system

Abstract

The system according to this embodiment aims to analyze the user's health data and provide personalized health advice and exercise / diet plans. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a feedback unit. The collection unit collects the user's health data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates health advice and exercise / meal plans based on the analysis results obtained by the analysis unit. The provision unit provides the plans generated by the generation unit. The feedback unit collects user feedback based on the plans provided by the provision unit and modifies the plans.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, providing individual health advice and exercise / diet plans based on users' health data has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze users' health data and provide individual health advice and exercise / diet plans.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a feedback unit. The collection unit collects the user's health data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates health advice and exercise / meal plans based on the analysis results obtained by the analysis unit. The provision unit provides the plans generated by the generation unit. The feedback unit collects user feedback based on the plans provided by the provision unit and modifies the plans. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's health data and provide personalized health advice and exercise / diet plans. [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 manages 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) The generative AI agent according to an embodiment of the present invention is a system that analyzes a user's health data and provides personalized health advice and exercise / diet plans. This generative AI agent collects the user's health data, and the generative AI analyzes that data to evaluate the user's health status. For example, the generative AI agent collects health data such as fitness data, dietary content, and medical history. Next, the generative AI agent analyzes the collected data using text analysis and image processing. For example, the generative AI agent analyzes exercise patterns from fitness data and evaluates nutritional balance from dietary content. Subsequently, the generative AI agent provides the user with optimal health advice. For example, it proposes an appropriate exercise plan for users who are not getting enough exercise and provides a balanced diet plan for users with unbalanced nutrition. Furthermore, the generative AI agent generates a customized exercise / diet plan according to the user's health status. This allows the user to implement a health plan that suits them. The generative AI agent also has an automatic feedback function, which collects data on the exercise and diet practiced by the user, and the generative AI analyzes that data to provide feedback. For example, the generative AI agent can evaluate the results of implementing the exercise plan and modify the plan as needed. This allows the user to always implement the optimal health plan. This generative AI agent will be an extremely useful tool in today's world, where the demand for personalized health services is surging with the growth of the health and fitness market. It aims to support everyone in optimally managing their own health and achieve overall improvements in health levels. This will enable the generative AI agent to efficiently collect, analyze, generate, provide, and provide feedback on users' health data.

[0029] The generation AI agent according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a feedback unit. The collection unit collects the user's health data. The collection unit collects health data such as fitness data, dietary information, and medical history. The collection unit can collect the user's exercise data using a wearable device, for example. The collection unit can also collect the user's dietary information in digital format. Furthermore, the collection unit can obtain the user's medical history from electronic medical records. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, text analysis or image processing. The analysis unit can analyze the user's dietary information and evaluate nutritional balance using, for example, natural language processing technology. The analysis unit can also analyze fitness data and evaluate exercise patterns using image recognition technology. The generation unit generates health advice and exercise / meal plans based on the analysis results obtained by the analysis unit. The generation unit generates optimal health advice for the user using, for example, generation AI. The generation unit can, for example, suggest an appropriate exercise plan to a user who is not getting enough exercise. It can also provide a balanced meal plan to a user with an unbalanced diet. The delivery unit provides the plan generated by the generation unit. The delivery unit can provide health advice and exercise / meal plans to the user, for example, through app notifications or email. It can also provide the plan to the user through a dashboard display. The feedback unit collects user feedback based on the plan provided by the delivery unit and modifies the plan. The feedback unit can, for example, collect user activity logs, and the generation AI can analyze that data to modify the plan. It can also collect user-entered feedback, and the generation AI can analyze that data to modify the plan. This enables the generation AI agent according to the embodiment to efficiently collect, analyze, generate, provide, and provide feedback on the user's health data.

[0030] The data collection unit collects user health data. This includes, for example, fitness data, dietary information, and medical history. Specifically, the unit can collect user exercise data using wearable devices. These devices have the capability to record data such as heart rate, steps, calories burned, and sleep patterns in real time. This allows for a detailed understanding of the user's daily exercise and activity levels. The unit can also collect user-entered dietary information digitally. Users can input their daily diet through a dedicated application, which then saves the data to the cloud. Furthermore, the unit can retrieve the user's medical history from electronic medical records. These records include past medical records, prescriptions, and allergy information. Collecting this data allows for a comprehensive assessment of the user's health status. The unit centrally manages this diverse data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department uses text analysis and image processing to analyze the data. Specifically, the analysis department can use natural language processing technology to analyze a user's diet and evaluate nutritional balance. By using natural language processing technology, it can analyze text data of the user's diet and automatically calculate the nutrients and calories of each food item. The analysis department can also use image recognition technology to analyze fitness data and evaluate exercise patterns. For example, it can analyze exercise data recorded by a user using a wearable device to evaluate the type, intensity, and duration of exercise. Furthermore, the analysis department can analyze collected medical history and assess the user's health risks. For example, based on past medical records and prescription drug data, it can predict the risk of specific diseases and take early countermeasures. This allows the analysis department to quickly and accurately analyze collected data and comprehensively evaluate the user's health status. Additionally, the analysis department can utilize historical data and statistical information to analyze long-term health trends. For example, based on past exercise and dietary data, it can track changes in the user's health status and predict future health risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit generates health advice and exercise / meal plans based on the analysis results obtained by the analysis unit. For example, the generation unit uses a generation AI to generate optimal health advice for the user. The generation AI provides advice tailored to the user's individual needs and goals based on the user's health data. For example, it can suggest an appropriate exercise plan for a user who is not getting enough exercise. The generation AI analyzes the user's exercise data and adjusts the type, intensity, and frequency of exercise to generate an optimal exercise plan. The generation unit can also provide a balanced meal plan for users with unbalanced nutrition. The generation AI analyzes the user's meal data, evaluates nutrient intake, and generates a meal plan to supplement necessary nutrients. Furthermore, the generation unit can consider the user's medical history and provide advice to mitigate health risks. For example, it can suggest preventive measures and lifestyle improvements for users at high risk of certain diseases. In this way, the generation unit can comprehensively evaluate the user's health status and provide health advice and exercise / meal plans tailored to individual needs. In addition, the generation unit can collect user feedback to continuously improve the generated plans, and the generation AI can analyze this data to revise the plans. This allows the generation unit to provide highly accurate health advice based on the latest information at all times, supporting the user's health management.

[0033] The service provider provides plans generated by the generation unit. For example, the service provider can provide users with health advice and exercise / diet plans via app notifications or email. Specifically, the service provider sends app notifications to users' smartphones, providing the generated plans in real time. Users can view and implement health advice and exercise / diet plans through the app. The service provider can also send generated plans to users via email, allowing users to access health advice and exercise / diet plans anytime, anywhere. Furthermore, the service provider can provide plans through a dashboard display. The dashboard visually displays the user's health data and generated plans, allowing users to see changes in their health status and plan progress at a glance. This enables the service provider to quickly provide users with appropriate health advice and exercise / diet plans, supporting their health management. Additionally, the service provider can collect user feedback, and the generation AI can analyze this data to modify the plans. This allows the service provider to consistently provide highly accurate health advice based on the latest information, supporting users' health management.

[0034] The Feedback Department collects user feedback based on the plan provided by the Service Provider and modifies the plan. Specifically, the Feedback Department collects user activity logs, and the Generating AI analyzes this data to modify the plan. For example, it records the exercise and meals a user has eaten, and the Generating AI analyzes this data to evaluate the plan's effectiveness. The Feedback Department can also collect user-inputted feedback, and the Generating AI analyzes this data to modify the plan. Users can input feedback on the generated plan through a dedicated application, and this data is stored in the cloud. Based on user feedback, the Generating AI adjusts the plan content and advice to support more effective health management. As a result, the Feedback Department can always provide highly accurate health advice based on the latest information by collecting user feedback and using the Generating AI to analyze the data and modify the plan. Furthermore, the Feedback Department can support users' health management by continuously collecting user activity logs and feedback, and using the Generating AI to analyze this data and modify the plan. As a result, the Feedback Department can always provide highly accurate health advice based on the latest information by collecting user feedback and using the Generating AI to analyze the data and modify the plan.

[0035] The data collection unit can collect health data such as the user's fitness data, dietary information, and medical history. For example, the data collection unit can collect the user's exercise data using a wearable device. For example, the data collection unit can collect data such as the user's steps, heart rate, and exercise time. The data collection unit can also collect the user's dietary information in digital format. For example, the data collection unit can collect data such as the calories consumed, the types of nutrients, and the timing of meals. Furthermore, the data collection unit can obtain the user's medical history from electronic medical records. For example, the data collection unit can collect data such as the user's past medical history, allergy information, and medication history. In this way, the data collection unit can comprehensively collect the user's health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from a wearable device into a generating AI and have the generating AI perform data analysis.

[0036] The analysis unit can analyze collected health data using text analysis and image processing. For example, the analysis unit can analyze a user's diet using natural language processing technology and evaluate nutritional balance. For example, the analysis unit can evaluate calorie intake and nutrient balance from the diet information entered by the user. The analysis unit can also analyze fitness data using image recognition technology and evaluate exercise patterns. For example, the analysis unit can evaluate the frequency and intensity of exercise from the user's exercise data. Furthermore, the analysis unit can analyze health data from multiple perspectives using statistical analysis technology. For example, the analysis unit can statistically analyze a user's health data and evaluate health risks. In this way, the analysis unit can analyze health data from multiple perspectives. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected health data into a generating AI and have the generating AI perform the data analysis.

[0037] The generation unit can generate optimal health advice and exercise / meal plans for the user based on the analysis results. For example, the generation unit can use a generation AI to generate optimal health advice for the user. For example, the generation unit can suggest an appropriate exercise plan to a user who is not getting enough exercise. The generation unit can also provide a balanced meal plan to a user whose nutritional balance is unbalanced. Furthermore, the generation unit can generate customized exercise and meal plans according to the user's health condition. For example, the generation unit can generate individual exercise and meal plans based on the user's health data. This allows the generation unit to provide optimal health advice and plans to the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the analysis results into a generation AI and have the generation AI generate health advice and plans.

[0038] The service provider can provide users with generated health advice and exercise / meal plans. The service provider can provide users with health advice and exercise / meal plans, for example, through app notifications or email. For example, the service provider can send notifications to users' smartphones to display health advice and plans. The service provider can also provide plans to users through dashboard displays. For example, the service provider can display health advice and plans to users through web applications or mobile applications. Furthermore, the service provider can send health advice and plans to users via email. For example, the service provider can send health advice and plans to users' email addresses. This allows the service provider to provide users with generated health advice and plans. 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 the generated health advice and plans into a generating AI and have the generating AI select the delivery method.

[0039] The feedback unit can collect user feedback, and the generating AI can analyze that data to modify the plan. For example, the feedback unit can collect user activity logs, and the generating AI can analyze that data to modify the plan. For example, the feedback unit can collect data on exercise and diet practiced by the user, and the generating AI can modify the plan based on that data. The feedback unit can also collect user input, and the generating AI can analyze that data to modify the plan. For example, the feedback unit can collect feedback entered by the user through the application, and the generating AI can modify the plan based on that data. Furthermore, the feedback unit can continuously collect user health data, and the generating AI can analyze that data to modify the plan. For example, the feedback unit can periodically collect user health data, and the generating AI can modify the plan based on that data. In this way, the feedback unit can modify the plan based on user feedback. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the collected feedback data into the generating AI and have the generating AI perform the plan modification.

[0040] The data collection unit can analyze the user's past health data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on the devices and applications the user has used in the past. For example, the data collection unit can suggest the optimal collection method based on the wearable devices the user has used in the past. The data collection unit can also analyze the user's past data collection frequency and set an appropriate collection interval. For example, the data collection unit can set an appropriate collection interval based on the user's past data collection frequency. Furthermore, the data collection unit can suggest the optimal collection timing based on the user's past data collection patterns. For example, the data collection unit can suggest the optimal collection timing based on the user's past data collection patterns. This allows the data collection unit to select the optimal collection method based on the user's past history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data collection history into a generating AI and have the generating AI select the optimal collection method.

[0041] The data collection unit can filter health data based on the user's current lifestyle and areas of interest. For example, if the user is on a diet, the data collection unit can prioritize collecting data on their diet. For example, if the user is on a diet, the data collection unit can prioritize collecting data on calorie intake and nutrients. The data collection unit can also prioritize collecting fitness data if the user is training. For example, if the user is training, the data collection unit can prioritize collecting data on exercise frequency and intensity. Furthermore, if the user has just undergone a health checkup, the data collection unit can prioritize collecting data on their medical history. For example, if the user has just undergone a health checkup, the data collection unit can prioritize collecting data on their past medical history and allergy information. This allows the data collection unit to prioritize collecting data that is appropriate to the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit can prioritize the collection of exercise data at high altitude. For example, if the user is at high altitude, the data collection unit can prioritize the collection of heart rate and oxygen saturation data at high altitude. The data collection unit can also prioritize the collection of activity data in urban areas if the user is in an urban area. For example, if the user is in an urban area, the data collection unit can prioritize the collection of step count and exercise time data. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of meal and exercise data at the travel destination. For example, if the user is traveling, the data collection unit can prioritize the collection of meal content and exercise patterns at the travel destination. In this way, the data collection unit can collect highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data when collecting health data. For example, the data collection unit can collect dietary data based on the meal content shared by the user on social media. For example, the data collection unit can collect data on calorie intake and nutrients based on photos and comments of meals shared by the user on social media. The data collection unit can also collect fitness data based on exercise records shared by the user on social media. For example, the data collection unit can collect data on exercise frequency and intensity based on photos and comments of exercise shared by the user on social media. Furthermore, the data collection unit can update the medical history based on health information shared by the user on social media. For example, the data collection unit can update the medical history based on health checkup results and medical history information shared by the user on social media. In this way, the data collection unit can collect relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can analyze high-importance data (e.g., heart rate, blood pressure) in detail. For example, the analysis unit can analyze heart rate and blood pressure data in detail. The analysis unit can also analyze moderately important data (e.g., steps taken, calorie consumption) to a moderate degree. For example, the analysis unit can analyze steps taken and calorie consumption data to a moderate degree. Furthermore, the analysis unit can also analyze low-importance data (e.g., water intake) in a simplified manner. For example, the analysis unit can analyze water intake data in a simplified manner. In this way, the analysis unit can adjust the level of detail of the analysis according to the importance of the health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply an exercise pattern analysis algorithm to fitness data. For example, the analysis unit can apply an exercise pattern analysis algorithm to fitness data to evaluate the frequency and intensity of exercise. The analysis unit can also apply a nutritional balance evaluation algorithm to dietary content. For example, the analysis unit can apply a nutritional balance evaluation algorithm to dietary content to evaluate the balance of calories and nutrients. Furthermore, the analysis unit can apply a health risk evaluation algorithm to medical history. For example, the analysis unit can apply a health risk evaluation algorithm to medical history to evaluate health risks based on past medical history and allergy information. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of health data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The generation unit can adjust the level of detail in the generated health advice and plans based on their importance. For example, the generation unit can include detailed explanations for highly important advice and plans. For example, the generation unit can provide content with detailed explanations for highly important health advice and plans. The generation unit can also provide a moderate level of detail for advice and plans of moderate importance. For example, the generation unit can provide content with a moderate level of detail for health advice and plans of moderate importance. Furthermore, the generation unit can include concise explanations for less important advice and plans. For example, the generation unit can provide content with concise explanations for less important health advice and plans. This allows the generation unit to adjust the level of detail in the generated health advice and plans according to their importance. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the importance of the health advice and plans into the generation AI and have the generation AI adjust the level of detail in the generated health advice and plans.

[0047] The generation unit can apply different generation algorithms depending on the category of health advice or plan during generation. For example, the generation unit can apply an exercise pattern generation algorithm to a fitness plan. For example, the generation unit can apply an exercise pattern generation algorithm to a fitness plan to determine the type and frequency of exercise. The generation unit can also apply a nutrition balance generation algorithm to a meal plan. For example, the generation unit can apply a nutrition balance generation algorithm to a meal plan to determine the menu and quantity of meals. Furthermore, the generation unit can apply a health risk assessment generation algorithm to health advice. For example, the generation unit can apply a health risk assessment generation algorithm to health advice to assess the user's health risk. This allows the generation unit to apply an appropriate generation algorithm depending on the category of health advice or plan. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the categories of health advice or plans into a generation AI and have the generation AI perform the application of the generation algorithm.

[0048] The delivery unit can select the optimal delivery method by referring to the user's past health advice and plan history. For example, the delivery unit can prioritize using delivery methods that the user has preferred in the past (e.g., email, app notifications). For example, the delivery unit can select the optimal delivery method based on the email and app notifications that the user has preferred in the past. The delivery unit can also select the most effective delivery method from the user's past history. For example, the delivery unit can select the most effective delivery method based on the user's past history. Furthermore, the delivery unit can customize the delivery method based on the user's past feedback. For example, the delivery unit can customize the delivery method based on the user's past feedback. This allows the delivery unit to select the optimal delivery method based on the user's past history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's past health advice and plan history into a generating AI and have the generating AI select the optimal delivery method.

[0049] The service provider can customize the means of delivering health advice and plans based on the user's current lifestyle at the time of delivery. For example, if the user is busy, the service provider can provide concise notifications or reminders. For example, if the user is busy, the service provider can provide health advice and plans through concise notifications or reminders. The service provider can also provide notifications with detailed explanations if the user is relaxed. For example, if the user is relaxed, the service provider can provide health advice and plans through notifications with detailed explanations. Furthermore, if the user is traveling, the service provider can provide advice and plans tailored to their travel destination. For example, if the user is traveling, the service provider can provide health advice and plans tailored to their travel destination. This allows the service provider to customize the means of delivery according to the user's lifestyle. 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 the user's current lifestyle data into a generating AI and have the generating AI perform the customization of the means of delivery.

[0050] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is at high altitude, the service provider can provide advice on exercise at high altitude. For example, if the user is at high altitude, the service provider can provide health advice on exercise at high altitude. The service provider can also provide advice on activities in urban areas if the user is in an urban area. For example, if the user is in an urban area, the service provider can provide health advice on activities in urban areas. Furthermore, if the user is traveling, the service provider can provide meal and exercise plans tailored to the travel destination. For example, if the user is traveling, the service provider can provide meal and exercise plans tailored to the travel destination. This allows the service provider to select the optimal service delivery method based on 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 the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0051] The service provider can analyze the user's social media activity at the time of delivery and propose means of providing health advice and plans. For example, the service provider can provide meal plans based on the content of meals shared by the user on social media. For example, the service provider can provide meal plans based on photos and comments of meals shared by the user on social media. The service provider can also provide exercise plans based on exercise records shared by the user on social media. For example, the service provider can provide exercise plans based on photos and comments of exercise shared by the user on social media. Furthermore, the service provider can provide health advice based on health information shared by the user on social media. For example, the service provider can provide health advice based on the results of health checkups and medical history information shared by the user on social media. In this way, the service provider can propose means of delivery based on the user's social media activity. 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 the user's social media activity data into a generating AI and have the generating AI execute the proposal of means of delivery.

[0052] The feedback unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback unit can prioritize using feedback methods that the user has preferred in the past (e.g., surveys, voice memos). For example, the feedback unit can select the optimal collection method based on surveys and voice memos that the user has preferred in the past. The feedback unit can also select the most effective collection method from the user's past feedback history. For example, the feedback unit can select the most effective collection method based on the user's past feedback history. Furthermore, the feedback unit can customize the collection method based on the user's past feedback. For example, the feedback unit can customize the collection method based on the user's past feedback. This allows the feedback unit to select the optimal collection method based on the user's past history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past feedback history into a generating AI and have the generating AI select the optimal collection method.

[0053] The feedback unit can customize the means of collecting feedback based on the user's current living situation when collecting feedback. For example, if the user is busy, the feedback unit can provide a concise feedback form. For example, if the user is busy, the feedback unit can collect feedback through a concise feedback form. The feedback unit can also provide a detailed feedback form if the user is relaxed. For example, if the user is relaxed, the feedback unit can collect feedback through a detailed feedback form. Furthermore, if the user is traveling, the feedback unit can provide a feedback form tailored to their travel destination. For example, if the user is traveling, the feedback unit can collect feedback through a feedback form tailored to their travel destination. This allows the feedback unit to customize the means of collection according to the user's living situation. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the collection means.

[0054] The feedback unit can select the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, if the user is at high altitude, the feedback unit can collect feedback on activities at high altitude. For example, if the user is at high altitude, the feedback unit can collect feedback on exercise and health status at high altitude. The feedback unit can also collect feedback on activities in urban areas if the user is in urban areas. For example, if the user is in urban areas, the feedback unit can collect feedback on exercise and health status in urban areas. Furthermore, if the user is traveling, the feedback unit can collect feedback on meals and exercise at their travel destination. For example, if the user is traveling, the feedback unit can collect feedback on meals and exercise at their travel destination. This allows the feedback unit to select the optimal collection method based on the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal collection method.

[0055] The feedback unit can analyze the user's social media activity and propose methods for collecting feedback when collecting it. For example, the feedback unit can collect feedback on meals based on the content of meals shared by the user on social media. For example, the feedback unit can collect feedback on meals based on photos and comments of meals shared by the user on social media. The feedback unit can also collect feedback on exercise based on exercise records shared by the user on social media. For example, the feedback unit can collect feedback on exercise based on photos and comments of exercise shared by the user on social media. Furthermore, the feedback unit can collect feedback on health based on health information shared by the user on social media. For example, the feedback unit can collect feedback on health based on the results of health checkups and medical history information shared by the user on social media. This allows the feedback unit to propose collection methods based on the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of collection methods.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The generating AI agent can adjust the frequency and timing of data collection by considering the user's lifestyle and daily behavior patterns when collecting user health data. For example, if a user has a habit of jogging every morning, the collection unit can collect exercise data during that time. Similarly, if a user has a habit of eating at night, the collection of meal data can be concentrated at night. Furthermore, if a user has time to relax on weekends, medical history data can be collected during that time. In this way, the collection unit can collect health data at the optimal time according to the user's lifestyle.

[0058] The generating AI agent can customize the scope of data collection based on the user's health goals and interests when collecting user health data. For example, if the user's goal is weight loss, the collection unit can prioritize collecting data on weight and calorie intake. If the user is interested in strength training, it can also collect data on the frequency and intensity of strength training. Furthermore, if the user is interested in consuming a specific nutrient, it can collect data on that nutrient. This allows the collection unit to customize the scope of data collection according to the user's health goals and interests.

[0059] The generating AI agent can analyze a user's health data and evaluate their current health status by comparing it to the user's past health data. For example, the analysis unit can compare the user's past exercise data with their current exercise data to evaluate improvements or declines in exercise performance. It can also compare the user's past dietary data with their current dietary data to evaluate changes in nutritional balance. Furthermore, it can compare the user's past medical history with their current health data to evaluate changes in health risks. This allows the analysis unit to evaluate the user's current health status by comparing it to their past health data.

[0060] The generating AI agent can benchmark a user's health data by comparing it to data from other users when analyzing the data. For example, the analysis unit can benchmark a user's exercise data against data from other users of the same age group to assess exercise performance. It can also benchmark a user's dietary data against data from other users following the same meal plan to assess nutritional balance. Furthermore, it can benchmark a user's health risk by comparing their medical history against data from other users with similar health risks. In this way, the analysis unit can benchmark a user's health data against data from other users.

[0061] The generating AI agent can monitor the user's health data in real time and detect anomalies when analyzing the data. For example, the analysis unit can monitor the user's heart rate data in real time and detect abnormal heart rates. It can also monitor the user's blood pressure data in real time and detect abnormal blood pressure. Furthermore, it can monitor the user's blood glucose level data in real time and detect abnormal blood glucose levels. As a result, the analysis unit can monitor the user's health data in real time and detect anomalies.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects the user's health data. The data collection unit collects health data such as fitness data, dietary information, and medical history. The data collection unit can collect the user's exercise data using a wearable device. It can also collect the user's dietary information in digital format. Furthermore, it can obtain the user's medical history from electronic medical records. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using text analysis and image processing. For example, it can use natural language processing technology to analyze the user's diet and evaluate nutritional balance. It can also use image recognition technology to analyze fitness data and evaluate exercise patterns. Step 3: The generation unit generates health advice and exercise / meal plans based on the analysis results obtained by the analysis unit. The generation unit uses generation AI to generate optimal health advice for the user. For example, it can suggest an appropriate exercise plan to users who are not getting enough exercise, and provide a balanced meal plan to users whose nutritional balance is unbalanced. Step 4: The delivery unit provides the plan generated by the generation unit. The delivery unit can provide health advice and exercise / meal plans to users via app notifications or email. It can also provide plans to users through a dashboard display. Step 5: The Feedback Department collects user feedback based on the plan provided by the Delivery Department and modifies the plan. The Feedback Department can collect user behavior logs and use the Generating AI to analyze that data and modify the plan. It can also collect user-entered feedback and use the Generating AI to analyze that data and modify the plan.

[0064] (Example of form 2) The generative AI agent according to an embodiment of the present invention is a system that analyzes a user's health data and provides personalized health advice and exercise / diet plans. This generative AI agent collects the user's health data, and the generative AI analyzes that data to evaluate the user's health status. For example, the generative AI agent collects health data such as fitness data, dietary content, and medical history. Next, the generative AI agent analyzes the collected data using text analysis and image processing. For example, the generative AI agent analyzes exercise patterns from fitness data and evaluates nutritional balance from dietary content. Subsequently, the generative AI agent provides the user with optimal health advice. For example, it proposes an appropriate exercise plan for users who are not getting enough exercise and provides a balanced diet plan for users with unbalanced nutrition. Furthermore, the generative AI agent generates a customized exercise / diet plan according to the user's health status. This allows the user to implement a health plan that suits them. The generative AI agent also has an automatic feedback function, which collects data on the exercise and diet practiced by the user, and the generative AI analyzes that data to provide feedback. For example, the generative AI agent can evaluate the results of implementing the exercise plan and modify the plan as needed. This allows the user to always implement the optimal health plan. This generative AI agent will be an extremely useful tool in today's world, where the demand for personalized health services is surging with the growth of the health and fitness market. It aims to support everyone in optimally managing their own health and achieve overall improvements in health levels. This will enable the generative AI agent to efficiently collect, analyze, generate, provide, and provide feedback on users' health data.

[0065] The generation AI agent according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a feedback unit. The collection unit collects the user's health data. The collection unit collects health data such as fitness data, dietary information, and medical history. The collection unit can collect the user's exercise data using a wearable device, for example. The collection unit can also collect the user's dietary information in digital format. Furthermore, the collection unit can obtain the user's medical history from electronic medical records. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, text analysis or image processing. The analysis unit can analyze the user's dietary information and evaluate nutritional balance using, for example, natural language processing technology. The analysis unit can also analyze fitness data and evaluate exercise patterns using image recognition technology. The generation unit generates health advice and exercise / meal plans based on the analysis results obtained by the analysis unit. The generation unit generates optimal health advice for the user using, for example, generation AI. The generation unit can, for example, suggest an appropriate exercise plan to a user who is not getting enough exercise. It can also provide a balanced meal plan to a user with an unbalanced diet. The delivery unit provides the plan generated by the generation unit. The delivery unit can provide health advice and exercise / meal plans to the user, for example, through app notifications or email. It can also provide the plan to the user through a dashboard display. The feedback unit collects user feedback based on the plan provided by the delivery unit and modifies the plan. The feedback unit can, for example, collect user activity logs, and the generation AI can analyze that data to modify the plan. It can also collect user-entered feedback, and the generation AI can analyze that data to modify the plan. This enables the generation AI agent according to the embodiment to efficiently collect, analyze, generate, provide, and provide feedback on the user's health data.

[0066] The data collection unit collects user health data. This includes, for example, fitness data, dietary information, and medical history. Specifically, the unit can collect user exercise data using wearable devices. These devices have the capability to record data such as heart rate, steps, calories burned, and sleep patterns in real time. This allows for a detailed understanding of the user's daily exercise and activity levels. The unit can also collect user-entered dietary information digitally. Users can input their daily diet through a dedicated application, which then saves the data to the cloud. Furthermore, the unit can retrieve the user's medical history from electronic medical records. These records include past medical records, prescriptions, and allergy information. Collecting this data allows for a comprehensive assessment of the user's health status. The unit centrally manages this diverse data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis department analyzes the data collected by the data collection department. For example, the analysis department uses text analysis and image processing to analyze the data. Specifically, the analysis department can use natural language processing technology to analyze a user's diet and evaluate nutritional balance. By using natural language processing technology, it can analyze text data of the user's diet and automatically calculate the nutrients and calories of each food item. The analysis department can also use image recognition technology to analyze fitness data and evaluate exercise patterns. For example, it can analyze exercise data recorded by a user using a wearable device to evaluate the type, intensity, and duration of exercise. Furthermore, the analysis department can analyze collected medical history and assess the user's health risks. For example, based on past medical records and prescription drug data, it can predict the risk of specific diseases and take early countermeasures. This allows the analysis department to quickly and accurately analyze collected data and comprehensively evaluate the user's health status. Additionally, the analysis department can utilize historical data and statistical information to analyze long-term health trends. For example, based on past exercise and dietary data, it can track changes in the user's health status and predict future health risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0068] The generation unit generates health advice and exercise / meal plans based on the analysis results obtained by the analysis unit. For example, the generation unit uses a generation AI to generate optimal health advice for the user. The generation AI provides advice tailored to the user's individual needs and goals based on the user's health data. For example, it can suggest an appropriate exercise plan for a user who is not getting enough exercise. The generation AI analyzes the user's exercise data and adjusts the type, intensity, and frequency of exercise to generate an optimal exercise plan. The generation unit can also provide a balanced meal plan for users with unbalanced nutrition. The generation AI analyzes the user's meal data, evaluates nutrient intake, and generates a meal plan to supplement necessary nutrients. Furthermore, the generation unit can consider the user's medical history and provide advice to mitigate health risks. For example, it can suggest preventive measures and lifestyle improvements for users at high risk of certain diseases. In this way, the generation unit can comprehensively evaluate the user's health status and provide health advice and exercise / meal plans tailored to individual needs. In addition, the generation unit can collect user feedback to continuously improve the generated plans, and the generation AI can analyze this data to revise the plans. This allows the generation unit to provide highly accurate health advice based on the latest information at all times, supporting the user's health management.

[0069] The service provider provides plans generated by the generation unit. For example, the service provider can provide users with health advice and exercise / diet plans via app notifications or email. Specifically, the service provider sends app notifications to users' smartphones, providing the generated plans in real time. Users can view and implement health advice and exercise / diet plans through the app. The service provider can also send generated plans to users via email, allowing users to access health advice and exercise / diet plans anytime, anywhere. Furthermore, the service provider can provide plans through a dashboard display. The dashboard visually displays the user's health data and generated plans, allowing users to see changes in their health status and plan progress at a glance. This enables the service provider to quickly provide users with appropriate health advice and exercise / diet plans, supporting their health management. Additionally, the service provider can collect user feedback, and the generation AI can analyze this data to modify the plans. This allows the service provider to consistently provide highly accurate health advice based on the latest information, supporting users' health management.

[0070] The Feedback Department collects user feedback based on the plan provided by the Service Provider and modifies the plan. Specifically, the Feedback Department collects user activity logs, and the Generating AI analyzes this data to modify the plan. For example, it records the exercise and meals a user has eaten, and the Generating AI analyzes this data to evaluate the plan's effectiveness. The Feedback Department can also collect user-inputted feedback, and the Generating AI analyzes this data to modify the plan. Users can input feedback on the generated plan through a dedicated application, and this data is stored in the cloud. Based on user feedback, the Generating AI adjusts the plan content and advice to support more effective health management. As a result, the Feedback Department can always provide highly accurate health advice based on the latest information by collecting user feedback and using the Generating AI to analyze the data and modify the plan. Furthermore, the Feedback Department can support users' health management by continuously collecting user activity logs and feedback, and using the Generating AI to analyze this data and modify the plan. As a result, the Feedback Department can always provide highly accurate health advice based on the latest information by collecting user feedback and using the Generating AI to analyze the data and modify the plan.

[0071] The data collection unit can collect health data such as the user's fitness data, dietary information, and medical history. For example, the data collection unit can collect the user's exercise data using a wearable device. For example, the data collection unit can collect data such as the user's steps, heart rate, and exercise time. The data collection unit can also collect the user's dietary information in digital format. For example, the data collection unit can collect data such as the calories consumed, the types of nutrients, and the timing of meals. Furthermore, the data collection unit can obtain the user's medical history from electronic medical records. For example, the data collection unit can collect data such as the user's past medical history, allergy information, and medication history. In this way, the data collection unit can comprehensively collect the user's health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from a wearable device into a generating AI and have the generating AI perform data analysis.

[0072] The analysis unit can analyze collected health data using text analysis and image processing. For example, the analysis unit can analyze a user's diet using natural language processing technology and evaluate nutritional balance. For example, the analysis unit can evaluate calorie intake and nutrient balance from the diet information entered by the user. The analysis unit can also analyze fitness data using image recognition technology and evaluate exercise patterns. For example, the analysis unit can evaluate the frequency and intensity of exercise from the user's exercise data. Furthermore, the analysis unit can analyze health data from multiple perspectives using statistical analysis technology. For example, the analysis unit can statistically analyze a user's health data and evaluate health risks. In this way, the analysis unit can analyze health data from multiple perspectives. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected health data into a generating AI and have the generating AI perform the data analysis.

[0073] The generation unit can generate optimal health advice and exercise / meal plans for the user based on the analysis results. For example, the generation unit can use a generation AI to generate optimal health advice for the user. For example, the generation unit can suggest an appropriate exercise plan to a user who is not getting enough exercise. The generation unit can also provide a balanced meal plan to a user whose nutritional balance is unbalanced. Furthermore, the generation unit can generate customized exercise and meal plans according to the user's health condition. For example, the generation unit can generate individual exercise and meal plans based on the user's health data. This allows the generation unit to provide optimal health advice and plans to the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the analysis results into a generation AI and have the generation AI generate health advice and plans.

[0074] The service provider can provide users with generated health advice and exercise / meal plans. The service provider can provide users with health advice and exercise / meal plans, for example, through app notifications or email. For example, the service provider can send notifications to users' smartphones to display health advice and plans. The service provider can also provide plans to users through dashboard displays. For example, the service provider can display health advice and plans to users through web applications or mobile applications. Furthermore, the service provider can send health advice and plans to users via email. For example, the service provider can send health advice and plans to users' email addresses. This allows the service provider to provide users with generated health advice and plans. 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 the generated health advice and plans into a generating AI and have the generating AI select the delivery method.

[0075] The feedback unit can collect user feedback, and the generating AI can analyze that data to modify the plan. For example, the feedback unit can collect user activity logs, and the generating AI can analyze that data to modify the plan. For example, the feedback unit can collect data on exercise and diet practiced by the user, and the generating AI can modify the plan based on that data. The feedback unit can also collect user input, and the generating AI can analyze that data to modify the plan. For example, the feedback unit can collect feedback entered by the user through the application, and the generating AI can modify the plan based on that data. Furthermore, the feedback unit can continuously collect user health data, and the generating AI can analyze that data to modify the plan. For example, the feedback unit can periodically collect user health data, and the generating AI can modify the plan based on that data. In this way, the feedback unit can modify the plan based on user feedback. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the collected feedback data into the generating AI and have the generating AI perform the plan modification.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect health data during times when the user is relaxed. For example, the data collection unit can collect exercise data during times when the user is relaxed. Also, if the user is tired, the data collection unit can collect health data after rest. For example, the data collection unit can collect dietary data after the user has rested. Furthermore, if the user is excited, the data collection unit can collect health data after their emotions have calmed down. For example, the data collection unit can collect medical history after the user's emotions have calmed down. This allows the data collection unit to collect health data at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of data collection.

[0077] The data collection unit can analyze the user's past health data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on the devices and applications the user has used in the past. For example, the data collection unit can suggest the optimal collection method based on the wearable devices the user has used in the past. The data collection unit can also analyze the user's past data collection frequency and set an appropriate collection interval. For example, the data collection unit can set an appropriate collection interval based on the user's past data collection frequency. Furthermore, the data collection unit can suggest the optimal collection timing based on the user's past data collection patterns. For example, the data collection unit can suggest the optimal collection timing based on the user's past data collection patterns. This allows the data collection unit to select the optimal collection method based on the user's past history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data collection history into a generating AI and have the generating AI select the optimal collection method.

[0078] The data collection unit can filter health data based on the user's current lifestyle and areas of interest. For example, if the user is on a diet, the data collection unit can prioritize collecting data on their diet. For example, if the user is on a diet, the data collection unit can prioritize collecting data on calorie intake and nutrients. The data collection unit can also prioritize collecting fitness data if the user is training. For example, if the user is training, the data collection unit can prioritize collecting data on exercise frequency and intensity. Furthermore, if the user has just undergone a health checkup, the data collection unit can prioritize collecting data on their medical history. For example, if the user has just undergone a health checkup, the data collection unit can prioritize collecting data on their past medical history and allergy information. This allows the data collection unit to prioritize collecting data that is appropriate to the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0079] The data collection unit can estimate the user's emotions and determine the priority of health data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting stress-related data. For example, if the user is stressed, the data collection unit may prioritize collecting heart rate and blood pressure data. Also, if the user is relaxed, the data collection unit may collect overall health data in a balanced manner. For example, if the user is relaxed, the data collection unit may prioritize collecting exercise data and dietary data. Furthermore, if the user is tired, the data collection unit may prioritize collecting data related to rest and sleep. For example, if the user is tired, the data collection unit may prioritize collecting data on sleep duration and sleep quality. In this way, the data collection unit can determine the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then determine the priority of the data to be collected.

[0080] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit can prioritize the collection of exercise data at high altitude. For example, if the user is at high altitude, the data collection unit can prioritize the collection of heart rate and oxygen saturation data at high altitude. The data collection unit can also prioritize the collection of activity data in urban areas if the user is in an urban area. For example, if the user is in an urban area, the data collection unit can prioritize the collection of step count and exercise time data. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of meal and exercise data at the travel destination. For example, if the user is traveling, the data collection unit can prioritize the collection of meal content and exercise patterns at the travel destination. In this way, the data collection unit can collect highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0081] The data collection unit can analyze the user's social media activity and collect relevant data when collecting health data. For example, the data collection unit can collect dietary data based on the meal content shared by the user on social media. For example, the data collection unit can collect data on calorie intake and nutrients based on photos and comments of meals shared by the user on social media. The data collection unit can also collect fitness data based on exercise records shared by the user on social media. For example, the data collection unit can collect data on exercise frequency and intensity based on photos and comments of exercise shared by the user on social media. Furthermore, the data collection unit can update the medical history based on health information shared by the user on social media. For example, the data collection unit can update the medical history based on health checkup results and medical history information shared by the user on social media. In this way, the data collection unit can collect relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0082] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can focus on analyzing stress-related data. For example, if the user is stressed, the analysis unit can focus on analyzing heart rate and blood pressure data. Also, if the user is relaxed, the analysis unit can analyze overall health data in a balanced way. For example, if the user is relaxed, the analysis unit can analyze exercise data and diet data in a balanced way. Furthermore, if the user is tired, the analysis unit can focus on analyzing data related to rest and sleep. For example, if the user is tired, the analysis unit can focus on analyzing sleep duration and sleep quality data. In this way, the analysis unit can adjust the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust the data analysis method.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can analyze high-importance data (e.g., heart rate, blood pressure) in detail. For example, the analysis unit can analyze heart rate and blood pressure data in detail. The analysis unit can also analyze moderately important data (e.g., steps taken, calorie consumption) to a moderate degree. For example, the analysis unit can analyze steps taken and calorie consumption data to a moderate degree. Furthermore, the analysis unit can also analyze low-importance data (e.g., water intake) in a simplified manner. For example, the analysis unit can analyze water intake data in a simplified manner. In this way, the analysis unit can adjust the level of detail of the analysis according to the importance of the health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply an exercise pattern analysis algorithm to fitness data. For example, the analysis unit can apply an exercise pattern analysis algorithm to fitness data to evaluate the frequency and intensity of exercise. The analysis unit can also apply a nutritional balance evaluation algorithm to dietary content. For example, the analysis unit can apply a nutritional balance evaluation algorithm to dietary content to evaluate the balance of calories and nutrients. Furthermore, the analysis unit can apply a health risk evaluation algorithm to medical history. For example, the analysis unit can apply a health risk evaluation algorithm to medical history to evaluate health risks based on past medical history and allergy information. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of health data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0085] The generation unit can estimate the user's emotions and adjust the way it presents health advice and plans based on those estimated emotions. For example, if the user is stressed, the generation unit can use a simple and easy-to-understand presentation. For instance, if the user is stressed, the generation unit can provide health advice and plans in concise language. Furthermore, if the user is relaxed, the generation unit can use a presentation that includes detailed explanations. For instance, if the user is relaxed, the generation unit can provide health advice and plans that include detailed explanations. Additionally, if the user is excited, the generation unit can use a visually stimulating presentation. For example, if the user is excited, the generation unit can provide health advice and plans using visually appealing graphics or animations. This allows the generation unit to adjust the presentation of health advice and plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the expression method.

[0086] The generation unit can adjust the level of detail in the generated health advice and plans based on their importance. For example, the generation unit can include detailed explanations for highly important advice and plans. For example, the generation unit can provide content with detailed explanations for highly important health advice and plans. The generation unit can also provide a moderate level of detail for advice and plans of moderate importance. For example, the generation unit can provide content with a moderate level of detail for health advice and plans of moderate importance. Furthermore, the generation unit can include concise explanations for less important advice and plans. For example, the generation unit can provide content with concise explanations for less important health advice and plans. This allows the generation unit to adjust the level of detail in the generated health advice and plans according to their importance. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the importance of the health advice and plans into the generation AI and have the generation AI adjust the level of detail in the generated health advice and plans.

[0087] The generation unit can apply different generation algorithms depending on the category of health advice or plan during generation. For example, the generation unit can apply an exercise pattern generation algorithm to a fitness plan. For example, the generation unit can apply an exercise pattern generation algorithm to a fitness plan to determine the type and frequency of exercise. The generation unit can also apply a nutrition balance generation algorithm to a meal plan. For example, the generation unit can apply a nutrition balance generation algorithm to a meal plan to determine the menu and quantity of meals. Furthermore, the generation unit can apply a health risk assessment generation algorithm to health advice. For example, the generation unit can apply a health risk assessment generation algorithm to health advice to assess the user's health risk. This allows the generation unit to apply an appropriate generation algorithm depending on the category of health advice or plan. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the categories of health advice or plans into a generation AI and have the generation AI perform the application of the generation algorithm.

[0088] The service provider can estimate the user's emotions and adjust the way health advice and plans are delivered based on the estimated emotions. For example, if the user is stressed, the service provider can deliver the advice and plans in a simple and easy-to-understand manner. For example, if the user is stressed, the service provider can deliver the advice and plans in concise language. If the user is relaxed, the service provider can deliver the advice and plans in a manner that includes detailed explanations. For example, if the user is relaxed, the service provider can deliver the advice and plans in a manner that includes detailed explanations. Furthermore, if the user is excited, the service provider can deliver the advice and plans in a visually stimulating manner. For example, if the user is excited, the service provider can deliver the advice and plans using visually appealing graphics or animations. This allows the service provider to adjust the way health advice and plans are delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​adjust the delivery method.

[0089] The delivery unit can select the optimal delivery method by referring to the user's past health advice and plan history. For example, the delivery unit can prioritize using delivery methods that the user has preferred in the past (e.g., email, app notifications). For example, the delivery unit can select the optimal delivery method based on the email and app notifications that the user has preferred in the past. The delivery unit can also select the most effective delivery method from the user's past history. For example, the delivery unit can select the most effective delivery method based on the user's past history. Furthermore, the delivery unit can customize the delivery method based on the user's past feedback. For example, the delivery unit can customize the delivery method based on the user's past feedback. This allows the delivery unit to select the optimal delivery method based on the user's past history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's past health advice and plan history into a generating AI and have the generating AI select the optimal delivery method.

[0090] The service provider can customize the means of delivering health advice and plans based on the user's current lifestyle at the time of delivery. For example, if the user is busy, the service provider can provide concise notifications or reminders. For example, if the user is busy, the service provider can provide health advice and plans through concise notifications or reminders. The service provider can also provide notifications with detailed explanations if the user is relaxed. For example, if the user is relaxed, the service provider can provide health advice and plans through notifications with detailed explanations. Furthermore, if the user is traveling, the service provider can provide advice and plans tailored to their travel destination. For example, if the user is traveling, the service provider can provide health advice and plans tailored to their travel destination. This allows the service provider to customize the means of delivery according to the user's lifestyle. 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 the user's current lifestyle data into a generating AI and have the generating AI perform the customization of the means of delivery.

[0091] The service provider can estimate the user's emotions and determine the priority of providing health advice and plans based on the estimated emotions. For example, if the user is stressed, the service provider can prioritize providing stress-reducing advice. For example, if the user is stressed, the service provider can prioritize providing health advice related to stress reduction. The service provider can also provide a balanced range of overall health advice if the user is relaxed. For example, if the user is relaxed, the service provider can provide a balanced range of overall health advice. Furthermore, if the user is tired, the service provider can prioritize providing advice related to rest and sleep. For example, if the user is tired, the service provider can prioritize providing health advice related to rest and sleep. In this way, the service provider can determine the priority of services provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​determine the priority of services to be provided.

[0092] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is at high altitude, the service provider can provide advice on exercise at high altitude. For example, if the user is at high altitude, the service provider can provide health advice on exercise at high altitude. The service provider can also provide advice on activities in urban areas if the user is in an urban area. For example, if the user is in an urban area, the service provider can provide health advice on activities in urban areas. Furthermore, if the user is traveling, the service provider can provide meal and exercise plans tailored to the travel destination. For example, if the user is traveling, the service provider can provide meal and exercise plans tailored to the travel destination. This allows the service provider to select the optimal service delivery method based on 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 the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0093] The service provider can analyze the user's social media activity at the time of delivery and propose means of providing health advice and plans. For example, the service provider can provide meal plans based on the content of meals shared by the user on social media. For example, the service provider can provide meal plans based on photos and comments of meals shared by the user on social media. The service provider can also provide exercise plans based on exercise records shared by the user on social media. For example, the service provider can provide exercise plans based on photos and comments of exercise shared by the user on social media. Furthermore, the service provider can provide health advice based on health information shared by the user on social media. For example, the service provider can provide health advice based on the results of health checkups and medical history information shared by the user on social media. In this way, the service provider can propose means of delivery based on the user's social media activity. 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 the user's social media activity data into a generating AI and have the generating AI execute the proposal of means of delivery.

[0094] The feedback unit can estimate the user's emotions and adjust the method of collecting feedback based on the estimated user emotions. For example, if the user is stressed, the feedback unit can provide a concise feedback form. For example, if the user is stressed, the feedback unit can collect feedback through a concise feedback form. The feedback unit can also provide a detailed feedback form if the user is relaxed. For example, if the user is relaxed, the feedback unit can collect feedback through a detailed feedback form. Furthermore, if the user is excited, the feedback unit can provide a visually stimulating feedback form. For example, if the user is excited, the feedback unit can collect feedback through a visually appealing feedback form. This allows the feedback unit to adjust the method of collecting feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user emotion data into a generating AI and have the generating AI adjust the data collection method.

[0095] The feedback unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback unit can prioritize using feedback methods that the user has preferred in the past (e.g., surveys, voice memos). For example, the feedback unit can select the optimal collection method based on surveys and voice memos that the user has preferred in the past. The feedback unit can also select the most effective collection method from the user's past feedback history. For example, the feedback unit can select the most effective collection method based on the user's past feedback history. Furthermore, the feedback unit can customize the collection method based on the user's past feedback. For example, the feedback unit can customize the collection method based on the user's past feedback. This allows the feedback unit to select the optimal collection method based on the user's past history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past feedback history into a generating AI and have the generating AI select the optimal collection method.

[0096] The feedback unit can customize the means of collecting feedback based on the user's current living situation when collecting feedback. For example, if the user is busy, the feedback unit can provide a concise feedback form. For example, if the user is busy, the feedback unit can collect feedback through a concise feedback form. The feedback unit can also provide a detailed feedback form if the user is relaxed. For example, if the user is relaxed, the feedback unit can collect feedback through a detailed feedback form. Furthermore, if the user is traveling, the feedback unit can provide a feedback form tailored to their travel destination. For example, if the user is traveling, the feedback unit can collect feedback through a feedback form tailored to their travel destination. This allows the feedback unit to customize the means of collection according to the user's living situation. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the collection means.

[0097] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit can prioritize collecting feedback related to stress reduction. For example, if the user is stressed, the feedback unit can prioritize collecting feedback related to stress reduction. For example, if the user is relaxed, the feedback unit can prioritize collecting overall health feedback in a balanced manner. For example, if the user is relaxed, the feedback unit can prioritize collecting overall health feedback in a balanced manner. Furthermore, if the user is tired, the feedback unit can prioritize collecting feedback related to rest and sleep. For example, if the user is tired, the feedback unit can prioritize collecting feedback related to rest and sleep. In this way, the feedback unit can determine the priority of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user emotion data into a generating AI and have the generating AI determine the priority of the feedback.

[0098] The feedback unit can select the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, if the user is at high altitude, the feedback unit can collect feedback on activities at high altitude. For example, if the user is at high altitude, the feedback unit can collect feedback on exercise and health status at high altitude. The feedback unit can also collect feedback on activities in urban areas if the user is in urban areas. For example, if the user is in urban areas, the feedback unit can collect feedback on exercise and health status in urban areas. Furthermore, if the user is traveling, the feedback unit can collect feedback on meals and exercise at their travel destination. For example, if the user is traveling, the feedback unit can collect feedback on meals and exercise at their travel destination. This allows the feedback unit to select the optimal collection method based on the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal collection method.

[0099] The feedback unit can analyze the user's social media activity and propose methods for collecting feedback when collecting it. For example, the feedback unit can collect feedback on meals based on the content of meals shared by the user on social media. For example, the feedback unit can collect feedback on meals based on photos and comments of meals shared by the user on social media. The feedback unit can also collect feedback on exercise based on exercise records shared by the user on social media. For example, the feedback unit can collect feedback on exercise based on photos and comments of exercise shared by the user on social media. Furthermore, the feedback unit can collect feedback on health based on health information shared by the user on social media. For example, the feedback unit can collect feedback on health based on the results of health checkups and medical history information shared by the user on social media. This allows the feedback unit to propose collection methods based on the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of collection methods.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The generating AI agent can adjust the frequency and timing of data collection by considering the user's lifestyle and daily behavior patterns when collecting user health data. For example, if a user has a habit of jogging every morning, the collection unit can collect exercise data during that time. Similarly, if a user has a habit of eating at night, the collection of meal data can be concentrated at night. Furthermore, if a user has time to relax on weekends, medical history data can be collected during that time. In this way, the collection unit can collect health data at the optimal time according to the user's lifestyle.

[0102] The generating AI agent can customize the scope of data collection based on the user's health goals and interests when collecting user health data. For example, if the user's goal is weight loss, the collection unit can prioritize collecting data on weight and calorie intake. If the user is interested in strength training, it can also collect data on the frequency and intensity of strength training. Furthermore, if the user is interested in consuming a specific nutrient, it can collect data on that nutrient. This allows the collection unit to customize the scope of data collection according to the user's health goals and interests.

[0103] The generating AI agent can analyze a user's health data and evaluate their current health status by comparing it to the user's past health data. For example, the analysis unit can compare the user's past exercise data with their current exercise data to evaluate improvements or declines in exercise performance. It can also compare the user's past dietary data with their current dietary data to evaluate changes in nutritional balance. Furthermore, it can compare the user's past medical history with their current health data to evaluate changes in health risks. This allows the analysis unit to evaluate the user's current health status by comparing it to their past health data.

[0104] The generating AI agent can benchmark a user's health data by comparing it to data from other users when analyzing the data. For example, the analysis unit can benchmark a user's exercise data against data from other users of the same age group to assess exercise performance. It can also benchmark a user's dietary data against data from other users following the same meal plan to assess nutritional balance. Furthermore, it can benchmark a user's health risk by comparing their medical history against data from other users with similar health risks. In this way, the analysis unit can benchmark a user's health data against data from other users.

[0105] The generating AI agent can monitor the user's health data in real time and detect anomalies when analyzing the data. For example, the analysis unit can monitor the user's heart rate data in real time and detect abnormal heart rates. It can also monitor the user's blood pressure data in real time and detect abnormal blood pressure. Furthermore, it can monitor the user's blood glucose level data in real time and detect abnormal blood glucose levels. As a result, the analysis unit can monitor the user's health data in real time and detect anomalies.

[0106] The generating AI agent can estimate the user's emotions and adjust the content of health advice and plans based on those emotions. For example, if the user is feeling stressed, the generating unit can suggest exercise and meal plans that are effective in reducing stress. If the user is relaxed, it can also provide health advice to help maintain that relaxation. Furthermore, if the user is agitated, it can suggest relaxation methods to calm them down. In this way, the generating unit can adjust the content of health advice and plans according to the user's emotions.

[0107] The generating AI agent can estimate the user's emotions and adjust the timing of health advice and plans based on those estimated emotions. For example, if the user is feeling stressed, the agent can provide health advice during a time when the user is relaxed. If the user is tired, it can provide health advice after they have rested. Furthermore, if the user is agitated, it can provide health advice after their emotions have calmed down. This allows the agent to adjust the timing of health advice and plans according to the user's emotions.

[0108] The generating AI agent can estimate the user's emotions and customize how health advice and plans are delivered based on those estimated emotions. For example, if the user is stressed, the agent can provide health advice in a simple and easy-to-understand way. If the user is relaxed, it can provide health advice with more detailed explanations. Furthermore, if the user is excited, it can provide health advice in a visually stimulating way. This allows the agent to customize how health advice and plans are delivered according to the user's emotions.

[0109] The generating AI agent can estimate the user's emotions and adjust how feedback is collected based on those emotions. For example, if the user is stressed, the feedback unit can provide a concise feedback form. If the user is relaxed, it can provide a more detailed feedback form. Furthermore, if the user is excited, it can provide a visually stimulating feedback form. This allows the feedback unit to adjust how feedback is collected according to the user's emotions.

[0110] The generating AI agent can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is feeling stressed, the feedback unit can prioritize collecting stress-reducing feedback. If the user is relaxed, it can also collect a balanced range of overall health feedback. Furthermore, if the user is tired, it can prioritize collecting feedback related to rest and sleep. In this way, the feedback unit can prioritize feedback according to the user's emotions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects the user's health data. The data collection unit collects health data such as fitness data, dietary information, and medical history. The data collection unit can collect the user's exercise data using a wearable device. It can also collect the user's dietary information in digital format. Furthermore, it can obtain the user's medical history from electronic medical records. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using text analysis and image processing. For example, it can use natural language processing technology to analyze the user's diet and evaluate nutritional balance. It can also use image recognition technology to analyze fitness data and evaluate exercise patterns. Step 3: The generation unit generates health advice and exercise / meal plans based on the analysis results obtained by the analysis unit. The generation unit uses generation AI to generate optimal health advice for the user. For example, it can suggest an appropriate exercise plan to users who are not getting enough exercise, and provide a balanced meal plan to users whose nutritional balance is unbalanced. Step 4: The delivery unit provides the plan generated by the generation unit. The delivery unit can provide health advice and exercise / meal plans to users via app notifications or email. It can also provide plans to users through a dashboard display. Step 5: The Feedback Department collects user feedback based on the plan provided by the Delivery Department and modifies the plan. The Feedback Department can collect user behavior logs and use the Generating AI to analyze that data and modify the plan. It can also collect user-entered feedback and use the Generating AI to analyze that data and modify the plan.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects the user's exercise data using a wearable device. Alternatively, the collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and obtains medical history from electronic medical records. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using text analysis and image processing. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates health advice and exercise / meal plans using generation AI. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the plan to the user via app notifications or email. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects the user's behavior log, and the generation AI analyzes that data to modify the plan. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects the user's exercise data using a wearable device. Alternatively, the collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and obtains medical history from electronic medical records. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using text analysis and image processing. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates health advice and exercise / meal plans using generation AI. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the plan to the user via app notifications or email. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects the user's behavior log, and the generation AI analyzes that data to modify the plan. 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] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects the user's exercise data using a wearable device. The collection unit is also implemented by the specific processing unit 290 of the data processing unit 12 and obtains medical history from electronic medical records. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using text analysis and image processing. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates health advice and exercise / meal plans using generation AI. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the plan to the user via app notifications or email. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects the user's behavior log, and the generation AI analyzes that data to modify the plan. 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] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[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 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.

[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 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).

[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] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and feedback unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects the user's exercise data using a wearable device. Alternatively, the collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and obtains medical history from electronic medical records. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the data using text analysis and image processing. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates health advice and exercise / meal plans using generation AI. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the plan to the user via app notifications or email. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and collects the user's behavior log, and the generation AI analyzes that data to modify the plan. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A collection unit that collects user health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates health advice and exercise / meal plans based on the analysis results obtained by the aforementioned analysis unit, A providing unit that provides the plan generated by the generation unit, The system includes a feedback unit that collects user feedback based on the plan provided by the aforementioned provisioning unit and modifies the plan. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects user health data such as fitness data, dietary information, and medical history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected health data is analyzed using text analysis and image processing. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on the analysis results, the system generates personalized health advice and exercise / meal plans for the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides users with generated health advice and exercise / meal plans. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is The system collects user feedback, and a generative AI analyzes that data to refine the plan. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past health data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the health data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting health data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting health data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate user emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts how health advice and plans are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the level of detail is adjusted based on the importance of the health advice and plan. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, different generation algorithms are applied depending on the category of health advice or plan. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts how health advice and plans are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, The system selects the optimal delivery method by referring to the user's past health advice and plan history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, the method of delivering health advice and plans will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes providing health advice and plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and suggest ways to deliver health advice and plans. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When collecting feedback, the system selects the optimal collection method by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When collecting feedback, customize the feedback collection method based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When collecting feedback, the optimal collection method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When collecting feedback, we analyze users' social media activity and suggest methods for collecting feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates health advice and exercise / meal plans based on the analysis results obtained by the aforementioned analysis unit, A providing unit that provides the plan generated by the generation unit, The system includes a feedback unit that collects user feedback based on the plan provided by the aforementioned provisioning unit and modifies the plan. A system characterized by the following features.

2. The aforementioned collection unit is Collects user health data such as fitness data, dietary information, and medical history. The system according to feature 1.

3. The aforementioned analysis unit is The collected health data is analyzed using text analysis and image processing. The system according to feature 1.

4. The generating unit is Based on the analysis results, the system generates optimal health advice and exercise / meal plans for the user. The system according to feature 1.

5. The aforementioned supply unit is, Provides users with generated health advice and exercise / meal plans. The system according to feature 1.

6. The aforementioned feedback unit is The system collects user feedback, and a generating AI analyzes that data to revise the plan. The system according to feature 1.

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

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

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