System

Abstract

To provide a system capable of analyzing medical record data and proposing preventive or treatment measures.SOLUTION: A system in an embodiment includes a collection unit, an analysis unit, a providing unit, and a proposal unit. The collection unit collects medical record data. The analysis unit analyzes medical record data collected by the collection unit. The providing unit provides analysis results obtained by the analysis unit. The proposal unit proposes preventive or treatment measures based on the analysis results provided by the providing unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has not effectively utilized medical record data to suggest preventive measures and treatments, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze medical record data and suggest preventive or therapeutic measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a suggestion unit. The collection unit collects medical chart data. The analysis unit analyzes the medical chart data collected by the collection unit. The provision unit provides the analysis results obtained by the analysis unit. The suggestion unit suggests preventive measures or treatment methods based on the analysis results provided by the provision unit. Effect of the Invention

[0007] The system according to the embodiment can analyze the medical record data and suggest preventive measures or treatments. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) The medical data sharing system according to the embodiment of the present invention is a platform for sharing medical record data, which is becoming increasingly digitized in the medical field. In this medical data sharing system, each medical institution uploads electronic medical record data to the platform, and the generation AI analyzes the uploaded medical record data and analyzes the individual's medical history, making it possible to link with the tendency of illness, lifestyle habits, and external vital data. This can contribute to the development of medicine. For example, each medical institution uploads electronic medical record data to the platform. At this time, the medical record data is uploaded in a standardized format. For example, it includes medical records, test results, prescription information, etc. This data is input to the generation AI. Next, the generation AI analyzes the uploaded medical record data. The generation AI analyzes the individual's medical history in detail to identify the tendency of illness. For example, it can find a tendency to be susceptible to a certain disease based on past medical records and test results. It also links with lifestyle habits and external vital data. For example, it analyzes data such as exercise habits, dietary content, and sleep patterns to clarify the association with health conditions. Furthermore, the generation AI provides useful information to medical professionals based on the analysis results. For example, it can suggest preventive measures and treatments for patients who are susceptible to certain diseases. It can also point out areas for lifestyle improvements and provide health management advice. This system will promote the sharing of medical record data in medical settings and enable linkage with individuals' medical history, lifestyle habits, and external vital data. It is expected that this will improve the quality of medical care and lead to more effective health management for patients. As a result, the medical data sharing system can promote the sharing of medical record data in medical settings and enable linkage with individuals' medical history, lifestyle habits, and external vital data.

[0029] The medical data sharing system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a proposal unit. The collection unit collects medical record data. The medical record data includes, for example, electronic medical records, paper medical records, medical records, etc., but is not limited to such examples. The collection unit collects, for example, electronic medical record data directly from medical institutions. The collection unit can also scan paper medical records, convert them into digital data, and collect them. Furthermore, the collection unit can collect data such as medical records, test results, and prescription information. For example, the collection unit automatically acquires data from an electronic medical record system and uploads it to a platform. Paper medical records are digitized and collected using a scanner. Medical records, test results, and prescription information are collected in a standardized format. The analysis unit uses a generation AI to analyze the medical record data collected by the collection unit. The analysis includes, for example, analyzing an individual's medical history in detail to identify a tendency to contract a disease, but is not limited to such examples. For example, the generation AI finds a tendency to be susceptible to a specific disease based on past medical records and test results. The analysis unit also cooperates with lifestyle habits and external vital data. For example, the generation AI analyzes data such as exercise habits, dietary content, and sleep patterns to clarify the association with health conditions. The generation AI analyzes medical record data using a text generation AI (e.g., LLM). The generation AI understands the contents of the medical record data using natural language processing technology and generates analysis results. The provision unit provides the analysis results obtained by the analysis unit. The provision includes, for example, but is not limited to, providing useful information to medical professionals. For example, the provision unit proposes preventive measures and treatment methods for patients who are prone to specific diseases. The provision unit can also point out improvements in lifestyle habits and provide health management advice. The provision unit provides useful information to medical professionals based on the results of the analysis by the generation AI. The suggestion unit proposes preventive measures and treatment methods based on the analysis results provided by the provision unit. The suggestion includes, for example, but is not limited to, proposing preventive measures and treatment methods for patients who are prone to specific diseases. For example, the suggestion unit suggests preventive measures and treatments based on the results of the analysis by the generative AI.The suggestion unit can also provide advice on health management by pointing out improvements to lifestyle habits based on the results of the analysis by the generation AI. As a result, the medical data sharing system according to the embodiment can efficiently collect, analyze, provide, and suggest medical record data.

[0030] The collection unit collects medical record data. The medical record data includes, for example, electronic medical records, paper medical records, medical records, etc., but is not limited to such examples. For example, the collection unit collects electronic medical record data directly from medical institutions. The collection unit can also scan paper medical records, convert them into digital data, and collect them. Furthermore, the collection unit can collect data such as medical records, test results, and prescription information. For example, the collection unit automatically acquires data from an electronic medical record system and uploads it to a platform. Paper medical records are digitized and collected using a scanner. Medical records, test results, and prescription information are collected in a standardized format. The collection unit centrally manages these data and performs data cleansing processing to prevent duplication and loss of data. For example, the collection unit converts data provided by different medical institutions into a unified format and integrates it into a database. Furthermore, the collection unit protects the data using encryption technology to ensure data security. This allows the collection unit to collect reliable data while protecting the privacy of patients. Furthermore, the collection unit can update data in real time and always keep the latest information. For example, every time a medical examination is performed, new data is automatically acquired from the electronic medical record system and added to the database, allowing the collection department to always provide the latest medical information.

[0031] The analysis unit uses the generation AI to analyze the medical record data collected by the collection unit. The analysis includes, for example, but is not limited to, analyzing an individual's medical history in detail to identify a tendency to contract a disease. For example, the generation AI finds a tendency to be susceptible to a particular disease based on past medical records and test results. The analysis unit also links with lifestyle habits and external vital data. For example, the generation AI analyzes data such as exercise habits, dietary content, and sleep patterns to clarify the association with health conditions. The generation AI analyzes the medical record data using a text generation AI (e.g., LLM). The generation AI uses natural language processing technology to understand the contents of the medical record data and generate analysis results. Specifically, the generation AI analyzes the text of the medical record data and extracts important keywords and phrases. This allows the generation AI to grasp the patient's medical history and medical treatment in detail and generate analysis results. Furthermore, the generation AI uses a machine learning algorithm to learn patterns from past data and predict future risks. For example, generative AI can identify patients who are susceptible to certain diseases based on past medical records and suggest preventive measures. Generative AI can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The providing unit provides the analysis results obtained by the analyzing unit. The provision includes, for example, providing useful information to medical professionals, but is not limited to such examples. For example, the providing unit suggests preventive measures and treatment methods for patients who are prone to a particular disease. The providing unit can also point out improvements in lifestyle habits and provide health management advice. The providing unit provides useful information to medical professionals based on the results of the analysis by the generating AI. Specifically, the providing unit provides a dashboard for visually displaying the analysis results in an easy-to-understand manner. The dashboard displays the patient's health condition and risk assessment, as well as recommended preventive measures and treatment methods. This allows medical professionals to quickly and accurately grasp the patient's condition and take appropriate measures. The providing unit can also provide the analysis results in the form of a report. The report contains detailed analysis results and recommended measures, which medical professionals can refer to. Furthermore, the providing unit can also notify medical professionals of the analysis results via email or a messaging system. This allows medical professionals to always receive the latest information and respond quickly. The providing unit can also provide information to patients based on the analysis results. For example, patients will be notified of advice on lifestyle improvements and health management, allowing them to understand their own health conditions and take appropriate measures.

[0033] The suggestion unit proposes preventive measures and treatments based on the analysis results provided by the provision unit. The suggestions include, but are not limited to, proposing preventive measures and treatments to patients who are prone to certain diseases. For example, the suggestion unit proposes preventive measures and treatments based on the results of the analysis by the generation AI. Specifically, the suggestion unit considers the patient's medical history and lifestyle habits and proposes preventive measures and treatments that are individually customized. For example, the suggestion unit recommends that patients who are prone to certain diseases undergo regular health checkups and specific tests. In addition, the suggestion unit points out improvements to lifestyle habits and provides specific advice. For example, this includes reviewing exercise habits, improving diet, adjusting sleep patterns, etc. The suggestion unit can also point out improvements to lifestyle habits and provide health management advice based on the results of the analysis by the generation AI. This allows the suggestion unit to help patients improve their health and work to prevent and detect diseases early. The suggestion unit also proposes specific treatments and preventive measures for patients to medical professionals. For example, this includes prescribing specific drugs, reviewing treatment plans, and recommending rehabilitation. This allows the suggestion unit to support medical professionals in providing optimal treatment to patients. Furthermore, based on the analysis results, the suggestion unit can also suggest preventive measures and reviews of treatment policies for the entire medical institution. This improves the medical quality of the entire medical institution and enables efficient health management of patients.

[0034] The medical data sharing system includes a collection unit that collects lifestyle habits or external vital data. The collection unit collects lifestyle habits and external vital data. The lifestyle habits include, for example, diet, exercise, sleep, etc., but are not limited to such examples. The external vital data include, for example, heart rate, blood pressure, body temperature, etc., but are not limited to such examples. The collection unit collects data such as heart rate, blood pressure, and body temperature, for example, using a wearable device. The collection unit can also collect data such as diet, exercise, and sleep, using a smartphone app. For example, the collection unit automatically acquires data from the wearable device and uploads it to a platform. The smartphone app collects data entered by the user and uploads it to the platform. This makes it possible to collect lifestyle habits and external vital data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data acquired from the wearable device to a generation AI and have the generation AI execute an analysis of the data.

[0035] The medical data sharing system includes a standardization unit that performs data standardization. The standardization unit performs data standardization. Standardization includes, for example, standardization of data format, application of standardization protocol, and the like, but is not limited to such examples. For example, the standardization unit standardizes the format of electronic medical record data and uploads it to the platform. The standardization unit can also standardize data such as medical records, test results, and prescription information. For example, the standardization unit converts electronic medical record data into HL7 (Health Level 7) format and uploads it to the platform. The medical records, test results, and prescription information are formatted according to the standardization protocol. This enables data standardization. A part or all of the above-mentioned processing in the standardization unit may be performed, for example, using AI, or may be performed without using AI. For example, the standardization unit can input electronic medical record data to a generation AI and cause the generation AI to perform data standardization.

[0036] The medical data sharing system includes a visualization unit that visualizes the analysis results. The visualization unit visualizes the analysis results. Examples of visualization include, but are not limited to, graphs, charts, dashboards, and the like. For example, the visualization unit displays the analysis results as graphs or charts, and provides them in a format that is visually easy to understand. The visualization unit can also centrally manage the analysis results using a dashboard. For example, the visualization unit displays the analysis results as line graphs or bar graphs, and visually shows fluctuations in data. The dashboard displays multiple analysis results on one screen, making it easier to grasp the overall situation. This makes it possible to visualize the analysis results. Some or all of the above-mentioned processing in the visualization unit may be performed, for example, using AI, or may be performed without using AI. For example, the visualization unit can input the analysis results to a generation AI and cause the generation AI to generate graphs and charts for visualization.

[0037] The collection unit can analyze the data collection method of each medical institution and select an appropriate collection protocol. The collection unit, for example, compares the data collection methods of each medical institution and selects the most efficient method. The collection unit can also analyze the data collection methods of each medical institution and create a common protocol. Furthermore, the collection unit can evaluate the data collection methods of each medical institution and propose an optimal collection protocol. For example, the collection unit investigates the data collection procedures of each medical institution and identifies an efficient collection method. The common protocol ensures consistency in data collection and improves the quality of data. The collection unit evaluates the conditions and procedures of data collection and proposes an optimal collection protocol. This makes it possible to select an optimal collection protocol. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the data collection method of each medical institution to the generation AI and cause the generation AI to select the optimal collection protocol.

[0038] The collection unit can perform filtering based on the patient's current health condition and treatment history when collecting medical record data. For example, the collection unit collects only necessary data taking into account the patient's current health condition. In addition, the collection unit can preferentially collect highly relevant data based on the patient's treatment history. Furthermore, the collection unit can also exclude unnecessary data based on the patient's health condition and treatment history. For example, the collection unit evaluates the patient's health condition and selects necessary data. The treatment history is evaluated based on past medical records and prescription information. The collection unit preferentially collects highly relevant data and excludes unnecessary data. This makes it possible to collect only necessary data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the patient's health condition and treatment history to the generation AI and cause the generation AI to perform data filtering.

[0039] When collecting medical record data, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the patient. For example, the collection unit preferentially collects data related to diseases specific to the region based on the geographical location information of the patient. In addition, the collection unit can preferentially collect data from nearby medical institutions in consideration of the geographical location information of the patient. Furthermore, the collection unit can preferentially collect data related to environmental factors based on the geographical location information of the patient. For example, the collection unit acquires the geographical location information of the patient from GPS data or address information, and collects data related to diseases specific to the region. Data from nearby medical institutions is collected based on the treatment history and medical records of the patient. The collection unit collects data related to environmental factors and clarifies the association with the patient's health condition. This makes it possible to collect data based on the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the patient to the generation AI and cause the generation AI to execute collection of highly relevant data.

[0040] The collection unit can analyze the social media activity of the patient and collect related data when collecting the medical record data. For example, the collection unit analyzes the social media activity of the patient and collects posts related to health. The collection unit can also collect data related to lifestyle habits based on the social media activity of the patient. Furthermore, the collection unit can also collect data related to the health condition in consideration of the social media activity of the patient. For example, the collection unit analyzes the social media posts of the patient and extracts information related to health. The data related to lifestyle habits includes information on diet, exercise, sleep, etc. The collection unit collects data related to the health condition and uses it to help manage the health of the patient. This makes it possible to collect data based on social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the social media activity of the patient to the generation AI and cause the generation AI to collect related data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the medical record data during the analysis. The analysis unit, for example, performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis stepwise according to the importance. For example, the analysis unit evaluates the importance of the medical record data and performs a detailed analysis. Data with low importance is analyzed simply. The analysis unit adjusts the level of detail of the analysis stepwise according to the importance. This makes it possible to adjust the level of detail of the analysis according to the importance. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the medical record data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of medical record data during analysis. For example, the analysis unit applies a diagnostic algorithm to medical records. In addition, the analysis unit can apply a test data analysis algorithm to test results. Furthermore, the analysis unit can also apply a drug effect analysis algorithm to prescription information. For example, the analysis unit applies a diagnostic algorithm to medical records to generate diagnostic results. For test results, the analysis unit applies a test data analysis algorithm to analyze the test results. For prescription information, the analysis unit applies a drug effect analysis algorithm to analyze the effects of the drug. This makes it possible to apply an analysis algorithm according to the category. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of medical record data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0043] The analysis unit can determine the priority of the analysis based on the submission time of the medical record data during the analysis. For example, the analysis unit gives priority to the analysis of the latest medical record data. In addition, the analysis unit can postpone data submitted earlier. Furthermore, the analysis unit can adjust the priority of the analysis stepwise according to the submission time. For example, the analysis unit evaluates the submission time of the medical record data and gives priority to the analysis of the latest data. Data submitted earlier is postponed. The analysis unit adjusts the priority of the analysis stepwise according to the submission time. This makes it possible to determine the priority of the analysis based on the submission time. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the submission time of the medical record data to the generation AI and cause the generation AI to execute the determination of the priority of the analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the medical record data during analysis. The analysis unit, for example, prioritizes the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. Furthermore, the analysis unit can also adjust the order of analysis stepwise according to the relevance. For example, the analysis unit evaluates the relevance of the medical record data and prioritizes the analysis of highly relevant data. The analysis unit postpones the analysis of less relevant data. The analysis unit adjusts the order of analysis stepwise according to the relevance. This makes it possible to adjust the order of analysis based on the relevance. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the medical record data to the generation AI and cause the generation AI to adjust the order of analysis.

[0045] The providing unit can adjust the level of detail of the provision based on the importance of the analysis result at the time of provision. For example, the providing unit provides detailed information for an analysis result with high importance. In addition, the providing unit can provide simple information for an analysis result with low importance. Furthermore, the providing unit can also adjust the level of detail of the provision stepwise according to the importance. For example, the providing unit evaluates the importance of the analysis result and provides detailed information. For an analysis result with low importance, simple information is provided. The providing unit adjusts the level of detail of the provision stepwise according to the importance. This makes it possible to adjust the level of detail of the provision according to the importance. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance of the analysis result to the generating AI and cause the generating AI to adjust the level of detail of the provision.

[0046] The providing unit can apply different providing algorithms depending on the category of the analysis result at the time of providing. For example, the providing unit applies a diagnostic information providing algorithm to the medical record. In addition, the providing unit can apply a test data providing algorithm to the test result. Furthermore, the providing unit can also apply a drug information providing algorithm to the prescription information. For example, the providing unit applies a diagnostic information providing algorithm to the medical record to provide the diagnosis result. For the test result, the providing unit applies a test data providing algorithm to provide the test result. For the prescription information, the drug information providing algorithm is applied to provide the effect of the drug. This makes it possible to apply a providing algorithm according to the category. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the category of the analysis result to the generating AI and cause the generating AI to apply a different providing algorithm.

[0047] The providing unit can determine the priority of provision based on the submission time of the analysis results at the time of provision. The providing unit, for example, provides the latest analysis result preferentially. In addition, the providing unit can postpone the analysis result submitted earlier. Furthermore, the providing unit can gradually adjust the priority of provision according to the submission time. For example, the providing unit evaluates the submission time of the analysis result and provides the latest analysis result preferentially. The analysis result submitted earlier is postponed. The providing unit gradually adjusts the priority of provision according to the submission time. This makes it possible to determine the priority of provision based on the submission time. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the submission time of the analysis result to the generation AI and cause the generation AI to execute the determination of the priority of provision.

[0048] The providing unit can adjust the order of provision based on the relevance of the analysis results at the time of provision. The providing unit, for example, provides analysis results with high relevance preferentially. In addition, the providing unit can postpone analysis results with low relevance. Furthermore, the providing unit can adjust the order of provision stepwise according to the relevance. For example, the providing unit evaluates the relevance of the analysis results and provides analysis results with high relevance preferentially. Analysis results with low relevance are postponed. The providing unit adjusts the order of provision stepwise according to the relevance. This makes it possible to adjust the order of provision based on the relevance. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of the analysis results to the generating AI and cause the generating AI to adjust the order of provision.

[0049] The suggestion unit can adjust the level of detail of the proposal based on the importance of the preventive measures and treatments at the time of proposal. For example, the suggestion unit makes a detailed proposal for preventive measures and treatments with high importance. In addition, the suggestion unit can make a simplified proposal for preventive measures and treatments with low importance. Furthermore, the suggestion unit can adjust the level of detail of the proposal stepwise according to the importance. For example, the suggestion unit evaluates the importance of preventive measures and treatments and makes a detailed proposal. For preventive measures and treatments with low importance, a simplified proposal is made. The suggestion unit adjusts the level of detail of the proposal stepwise according to the importance. This makes it possible to adjust the level of detail of the proposal according to the importance. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the importance of preventive measures and treatments to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0050] The suggestion unit can apply different proposal algorithms according to categories of preventive measures and treatments at the time of proposal. For example, the suggestion unit applies a lifestyle improvement algorithm to lifestyle-related diseases. In addition, the suggestion unit can apply an infection prevention algorithm to infectious diseases. Furthermore, the suggestion unit can apply a long-term management algorithm to chronic diseases. For example, the suggestion unit applies a lifestyle improvement algorithm to lifestyle-related diseases and proposes lifestyle improvements. For infectious diseases, the suggestion unit applies an infection prevention algorithm and proposes infection prevention measures. For chronic diseases, the suggestion unit applies a long-term management algorithm and proposes a long-term treatment plan. This makes it possible to apply a proposal algorithm according to a category. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input categories of preventive measures and treatments to the generation AI and cause the generation AI to apply different proposal algorithms.

[0051] The suggestion unit can determine the priority of the proposal based on the submission time of the preventive measures and treatments at the time of proposal. The suggestion unit, for example, preferentially proposes the latest preventive measures and treatments. In addition, the suggestion unit can postpone preventive measures and treatments that have been submitted earlier. Furthermore, the suggestion unit can gradually adjust the priority of the proposal according to the submission time. For example, the suggestion unit evaluates the submission time of the preventive measures and treatments, and preferentially proposes the latest preventive measures and treatments. Preventive measures and treatments that have been submitted earlier are postponed. The suggestion unit gradually adjusts the priority of the proposal according to the submission time. This makes it possible to determine the priority of the proposal based on the submission time. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the submission time of the preventive measures and treatments to the generation AI, and cause the generation AI to execute the determination of the priority of the proposal.

[0052] The suggestion unit can adjust the order of proposals based on the relevance of preventive measures and treatments at the time of proposal. For example, the suggestion unit preferentially suggests highly relevant preventive measures and treatments. In addition, the suggestion unit can postpone less relevant preventive measures and treatments. Furthermore, the suggestion unit can adjust the order of proposals stepwise according to the relevance. For example, the suggestion unit evaluates the relevance of preventive measures and treatments, and preferentially suggests highly relevant preventive measures and treatments. Less relevant preventive measures and treatments are postponed. The suggestion unit adjusts the order of proposals stepwise according to the relevance. This makes it possible to adjust the order of proposals based on the relevance. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the relevance of preventive measures and treatments to the generation AI, and cause the generation AI to adjust the order of proposals.

[0053] During standardization, the standardization unit can analyze the data format of each medical institution and select the optimal standardization protocol. For example, the standardization unit compares the data formats of each medical institution and selects the most efficient standardization protocol. The standardization unit can also analyze the data formats of each medical institution and create a common standardization protocol. Furthermore, the standardization unit can evaluate the data formats of each medical institution and propose the optimal standardization protocol. For example, the standardization unit investigates the data formats of each medical institution and identifies an efficient standardization protocol. The common standardization protocol ensures consistency of the data format and improves the quality of the data. The standardization unit evaluates the conditions and procedures of the data format and proposes the optimal standardization protocol. This makes it possible to select the optimal standardization protocol. Part or all of the above-mentioned processing in the standardization unit may be performed, for example, using AI or may be performed without using AI. For example, the standardization unit can input the data formats of each medical institution to the generation AI and cause the generation AI to select the optimal standardization protocol.

[0054] The standardization unit can adjust the order of standardization based on the submission time of data during standardization. For example, the standardization unit prioritizes standardization of the latest data. In addition, the standardization unit can postpone data submitted earlier. Furthermore, the standardization unit can adjust the order of standardization stepwise depending on the submission time. For example, the standardization unit evaluates the submission time of data and prioritizes standardization of the latest data. Data submitted earlier is postponed. The standardization unit adjusts the order of standardization stepwise depending on the submission time. This makes it possible to adjust the order of standardization based on the submission time. Part or all of the above-mentioned processing in the standardization unit may be performed using, for example, AI, or may be performed without using AI. For example, the standardization unit can input the submission time of data to the generation AI and cause the generation AI to adjust the order of standardization.

[0055] The visualization unit can adjust the level of detail of the visualization based on the importance of the analysis result at the time of visualization. For example, the visualization unit performs detailed visualization for an analysis result with high importance. In addition, the visualization unit can perform simple visualization for an analysis result with low importance. Furthermore, the visualization unit can adjust the level of detail of the visualization stepwise according to the importance. For example, the visualization unit evaluates the importance of the analysis result and performs detailed visualization. For an analysis result with low importance, it performs simple visualization. The visualization unit adjusts the level of detail of the visualization stepwise according to the importance. This makes it possible to adjust the level of detail of the visualization according to the importance. A part or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the visualization.

[0056] The visualization unit can determine the priority of visualization based on the submission time of the analysis results at the time of visualization. For example, the visualization unit preferentially visualizes the latest analysis results. In addition, the visualization unit can postpone analysis results submitted earlier. Furthermore, the visualization unit can gradually adjust the priority of visualization according to the submission time. For example, the visualization unit evaluates the submission time of the analysis results and preferentially visualizes the latest analysis results. Analysis results submitted earlier are postponed. The visualization unit gradually adjusts the priority of visualization according to the submission time. This makes it possible to determine the priority of visualization based on the submission time. Part or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the submission time of the analysis results to the generation AI and cause the generation AI to execute the determination of the priority of visualization.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The medical data sharing system may further include an anonymization unit that performs data anonymization. The anonymization unit deletes or masks information that can identify an individual from the collected medical record data. For example, information such as the patient's name, address, and contact information is deleted to generate anonymized data. The anonymization unit may also adjust the level of anonymization of the data. For example, necessary information may be left while other information may be anonymized depending on a specific research purpose. Furthermore, the anonymization unit may evaluate the quality of the anonymized data and perform re-anonymization as necessary. This makes it possible to maintain the usefulness of the data while ensuring the protection of personal information.

[0059] The medical data sharing system can further include a consistency check unit that checks the consistency of the data. The consistency check unit checks the consistency and accuracy of the collected medical record data. For example, it checks whether data from different medical institutions for the same patient is consistent. The consistency check unit can also detect missing or duplicated data and correct it as necessary. Furthermore, the consistency check unit can set rules for maintaining the consistency of the data and check the data periodically. This makes it possible to improve the quality of the data and realize highly reliable medical data sharing.

[0060] The medical data sharing system can further include a backup unit that backs up data. The backup unit periodically backs up collected medical record data to prepare for data loss or damage. For example, the backup unit backs up data on a daily or weekly schedule. The backup unit can also store data in different locations to enable data recovery in the event of a disaster. Furthermore, the backup unit can check the integrity of the backup data and perform a restoration test as necessary. This ensures the safety of the data and supports the continuous use of medical data.

[0061] The medical data sharing system can further include an access control unit that controls access to data. The access control unit manages access rights to the collected medical record data. For example, access rights are set for each medical professional, allowing them to view only the necessary data. The access control unit can also record the data viewing history and detect unauthorized access. Furthermore, the access control unit can periodically review the access rights to data and update them as necessary. This makes it possible to strengthen data security and promote appropriate data use.

[0062] The medical data sharing system can further include an encryption unit that encrypts data. The encryption unit encrypts collected medical record data to prevent data leakage. For example, the encryption unit encrypts data when it is transmitted or stored, to prevent unauthorized access by third parties. The encryption unit can also manage encryption keys and periodically update the keys. Furthermore, the encryption unit can decrypt encrypted data and make the data viewable only when necessary. This ensures the confidentiality of the data and enables safe medical data sharing.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The collection department collects medical record data. Medical record data includes electronic medical records, paper medical records, medical records, etc. The collection department collects electronic medical record data directly from medical institutions, scans paper medical records to convert them into digital data, and collects data such as medical records, test results, and prescription information. For example, data is automatically obtained from the electronic medical record system and uploaded to the platform. Paper medical records are digitized using a scanner, and medical records, test results, and prescription information are collected in a standardized format. Step 2: The analysis unit uses the generation AI to analyze the medical record data collected by the collection unit. The analysis includes detailed analysis of an individual's medical history and identifying trends in illness. The generation AI finds out tendencies toward certain diseases based on past medical records and test results. It also works with lifestyle habits and external vital signs data to analyze data such as exercise habits, diet, and sleep patterns, and clarify the correlation with health conditions. The generation AI uses text generation AI (e.g., LLM) to analyze the medical record data, and uses natural language processing technology to understand the contents of the medical record data and generate analysis results. Step 3: The provision unit provides the analysis results obtained by the analysis unit. Providing includes providing useful information to medical professionals. For example, it may suggest preventive measures and treatments for patients who are prone to certain diseases, point out ways to improve lifestyle habits, and provide health management advice. The provision unit provides useful information to medical professionals based on the results of analysis by the generation AI. Step 4: The suggestion unit proposes preventive measures and treatments based on the analysis results provided by the provision unit. The suggestions include proposing preventive measures and treatments for patients who are prone to certain diseases. For example, the suggestion unit proposes preventive measures and treatments based on the results of the analysis by the generation AI, points out areas for improving lifestyle habits, and provides health management advice.

[0065] (Example 2) The medical data sharing system according to the embodiment of the present invention is a platform for sharing medical record data, which is becoming increasingly digitized in the medical field. In this medical data sharing system, each medical institution uploads electronic medical record data to the platform, and the generation AI analyzes the uploaded medical record data and analyzes the individual's medical history, making it possible to link with the tendency of illness, lifestyle habits, and external vital data. This can contribute to the development of medicine. For example, each medical institution uploads electronic medical record data to the platform. At this time, the medical record data is uploaded in a standardized format. For example, it includes medical records, test results, prescription information, etc. This data is input to the generation AI. Next, the generation AI analyzes the uploaded medical record data. The generation AI analyzes the individual's medical history in detail to identify the tendency of illness. For example, it can find a tendency to be susceptible to a certain disease based on past medical records and test results. It also links with lifestyle habits and external vital data. For example, it analyzes data such as exercise habits, dietary content, and sleep patterns to clarify the association with health conditions. Furthermore, the generation AI provides useful information to medical professionals based on the analysis results. For example, it can suggest preventive measures and treatments for patients who are susceptible to certain diseases. It can also point out areas for lifestyle improvements and provide health management advice. This system will promote the sharing of medical record data in medical settings and enable linkage with individuals' medical history, lifestyle habits, and external vital data. It is expected that this will improve the quality of medical care and lead to more effective health management for patients. As a result, the medical data sharing system can promote the sharing of medical record data in medical settings and enable linkage with individuals' medical history, lifestyle habits, and external vital data.

[0066] The medical data sharing system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a proposal unit. The collection unit collects medical record data. The medical record data includes, for example, electronic medical records, paper medical records, medical records, etc., but is not limited to such examples. The collection unit collects, for example, electronic medical record data directly from medical institutions. The collection unit can also scan paper medical records, convert them into digital data, and collect them. Furthermore, the collection unit can collect data such as medical records, test results, and prescription information. For example, the collection unit automatically acquires data from an electronic medical record system and uploads it to a platform. Paper medical records are digitized and collected using a scanner. Medical records, test results, and prescription information are collected in a standardized format. The analysis unit uses a generation AI to analyze the medical record data collected by the collection unit. The analysis includes, for example, analyzing an individual's medical history in detail to identify a tendency to contract a disease, but is not limited to such examples. For example, the generation AI finds a tendency to be susceptible to a specific disease based on past medical records and test results. The analysis unit also cooperates with lifestyle habits and external vital data. For example, the generation AI analyzes data such as exercise habits, dietary content, and sleep patterns to clarify the association with health conditions. The generation AI analyzes medical record data using a text generation AI (e.g., LLM). The generation AI understands the contents of the medical record data using natural language processing technology and generates analysis results. The provision unit provides the analysis results obtained by the analysis unit. The provision includes, for example, but is not limited to, providing useful information to medical professionals. For example, the provision unit proposes preventive measures and treatment methods for patients who are prone to specific diseases. The provision unit can also point out improvements in lifestyle habits and provide health management advice. The provision unit provides useful information to medical professionals based on the results of the analysis by the generation AI. The suggestion unit proposes preventive measures and treatment methods based on the analysis results provided by the provision unit. The suggestion includes, for example, but is not limited to, proposing preventive measures and treatment methods for patients who are prone to specific diseases. For example, the suggestion unit suggests preventive measures and treatments based on the results of the analysis by the generative AI.The suggestion unit can also provide advice on health management by pointing out improvements to lifestyle habits based on the results of the analysis by the generation AI. As a result, the medical data sharing system according to the embodiment can efficiently collect, analyze, provide, and suggest medical record data.

[0067] The collection unit collects medical record data. The medical record data includes, for example, electronic medical records, paper medical records, medical records, etc., but is not limited to such examples. For example, the collection unit collects electronic medical record data directly from medical institutions. The collection unit can also scan paper medical records, convert them into digital data, and collect them. Furthermore, the collection unit can collect data such as medical records, test results, and prescription information. For example, the collection unit automatically acquires data from an electronic medical record system and uploads it to a platform. Paper medical records are digitized and collected using a scanner. Medical records, test results, and prescription information are collected in a standardized format. The collection unit centrally manages these data and performs data cleansing processing to prevent duplication and loss of data. For example, the collection unit converts data provided by different medical institutions into a unified format and integrates it into a database. Furthermore, the collection unit protects the data using encryption technology to ensure data security. This allows the collection unit to collect reliable data while protecting the privacy of patients. Furthermore, the collection unit can update data in real time and always keep the latest information. For example, every time a medical examination is performed, new data is automatically acquired from the electronic medical record system and added to the database, allowing the collection department to always provide the latest medical information.

[0068] The analysis unit uses the generation AI to analyze the medical record data collected by the collection unit. The analysis includes, for example, but is not limited to, analyzing an individual's medical history in detail to identify a tendency to contract a disease. For example, the generation AI finds a tendency to be susceptible to a particular disease based on past medical records and test results. The analysis unit also links with lifestyle habits and external vital data. For example, the generation AI analyzes data such as exercise habits, dietary content, and sleep patterns to clarify the association with health conditions. The generation AI analyzes the medical record data using a text generation AI (e.g., LLM). The generation AI uses natural language processing technology to understand the contents of the medical record data and generate analysis results. Specifically, the generation AI analyzes the text of the medical record data and extracts important keywords and phrases. This allows the generation AI to grasp the patient's medical history and medical treatment in detail and generate analysis results. Furthermore, the generation AI uses a machine learning algorithm to learn patterns from past data and predict future risks. For example, generative AI can identify patients who are susceptible to certain diseases based on past medical records and suggest preventive measures. Generative AI can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0069] The providing unit provides the analysis results obtained by the analyzing unit. The provision includes, for example, providing useful information to medical professionals, but is not limited to such examples. For example, the providing unit suggests preventive measures and treatment methods for patients who are prone to a particular disease. The providing unit can also point out improvements in lifestyle habits and provide health management advice. The providing unit provides useful information to medical professionals based on the results of the analysis by the generating AI. Specifically, the providing unit provides a dashboard for visually displaying the analysis results in an easy-to-understand manner. The dashboard displays the patient's health condition and risk assessment, as well as recommended preventive measures and treatment methods. This allows medical professionals to quickly and accurately grasp the patient's condition and take appropriate measures. The providing unit can also provide the analysis results in the form of a report. The report contains detailed analysis results and recommended measures, which medical professionals can refer to. Furthermore, the providing unit can also notify medical professionals of the analysis results via email or a messaging system. This allows medical professionals to always receive the latest information and respond quickly. The providing unit can also provide information to patients based on the analysis results. For example, patients will be notified of advice on lifestyle improvements and health management, allowing them to understand their own health conditions and take appropriate measures.

[0070] The suggestion unit proposes preventive measures and treatments based on the analysis results provided by the provision unit. The suggestions include, but are not limited to, proposing preventive measures and treatments to patients who are prone to certain diseases. For example, the suggestion unit proposes preventive measures and treatments based on the results of the analysis by the generation AI. Specifically, the suggestion unit considers the patient's medical history and lifestyle habits and proposes preventive measures and treatments that are individually customized. For example, the suggestion unit recommends that patients who are prone to certain diseases undergo regular health checkups and specific tests. In addition, the suggestion unit points out improvements to lifestyle habits and provides specific advice. For example, this includes reviewing exercise habits, improving diet, adjusting sleep patterns, etc. The suggestion unit can also point out improvements to lifestyle habits and provide health management advice based on the results of the analysis by the generation AI. This allows the suggestion unit to help patients improve their health and work to prevent and detect diseases early. The suggestion unit also proposes specific treatments and preventive measures for patients to medical professionals. For example, this includes prescribing specific drugs, reviewing treatment plans, and recommending rehabilitation. This allows the suggestion unit to support medical professionals in providing optimal treatment to patients. Furthermore, based on the analysis results, the suggestion unit can also suggest preventive measures and reviews of treatment policies for the entire medical institution. This improves the medical quality of the entire medical institution and enables efficient health management of patients.

[0071] The medical data sharing system includes a collection unit that collects lifestyle habits or external vital data. The collection unit collects lifestyle habits and external vital data. The lifestyle habits include, for example, diet, exercise, sleep, etc., but are not limited to such examples. The external vital data include, for example, heart rate, blood pressure, body temperature, etc., but are not limited to such examples. The collection unit collects data such as heart rate, blood pressure, and body temperature, for example, using a wearable device. The collection unit can also collect data such as diet, exercise, and sleep, using a smartphone app. For example, the collection unit automatically acquires data from the wearable device and uploads it to a platform. The smartphone app collects data entered by the user and uploads it to the platform. This makes it possible to collect lifestyle habits and external vital data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data acquired from the wearable device to a generation AI and have the generation AI execute an analysis of the data.

[0072] The medical data sharing system includes a standardization unit that performs data standardization. The standardization unit performs data standardization. Standardization includes, for example, standardization of data format, application of standardization protocol, and the like, but is not limited to such examples. For example, the standardization unit standardizes the format of electronic medical record data and uploads it to the platform. The standardization unit can also standardize data such as medical records, test results, and prescription information. For example, the standardization unit converts electronic medical record data into HL7 (Health Level 7) format and uploads it to the platform. The medical records, test results, and prescription information are formatted according to the standardization protocol. This enables data standardization. A part or all of the above-mentioned processing in the standardization unit may be performed, for example, using AI, or may be performed without using AI. For example, the standardization unit can input electronic medical record data to a generation AI and cause the generation AI to perform data standardization.

[0073] The medical data sharing system includes a visualization unit that visualizes the analysis results. The visualization unit visualizes the analysis results. Examples of visualization include, but are not limited to, graphs, charts, dashboards, and the like. For example, the visualization unit displays the analysis results as graphs or charts, and provides them in a format that is visually easy to understand. The visualization unit can also centrally manage the analysis results using a dashboard. For example, the visualization unit displays the analysis results as line graphs or bar graphs, and visually shows fluctuations in data. The dashboard displays multiple analysis results on one screen, making it easier to grasp the overall situation. This makes it possible to visualize the analysis results. Some or all of the above-mentioned processing in the visualization unit may be performed, for example, using AI, or may be performed without using AI. For example, the visualization unit can input the analysis results to a generation AI and cause the generation AI to generate graphs and charts for visualization.

[0074] The collection unit can estimate the user's emotion and adjust the collection timing of the medical record data based on the estimated emotion of the user. For example, when the user is feeling stressed, the collection unit delays the collection timing and collects data in a relaxed state. Also, when the user is relaxed, the collection unit can instantly collect data and obtain highly accurate information. Furthermore, when the user is in a hurry, the collection unit can adjust the collection timing to quickly collect data. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The collection unit calculates an emotion score based on the change in facial expression and adjusts the collection timing. Also, the collection unit can record the user's voice and estimate the emotion using a voice analysis technology. The collection unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the collection timing. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The collection unit calculates an emotion score based on the fluctuation in the heart rate and adjusts the collection timing. This enables adjustment of collection timing according to the user's emotions. Estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input image data of the user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0075] The collection unit can analyze the data collection method of each medical institution and select an appropriate collection protocol. The collection unit, for example, compares the data collection methods of each medical institution and selects the most efficient method. The collection unit can also analyze the data collection methods of each medical institution and create a common protocol. Furthermore, the collection unit can evaluate the data collection methods of each medical institution and propose an optimal collection protocol. For example, the collection unit investigates the data collection procedures of each medical institution and identifies an efficient collection method. The common protocol ensures consistency in data collection and improves the quality of data. The collection unit evaluates the conditions and procedures of data collection and proposes an optimal collection protocol. This makes it possible to select an optimal collection protocol. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the data collection method of each medical institution to the generation AI and cause the generation AI to select the optimal collection protocol.

[0076] The collection unit can perform filtering based on the patient's current health condition and treatment history when collecting medical record data. For example, the collection unit collects only necessary data taking into account the patient's current health condition. In addition, the collection unit can preferentially collect highly relevant data based on the patient's treatment history. Furthermore, the collection unit can also exclude unnecessary data based on the patient's health condition and treatment history. For example, the collection unit evaluates the patient's health condition and selects necessary data. The treatment history is evaluated based on past medical records and prescription information. The collection unit preferentially collects highly relevant data and excludes unnecessary data. This makes it possible to collect only necessary data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the patient's health condition and treatment history to the generation AI and cause the generation AI to perform data filtering.

[0077] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is stressed, the collection unit postpones data of low importance. Furthermore, when the user is relaxed, the collection unit can preferentially collect data of high importance. Furthermore, when the user is in a hurry, the collection unit can also prioritize data that can be collected quickly. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The collection unit calculates an emotion score based on the change in facial expression and determines the priority of data to be collected. Furthermore, the collection unit can record the user's voice and estimate the emotion using a voice analysis technique. The collection unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of data to be collected. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The collection unit calculates an emotion score based on the fluctuation of the heart rate and determines the priority of data to be collected. This makes it possible to determine the priority of data according to the user's emotion. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0078] When collecting medical record data, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the patient. For example, the collection unit preferentially collects data related to diseases specific to the region based on the geographical location information of the patient. In addition, the collection unit can preferentially collect data from nearby medical institutions in consideration of the geographical location information of the patient. Furthermore, the collection unit can preferentially collect data related to environmental factors based on the geographical location information of the patient. For example, the collection unit acquires the geographical location information of the patient from GPS data or address information, and collects data related to diseases specific to the region. Data from nearby medical institutions is collected based on the treatment history and medical records of the patient. The collection unit collects data related to environmental factors and clarifies the association with the patient's health condition. This makes it possible to collect data based on the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the patient to the generation AI and cause the generation AI to execute collection of highly relevant data.

[0079] The collection unit can analyze the social media activity of the patient and collect related data when collecting the medical record data. For example, the collection unit analyzes the social media activity of the patient and collects posts related to health. The collection unit can also collect data related to lifestyle habits based on the social media activity of the patient. Furthermore, the collection unit can also collect data related to the health condition in consideration of the social media activity of the patient. For example, the collection unit analyzes the social media posts of the patient and extracts information related to health. The data related to lifestyle habits includes information on diet, exercise, sleep, etc. The collection unit collects data related to the health condition and uses it to help manage the health of the patient. This makes it possible to collect data based on social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the social media activity of the patient to the generation AI and cause the generation AI to collect related data.

[0080] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit provides a detailed analysis result. Furthermore, when the user is stressed, the analysis unit can provide a concise analysis result. Furthermore, when the user is in a hurry, the analysis unit can provide an analysis result that covers the main points. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit calculates an emotion score based on the change in facial expression and adjusts the expression method of the analysis. Furthermore, the analysis unit can record the user's voice and estimate the emotion using a voice analysis technology. The analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the expression method of the analysis. Furthermore, the analysis unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The analysis unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the expression method of the analysis. This makes it possible to adjust the expression method of the analysis according to the user's emotion. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI or may be performed without using an AI. For example, the analysis unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the medical record data during the analysis. The analysis unit, for example, performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis stepwise according to the importance. For example, the analysis unit evaluates the importance of the medical record data and performs a detailed analysis. Data with low importance is analyzed simply. The analysis unit adjusts the level of detail of the analysis stepwise according to the importance. This makes it possible to adjust the level of detail of the analysis according to the importance. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the medical record data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of medical record data during analysis. For example, the analysis unit applies a diagnostic algorithm to medical records. In addition, the analysis unit can apply a test data analysis algorithm to test results. Furthermore, the analysis unit can also apply a drug effect analysis algorithm to prescription information. For example, the analysis unit applies a diagnostic algorithm to medical records to generate diagnostic results. For test results, the analysis unit applies a test data analysis algorithm to analyze the test results. For prescription information, the analysis unit applies a drug effect analysis algorithm to analyze the effects of the drug. This makes it possible to apply an analysis algorithm according to the category. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of medical record data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0083] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit performs a detailed analysis. Furthermore, when the user is stressed, the analysis unit can perform a brief analysis. Furthermore, when the user is in a hurry, the analysis unit can perform an analysis that is completed in a short time. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit calculates an emotion score based on the change in the facial expression and adjusts the length of the analysis. Furthermore, the analysis unit can record the user's voice and estimate the emotion using a voice analysis technique. The analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the analysis. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The analysis unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the length of the analysis. This makes it possible to adjust the length of the analysis according to the user's emotion. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI or may be performed without using an AI. For example, the analysis unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0084] The analysis unit can determine the priority of the analysis based on the submission time of the medical record data during the analysis. For example, the analysis unit gives priority to the analysis of the latest medical record data. In addition, the analysis unit can postpone data submitted earlier. Furthermore, the analysis unit can adjust the priority of the analysis stepwise according to the submission time. For example, the analysis unit evaluates the submission time of the medical record data and gives priority to the analysis of the latest data. Data submitted earlier is postponed. The analysis unit adjusts the priority of the analysis stepwise according to the submission time. This makes it possible to determine the priority of the analysis based on the submission time. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the submission time of the medical record data to the generation AI and cause the generation AI to execute the determination of the priority of the analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the medical record data during analysis. The analysis unit, for example, prioritizes the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. Furthermore, the analysis unit can also adjust the order of analysis stepwise according to the relevance. For example, the analysis unit evaluates the relevance of the medical record data and prioritizes the analysis of highly relevant data. The analysis unit postpones the analysis of less relevant data. The analysis unit adjusts the order of analysis stepwise according to the relevance. This makes it possible to adjust the order of analysis based on the relevance. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the medical record data to the generation AI and cause the generation AI to adjust the order of analysis.

[0086] The providing unit can estimate the user's emotion and adjust the method of expressing the information to be provided based on the estimated user's emotion. For example, when the user is relaxed, the providing unit provides detailed information. Furthermore, when the user is stressed, the providing unit can provide concise information. Furthermore, when the user is in a hurry, the providing unit can also provide information that covers the main points. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit calculates an emotion score based on the change in the facial expression and adjusts the method of expressing the information. Furthermore, the providing unit can record the user's voice and estimate the emotion using a voice analysis technology. The providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the method of expressing the information. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The providing unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the method of expressing the information. This makes it possible to adjust the method of expressing the information according to the user's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0087] The providing unit can adjust the level of detail of the provision based on the importance of the analysis result at the time of provision. For example, the providing unit provides detailed information for an analysis result with high importance. In addition, the providing unit can provide simple information for an analysis result with low importance. Furthermore, the providing unit can also adjust the level of detail of the provision stepwise according to the importance. For example, the providing unit evaluates the importance of the analysis result and provides detailed information. For an analysis result with low importance, simple information is provided. The providing unit adjusts the level of detail of the provision stepwise according to the importance. This makes it possible to adjust the level of detail of the provision according to the importance. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance of the analysis result to the generating AI and cause the generating AI to adjust the level of detail of the provision.

[0088] The providing unit can apply different providing algorithms depending on the category of the analysis result at the time of providing. For example, the providing unit applies a diagnostic information providing algorithm to the medical record. In addition, the providing unit can apply a test data providing algorithm to the test result. Furthermore, the providing unit can also apply a drug information providing algorithm to the prescription information. For example, the providing unit applies a diagnostic information providing algorithm to the medical record to provide the diagnosis result. For the test result, the providing unit applies a test data providing algorithm to provide the test result. For the prescription information, the drug information providing algorithm is applied to provide the effect of the drug. This makes it possible to apply a providing algorithm according to the category. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the category of the analysis result to the generating AI and cause the generating AI to apply a different providing algorithm.

[0089] The providing unit can estimate the user's emotion and adjust the length of the information to be provided based on the estimated user's emotion. For example, when the user is relaxed, the providing unit provides detailed information. Furthermore, when the user is stressed, the providing unit can provide concise information. Furthermore, when the user is in a hurry, the providing unit can also provide information that can be understood in a short time. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit calculates an emotion score based on the change in the facial expression and adjusts the length of the information. Furthermore, the providing unit can record the user's voice and estimate the emotion using a voice analysis technology. The providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the information. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The providing unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the length of the information. This makes it possible to adjust the length of the information according to the user's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0090] The providing unit can determine the priority of provision based on the submission time of the analysis results at the time of provision. The providing unit, for example, provides the latest analysis result preferentially. In addition, the providing unit can postpone the analysis result submitted earlier. Furthermore, the providing unit can gradually adjust the priority of provision according to the submission time. For example, the providing unit evaluates the submission time of the analysis result and provides the latest analysis result preferentially. The analysis result submitted earlier is postponed. The providing unit gradually adjusts the priority of provision according to the submission time. This makes it possible to determine the priority of provision based on the submission time. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the submission time of the analysis result to the generation AI and cause the generation AI to execute the determination of the priority of provision.

[0091] The providing unit can adjust the order of provision based on the relevance of the analysis results at the time of provision. The providing unit, for example, provides analysis results with high relevance preferentially. In addition, the providing unit can postpone analysis results with low relevance. Furthermore, the providing unit can adjust the order of provision stepwise according to the relevance. For example, the providing unit evaluates the relevance of the analysis results and provides analysis results with high relevance preferentially. Analysis results with low relevance are postponed. The providing unit adjusts the order of provision stepwise according to the relevance. This makes it possible to adjust the order of provision based on the relevance. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of the analysis results to the generating AI and cause the generating AI to adjust the order of provision.

[0092] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated emotion of the user. For example, when the user is relaxed, the suggestion unit makes a detailed suggestion. Furthermore, when the user is stressed, the suggestion unit can make a concise suggestion. Furthermore, when the user is in a hurry, the suggestion unit can make a suggestion that focuses on the main points. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The suggestion unit calculates an emotion score based on the change in the facial expression and adjusts the expression method of the proposal. Furthermore, the suggestion unit can record the user's voice and estimate the emotion using a voice analysis technology. The suggestion unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the expression method of the proposal. Furthermore, the suggestion unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The suggestion unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the expression method of the proposal. This makes it possible to adjust the expression method of the proposal according to the user's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proposal unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0093] The suggestion unit can adjust the level of detail of the proposal based on the importance of the preventive measures and treatments at the time of proposal. For example, the suggestion unit makes a detailed proposal for preventive measures and treatments with high importance. In addition, the suggestion unit can make a simplified proposal for preventive measures and treatments with low importance. Furthermore, the suggestion unit can adjust the level of detail of the proposal stepwise according to the importance. For example, the suggestion unit evaluates the importance of preventive measures and treatments and makes a detailed proposal. For preventive measures and treatments with low importance, a simplified proposal is made. The suggestion unit adjusts the level of detail of the proposal stepwise according to the importance. This makes it possible to adjust the level of detail of the proposal according to the importance. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the importance of preventive measures and treatments to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0094] The suggestion unit can apply different proposal algorithms according to categories of preventive measures and treatments at the time of proposal. For example, the suggestion unit applies a lifestyle improvement algorithm to lifestyle-related diseases. In addition, the suggestion unit can apply an infection prevention algorithm to infectious diseases. Furthermore, the suggestion unit can apply a long-term management algorithm to chronic diseases. For example, the suggestion unit applies a lifestyle improvement algorithm to lifestyle-related diseases and proposes lifestyle improvements. For infectious diseases, the suggestion unit applies an infection prevention algorithm and proposes infection prevention measures. For chronic diseases, the suggestion unit applies a long-term management algorithm and proposes a long-term treatment plan. This makes it possible to apply a proposal algorithm according to a category. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input categories of preventive measures and treatments to the generation AI and cause the generation AI to apply different proposal algorithms.

[0095] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, when the user is relaxed, the suggestion unit makes a detailed suggestion. Furthermore, when the user is stressed, the suggestion unit can make a concise suggestion. Furthermore, when the user is in a hurry, the suggestion unit can make a suggestion that can be understood in a short time. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The suggestion unit calculates an emotion score based on the change in the facial expression and adjusts the length of the suggestion. Furthermore, the suggestion unit can record the user's voice and estimate the emotion using a voice analysis technology. The suggestion unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the suggestion. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The suggestion unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the length of the suggestion. This makes it possible to adjust the length of the suggestion according to the user's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proposal unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0096] The suggestion unit can determine the priority of the proposal based on the submission time of the preventive measures and treatments at the time of proposal. The suggestion unit, for example, preferentially proposes the latest preventive measures and treatments. In addition, the suggestion unit can postpone preventive measures and treatments that have been submitted earlier. Furthermore, the suggestion unit can gradually adjust the priority of the proposal according to the submission time. For example, the suggestion unit evaluates the submission time of the preventive measures and treatments, and preferentially proposes the latest preventive measures and treatments. Preventive measures and treatments that have been submitted earlier are postponed. The suggestion unit gradually adjusts the priority of the proposal according to the submission time. This makes it possible to determine the priority of the proposal based on the submission time. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the submission time of the preventive measures and treatments to the generation AI, and cause the generation AI to execute the determination of the priority of the proposal.

[0097] The suggestion unit can adjust the order of proposals based on the relevance of preventive measures and treatments at the time of proposal. For example, the suggestion unit preferentially suggests highly relevant preventive measures and treatments. In addition, the suggestion unit can postpone less relevant preventive measures and treatments. Furthermore, the suggestion unit can adjust the order of proposals stepwise according to the relevance. For example, the suggestion unit evaluates the relevance of preventive measures and treatments, and preferentially suggests highly relevant preventive measures and treatments. Less relevant preventive measures and treatments are postponed. The suggestion unit adjusts the order of proposals stepwise according to the relevance. This makes it possible to adjust the order of proposals based on the relevance. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the relevance of preventive measures and treatments to the generation AI, and cause the generation AI to adjust the order of proposals.

[0098] The standardization unit can estimate the user's emotion and adjust the data standardization method based on the estimated user's emotion. For example, when the user is relaxed, the standardization unit performs detailed standardization. Furthermore, when the user is stressed, the standardization unit can perform simple standardization. Furthermore, when the user is in a hurry, the standardization unit can perform standardization that can be completed in a short time. For example, the standardization unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The standardization unit calculates an emotion score based on the change in the facial expression and adjusts the standardization method. Furthermore, the standardization unit can record the user's voice and estimate the emotion using a voice analysis technology. The standardization unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the standardization method. Furthermore, the standardization unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The standardization unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the standardization method. This makes it possible to adjust the data standardization method according to the user's emotion. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the standardization unit may be performed, for example, using AI or without using AI. For example, the standardization unit can input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0099] During standardization, the standardization unit can analyze the data format of each medical institution and select the optimal standardization protocol. For example, the standardization unit compares the data formats of each medical institution and selects the most efficient standardization protocol. The standardization unit can also analyze the data formats of each medical institution and create a common standardization protocol. Furthermore, the standardization unit can evaluate the data formats of each medical institution and propose the optimal standardization protocol. For example, the standardization unit investigates the data formats of each medical institution and identifies an efficient standardization protocol. The common standardization protocol ensures consistency of the data format and improves the quality of the data. The standardization unit evaluates the conditions and procedures of the data format and proposes the optimal standardization protocol. This makes it possible to select the optimal standardization protocol. Part or all of the above-mentioned processing in the standardization unit may be performed, for example, using AI or may be performed without using AI. For example, the standardization unit can input the data formats of each medical institution to the generation AI and cause the generation AI to select the optimal standardization protocol.

[0100] The standardization unit can estimate the user's emotion and determine the priority of standardization based on the estimated emotion of the user. For example, when the user is relaxed, the standardization unit prioritizes standardization of data with high importance. Also, when the user is stressed, the standardization unit can postpone data with low importance. Furthermore, when the user is in a hurry, the standardization unit can prioritize data that can be quickly standardized. For example, the standardization unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The standardization unit calculates an emotion score based on the change in facial expression and determines the priority of standardization. Also, the standardization unit can record the user's voice and estimate the emotion using a voice analysis technology. The standardization unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of standardization. Furthermore, the standardization unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The standardization unit calculates an emotion score based on the fluctuation of the heart rate and determines the priority of standardization. This makes it possible to determine the priority of standardization according to the user's emotion. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the standardization unit may be performed, for example, using AI or without using AI. For example, the standardization unit can input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0101] The standardization unit can adjust the order of standardization based on the submission time of data during standardization. For example, the standardization unit prioritizes standardization of the latest data. In addition, the standardization unit can postpone data submitted earlier. Furthermore, the standardization unit can adjust the order of standardization stepwise depending on the submission time. For example, the standardization unit evaluates the submission time of data and prioritizes standardization of the latest data. Data submitted earlier is postponed. The standardization unit adjusts the order of standardization stepwise depending on the submission time. This makes it possible to adjust the order of standardization based on the submission time. Part or all of the above-mentioned processing in the standardization unit may be performed using, for example, AI, or may be performed without using AI. For example, the standardization unit can input the submission time of data to the generation AI and cause the generation AI to adjust the order of standardization.

[0102] The visualization unit can estimate the user's emotion and adjust the visualization expression method based on the estimated user's emotion. For example, when the user is relaxed, the visualization unit performs detailed visualization. Furthermore, when the user is stressed, the visualization unit can perform concise visualization. Furthermore, when the user is in a hurry, the visualization unit can perform visualization that focuses on the main points. For example, the visualization unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The visualization unit calculates an emotion score based on the change in the facial expression and adjusts the visualization expression method. Furthermore, the visualization unit can record the user's voice and estimate the emotion using a voice analysis technology. The visualization unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the visualization expression method. Furthermore, the visualization unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The visualization unit calculates an emotion score based on the fluctuation in the heart rate and adjusts the visualization expression method. This makes it possible to adjust the visualization expression method according to the user's emotions. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the visualization unit may be performed, for example, using AI, or may be performed without using AI. For example, the visualization unit can input image data of the user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0103] The visualization unit can adjust the level of detail of the visualization based on the importance of the analysis result at the time of visualization. For example, the visualization unit performs detailed visualization for an analysis result with high importance. In addition, the visualization unit can perform simple visualization for an analysis result with low importance. Furthermore, the visualization unit can adjust the level of detail of the visualization stepwise according to the importance. For example, the visualization unit evaluates the importance of the analysis result and performs detailed visualization. For an analysis result with low importance, it performs simple visualization. The visualization unit adjusts the level of detail of the visualization stepwise according to the importance. This makes it possible to adjust the level of detail of the visualization according to the importance. A part or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the visualization.

[0104] The visualization unit can estimate the user's emotion and adjust the length of the visualization based on the estimated user's emotion. For example, when the user is relaxed, the visualization unit performs detailed visualization. Also, when the user is stressed, the visualization unit can perform concise visualization. Furthermore, when the user is in a hurry, the visualization unit can perform visualization that can be understood in a short time. For example, the visualization unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The visualization unit calculates an emotion score based on the change in the facial expression and adjusts the length of the visualization. Also, the visualization unit can record the user's voice and estimate the emotion using a voice analysis technology. The visualization unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the visualization. Furthermore, the visualization unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The visualization unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the length of the visualization. This enables the length of visualization to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the visualization unit may be performed, for example, using AI or without using AI. For example, the visualization unit can input image data of the user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotion.

[0105] The visualization unit can determine the priority of visualization based on the submission time of the analysis results at the time of visualization. For example, the visualization unit preferentially visualizes the latest analysis results. In addition, the visualization unit can postpone analysis results submitted earlier. Furthermore, the visualization unit can gradually adjust the priority of visualization according to the submission time. For example, the visualization unit evaluates the submission time of the analysis results and preferentially visualizes the latest analysis results. Analysis results submitted earlier are postponed. The visualization unit gradually adjusts the priority of visualization according to the submission time. This makes it possible to determine the priority of visualization based on the submission time. Part or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the submission time of the analysis results to the generation AI and cause the generation AI to execute the determination of the priority of visualization.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The medical data sharing system may further include an anonymization unit that performs data anonymization. The anonymization unit deletes or masks information that can identify an individual from the collected medical record data. For example, information such as the patient's name, address, and contact information is deleted to generate anonymized data. The anonymization unit may also adjust the level of anonymization of the data. For example, necessary information may be left while other information may be anonymized depending on a specific research purpose. Furthermore, the anonymization unit may evaluate the quality of the anonymized data and perform re-anonymization as necessary. This makes it possible to maintain the usefulness of the data while ensuring the protection of personal information.

[0108] The medical data sharing system can further include a consistency check unit that checks the consistency of the data. The consistency check unit checks the consistency and accuracy of the collected medical record data. For example, it checks whether data from different medical institutions for the same patient is consistent. The consistency check unit can also detect missing or duplicated data and correct it as necessary. Furthermore, the consistency check unit can set rules for maintaining the consistency of the data and check the data periodically. This makes it possible to improve the quality of the data and realize highly reliable medical data sharing.

[0109] The medical data sharing system can further include a backup unit that backs up data. The backup unit periodically backs up collected medical record data to prepare for data loss or damage. For example, the backup unit backs up data on a daily or weekly schedule. The backup unit can also store data in different locations to enable data recovery in the event of a disaster. Furthermore, the backup unit can check the integrity of the backup data and perform a restoration test as necessary. This ensures the safety of the data and supports the continuous use of medical data.

[0110] The medical data sharing system can further include an access control unit that controls access to data. The access control unit manages access rights to the collected medical record data. For example, access rights are set for each medical professional, allowing them to view only the necessary data. The access control unit can also record the data viewing history and detect unauthorized access. Furthermore, the access control unit can periodically review the access rights to data and update them as necessary. This makes it possible to strengthen data security and promote appropriate data use.

[0111] The medical data sharing system can further include an encryption unit that encrypts data. The encryption unit encrypts collected medical record data to prevent data leakage. For example, the encryption unit encrypts data when it is transmitted or stored, to prevent unauthorized access by third parties. The encryption unit can also manage encryption keys and periodically update the keys. Furthermore, the encryption unit can decrypt encrypted data and make the data viewable only when necessary. This ensures the confidentiality of the data and enables safe medical data sharing.

[0112] The medical data sharing system can further estimate the user's emotions and adjust the way data is displayed based on the estimated user's emotions. For example, if the user is feeling stressed, the data display can be simplified and only important information can be emphasized. If the user is relaxed, detailed data can be displayed to make it easier to grasp the overall picture. Furthermore, if the user is in a hurry, data can be displayed with only the main points, supporting quick decision-making. This enables flexible data display according to the user's emotions, and is expected to reduce the burden on medical professionals.

[0113] The medical data sharing system can further estimate the user's emotions and adjust the timing of notifications based on the estimated user's emotions. For example, if the user is feeling stressed, the notification can be delayed and sent in a relaxed state. Also, if the user is relaxed, the notification can be sent immediately to encourage a quick response. Furthermore, if the user is in a hurry, only important notifications can be sent as a priority, and other notifications can be postponed. This makes it possible to adjust the timing of notifications appropriately according to the user's emotions, and is expected to reduce the stress of medical professionals.

[0114] The medical data sharing system can further estimate the user's emotions and filter data based on the estimated user's emotions. For example, if the user is feeling stressed, data of low importance is hidden and only important data is displayed. If the user is relaxed, all data can be displayed and detailed information can be provided. Furthermore, if the user is in a hurry, only data that can be checked quickly can be displayed and other data can be postponed. This makes it possible to filter data according to the user's emotions, and is expected to support medical professionals in using data more efficiently.

[0115] The medical data sharing system can further estimate the user's emotions and adjust the data update frequency based on the estimated user's emotions. For example, if the user is feeling stressed, the data update frequency can be reduced to provide stable information. Also, if the user is relaxed, the data update frequency can be increased to provide the latest information. Furthermore, if the user is in a hurry, only important data can be updated as a priority, and other data can be postponed. This makes it possible to adjust the data update frequency according to the user's emotions, which is expected to reduce the burden on medical professionals.

[0116] The medical data sharing system can further estimate the user's emotions and adjust the data display format based on the estimated user's emotions. For example, if the user is stressed, the data can be displayed in a concise list format to highlight important information. If the user is relaxed, the data can be displayed in a detailed table format to make it easier to grasp the overall picture. Furthermore, if the user is in a hurry, the data can be displayed in a card format that focuses on the main points to support quick decision-making. This makes it possible to flexibly adjust the data display format according to the user's emotions, and is expected to support medical professionals in using data more efficiently.

[0117] The process flow of the second embodiment will be briefly described below.

[0118] Step 1: The collection department collects medical record data. Medical record data includes electronic medical records, paper medical records, medical records, etc. The collection department collects electronic medical record data directly from medical institutions, scans paper medical records to convert them into digital data, and collects data such as medical records, test results, and prescription information. For example, data is automatically obtained from the electronic medical record system and uploaded to the platform. Paper medical records are digitized using a scanner, and medical records, test results, and prescription information are collected in a standardized format. Step 2: The analysis unit uses the generation AI to analyze the medical record data collected by the collection unit. The analysis includes detailed analysis of an individual's medical history and identifying trends in illness. The generation AI finds out tendencies toward certain diseases based on past medical records and test results. It also works with lifestyle habits and external vital signs data to analyze data such as exercise habits, diet, and sleep patterns, and clarify the correlation with health conditions. The generation AI uses text generation AI (e.g., LLM) to analyze the medical record data, and uses natural language processing technology to understand the contents of the medical record data and generate analysis results. Step 3: The provision unit provides the analysis results obtained by the analysis unit. Providing includes providing useful information to medical professionals. For example, it may suggest preventive measures and treatments for patients who are prone to certain diseases, point out ways to improve lifestyle habits, and provide health management advice. The provision unit provides useful information to medical professionals based on the results of analysis by the generation AI. Step 4: The suggestion unit proposes preventive measures and treatments based on the analysis results provided by the provision unit. The suggestions include proposing preventive measures and treatments for patients who are prone to certain diseases. For example, the suggestion unit proposes preventive measures and treatments based on the results of the analysis by the generation AI, points out areas for improving lifestyle habits, and provides health management advice.

[0119] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0121] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, suggestion unit, standardization unit, and visualization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14, and collects electronic medical record data and lifestyle habit data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the medical record data using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the analysis result to a medical professional. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests preventive measures and treatment methods. The standardization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and standardizes data. The visualization unit is realized, for example, by the control unit 46A of the smart device 14, and visualizes the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0130] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0133] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0135] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, suggestion unit, standardization unit, and visualization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214, and collects electronic medical record data and lifestyle habit data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the medical record data using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the analysis result to a medical professional. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests preventive measures and treatment methods. The standardization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and standardizes data. The visualization unit is realized, for example, by the control unit 46A of the smart glasses 214, and visualizes the analysis result. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0149] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, suggestion unit, standardization unit, and visualization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314, and collects electronic medical record data and lifestyle habit data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the medical record data using a generation AI. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the analysis results to medical professionals. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests preventive measures and treatment methods. The standardization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and standardizes data. The visualization unit is realized, for example, by the control unit 46A of the headset type terminal 314, and visualizes the analysis results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a 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.

[0157] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0166] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, suggestion unit, standardization unit, and visualization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414, and collects electronic medical record data and lifestyle habit data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the medical record data using a generation AI. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the analysis results to medical professionals. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests preventive measures and treatment methods. The standardization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and standardizes data. The visualization unit is realized, for example, by the control unit 46A of the robot 414, and visualizes the analysis results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0173] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0177] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] In addition, 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 upon request from the data processing device 12.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0184] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0188] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0190] (Appendix 1) A collection unit that collects medical record data; an analysis unit that analyzes the medical record data collected by the collection unit; a providing unit that provides an analysis result obtained by the analysis unit; A suggestion unit that proposes a preventive measure or a treatment method based on the analysis result provided by the provision unit. A system characterized by: (Appendix 2) Equipped with a collection unit that collects lifestyle habits or external vital data 2. The system of claim 1, (Appendix 3) Equipping a standardization department to standardize data 2. The system of claim 1, (Appendix 4) Equipped with a visualization section to visualize analysis results 2. The system of claim 1, (Appendix 5) The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting medical record data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 6) The collecting unit includes: Analyze the data collection methods of each medical institution and select the appropriate collection protocol 2. The system of claim 1, (Appendix 7) The collecting unit includes: When collecting medical record data, filter it based on the patient's current health status and treatment history. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting medical record data, consider the geographic location of the patient to prioritize collection of the most relevant data 2. The system of claim 1, (Appendix 10) The collecting unit includes: Analyze patients' social media activity and collect relevant data when collecting medical records 2. The system of claim 1, (Appendix 11) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, adjust the level of detail based on the importance of the medical record data 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of medical record data. 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 15) The analysis unit is At the time of analysis, the analysis priority will be determined based on the time of submission of medical record data. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, adjust the order of analysis based on the relevance of the medical record data 2. The system of claim 1, (Appendix 17) The providing unit is Estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 18) The providing unit is At the time of provision, the level of detail provided will be adjusted based on the importance of the analysis results. 2. The system of claim 1, (Appendix 19) The providing unit is When serving, different serving algorithms are applied depending on the category of analysis results. 2. The system of claim 1, (Appendix 20) The providing unit is The system estimates the user's emotions and adjusts the length of information provided based on the estimated user emotions. 2. The system of claim 1, (Appendix 21) The providing unit is At the time of provision, the priority of provision will be determined based on the time of submission of analysis results. 2. The system of claim 1, (Appendix 22) The providing unit is At the time of serving, adjust the order of serving based on the relevance of the analysis results 2. The system of claim 1, (Appendix 23) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 24) The suggestion unit, When making recommendations, adjust the level of detail in the recommendations based on the importance of the preventive or therapeutic measures 2. The system of claim 1, (Appendix 25) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of preventive measures or treatments. 2. The system of claim 1, (Appendix 26) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 27) The suggestion unit, Prioritize proposals based on when preventive or therapeutic measures are submitted 2. The system of claim 1, (Appendix 28) The suggestion unit, When making suggestions, adjust the order of suggestions based on the relevance of preventive and therapeutic measures 2. The system of claim 1, (Appendix 29) The standardization unit includes: Infer user sentiment and adjust the data standardization method based on the inferred user sentiment 2. The system of claim 1, (Appendix 30) The standardization unit includes: When standardizing, analyze the data format of each medical institution and select the optimal standardization protocol. 2. The system of claim 1, (Appendix 31) The standardization unit includes: Estimating user sentiment and determining standardization priorities based on the estimated user sentiment 2. The system of claim 1, (Appendix 32) The standardization unit includes: During standardization, adjust the standardization order based on when the data was submitted 2. The system of claim 1, (Appendix 33) The visualization unit is Infer user emotions and adjust visualizations based on the inferred emotions 2. The system of claim 1, (Appendix 34) The visualization unit is At visualization time, adjust the visualization detail based on the importance of the analysis results 2. The system of claim 1, (Appendix 35) The visualization unit is Infer user emotion and adjust the length of the visualization based on the inferred user emotion 2. The system of claim 1, (Appendix 36) The visualization unit is During visualization, prioritize visualization based on when analysis results are submitted 2. The system of claim 1, [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects medical record data; an analysis unit that analyzes the medical record data collected by the collection unit; a providing unit that provides an analysis result obtained by the analysis unit; A suggestion unit that proposes a preventive measure or a treatment method based on the analysis result provided by the provision unit. A system characterized by:

2. Equipped with a collection unit that collects lifestyle habits or external vital data 2. The system of claim 1 .

3. Equipping a standardization department to standardize data 2. The system of claim 1 .

4. Equipped with a visualization section to visualize analysis results 2. The system of claim 1 .

5. The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting medical record data based on the estimated user's emotion.

2. The system of claim 1 .

6. The collecting unit includes: Analyze the data collection methods of each medical institution and select the appropriate collection protocol 2. The system of claim 1 .

7. The collecting unit includes: When collecting medical record data, filter it based on the patient's current health status and treatment history.

2. The system of claim 1 .

8. The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1 .

9. The collecting unit includes: When collecting medical records, consider the geographic location of the patient to prioritize collection of the most relevant data 2. The system of claim 1 .

10. The collecting unit includes: Analyze patients' social media activity and collect relevant data when collecting medical records 2. The system of claim 1 .

11. The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1 .

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