system

Abstract

The system according to this embodiment aims to propose an optimal exercise program for pregnant women, monitor their implementation status, and provide feedback. [Solution] The system according to the embodiment comprises a data collection unit, a proposal unit, a monitoring unit, and a feedback unit. The data collection unit collects data on the pregnant woman's physical condition. The proposal unit proposes an optimal exercise program for the pregnant woman based on the data collected by the data collection unit. The monitoring unit monitors the implementation status of the exercise program proposed by the proposal unit. The feedback unit provides feedback based on the implementation status monitored by the monitoring unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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, a suitable exercise program for pregnant women has not been sufficiently proposed and the implementation status has not been sufficiently monitored, leaving room for improvement.

[0005] The system according to the embodiment aims to propose an optimal exercise program for pregnant women, monitor the implementation status, and provide feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a proposal unit, a monitoring unit, and a feedback unit. The data collection unit collects data on the pregnant woman's physical condition. The proposal unit proposes an optimal exercise program for the pregnant woman based on the data collected by the data collection unit. The monitoring unit monitors the implementation status of the exercise program proposed by the proposal unit. The feedback unit provides feedback based on the implementation status monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose an optimal exercise program for pregnant women, monitor their implementation status, and provide feedback. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple 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) An AI agent system according to an embodiment of the present invention is a system that proposes an exercise program suitable for pregnant women, monitors their implementation status, and provides feedback. This AI agent system proposes the optimal exercise according to the pregnant woman's physical condition and stage of pregnancy, and provides advice and support in real time. It also responds to consultations and questions in chat format, resolving the pregnant woman's anxieties and doubts. For example, the AI ​​agent system collects information about the pregnant woman's physical condition and stage of pregnancy. For example, it collects data such as the pregnant woman's weight, blood pressure, heart rate, and gestational age. This information is input into the AI ​​agent system. Next, the AI ​​agent system analyzes the collected information and proposes the optimal exercise program for the pregnant woman. For example, it proposes light stretching or walking in the early stages of pregnancy, and yoga or light strength training in the later stages of pregnancy. In this way, it provides an exercise program that is appropriate for the pregnant woman's physical condition and stage of pregnancy. Furthermore, the AI ​​agent system monitors the implementation status of the proposed exercise program in real time. For example, while the pregnant woman is exercising, the AI ​​agent system monitors her heart rate and exercise intensity and provides appropriate feedback. This allows the pregnant woman to exercise safely. The AI ​​agent system also responds to consultations and questions in chat format. For example, if a pregnant woman has questions or concerns about exercise, the AI ​​agent system provides advice and support in real time. This allows the pregnant woman's anxieties and questions to be resolved quickly. As a result, the AI ​​agent system enables pregnant women to have a safe and healthy pregnancy. The AI ​​agent system suggests the most suitable exercises according to the pregnant woman's physical condition and stage of pregnancy, and provides advice and support in real time, supporting a healthy life for both mother and child.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, a suggestion unit, a monitoring unit, and a feedback unit. The data collection unit collects data on the pregnant woman's physical condition. For example, the data collection unit collects data such as the pregnant woman's weight, blood pressure, heart rate, and gestational age. For example, the data collection unit uses a scale to measure the pregnant woman's weight. For example, the data collection unit uses a blood pressure monitor to measure the pregnant woman's blood pressure. For example, the data collection unit uses a heart rate monitor to measure the pregnant woman's heart rate. The suggestion unit suggests an optimal exercise program for the pregnant woman based on the data collected by the data collection unit. For example, the suggestion unit suggests light stretching or walking in the early stages of pregnancy. For example, the suggestion unit suggests yoga or light strength training in the late stages of pregnancy. For example, the suggestion unit suggests aquatic exercise in the middle stages of pregnancy. The monitoring unit monitors the implementation status of the exercise program suggested by the suggestion unit. For example, the monitoring unit monitors the heart rate and exercise intensity while the pregnant woman is exercising. For example, the monitoring unit monitors the duration of exercise while the pregnant woman is exercising. The monitoring unit, for example, monitors the frequency of exercise while the pregnant woman is exercising. The feedback unit provides feedback based on the exercise status monitored by the monitoring unit. For example, the feedback unit advises the pregnant woman to stop exercising if her heart rate is too high while she is exercising. For example, the feedback unit advises the pregnant woman to increase the intensity of her exercise if it is too low while she is exercising. For example, the feedback unit advises the pregnant woman to extend the duration of her exercise if it is too short while she is exercising. In this way, the AI ​​agent system according to the embodiment can support the health of pregnant women by collecting their physical condition data, proposing an optimal exercise program, monitoring their exercise status, and providing feedback.

[0030] The data collection unit collects data on the pregnant woman's physical condition. For example, it collects data such as the pregnant woman's weight, blood pressure, heart rate, and gestational age. Specifically, it uses a scale to measure the pregnant woman's weight and regularly records weight fluctuations. The scale is digital and has a function to automatically transmit measurement results to the cloud. The blood pressure monitor is either an upper arm or wrist type, allowing pregnant women to easily measure their blood pressure at home. The measurement results are transmitted to the data collection unit via Bluetooth® or Wi-Fi. The heart rate monitor is either a chest-worn or wristband type, monitoring the heart rate 24 hours a day. This allows for real-time monitoring of fluctuations in the pregnant woman's heart rate. Gestational age is collected either by the pregnant woman or based on data provided by a medical institution. This data is centrally managed by the data collection unit and serves as foundational data for comprehensively evaluating the pregnant woman's health. The data collection unit regularly updates this data, allowing for real-time monitoring of changes in the pregnant woman's physical condition. Furthermore, the data collection unit performs regular calibration and device maintenance to ensure data accuracy. This allows the data collection unit to accurately and efficiently collect data on the pregnant woman's physical condition, thereby improving the overall reliability of the system.

[0031] The proposal department proposes optimal exercise programs for pregnant women based on data collected by the data collection department. For example, the proposal department suggests light stretching and walking during the first trimester. Specifically, it recommends light stretching and walking that puts minimal strain on the body during the first trimester, promoting muscle relaxation and blood circulation. For the second trimester, it suggests aquatic exercises. Aquatic exercises utilize the buoyancy of water to reduce stress on joints and muscles while strengthening the entire body. For the third trimester, it suggests yoga and light strength training. Yoga enhances relaxation through breathing techniques and poses, supporting the preparation of physical strength for childbirth. Light strength training is important for maintaining physical strength during childbirth and promoting postpartum recovery. The proposal department uses AI to analyze collected data and automatically generates optimal exercise programs tailored to the pregnant woman's physical condition and gestational week. For example, it adjusts the intensity and frequency of exercise considering fluctuations in the pregnant woman's weight, blood pressure, and heart rate. The proposal department also reviews the exercise programs periodically based on feedback from pregnant women, customizing them to meet individual needs. This allows the proposal department to provide pregnant women with safe and effective exercise programs and support their health maintenance.

[0032] The monitoring unit monitors the implementation status of the exercise program proposed by the proposal unit. For example, the monitoring unit monitors the heart rate and exercise intensity while the pregnant woman is exercising. Specifically, it uses a heart rate monitor to monitor the pregnant woman's heart rate in real time and records fluctuations in heart rate during exercise. Exercise intensity is measured using an accelerometer and gyroscope to evaluate the type and intensity of exercise. For example, the monitoring unit monitors the duration of exercise while the pregnant woman is exercising. The duration is determined by recording the start and end times of the exercise and calculating the duration. Furthermore, for example, the monitoring unit monitors the frequency of exercise while the pregnant woman is exercising. The frequency of exercise is recorded on a weekly and monthly basis to evaluate how well the pregnant woman is following the proposed exercise program. The monitoring unit collects this data and comprehensively evaluates the pregnant woman's exercise status. In addition, the monitoring unit can detect abnormal data and risks during exercise early and issue warnings as needed. For example, if the heart rate is abnormally high or the exercise intensity is excessively high, the system will notify the pregnant woman to stop exercising. This allows the monitoring unit to effectively monitor the progress of the exercise program while ensuring the pregnant woman's safety.

[0033] The Feedback Department provides feedback based on the implementation status monitored by the Monitoring Department. For example, the Feedback Department advises pregnant women to stop exercising if their heart rate is too high. Specifically, if the heart rate exceeds the set safety range, the Feedback Department notifies the pregnant woman to stop exercising and rest. For example, the Feedback Department advises pregnant women to increase the intensity of their exercise if it is too low. If the exercise intensity does not meet the standards of the proposed program, the Feedback Department instructs the pregnant woman to increase the exercise intensity and suggests specific methods. For example, the Feedback Department advises pregnant women to extend the duration of their exercise if it is too short. If the duration does not meet the standards of the proposed program, the Feedback Department encourages the pregnant woman to continue exercising and provides specific advice on how to achieve the target time. Furthermore, the Feedback Department collects feedback from pregnant women and uses it to improve the exercise program. For example, by reporting discomfort or difficulties experienced by pregnant women during exercise, the Proposal Department can review the exercise program and adjust it to be more appropriate. This allows the feedback unit to provide appropriate advice to pregnant women and maximize the effectiveness of their exercise programs.

[0034] The AI ​​agent system includes a consultation department that handles inquiries and questions via chat. The consultation department can, for example, answer questions from pregnant women via text chat, voice chat, or video chat. This allows for real-time advice and support when pregnant women have questions or concerns about exercise.

[0035] The data collection unit can collect data such as the pregnant woman's weight, blood pressure, heart rate, and gestational age. For example, the data collection unit uses a scale to measure the pregnant woman's weight. For example, the data collection unit uses a blood pressure monitor to measure the pregnant woman's blood pressure. For example, the data collection unit uses a heart rate monitor to measure the pregnant woman's heart rate. By collecting detailed data on the pregnant woman's physical condition, it becomes possible to suggest a more appropriate exercise program.

[0036] The suggestion department can propose light stretching and walking during early pregnancy, and yoga and light strength training during late pregnancy. For example, the suggestion department can propose light stretching during early pregnancy. For example, the suggestion department can propose walking during early pregnancy. For example, the suggestion department can propose yoga during late pregnancy. For example, the suggestion department can propose light strength training during late pregnancy. By doing so, the health of pregnant women can be supported by proposing an optimal exercise program tailored to each stage of pregnancy.

[0037] The monitoring unit can monitor the heart rate and exercise intensity of a pregnant woman while she is exercising. For example, the monitoring unit can monitor the heart rate while the pregnant woman is exercising. For example, the monitoring unit can monitor the exercise intensity while the pregnant woman is exercising. For example, the monitoring unit can monitor the duration of exercise while the pregnant woman is exercising. This allows for real-time monitoring of the pregnant woman's physical condition during exercise, enabling her to exercise safely.

[0038] The feedback department can provide appropriate feedback based on monitored performance. For example, the feedback department may advise a pregnant woman to stop exercising if her heart rate is too high. For example, the feedback department may advise a pregnant woman to increase the intensity of her exercise if it is too low. For example, the feedback department may advise a pregnant woman to extend the duration of her exercise if it is too short. In this way, by providing feedback based on the performance of the exercise program, it is possible to support pregnant women's exercise.

[0039] The data collection unit can analyze the pregnant woman's past health data and select the optimal data collection method. For example, the data collection unit selects the most effective data collection method based on the pregnant woman's past health data. For example, the data collection unit selects a method for collecting data at a specific time of day based on the pregnant woman's past health data. For example, the data collection unit analyzes the pregnant woman's past health data and adjusts the data collection frequency. In this way, by analyzing past health data, the optimal data collection method can be selected and data can be collected efficiently.

[0040] The data collection unit can filter the health data collected based on the pregnant woman's current lifestyle and activity level. For example, the data collection unit filters the data to be collected considering the pregnant woman's current lifestyle. For example, the data collection unit selects the type of data to be collected based on the pregnant woman's activity level. For example, the data collection unit adjusts the frequency of data collection according to the pregnant woman's lifestyle and activity level. By filtering the data according to the pregnant woman's lifestyle and activity level, more relevant data can be collected.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the pregnant woman's geographical location when collecting health data. For example, if the pregnant woman is at home, the data collection unit prioritizes collecting health data in a relaxed state. If the pregnant woman is out, the data collection unit prioritizes collecting health data during activities. If the pregnant woman is in a hospital, the data collection unit prioritizes collecting medical-related data. By considering the pregnant woman's geographical location, the data collection unit can prioritize the collection of more relevant data.

[0042] The data collection unit can analyze the social media activity of pregnant women when collecting health data and collect relevant data. For example, the data collection unit can analyze signs of stress and relaxation from the pregnant woman's social media activity and collect relevant data. For example, the data collection unit can estimate activity levels from the pregnant woman's social media activity and collect relevant data. For example, the data collection unit can analyze changes in emotions from the pregnant woman's social media activity and collect relevant data. In this way, by analyzing the social media activity of pregnant women, relevant data can be collected and more detailed health data can be obtained.

[0043] The suggestion function can adjust the level of detail in the exercise program based on the pregnant woman's health data when making a suggestion. For example, if the pregnant woman's health data is good, the suggestion function will suggest a detailed exercise program. For example, if the pregnant woman's health data is unstable, the suggestion function will suggest a simplified exercise program. For example, the suggestion function will adjust the intensity of the exercise program based on the pregnant woman's health data. In this way, by adjusting the level of detail in the exercise program based on the pregnant woman's health data, a more appropriate exercise program can be suggested.

[0044] The suggestion function can apply different exercise programs depending on the stage of pregnancy of the pregnant woman. For example, the suggestion function might suggest light stretching or walking in the early stages of pregnancy. For example, it might suggest yoga or light strength training in the second trimester. For example, it might suggest relaxation exercises or breathing exercises in the third trimester. By applying different exercise programs according to the stage of pregnancy, it is possible to suggest a more appropriate exercise program.

[0045] The proposal department can determine the priority of exercise programs based on when the pregnant woman's health data is submitted. For example, if the pregnant woman's health data is up-to-date, the proposal department will prioritize suggesting exercise programs. If the pregnant woman's health data is outdated, the proposal department will prompt an update and suggest exercise programs based on the latest data. The proposal department can adjust the priority of exercise programs based on when the pregnant woman's health data is submitted. By prioritizing exercise programs based on when the pregnant woman's health data is submitted, it is possible to suggest more appropriate exercise programs.

[0046] The suggestion unit can adjust the order of exercise programs based on the relationship of the pregnant woman's health data when making a suggestion. For example, if the pregnant woman's health data is good, the suggestion unit will prioritize high-intensity exercise programs. For example, if the pregnant woman's health data is unstable, the suggestion unit will prioritize low-intensity exercise programs. The suggestion unit adjusts the order of exercise programs based on the relationship of the pregnant woman's health data. By doing so, it can suggest a more appropriate exercise program by adjusting the order of exercise programs based on the relationship of the pregnant woman's health data.

[0047] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships of the pregnant woman's health data during monitoring. For example, the monitoring unit monitors by considering the interrelationship between the pregnant woman's heart rate and blood pressure. For example, the monitoring unit monitors by considering the interrelationship between the pregnant woman's weight and activity level. For example, the monitoring unit analyzes the interrelationships of the pregnant woman's health data to improve the accuracy of monitoring. In this way, the accuracy of monitoring can be improved by considering the interrelationships of the pregnant woman's health data.

[0048] The monitoring unit can perform monitoring while considering the pregnant woman's attribute information. For example, the monitoring unit can monitor while considering the pregnant woman's age and weight. For example, the monitoring unit can monitor while considering the pregnant woman's gestational age. For example, the monitoring unit can monitor while considering the pregnant woman's health status. This allows for more appropriate monitoring by considering the pregnant woman's attribute information.

[0049] The monitoring unit can perform monitoring while considering the geographical distribution of pregnant women. For example, if a pregnant woman is at home, the monitoring unit will monitor her health data at home. For example, if a pregnant woman is out, the monitoring unit will monitor her health data at her destination. For example, if a pregnant woman is in a hospital, the monitoring unit will monitor her health data at the hospital. This allows for more appropriate monitoring by considering the geographical distribution of pregnant women.

[0050] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature on pregnant women during monitoring. For example, the monitoring unit monitors by referring to the latest research on the physical condition of pregnant women. For example, the monitoring unit monitors by referring to relevant literature on the health status of pregnant women. For example, the monitoring unit improves the accuracy of monitoring by comparing the physical condition data of pregnant women with relevant literature. In this way, the accuracy of monitoring can be improved by referring to relevant literature on pregnant women.

[0051] The feedback unit can analyze the pregnant woman's health data during the feedback process to select the most appropriate feedback method. For example, if the pregnant woman's health data is good, the feedback unit will provide detailed feedback. For example, if the pregnant woman's health data is unstable, the feedback unit will provide concise feedback. For example, the feedback unit will analyze the pregnant woman's health data and adjust the content of the feedback. In this way, by analyzing the pregnant woman's health data, the optimal feedback method can be selected, and more appropriate feedback can be provided.

[0052] The feedback unit can customize the means of feedback based on the pregnant woman's current living situation. For example, if the pregnant woman is at home, the feedback unit provides feedback in a relaxed environment. For example, if the pregnant woman is out, the feedback unit provides concise and quick feedback. The feedback unit adjusts the means of feedback according to the pregnant woman's living situation. This allows for the provision of more appropriate feedback by customizing the means of feedback based on the pregnant woman's current living situation.

[0053] The feedback unit can select the most appropriate feedback method when providing feedback, taking into account the pregnant woman's geographical location. For example, if the pregnant woman is at home, the feedback unit will provide feedback at home. For example, if the pregnant woman is out, the feedback unit will provide feedback at her location. For example, if the pregnant woman is in a hospital, the feedback unit will provide medical-related feedback. This allows for the selection of a more appropriate feedback method by considering the pregnant woman's geographical location.

[0054] The feedback unit can analyze the pregnant woman's social media activity during the feedback process and propose methods for providing feedback. For example, the feedback unit can analyze signs of stress and relaxation from the pregnant woman's social media activity and provide relevant feedback. For example, the feedback unit can estimate the activity level from the pregnant woman's social media activity and provide relevant feedback. For example, the feedback unit can analyze changes in emotion from the pregnant woman's social media activity and provide relevant feedback. In this way, by analyzing the pregnant woman's social media activity, it is possible to propose more appropriate methods for providing feedback.

[0055] The consultation department can select the most appropriate course of action by referring to the pregnant woman's past consultation history during a consultation. For example, the consultation department selects the most appropriate course of action based on the pregnant woman's past consultation history. For example, the consultation department selects a course of action for a specific problem from the pregnant woman's past consultation history. For example, the consultation department analyzes the pregnant woman's past consultation history and adjusts the course of action. In this way, by referring to the pregnant woman's past consultation history, a more appropriate course of action can be selected.

[0056] The consultation department can select the most appropriate response method during a consultation, taking into account the pregnant woman's device information. For example, if the pregnant woman is using a smartphone, the consultation department will provide a response method optimized for smartphones. For example, if the pregnant woman is using a tablet, the consultation department will provide a response method optimized for tablets. For example, if the pregnant woman is using a personal computer, the consultation department will provide a response method optimized for personal computers. This allows for the selection of a more appropriate response method by considering the pregnant woman's device information.

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

[0058] The data collection unit can analyze a pregnant woman's past health data and select the optimal data collection method. For example, it can select the most effective collection method based on the pregnant woman's past health data. It can also select a method for collecting data at specific time periods based on the pregnant woman's past health data. Furthermore, it can analyze the pregnant woman's past health data and adjust the collection frequency. This allows for the selection of the optimal data collection method and efficient data collection by analyzing past health data.

[0059] The feedback unit can analyze the pregnant woman's health data during the feedback process to select the most appropriate feedback method. For example, if the pregnant woman's health data is good, detailed feedback can be provided. If the pregnant woman's health data is unstable, concise feedback can be provided. The feedback content can be adjusted by analyzing the pregnant woman's health data. This allows for the selection of the most appropriate feedback method and the provision of more suitable feedback by analyzing the pregnant woman's health data.

[0060] The data collection unit can filter the health data collected based on the pregnant woman's current lifestyle and activity level. For example, it can filter the data to be collected considering the pregnant woman's current lifestyle. It can select the type of data to be collected based on the pregnant woman's activity level. It can adjust the frequency of data collection according to the pregnant woman's lifestyle and activity level. This allows for the collection of more relevant data by filtering the data according to the pregnant woman's lifestyle and activity level.

[0061] The proposal function can adjust the level of detail in the exercise program based on the pregnant woman's health data during the proposal process. For example, if the pregnant woman's health data is good, a detailed exercise program can be proposed. If the pregnant woman's health data is unstable, a simplified exercise program can be proposed. The intensity of the exercise program can also be adjusted based on the pregnant woman's health data. By adjusting the level of detail in the exercise program based on the pregnant woman's health data, a more appropriate exercise program can be proposed.

[0062] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships of the pregnant woman's health data during monitoring. For example, it can monitor by considering the interrelationship between the pregnant woman's heart rate and blood pressure. It can monitor by considering the interrelationship between the pregnant woman's weight and activity level. By analyzing the interrelationships of the pregnant woman's health data, the accuracy of monitoring can be improved. In this way, the accuracy of monitoring can be improved by considering the interrelationships of the pregnant woman's health data.

[0063] The consultation department can select the most appropriate response method during a consultation by considering the pregnant woman's device information. For example, if the pregnant woman is using a smartphone, a response method optimized for smartphones can be provided. If the pregnant woman is using a tablet, a response method optimized for tablets can be provided. If the pregnant woman is using a personal computer, a response method optimized for personal computers can be provided. In this way, by considering the pregnant woman's device information, a more appropriate response method can be selected.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects data on the pregnant woman's physical condition. The data collection unit collects data such as the pregnant woman's weight, blood pressure, heart rate, and gestational age. The data collection unit uses a scale to measure the pregnant woman's weight, a blood pressure monitor to measure the pregnant woman's blood pressure, and a heart rate monitor to measure the pregnant woman's heart rate. Step 2: The suggestion unit proposes an optimal exercise program for pregnant women based on the data collected by the collection unit. For example, the suggestion unit might suggest light stretching or walking during the first trimester of pregnancy. For example, it might suggest yoga or light strength training during the third trimester of pregnancy. For example, it might suggest aquatic exercises during the second trimester of pregnancy. Step 3: The monitoring unit monitors the implementation status of the exercise program proposed by the proposal unit. For example, the monitoring unit monitors the heart rate and exercise intensity while the pregnant woman is exercising. For example, the monitoring unit monitors the duration of exercise while the pregnant woman is exercising. For example, the monitoring unit monitors the frequency of exercise while the pregnant woman is exercising. Step 4: The Feedback Department provides feedback based on the implementation status monitored by the Monitoring Department. For example, the Feedback Department may advise a pregnant woman to stop exercising if her heart rate is too high while she is exercising. For example, the Feedback Department may advise a pregnant woman to increase the intensity of her exercise if it is too low while she is exercising. For example, the Feedback Department may advise a pregnant woman to extend the duration of her exercise if it is too short while she is exercising.

[0066] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that proposes an exercise program suitable for pregnant women, monitors their implementation status, and provides feedback. This AI agent system proposes the optimal exercise according to the pregnant woman's physical condition and stage of pregnancy, and provides advice and support in real time. It also responds to consultations and questions in chat format, resolving the pregnant woman's anxieties and doubts. For example, the AI ​​agent system collects information about the pregnant woman's physical condition and stage of pregnancy. For example, it collects data such as the pregnant woman's weight, blood pressure, heart rate, and gestational age. This information is input into the AI ​​agent system. Next, the AI ​​agent system analyzes the collected information and proposes the optimal exercise program for the pregnant woman. For example, it proposes light stretching or walking in the early stages of pregnancy, and yoga or light strength training in the later stages of pregnancy. In this way, it provides an exercise program that is appropriate for the pregnant woman's physical condition and stage of pregnancy. Furthermore, the AI ​​agent system monitors the implementation status of the proposed exercise program in real time. For example, while the pregnant woman is exercising, the AI ​​agent system monitors her heart rate and exercise intensity and provides appropriate feedback. This allows the pregnant woman to exercise safely. The AI ​​agent system also responds to consultations and questions in chat format. For example, if a pregnant woman has questions or concerns about exercise, the AI ​​agent system provides advice and support in real time. This allows the pregnant woman's anxieties and questions to be resolved quickly. As a result, the AI ​​agent system enables pregnant women to have a safe and healthy pregnancy. The AI ​​agent system suggests the most suitable exercises according to the pregnant woman's physical condition and stage of pregnancy, and provides advice and support in real time, supporting a healthy life for both mother and child.

[0067] The AI ​​agent system according to this embodiment comprises a data collection unit, a suggestion unit, a monitoring unit, and a feedback unit. The data collection unit collects data on the pregnant woman's physical condition. For example, the data collection unit collects data such as the pregnant woman's weight, blood pressure, heart rate, and gestational age. For example, the data collection unit uses a scale to measure the pregnant woman's weight. For example, the data collection unit uses a blood pressure monitor to measure the pregnant woman's blood pressure. For example, the data collection unit uses a heart rate monitor to measure the pregnant woman's heart rate. The suggestion unit suggests an optimal exercise program for the pregnant woman based on the data collected by the data collection unit. For example, the suggestion unit suggests light stretching or walking in the early stages of pregnancy. For example, the suggestion unit suggests yoga or light strength training in the late stages of pregnancy. For example, the suggestion unit suggests aquatic exercise in the middle stages of pregnancy. The monitoring unit monitors the implementation status of the exercise program suggested by the suggestion unit. For example, the monitoring unit monitors the heart rate and exercise intensity while the pregnant woman is exercising. For example, the monitoring unit monitors the duration of exercise while the pregnant woman is exercising. The monitoring unit, for example, monitors the frequency of exercise while the pregnant woman is exercising. The feedback unit provides feedback based on the exercise status monitored by the monitoring unit. For example, the feedback unit advises the pregnant woman to stop exercising if her heart rate is too high while she is exercising. For example, the feedback unit advises the pregnant woman to increase the intensity of her exercise if it is too low while she is exercising. For example, the feedback unit advises the pregnant woman to extend the duration of her exercise if it is too short while she is exercising. In this way, the AI ​​agent system according to the embodiment can support the health of pregnant women by collecting their physical condition data, proposing an optimal exercise program, monitoring their exercise status, and providing feedback.

[0068] The data collection unit collects data on the pregnant woman's health. For example, it collects data such as weight, blood pressure, heart rate, and gestational age. Specifically, it uses a scale to measure the pregnant woman's weight and regularly records weight fluctuations. The scale is digital and has a function to automatically transmit measurement results to the cloud. The blood pressure monitor is either an upper arm or wrist type, allowing pregnant women to easily measure their blood pressure at home. The measurement results are transmitted to the data collection unit via Bluetooth or Wi-Fi. The heart rate monitor is either a chest-worn or wristband type, monitoring heart rate 24 hours a day. This allows for real-time monitoring of fluctuations in the pregnant woman's heart rate. Gestational age is collected either by the pregnant woman or based on data provided by a medical institution. This data is centrally managed by the data collection unit and serves as foundational data for comprehensively evaluating the pregnant woman's health. The data collection unit regularly updates this data, allowing for real-time monitoring of changes in the pregnant woman's health. Furthermore, the data collection unit performs regular calibration and device maintenance to ensure data accuracy. This allows the data collection unit to accurately and efficiently collect data on the pregnant woman's physical condition, thereby improving the overall reliability of the system.

[0069] The proposal department proposes optimal exercise programs for pregnant women based on data collected by the data collection department. For example, the proposal department suggests light stretching and walking during the first trimester. Specifically, it recommends light stretching and walking that puts minimal strain on the body during the first trimester, promoting muscle relaxation and blood circulation. For the second trimester, it suggests aquatic exercises. Aquatic exercises utilize the buoyancy of water to reduce stress on joints and muscles while strengthening the entire body. For the third trimester, it suggests yoga and light strength training. Yoga enhances relaxation through breathing techniques and poses, supporting the preparation of physical strength for childbirth. Light strength training is important for maintaining physical strength during childbirth and promoting postpartum recovery. The proposal department uses AI to analyze collected data and automatically generates optimal exercise programs tailored to the pregnant woman's physical condition and gestational week. For example, it adjusts the intensity and frequency of exercise considering fluctuations in the pregnant woman's weight, blood pressure, and heart rate. The proposal department also reviews the exercise programs periodically based on feedback from pregnant women, customizing them to meet individual needs. This allows the proposal department to provide pregnant women with safe and effective exercise programs and support their health maintenance.

[0070] The monitoring unit monitors the implementation status of the exercise program proposed by the proposal unit. For example, the monitoring unit monitors the heart rate and exercise intensity while the pregnant woman is exercising. Specifically, it uses a heart rate monitor to monitor the pregnant woman's heart rate in real time and records fluctuations in heart rate during exercise. Exercise intensity is measured using an accelerometer and gyroscope to evaluate the type and intensity of exercise. For example, the monitoring unit monitors the duration of exercise while the pregnant woman is exercising. The duration is determined by recording the start and end times of the exercise and calculating the duration. Furthermore, for example, the monitoring unit monitors the frequency of exercise while the pregnant woman is exercising. The frequency of exercise is recorded on a weekly and monthly basis to evaluate how well the pregnant woman is following the proposed exercise program. The monitoring unit collects this data and comprehensively evaluates the pregnant woman's exercise status. In addition, the monitoring unit can detect abnormal data and risks during exercise early and issue warnings as needed. For example, if the heart rate is abnormally high or the exercise intensity is excessively high, the system will notify the pregnant woman to stop exercising. This allows the monitoring unit to effectively monitor the progress of the exercise program while ensuring the pregnant woman's safety.

[0071] The Feedback Department provides feedback based on the implementation status monitored by the Monitoring Department. For example, the Feedback Department advises pregnant women to stop exercising if their heart rate is too high. Specifically, if the heart rate exceeds the set safety range, the Feedback Department notifies the pregnant woman to stop exercising and rest. For example, the Feedback Department advises pregnant women to increase the intensity of their exercise if it is too low. If the exercise intensity does not meet the standards of the proposed program, the Feedback Department instructs the pregnant woman to increase the exercise intensity and suggests specific methods. For example, the Feedback Department advises pregnant women to extend the duration of their exercise if it is too short. If the duration does not meet the standards of the proposed program, the Feedback Department encourages the pregnant woman to continue exercising and provides specific advice on how to achieve the target time. Furthermore, the Feedback Department collects feedback from pregnant women and uses it to improve the exercise program. For example, by reporting discomfort or difficulties experienced by pregnant women during exercise, the Proposal Department can review the exercise program and adjust it to be more appropriate. This allows the feedback unit to provide appropriate advice to pregnant women and maximize the effectiveness of their exercise programs.

[0072] The AI ​​agent system includes a consultation department that handles inquiries and questions via chat. The consultation department can, for example, answer questions from pregnant women via text chat, voice chat, or video chat. This allows for real-time advice and support when pregnant women have questions or concerns about exercise.

[0073] The data collection unit can collect data such as the pregnant woman's weight, blood pressure, heart rate, and gestational age. For example, the data collection unit uses a scale to measure the pregnant woman's weight. For example, the data collection unit uses a blood pressure monitor to measure the pregnant woman's blood pressure. For example, the data collection unit uses a heart rate monitor to measure the pregnant woman's heart rate. By collecting detailed data on the pregnant woman's physical condition, it becomes possible to suggest a more appropriate exercise program.

[0074] The suggestion department can propose light stretching and walking during early pregnancy, and yoga and light strength training during late pregnancy. For example, the suggestion department can propose light stretching during early pregnancy. For example, the suggestion department can propose walking during early pregnancy. For example, the suggestion department can propose yoga during late pregnancy. For example, the suggestion department can propose light strength training during late pregnancy. By doing so, the health of pregnant women can be supported by proposing an optimal exercise program tailored to each stage of pregnancy.

[0075] The monitoring unit can monitor the heart rate and exercise intensity of a pregnant woman while she is exercising. For example, the monitoring unit can monitor the heart rate while the pregnant woman is exercising. For example, the monitoring unit can monitor the exercise intensity while the pregnant woman is exercising. For example, the monitoring unit can monitor the duration of exercise while the pregnant woman is exercising. This allows for real-time monitoring of the pregnant woman's physical condition during exercise, enabling her to exercise safely.

[0076] The feedback department can provide appropriate feedback based on monitored performance. For example, the feedback department may advise a pregnant woman to stop exercising if her heart rate is too high. For example, the feedback department may advise a pregnant woman to increase the intensity of her exercise if it is too low. For example, the feedback department may advise a pregnant woman to extend the duration of her exercise if it is too short. In this way, by providing feedback based on the performance of the exercise program, it is possible to support pregnant women's exercise.

[0077] The data collection unit can estimate the pregnant woman's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the pregnant woman is stressed, the data collection unit adjusts the timing to collect data when she is relaxed. For example, if the pregnant woman is relaxed, the data collection unit collects data frequently to obtain detailed data. For example, if the pregnant woman is tired, the data collection unit reduces the frequency of data collection and prioritizes rest. By adjusting the timing of data collection according to the pregnant woman's emotions, more appropriate data can be collected. 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.

[0078] The data collection unit can analyze the pregnant woman's past health data and select the optimal data collection method. For example, the data collection unit selects the most effective data collection method based on the pregnant woman's past health data. For example, the data collection unit selects a method for collecting data at a specific time of day based on the pregnant woman's past health data. For example, the data collection unit analyzes the pregnant woman's past health data and adjusts the data collection frequency. In this way, by analyzing past health data, the optimal data collection method can be selected and data can be collected efficiently.

[0079] The data collection unit can filter the health data collected based on the pregnant woman's current lifestyle and activity level. For example, the data collection unit filters the data to be collected considering the pregnant woman's current lifestyle. For example, the data collection unit selects the type of data to be collected based on the pregnant woman's activity level. For example, the data collection unit adjusts the frequency of data collection according to the pregnant woman's lifestyle and activity level. By filtering the data according to the pregnant woman's lifestyle and activity level, more relevant data can be collected.

[0080] The data collection unit can estimate the pregnant woman's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the pregnant woman is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the pregnant woman is relaxed, the data collection unit will prioritize collecting detailed physical condition data. For example, if the pregnant woman is tired, the data collection unit will prioritize collecting rest-related data. This allows for the collection of more important data by prioritizing the data to be collected according to the pregnant woman's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The data collection unit can prioritize the collection of highly relevant data by considering the pregnant woman's geographical location when collecting health data. For example, if the pregnant woman is at home, the data collection unit prioritizes collecting health data in a relaxed state. If the pregnant woman is out, the data collection unit prioritizes collecting health data during activities. If the pregnant woman is in a hospital, the data collection unit prioritizes collecting medical-related data. By considering the pregnant woman's geographical location, the data collection unit can prioritize the collection of more relevant data.

[0082] The data collection unit can analyze the social media activity of pregnant women when collecting health data and collect relevant data. For example, the data collection unit can analyze signs of stress and relaxation from the pregnant woman's social media activity and collect relevant data. For example, the data collection unit can estimate activity levels from the pregnant woman's social media activity and collect relevant data. For example, the data collection unit can analyze changes in emotions from the pregnant woman's social media activity and collect relevant data. In this way, by analyzing the social media activity of pregnant women, relevant data can be collected and more detailed health data can be obtained.

[0083] The suggestion unit can estimate the pregnant woman's emotions and adjust the method of suggesting exercise programs based on the estimated emotions. For example, if the pregnant woman is feeling stressed, the suggestion unit will suggest an exercise program with a relaxing effect. For example, if the pregnant woman is relaxed, the suggestion unit will suggest an exercise program aimed at improving physical fitness. For example, if the pregnant woman is tired, the suggestion unit will suggest light stretching or relaxation exercises. In this way, by adjusting the method of suggesting exercise programs according to the pregnant woman's emotions, a more appropriate exercise program can be suggested. 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.

[0084] The suggestion function can adjust the level of detail in the exercise program based on the pregnant woman's health data when making a suggestion. For example, if the pregnant woman's health data is good, the suggestion function will suggest a detailed exercise program. For example, if the pregnant woman's health data is unstable, the suggestion function will suggest a simplified exercise program. For example, the suggestion function will adjust the intensity of the exercise program based on the pregnant woman's health data. In this way, by adjusting the level of detail in the exercise program based on the pregnant woman's health data, a more appropriate exercise program can be suggested.

[0085] The suggestion function can apply different exercise programs depending on the stage of pregnancy of the pregnant woman. For example, the suggestion function might suggest light stretching or walking in the early stages of pregnancy. For example, it might suggest yoga or light strength training in the second trimester. For example, it might suggest relaxation exercises or breathing exercises in the third trimester. By applying different exercise programs according to the stage of pregnancy, it is possible to suggest a more appropriate exercise program.

[0086] The suggestion unit can estimate the pregnant woman's emotions and adjust the length of the exercise program based on the estimated emotions. For example, if the pregnant woman is stressed, the suggestion unit will suggest a short, relaxing exercise program. For example, if the pregnant woman is relaxed, the suggestion unit will suggest a longer exercise program. For example, if the pregnant woman is tired, the suggestion unit will suggest a short, light exercise program. In this way, by adjusting the length of the exercise program according to the pregnant woman's emotions, a more appropriate exercise program can be suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The proposal department can determine the priority of exercise programs based on when the pregnant woman's health data is submitted. For example, if the pregnant woman's health data is up-to-date, the proposal department will prioritize suggesting exercise programs. If the pregnant woman's health data is outdated, the proposal department will prompt an update and suggest exercise programs based on the latest data. The proposal department can adjust the priority of exercise programs based on when the pregnant woman's health data is submitted. By prioritizing exercise programs based on when the pregnant woman's health data is submitted, it is possible to suggest more appropriate exercise programs.

[0088] The suggestion unit can adjust the order of exercise programs based on the relationship of the pregnant woman's health data when making a suggestion. For example, if the pregnant woman's health data is good, the suggestion unit will prioritize high-intensity exercise programs. For example, if the pregnant woman's health data is unstable, the suggestion unit will prioritize low-intensity exercise programs. The suggestion unit adjusts the order of exercise programs based on the relationship of the pregnant woman's health data. By doing so, it can suggest a more appropriate exercise program by adjusting the order of exercise programs based on the relationship of the pregnant woman's health data.

[0089] The monitoring unit can estimate the pregnant woman's emotions and adjust the monitoring criteria based on the estimated emotions. For example, if the pregnant woman is stressed, the monitoring unit will focus on monitoring stress-related data. For example, if the pregnant woman is relaxed, the monitoring unit will monitor overall physical condition data. For example, if the pregnant woman is tired, the monitoring unit will monitor rest-related data. This allows for more appropriate monitoring by adjusting the monitoring criteria according to the pregnant woman's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships of the pregnant woman's health data during monitoring. For example, the monitoring unit monitors by considering the interrelationship between the pregnant woman's heart rate and blood pressure. For example, the monitoring unit monitors by considering the interrelationship between the pregnant woman's weight and activity level. For example, the monitoring unit analyzes the interrelationships of the pregnant woman's health data to improve the accuracy of monitoring. In this way, the accuracy of monitoring can be improved by considering the interrelationships of the pregnant woman's health data.

[0091] The monitoring unit can perform monitoring while considering the pregnant woman's attribute information. For example, the monitoring unit can monitor while considering the pregnant woman's age and weight. For example, the monitoring unit can monitor while considering the pregnant woman's gestational age. For example, the monitoring unit can monitor while considering the pregnant woman's health status. This allows for more appropriate monitoring by considering the pregnant woman's attribute information.

[0092] The monitoring unit can estimate the pregnant woman's emotions and adjust the order in which it displays the monitoring results based on the estimated emotions. For example, if the pregnant woman is stressed, the monitoring unit will display stress-related data first. For example, if the pregnant woman is relaxed, the monitoring unit will display overall physical condition data. For example, if the pregnant woman is tired, the monitoring unit will display rest-related data first. This allows for the provision of more appropriate information by adjusting the order in which the monitoring results are displayed according to the pregnant woman'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.

[0093] The monitoring unit can perform monitoring while considering the geographical distribution of pregnant women. For example, if a pregnant woman is at home, the monitoring unit will monitor her health data at home. For example, if a pregnant woman is out, the monitoring unit will monitor her health data at her destination. For example, if a pregnant woman is in a hospital, the monitoring unit will monitor her health data at the hospital. This allows for more appropriate monitoring by considering the geographical distribution of pregnant women.

[0094] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature on pregnant women during monitoring. For example, the monitoring unit monitors by referring to the latest research on the physical condition of pregnant women. For example, the monitoring unit monitors by referring to relevant literature on the health status of pregnant women. For example, the monitoring unit improves the accuracy of monitoring by comparing the physical condition data of pregnant women with relevant literature. In this way, the accuracy of monitoring can be improved by referring to relevant literature on pregnant women.

[0095] The feedback unit can estimate the pregnant woman's emotions and adjust the feedback method based on the estimated emotions. For example, if the pregnant woman is stressed, the feedback unit will provide relaxing feedback. For example, if the pregnant woman is relaxed, the feedback unit will provide detailed feedback. For example, if the pregnant woman is tired, the feedback unit will provide concise and easy-to-understand feedback. This allows for more appropriate feedback to be provided by adjusting the feedback method according to the pregnant woman'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.

[0096] The feedback unit can analyze the pregnant woman's health data during the feedback process to select the most appropriate feedback method. For example, if the pregnant woman's health data is good, the feedback unit will provide detailed feedback. For example, if the pregnant woman's health data is unstable, the feedback unit will provide concise feedback. For example, the feedback unit will analyze the pregnant woman's health data and adjust the content of the feedback. In this way, by analyzing the pregnant woman's health data, the optimal feedback method can be selected, and more appropriate feedback can be provided.

[0097] The feedback unit can customize the means of feedback based on the pregnant woman's current living situation. For example, if the pregnant woman is at home, the feedback unit provides feedback in a relaxed environment. For example, if the pregnant woman is out, the feedback unit provides concise and quick feedback. The feedback unit adjusts the means of feedback according to the pregnant woman's living situation. This allows for the provision of more appropriate feedback by customizing the means of feedback based on the pregnant woman's current living situation.

[0098] The feedback unit can estimate the pregnant woman's emotions and determine the priority of feedback based on the estimated emotions. For example, if the pregnant woman is stressed, the feedback unit will prioritize feedback related to stress reduction. For example, if the pregnant woman is relaxed, the feedback unit will prioritize detailed feedback. For example, if the pregnant woman is tired, the feedback unit will prioritize feedback related to rest. This allows for the prioritization of more important feedback based on the pregnant woman's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The feedback unit can select the most appropriate feedback method when providing feedback, taking into account the pregnant woman's geographical location. For example, if the pregnant woman is at home, the feedback unit will provide feedback at home. For example, if the pregnant woman is out, the feedback unit will provide feedback at her location. For example, if the pregnant woman is in a hospital, the feedback unit will provide medical-related feedback. This allows for the selection of a more appropriate feedback method by considering the pregnant woman's geographical location.

[0100] The feedback unit can analyze the pregnant woman's social media activity during the feedback process and propose methods for providing feedback. For example, the feedback unit can analyze signs of stress and relaxation from the pregnant woman's social media activity and provide relevant feedback. For example, the feedback unit can estimate the activity level from the pregnant woman's social media activity and provide relevant feedback. For example, the feedback unit can analyze changes in emotion from the pregnant woman's social media activity and provide relevant feedback. In this way, by analyzing the pregnant woman's social media activity, it is possible to propose more appropriate methods for providing feedback.

[0101] The counseling department can estimate the pregnant woman's emotions and adjust its approach to counseling based on those emotions. For example, if the pregnant woman is stressed, the counseling department will provide a relaxing approach. If the pregnant woman is relaxed, the counseling department will provide a detailed approach. If the pregnant woman is tired, the counseling department will provide a concise and easy-to-understand approach. By adjusting the counseling approach according to the pregnant woman's emotions, a more appropriate response can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The consultation department can select the most appropriate course of action by referring to the pregnant woman's past consultation history during a consultation. For example, the consultation department selects the most appropriate course of action based on the pregnant woman's past consultation history. For example, the consultation department selects a course of action for a specific problem from the pregnant woman's past consultation history. For example, the consultation department analyzes the pregnant woman's past consultation history and adjusts the course of action. In this way, by referring to the pregnant woman's past consultation history, a more appropriate course of action can be selected.

[0103] The counseling department can estimate the pregnant woman's emotions and determine the priority of consultations based on the estimated emotions. For example, if the pregnant woman is feeling stressed, the counseling department will prioritize consultations related to stress reduction. For example, if the pregnant woman is relaxed, the counseling department will prioritize detailed consultations. For example, if the pregnant woman is tired, the counseling department will prioritize consultations related to rest. In this way, by determining the priority of consultations according to the pregnant woman's emotions, more important consultations can be addressed preferentially. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The consultation department can select the most appropriate response method during a consultation, taking into account the pregnant woman's device information. For example, if the pregnant woman is using a smartphone, the consultation department will provide a response method optimized for smartphones. For example, if the pregnant woman is using a tablet, the consultation department will provide a response method optimized for tablets. For example, if the pregnant woman is using a personal computer, the consultation department will provide a response method optimized for personal computers. This allows for the selection of a more appropriate response method by considering the pregnant woman's device information.

[0105] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0106] The suggestion function can estimate the pregnant woman's emotions and adjust the method of suggesting exercise programs based on those emotions. For example, if the pregnant woman is feeling stressed, it can suggest an exercise program with a relaxing effect. If the pregnant woman is relaxed, it can suggest an exercise program aimed at improving physical fitness. If the pregnant woman is tired, it can suggest light stretching or relaxation exercises. By adjusting the method of suggesting exercise programs according to the pregnant woman's emotions, it is possible to suggest a more appropriate exercise program.

[0107] The data collection unit can analyze a pregnant woman's past health data and select the optimal data collection method. For example, it can select the most effective collection method based on the pregnant woman's past health data. It can also select a method for collecting data at specific time periods based on the pregnant woman's past health data. Furthermore, it can analyze the pregnant woman's past health data and adjust the collection frequency. This allows for the selection of the optimal data collection method and efficient data collection by analyzing past health data.

[0108] The monitoring unit can estimate the pregnant woman's emotions and adjust the monitoring criteria based on the estimated emotions. For example, if the pregnant woman is stressed, stress-related data can be monitored intensively. If the pregnant woman is relaxed, overall physical condition data can be monitored. If the pregnant woman is tired, rest-related data can be monitored. This allows for more appropriate monitoring by adjusting the monitoring criteria according to the pregnant woman's emotions.

[0109] The feedback unit can analyze the pregnant woman's health data during the feedback process to select the most appropriate feedback method. For example, if the pregnant woman's health data is good, detailed feedback can be provided. If the pregnant woman's health data is unstable, concise feedback can be provided. The feedback content can be adjusted by analyzing the pregnant woman's health data. This allows for the selection of the most appropriate feedback method and the provision of more suitable feedback by analyzing the pregnant woman's health data.

[0110] The counseling department can estimate the pregnant woman's emotions and adjust its approach based on those estimates. For example, if the pregnant woman is stressed, it can offer a relaxing approach. If the pregnant woman is relaxed, it can offer a more detailed approach. If the pregnant woman is tired, it can offer a concise and easy-to-understand approach. By adjusting the counseling approach according to the pregnant woman's emotions, it can provide a more appropriate response.

[0111] The data collection unit can filter the health data collected based on the pregnant woman's current lifestyle and activity level. For example, it can filter the data to be collected considering the pregnant woman's current lifestyle. It can select the type of data to be collected based on the pregnant woman's activity level. It can adjust the frequency of data collection according to the pregnant woman's lifestyle and activity level. This allows for the collection of more relevant data by filtering the data according to the pregnant woman's lifestyle and activity level.

[0112] The proposal function can adjust the level of detail in the exercise program based on the pregnant woman's health data during the proposal process. For example, if the pregnant woman's health data is good, a detailed exercise program can be proposed. If the pregnant woman's health data is unstable, a simplified exercise program can be proposed. The intensity of the exercise program can also be adjusted based on the pregnant woman's health data. By adjusting the level of detail in the exercise program based on the pregnant woman's health data, a more appropriate exercise program can be proposed.

[0113] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships of the pregnant woman's health data during monitoring. For example, it can monitor by considering the interrelationship between the pregnant woman's heart rate and blood pressure. It can monitor by considering the interrelationship between the pregnant woman's weight and activity level. By analyzing the interrelationships of the pregnant woman's health data, the accuracy of monitoring can be improved. In this way, the accuracy of monitoring can be improved by considering the interrelationships of the pregnant woman's health data.

[0114] The feedback unit can estimate the pregnant woman's emotions and prioritize feedback based on those emotions. For example, if the pregnant woman is stressed, feedback related to stress reduction can be prioritized. If the pregnant woman is relaxed, detailed feedback can be prioritized. If the pregnant woman is tired, feedback related to rest can be prioritized. This allows for the prioritization of feedback according to the pregnant woman's emotions, ensuring that more important feedback is provided first.

[0115] The consultation department can select the most appropriate response method during a consultation by considering the pregnant woman's device information. For example, if the pregnant woman is using a smartphone, a response method optimized for smartphones can be provided. If the pregnant woman is using a tablet, a response method optimized for tablets can be provided. If the pregnant woman is using a personal computer, a response method optimized for personal computers can be provided. In this way, by considering the pregnant woman's device information, a more appropriate response method can be selected.

[0116] The following briefly describes the processing flow for example form 2.

[0117] Step 1: The data collection unit collects data on the pregnant woman's physical condition. The data collection unit collects data such as the pregnant woman's weight, blood pressure, heart rate, and gestational age. The data collection unit uses a scale to measure the pregnant woman's weight, a blood pressure monitor to measure the pregnant woman's blood pressure, and a heart rate monitor to measure the pregnant woman's heart rate. Step 2: The suggestion unit proposes an optimal exercise program for pregnant women based on the data collected by the collection unit. For example, the suggestion unit might suggest light stretching or walking during the first trimester of pregnancy. For example, it might suggest yoga or light strength training during the third trimester of pregnancy. For example, it might suggest aquatic exercises during the second trimester of pregnancy. Step 3: The monitoring unit monitors the implementation status of the exercise program proposed by the proposal unit. For example, the monitoring unit monitors the heart rate and exercise intensity while the pregnant woman is exercising. For example, the monitoring unit monitors the duration of exercise while the pregnant woman is exercising. For example, the monitoring unit monitors the frequency of exercise while the pregnant woman is exercising. Step 4: The Feedback Department provides feedback based on the implementation status monitored by the Monitoring Department. For example, the Feedback Department may advise a pregnant woman to stop exercising if her heart rate is too high while she is exercising. For example, the Feedback Department may advise a pregnant woman to increase the intensity of her exercise if it is too low while she is exercising. For example, the Feedback Department may advise a pregnant woman to extend the duration of her exercise if it is too short while she is exercising.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the data collection unit, proposal unit, monitoring unit, feedback unit, and consultation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects the pregnant woman's physical condition data using the smart device 14's scale, blood pressure monitor, heart rate monitor, etc. The proposal unit is implemented in the data processing unit 12's specific processing unit 290 and proposes an optimal exercise program based on the collected data. The monitoring unit monitors the exercise performance using the smart device 14's sensors. The feedback unit is implemented in the data processing unit 12's specific processing unit 290 and provides feedback based on the monitored data. The consultation unit answers the pregnant woman's questions using the smart device 14's text chat, voice chat, and video chat functions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0122] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the data collection unit, proposal unit, monitoring unit, feedback unit, and consultation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the pregnant woman's physical condition data using the smart glasses 214's scale, blood pressure monitor, heart rate monitor, etc. The proposal unit is implemented in the data processing unit 12, for example, by the specific processing unit 290, and proposes an optimal exercise program based on the collected data. The monitoring unit monitors the exercise performance using the sensors of the smart glasses 214. The feedback unit is implemented in the data processing unit 12, for example, by the specific processing unit 290, and provides feedback based on the monitored data. The consultation unit answers the pregnant woman's questions using the text chat, voice chat, and video chat functions of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0138] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the data collection unit, proposal unit, monitoring unit, feedback unit, and consultation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects the pregnant woman's physical condition data using the scale, blood pressure monitor, heart rate monitor, etc., of the headset terminal 314. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes an optimal exercise program based on the collected data. The monitoring unit monitors the exercise performance using the sensors of the headset terminal 314. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides feedback based on the monitored data. The consultation unit answers the pregnant woman's questions using, for example, the text chat, voice chat, and video chat functions of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the data collection unit, proposal unit, monitoring unit, feedback unit, and consultation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects the pregnant woman's physical condition data using the robot 414's scale, blood pressure monitor, heart rate monitor, etc. The proposal unit is implemented in the data processing unit 12, for example, by the specific processing unit 290, and proposes an optimal exercise program based on the collected data. The monitoring unit monitors the exercise performance using the robot 414's sensors. The feedback unit is implemented in the data processing unit 12, for example, by the specific processing unit 290, and provides feedback based on the monitored data. The consultation unit answers the pregnant woman's questions using the robot 414's text chat, voice chat, and video chat functions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] (Note 1) A data collection department that collects data on the health condition of pregnant women, Based on the data collected by the aforementioned collection unit, a proposal unit proposes an optimal exercise program for pregnant women. A monitoring unit that monitors the implementation status of the exercise program proposed by the aforementioned proposal unit, The system includes a feedback unit that provides feedback based on the implementation status monitored by the monitoring unit. A system characterized by the following features. (Note 2) We have a consultation department that provides support and answers questions via chat. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We collect data such as the pregnant woman's weight, blood pressure, heart rate, and gestational age. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, In early pregnancy, we suggest light stretching and walking, and in late pregnancy, we suggest yoga and light strength training. The system described in Appendix 1, characterized by the features described herein. (Note 5) The monitoring unit, Monitor the heart rate and exercise intensity of pregnant women while they are exercising. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Provide appropriate feedback based on monitored implementation status. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the emotions of pregnant women and adjusts the timing of collecting physical condition data based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past health data of pregnant women and select the optimal data 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 pregnant woman's current lifestyle and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate the emotions of pregnant women and prioritize the 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 collection of highly relevant data will be prioritized, taking into account the geographical location of pregnant women. 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 the social media activity of pregnant women and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, We estimate the emotions of pregnant women and adjust the method of suggesting exercise programs based on the estimated emotions of pregnant women. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the exercise program based on the pregnant woman's health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, different exercise programs will be applied depending on the stage of pregnancy for the pregnant woman. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, The system estimates the pregnant woman's emotions and adjusts the length of the exercise program based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, prioritize exercise programs based on when the pregnant woman's health data is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, adjust the order of the exercise program based on the relevance of the pregnant woman's health data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The monitoring unit, We estimate the pregnant woman's emotions and adjust the monitoring criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The monitoring unit, During monitoring, we improve the accuracy of monitoring by considering the interrelationships between the pregnant woman's health data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The monitoring unit, During monitoring, the monitoring process should take into account the pregnant woman's attributes. The system described in Appendix 1, characterized by the features described herein. (Note 22) The monitoring unit, The system estimates the pregnant woman's emotions and adjusts the order in which monitoring results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The monitoring unit, During monitoring, the geographical distribution of pregnant women should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The monitoring unit, During monitoring, we refer to relevant literature on pregnant women to improve the accuracy of monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is The system estimates the pregnant woman's emotions and adjusts the feedback method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is During the feedback process, we analyze the pregnant woman's health data to select the most appropriate feedback method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When providing feedback, customize the feedback method based on the pregnant woman's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is The system estimates the pregnant woman'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 providing feedback, the most appropriate feedback method will be selected, taking into account the pregnant woman's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is During the feedback process, we will analyze the pregnant woman's social media activity and propose methods for providing feedback. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned consultation department, We estimate the pregnant woman's emotions and adjust the consultation approach based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned consultation department, During the consultation, the most appropriate course of action will be selected by referring to the pregnant woman's past consultation history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned consultation department, The system estimates the pregnant woman's emotions and determines the priority of consultations based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned consultation department, During consultations, the most appropriate course of action is selected, taking into account the pregnant woman's device information. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection department that collects data on the health condition of pregnant women, Based on the data collected by the aforementioned collection unit, a proposal unit proposes an optimal exercise program for pregnant women. A monitoring unit that monitors the implementation status of the exercise program proposed by the aforementioned proposal unit, The system includes a feedback unit that provides feedback based on the implementation status monitored by the monitoring unit. A system characterized by the following features.

2. We have a consultation department that provides support and answers questions via chat. The system according to feature 1.

3. The aforementioned collection unit is We collect data such as the pregnant woman's weight, blood pressure, heart rate, and gestational age. The system according to feature 1.

4. The aforementioned proposal section is, In early pregnancy, we suggest light stretching and walking, and in late pregnancy, we suggest yoga and light strength training. The system according to feature 1.

5. The monitoring unit, Monitor the heart rate and exercise intensity of pregnant women while they are exercising. The system according to feature 1.

6. The aforementioned feedback unit is Provide appropriate feedback based on monitored implementation status. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the emotions of pregnant women and adjusts the timing of collecting physical condition data based on these estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past health data of pregnant women and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting health data, filtering is performed based on the pregnant woman's current lifestyle and activity level. The system according to feature 1.

Images