagnostic support department considers the patient's social background when making a diagnosis. In this way, the accuracy of diagnoses can be improved by considering the interrelationships of patients.

[0094] The diagnostic support department can make diagnoses while considering the patient's attribute information. For example, the diagnostic support department can make diagnoses while considering the patient's age. For example, the diagnostic support department can make diagnoses while considering the patient's gender. For example, the diagnostic support department can make diagnoses while considering the patient's occupation. By considering the patient's attribute information, a more appropriate diagnosis can be made.

[0095] The diagnostic support unit can estimate the patient's emotions and adjust the order in which the diagnostic results are displayed based on the estimated emotions. For example, if the patient is relaxed, the diagnostic support unit will display detailed diagnostic results first. If the patient is stressed, for example, the diagnostic support unit will display concise diagnostic results first. If the patient is agitated, for example, the diagnostic support unit will display visually stimulating diagnostic results first. By adjusting the order in which the diagnostic results are displayed based on the patient's emotions, the system can provide diagnostic results that are easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The diagnostic support department can perform diagnoses while considering the geographical distribution of patients. For example, if a patient lives in a high-altitude area, the diagnostic support department will consider the health risks specific to high-altitude areas when performing the diagnosis. For example, if a patient lives in an urban area, the diagnostic support department will consider the health risks specific to urban areas when performing the diagnosis. For example, if a patient lives in a rural area, the diagnostic support department will consider the health risks specific to rural areas when performing the diagnosis. By considering the geographical distribution of patients, more appropriate diagnoses can be made.

[0097] The diagnostic support department can improve the accuracy of its diagnosis by referring to relevant literature on the patient during diagnostic support. For example, the diagnostic support department can refer to the latest research papers related to the patient's symptoms. For example, the diagnostic support department can refer to past literature related to the patient's medical history. For example, the diagnostic support department can refer to guidelines related to the patient's treatment plan. In this way, the accuracy of the diagnosis can be improved by referring to relevant literature on the patient.

[0098] The treatment suggestion unit can estimate the patient's emotions and adjust the method of treatment suggestion based on the estimated emotions. For example, if the patient is relaxed, the treatment suggestion unit will provide detailed treatment suggestions. For example, if the patient is stressed, the treatment suggestion unit will provide concise treatment suggestions. For example, if the patient is agitated, the treatment suggestion unit will provide visually stimulating treatment suggestions. In this way, by adjusting the method of treatment suggestion based on the patient's emotions, more appropriate treatment suggestions can be made. 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.

[0099] The treatment proposal unit can analyze the patient's past treatment history to select the optimal treatment method when proposing a treatment. For example, the treatment proposal unit can select the most effective treatment method based on the patient's past treatment history. For example, the treatment proposal unit can select a treatment method with fewer side effects based on the patient's past treatment history. For example, the treatment proposal unit can analyze the patient's past treatment history to select the most efficient treatment method. In this way, by analyzing the patient's past treatment history, the optimal treatment method can be selected and the effectiveness of the treatment can be improved.

[0100] The treatment suggestion unit can customize the means of suggesting treatment based on the patient's current health condition. For example, the treatment suggestion unit can suggest the optimal drug therapy based on the patient's current health condition. For example, the treatment suggestion unit can suggest the optimal rehabilitation program based on the patient's current health condition. For example, the treatment suggestion unit can suggest the optimal lifestyle improvement plan based on the patient's current health condition. By customizing the means of suggesting treatment based on the patient's current health condition, more appropriate treatment suggestions can be made.

[0101] The treatment suggestion unit can estimate the patient's emotions and prioritize treatment suggestions based on those emotions. For example, if the patient is relaxed, the unit will prioritize detailed treatment suggestions. If the patient is stressed, the unit will prioritize concise treatment suggestions. If the patient is agitated, the unit will prioritize visually stimulating treatment suggestions. By prioritizing treatment suggestions based on the patient's emotions, more important treatment suggestions can be prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The treatment proposal unit can select the most appropriate treatment method when proposing treatment, taking into account the patient's geographical location. For example, if the patient lives at high altitude, the treatment proposal unit will select a treatment method considering the health risks specific to high altitude. For example, if the patient lives in an urban area, the treatment proposal unit will select a treatment method considering the health risks specific to urban areas. For example, if the patient lives in a rural area, the treatment proposal unit will select a treatment method considering the health risks specific to rural areas. In this way, by considering the patient's geographical location, a more appropriate treatment method can be selected.

[0103] The treatment proposal department can analyze a patient's social media activity when proposing treatment and suggest appropriate treatment options. For example, if a patient posts about feeling stressed on social media, the department will suggest stress reduction treatment methods. If a patient posts about exercise, the department will suggest exercise therapy. If a patient posts about food, the department will suggest dietary therapy. By analyzing a patient's social media activity, the department can provide more appropriate treatment suggestions.

[0104] The report generation unit can estimate the patient's emotions and adjust the report's presentation based on the estimated emotions. For example, if the patient is relaxed, the report generation unit provides a detailed report. If the patient is stressed, the report generation unit provides a concise and to-the-point report. If the patient is agitated, the report generation unit provides a visually stimulating report. By adjusting the report's presentation based on the patient's emotions, it is possible to provide a report that is easy for the patient to understand. 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.

[0105] The report generation unit can adjust the level of detail in the report based on the importance of the diagnostic results when generating the report. For example, the report generation unit provides a detailed report for high-importance diagnostic results. For example, the report generation unit provides a concise report for low-importance diagnostic results. For example, the report generation unit sets the priority of the report according to its importance. This allows important information to be reported in detail by adjusting the level of detail in the report based on the importance of the diagnostic results.

[0106] The report generation unit can apply different report generation algorithms depending on the category of the diagnostic result when generating a report. For example, the report generation unit applies a standard report generation algorithm to basic diagnostic results such as heart rate and blood pressure. For advanced diagnostic results such as electroencephalogram (EEG) and heart rate variability, the report generation unit applies a specialized report generation algorithm. For psychological diagnostic results such as stress levels and cortisol levels, the report generation unit applies a psychological report generation algorithm. By applying different report generation algorithms depending on the category of the diagnostic result, a more accurate report can be provided.

[0107] The report generation unit can estimate the patient's emotions and adjust the length of the report based on the estimated emotions. For example, if the patient is relaxed, the report generation unit will provide a detailed report. For example, if the patient is stressed, the report generation unit will provide a concise report. For example, if the patient is agitated, the report generation unit will provide a visually stimulating report. By adjusting the length of the report based on the patient's emotions, it is possible to provide a report of an appropriate length for the patient. 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.

[0108] The report generation unit can determine the priority of reports based on when the diagnostic results were submitted. For example, the report generation unit may prioritize reflecting the latest diagnostic results in the report. For example, the report generation unit may reflect the current diagnostic results in the report while referring to past diagnostic results. For example, the report generation unit may set the priority of reports according to when the diagnostic results were submitted. This allows the latest information to be reported preferentially by determining the priority of reports based on when the diagnostic results were submitted.

[0109] The report generation unit can adjust the order of reports based on the relevance of the diagnostic results during report generation. For example, the report generation unit prioritizes reflecting highly relevant diagnostic results in the report. For example, the report generation unit postpones reflecting less relevant diagnostic results in the report. For example, the report generation unit sets the order of reports according to the relevance of the diagnostic results. In this way, by adjusting the order of reports based on the relevance of the diagnostic results, highly relevant information can be prioritized in the reports.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The data collection unit can adjust the types of biometric information collected based on the patient's living environment. For example, if the patient lives in a hot and humid environment, body temperature and sweating can be prioritized for collection. If the patient lives in a cold region, fluctuations in body temperature and blood pressure can be prioritized for collection. Furthermore, if the patient lives in an urban area, data on ambient noise and air quality can be collected, and this data can be correlated with biometric information for analysis. This makes it possible to collect biometric information tailored to the patient's living environment, resulting in more accurate data.

[0112] The analysis unit can estimate the patient's emotions and adjust the timing of the analysis based on those emotions. For example, if the patient is relaxed, it can select the timing for a detailed analysis. If the patient is stressed, the frequency of analysis can be reduced to lessen the patient's burden. Furthermore, if the patient is agitated, real-time analysis can be performed to provide results quickly. By adjusting the timing of the analysis according to the patient's emotions, more appropriate analysis results can be provided.

[0113] The diagnostic support department can make diagnoses while considering the patient's social background. For example, if the patient is elderly, the diagnosis can be made considering the specific health risks associated with aging. If the patient is a parent raising children, the diagnosis can be made considering parenting stress and sleep deprivation. Furthermore, if the patient is in a high-stress occupational environment, the diagnosis can be made considering their stress level. In this way, by considering the patient's social background, a more accurate diagnosis can be made.

[0114] The treatment suggestion system can estimate the patient's emotions and adjust the content of the treatment suggestion based on those emotions. For example, if the patient is relaxed, it can suggest a detailed treatment plan. If the patient is stressed, it can suggest a simple and easy-to-follow treatment plan. Furthermore, if the patient is agitated, it can suggest a visually stimulating treatment plan. By adjusting the content of the treatment suggestion based on the patient's emotions, it is possible to provide a more appropriate treatment plan.

[0115] The report generation unit can analyze a patient's past report viewing history when generating reports on diagnostic results and treatment plans, and select the most appropriate report format. For example, a patient who previously preferred detailed reports can be provided with a detailed report. Similarly, a patient who previously preferred concise reports can be provided with a concise report. Furthermore, a patient who previously preferred visual reports can be provided with a visually easy-to-understand report. In this way, by analyzing a patient's past report viewing history, the system can select the most appropriate report format and provide reports that are easy for the patient to understand.

[0116] The data collection unit can estimate the patient's emotions and adjust the types of data collected based on those estimates. For example, if the patient is relaxed, basic biometric information such as heart rate and blood pressure can be collected. If the patient is stressed, stress levels and cortisol levels can be collected. Furthermore, if the patient is agitated, electroencephalograms (EEGs) and heart rate variability can be collected. This allows for the collection of more important data by adjusting the types of data collected based on the patient's emotions.

[0117] The analysis unit can analyze past analysis results for patients and select the optimal analysis algorithm. For example, it can identify the most effective analysis algorithm from past analysis results. It can also select algorithms to improve the accuracy of the analysis based on past analysis results. Furthermore, it can analyze past analysis results and select the most efficient analysis algorithm. In this way, by analyzing past analysis results for patients, the optimal analysis algorithm can be selected and the accuracy of the analysis can be improved.

[0118] The diagnostic support unit can estimate the patient's emotions and prioritize diagnoses based on those emotions. For example, if the patient is relaxed, detailed diagnoses can be prioritized. If the patient is stressed, concise diagnoses can be prioritized. Furthermore, if the patient is agitated, visually stimulating diagnoses can be prioritized. By prioritizing diagnoses based on the patient's emotions, more important diagnoses can be prioritized.

[0119] The treatment proposal department can analyze a patient's past treatment history and select the optimal treatment method. For example, it can identify the most effective treatment method from past treatment history. It can also select a treatment method with fewer side effects based on past treatment history. Furthermore, it can analyze past treatment history to select the most efficient treatment method. In this way, by analyzing a patient's past treatment history, the optimal treatment method can be selected, and the effectiveness of the treatment can be improved.

[0120] The report generation unit can estimate the patient's emotions and prioritize reports based on those emotions. For example, if the patient is relaxed, a detailed report can be prioritized. If the patient is stressed, a concise report can be prioritized. Furthermore, if the patient is agitated, a visually stimulating report can be prioritized. By prioritizing reports based on the patient's emotions, more important reports can be delivered preferentially.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data acquisition unit collects the patient's biometric information in real time. This biometric information includes heart rate, blood pressure, and body temperature. The data acquisition unit can collect the patient's biometric information in real time using audio glasses. The audio glasses have built-in sensors for collecting the patient's biometric information and can collect data in real time. Step 2: The analysis unit uses generational AI to analyze the data collected by the data collection unit in real time. The analysis unit can analyze the collected data in real time and visualize it on a dashboard for healthcare professionals. The dashboard visually displays the collected data, enabling healthcare professionals to quickly grasp the information. Step 3: The diagnostic support unit uses generation AI to assist in diagnosis based on the data analyzed by the analysis unit. The diagnostic support unit can assist in diagnosis based on the patient's past medical history and current biological information. Step 4: The treatment proposal unit uses generation AI to propose the optimal treatment plan based on the diagnostic results obtained by the diagnostic support unit. The treatment proposal unit can propose the optimal treatment plan. Step 5: The report generation unit uses generation AI to automatically generate a detailed report of the treatment plan proposed by the treatment proposal unit. The report generation unit can automatically generate detailed reports of diagnostic results and treatment plans.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, diagnostic support unit, treatment proposal unit, and report generation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects the patient's biometric information in real time using the audio glasses of the smart device 14. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data in real time and visualizes it on a dashboard for healthcare professionals. The diagnostic support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which supports diagnosis based on the patient's past medical history and current biometric information. The treatment proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes an optimal treatment plan. The report generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates a detailed report of the diagnosis results and treatment plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, diagnostic support unit, treatment proposal unit, and report generation 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 collects the patient's biometric information in real time using the audio glasses of the smart glasses 214. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data in real time and visualizes it on a dashboard for healthcare professionals. The diagnostic support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which supports diagnosis based on the patient's past medical history and current biometric information. The treatment proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes an optimal treatment plan. The report generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which automatically generates a detailed report of the diagnosis results and treatment plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, diagnostic support unit, treatment proposal unit, and report generation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects the patient's biological information in real time using the audio glasses of the headset terminal 314. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data in real time and visualizes it on a dashboard for healthcare professionals. The diagnostic support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which supports diagnosis based on the patient's past medical history and current biological information. The treatment proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes an optimal treatment plan. The report generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates a detailed report of the diagnosis results and treatment plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the data collection unit, analysis unit, diagnostic support unit, treatment proposal unit, and report generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects the patient's biological information in real time using the audio glasses of the robot 414. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data in real time and visualizes it on a dashboard for medical professionals. The diagnostic support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which supports diagnosis based on the patient's past medical history and current biological information. The treatment proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes an optimal treatment plan. The report generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates a detailed report of the diagnosis results and treatment plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A data collection unit that collects patients' vital signs in real time, An analysis unit analyzes the data collected by the data acquisition unit, A diagnostic support unit that assists in diagnosis based on the data analyzed by the aforementioned analysis unit, A treatment proposal unit proposes a treatment plan based on the diagnostic results obtained by the aforementioned diagnostic support unit, The system includes a report generation unit that automatically generates a report of the treatment plan proposed by the aforementioned treatment proposal unit. A system characterized by the following features. (Note 2) The aforementioned data acquisition unit is Collect patient biometric information in real time using audio glasses. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed in real time and visualized on a dashboard for healthcare professionals. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned diagnostic support unit, Supporting diagnosis based on the patient's past medical history and current biometric information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned treatment proposal unit, Propose a treatment plan The system described in Appendix 1, characterized by the features described herein. (Note 6) The report generation unit, It automatically generates detailed reports of diagnostic results and treatment plans. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned data acquisition unit is The system estimates the patient's emotions and adjusts the timing of biometric data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned data acquisition unit is Analyze the patient's past biometric data collection history and select the appropriate collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned data acquisition unit is When collecting biometric data, 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 data acquisition unit is The system estimates the patient's emotions and prioritizes the biometric data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned data acquisition unit is When collecting biometric information, the system prioritizes the collection of highly relevant information, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned data acquisition unit is When collecting biometric data, analyze the patient's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the biological information. 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 category of biological information. 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 the analysis is determined based on when the biological information was collected. 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 the relevance of the biological information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned diagnostic support unit, The patient's emotions are estimated, and the diagnostic criteria are adjusted based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned diagnostic support unit, When providing diagnostic support, consider the relationships between patients to improve the accuracy of the diagnosis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned diagnostic support unit, When providing diagnostic support, the diagnosis should be made while taking into account the patient's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned diagnostic support unit, The system estimates the patient's emotions and adjusts the order in which the diagnostic results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned diagnostic support unit, When providing diagnostic support, consider the geographical distribution of patients when making a diagnosis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned diagnostic support unit, When providing diagnostic support, referencing relevant patient literature improves the accuracy of the diagnosis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned treatment proposal unit, The system estimates the patient's emotions and adjusts the treatment proposal based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned treatment proposal unit, When proposing treatment, we analyze the patient's past treatment history to select the most suitable treatment method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned treatment proposal unit, When proposing treatment, customize the treatment proposal based on the patient's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned treatment proposal unit, The system estimates the patient's emotions and prioritizes treatment suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned treatment proposal unit, When proposing treatment, the treatment method will be selected taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned treatment proposal unit, When proposing treatment, we analyze the patient's social media activity to suggest appropriate treatment options. The system described in Appendix 1, characterized by the features described herein. (Note 31) The report generation unit, The system estimates the patient's emotions and adjusts the way the report is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The report generation unit, When generating a report, adjust the level of detail in the report based on the importance of the diagnostic results. The system described in Appendix 1, characterized by the features described herein. (Note 33) The report generation unit, When generating reports, different report generation algorithms are applied depending on the category of the diagnostic results. The system described in Appendix 1, characterized by the features described herein. (Note 34) The report generation unit, The system estimates the patient's emotions and adjusts the length of the report based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The report generation unit, When generating reports, the priority of reports is determined based on when the diagnostic results were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The report generation unit, When generating reports, adjust the order of reports based on the relevance of the diagnostic results. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects patients' vital signs in real time, An analysis unit analyzes the data collected by the data acquisition unit, A diagnostic support unit that assists in diagnosis based on the data analyzed by the aforementioned analysis unit, A treatment proposal unit proposes a treatment plan based on the diagnostic results obtained by the aforementioned diagnostic support unit, The system includes a report generation unit that automatically generates a report of the treatment plan proposed by the aforementioned treatment proposal unit. A system characterized by the following features.

2. The aforementioned data acquisition unit is Collect patient biometric information in real time using audio glasses. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed in real time and visualized on a dashboard for healthcare professionals. The system according to feature 1.

4. The aforementioned diagnostic support unit, Supporting diagnosis based on the patient's past medical history and current biometric information. The system according to feature 1.

5. The aforementioned treatment proposal unit, Propose a treatment plan The system according to feature 1.

6. The report generation unit, It automatically generates detailed reports of diagnostic results and treatment plans. The system according to feature 1.

7. The aforementioned data acquisition unit is The system estimates the patient's emotions and adjusts the timing of biometric data collection based on the estimated emotions. The system according to feature 1.

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

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

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

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