system

Abstract

The system according to this embodiment aims to present an appropriate action plan based on the user's health check results and attribute information. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an evaluation unit, and a presentation unit. The reception unit receives input of health check results and attribute information from the user. The analysis unit analyzes the information received by the reception unit. The evaluation unit evaluates the user's health status based on the analysis results obtained by the analysis unit. The presentation unit presents a specific action plan based on the evaluation results obtained by the evaluation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it has not been fully done to present an appropriate action plan based on health diagnosis results and attribute information, and there is room for improvement.

[0005] The system according to the embodiment aims to present an appropriate action plan based on the user's health diagnosis results and attribute information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an evaluation unit, and a presentation unit. The reception unit receives input from the user, such as health checkup results and attribute information. The analysis unit analyzes the information received by the reception unit. The evaluation unit evaluates the user's health status based on the analysis results obtained by the analysis unit. The presentation unit presents a specific action plan based on the evaluation results obtained by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can present an appropriate action plan based on the user's health check results and attribute information. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health management support system according to an embodiment of the present invention is an AI agent that provides appropriate guidance on what actions to take now to people who are unsure how to address their health management needs. This health management support system works by having the user input their health checkup results and other health-related data, which the generating AI then analyzes to present the user with a concrete action plan. For example, the user inputs their health checkup results and attribute information (gender, age, place of residence, etc.). For example, they input health checkup data and medication data from the past few years. This information is input into the generating AI. Next, the generating AI analyzes the input information. The generating AI analyzes the health checkup results and medical data to evaluate the user's health status. For example, based on past health checkup data, the generating AI can predict the likelihood of developing a specific disease within the next five years. Based on the results of the generating AI's analysis, the AI ​​agent presents a concrete action plan. For example, if the generating AI determines that the user has a high risk of developing stomach cancer, it will recommend an endoscopy and suggest a hospital in the user's place of residence. Furthermore, the generating AI develops an optimal health plan according to the user's health status. For example, based on five years of health checkup and medication data, the system provides personalized health management advice. This system allows users to obtain specific action plans tailored to their health status, making health management easier. For instance, simply by entering health checkup results, the AI ​​generates information on what actions to take, enabling even those unsure of how to address their health issues to take appropriate action. This allows the health management support system to present specific action plans based on the user's health checkup results and attribute information.

[0029] The health management support system according to this embodiment comprises a reception unit, an analysis unit, an evaluation unit, and a presentation unit. The reception unit receives input from the user of health checkup results and attribute information. For example, the user can input health checkup results and attribute information (gender, age, place of residence, etc.) into the reception unit. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes health checkup results and medical data to evaluate the user's health status. The evaluation unit evaluates the user's health status based on the analysis results obtained by the analysis unit. For example, the evaluation unit can predict the likelihood of developing a specific disease within the next five years based on past health checkup data. The presentation unit presents a specific action plan based on the evaluation results obtained by the evaluation unit. For example, the presentation unit presents a specific action plan to the user based on the results evaluated by a generating AI. Thus, the health management support system according to this embodiment can present a specific action plan based on the user's health checkup results and attribute information.

[0030] The reception desk accepts input from users regarding health checkup results and attribute information. For example, users can input health checkup results and attribute information (gender, age, place of residence, etc.). Specifically, users input numerical data from their health checkup results and detailed attribute information such as gender, age, place of residence, occupation, and lifestyle habits through a dedicated web form or mobile application. This information is transmitted to the server using a secure communication protocol and safely stored in the database. The reception desk also has a function to automatically check the format and content of the input data and prompt the user to correct any deficiencies or errors. For example, if the numerical values ​​of the health checkup results are abnormally high or low, or if required fields are not entered, a warning message is displayed in real time to support the user in entering accurate information. Furthermore, the reception desk provides a function that allows users to refer to data they have entered in the past, enabling centralized management of regular health checkup results. This allows users to easily understand changes in their health status and enables continuous health management.

[0031] The Analysis Department analyzes information received by the Reception Department. For example, the Analysis Department analyzes health checkup results and medical data to assess the user's health status. Specifically, based on numerical data from health checkup results, it analyzes key health indicators such as blood pressure, blood sugar levels, and cholesterol levels, and evaluates whether these indicators are within the normal range. Furthermore, it uses AI to comprehensively analyze the user's attribute information, lifestyle data, and health checkup results to identify potential health risks. For example, the AI ​​learns from past health checkup and medical data, and by detecting specific patterns and trends, it predicts what kind of health problems the user may face in the future. The Analysis Department also provides a dashboard to visually display the user's health status in an easy-to-understand way, showing fluctuations in health indicators and the results of risk assessments in graphs and charts. This allows users to intuitively understand their own health status and obtain information to take necessary measures.

[0032] The evaluation department assesses the user's health status based on the analysis results obtained by the analysis department. For example, the evaluation department can predict the likelihood of developing a specific disease within the next five years based on past health checkup data. Specifically, it uses AI to analyze the user's health checkup results and attribute information, and compares them with past data to evaluate changes in health status and increases or decreases in risk. For example, the AI ​​analyzes fluctuations in the user's blood pressure and blood sugar levels to predict the risk of hypertension and diabetes. It also considers the user's lifestyle data (diet, exercise, smoking, alcohol consumption, etc.) and evaluates the impact of these factors on health. Furthermore, the evaluation department introduces a scoring system to comprehensively evaluate the user's health status, quantifying and displaying the level of health risk. This allows users to objectively understand their own health status and use it to set health management goals and formulate action plans.

[0033] The presentation unit presents a specific action plan based on the evaluation results obtained by the evaluation unit. For example, the presentation unit presents a specific action plan to the user based on the evaluation results of the generating AI. Specifically, the generating AI generates an individually customized action plan based on the user's health status and lifestyle data. For example, it suggests specific ingredients and recipes as a suggestion for improving diet, and presents an exercise menu tailored to the user's physical strength and health status as an exercise plan. It also provides specific advice and support programs for users with smoking or drinking habits to quit or reduce alcohol consumption. Furthermore, the presentation unit is equipped with support functions to help users execute the action plan, assisting them to act according to the plan through reminder and progress management functions. For example, it uses smartphone notification functions to remind users of meal and exercise timings, and records progress to provide feedback. In this way, the presentation unit can provide a specific action plan for users to continuously manage their health and support health improvement.

[0034] The reception desk can receive the user's health check results and attribute information. For example, the reception desk can receive the user's health check results and attribute information (gender, age, place of residence, etc.). The reception desk can receive the user's health check results and attribute information. As a result, the reception desk can receive the user's health check results and attribute information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without using AI.

[0035] The analysis unit can analyze health checkup results and medical data to evaluate the user's health status. For example, the analysis unit can analyze health checkup results and medical data to evaluate the user's health status. The analysis unit can analyze health checkup results and medical data to evaluate the user's health status. This allows the analysis unit to analyze health checkup results and medical data to evaluate the user's health status. Some or all of the above processing in the analysis unit may be performed using a generation AI, or not. For example, the analysis unit can input health checkup results and medical data into a generation AI and have the generation AI perform the evaluation of the user's health status.

[0036] The evaluation unit can predict the likelihood of developing a specific disease within the next five years based on past health checkup data. For example, the evaluation unit can predict the likelihood of developing a specific disease within the next five years based on past health checkup data. The evaluation unit can predict the likelihood of developing a specific disease within the next five years based on past health checkup data. This allows the evaluation unit to predict the likelihood of developing a specific disease within the next five years based on past health checkup data. Some or all of the above processing in the evaluation unit may be performed using or without a generating AI. For example, the evaluation unit can input past health checkup data into a generating AI and have the generating AI perform a prediction of the likelihood of developing a specific disease.

[0037] The presentation unit can present a specific action plan to the user based on the results evaluated by the generating AI. For example, the presentation unit presents a specific action plan to the user based on the results evaluated by the generating AI. The presentation unit can present a specific action plan to the user based on the results evaluated by the generating AI. In this way, the presentation unit can present a specific action plan to the user based on the results evaluated by the generating AI. Some or all of the above processing in the presentation unit may be performed using AI or not using AI. For example, the presentation unit can present an action plan using an AI model that presents a specific action plan to the user based on the results evaluated by the generating AI.

[0038] The presentation unit can suggest hospitals corresponding to the user's place of residence. The presentation unit can suggest hospitals corresponding to the user's place of residence. The presentation unit can suggest hospitals corresponding to the user's place of residence. In this way, the presentation unit can suggest hospitals corresponding to the user's place of residence. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the user's place of residence information into a generating AI and have the generating AI perform the task of suggesting corresponding hospitals.

[0039] The reception desk can analyze the user's past health checkup data and select the optimal input method. For example, the reception desk can suggest the optimal input format based on the data format the user has previously entered. The reception desk can prioritize suggesting input devices (smartphones, personal computers, etc.) that the user has used in the past. The reception desk can prompt the user for input at specific times based on the user's past input history. This allows the reception desk to analyze the user's past health checkup data and select the optimal input method. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past health checkup data into a generating AI and have the generating AI select the optimal input method.

[0040] The reception unit can filter the input of health checkup results based on the user's current lifestyle and areas of interest. For example, the reception unit can prioritize inputting health checkup items relevant to the user's current lifestyle (work, family, etc.). The reception unit can input relevant data based on the user's areas of interest (exercise, diet, etc.). The reception unit can adjust the input items to match the user's lifestyle. This allows the reception unit to filter based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The reception desk can prioritize inputting highly relevant data when entering health checkup results, taking into account the user's geographical location. For example, the reception desk can prioritize inputting data related to region-specific health risks based on the user's place of residence. The reception desk can prioritize inputting data related to the work environment, taking into account the user's workplace location. The reception desk can prioritize inputting data related to health risks during travel, taking into account the user's travel destination location. In this way, the reception desk can prioritize inputting highly relevant data, taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize inputting highly relevant data.

[0042] The reception unit can analyze the user's social media activity and input relevant data when inputting health check results. For example, the reception unit can extract health-related interests from the user's social media posts and input relevant data. The reception unit can analyze the user's social media friendships and input relevant data based on the health status of their friends. The reception unit can analyze the user's social media activity times and suggest the optimal input timing. This allows the reception unit to analyze the user's social media activity and input relevant data. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI input the relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the health checkup results. For example, the analysis unit can perform a detailed analysis on important health checkup results. The analysis unit can perform a concise analysis on general health checkup results. The analysis unit can perform a detailed analysis on health checkup results of high user interest. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the health checkup results. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance data of the health checkup results into a generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the health checkup results during analysis. For example, the analysis unit can apply a specific algorithm to blood test results. The analysis unit can apply a different algorithm to image diagnostic results. The analysis unit can apply a dedicated algorithm to electrocardiogram results. This allows the analysis unit to apply different analysis algorithms depending on the category of the health checkup results. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input health checkup result category data into a generation AI and have the generation AI execute the application of different analysis algorithms.

[0045] The analysis department can determine the priority of analysis based on the submission date of health checkup results. For example, the analysis department may prioritize the analysis of the most recent health checkup results. The analysis department can analyze the latest results while referring to past health checkup results. The analysis department can analyze older health checkup results more concisely. This allows the analysis department to determine the priority of analysis based on the submission date of health checkup results. Some or all of the above processing in the analysis department may be performed using a generation AI, or not. For example, the analysis department can input health checkup result submission date data into a generation AI and have the generation AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the health checkup results during the analysis. For example, the analysis unit can prioritize the analysis of health checkup results with high relevance. The analysis unit can postpone the analysis of health checkup results with low relevance. The analysis unit can evaluate the relevance of the health checkup results and perform the analysis in the optimal order. This allows the analysis unit to adjust the order of analysis based on the relevance of the health checkup results. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the relevance data of the health checkup results into a generative AI and have the generative AI perform the adjustment of the analysis order.

[0047] The evaluation unit can improve the accuracy of its evaluation by considering the interrelationships of health checkup results during the evaluation process. For example, the evaluation unit can evaluate by considering the interrelationships between blood test results and imaging diagnostic results. The evaluation unit can evaluate by considering the interrelationships between electrocardiogram results and blood pressure measurement results. The evaluation unit can analyze the interrelationships of all health checkup results to improve the accuracy of its evaluation. In this way, the evaluation unit can improve the accuracy of its evaluation by considering the interrelationships of health checkup results. Some or all of the above processing in the evaluation unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the evaluation unit can input data on the interrelationships of health checkup results into a generating AI and have the generating AI perform the improvement of evaluation accuracy.

[0048] The evaluation unit can perform evaluations while considering the attribute information of the person submitting the health check results. For example, the evaluation unit can adjust the evaluation criteria based on the submitter's age. The evaluation unit can adjust the evaluation criteria based on the submitter's gender. The evaluation unit can adjust the evaluation criteria based on the submitter's place of residence. This allows the evaluation unit to perform evaluations while considering the attribute information of the person submitting the health check results. Some or all of the above processing in the evaluation unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the evaluation unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the evaluation adjustments.

[0049] The evaluation unit can perform evaluations while considering the geographical distribution of health checkup results. For example, the evaluation unit can perform evaluations while considering region-specific health risks based on the submitter's place of residence. The evaluation unit can perform evaluations related to the work environment by considering the submitter's workplace location information. The evaluation unit can perform evaluations related to health risks during travel by considering the submitter's travel destination location information. In this way, the evaluation unit can perform evaluations while considering the geographical distribution of health checkup results. Some or all of the above processing in the evaluation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the evaluation unit can input geographical distribution data of health checkup results into a generation AI and have the generation AI perform adjustments to the evaluation.

[0050] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on health checkup results during the evaluation process. For example, the evaluation unit can refer to the latest research papers related to health checkup results. The evaluation unit can refer to past research data related to health checkup results. The evaluation unit can refer to specialized books related to health checkup results. In this way, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on health checkup results. Some or all of the above processing in the evaluation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the evaluation unit can input relevant literature data on health checkup results into a generating AI and have the generating AI perform the improvement of evaluation accuracy.

[0051] The presentation unit can optimize the current action plan by referring to past action plan data when presenting an action plan. For example, the presentation unit can propose the optimal action plan based on the user's past action plan data. The presentation unit can extract and propose effective action plans from the user's past action plan data. The presentation unit can analyze the user's past action plan data and propose the optimal action plan for their current health condition. This allows the presentation unit to optimize the current action plan by referring to past action plan data. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input past action plan data into a generating AI and have the generating AI perform the optimization of the current action plan.

[0052] The presentation unit can apply different action plan presentation methods to each category of health checkup results when presenting action plans. For example, an action plan based on blood test results may provide specific advice on diet and exercise. An action plan based on imaging diagnostic results may recommend regular checkups and visits to specialists. An action plan based on electrocardiogram results may suggest lifestyle improvements to maintain heart health. In this way, the presentation unit can apply different action plan presentation methods to each category of health checkup results. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input category data of health checkup results into a generating AI and have the generating AI execute the application of different action plan presentation methods.

[0053] The presentation unit can analyze changes in the action plan based on the submission timing of health check results when presenting the action plan. For example, the presentation unit updates the action plan based on the latest health check results. The presentation unit can analyze changes in the action plan while referring to past health check results. The presentation unit can present action plans based on older health check results in a concise manner. This allows the presentation unit to analyze changes in the action plan based on the submission timing of health check results. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input health check result submission timing data into a generating AI and have the generating AI perform an analysis of changes in the action plan.

[0054] The presentation unit can analyze the action plan by referring to relevant market data related to the health checkup results when presenting the action plan. For example, the presentation unit can propose an action plan by referring to the latest market data related to the health checkup results. The presentation unit can propose an action plan by referring to historical market data related to the health checkup results. The presentation unit can analyze market trends related to the health checkup results and propose the optimal action plan. This allows the presentation unit to analyze the action plan by referring to relevant market data related to the health checkup results. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input relevant market data related to the health checkup results into a generating AI and have the generating AI perform the analysis of the action plan.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can improve the accuracy of data entry by referring to the user's past health checkup data when receiving the user's health checkup results and attribute information. For example, the reception desk can suggest the optimal input format based on the data format the user has entered in the past. It can also prioritize suggesting input devices (smartphones, personal computers, etc.) that the user has used in the past. Furthermore, it can prompt the user to enter data at specific times based on their past input history. This allows the reception desk to analyze the user's past health checkup data and select the optimal input method.

[0057] The analysis department can improve the accuracy of its analysis of users' health checkup results and medical data by considering their lifestyle data. For example, the analysis department can collect data on users' diet, exercise, and sleep, and analyze it in conjunction with their health checkup results. It can also perform a more comprehensive health assessment by considering the user's stress level and mental health data. Furthermore, it can assess health risks by considering the user's living environment (e.g., climate and air pollution levels in their place of residence). As a result, the analysis department can perform a more accurate health assessment by considering the user's lifestyle data.

[0058] The evaluation unit can improve the accuracy of its assessments of users' health checkup results by considering the user's genetic information. For example, the evaluation unit can predict the risk of developing specific diseases based on the user's genetic test results. It can also assess genetic health risks by considering the user's family history. Furthermore, it can integrate the user's genetic information with health checkup results to provide a more personalized health management plan. As a result, the evaluation unit can perform more accurate health assessments by taking the user's genetic information into account.

[0059] The presentation unit can customize action plans to the user, taking into account the user's health goals. For example, it can propose specific action plans based on the user's short-term and long-term health goals. It can also adjust the priority of action plans according to the user's health goals. Furthermore, it can monitor the user's progress toward achieving their health goals and update the action plan as needed. This allows the presentation unit to provide more effective action plans that take the user's health goals into consideration.

[0060] The reception desk can prioritize inputting highly relevant data when receiving users' health check results and attribute information, taking into account the user's geographical location. For example, based on the user's place of residence, it can prioritize inputting data related to region-specific health risks. It can also prioritize inputting data related to the work environment, taking into account the user's workplace location. Furthermore, it can prioritize inputting data related to health risks during travel, taking into account the user's travel destination location. This allows the reception desk to prioritize inputting highly relevant data, taking into account the user's geographical location.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk accepts health check results and attribute information from users. For example, users can enter their health check results and attribute information (gender, age, place of residence, etc.). Step 2: The analysis department analyzes the information received by the reception department. For example, they analyze health checkup results and medical data to evaluate the user's health status. Step 3: The evaluation unit assesses the user's health status based on the analysis results obtained by the analysis unit. For example, it can predict the likelihood of developing a specific disease within the next five years based on past health checkup data. Step 4: The presentation unit presents a specific action plan based on the evaluation results obtained by the evaluation unit. For example, it presents a specific action plan to the user based on the evaluation results of the generating AI.

[0063] (Example of form 2) The health management support system according to an embodiment of the present invention is an AI agent that provides appropriate guidance on what actions to take now to people who are unsure how to address their health management needs. This health management support system works by having the user input their health checkup results and other health-related data, which the generating AI then analyzes to present the user with a concrete action plan. For example, the user inputs their health checkup results and attribute information (gender, age, place of residence, etc.). For example, they input health checkup data and medication data from the past few years. This information is input into the generating AI. Next, the generating AI analyzes the input information. The generating AI analyzes the health checkup results and medical data to evaluate the user's health status. For example, based on past health checkup data, the generating AI can predict the likelihood of developing a specific disease within the next five years. Based on the results of the generating AI's analysis, the AI ​​agent presents a concrete action plan. For example, if the generating AI determines that the user has a high risk of developing stomach cancer, it will recommend an endoscopy and suggest a hospital in the user's place of residence. Furthermore, the generating AI develops an optimal health plan according to the user's health status. For example, based on five years of health checkup and medication data, the system provides personalized health management advice. This system allows users to obtain specific action plans tailored to their health status, making health management easier. For instance, simply by entering health checkup results, the AI ​​generates information on what actions to take, enabling even those unsure of how to address their health issues to take appropriate action. This allows the health management support system to present specific action plans based on the user's health checkup results and attribute information.

[0064] The health management support system according to this embodiment comprises a reception unit, an analysis unit, an evaluation unit, and a presentation unit. The reception unit receives input from the user of health checkup results and attribute information. For example, the user can input health checkup results and attribute information (gender, age, place of residence, etc.) into the reception unit. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes health checkup results and medical data to evaluate the user's health status. The evaluation unit evaluates the user's health status based on the analysis results obtained by the analysis unit. For example, the evaluation unit can predict the likelihood of developing a specific disease within the next five years based on past health checkup data. The presentation unit presents a specific action plan based on the evaluation results obtained by the evaluation unit. For example, the presentation unit presents a specific action plan to the user based on the results evaluated by a generating AI. Thus, the health management support system according to this embodiment can present a specific action plan based on the user's health checkup results and attribute information.

[0065] The reception desk accepts input from users regarding health checkup results and attribute information. For example, users can input health checkup results and attribute information (gender, age, place of residence, etc.). Specifically, users input numerical data from their health checkup results and detailed attribute information such as gender, age, place of residence, occupation, and lifestyle habits through a dedicated web form or mobile application. This information is transmitted to the server using a secure communication protocol and safely stored in the database. The reception desk also has a function to automatically check the format and content of the input data and prompt the user to correct any deficiencies or errors. For example, if the numerical values ​​of the health checkup results are abnormally high or low, or if required fields are not entered, a warning message is displayed in real time to support the user in entering accurate information. Furthermore, the reception desk provides a function that allows users to refer to data they have entered in the past, enabling centralized management of regular health checkup results. This allows users to easily understand changes in their health status and enables continuous health management.

[0066] The Analysis Department analyzes information received by the Reception Department. For example, the Analysis Department analyzes health checkup results and medical data to assess the user's health status. Specifically, based on numerical data from health checkup results, it analyzes key health indicators such as blood pressure, blood sugar levels, and cholesterol levels, and evaluates whether these indicators are within the normal range. Furthermore, it uses AI to comprehensively analyze the user's attribute information, lifestyle data, and health checkup results to identify potential health risks. For example, the AI ​​learns from past health checkup and medical data, and by detecting specific patterns and trends, it predicts what kind of health problems the user may face in the future. The Analysis Department also provides a dashboard to visually display the user's health status in an easy-to-understand way, showing fluctuations in health indicators and the results of risk assessments in graphs and charts. This allows users to intuitively understand their own health status and obtain information to take necessary measures.

[0067] The evaluation department assesses the user's health status based on the analysis results obtained by the analysis department. For example, the evaluation department can predict the likelihood of developing a specific disease within the next five years based on past health checkup data. Specifically, it uses AI to analyze the user's health checkup results and attribute information, and compares them with past data to evaluate changes in health status and increases or decreases in risk. For example, the AI ​​analyzes fluctuations in the user's blood pressure and blood sugar levels to predict the risk of hypertension and diabetes. It also considers the user's lifestyle data (diet, exercise, smoking, alcohol consumption, etc.) and evaluates the impact of these factors on health. Furthermore, the evaluation department introduces a scoring system to comprehensively evaluate the user's health status, quantifying and displaying the level of health risk. This allows users to objectively understand their own health status and use it to set health management goals and formulate action plans.

[0068] The presentation unit presents a specific action plan based on the evaluation results obtained by the evaluation unit. For example, the presentation unit presents a specific action plan to the user based on the evaluation results of the generating AI. Specifically, the generating AI generates an individually customized action plan based on the user's health status and lifestyle data. For example, it suggests specific ingredients and recipes as a suggestion for improving diet, and presents an exercise menu tailored to the user's physical strength and health status as an exercise plan. It also provides specific advice and support programs for users with smoking or drinking habits to quit or reduce alcohol consumption. Furthermore, the presentation unit is equipped with support functions to help users execute the action plan, assisting them to act according to the plan through reminder and progress management functions. For example, it uses smartphone notification functions to remind users of meal and exercise timings, and records progress to provide feedback. In this way, the presentation unit can provide a specific action plan for users to continuously manage their health and support health improvement.

[0069] The reception desk can receive the user's health check results and attribute information. For example, the reception desk can receive the user's health check results and attribute information (gender, age, place of residence, etc.). The reception desk can receive the user's health check results and attribute information. As a result, the reception desk can receive the user's health check results and attribute information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without using AI.

[0070] The analysis unit can analyze health checkup results and medical data to evaluate the user's health status. For example, the analysis unit can analyze health checkup results and medical data to evaluate the user's health status. The analysis unit can analyze health checkup results and medical data to evaluate the user's health status. This allows the analysis unit to analyze health checkup results and medical data to evaluate the user's health status. Some or all of the above processing in the analysis unit may be performed using a generation AI, or not. For example, the analysis unit can input health checkup results and medical data into a generation AI and have the generation AI perform the evaluation of the user's health status.

[0071] The evaluation unit can predict the likelihood of developing a specific disease within the next five years based on past health checkup data. For example, the evaluation unit can predict the likelihood of developing a specific disease within the next five years based on past health checkup data. The evaluation unit can predict the likelihood of developing a specific disease within the next five years based on past health checkup data. This allows the evaluation unit to predict the likelihood of developing a specific disease within the next five years based on past health checkup data. Some or all of the above processing in the evaluation unit may be performed using or without a generating AI. For example, the evaluation unit can input past health checkup data into a generating AI and have the generating AI perform a prediction of the likelihood of developing a specific disease.

[0072] The presentation unit can present a specific action plan to the user based on the results evaluated by the generating AI. For example, the presentation unit presents a specific action plan to the user based on the results evaluated by the generating AI. The presentation unit can present a specific action plan to the user based on the results evaluated by the generating AI. In this way, the presentation unit can present a specific action plan to the user based on the results evaluated by the generating AI. Some or all of the above processing in the presentation unit may be performed using AI or not using AI. For example, the presentation unit can present an action plan using an AI model that presents a specific action plan to the user based on the results evaluated by the generating AI.

[0073] The presentation unit can suggest hospitals corresponding to the user's place of residence. The presentation unit can suggest hospitals corresponding to the user's place of residence. The presentation unit can suggest hospitals corresponding to the user's place of residence. In this way, the presentation unit can suggest hospitals corresponding to the user's place of residence. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the user's place of residence information into a generating AI and have the generating AI perform the task of suggesting corresponding hospitals.

[0074] The reception desk can estimate the user's emotions and adjust the timing of health checkup result input based on the estimated emotions. For example, if the user is feeling stressed, the reception desk may prompt them to input their health checkup results during a time when they can relax. If the user is busy, the reception desk can narrow down the input fields to allow for quick input. If the user is relaxed, the reception desk may prompt for detailed input to collect more information. This allows the reception desk to adjust the timing of health checkup result input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The reception desk can analyze the user's past health checkup data and select the optimal input method. For example, the reception desk can suggest the optimal input format based on the data format the user has previously entered. The reception desk can prioritize suggesting input devices (smartphones, personal computers, etc.) that the user has used in the past. The reception desk can prompt the user for input at specific times based on the user's past input history. This allows the reception desk to analyze the user's past health checkup data and select the optimal input method. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past health checkup data into a generating AI and have the generating AI select the optimal input method.

[0076] The reception unit can filter the input of health checkup results based on the user's current lifestyle and areas of interest. For example, the reception unit can prioritize inputting health checkup items relevant to the user's current lifestyle (work, family, etc.). The reception unit can input relevant data based on the user's areas of interest (exercise, diet, etc.). The reception unit can adjust the input items to match the user's lifestyle. This allows the reception unit to filter based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0077] The reception desk can estimate the user's emotions and determine the priority of the health check results to be entered based on the estimated emotions. For example, if the user is feeling stressed, the reception desk may prompt them to prioritize entering important items. If the user is relaxed, the reception desk may prompt them to enter detailed items. If the user is in a hurry, the reception desk may prompt them to enter only the most important items. This allows the reception desk to determine the priority of the health check results to be entered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The reception desk can prioritize inputting highly relevant data when entering health checkup results, taking into account the user's geographical location. For example, the reception desk can prioritize inputting data related to region-specific health risks based on the user's place of residence. The reception desk can prioritize inputting data related to the work environment, taking into account the user's workplace location. The reception desk can prioritize inputting data related to health risks during travel, taking into account the user's travel destination location. In this way, the reception desk can prioritize inputting highly relevant data, taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize inputting highly relevant data.

[0079] The reception unit can analyze the user's social media activity and input relevant data when inputting health check results. For example, the reception unit can extract health-related interests from the user's social media posts and input relevant data. The reception unit can analyze the user's social media friendships and input relevant data based on the health status of their friends. The reception unit can analyze the user's social media activity times and suggest the optimal input timing. This allows the reception unit to analyze the user's social media activity and input relevant data. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI input the relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a concise analysis result that gets straight to the point. In this way, the analysis unit can adjust the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the health checkup results. For example, the analysis unit can perform a detailed analysis on important health checkup results. The analysis unit can perform a concise analysis on general health checkup results. The analysis unit can perform a detailed analysis on health checkup results of high user interest. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the health checkup results. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance data of the health checkup results into a generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the health checkup results during analysis. For example, the analysis unit can apply a specific algorithm to blood test results. The analysis unit can apply a different algorithm to image diagnostic results. The analysis unit can apply a dedicated algorithm to electrocardiogram results. This allows the analysis unit to apply different analysis algorithms depending on the category of the health checkup results. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input health checkup result category data into a generation AI and have the generation AI execute the application of different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. If the user is excited, the analysis unit can provide an analysis with visually stimulating effects. This allows the analysis unit to adjust the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The analysis department can determine the priority of analysis based on the submission date of health checkup results. For example, the analysis department may prioritize the analysis of the most recent health checkup results. The analysis department can analyze the latest results while referring to past health checkup results. The analysis department can analyze older health checkup results more concisely. This allows the analysis department to determine the priority of analysis based on the submission date of health checkup results. Some or all of the above processing in the analysis department may be performed using a generation AI, or not. For example, the analysis department can input health checkup result submission date data into a generation AI and have the generation AI determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the health checkup results during the analysis. For example, the analysis unit can prioritize the analysis of health checkup results with high relevance. The analysis unit can postpone the analysis of health checkup results with low relevance. The analysis unit can evaluate the relevance of the health checkup results and perform the analysis in the optimal order. This allows the analysis unit to adjust the order of analysis based on the relevance of the health checkup results. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the relevance data of the health checkup results into a generative AI and have the generative AI perform the adjustment of the analysis order.

[0086] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is tense, the evaluation unit may relax strict evaluation criteria. If the user is relaxed, the evaluation unit may apply detailed evaluation criteria. If the user is in a hurry, the evaluation unit may apply concise evaluation criteria. In this way, the evaluation unit can adjust the evaluation criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using or without a generative AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The evaluation unit can improve the accuracy of its evaluation by considering the interrelationships of health checkup results during the evaluation process. For example, the evaluation unit can evaluate by considering the interrelationships between blood test results and imaging diagnostic results. The evaluation unit can evaluate by considering the interrelationships between electrocardiogram results and blood pressure measurement results. The evaluation unit can analyze the interrelationships of all health checkup results to improve the accuracy of its evaluation. In this way, the evaluation unit can improve the accuracy of its evaluation by considering the interrelationships of health checkup results. Some or all of the above processing in the evaluation unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the evaluation unit can input data on the interrelationships of health checkup results into a generating AI and have the generating AI perform the improvement of evaluation accuracy.

[0088] The evaluation unit can perform evaluations while considering the attribute information of the person submitting the health check results. For example, the evaluation unit can adjust the evaluation criteria based on the submitter's age. The evaluation unit can adjust the evaluation criteria based on the submitter's gender. The evaluation unit can adjust the evaluation criteria based on the submitter's place of residence. This allows the evaluation unit to perform evaluations while considering the attribute information of the person submitting the health check results. Some or all of the above processing in the evaluation unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the evaluation unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the evaluation adjustments.

[0089] The evaluation unit can estimate the user's emotions and adjust the order in which the evaluation results are displayed based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can display important results first. If the user is relaxed, the evaluation unit can display detailed results sequentially. If the user is in a hurry, the evaluation unit can display concise results first. In this way, the evaluation unit can adjust the order in which the evaluation results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using or without a generative AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The evaluation unit can perform evaluations while considering the geographical distribution of health checkup results. For example, the evaluation unit can perform evaluations while considering region-specific health risks based on the submitter's place of residence. The evaluation unit can perform evaluations related to the work environment by considering the submitter's workplace location information. The evaluation unit can perform evaluations related to health risks during travel by considering the submitter's travel destination location information. In this way, the evaluation unit can perform evaluations while considering the geographical distribution of health checkup results. Some or all of the above processing in the evaluation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the evaluation unit can input geographical distribution data of health checkup results into a generation AI and have the generation AI perform adjustments to the evaluation.

[0091] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on health checkup results during the evaluation process. For example, the evaluation unit can refer to the latest research papers related to health checkup results. The evaluation unit can refer to past research data related to health checkup results. The evaluation unit can refer to specialized books related to health checkup results. In this way, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on health checkup results. Some or all of the above processing in the evaluation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the evaluation unit can input relevant literature data on health checkup results into a generating AI and have the generating AI perform the improvement of evaluation accuracy.

[0092] The presentation unit can estimate the user's emotions and adjust how the action plan is displayed based on the estimated emotions. For example, if the user is nervous, the presentation unit can provide a simple and highly visible display. If the user is relaxed, the presentation unit can provide a display that includes detailed information. If the user is in a hurry, the presentation unit can provide a concise display. In this way, the presentation unit can adjust how the action plan is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The presentation unit can optimize the current action plan by referring to past action plan data when presenting an action plan. For example, the presentation unit can propose the optimal action plan based on the user's past action plan data. The presentation unit can extract and propose effective action plans from the user's past action plan data. The presentation unit can analyze the user's past action plan data and propose the optimal action plan for their current health condition. This allows the presentation unit to optimize the current action plan by referring to past action plan data. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input past action plan data into a generating AI and have the generating AI perform the optimization of the current action plan.

[0094] The presentation unit can apply different action plan presentation methods to each category of health checkup results when presenting action plans. For example, an action plan based on blood test results may provide specific advice on diet and exercise. An action plan based on imaging diagnostic results may recommend regular checkups and visits to specialists. An action plan based on electrocardiogram results may suggest lifestyle improvements to maintain heart health. In this way, the presentation unit can apply different action plan presentation methods to each category of health checkup results. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input category data of health checkup results into a generating AI and have the generating AI execute the application of different action plan presentation methods.

[0095] The presentation unit can estimate the user's emotions and adjust the importance of the action plan based on the estimated emotions. For example, if the user is nervous, the presentation unit may present important action plans first. If the user is relaxed, the presentation unit may present detailed action plans sequentially. If the user is in a hurry, the presentation unit may present concise action plans first. In this way, the presentation unit can adjust the importance of the action plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The presentation unit can analyze changes in the action plan based on the submission timing of health check results when presenting the action plan. For example, the presentation unit updates the action plan based on the latest health check results. The presentation unit can analyze changes in the action plan while referring to past health check results. The presentation unit can present action plans based on older health check results in a concise manner. This allows the presentation unit to analyze changes in the action plan based on the submission timing of health check results. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input health check result submission timing data into a generating AI and have the generating AI perform an analysis of changes in the action plan.

[0097] The presentation unit can analyze the action plan by referring to relevant market data related to the health checkup results when presenting the action plan. For example, the presentation unit can propose an action plan by referring to the latest market data related to the health checkup results. The presentation unit can propose an action plan by referring to historical market data related to the health checkup results. The presentation unit can analyze market trends related to the health checkup results and propose the optimal action plan. This allows the presentation unit to analyze the action plan by referring to relevant market data related to the health checkup results. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input relevant market data related to the health checkup results into a generating AI and have the generating AI perform the analysis of the action plan.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can improve the accuracy of data entry by referring to the user's past health checkup data when receiving the user's health checkup results and attribute information. For example, the reception desk can suggest the optimal input format based on the data format the user has entered in the past. It can also prioritize suggesting input devices (smartphones, personal computers, etc.) that the user has used in the past. Furthermore, it can prompt the user to enter data at specific times based on their past input history. This allows the reception desk to analyze the user's past health checkup data and select the optimal input method.

[0100] The analysis department can improve the accuracy of its analysis of users' health checkup results and medical data by considering their lifestyle data. For example, the analysis department can collect data on users' diet, exercise, and sleep, and analyze it in conjunction with their health checkup results. It can also perform a more comprehensive health assessment by considering the user's stress level and mental health data. Furthermore, it can assess health risks by considering the user's living environment (e.g., climate and air pollution levels in their place of residence). As a result, the analysis department can perform a more accurate health assessment by considering the user's lifestyle data.

[0101] The evaluation unit can improve the accuracy of its assessments of users' health checkup results by considering the user's genetic information. For example, the evaluation unit can predict the risk of developing specific diseases based on the user's genetic test results. It can also assess genetic health risks by considering the user's family history. Furthermore, it can integrate the user's genetic information with health checkup results to provide a more personalized health management plan. As a result, the evaluation unit can perform more accurate health assessments by taking the user's genetic information into account.

[0102] The presentation unit can customize action plans to the user, taking into account the user's health goals. For example, it can propose specific action plans based on the user's short-term and long-term health goals. It can also adjust the priority of action plans according to the user's health goals. Furthermore, it can monitor the user's progress toward achieving their health goals and update the action plan as needed. This allows the presentation unit to provide more effective action plans that take the user's health goals into consideration.

[0103] The reception desk can estimate the user's emotions and adjust the timing of health checkup result input based on those estimates. For example, if a user is feeling stressed, it can prompt them to input their health checkup results during a time when they can relax. If a user is busy, the input fields can be narrowed down to allow for quicker input. Furthermore, if a user is relaxed, it can encourage more detailed input to collect more information. In this way, the reception desk can adjust the timing of health checkup result input based on the user's emotions.

[0104] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results that get straight to the point. In this way, the analysis unit can adjust the presentation of the analysis based on the user's emotions.

[0105] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on those emotions. For example, if the user is stressed, strict evaluation criteria can be relaxed. If the user is relaxed, detailed evaluation criteria can be applied. Furthermore, if the user is in a hurry, concise evaluation criteria can be applied. In this way, the evaluation unit can adjust the evaluation criteria based on the user's emotions.

[0106] The presentation unit can estimate the user's emotions and adjust how the action plan is displayed based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. In this way, the presentation unit can adjust how the action plan is displayed based on the user's emotions.

[0107] The presentation unit can estimate the user's emotions and adjust the importance of the action plan based on those emotions. For example, if the user is nervous, the most important action plan can be presented first. If the user is relaxed, a more detailed action plan can be presented sequentially. Furthermore, if the user is in a hurry, a concise action plan can be presented first. In this way, the presentation unit can adjust the importance of the action plan based on the user's emotions.

[0108] The reception desk can prioritize inputting highly relevant data when receiving users' health check results and attribute information, taking into account the user's geographical location. For example, based on the user's place of residence, it can prioritize inputting data related to region-specific health risks. It can also prioritize inputting data related to the work environment, taking into account the user's workplace location. Furthermore, it can prioritize inputting data related to health risks during travel, taking into account the user's travel destination location. This allows the reception desk to prioritize inputting highly relevant data, taking into account the user's geographical location.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk accepts health check results and attribute information from users. For example, users can enter their health check results and attribute information (gender, age, place of residence, etc.). Step 2: The analysis department analyzes the information received by the reception department. For example, they analyze health checkup results and medical data to evaluate the user's health status. Step 3: The evaluation unit assesses the user's health status based on the analysis results obtained by the analysis unit. For example, it can predict the likelihood of developing a specific disease within the next five years based on past health checkup data. Step 4: The presentation unit presents a specific action plan based on the evaluation results obtained by the evaluation unit. For example, it presents a specific action plan to the user based on the evaluation results of the generating AI.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the reception unit, analysis unit, evaluation unit, and presentation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives input of health check results and attribute information from the user using the touch panel 38A and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the health check results and medical data. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts the risk of developing a disease based on past health check data. The presentation unit presents a specific action plan to the user using the display 40A and speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, evaluation unit, and presentation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives input of health check results and attribute information from the user using the microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the health check results and medical data. The evaluation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and predicts the risk of developing a disease based on past health check data. The presentation unit presents a specific action plan to the user using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, evaluation unit, and presentation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives input of health check results and attribute information from the user using the microphone 238 of the headset terminal 314. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the health check results and medical data. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and predicts the risk of developing a disease based on past health check data. The presentation unit presents a specific action plan to the user using, for example, the speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the reception unit, analysis unit, evaluation unit, and presentation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives input of health check results and attribute information from the user using the microphone 238 of the robot 414. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the health check results and medical data. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and predicts the risk of developing a disease based on past health check data. The presentation unit presents a specific action plan to the user using, for example, the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A reception area that accepts users' health checkup results and attribute information, An analysis unit analyzes the information received by the aforementioned reception unit, An evaluation unit that evaluates the user's health status based on the analysis results obtained by the aforementioned analysis unit, The system includes a presentation unit that presents a specific action plan based on the evaluation results obtained by the evaluation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Accepts user health check results and attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We analyze health checkup results and medical data to evaluate the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit, Based on past health checkup data, we predict the likelihood of developing a specific disease within the next five years. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is, Based on the results of the AI's evaluation, the system presents the user with a concrete action plan. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is, We propose hospitals that are located near the user's place of residence. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of health checkup result input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past health checkup data and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering health checkup results, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the health check results to be entered based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering health checkup results, the system prioritizes inputting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter their health checkup results, the system analyzes their social media activity and inputs relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the health checkup results. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of the health checkup results. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, the priority of the analysis will be determined based on when the health checkup results were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, the order of analysis will be adjusted based on the relevance of the health checkup results. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, During evaluation, consider the interrelationships between health checkup results to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, During the evaluation, the attribute information of the person who submitted the health check results will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, It estimates the user's emotions and adjusts the order in which evaluation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During the evaluation, the geographical distribution of health checkup results will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, During evaluation, we improve the accuracy of the evaluation by referring to relevant literature on health checkup results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is, It estimates the user's emotions and adjusts how the action plan is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, When presenting an action plan, we optimize the current action plan by referring to past action plan data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is, When presenting action plans, different methods for presenting action plans will be applied depending on the category of health checkup results. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is, It estimates the user's emotions and adjusts the importance of the action plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is, When presenting an action plan, analyze how the plan may change based on when the health checkup results are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is, When presenting an action plan, we analyze the plan by referring to relevant market data related to the health checkup results. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts users' health checkup results and attribute information, An analysis unit analyzes the information received by the aforementioned reception unit, An evaluation unit that evaluates the user's health status based on the analysis results obtained by the aforementioned analysis unit, The system includes a presentation unit that presents a specific action plan based on the evaluation results obtained by the evaluation unit. A system characterized by the following features.

2. The aforementioned reception unit is Accepts user health check results and attribute information. The system according to feature 1.

3. The aforementioned analysis unit is We analyze health checkup results and medical data to evaluate the user's health status. The system according to feature 1.

4. The evaluation unit, Based on past health checkup data, we predict the likelihood of developing a specific disease within the next five years. The system according to feature 1.

5. The aforementioned display unit is, Based on the evaluation results from the generating AI, the system presents the user with a concrete action plan. The system according to feature 1.

6. The aforementioned display unit is, We propose hospitals that are located near the user's place of residence. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of health checkup result input based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past health checkup data and select the optimal input method. The system according to feature 1.

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