system

Abstract

The system according to this embodiment aims to provide individualized health plans based on health checkup records and daily health data. [Solution] The system according to the embodiment comprises a sharing unit, a generation unit, a reception unit, and a provision unit. The sharing unit shares records of health checkups. The generation unit generates individual plans based on the records shared by the sharing unit. The reception unit receives daily reports. The provision unit reviews and provides plans based on the reports received by the reception unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that individual health plans have not been sufficiently provided by effectively utilizing the records of health check - ups and daily health data.

[0005] The system according to the embodiment aims to provide an individual health plan based on the records of health check - ups and daily health data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a sharing unit, a generation unit, a reception unit, and a provision unit. The sharing unit shares records of health examinations. The generation unit generates individual plans based on the records shared by the sharing unit. The reception unit receives daily reports. The provision unit reviews and provides plans based on the reports received by the reception unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide individualized health plans based on health checkup records and daily health data. [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, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) A health management system according to an embodiment of the present invention is a system that shares health checkup records and test results with AI and provides insights into potential risks and treatment options. The health management system generates meal plans and exercise plans by sharing the user's current health status and future goals with an AI personal trainer. The user reports what they ate each day via chat, and the AI ​​provides balanced meal plans, supplement suggestions, and revised exercise plans and schedules. For example, the health management system allows the user to share health checkup records and test results with the AI. For example, the user inputs blood test results and electrocardiogram data into the AI. This information serves as basic data for the AI ​​to provide potential risks and treatment options. Next, the health management system shares the user's current health status and future goals with the AI ​​personal trainer. For example, the user sets goals such as wanting to lose weight or increase muscle mass. Based on this information, the AI ​​generates individual meal plans and exercise plans. Furthermore, the health management system allows the user to report what they ate each day via chat. For example, the user reports what they ate for breakfast, what they ate for lunch, etc. This information serves as data for the AI ​​to provide balanced meal plans and supplement suggestions. The AI ​​reviews meal and exercise plans based on user reports and provides schedules. For example, if calorie intake is high, it suggests increasing exercise. It also suggests supplements if there is a deficiency in specific nutrients. This allows the health management system to recognize potential risks that users may not be aware of, enabling more efficient health management. For instance, using a chat tool to implement a PDCA (Plan-Do-Check-Act) cycle allows for continuous health support. Furthermore, the health management system can utilize devices for health management. For example, daily meal reports and heart rate / exercise records from health management apps are accumulated as data. This accumulated data is also used to train the AI, resulting in more accurate health support. This allows the health management system to efficiently manage the user's health.

[0029] The health management system according to this embodiment comprises a sharing unit, a generation unit, a reception unit, and a provision unit. The sharing unit shares health checkup records. The sharing unit shares, for example, blood test results and electrocardiogram data. The sharing unit can, for example, directly read health checkup records submitted in digital format. The generation unit generates individual plans based on the records shared by the sharing unit. The generation unit generates, for example, meal plans and exercise plans based on the user's goals. The generation unit generates, for example, meal plans that take into account calorie restriction and nutritional balance. The generation unit generates, for example, exercise plans that take into account the type and frequency of exercise. The reception unit receives daily reports. The reception unit receives, for example, daily reports of meals eaten by the user via chat. The reception unit inputs, for example, the contents of meals consumed by the user. The reception unit receives, for example, reports of exercise by the user. The provision unit reviews and provides plans based on the reports received by the reception unit. The provision unit makes, for example, suggestions to increase exercise if calorie intake is high. The provisioning unit, for example, suggests supplements if a specific nutrient is deficient. The provisioning unit, for example, reviews meal plans and exercise plans based on user reports and provides schedules. This allows the health management system according to the embodiment to efficiently manage the user's health. Some or all of the above-described processes in the sharing unit, generation unit, reception unit, and provisioning unit may be performed using AI, for example, or without AI. For example, the sharing unit can input health checkup records into the AI, which can then analyze and share the records. The generation unit can input user goals into the AI, which can then generate plans based on those goals. The reception unit can input user reports into the AI, which can then analyze and accept the reports. The provisioning unit can input user reports into the AI, which can then revise and provide plans based on those reports.

[0030] The shared section allows for the sharing of health checkup records. For example, it can share blood test results and electrocardiogram data. Specifically, the shared section can directly read health checkup records submitted in digital format. This eliminates the need for users to manually enter paper records. The shared section uses a cloud-based database to centrally manage data from multiple medical institutions and user devices. For example, it can directly retrieve data from hospital electronic medical record systems and make it accessible from users' smartphones and computers. Furthermore, the shared section uses encryption technology to send and receive data to ensure data security. This allows for the rapid sharing of necessary information while protecting user privacy. In addition, the shared section uses data version control to maintain data integrity and allows for comparison of past and current records. This enables tracking changes in the user's health status and taking appropriate action. Moreover, the shared section can use AI to analyze health checkup records and automatically detect abnormal values ​​and risk factors. For example, it can identify the risk of high blood sugar or high cholesterol from blood test results and notify the user. This enables early health management and helps prevent serious health problems.

[0031] The generation unit generates individual plans based on records shared by the sharing unit. For example, the generation unit generates meal plans and exercise plans based on the user's goals. Specifically, the generation unit analyzes the user's health check results and daily activity data to generate meal plans that consider calorie restriction and nutritional balance. For example, if the user aims to lose weight, the generation unit will suggest a low-calorie, high-nutrient meal plan. If the user aims to increase muscle mass, it will suggest a meal plan that includes a lot of protein. Furthermore, the generation unit determines the type and frequency of exercise considering the user's exercise habits and fitness level. For example, it will suggest light aerobic exercise for beginners and high-intensity interval training for advanced users. The generation unit uses AI to automatically generate the optimal plan for the user's goals and health condition. For example, the user inputs their goals as a prompt to the AI, and the AI ​​generates the optimal plan based on past data and the latest research. This allows users to easily obtain a health management plan that suits them, even without specialized knowledge. Furthermore, the generation unit can continuously review and optimize the plan based on user feedback. For example, the user can report their satisfaction with the plan and its effectiveness, and the plan will be adjusted accordingly. This allows the generation unit to respond flexibly to user needs and support long-term health management.

[0032] The reception desk receives daily reports. For example, users report their daily meals via chat. Specifically, users can input the content and quantity of their meals using their smartphones or computers. The reception desk automatically analyzes this data and calculates calorie and nutrient intake. Furthermore, users can also report their exercise, for example, by inputting the distance, time, and calories burned during running. The reception desk centrally manages this data and can monitor the user's health status in real time. The reception desk uses AI to analyze user reports and automatically detect abnormal values ​​and inappropriate behaviors. For example, it issues warnings if calorie intake significantly exceeds the target or if exercise levels are insufficient. The reception desk can also provide daily health management advice based on user reports. For example, it may suggest foods or supplements to replenish a specific nutrient if there is a deficiency. In addition, the reception desk can track long-term changes in health status based on user reports and coordinate with medical institutions as needed. This allows the reception desk to support users' health management and facilitate early problem detection and appropriate responses.

[0033] The service department reviews and provides plans based on reports received by the reception department. Specifically, it suggests increasing exercise if calorie intake is high. For example, if a user exceeds their target calorie intake, the service department suggests additional exercise and provides specific exercise menus and times. It also suggests supplements if there is a deficiency in specific nutrients. For example, if there is a vitamin D deficiency, it suggests foods or supplements containing vitamin D. The service department reviews meal and exercise plans and provides schedules based on user reports. For example, it adjusts the next day's meal and exercise plans based on data reported by the user and notifies the user. The service department uses AI to analyze user report data and automatically generate optimal plans. For example, the user's report is input as a prompt to the AI, and the AI ​​reviews the plan based on past data and the latest research. This ensures that users always receive health management plans based on the latest information. Furthermore, the service department can continuously improve plans based on user feedback. For example, users report their satisfaction with and effectiveness of the plan, and the plan is adjusted accordingly. This allows the service department to respond flexibly to user needs and support long-term health management.

[0034] The storage unit stores data using devices. For example, the storage unit stores data using smartphones or wearable devices. For example, the storage unit records heart rate and exercise levels using health management apps. For example, the storage unit records blood pressure and blood sugar levels using medical devices. This enables more detailed health management by storing data using devices. Some or all of the above-mentioned processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input data acquired from a smartphone into a generating AI and have the generating AI perform data analysis.

[0035] The cognitive unit recognizes potential risks. For example, the cognitive unit recognizes potential risks by analyzing health checkup records and test results. For example, the cognitive unit recognizes potential risks by analyzing lifestyle habits and environmental risks. For example, the cognitive unit recognizes potential risks by analyzing the user's health status. By recognizing potential risks, the user can understand risks that they themselves may not be aware of. Some or all of the above processing in the cognitive unit may be performed using AI, for example, or without AI. For example, the cognitive unit can input health checkup records into a generating AI and have the generating AI perform the recognition of potential risks.

[0036] The cycle unit runs the PDCA cycle. The cycle unit assists, for example, in planning. The cycle unit assists, for example, in execution. The cycle unit provides, for example, criteria for checking. The cycle unit assists, for example, in taking action. By running the PDCA cycle, continuous health support is achieved. Some or all of the above processes in the cycle unit may be performed using, for example, AI, or not using AI. For example, the cycle unit can input how to plan into a generating AI and have the generating AI execute how to run the PDCA cycle.

[0037] The sharing unit shares blood test results and electrocardiogram data. The sharing unit shares, for example, blood glucose levels and cholesterol levels. The sharing unit shares, for example, heart rate and rhythm. The sharing unit shares, for example, abnormality detection data. This allows for more detailed health management by sharing blood test results and electrocardiogram data. Some or all of the above processing in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input blood test results into a generating AI and have the generating AI perform data analysis.

[0038] The generation unit generates meal plans and exercise plans based on the user's goals. For example, the generation unit generates a meal plan with the goal of weight loss. For example, the generation unit generates an exercise plan with the goal of muscle strengthening. For example, the generation unit generates a plan with the goal of maintaining health. This enables personalized health management by generating plans based on the user's goals. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's goals into a generation AI and have the generation AI execute the plan generation.

[0039] The service provider will suggest increasing exercise if calorie intake is high. The service provider will, for example, suggest increasing exercise if calorie intake is high. The service provider will, for example, suggest increasing exercise if calorie intake is high. The service provider will, for example, suggest increasing exercise if calorie intake is high. This makes it possible to manage health in a balanced way by suggesting to increase exercise when calorie intake is high. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input calorie intake data into a generating AI and have the generating AI execute exercise suggestions.

[0040] The supply department will suggest supplements if there is a deficiency in a specific nutrient. For example, if there is a vitamin deficiency, the supply department will suggest a vitamin supplement. For example, if there is a mineral deficiency, the supply department will suggest a mineral supplement. For example, if there is a protein deficiency, the supply department will suggest a protein supplement. This makes it possible to manage health in a nutritionally balanced way by suggesting supplements when there is a deficiency in a specific nutrient. Some or all of the above processing in the supply department may be performed using AI, for example, or without AI. For example, the supply department can input nutrient data into a generating AI and have the generating AI make supplement suggestions.

[0041] The sharing unit analyzes the user's past health data when sharing health checkup records and selects the optimal sharing method. For example, the sharing unit may select email sharing based on the user's past health data. For example, the sharing unit may select in-app notification sharing based on the user's past health data. For example, the sharing unit may select paper-based sharing based on the user's past health data. This allows the optimal sharing method to be selected by analyzing the user's past health data. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input past health data into a generating AI and have the generating AI select the sharing method.

[0042] The sharing unit filters the shared health checkup records based on the user's current health status and lifestyle. For example, the sharing unit shares only important data based on the user's current health status. For example, the sharing unit prioritizes sharing highly relevant data based on the user's lifestyle. For example, the sharing unit comprehensively considers the user's health status and lifestyle to share the most relevant data. This allows for the sharing of highly relevant data by filtering based on the user's current health status and lifestyle. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input data on the current health status and lifestyle into a generating AI and have the generating AI perform the filtering.

[0043] The sharing function prioritizes sharing of highly relevant records when sharing health checkup records, taking into account the user's geographical location. For example, if the user is in a specific region, the sharing function prioritizes sharing health checkup records related to that region. For example, if the user is traveling, the sharing function prioritizes sharing health checkup records related to the travel destination. For example, if the user is at home, the sharing function prioritizes sharing health checkup records related to lifestyle habits at home. This allows for the priority sharing of highly relevant records by considering the user's geographical location. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can input geographical location information into a generating AI and have the generating AI determine the priority of records.

[0044] The sharing unit analyzes the user's social media activity when sharing health checkup records and shares relevant records. For example, if the user makes health-related posts on social media, the sharing unit shares health checkup records related to that content. For example, if the user participates in a specific health community, the sharing unit shares health checkup records related to that community. For example, if the user shows interest in a specific health topic on social media, the sharing unit shares health checkup records related to that topic. In this way, relevant records can be shared by analyzing the user's social media activity. Some or all of the above processing in the sharing unit may be performed using AI, for example, or not using AI. For example, the sharing unit can input social media activity data into a generating AI and have the generating AI perform the record sharing.

[0045] The generation unit adjusts the level of detail of the plan based on the user's health goals when generating the plan. For example, if the user wants to lose weight, the generation unit will generate a plan that emphasizes calorie restriction. For example, if the user wants to gain muscle, the generation unit will generate a plan that emphasizes protein intake. For example, if the user wants to improve their overall health, the generation unit will generate a balanced plan. This allows for individualized health management by adjusting the level of detail of the plan based on the user's health goals. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health goals into a generation AI and have the generation AI perform the adjustment of the level of detail of the plan.

[0046] The generation unit applies different generation algorithms depending on the user's health condition when generating a plan. For example, if the user has diabetes, the generation unit applies an algorithm to generate a low-carbohydrate meal plan. For example, if the user has high blood pressure, the generation unit applies an algorithm to generate a low-sodium meal plan. For example, if the user has allergies, the generation unit applies an algorithm to generate a meal plan that avoids allergens. By applying different generation algorithms according to the user's health condition, a more appropriate plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health condition into a generation AI and have the generation AI execute the application of the generation algorithm.

[0047] The generation unit determines plan priorities based on when the user's health checkup records are submitted. For example, if the user has recently had a health checkup, the generation unit prioritizes generating plans based on the results. For example, the generation unit determines plan priorities based on the results of health checkups the user has had in the past. For example, if the user has regular health checkups, the generation unit determines plan priorities based on the frequency of those checkups. This allows the generation unit to provide plans that correspond to the user's latest health status by determining plan priorities based on when the user's health checkup records are submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submission dates of health checkup records into a generation AI and have the generation AI perform the determination of plan priorities.

[0048] The generation unit adjusts the order of plans based on the relevance of the user's health status when generating plans. For example, if a user has multiple health problems, the generation unit prioritizes the plan that addresses the most important problem. For example, if a user's health status changes suddenly, the generation unit prioritizes a plan that addresses that change. For example, if a user's health status is stable, the generation unit prioritizes a plan based on long-term goals. This allows the generation unit to prioritize plans that address the most important health problems by adjusting the order of plans based on the relevance of the user's health status. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input health status data into a generation AI and have the generation AI perform the adjustment of the order of plans.

[0049] The reception department analyzes the user's past reporting history when a report is received and selects the optimal reception method. For example, the reception department may prioritize suggesting reporting methods that the user has frequently used in the past. For example, the reception department may predict and suggest reporting methods to be used during specific time periods based on the user's past reporting history. For example, the reception department may select the optimal reporting method based on the user's past reporting history. In this way, the optimal reception method can be selected by analyzing the user's past reporting history. Some or all of the above processes in the reception department may be performed using AI, for example, or without AI. For example, the reception department may input past reporting history into a generating AI and have the generating AI perform the selection of the reception method.

[0050] The reception unit selects the optimal reception method when receiving a report, taking into account the user's device information. For example, if the user is using a smartphone, the reception unit provides a reception method adapted to the screen size. For example, if the user is using a tablet, the reception unit provides a reception method optimized for a large screen. For example, if the user is using a smartwatch, the reception unit provides a concise and highly visible reception method. This allows the reception unit to select the optimal reception method by considering the user's device information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input device information into a generating AI and have the generating AI perform the selection of the reception method.

[0051] The service provider analyzes the user's past health data to select the optimal delivery method when providing a plan. For example, the service provider may select delivery via email based on the user's past health data. For example, the service provider may select delivery via in-app notification based on the user's past health data. For example, the service provider may select delivery on paper based on the user's past health data. This allows the service provider to select the optimal delivery method by analyzing the user's past health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input past health data into a generating AI and have the generating AI select the delivery method.

[0052] The service provider customizes the service content based on the user's current health status and lifestyle when providing a plan. For example, the service provider may provide a meal plan fortified with specific nutrients based on the user's current health status. For example, the service provider may provide a feasible exercise plan based on the user's lifestyle. For example, the service provider may provide an optimal plan by comprehensively considering the user's health status and lifestyle. This enables individualized health management by customizing the service content based on the user's current health status and lifestyle. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input data on the user's current health status and lifestyle into a generating AI and have the generating AI perform the customization of the service content.

[0053] The service provider selects the optimal delivery method when providing a plan, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider will provide a plan related to that region. For example, if the user is traveling, the service provider will provide a plan related to their travel destination. For example, if the user is at home, the service provider will provide a plan related to their lifestyle at home. This allows the service provider to select the optimal delivery method by taking into account the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI perform the selection of the delivery method.

[0054] The service provider analyzes the user's social media activity when providing a plan and adjusts the content accordingly. For example, if the user posts health-related content on social media, the service provider will provide a plan related to that content. For example, if the user participates in a specific health community, the service provider will provide a plan related to that community. For example, if the user shows interest in a specific health topic on social media, the service provider will provide a plan related to that topic. In this way, by analyzing the user's social media activity, relevant plans can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input social media activity data into a generating AI and have the generating AI adjust the content of the service.

[0055] The storage unit analyzes the user's past data to select the optimal storage method when storing data. For example, the storage unit proposes the optimal data input method based on the user's past data. For example, the storage unit proposes automatic data input based on the user's past data. For example, the storage unit adjusts the data storage frequency based on the user's past data. In this way, the optimal storage method can be selected by analyzing the user's past data. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input past data into a generating AI and have the generating AI perform the selection of the storage method.

[0056] The storage unit selects the optimal storage method when storing data, taking into account the user's device information. For example, if the user is using a smartphone, the storage unit provides a data input method that matches the screen size. For example, if the user is using a tablet, the storage unit provides a data input method optimized for a large screen. For example, if the user is using a smartwatch, the storage unit provides a simple and highly visible data input method. This allows the storage unit to select the optimal storage method by taking into account the user's device information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input device information into a generating AI and have the generating AI perform the selection of the storage method.

[0057] The cognitive unit, upon recognizing a potential risk, analyzes the user's past health data to select the optimal recognition method. For example, the cognitive unit proposes the optimal risk recognition method based on the user's past health data. For example, the cognitive unit adjusts the frequency of risk recognition based on the user's past health data. For example, the cognitive unit adjusts the timing of risk recognition based on the user's past health data. In this way, the optimal recognition method can be selected by analyzing the user's past health data. Some or all of the above processing in the cognitive unit may be performed using AI, for example, or without AI. For example, the cognitive unit can input past health data into a generating AI and have the generating AI perform the selection of a recognition method.

[0058] The cognitive unit selects the optimal risk assessment method when assessing potential risks, taking into account the user's geographical location. For example, if the user is in a specific region, the cognitive unit prioritizes recognizing risks associated with that region. For example, if the user is traveling, the cognitive unit prioritizes recognizing risks associated with the travel destination. For example, if the user is at home, the cognitive unit prioritizes recognizing risks associated with lifestyle habits at home. This allows the optimal risk assessment method to be selected by considering the user's geographical location. Some or all of the above processing in the cognitive unit may be performed using AI, for example, or without AI. For example, the cognitive unit can input geographical location information into a generating AI and have the generating AI perform the selection of the assessment method.

[0059] The cycle unit analyzes the user's past data to select the optimal cycle method when executing the PDCA cycle. For example, the cycle unit proposes the optimal PDCA cycle method based on the user's past data. For example, the cycle unit adjusts the frequency of the PDCA cycle based on the user's past data. For example, the cycle unit adjusts the timing of the PDCA cycle based on the user's past data. In this way, the optimal PDCA cycle method can be selected by analyzing the user's past data. Some or all of the above processes in the cycle unit may be performed using AI, for example, or without AI. For example, the cycle unit can input past data into a generating AI and have the generating AI perform the selection of the cycle method.

[0060] The cycle unit selects the optimal cycle method when executing the PDCA cycle, taking into account the user's device information. For example, if the user is using a smartphone, the cycle unit provides a PDCA cycle method adapted to the screen size. For example, if the user is using a tablet, the cycle unit provides a PDCA cycle method optimized for a large screen. For example, if the user is using a smartwatch, the cycle unit provides a concise and highly visible PDCA cycle method. This allows for the selection of the optimal PDCA cycle method by considering the user's device information. Some or all of the above processing in the cycle unit may be performed using AI, for example, or without AI. For example, the cycle unit can input device information into a generating AI and have the generating AI perform the selection of the cycle method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The health management system can also include an evaluation unit that acquires user sleep data and assesses sleep quality. This evaluation unit analyzes, for example, the user's sleep duration, the ratio of deep to light sleep, and the number of nighttime awakenings. Based on this data, the evaluation unit can assess the user's sleep quality and provide advice for improvement. For example, if sleep quality is poor, it can provide advice on relaxation methods before bed or how to choose appropriate bedding. Furthermore, the evaluation unit can integrate the user's sleep data with other health data to perform an overall health assessment. This allows users to understand their own sleep quality and receive specific advice for improvement.

[0063] The health management system can also include a stress measurement unit to measure the user's stress level. This unit measures stress levels by analyzing physiological data such as heart rate variability and skin electrical activity. Based on this data, the unit can assess the user's stress level in real time and provide advice for stress reduction. For example, if the stress level is high, it can suggest relaxation methods such as deep breathing or meditation. Furthermore, the unit can monitor fluctuations in the user's stress level over the long term and evaluate the effectiveness of stress management. This allows users to understand their own stress levels and take appropriate measures.

[0064] The health management system can also include a nutrition assessment unit that analyzes the user's dietary data in detail and evaluates nutritional balance. For example, the nutrition assessment unit analyzes the calories, protein, fat, carbohydrates, vitamins, minerals, and other nutrients in the meals the user consumes. Based on this data, the nutrition assessment unit can evaluate the user's nutritional balance and provide advice for improvement. For instance, if a specific nutrient is deficient, it can suggest foods or supplements containing that nutrient. Furthermore, the nutrition assessment unit can integrate the user's dietary data with other health data to perform an overall health assessment. This allows the user to understand their nutritional balance and receive specific advice for improvement.

[0065] The health management system may also include an exercise evaluation unit that analyzes the user's exercise data in detail and evaluates the effectiveness of the exercise. For example, the exercise evaluation unit analyzes the type, duration, intensity, and calories burned of the exercise performed by the user. Based on this data, the exercise evaluation unit can evaluate the effectiveness of the user's exercise and provide advice for improvement. For example, if the exercise is ineffective, it can suggest changing the type or intensity of the exercise. Furthermore, the exercise evaluation unit can integrate the user's exercise data with other health data to perform an overall assessment of their health status. This allows the user to understand the effectiveness of their exercise and receive specific advice for improvement.

[0066] The health management system may also include a comparison unit that compares a user's health data with that of other users. For example, the comparison unit might compare the user's health data with that of other users of the same age or gender. Based on this data, the comparison unit can relatively evaluate the user's health status and provide advice for improvement. For instance, if a user's health status is inferior to that of other users, it can suggest specific ways to improve it. Furthermore, the comparison unit can protect privacy by anonymizing the user's health data during comparison. This allows users to compare their health status with others and receive specific advice for improvement.

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

[0068] Step 1: The shared section allows for the sharing of health checkup records. For example, it can directly read health checkup records submitted in digital format, such as blood test results and electrocardiogram data. Step 2: The generation unit generates individual plans based on the records shared by the sharing unit. For example, it generates meal plans and exercise plans based on the user's goals, such as meal plans that take into account calorie restrictions and nutritional balance, and exercise plans that take into account the type and frequency of exercise. Step 3: The reception desk receives daily reports. For example, users report the meals they ate each day via chat, entering details of the food consumed and the type of exercise they did. Step 4: The service department reviews and provides the plan based on the report received by the reception department. For example, they may suggest increasing exercise if calorie intake is high, or suggest supplements if there is a deficiency in a particular nutrient. Based on the user's report, they revise the meal plan and exercise plan and provide a schedule.

[0069] (Example of form 2) A health management system according to an embodiment of the present invention is a system that shares health checkup records and test results with AI and provides insights into potential risks and treatment options. The health management system generates meal plans and exercise plans by sharing the user's current health status and future goals with an AI personal trainer. The user reports what they ate each day via chat, and the AI ​​provides balanced meal plans, supplement suggestions, and revised exercise plans and schedules. For example, the health management system allows the user to share health checkup records and test results with the AI. For example, the user inputs blood test results and electrocardiogram data into the AI. This information serves as basic data for the AI ​​to provide potential risks and treatment options. Next, the health management system shares the user's current health status and future goals with the AI ​​personal trainer. For example, the user sets goals such as wanting to lose weight or increase muscle mass. Based on this information, the AI ​​generates individual meal plans and exercise plans. Furthermore, the health management system allows the user to report what they ate each day via chat. For example, the user reports what they ate for breakfast, what they ate for lunch, etc. This information serves as data for the AI ​​to provide balanced meal plans and supplement suggestions. The AI ​​reviews meal and exercise plans based on user reports and provides schedules. For example, if calorie intake is high, it suggests increasing exercise. It also suggests supplements if there is a deficiency in specific nutrients. This allows the health management system to recognize potential risks that users may not be aware of, enabling more efficient health management. For instance, using a chat tool to implement a PDCA (Plan-Do-Check-Act) cycle allows for continuous health support. Furthermore, the health management system can utilize devices for health management. For example, daily meal reports and heart rate / exercise records from health management apps are accumulated as data. This accumulated data is also used to train the AI, resulting in more accurate health support. This allows the health management system to efficiently manage the user's health.

[0070] The health management system according to this embodiment comprises a sharing unit, a generation unit, a reception unit, and a provision unit. The sharing unit shares health checkup records. The sharing unit shares, for example, blood test results and electrocardiogram data. The sharing unit can, for example, directly read health checkup records submitted in digital format. The generation unit generates individual plans based on the records shared by the sharing unit. The generation unit generates, for example, meal plans and exercise plans based on the user's goals. The generation unit generates, for example, meal plans that take into account calorie restriction and nutritional balance. The generation unit generates, for example, exercise plans that take into account the type and frequency of exercise. The reception unit receives daily reports. The reception unit receives, for example, daily reports of meals eaten by the user via chat. The reception unit inputs, for example, the contents of meals consumed by the user. The reception unit receives, for example, reports of exercise by the user. The provision unit reviews and provides plans based on the reports received by the reception unit. The provision unit makes, for example, suggestions to increase exercise if calorie intake is high. The provisioning unit, for example, suggests supplements if a specific nutrient is deficient. The provisioning unit, for example, reviews meal plans and exercise plans based on user reports and provides schedules. This allows the health management system according to the embodiment to efficiently manage the user's health. Some or all of the above-described processes in the sharing unit, generation unit, reception unit, and provisioning unit may be performed using AI, for example, or without AI. For example, the sharing unit can input health checkup records into the AI, which can then analyze and share the records. The generation unit can input user goals into the AI, which can then generate plans based on those goals. The reception unit can input user reports into the AI, which can then analyze and accept the reports. The provisioning unit can input user reports into the AI, which can then revise and provide plans based on those reports.

[0071] The shared section allows for the sharing of health checkup records. For example, it can share blood test results and electrocardiogram data. Specifically, the shared section can directly read health checkup records submitted in digital format. This eliminates the need for users to manually enter paper records. The shared section uses a cloud-based database to centrally manage data from multiple medical institutions and user devices. For example, it can directly retrieve data from hospital electronic medical record systems and make it accessible from users' smartphones and computers. Furthermore, the shared section uses encryption technology to send and receive data to ensure data security. This allows for the rapid sharing of necessary information while protecting user privacy. In addition, the shared section uses data version control to maintain data integrity and allows for comparison of past and current records. This enables tracking changes in the user's health status and taking appropriate action. Moreover, the shared section can use AI to analyze health checkup records and automatically detect abnormal values ​​and risk factors. For example, it can identify the risk of high blood sugar or high cholesterol from blood test results and notify the user. This enables early health management and helps prevent serious health problems.

[0072] The generation unit generates individual plans based on records shared by the sharing unit. For example, the generation unit generates meal plans and exercise plans based on the user's goals. Specifically, the generation unit analyzes the user's health check results and daily activity data to generate meal plans that consider calorie restriction and nutritional balance. For example, if the user aims to lose weight, the generation unit will suggest a low-calorie, high-nutrient meal plan. If the user aims to increase muscle mass, it will suggest a meal plan that includes a lot of protein. Furthermore, the generation unit determines the type and frequency of exercise considering the user's exercise habits and fitness level. For example, it will suggest light aerobic exercise for beginners and high-intensity interval training for advanced users. The generation unit uses AI to automatically generate the optimal plan for the user's goals and health condition. For example, the user inputs their goals as a prompt to the AI, and the AI ​​generates the optimal plan based on past data and the latest research. This allows users to easily obtain a health management plan that suits them, even without specialized knowledge. Furthermore, the generation unit can continuously review and optimize the plan based on user feedback. For example, the user can report their satisfaction with the plan and its effectiveness, and the plan will be adjusted accordingly. This allows the generation unit to respond flexibly to user needs and support long-term health management.

[0073] The reception desk receives daily reports. For example, users report their daily meals via chat. Specifically, users can input the content and quantity of their meals using their smartphones or computers. The reception desk automatically analyzes this data and calculates calorie and nutrient intake. Furthermore, users can also report their exercise, for example, by inputting the distance, time, and calories burned during running. The reception desk centrally manages this data and can monitor the user's health status in real time. The reception desk uses AI to analyze user reports and automatically detect abnormal values ​​and inappropriate behaviors. For example, it issues warnings if calorie intake significantly exceeds the target or if exercise levels are insufficient. The reception desk can also provide daily health management advice based on user reports. For example, it may suggest foods or supplements to replenish a specific nutrient if there is a deficiency. In addition, the reception desk can track long-term changes in health status based on user reports and coordinate with medical institutions as needed. This allows the reception desk to support users' health management and facilitate early problem detection and appropriate responses.

[0074] The service department reviews and provides plans based on reports received by the reception department. Specifically, it suggests increasing exercise if calorie intake is high. For example, if a user exceeds their target calorie intake, the service department suggests additional exercise and provides specific exercise menus and times. It also suggests supplements if there is a deficiency in specific nutrients. For example, if there is a vitamin D deficiency, it suggests foods or supplements containing vitamin D. The service department reviews meal and exercise plans and provides schedules based on user reports. For example, it adjusts the next day's meal and exercise plans based on data reported by the user and notifies the user. The service department uses AI to analyze user report data and automatically generate optimal plans. For example, the user's report is input as a prompt to the AI, and the AI ​​reviews the plan based on past data and the latest research. This ensures that users always receive health management plans based on the latest information. Furthermore, the service department can continuously improve plans based on user feedback. For example, users report their satisfaction with and effectiveness of the plan, and the plan is adjusted accordingly. This allows the service department to respond flexibly to user needs and support long-term health management.

[0075] The storage unit stores data using devices. For example, the storage unit stores data using smartphones or wearable devices. For example, the storage unit records heart rate and exercise levels using health management apps. For example, the storage unit records blood pressure and blood sugar levels using medical devices. This enables more detailed health management by storing data using devices. Some or all of the above-mentioned processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input data acquired from a smartphone into a generating AI and have the generating AI perform data analysis.

[0076] The cognitive unit recognizes potential risks. For example, the cognitive unit recognizes potential risks by analyzing health checkup records and test results. For example, the cognitive unit recognizes potential risks by analyzing lifestyle habits and environmental risks. For example, the cognitive unit recognizes potential risks by analyzing the user's health status. By recognizing potential risks, the user can understand risks that they themselves may not be aware of. Some or all of the above processing in the cognitive unit may be performed using AI, for example, or without AI. For example, the cognitive unit can input health checkup records into a generating AI and have the generating AI perform the recognition of potential risks.

[0077] The cycle unit runs the PDCA cycle. The cycle unit assists, for example, in planning. The cycle unit assists, for example, in execution. The cycle unit provides, for example, criteria for checking. The cycle unit assists, for example, in taking action. By running the PDCA cycle, continuous health support is achieved. Some or all of the above processes in the cycle unit may be performed using, for example, AI, or not using AI. For example, the cycle unit can input how to plan into a generating AI and have the generating AI execute how to run the PDCA cycle.

[0078] The sharing unit shares blood test results and electrocardiogram data. The sharing unit shares, for example, blood glucose levels and cholesterol levels. The sharing unit shares, for example, heart rate and rhythm. The sharing unit shares, for example, abnormality detection data. This allows for more detailed health management by sharing blood test results and electrocardiogram data. Some or all of the above processing in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input blood test results into a generating AI and have the generating AI perform data analysis.

[0079] The generation unit generates meal plans and exercise plans based on the user's goals. For example, the generation unit generates a meal plan with the goal of weight loss. For example, the generation unit generates an exercise plan with the goal of muscle strengthening. For example, the generation unit generates a plan with the goal of maintaining health. This enables personalized health management by generating plans based on the user's goals. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's goals into a generation AI and have the generation AI execute the plan generation.

[0080] The service provider will suggest increasing exercise if calorie intake is high. The service provider will, for example, suggest increasing exercise if calorie intake is high. The service provider will, for example, suggest increasing exercise if calorie intake is high. The service provider will, for example, suggest increasing exercise if calorie intake is high. This makes it possible to manage health in a balanced way by suggesting to increase exercise when calorie intake is high. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input calorie intake data into a generating AI and have the generating AI execute exercise suggestions.

[0081] The supply department will suggest supplements if there is a deficiency in a specific nutrient. For example, if there is a vitamin deficiency, the supply department will suggest a vitamin supplement. For example, if there is a mineral deficiency, the supply department will suggest a mineral supplement. For example, if there is a protein deficiency, the supply department will suggest a protein supplement. This makes it possible to manage health in a nutritionally balanced way by suggesting supplements when there is a deficiency in a specific nutrient. Some or all of the above processing in the supply department may be performed using AI, for example, or without AI. For example, the supply department can input nutrient data into a generating AI and have the generating AI make supplement suggestions.

[0082] The sharing unit estimates the user's emotions and adjusts the timing of sharing the health checkup record based on the estimated emotions. For example, if the user is stressed, the sharing unit will share the health checkup record during a time when the user can relax. For example, if the user is relaxed, the sharing unit will share the health checkup record immediately. For example, if the user is busy, the sharing unit will share the health checkup record during a time when the user has free time. This reduces the user's burden by adjusting the sharing timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 sharing unit may be performed using AI, for example, or not using AI. For example, the sharing unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the sharing timing.

[0083] The sharing unit analyzes the user's past health data when sharing health checkup records and selects the optimal sharing method. For example, the sharing unit may select email sharing based on the user's past health data. For example, the sharing unit may select in-app notification sharing based on the user's past health data. For example, the sharing unit may select paper-based sharing based on the user's past health data. This allows the optimal sharing method to be selected by analyzing the user's past health data. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input past health data into a generating AI and have the generating AI select the sharing method.

[0084] The sharing unit filters the shared health checkup records based on the user's current health status and lifestyle. For example, the sharing unit shares only important data based on the user's current health status. For example, the sharing unit prioritizes sharing highly relevant data based on the user's lifestyle. For example, the sharing unit comprehensively considers the user's health status and lifestyle to share the most relevant data. This allows for the sharing of highly relevant data by filtering based on the user's current health status and lifestyle. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input data on the current health status and lifestyle into a generating AI and have the generating AI perform the filtering.

[0085] The sharing unit estimates the user's emotions and determines the priority of health check records to share based on the estimated emotions. For example, if the user is stressed, the sharing unit will postpone sharing records of lower importance. For example, if the user is relaxed, the sharing unit will immediately share all records. For example, if the user is busy, the sharing unit will prioritize sharing only the most important records. This ensures that important records are shared preferentially by determining priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input user emotion data into a generative AI and have the generative AI perform the priority determination.

[0086] The sharing function prioritizes sharing of highly relevant records when sharing health checkup records, taking into account the user's geographical location. For example, if the user is in a specific region, the sharing function prioritizes sharing health checkup records related to that region. For example, if the user is traveling, the sharing function prioritizes sharing health checkup records related to the travel destination. For example, if the user is at home, the sharing function prioritizes sharing health checkup records related to lifestyle habits at home. This allows for the priority sharing of highly relevant records by considering the user's geographical location. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can input geographical location information into a generating AI and have the generating AI determine the priority of records.

[0087] The sharing unit analyzes the user's social media activity when sharing health checkup records and shares relevant records. For example, if the user makes health-related posts on social media, the sharing unit shares health checkup records related to that content. For example, if the user participates in a specific health community, the sharing unit shares health checkup records related to that community. For example, if the user shows interest in a specific health topic on social media, the sharing unit shares health checkup records related to that topic. In this way, relevant records can be shared by analyzing the user's social media activity. Some or all of the above processing in the sharing unit may be performed using AI, for example, or not using AI. For example, the sharing unit can input social media activity data into a generating AI and have the generating AI perform the record sharing.

[0088] The generation unit estimates the user's emotions and adjusts the way the plan is presented based on the estimated emotions. For example, if the user is stressed, the generation unit generates a simple and easy-to-understand plan. For example, if the user is relaxed, the generation unit generates a plan with detailed explanations. For example, if the user is in a hurry, the generation unit generates a short, concise plan. By adjusting the way the plan is presented based on the user's emotions, the system can provide a plan that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the plan is presented.

[0089] The generation unit adjusts the level of detail of the plan based on the user's health goals when generating the plan. For example, if the user wants to lose weight, the generation unit will generate a plan that emphasizes calorie restriction. For example, if the user wants to gain muscle, the generation unit will generate a plan that emphasizes protein intake. For example, if the user wants to improve their overall health, the generation unit will generate a balanced plan. This allows for individualized health management by adjusting the level of detail of the plan based on the user's health goals. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health goals into a generation AI and have the generation AI perform the adjustment of the level of detail of the plan.

[0090] The generation unit applies different generation algorithms depending on the user's health condition when generating a plan. For example, if the user has diabetes, the generation unit applies an algorithm to generate a low-carbohydrate meal plan. For example, if the user has high blood pressure, the generation unit applies an algorithm to generate a low-sodium meal plan. For example, if the user has allergies, the generation unit applies an algorithm to generate a meal plan that avoids allergens. By applying different generation algorithms according to the user's health condition, a more appropriate plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health condition into a generation AI and have the generation AI execute the application of the generation algorithm.

[0091] The generation unit estimates the user's emotions and adjusts the length of the plan based on the estimated emotions. For example, if the user is stressed, the generation unit generates a short, concise plan. For example, if the user is relaxed, the generation unit generates a longer plan with detailed explanations. For example, if the user is in a hurry, the generation unit generates a short plan that can be executed quickly. By adjusting the length of the plan based on the user's emotions, the system can provide the user with a plan of an appropriate length. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI adjust the length of the plan.

[0092] The generation unit determines plan priorities based on when the user's health checkup records are submitted. For example, if the user has recently had a health checkup, the generation unit prioritizes generating plans based on the results. For example, the generation unit determines plan priorities based on the results of health checkups the user has had in the past. For example, if the user has regular health checkups, the generation unit determines plan priorities based on the frequency of those checkups. This allows the generation unit to provide plans that correspond to the user's latest health status by determining plan priorities based on when the user's health checkup records are submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submission dates of health checkup records into a generation AI and have the generation AI perform the determination of plan priorities.

[0093] The generation unit adjusts the order of plans based on the relevance of the user's health status when generating plans. For example, if a user has multiple health problems, the generation unit prioritizes the plan that addresses the most important problem. For example, if a user's health status changes suddenly, the generation unit prioritizes a plan that addresses that change. For example, if a user's health status is stable, the generation unit prioritizes a plan based on long-term goals. This allows the generation unit to prioritize plans that address the most important health problems by adjusting the order of plans based on the relevance of the user's health status. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input health status data into a generation AI and have the generation AI perform the adjustment of the order of plans.

[0094] The reception desk estimates the user's emotions and adjusts the reporting process based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. If the user is relaxed, for example, the reception desk provides detailed input options and suggests a customizable input method. If the user is in a hurry, for example, the reception desk prioritizes voice input and receives the report quickly. This provides a user-friendly interface by adjusting the reporting process based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 user emotion data into a generative AI and have the generative AI adjust the reporting process.

[0095] The reception department analyzes the user's past reporting history when a report is received and selects the optimal reception method. For example, the reception department may prioritize suggesting reporting methods that the user has frequently used in the past. For example, the reception department may predict and suggest reporting methods to be used during specific time periods based on the user's past reporting history. For example, the reception department may select the optimal reporting method based on the user's past reporting history. In this way, the optimal reception method can be selected by analyzing the user's past reporting history. Some or all of the above processes in the reception department may be performed using AI, for example, or without AI. For example, the reception department may input past reporting history into a generating AI and have the generating AI perform the selection of the reception method.

[0096] The reception desk estimates the user's emotions and prioritizes reports based on the estimated emotions. For example, if the user is stressed, the reception desk will postpone less important reports. For example, if the user is relaxed, the reception desk will accept all reports immediately. For example, if the user is busy, the reception desk will prioritize accepting only the most important reports. This ensures that important reports are received preferentially by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of reports.

[0097] The reception unit selects the optimal reception method when receiving a report, taking into account the user's device information. For example, if the user is using a smartphone, the reception unit provides a reception method adapted to the screen size. For example, if the user is using a tablet, the reception unit provides a reception method optimized for a large screen. For example, if the user is using a smartwatch, the reception unit provides a concise and highly visible reception method. This allows the reception unit to select the optimal reception method by considering the user's device information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input device information into a generating AI and have the generating AI perform the selection of the reception method.

[0098] The service provider estimates the user's emotions and adjusts the way the plan is delivered based on the estimated emotions. For example, if the user is stressed, the service provider will provide a simple and easy-to-understand plan. If the user is relaxed, the service provider will provide a plan with detailed explanations. If the user is in a hurry, the service provider will provide a short, to-the-point plan. By adjusting the way the plan is delivered based on the user's emotions, the service provider can provide a plan that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way the plan is delivered.

[0099] The service provider analyzes the user's past health data to select the optimal delivery method when providing a plan. For example, the service provider may select delivery via email based on the user's past health data. For example, the service provider may select delivery via in-app notification based on the user's past health data. For example, the service provider may select delivery on paper based on the user's past health data. This allows the service provider to select the optimal delivery method by analyzing the user's past health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input past health data into a generating AI and have the generating AI select the delivery method.

[0100] The service provider customizes the service content based on the user's current health status and lifestyle when providing a plan. For example, the service provider may provide a meal plan fortified with specific nutrients based on the user's current health status. For example, the service provider may provide a feasible exercise plan based on the user's lifestyle. For example, the service provider may provide an optimal plan by comprehensively considering the user's health status and lifestyle. This enables individualized health management by customizing the service content based on the user's current health status and lifestyle. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input data on the user's current health status and lifestyle into a generating AI and have the generating AI perform the customization of the service content.

[0101] The service provider estimates the user's emotions and prioritizes plans based on the estimated emotions. For example, if the user is stressed, the service provider will postpone less important plans. For example, if the user is relaxed, the service provider will immediately provide all plans. For example, if the user is busy, the service provider will prioritize providing only the most important plans. This ensures that important plans are prioritized by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of plans.

[0102] The service provider selects the optimal delivery method when providing a plan, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider will provide a plan related to that region. For example, if the user is traveling, the service provider will provide a plan related to their travel destination. For example, if the user is at home, the service provider will provide a plan related to their lifestyle at home. This allows the service provider to select the optimal delivery method by taking into account the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI perform the selection of the delivery method.

[0103] The service provider analyzes the user's social media activity when providing a plan and adjusts the content accordingly. For example, if the user posts health-related content on social media, the service provider will provide a plan related to that content. For example, if the user participates in a specific health community, the service provider will provide a plan related to that community. For example, if the user shows interest in a specific health topic on social media, the service provider will provide a plan related to that topic. In this way, by analyzing the user's social media activity, relevant plans can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input social media activity data into a generating AI and have the generating AI adjust the content of the service.

[0104] The storage unit estimates the user's emotions and adjusts the data storage method based on the estimated emotions. For example, if the user is stressed, the storage unit provides a simple data input interface. For example, if the user is relaxed, the storage unit provides detailed data input options. For example, if the user is in a hurry, the storage unit prioritizes voice input and stores data quickly. This allows for a user-friendly data input interface by adjusting the data storage method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input user emotion data into a generative AI and have the generative AI adjust the data storage method.

[0105] The storage unit analyzes the user's past data to select the optimal storage method when storing data. For example, the storage unit proposes the optimal data input method based on the user's past data. For example, the storage unit proposes automatic data input based on the user's past data. For example, the storage unit adjusts the data storage frequency based on the user's past data. In this way, the optimal storage method can be selected by analyzing the user's past data. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input past data into a generating AI and have the generating AI perform the selection of the storage method.

[0106] The storage unit estimates the user's emotions and prioritizes data based on the estimated emotions. For example, if the user is stressed, the storage unit will postpone storing less important data. For example, if the user is relaxed, the storage unit will immediately store all data. For example, if the user is busy, the storage unit will prioritize storing only the most important data. This allows for the priority storage of important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input user emotion data into a generative AI and have the generative AI determine the data prioritization.

[0107] The storage unit selects the optimal storage method when storing data, taking into account the user's device information. For example, if the user is using a smartphone, the storage unit provides a data input method that matches the screen size. For example, if the user is using a tablet, the storage unit provides a data input method optimized for a large screen. For example, if the user is using a smartwatch, the storage unit provides a simple and highly visible data input method. This allows the storage unit to select the optimal storage method by taking into account the user's device information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input device information into a generating AI and have the generating AI perform the selection of the storage method.

[0108] The cognitive unit estimates the user's emotions and adjusts the method of perceiving potential risks based on the estimated user emotions. For example, if the user is stressed, the cognitive unit provides a simple and easy-to-understand method of risk perception. For example, if the user is relaxed, the cognitive unit provides a method of risk perception that includes detailed explanations. For example, if the user is in a hurry, the cognitive unit provides a concise and to-the-point method of risk perception. By adjusting the method of perceiving potential risks based on the user's emotions, a risk perception method that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cognitive unit may be performed using AI, for example, or not using AI. For example, the cognitive unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the risk perception method.

[0109] The cognitive unit, upon recognizing a potential risk, analyzes the user's past health data to select the optimal recognition method. For example, the cognitive unit proposes the optimal risk recognition method based on the user's past health data. For example, the cognitive unit adjusts the frequency of risk recognition based on the user's past health data. For example, the cognitive unit adjusts the timing of risk recognition based on the user's past health data. In this way, the optimal recognition method can be selected by analyzing the user's past health data. Some or all of the above processing in the cognitive unit may be performed using AI, for example, or without AI. For example, the cognitive unit can input past health data into a generating AI and have the generating AI perform the selection of a recognition method.

[0110] The cognitive unit estimates the user's emotions and prioritizes potential risks based on the estimated emotions. For example, if the user is stressed, the cognitive unit will postpone less important risks. For example, if the user is relaxed, the cognitive unit will immediately recognize all risks. For example, if the user is busy, the cognitive unit will prioritize recognizing only the most important risks. This allows for the priority recognition of important risks by prioritizing potential risks based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the cognitive unit may be performed using AI or not. For example, the cognitive unit can input user emotion data into a generative AI and have the generative AI perform the determination of risk prioritization.

[0111] The cognitive unit selects the optimal risk assessment method when assessing potential risks, taking into account the user's geographical location. For example, if the user is in a specific region, the cognitive unit prioritizes recognizing risks associated with that region. For example, if the user is traveling, the cognitive unit prioritizes recognizing risks associated with the travel destination. For example, if the user is at home, the cognitive unit prioritizes recognizing risks associated with lifestyle habits at home. This allows the optimal risk assessment method to be selected by considering the user's geographical location. Some or all of the above processing in the cognitive unit may be performed using AI, for example, or without AI. For example, the cognitive unit can input geographical location information into a generating AI and have the generating AI perform the selection of the assessment method.

[0112] The cycle unit estimates the user's emotions and adjusts the PDCA cycle based on the estimated emotions. For example, if the user is stressed, the cycle unit provides a simple and easy-to-understand PDCA cycle. If the user is relaxed, the cycle unit provides a PDCA cycle with detailed explanations. If the user is in a hurry, the cycle unit provides a short, to-the-point PDCA cycle. By adjusting the PDCA cycle based on the user's emotions, it is possible to provide a PDCA cycle that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the cycle unit may be performed using AI, for example, or without AI. For example, the cycle unit can input user emotion data into a generative AI and have the generative AI adjust the PDCA cycle.

[0113] The cycle unit analyzes the user's past data to select the optimal cycle method when executing the PDCA cycle. For example, the cycle unit proposes the optimal PDCA cycle method based on the user's past data. For example, the cycle unit adjusts the frequency of the PDCA cycle based on the user's past data. For example, the cycle unit adjusts the timing of the PDCA cycle based on the user's past data. In this way, the optimal PDCA cycle method can be selected by analyzing the user's past data. Some or all of the above processes in the cycle unit may be performed using AI, for example, or without AI. For example, the cycle unit can input past data into a generating AI and have the generating AI perform the selection of the cycle method.

[0114] The cycle unit estimates the user's emotions and determines the priority of the PDCA cycle based on the estimated emotions. For example, if the user is stressed, the cycle unit will postpone less important cycles. For example, if the user is relaxed, the cycle unit will execute all cycles immediately. For example, if the user is busy, the cycle unit will prioritize executing only the most important cycles. This allows important cycles to be executed preferentially by determining the priority of the PDCA cycle based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 cycle unit may be performed using AI or not using AI. For example, the cycle unit can input user emotion data into a generative AI and have the generative AI determine the priority of the cycles.

[0115] The cycle unit selects the optimal cycle method when executing the PDCA cycle, taking into account the user's device information. For example, if the user is using a smartphone, the cycle unit provides a PDCA cycle method adapted to the screen size. For example, if the user is using a tablet, the cycle unit provides a PDCA cycle method optimized for a large screen. For example, if the user is using a smartwatch, the cycle unit provides a concise and highly visible PDCA cycle method. This allows for the selection of the optimal PDCA cycle method by considering the user's device information. Some or all of the above processing in the cycle unit may be performed using AI, for example, or without AI. For example, the cycle unit can input device information into a generating AI and have the generating AI perform the selection of the cycle method.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The health management system can also include an evaluation unit that acquires user sleep data and assesses sleep quality. This evaluation unit analyzes, for example, the user's sleep duration, the ratio of deep to light sleep, and the number of nighttime awakenings. Based on this data, the evaluation unit can assess the user's sleep quality and provide advice for improvement. For example, if sleep quality is poor, it can provide advice on relaxation methods before bed or how to choose appropriate bedding. Furthermore, the evaluation unit can integrate the user's sleep data with other health data to perform an overall health assessment. This allows users to understand their own sleep quality and receive specific advice for improvement.

[0118] The health management system can also include a stress measurement unit to measure the user's stress level. This unit measures stress levels by analyzing physiological data such as heart rate variability and skin electrical activity. Based on this data, the unit can assess the user's stress level in real time and provide advice for stress reduction. For example, if the stress level is high, it can suggest relaxation methods such as deep breathing or meditation. Furthermore, the unit can monitor fluctuations in the user's stress level over the long term and evaluate the effectiveness of stress management. This allows users to understand their own stress levels and take appropriate measures.

[0119] The health management system may also include an emotion advice unit that estimates the user's emotions and provides health advice based on those emotions. The emotion advice unit estimates emotions from, for example, the user's facial expressions, voice, and text input. Based on the estimated emotions, the emotion advice unit can provide health advice tailored to the user's mood. For example, if the user is feeling stressed, it can suggest relaxation methods or exercises to reduce stress. If the user is relaxed, it can suggest new challenges to maintain their health. This allows users to receive appropriate health advice that matches their emotions.

[0120] The health management system can also include a nutrition assessment unit that analyzes the user's dietary data in detail and evaluates nutritional balance. For example, the nutrition assessment unit analyzes the calories, protein, fat, carbohydrates, vitamins, minerals, and other nutrients in the meals the user consumes. Based on this data, the nutrition assessment unit can evaluate the user's nutritional balance and provide advice for improvement. For instance, if a specific nutrient is deficient, it can suggest foods or supplements containing that nutrient. Furthermore, the nutrition assessment unit can integrate the user's dietary data with other health data to perform an overall health assessment. This allows the user to understand their nutritional balance and receive specific advice for improvement.

[0121] The health management system may also include an exercise evaluation unit that analyzes the user's exercise data in detail and evaluates the effectiveness of the exercise. For example, the exercise evaluation unit analyzes the type, duration, intensity, and calories burned of the exercise performed by the user. Based on this data, the exercise evaluation unit can evaluate the effectiveness of the user's exercise and provide advice for improvement. For example, if the exercise is ineffective, it can suggest changing the type or intensity of the exercise. Furthermore, the exercise evaluation unit can integrate the user's exercise data with other health data to perform an overall assessment of their health status. This allows the user to understand the effectiveness of their exercise and receive specific advice for improvement.

[0122] The health management system may also include an emotion-motor unit that estimates the user's emotions and adjusts the exercise plan based on those emotions. The emotion-motor unit estimates emotions from, for example, the user's facial expressions, voice, and text input. Based on the estimated emotions, the emotion-motor unit can provide an exercise plan tailored to the user's mood. For example, if the user is feeling stressed, it can suggest relaxing yoga or stretching. Conversely, if the user is relaxed, it can suggest energetic exercises. This allows the user to receive an appropriate exercise plan that matches their emotions.

[0123] The health management system may also include an emotional meal function that estimates the user's emotions and adjusts the meal plan based on those emotions. This emotional meal function estimates emotions from, for example, the user's facial expressions, voice, and text input. Based on the estimated emotions, it can provide a meal plan tailored to the user's mood. For example, if the user is stressed, it can suggest foods or herbal teas with relaxing effects. If the user is relaxed, it can suggest a nutritionally balanced meal. This allows the user to receive an appropriate meal plan that matches their emotions.

[0124] The health management system may also include an emotion targeting unit that estimates the user's emotions and sets health goals based on those estimated emotions. The emotion targeting unit estimates emotions from, for example, the user's facial expressions, voice, and text input. Based on the estimated emotions, the emotion targeting unit can set health goals tailored to the user's mood. For example, if the user is feeling stressed, a relaxation-focused goal can be set. Conversely, if the user is relaxed, a challenging goal can be set. This allows users to set appropriate health goals that match their emotions.

[0125] The health management system may also include an emotion display unit that estimates the user's emotions and adjusts the display method of health data based on the estimated emotions. The emotion display unit estimates emotions from, for example, the user's facial expressions, voice, or text input. Based on the estimated emotions, the emotion display unit can provide a display method of health data that matches the user's mood. For example, if the user is stressed, a simple and easy-to-understand display method can be provided. Conversely, if the user is relaxed, detailed data can be displayed. This allows the user to receive an appropriate display method of health data that reflects their emotions.

[0126] The health management system may also include a comparison unit that compares a user's health data with that of other users. For example, the comparison unit might compare the user's health data with that of other users of the same age or gender. Based on this data, the comparison unit can relatively evaluate the user's health status and provide advice for improvement. For instance, if a user's health status is inferior to that of other users, it can suggest specific ways to improve it. Furthermore, the comparison unit can protect privacy by anonymizing the user's health data during comparison. This allows users to compare their health status with others and receive specific advice for improvement.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The shared section allows for the sharing of health checkup records. For example, it can directly read health checkup records submitted in digital format, such as blood test results and electrocardiogram data. Step 2: The generation unit generates individual plans based on the records shared by the sharing unit. For example, it generates meal plans and exercise plans based on the user's goals, such as meal plans that take into account calorie restrictions and nutritional balance, and exercise plans that take into account the type and frequency of exercise. Step 3: The reception desk receives daily reports. For example, users report the meals they ate each day via chat, entering details of the food consumed and the type of exercise they did. Step 4: The service department reviews and provides the plan based on the report received by the reception department. For example, they may suggest increasing exercise if calorie intake is high, or suggest supplements if there is a deficiency in a particular nutrient. Based on the user's report, they revise the meal plan and exercise plan and provide a schedule.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the sharing unit, generation unit, reception unit, provision unit, storage unit, recognition unit, and cycle unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the sharing unit shares health check records via the control unit 46A of the smart device 14 and analyzes them via the specific processing unit 290 of the data processing unit 12. The generation unit generates a plan based on the user's goals via the specific processing unit 290 of the data processing unit 12. The reception unit receives user reports via the control unit 46A of the smart device 14. The provision unit reviews and provides the plan via the specific processing unit 290 of the data processing unit 12. The storage unit stores data via the control unit 46A of the smart device 14 and analyzes it via the specific processing unit 290 of the data processing unit 12. The recognition unit recognizes potential risks via the specific processing unit 290 of the data processing unit 12. The cycle unit runs the PDCA cycle via the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the sharing unit, generation unit, reception unit, provision unit, storage unit, recognition unit, and cycle unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the sharing unit shares health check records via the control unit 46A of the smart glasses 214 and analyzes them via the specific processing unit 290 of the data processing unit 12. The generation unit generates a plan based on the user's goals via the specific processing unit 290 of the data processing unit 12. The reception unit receives user reports via the control unit 46A of the smart glasses 214. The provision unit reviews and provides the plan via the specific processing unit 290 of the data processing unit 12. The storage unit stores data via the control unit 46A of the smart glasses 214 and analyzes it via the specific processing unit 290 of the data processing unit 12. The recognition unit recognizes potential risks via the specific processing unit 290 of the data processing unit 12. The cycle unit runs the PDCA cycle via the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the sharing unit, generation unit, reception unit, provision unit, storage unit, recognition unit, and cycle unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the sharing unit shares health check records via the control unit 46A of the headset terminal 314 and analyzes them via the specific processing unit 290 of the data processing unit 12. The generation unit generates a plan based on the user's goals via the specific processing unit 290 of the data processing unit 12. The reception unit receives user reports via the control unit 46A of the headset terminal 314. The provision unit reviews and provides the plan via the specific processing unit 290 of the data processing unit 12. The storage unit stores data via the control unit 46A of the headset terminal 314 and analyzes it via the specific processing unit 290 of the data processing unit 12. The recognition unit recognizes potential risks via the specific processing unit 290 of the data processing unit 12. The cycle unit runs the PDCA cycle via the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the sharing unit, generation unit, reception unit, provision unit, storage unit, recognition unit, and cycle unit, is realized, for example, by at least one of the robot 414 and the data processing unit 12. For example, the sharing unit shares health check records via the control unit 46A of the robot 414 and analyzes them via the specific processing unit 290 of the data processing unit 12. The generation unit generates a plan based on the user's goals via the specific processing unit 290 of the data processing unit 12. The reception unit receives user reports via the control unit 46A of the robot 414. The provision unit reviews and provides the plan via the specific processing unit 290 of the data processing unit 12. The storage unit stores data via the control unit 46A of the robot 414 and analyzes it via the specific processing unit 290 of the data processing unit 12. The recognition unit recognizes potential risks via the specific processing unit 290 of the data processing unit 12. The cycle unit runs the PDCA cycle via the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) A shared section for sharing health checkup records, A generation unit that generates individual plans based on records shared by the aforementioned shared unit, The reception desk that receives daily reports, The system comprises a provisioning unit that reviews and provides plans based on reports received by the aforementioned receiving unit. A system characterized by the following features. (Note 2) It is equipped with a storage unit that stores data using the device. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a recognition unit that recognizes potential risks. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with a cycle unit that runs the PDCA cycle. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned shared portion is, Share blood test results and electrocardiogram data The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generates meal plans and exercise plans based on the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, If calorie intake is high, we suggest increasing exercise. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, We suggest supplements if there is a deficiency in specific nutrients. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned shared portion is, The system estimates the user's emotions and adjusts the timing of sharing health checkup records based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned shared portion is, When sharing health checkup records, the system analyzes the user's past health data and selects the optimal sharing method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned shared portion is, When sharing health checkup records, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned shared portion is, It estimates the user's emotions and prioritizes which health check records to share based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned shared portion is, When sharing health checkup records, the system prioritizes sharing records that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned shared portion is, When sharing health checkup records, analyze users' social media activity and share relevant records. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts how the plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating a plan, adjust the level of detail in the plan based on the user's health goals. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a plan, different generation algorithms are applied depending on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the length of the plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating a plan, the plan priority is determined based on when the user's health checkup records were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a plan, the order of the plans is adjusted based on the relevance of the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reception unit is We estimate the user's emotions and adjust the reporting method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reception unit is When a report is received, the system analyzes the user's past reporting history and selects the most suitable method of receiving the report. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reception unit is The system estimates user sentiment and prioritizes reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reception unit is When receiving a report, the system will select the most appropriate submission method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the plan is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing a plan, we analyze the user's past health data to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing a plan, the content offered will be customized based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates user sentiment and determines plan priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing a plan, the optimal delivery method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing a plan, we analyze the user's social media activity and adjust the content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 31) The storage unit is We estimate the user's emotions and adjust the data collection method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The storage unit is When accumulating data, the system analyzes the user's past data to select the optimal storage method. The system described in Appendix 2, characterized by the features described herein. (Note 33) The storage unit is It estimates user sentiment and prioritizes data based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 34) The storage unit is When accumulating data, the optimal storage method is selected considering the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned recognition unit, It estimates the user's emotions and adjusts how potential risks are perceived based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned recognition unit, When identifying potential risks, the system analyzes the user's past health data to select the optimal assessment method. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned recognition unit, It estimates user sentiment and prioritizes potential risks based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned recognition unit, When recognizing potential risks, the optimal recognition method is selected considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 39) The cycle unit is We estimate the user's emotions and adjust the PDCA cycle based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The cycle unit is When executing the PDCA cycle, analyze the user's past data to select the optimal cycle method. The system described in Appendix 4, characterized by the features described herein. (Note 41) The cycle unit is Estimate user emotions and determine the priorities of the PDCA cycle based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The cycle unit is When executing the PDCA cycle, the optimal cycle method is selected considering the user's device information. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0201] 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 shared section for sharing health checkup records, A generation unit that generates individual plans based on records shared by the aforementioned shared unit, The reception desk that receives daily reports, The system comprises a provisioning unit that reviews and provides plans based on reports received by the aforementioned receiving unit. A system characterized by the following features.

2. It is equipped with a storage unit that stores data using the device. The system according to feature 1.

3. It is equipped with a recognition unit that recognizes potential risks. The system according to feature 1.

4. It is equipped with a cycle unit that runs the PDCA cycle. The system according to feature 1.

5. The aforementioned shared portion is, Share blood test results and electrocardiogram data The system according to feature 1.

6. The generating unit is Generates meal plans and exercise plans based on the user's goals. The system according to feature 1.

7. The aforementioned supply unit is, If calorie intake is high, we suggest increasing exercise. The system according to feature 1.

8. The aforementioned supply unit is, We suggest supplements if there is a deficiency in specific nutrients. The system according to feature 1.

9. The aforementioned shared portion is, The system estimates the user's emotions and adjusts the timing of sharing health checkup records based on those emotions. The system according to feature 1.

10. The aforementioned shared portion is, When sharing health checkup records, the system analyzes the user's past health data and selects the optimal sharing method. The system according to feature 1.

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